Title Characterization of post-traumatic - ARAN Home

Transcription

Provided by the author(s) and NUI Galway in accordance with publisher policies. Please cite the published
version when available.
Title
Author(s)
Characterization of post-traumatic immunosuppression and its
partial reversal by autologous salvaged blood transfusion
Islam, Nahidul
Publication
Date
2015-01-28
Item record
http://hdl.handle.net/10379/4868
Downloaded 2016-11-17T10:50:13Z
Some rights reserved. For more information, please see the item record link above.
CHARACTERIZATION OF POST-TRAUMATIC
IMMUNOSUPPRESSION AND ITS PARTIAL REVERSAL BY
AUTOLOGOUS SALVAGED BLOOD TRANSFUSION
A thesis submitted to the National University of Ireland Galway in fulfilment of the
requirements for the degree of
Doctor of Philosophy
by
Nahidul Islam
B.Sc., M.Sc., M.Sc. (Research)
Immunology and Transplant Biology Group,
Regenerative Medicine Institute,
National Centre for Biomedical Engineering Science, Biosciences,
National University of Ireland, Galway
Thesis Supervisors:
Professor Benjamin A Bradley
Avon Orthopaedic Centre, Southmead Hospital, University of Bristol, UK
Professor Rhodri Ceredig
Regenerative Medicine Institute, NCBES, Biosciences, NUI Galway, Ireland
July 2014
Characterization of Post-Traumatic Immunosuppression and Its Partial Reversal By Autologous Salvaged Blood Transfusion
TABLE OF CONTENTS
Declaration
VII
Abstract
VIII
Acknowledgement
X
Dedications
XI
Abbreviations
XII
List of Figures
XV
List of Tables
XVIII
1-37
Chapter - I : Introduction
1.1. Post-traumatic Immunosuppression (PTI)
2
1.1.1. Definitions
3
1.1.2. Historical Context
4
1.1.3. Systemic Inflammatory Response Syndrome
5
1.2. Distinguishing Sterile Trauma from Sepsis
9
1.3. Immunological changes following Sterile Trauma
12
1.3.1. Local Site of Trauma
13
1.3.2. Ischaemic-Reperfusion Injury
14
1.3.3. Hypothalamus - Pituitary - Endocrine axis
16
1.3.4. Changes in Lympho-haemopoiesis
16
1.3.5. Changes in Blood Plasma
20
1.3.6. Changes in Liver
23
1.4. Repairing Injured Tissue
23
1.4.1. Cell Activation Triggers
23
1.4.2. Soluble Factors
24
1.4.3. MSCs, the Warden of Tissue Repair
26
1.5. Aggravating Factors of PTI
27
1.6. Immunological Consequences of PTI
28
1.7. Treatment of PTI
30
1.8. Autologous Blood Transfusion; Types of transfusion
31
1.9. Total Knee Arthroplasty and Postoperative Salvaged Blood Transfusion
34
II
1.10. Knowledge Gaps at the time of Project Design
36
1.11. Study Aims and Hypotheses
37
38 - 63
Chapter - II : Materials and Methods
2.1. Study Design
39
2.2. Ethical Approval
39
2.3. Patient Selection and Recruitment
39
2.4. Haematological and Biochemical Data
40
2.5. Blood Transfusion Procedure
40
2.5.1.
Sampling of Blood from Patients
42
2.5.2.
Blood Sample Collection, Isolation of Plasma
44
2.5.3.
Separation and Storage of PBMC
45
2.6. Cell Culture of Stored PBMC
45
2.7. Blood Collection for Proteomic and Glycomic Analyses
45
2.8. Flow Cytometric Bead Array
46
2.9.Enzyme Linked Immuno-Sorbent Assay (ELISA)
48
2.10. Human Whole Blood Culture Assay
51
2.11. Automated Liquid Handing
51
2.12. High Abundant Protein Depletion from Blood Plasma
52
2.12.1. Importance of Depletion
52
2.12.2. Depletion Technique
53
2.13. Quantification of Proteins (Bradford Assay)
54
2.14. One Dimensional Gel Electrophoresis
55
2.15. Proteomic Array
56
2.16. Glycomic Array (Lectin Profiling)
59
2.17. Statistical Analyses of the Study
62
III
Chapter – III : RESULTS: Characterization of Post-traumatic
64 - 91
Immunosuppression (PTI) and Assessment of the Effect of Autologous
Salvaged Blood Transfusion
3.1. Patients‟ Characteristics
65
3.2. Damage Associated Molecular Patterns
67
3.3. Pro-inflammatory Cytokines
69
3.4. Anti-inflammatory Cytokines
71
3.5. Chemokines
73
3.6. Interleukin-22
74
3.7. Soluble IL-6R, Soluble gp130 and ADAM-17
75
3.8. Soluble CD14
76
3.9. Lysozyme Activity
77
3.10. CD-24, Siglec-10 and Siglec-2
78
3.11. Complement Split Protein – C5a
79
3.12. In-vitro PBMC Culture With or Without LPS Stimulation
80
3.13. Summary of Postoperative Observations
85
3.14. Correlation Between Postoperative Changes in Different Biomarkers
89
Chapter – IV : RESULTS: Constituents of Salvaged Blood
92-110
4.1. Damage Associated Molecular Patterns
93
95
97
4.4. Chemokines
99
4.5. Interleukin-22
100
4.6. Soluble IL-6R, Soluble gp130 and ADAM-17
101
4.7. Soluble CD14
102
103
4.9. CD-24, Siglec-10 and Siglec-2
104
4.10. Complement Split Protein – C5a
105
4.11. Summary of Observations
106
4.12. Correlation on Fold Changes in Salvaged Bloods Between all different
Biomarkers
108
IV
Chapter – V : RESULTS: Proteomic and Glycomic Profiling of Salvaged
111 - 121
Blood
5.1. One Dimensional Gel Electrophoresis
112
5.2. Tandem Mass Tagging
113
5.3. Lectin Array
118
Chapter – VI : Discussion
122 - 144
6.1. Characterization of Post-traumatic Immunosuppression and Assessment
123
of the Effect of Autologous Salvaged Blood Transfusion
6.2. Constituents of Salvaged Blood
130
6.3. Diverse Roles of Interleukin-6
135
6.4. Proteomic and Glycomic Profiling of Venous and Salvaged Blood
136
6.5. Differences with Other‟s Results
137
6.6. Biological Insights into Effects of Sterile Trauma
138
6.6.1. Enhancement in Antimicrobial Properties
138
6.6.2. Role of CD24/Siglec-10 axis in Immunology
139
6.7. Clinical Significance of Findings
141
6.8. Limitations of this Study
142
6.9. Future Directions
143
Chapter – VII : Appendices
145 - 182
Appendix-I: Levels of Biomarkers in Wound Blood
146
Appendix-II: Levels of Biomarkers Postoperatively Following Major Surgery
147
Appendix-III(A): Correlation Between Biomarker Levels and Transfused
151
Blood Volume
Appendix-III(B): Correlation Between Biomarker Levels and Length of
152
Hospital Stay
Appendix-III(C): Correlation Between Biomarker Levels and Drained Blood
Volume
153
V
Appendix-IV: Pre- and Post-Operative Levels of Different Biomarkers
154
Appendix-V: Levels of Different Biomarkers in Peripheral and Salvaged
155
Blood
Appendix-VI: In Vitro Studies on sCD14, IL-1β, And TNF-α Productions by
156
Fresh PBMCs and Human Whole Blood Culture
Appendix-VII: Simulation of Salvaged Blood Collection Conditions
158
Appendix-VIII: Effect of Lysozyme on Productions Of Different Biomarkers
160
In Human Whole Blood Culture
Appendix-IX: Details of Biomarkers Assessed by Tandem Mass Tagging
161
Appendix-X: Patients‟ Consent Form
170
Appendix-XI: Funding Bodies
174
Appendix-XII: Publications, Presentations and Achievements
175
Appendix-XIII: Additional Research Projects
177
Appendix-XIV: Lists of Reagents
179
Appendix-XV: Cross-Reactivity of the Measured Analytes
182
Chapter – VIII : References
183 - 208
VI
DECLARATION
I, Nahidul Islam, confirm that all the laboratory work presented in this thesis is my
own. Where information has been derived from other sources, I confirm that this has
been indicated in the thesis.
I also declare that Professor Benjamin Bradley was the principle investigator and
originator of this study. Benjamin, I, Professor Gordon Bannister, Professor Ashley
Blom and Mr. Michael Whitehouse designed the study. Gordon. Ashley, Michael and
Mr. Sanchit Mehandale organized patient recruitment and administered the surgical
and post-operative treatment protocol. I collected, processed and preserved all blood
samples and conducted all laboratory based assays. Dr. Enda O‟Connell and I
developed and optimized automated ELISA assays. Benjamin and Professor Rhodri
Ceredig supervised and guided all the laboratory studies. Benjamin and Rhodri also
helped me in the interpretations of the data.
VII
ABSTRACT
Documentary evidence of post-traumatic immunosuppression (PTI) is
traceable back to 1856. Although improved hygiene has ameliorated many risks,
vulnerability to systemic infection following major surgery or closed injury persists.
Underlying mechanisms remain enigmatic and few effective treatments exist. Knee
replacement surgery offers an ideal clinical model to characterize immune status
after sterile trauma in terms of blood biomarkers.
The main objective was to identify biomarkers of PTI, and establish how
these were altered by anti-coagulated salvaged blood transfusion.
A prospective non-randomized cohort study involved 43 patients undergoing
primary total knee arthroplasty, 25 of whom received anti-coagulated salvaged blood
transfusions collected post-operatively, and 18 non-transfused patients. Biomarkers
of sterile trauma included haematological values, Damage-Associated-MolecularPatterns (DAMPs), cytokines, and chemokines. Salvaged blood was analysed within
one hour and six hours after commencing collection. Biomarker levels were
expressed as fold-changes over pre-operative values.
Two groups of biomarkers were revealed: the first were termed as “common
biomarkers of sterile trauma” that were common to all 43 patients with no
differences in changes between the two cohorts; whereas the second were termed as
“Salvaged Blood Sensitive Biomarkers of sterile trauma” as these were reversed by
anti-coagulated salvaged blood re-infusion. The former included the following:
leukocytosis, monocytosis, neutrophilia, erythropenia, decreased haemoglobin and
haematocrit values, thrombocytopenia, lymphopenia, and also decreased numbers of
eosinophils and basophils; increased levels of: Interleukin (IL)-6, IL-1-ReceptorAntagonist (IL-1RA), IL-8, Heat-Shock-Protein-(HSP)-70, Calgranulin-S100-A8/9,
α-Defensins, Heat Stable Antigen (CD24), Sialic Acid Binding Ig-Like Protein-10
(Siglec-10), Soluble CD14, Lysozyme and Anaphylatoxin C5a; and, decreased levels
of Soluble IL-6 Receptor, Soluble gp130 and High Mobility Group Box Protein-1
(HMGB-1). SBS-BST included increased levels of: IL-1-β, IL-2, IL-17A, Interferon-
VIII
gamma (IFN-γ), Tumour-Necrosis-Factor-alpha (TNF-α), Annexin-A2, ADAM-17
and IL-22; and decreased levels of: IL-4, IL-5, IL-10, and IL-13.
Analyses of salvaged blood revealed two groups of biomarkers. The first
group was termed as “Stable biomarkers of salvaged blood” that had no additional
production during the collection period in the bag, and were assumed to have been
continuously synthesized in-vivo within the wound site. Stable biomarkers included:
sustained high levels of certain DAMPs including: Calgranulin-S100-A8/9, alphaDefensin, HSP-27, HSP-60, HSP-70, α-Defensins IL-9; and low levels of IL-13. The
second group termed as “Dynamic biomarkers of salvaged blood”, since on-going
synthesis or reduction was observed during ex-vivo collections of salvaged blood.
These included: increasing levels of Annexin-A2, IL-1-β, IL-1RA, IL-2, IL-4, IL-6,
IL-8, IL-10, IL-12p70, IL-17A, IFN-γ, TNF-α, Transforming-Growth-Factor-Beta-1
(TGF-β1), Monocyte-Chemotactic-Protein-1, Macrophage-Inflammatory-Protein-1alpha, IL-22, ADAM-17, Soluble CD14, Lysozyme; and, decreasing levels of
HMGB-1 and Keratinocyte Growth Factor.
This study also indicated that sterile trauma human enhanced levels of antimicrobial
proteins, including soluble CD14, Lysozyme, Calgranulin and alpha-Defensin.
Furthermore elevated levels of Siglec-10 and soluble CD24 suggested enhanced
regulation of autoimmune reaction to neoantigens exposed by necrotic tissues.
This study showed that whereas early salvaged blood reflected suppressed immune
status associated with PTI, it developed immunostimulatory properties ex-vivo that
upon reinfusion subsequently reversed PTI. The detailed characterisations of
immunostimulatory constituents of salvaged blood responsible for this phenomenon
and their therapeutic potential have yet to be performed.
IX
ACKNOWLEDGEMENTS
I would like to gratefully acknowledge my supervisor Prof. Ben Bradley for
accepting me in his Research team. His enthusiasm, support, honesty, generosity and
professional help accompanied all the period of preparing and finishing this work.
Thanks for teaching me how to critically think, design and complete an experiment
independently.
Also, I am very grateful to my supervisor Prof. Rhodri Ceredig for his never ending
support, ideas, advice, inspiration and critique since 2011. Long may it continue.
Thanks for valuing my ideas and your support whenever I needed.
Thanks to the surgical team in Bristol, Prof. Ashley, Prof. Gordon, Mike and Sanchit;
without them this study would not be possible. Special thanks to all the patients who
happily donated their blood several times for this study; nurses and other hospital
staffs also deserve thanks for their support during my stay in Southmead hospital.
Thanks to Prof. Lokesh Joshi for allowing me to do the lectin microarray in his lab.
Also thanks to Catherine for her wonderful help in setting up the glycobiology
experiment, that otherwise would be difficult for me to complete. Very special
thanks to Satbir for her support in proteomic and glycomic data analyses.
Very special thanks to Dr. Michael Hall for his time and guidance in Institute of
Technology Tralee. Also, thanks to Dr. Joanna Tierney and Felicity Bentley for
training me in the required laboratory techniques.
Dr. Jane Eastlake (Bristol), thank you for the study on microvesicular cytokines.
Also, thanks to Dr. Eugene McCarthy (Galway-Mayo Institute of Technology), Dr.
Dilip Rai (Teagasc) and Mr. Brendan Harhen (NUI Galway) for their advice on
proteomic studies.
Prof. Matthew Griffin, thanks for sharing your knowledge bank and advices
throughout the years. Also thanks to all the Immunology group members in REMEDI
and other NCBES colleagues for their fun and for not saying „no‟ whenever I needed
a helping hand; especially Claas for showing me how to handle Accuri-C6.
I would also like to thank the funding bodies for supporting this work, including Irish
Research Council, Science Foundation Ireland, Enterprise Ireland and North Bristol
NHS Orthopaedic Trust.
No words can describe my gratitude to my mother (Shirin), wife (Sonny), brother
(Shahin), sister (Nigar), sister-in-law (Shahnaj) and brother-in-law (Mostafa). Thank
you all for the encouragement and support which allowed me to pursue my dreams.
Also, I would like to thank my friends, specially, Anwar, Dipankar, Sumon, Mahbub
and Rasel for fun and inspirations.
X
Dedicated to my mother
SHIRIN AKTER
XI
ABBREVIATIONS
AAL
ABL
ACA
ACD
ACTH
ADAM-17
AIA
AMP
ANH
ANOVA
APC
APP
ASBT
BCL2
BM
BSA
BST
CAA
CARS
C-BST
CCL
CCM
CCR-2
CD
CPT
CRF
CRP
CXCL
CXCR
DAMPs
DC
DMSO
DSA
EGFR
ELISA
FCS
FGF
FSAP
GalNAc
G-CSF
GFP
GI
Aleuria aurentia lectin
Agaricus bisporus lectin
Amaranthus caudatus agglutinin
Acid Citrate Dextrose
adreno-cortico-tropic-hormone
A Disintegrin And Metalloproteinase - domain 17
Artocarpus integrifolia lectin
antimicrobial peptide
Acute normovolemic hemodilution
Analysis of variance
antigen presenting cells
Acute Phase Proteins
Autologous salvaged blood transfusion
B-cell lymphoma 2
Bone marrow
Bovine serum albumin
Biomarkers of sterile trauma
Caragana arborescens lectin
Compensatory Anti-inflammatory Response Syndrome
Common Biomarkers of Sterile Trauma
Chemokine Ligand
complete culture medium
C-C chemokine receptor type 2
Cluster of Differentiation
cell preparation tube
corticotrophin releasing factor
C Reactive Protein
C-X-C motif chemokine ligand
CXC chemokine receptor
damage associated molecular patterns
dendritic cells
Di-methyl sulfoxide
Datura stramonium Agglutinin
epidermal growth factor receptor
Enzyme linked immuno-sorbent assay
Foetal calf serum
fibroblast growth factor
Factor VII-activating protease
N-Acetylgalactosamine
granulocyte colony-stimulating factor
Green fluorescence protein
Gastrointestinal
XII
GlcNAc
gp130
Hb
Hct
HIF
HIV
HLA-DR
HMGB-1
HPE
HRP
HSP
ICU
IDR-peptides
IFN-γ
Ig
ILIL-1RA
IQR
kDa
KGF
LacNAc
LBP
LC MS
LEL
LPS
MARS
MCP-1
MDC
MDSCs
MFI
MHC
MIP-1α
MOF
MSC
NeuAc
NFkB
NHS
NK
NKp
NLR
NO
NSAID
NSBT
PAMP
PBMC
N-Acetylglucosamine
Glycoprotein-130
Haemoglobin
haematocrit
hypoxia induced transcription factor
human immunodeficiency virus
human leukocyte antigen - DR
High-mobility group box 1 protein
hypothalamus-pituitary-endocrine
horseradish peroxidase
Heat shock protein
Intensive care unit
Innate defence regulator peptides
Interferon-γ
Immunoglobulin
Interleukin Interleukin-1-receptor antagonist
inter-quartile-range
kilo Dalton
Keratinocyte growth factor
N-Acetyl-D-lactosamine
Lipopolysaccharide binding protein
Liquid chromatography - Mass spectrometry
Lycopersicon Esculentum (Tomato) Lectin
Lipopolysaccharide
Multiple-affinity removal spin
Monocyte Chemoattractant Protein - 1
Myeloid dendritic cells
myeloid derived suppressor cells
Mean fluorescence intensity
major histocompatibility complex
Macrophage Inflammatory Protein - 1α
Multiple organ failure
mesenchymal stromal cells
N-acetylneuraminic acid
nuclear factor kappa-light-chain-enhancer of activated B cells
National health service
Natural Killer
Natural killer cell precursor
NOD-like receptor (nucleotide-binding oligomerization domain receptors)
nitrous oxide
Non-steroidal anti-inflammatory drugs
No salvaged blood transfusion
Pathogen associated molecular patterns
peripheral blood mononuclear cells
XIII
PBS
PD-1
PDC
PE
PGE2
PHA-E
PHA-L
PICS
PTI
RBC
RCA-1
RFU
rIFN-γ
ROS
RT
S-100A8/A9
SAA
SBS
sCD14
SD
SDF-1
sgp130
Siglec
sIL-6
sIL-6R
SIRS
SNA
sTNF-R
TGF-β
TIMP
TKA
TLR
TMT
TNF-α
TSB
TSG-6
UTI
vCJD
WBC
WGA
WSB
β-Gal
Phosphate buffered saline
Programmed Cell Death - 1
Plasmacytoid dendritic cells
Phycoerythrin
Prostaglandin-E2
Phytohemagglutinin-Erythrocytes
Phytohemagglutinin-Leukocytes
persistent inflammation, immunosuppression, and catabolism syndrome
Post-Traumatic Immunosuppression
red blood cells
Ricinus communis agglutinin - 1
Relative fluorescence unit
Recombinant IFN-γ
Reactive oxygen species
Room temperature
S100 proteins A8/A9; also called Calgranulin
Serum Amyloid-A
Salvaged Blood Sensitive
Soluble CD-14
Standard deviation
Stromal cell-derived factor-1
Soluble Glycoprotein-130
Sialic acid-binding immunoglobulin-type lectins
Soluble Interleukin-6
Soluble Interleukin-6 receptor
Systemic Inflammatory Response Syndrome
Sambucus nigra bark lectin
Soluble tumour necrosis factor receptor
Transforming growth factor - beta
tissue inhibitors of metalloproteinases
Total Knee Arthroplasty
Toll like receptor
Tandem Mass Tag
Tumour Necrosis Factor - α
transfused salvaged blood
TNF-α stimulated gene/protein 6
Urinary tract infections
variant Creutzfeldt–Jakob disease
white blood cells
Wheat germ agglutinin
wound site blood
Beta-Galactoside
XIV
LIST OF FIGURES
Chapter – I : Introduction
Fig. 1.1
Coxcomb Diagram of Florence Nightingale
5
Fig. 1.2
SIRS-CARS paradigm
7
Fig. 1.3
Persistent inflammation, immunosuppression and catabolism
syndrome
8-9
Fig. 1.4
CD24-Siglec-G pathway discriminates between Sterile Trauma
and Infectious Sepsis
11
Fig. 1.5
Cartoon of hypothetical events following sterile trauma and
infection
13
Fig. 1.6
IL-6 Classical Signalling and Trans-Signalling
25
Chapter – II : Materials and Methods
Fig. 2.1
Dideco-797 recovery device for postoperative autologous
salvaged blood from knee joint replacement operation site.
41
Fig. 2.2
Experimental Design
43
Fig. 2.3
Isolation of mononuclear cells using cell preparation tube
44
Fig. 2.4
Isolation of plasma using protease inhibitor tubes (P100 Tube)
46
Fig. 2.5(A)
Different Steps of Flow Cytometric Bead Array
47
Fig. 2.5(B)
Gating Strategy for Flow Cytometric Bead Array Technique
47
Fig. 2.6
JANUS automated liquid handling system.
51
Fig. 2.7
Depletion of proteins by MARS cartridge
54
Fig. 2.8
Schematic Diagram of Tandem Mass Tagging.
57
Chapter – III : Characterization of Post-Traumatic Immunosuppression
(PTI) and the Effect of Autologous Salvaged Blood Transfusion on PTI
Fig. 3.1
Damage Associated Molecular Patterns
67 – 68
Fig. 3.2
Pro-inflammatory Cytokines
69 – 70
Fig. 3.3
Anti-inflammatory Cytokines
71 - 72
Fig. 3.4
Chemokines
73
Fig. 3.5
Interleukin-22
74
Fig. 3.6
sIL-6R, sgp130, and ADAM-17
75
XV
Fig. 3.7
Soluble CD14
76
Fig. 3.8
Lysozyme Activity
77
Fig. 3.9
CD-24, Siglec-10 and Siglec-2
78
Fig. 3.10
Complement C5a
79
Fig. 3.11(A-F)
Biomarker Profiles of PBMCs Challenged with LPS
81
Fig. 3.11(G-H) Biomarker Profiles of PBMCs Challenged with LPS (Cont.)
82
Fig. 3.11(I-J)
Biomarker Profiles of PBMCs Challenged with LPS (Cont.)
83
Fig. 3.11(K-N) Biomarker Profiles of PBMCs Challenged with LPS (Cont.)
84
Chapter – IV : Constituents of Salvaged Blood
Fig. 4.1
Damage Associated Molecular Patterns
93 - 94
Fig. 4.2
Pro-inflammatory Cytokines
95 - 96
Fig. 4.3
Anti-inflammatory Cytokines
97 - 98
Fig. 4.4
Chemokines
99
Fig. 4.5
Interleukin-22 and Keratinocyte Growth Factor
100
Fig. 4.6
sIL-6R, sgp130, and ADAM-17
101
Fig. 4.7
Soluble CD14
102
Fig. 4.8
Lysozyme Activity
103
Fig. 4.9
CD-24, Siglec-10 and Siglec-2
104
Fig. 4.10
Complement C5a
105
Chapter – V : Proteomic and Glycomic Profiling of Salvaged Blood
Fig. 5.1
One-Dimensional gel electrophoresis of venous and salvaged
blood plasma samples.
112
Fig. 5.2
Alterations in TMT-profiles between the studied plasma samples
114
Fig. 5.3 (A)
Heat map analyses on the complete proteomic profile of the
study samples (No clustering).
116
Fig. 5.3 (B)
Heat map analyses on individual clusters obtained from the
complete proteomic map (Clusters 1 to 3)
117
Fig. 5.4
Lectin Array Data for venous and salvaged blood plasma
samples from individual patient.
Fig. 5.5
Heat map analyses on lectin binding specificity of venous and
salvaged blood samples from individual patient.
XVI
119 - 120
121
Chapter – VI : Discussion
Fig. 6.1
Biomarkers to Assess PTI following Major Surgery
128
Fig. 6.2
Reversion of PTI by reinfusion of salvaged blood
128
Fig. 6.3
Radar plot of the postoperative fold changes in the biomarkers of
sterile trauma; distinguishing two distinct panels of biomarkers
of sterile trauma: common biomarkers and salvaged blood
sensitive biomarkers
129
Fig. 6.4
Radar plot of fold changes in different biomarkers of sterile
trauma in the surgical wound site and in the transfused salvaged
blood; distinguishing two distinct panels of biomarkers in the
transfused blood: stable biomarkers and dynamic biomarkers
133
Fig. 6.5
Radar plot of fold changes in different biomarkers of sterile
trauma in the five-hourly incubated venous blood
134
Fig. 6.6
Hypotheses on the immunological responses mediated by soluble
forms of CD24 and Siglec-10
140
Chapter – VII : Appendices
Fig. 7.1
Effect of LPS addition on sCD-14 production by fresh PBMCs
and human whole blood culture
156
Fig. 7.2
Effect of LPS addition on pro-inflammatory cytokine production
by fresh PBMCs
157
Fig. 7.3
Simulated Salvaged Blood Results.
Fig. 7.4
Effect of lysozyme on production of different biomarkers in
human whole blood culture
XVII
158 - 159
160
LIST OF TABLES
Chapter – I : Introduction
Table. 1.1
Lists of studies investigating the changes in Biomarker levels
in the surgical wound site.
15
Table. 1.2
Lists of investigations on changes in Biomarker levels in
postoperative venous blood following major surgery
22
Chapter – II : Materials and Methods
Table. 2.1
Details of Biomarkers Assessed
Table. 2.2
Bradford protein detection assay set-up
49 - 50
55
Chapter – III : Characterization of Post-Traumatic Immunosuppression
(PTI) and the Effect of Autologous Salvaged Blood Transfusion on PTI
Table. 3.1
Patient Characteristics and Haematological Information
66
Table. 3.2
Common Biomarkers of Sterile Trauma (C-BST)
86
Table. 3.3
Salvaged Blood Sensitive Biomarkers of Sterile Trauma
(SBS-BST)
87
Table. 3.4
Spearman Correlation between changes in different
biomarkers in NSBT
89
Table. 3.5
Spearman Correlation between changes in different
biomarkers in ASBT
91
Chapter – IV : Constituents of Salvaged Blood
Table. 4.1
Constituents of Salvaged Blood at One Hour (WSB) and Six
Hours (TSB) After Surgery
107
Table. 4.2
Correlations on fold changes between different Biomarkers
in the surgical wound site (WSB/Peripheral)
109
Table. 4.3
Correlations on fold changes between different Biomarkers
during the blood collection period (TSB/WSB)
110
XVIII
Chapter – V : Proteomic and Glycomic Profiling of Salvaged Blood
Table. 5.1
Summary of Mass Spectrometric Analysis of the study
samples
114
Chapter – VIII : Appendices
Table 7.1
Levels of Biomarkers in Wound Blood
Table 7.2
Levels of Biomarkers Postoperatively Following Major
Surgery
147 - 150
Table 7.3 (A)
Correlation Between Biomarker Levels and Length of
Hospital Stay
151
Table 7.3 (B)
Correlation Between Biomarker Levels and Drained Blood
Volume
152
Table 7.3 (C)
Correlation Between Biomarker Levels and Transfused
Blood Volume
153
Table 7.4
Pre- and Post-Operative Levels of Different Biomarkers
154
Table 7.5
Levels of Different Biomarkers in Peripheral and Salvaged
Blood
155
Table 7.6
Details of Biomarkers Assessed by Tandem Mass Tagging
161 - 169
Table 7.7
List of Reagents
179
Table 7.8
Cross-Reactivity of Measured Biomarkers
182
XIX
146
CHAPTER – I
INTRODUCTION
Chapter - I
Introduction
1.1 Post-traumatic Immunosuppression
Every organism, from both the plant and animal kingdoms are exposed to
infections and/or different accidental or environmental injuries countless times in
their life. To combat these insults, organisms have developed self-defence
mechanisms that have co-evolved with that of animals and plants. Single-cellular
organisms from both prokaryotes and eukaryotes develop defence mechanisms
against viruses. Plants also develop defences against injuries induced by infectious
pathogens, environmental damage (e.g. rain or wind) or from the activities of
herbivores. Organisms in the oceans also develop defences to combat infectious
pathogens or climate changes such as acidification or warming of oceanic water.
The non-specific or “innate” immune system has been protecting plants and
animals (both vertebrates and invertebrates) from infections for billions of years. The
innate immune system is activated once there is engagement between cell surface
receptors expressed by certain cell types and stimulatory molecules. The number and
complexity of cell surface receptors mediating recognition by innate immune cells
are limited. These receptors generally recognise common elements shared by
stimulatory molecules and cannot distinguish between the bindings of different,
closely related, structural compounds. The former are known collectively as “pattern
recognition receptors (PRRs)”. Patterns in molecules from different pathogenic
microorganisms are recognized by these PRRs but they cannot distinguish between
the similar molecules expressed by different pathogens. Thus, the innate immune
response is a prototypic response lacking specificity. To develop a degree of
specificity in immune responses, the so-called adaptive immune system evolved.
Adaptive immunity is initiated by the activation of lymphocytes whose receptors for
antigen recognition are generated by somatic rearrangement of receptor-coding
genes, are clonally distributed and are of exquisite specificity. In addition, because
the specific immune response involves both clonal expansion and differentiation, the
adaptive immune response also shows memory, the hallmark of such a response.
In parallel to the body‟s response to infections, recent studies support that,
following non-infectious accidental/environmental injury, the affected cells/tissues
upregulate the secretion of endogenous signalling molecules that PRRs can
2
Chapter - I
Introduction
recognize, a phenomenon that also exists in primitive organisms. This indicates the
evolutionary development of immunity to facilitate self-survival. Heat shock proteins
(HSPs), as one of the ancient stress proteins, are systemically upregulated in man
following trauma (Szerafin, Hoetzenecker et al. 2008). HSP are conserved among
vertebrates and invertebrates (Klaenhammer, Kleerebezem et al. 2012) as well as
plants (Goodnough, Brecher et al. 1999). Thus endogenous signalling molecules
reflect the evolution of innate immunity.
Recent investigations show that, accidental injury or other infectious- or noninfectious trauma results in initial inflammatory events followed by a transient antiinflammatory response whose function is presumably to limit collateral damage
initiated by the former inflammatory response. The balance between these two events
therefore directs whether the patient would be immunologically suppressed or hyperactivated following injury. This thesis will deal with a careful analysis of the
responses of a group of patients to non-infectious surgical trauma. Before further
deliberation on immunological responses to injury/infection, below are definitions of
the frequently used terms in this thesis:
1.1.1
Definitions:
1.1.1.1 Inflammation Inflammation has many definitions.
Clinical Concept – John Hunter, the father of modern surgery first described
his own experiences about the medical situations of wounded soldiers in his
book “A Treatise on Blood, Inflammation and Gun-Shot Wounds” (Hunter
1794). His outstanding surgical and experimental observations on
“inflammation” suggested four clinical signs namely redness, heat, swelling,
and pain. The terminology of “Inflammation” by Hunter had no clear
understanding about the immunological responses. Additionally John‟s
studies also overlooked the fact “inflammation caused by acquired
infections” in the wounded subjects.
Immunological Concept – Inflammation became an important term in
immunology mainly since last few decades. Pro-/Anti-inflammatory
paradigm is the basic concept of inflammation that reflected in balance
between two opposing cytokine networks that activate/suppress immunity.
3
Chapter - I
Introduction
1.1.1.2 Sterile Trauma refers to tissue damage devoid of primary wound infection,
and is best exemplified by elective open surgery, for example in knee joint
arthroplasty. Inflammation following sterile trauma without any exposure to
microbial pathogens is termed as „sterile inflammation‟ (Chen and Nunez
2010).
1.1.1.3 Immune status is the level of appropriately targeted resistance to internal,
opportunistic and external pathogenic micro-organisms, where resistance is
attributable to multiple innate or acquired mechanisms located throughout the
organism. There is no single measure of immune status, but rather multiple
biomarkers that make up a profile relevant to efficacy of broad aspects of
innate and adaptive immunity.
1.1.1.4 Post-Traumatic Immunosuppression (PTI) is the condition of suppressed
immune status that follows sterile trauma. It ranges from mild to severe,
where mild is exemplified by strenuous exercise and severe by
immunosuppression induced by multiple extensive polytrauma or major open
surgery.
1.1.2. Historical Context of PTI
In 1856 Florence Nightingale, „the lady with the lamp’ drew attention to the
„utter insignificance‟ of risk of dying from battle wounds acquired during the
Crimean War compared to the risk of dying from subsequent zymotic (infectious)
diseases acquired within the Scutari Hospital (Florence 1999). Her meticulous data
collection on mortality of British soldiers, and generation of “Coxcomb Diagram”
established her as one of the earliest biostatisticians. In that revolutionary diagram
Nightingale reported more deaths due to zymotic diseases till March 1855 as
represented in the blue colour, compared to the deaths due to wound as represented
in pink colour. But, after arrival of sanitation commissions at April 1855, deaths from
the zymotic diseases became dramatically reduced continuously at the following
months.
4
Chapter - I
Introduction
Figure 1.1: Coxcomb Diagram of Florence Nightingale
Nightingale‟s continuous improvements in hospital hygiene gradually reduced
deaths from infectious diseases in patients with combat injuries. However more and
more new infections are still threatening the battle wound patients (Murray 2008,
Warkentien, Rodriguez et al. 2012). And therefore, although mortality rate in combat
injury decreased accordingly with continuous advancement in research, deaths from
systemic and battle wound infections are being reported even in this modern days.
An important but poorly highlighted question was “why the wounded patients
were acquiring systemic infections even in the hygienic environment?” Research of
last two decades opened the window and “imbalanced immunity in patients following
trauma” is that important factor that increases patients‟ vulnerability to acquire
infections. Therefore, although sanitation has been improved in the hospitals, an
equivalent phenomenon of post-traumatic deaths from systemic infections persists to
this day.
1.1.3. Systemic Inflammatory response Syndrome (SIRS)
In 1991 Dr Roger Bone introduced the term „Systemic Inflammatory
Response Syndrome (SIRS)” to describe physiological changes common to all cases
of burn or trauma, irrespective of cause. This paradigm included four physiological
changes namely, increased body temperature, elevated heart rate, tachypnea or
5
Chapter - I
Introduction
hyperventilation, and leukocytosis or lekocytopenia (Bone, Balk et al. 1992). Until
now SIRS has been the dominant paradigm adopted by clinicians worldwide, despite
its obvious limitations. For example, body temperature can increase following sepsis
and sterile trauma, but also after myocardial infarction, pulmonary embolism and
strenuous exercise. Elevated heart rate and tachypnea can occur following sepsis and
sterile trauma, but other physiological complications such as cardiac and respiratory
failures, hypovolemic shock and erythropenia can also affect these parameters. On
the other hand alteration in white blood cell count also can happen in different
disease conditions such as heart failure, pancreatitis and burns.
Five years later, Bone modified his SIRS paradigm by adding a sequel named
“Compensatory Anti-inflammatory Response Syndrome (CARS)”. CARS was
characterized by decrease in antigen presentation, macrophage paralysis, decrease in
T-cell proliferation, increase in T-cell and dendritic cell apoptosis, and shift in the Tcell subsets from Th1 to Th2 phenotype (Bone 1996).
In 2001, at the “International Sepsis Definitions Conference” SIRS-CARS
paradigm was expanded to include many more signs and symptoms (Levy, Fink et al.
2003). However, these concepts fitted poorly with the immuno-functional parameters
and failed to anticipate the clinical outcomes (Costa, Benedetto et al. 1989, Burton,
Nicholson et al. 2004, Buvanendran, Kroin et al. 2006, Philippou, Maridaki et al.
2012, Prockop and Oh 2012).
In particular, the SIRS-CARS paradigm fails to
distinguish sterile trauma from sepsis.
In 2011 Xiao et al proposed a modification of SIRS-CARS based on
observations of “A genomic storm” in critically injured patients (Xiao, Mindrinos et
al. 2011). Whereas SIRS-CARS after severe injury, conceived of initial SIRS
followed by suppression of adaptive immunity (CARS), followed by a second hit
leading to organ dysfunction (Fig 1.2A), their proposed paradigm (Fig 1.2B)
conceived of immediate induction of pro- and anti-inflammatory cytokine genes
involved in innate immunity and suppression of genes of adaptive immunity. Xiao
rejected the “second hit phenomenon” and further hypothesized that these delayed
recovery in patients who have complicated clinical outcomes was due to prolonged
imbalanced immunity.
6
Chapter - I
Introduction
A
Figure 1.2 : SIRS-CARS paradigm and proposed paradigm by Xiao et al (Xiao,
Mindrinos et al. 2011). SIRS: Systemic Inflammatory Response Syndrome; CARS:
Compensatory Anti-inflammatory Response Syndrome. (A) SIRS-CARS paradigm leading to death
following severe sepsis and injury; a consequence of initial overproduction of pro-inflammatory
mediators that leads to endothelial and tissue damage and thus multiple organ failure. After survival
following this initial SIRS phase, a CARS phase results in the suppression of adaptive immunity. A
second insult to these patients such as injury/surgery/nosocomial infection results may lead to
recurrent SIRS, called the „„second-hit‟‟. (B) Proposed model of simultaneous induction of both proinflammatory and anti-inflammatory genes involved in innate immunity whereas suppression of the
genes involved in adaptive immunity.
7
Chapter - I
Introduction
Xiao et al‟s paradigm was derived from studying injured patients in a multicentric study where all the post-injury collected samples varied from one to twelve
hours after injury. Furthermore, gene expression data did not necessarily reflect
levels of active proteins. Nonetheless they showed elevations in Interleukin-6 (IL-6),
Interleukin-1 receptor antagonist (IL-1RA), Interleukin-8 (IL-8) and Monocyte
chemoattractant protein-1 (MCP-1) (Xiao, Mindrinos et al. 2011).
Another concept is termed as “persistent inflammation, immunosuppression,
and catabolism syndrome” (PICS) (Gentile, Cuenca et al. 2012). Trauma or sepsis
induces an immediate release of myeloid derived suppressor cells (MDSCs) from the
bone marrow which populates the haemopoietic organs and suppresses both
proliferation and function of T-cells (Makarenkova, Bansal et al. 2006, Gentile,
Cuenca et al. 2012). Poor nutritional status, delayed wound healing and increased
susceptibility to infections are associated with protein loss for a prolonged period
following surgical trauma with a persistent low grade inflammation accompanying
imbalanced immunity (decreased T-cell proliferation, decreased functional capacity
of macrophages and T-cells, and increased MDSCs) as shown in Figure 1.3 (Gentile,
Cuenca et al. 2012). However, this concept was based mostly on animal models. It
would be important to see whether preventing the suppressive functions of MDSCs
and/or inhibiting the protein catabolism by nutritional therapies would benefit the
immune-compromised patients following sterile trauma or sepsis.
A
8
Chapter - I
Introduction
B
Figure 1.3 : Persistent inflammation, immunosuppression, and
catabolism syndrome – proposed by Gentile (Gentile, Cuenca et al. 2012).
Following the initial insult (sepsis/trauma) simultaneous SIRS-CARS events in patients resulted in the
longer hospital stay of the patients in ICU with controllable organ dysfunctions; however patients met
no established criteria for late multiple organ failure. These patients experienced protein catabolism,
poor wound healing and recurrent infections. Patients had persistent low-grade inflammation with
defects in their innate and adaptive immune system. These defects included: macrophage paralysis,
persistent increased myeloid derived suppressor cell numbers, decreased effector T-cell number and
function.
1.2. Distinguishing Sterile Trauma from Sepsis
One of the key unanswered questions at the present time in the field of
inflammation research is how to distinguish PTI induced by sterile trauma from that
induced by sepsis.
In a recent publication Savage et al analysed the secretory
capacity of glial cells stimulated with different Damage Associate Molecular Patterns
(DAMPs; to mimic the sterile trauma condition) as well as the Pathogen Associated
Molecular Pattern (PAMP) molecule such as LPS (to mimic sepsis). Differences
were seen in the secretions of IL-1β by the cells: increased if stimulated by PAMPs
but unchanged if stimulated by DAMPs. However, secretion of IL-6 and CXCL-1
were identical in both situations (Savage, Lopez-Castejon et al. 2012). This study
thus highlighted the differences in cellular responses following sterile versus non9
Chapter - I
Introduction
sterile activation and open new avenues to investigate the consequences of
inflammasome activation on these processes.
Johnson et al also showed significant variations in the gene expression profile
of whole blood cells taken from patients with sterile trauma versus early sepsis
(Johnson, Lissauer et al. 2007). Results obtained highlighted numerous genes that
were preferentially increased in early sepsis. These unique genes could be subdivided
into four broad categories namely: innate immunity, cytokine receptors, T helper cell
differentiation, and protein synthesis (Johnson, Lissauer et al. 2007). Although their
study built a platform for future understanding the distinction between sterile trauma
and sepsis, gene expression analysis needed to be confirmed at the protein levels
with additional proteomic studies.
Liu et al distinguished certain differences between sterile trauma and sepsis
(Liu, Chen et al. 2009). Thus, following sterile trauma, secreted DAMPs made a trimolecular complex with the membrane bound CD24 molecule and the transmembrane glycoprotein sialic-acid binding immunoglobulin type lectin-10 (Siglec10). This complex then inhibits TLR/NLR-mediated inflammation. In contrast,
following microbial infections, both PAMPs and released DAMPs interact with
TLR/NLR mediated inflammatory reactions. This is due to pathogen encoded
sialidase enzyme that prevents the interaction between the DAMPs/CD24 pair and
Siglec-10; thereby preventing the formation of tri-molecular complex (Chen, Tang et
al. 2009, Liu, Chen et al. 2009, Chen, Chen et al. 2011). These in vivo mouse models
and in vitro human studies need to be validated in a clinical setting. CD24 and
Siglec-10 are also present in the circulation as soluble molecules. Figure 1.4
schematically represents the mechanisms of membrane bound CD24 and Siglec-10 in
sterile and infectious situations.
10
Chapter - I
Introduction
Figure 1.4: CD24-Siglec-G pathway discriminates between Sterile Trauma and
Infectious Sepsis. This is a schematic diagram to describe how sepsis and sterile trauma can be
distinguished. Following injury, DAMPs make complex with CD24 which then bind to Siglec-10.
This tri-molecular complex can then inhibit TLR-NFkB mediated inflammatory activities. However,
in presence of microbes, PAMPs from microbes and DAMPs from the host both bind to the TLR/NLR
receptors thereby promoting inflammation. Pathogen encoded Sialidase enzyme that can block the
binding between CD24 and Siglec-10 thereby stop the immunosuppressive mechanism.
11
Chapter - I
1.3.
Introduction
Immunological changes following sterile trauma
Sterile trauma involves both local and systemic responses representing a
complex balance between pro- and anti-inflammatory mediators. Numerous studies
have described the immune pathways in relation to sterile trauma (Flohe, Flohe et al.
2008, Chen and Nunez 2010, Rock, Latz et al. 2010). Briefly, at the trauma site,
there is an immediate release of endogenous danger signals named damage
associated molecular patterns (DAMPs) which increases local production of different
cytokines, chemokines, and other soluble factors by resident cells. Subsequently, via
DAMPs and chemokines, there is further recruitment of neutrophils, monocytes and
mesenchymal stromal cells (MSC) into the local site of inflammation (McDonald,
Pittman et al. 2010, Shi and Pamer 2011, Han, Jing et al. 2012, Kolaczkowska and
Kubes 2013). These newly recruited cells differentiate locally into cells secreting
predominantly anti-inflammatory cytokines (Arnold, Henry et al. 2007, Han, Jing et
al. 2012). Additionally, neutrophils and monocytes in the injured site are guided by
DAMP induced inflammasomes to play a role in apoptosis/necroptosis/NETosis and
controlled clearing of damaged tissues (Kaczmarek, Vandenabeele et al. 2013).
DAMPs also trigger the release of IL-6 that plays important role in hypothalamuspituitary-endocrine (HPE) axis to release steroids and in turn triggers the release of
other immunosuppressive mediators to control inflammations at the wound. IL-6 also
induces hepatocytes to release acute phase proteins to mediate post-traumatic antiinflammatory activities. All the cells and soluble factors at the inflamed site
contribute to tissue remodelling and wound healing (Chen and Nunez 2010, Rock,
Latz et al. 2010). Although the above concept is established, the systemic
manifestations of sterile trauma are poorly described and not clearly distinguished
from the sequel of events following sepsis. In case of sepsis, PAMPs from
microorganisms and endogenous release of DAMPs, both trigger the inflammatory
activities at the site of infection. Role of IL-6 in sepsis is not distinguishable from
that in sterile trauma.
Sepsis may accelerate differentiation of local/recruited
immune cells into inflammatory phenotype or delayed apoptosis; whereas in sterile
trauma there may be an evolutionary control on this events. This control of local
inflammation following sterile trauma may be facilitated by CD24/Siglec-10
mediated immunosuppressive pathways and also by other yet unknown mechanisms.
12
Chapter - I
Introduction
Figure 1.5 schematically describes the sequel of events following sterile trauma and
sepsis.
Figure 1.5: Cartoon of hypothetical events following sterile trauma and
infection. Immediate release of endogenous danger signals also called DAMPs trigger the release of
a panel of chemokines and cytokines in the injured site. This leads to the recruitment of immune cells
to the site of tissue injury from the reservoirs bone marrow and spleen as well as from the circulation.
On the other hand injury triggers the release of IL-6 that stimulates the HPE axis to release
corticotrophin releasing hormone which in turn releases ACTH and subsequently cortisol. Cortisol
plays important role in elevating the levels of IL-6, IL-RA, IL-8 and IL-10 in blood. Additionally, IL6 also triggers the release of acute phase proteins by the liver, which also plays immunosuppressive
role in the site of injury. The recruited immune cells plays their role accordingly to favour apoptosis,
phagocytose the damaged tissue, and also triggers the proliferation and differentiation of the
appropriate immune cells to favour local wound healing. Following Infections, pathogen associated
molecular patterns from the microbes and endogenous DAMPs play the similar role as previously
described. However, importantly, the recruited immune cells at the site of infection may take longer
time due to delayed apoptosis leading to inflammation in the infected area.
1.3.1. Local Site of Trauma
1.3.1.1. Skin injury
Human skin acts as a physical barrier against foreign microbial infections,
aided by local production of antimicrobial peptides (AMP). Keratinocytes and
resident cells of the innate immune system reduce the epidermal microbial load
13
Chapter - I
Introduction
following transactivation by epidermal growth factor EGF which triggers the release
of AMPs (Sorensen, Thapa et al. 2006).
Injury to the skin results in immediate haemostasis, followed by infiltration of
neutrophils and macrophages and simultaneous production of cytokines such as
interleukin-6 (IL-6) and IL-8 (Sjogren and Anderson 2009). IL-6 plays a role in
fibroblast proliferation, collagen deposition and angiogenesis. Interestingly, a strong
correlation exists between local IL-6 concentrations and the speed of wound healing
(Lin, Kondo et al. 2003).
1.3.1.2. Muscle injury
Factors released by damaged muscles activate resident inflammatory cells,
which release chemotactic agents that signal circulating inflammatory cells to
infiltrate the damaged muscle. Subsequently, inflammatory cytokines are produced
locally. Damage to muscle results in increased production and secretion of IL-1β and
TNF-α, which triggers increased secretion of IL-6 and subsequent recruitment of
neutrophils to the injured tissue (Hashizume, Higuchi et al. 2011, Philippou,
Maridaki et al. 2012).
1.3.2.
Ischemia-Reperfusion injury
Major orthopaedic surgery is frequently carried out under tourniquet, creating
tissue hypoxia which following tourniquet release gives rise to ischaemia-reperfusion
injury to tissues. A key component of a cell‟s response to hypoxia is up-regulation of
hypoxia-induced-transcription-factor (HIF) expression (Kaelin and Ratcliffe 2008).
In hypoxic conditions, increasing HIF and Toll like receptor (TLR) signalling
synergistically activate the NFkB pathway thereby triggering recruitment of
monocytes, phagocytosis, release of IL-18, TNF-α and DAMPs (Kuhlicke, Frick et
al. 2007). HIF also inhibits apoptosis of recruited neutrophils at the site of injury
thereby prolonging their lifespan (Eltzschig and Carmeliet 2011). However, HIF upregulation in T-cells stimulates IL-10 production, inducing a shift from inflammatory
Th1 phenotype to anti-inflammatory Th2 phenotype (Eltzschig and Carmeliet 2011).
Hypoxia increases adenosine levels, which regulates innate immunity and controls
inflammation (Ohta and Sitkovsky 2001). Following tourniquet release, reperfusion
injury may also enhances the expression of inflammatory cytokines such as IL-17
14
Chapter - I
Introduction
that facilitate further recruitment of leukocytes to the site of injury (van Golen,
Reiniers et al. 2013); however DAMP driven immunological responses may direct to
anti-inflammatory events to facilitate the healing.
A literature review of studies showed considerable variations in the levels of
different soluble mediators in thesurgical wound site (1 to 48 hours after surgery;
collection time-point varied between studies) comparing with the preoperative
venous levels. This is summarized in Table 1.1 with more extensive results as shown
in Appendix-I. There is general agreement that increased levels of IL-1, IL-4, IL-6,
IL-8, IL-10, TNF-α, PGE2 and complement proteins- C3 and C5 were seen postoperatively.
Table – 1.1: Lists of studies investigating the changes in biomarkers levels in the
surgical wound site.
Biomarker
Type
Proinflammatory
Cytokines
Antiinflammatory
Cytokines
Chemokines
Others
Biomarker
Name
IL-1β
IL-2
IL-6 *
IL-12
IL-17
IFN-γ
TNF-α
IL-4
IL-5
IL-10
IL-13
IL-1RA
IL-8
MCP-1
MIP-1α
PGE2
sIL-6R
C3
C5
Platelet
No. of Investigations
Changes in wound site biomarker levels
relative to pre-operative baseline levels
Decrease No Change Increase N.D.
1
1
4
2
1
1
1
1
4
1
1
1
1
1
-
10
24
6
2
3
2
12
1
2
7
4
4
1
0
-
Total No.
of Studies
13
1
24
1
1
1
10
3
1
5
1
2
12
1
1
2
1
7
4
8
* IL-6 is a cytokine which recently has been described as an anti-inflammatory cytokine. Please see page 25
15
Chapter - I
Introduction
1.3.3. Hypothalamus - Pituitary - Endocrine axis
The response to sterile trauma involves activation of the hypothalamus-pituitaryendocrine (HPE) axis and subsequent changes in different hormonal levels. There are
complex functional inter-relationships between HPE and the immune system. Upon
trauma, corticotrophin releasing factor (CRF), a hypothalamic releasing substance
secreted by the anterior pituitary gland, immediately stimulates the release of adrenocortico-tropic-hormone (ACTH) (Burton, Nicholson et al. 2004). ACTH rapidly
stimulates production of glucocorticoid cortisol. The production of ACTH following
trauma overrides cortisol-dependent ACTH down-regulation, ultimately resulting in
continuous increases of both ACTH and cortisol (Burton, Nicholson et al. 2004).
Secretion of inflammatory cytokines such as IL-1, IL-6 and TNF-α has impacts on
the HPE-axis (Besedovsky, del Rey et al. 1991).
1.3.3.1. Prostaglandin E2 (PGE2)
Following sterile injury, PGE2, the breakdown product of arachidonic acid,
also plays an important role in immunological responses (Buvanendran, Kroin et al.
2006). By shifting the Th1 to Th2 balance, PGE2 inhibits hyper-inflammation and
promotes tissue repair (Gilroy, Colville-Nash et al. 1999, Fukunaga, Kohli et al.
2005, Wallace 2006). PGE2 crosses the blood-brain barrier to stimulate the
thermoregulatory centre responsible for febrile reactions associated with trauma
(Hamzic 2013).
1.3.4.
Changes in Lympho-haemopoiesis
Increases in the number of neutrophils, monocytes, and MSCs in blood reflect
recruitment of these cells from extra-vascular compartments (spleen and bone
marrow) after sterile injury (Seebach, Henrich et al. 2007, Tsou, Peters et al. 2007,
McDonald and Kubes 2011). In contrast, depending on the degree of injury,
reduction in the levels of red blood cells (RBC) and lymphocytes are seen (Kimura,
Shimizu et al. 2010). Effects of trauma on individual cell types are briefly discussed
below:
16
Chapter - I
Introduction
1.3.4.1. Neutrophils
During sterile trauma, neutrophils egress from the extra vascular spaces into
the circulation to be recruited to the site of injury. This neutrophil egression is
facilitated by the CXC chemokine receptors CXCR-4 and CXCR-2 (Suratt, Petty et
al. 2004). CXCR-4 ligands are involved in retaining neutrophils in the bone marrow,
but CXCR-2 ligands inhibit CXCR-4 dependant retention thereby encourages egress
(Suratt, Petty et al. 2004, McDonald and Kubes 2011). Granulocyte colonystimulating factor (G-CSF) also helps in the mobilization of neutrophils by altering
the balance between ligation of CXCR4 and CXCR2 (Suratt, Petty et al. 2004).
Sentinel neutrophils in peripheral blood can also be rapidly recruited to the site of
injury (Arancibia, Beltran et al. 2007, Kolaczkowska and Kubes 2013). By their
interaction with different damage-associated molecular patterns (DAMPs), these
sentinel cells release different inflammatory cytokines and chemo-attractants which
recruit additional neutrophils to the inflammatory site (Williams, Azcutia et al. 2011,
Kolaczkowska and Kubes 2013). Ubiquitin, an endogenous CXCR4 agonist, also
released following trauma and burn, indicate a natural defence by controlling the
extent of neutrophil release from the bone marrow to the site of injury (Majetschak,
Zedler et al. 2008, Majetschak 2011).
1.3.4.2. Monocytes/Macrophages
The mechanisms involved in the egression of monocytes from the bone
marrow to the injury site are still poorly understood. In mouse, Tsou et al illustrated
the recruitment of Ly-6+ monocytes from the bone marrow into the circulation by a
CCR-2 mediated pathway (Tsou, Peters et al. 2007, Shi and Pamer 2011). Additional
investigation suggested the spleen as another reservoir for monocyte recruitment to
damaged tissues (Swirski, Nahrendorf et al. 2009). Importantly, emigration of
monocytes from the bone marrow to the injured site is independent of neutrophil
recruitment (Henderson, Hobbs et al. 2003). Furthermore, decreased expression of
HLA-DR by circulating monocytes was also found in patients following trauma,
major surgery and burns (Hensler, Hecker et al. 1997, Kimura, Shimizu et al. 2010).
17
Chapter - I
Introduction
1.3.4.3. NK cells
Functional activity of natural killer (NK) cells as a first line responder of
innate immunity is decreased following traumatic injury (Blazar, Rodrick et al. 1986,
Gharehbaghian, Haque et al. 2004). Number of CD16+CD56+ NK cells was
decreased one day after surgery and decrease was reported even after one week
following knee joint replacement surgery (Munoz, Cobos et al. 2006).
Gharehbaghian et al also showed significant suppression of NKp frequencies at fifth
postoperative days following joint replacement surgery; however the exact CD
phenotype of that NKp was not determined (Gharehbaghian, Haque et al. 2004).
1.3.4.4. Dendritic Cells and other Antigen Presenting Cells
Antigen presenting cells (APC) such as dendritic cells (DC) interacts with
lymphocytes to trigger adaptive immunity. Kawasaki et al reported decreased antigen
presentation capacity by splenic DC in mice following trauma/haemorrhage
associated with decreased expression of MHC class II, IL-12 and IFN-γ (Kawasaki,
Fujimi et al. 2006). Total number of DCs although showed transient increase after
surgery, decreased at postoperative day 2-3 (Ho, Lopez et al. 2001). Recent studies
showed decreased myeloid DC (MDC) but no change in plasmacytoid DC (PDC) 3
to 5 days after surgery (Henrich, Maier et al. 2009). MDCs may possibly have been
recruited to the surgery site. Circulating MDCs but not PDCs were shown by Maier
et al to undergo apoptosis with overall increases in the expression of anti-apoptotic
markers (Maier, Geiger et al. 2009). Several studies in DCs from circulating blood
showed increased expressions of genes for chemotaxis including CCL5, CXCL5 and
CXCL4, anti-apoptosis (such as TIMP-1, BCL2), and inflammation (such as NF-kB)
(Maier, Wutzler et al. 2008, Maier, Geiger et al. 2009, Maier, Geiger et al. 2009,
Geiger, Maier et al. 2013). These may indicate a role for DC in the recruitment of
innate immune cells at the injury site.
1.3.4.5. Myeloid derive Suppressor Cells (MDSCs)
MDSCc are a mixed cell populations originated from the myeloid origin and
are able to suppressing T cell responses. Numbers of CD14+ APC cells were found to
be significantly elevated following surgery for at least 24 hours with relatively more
increases in CD16+, CD80+, and CD86+ APCs (Albertsmeier, Quaiser et al. 2014).
18
Chapter - I
Introduction
More importantly, there was a massive increase in the number of CD14+HLADRmyeloid derived suppressor cells (MDSCs) (Filipazzi, Valenti et al. 2007), which
may be involved in immunosuppression following trauma (Cuenca, Delano et al.
2011). However, future phenotypic and functional studies are needed to confirm the
subtype of these CD14+ APCs and their role in trauma induced immunological
responses.
1.3.4.6. Lymphocytes
A significant decrease in total CD3+ T-lymphocytes following trauma is
associated with shifting from Th1 to Th2 phenotype mediated by regulatory T-cells
(Hensler, Hecker et al. 1997, Marik and Flemmer 2012). Albertsmeier et al showed
decrease in counts for CD3+, CD4+ and CD28+ T-cells immediately after surgery and
no changes in CD8+ T cell subsets; whereas increased CD4+CD25+CD127regulatory T-cells (Tregs cells) that are involved in shifting from Th1 to Th2
phenotype (Albertsmeier, Quaiser et al. 2014). They hypothesized that, suppression
of T-cells following trauma is associated with increased Tregs cells and MDSCs
(Albertsmeier, Quaiser et al. 2014). In vitro stimulation studies also showed reduced
secretion of IFN-γ, IL-2 and TNF-α by postoperative T-cells (Hensler, Hecker et al.
1997). On the other hand, Munoz et al showed no postoperative changes in CD19+ B
lymphocyte counts from first postoperative day until seventh (Munoz, Cobos et al.
2006).
1.3.4.7. Eosinophils and Basophils
Postoperative eosinophil counts greatly decline and then return to baseline
after two days (Roche, Thorn et al. 1950). Decreases may be associated with the
increased ACTH secretions through signalling by the HPE-axis (Roche, Thorn et al.
1950, Burton, Nicholson et al. 2004). Basophils also decreased one day after surgery
and returned to normal at day five and there is significant release in histamines
(Laroche, Chrysanthou et al. 1992). However, phenotypic changes eosinophils and
basophils following major surgeries are poorly documented.
19
Chapter - I
Introduction
1.3.4.8. Mesenchymal Stromal Cells
Recent investigation showed increased numbers of circulatory mesenchymal
stromal cells (MSC) in the peripheral blood of burn and trauma patients (Mansilla,
Marin et al. 2006, Seebach, Henrich et al. 2007). There appears to be a direct
correlation between the proliferation rate of cultured bone marrow MSCs from
patients with multiple traumas and trauma severity of their (Seebach, Henrich et al.
2007). How MSC egresses from bone marrow and are recruited to the injury site
remains unclear.
1.3.5.
Changes in blood plasma
Alteration in soluble biomarkers in blood plasma following trauma are briefly
outlined below.
1.3.5.1. Cytokines
Complex changes in the levels of different cytokines occur after trauma. The
most common changes are in IL-6. Miller et al showed a post-traumatic decrease of
IL-2, IFN-γ, and IL-12 levels, whereas IL-10 and IL-4 levels were increased. This
was associated with a shift from Th1 to Th2 T-cell responses (Miller, Rashid et al.
2007). A comprehensive list of 109 investigations at different postoperative time
periods is summarized in Table-1.2 and further detailed Appendix-II.
1.3.5.2. Chemokines
Increased chemokines play important roles in the recruitment of mononuclear
phagocytic cells from the BM and spleen reservoirs to the site of injury (Gale and
McColl 1999). IL-8/CXCL-8 rises transiently following surgery; but drops back to
baseline within few days. Table 1.2 and Appendix Table II reported postoperative
changes in chemokines such as IL-8, MCP-1 and MIP-1α following surgical insults.
1.3.5.3. Damage Associated Molecular Patterns (DAMPs)
DAMPs, also called endogenous danger factors or alarmins, are a class of
molecules that play vital role in the recruitment of immune cells to the site of injury
with increase in the production of chemo-attractants and cytokines. DAMPs are
released at the wound site during the trauma period and examples include HMGB-1,
S100 proteins, heat shock proteins and α-defensins (Manson, Thiemermann et al.
20
Chapter - I
Introduction
2012). DAMPs in the venous blood would help better characterization of posttraumatic immunity, but are still poorly understood. Table 1.2 summarizes
postoperative changes in DAMPs studied to date.
1.3.5.4. Other Soluble factors
The levels of haemoglobin and haematocrit drop following major surgery
(Khurana, Zafar et al. 2012) possibly due to the blood loss, haemodilution,
inflammation and other reasons . Complement activation produces such as C5a and
C3a increase post-operatively (Burk, Martin et al. 2012). Release of histones and
nucleosomes from the damaged cells allows factor VII-activating protease (FSAP) to
trigger complement pathway and generation of C5a (Kanse, Gallenmueller et al.
2012). Soluble CD-14, a marker of monocyte activation increases following surgery
(Bastian, Tamburstuen et al. 2011); whereas decreases reported in soluble IL-6
receptor (sIL-6R) (Kristiansson, Soop et al. 1998) and soluble gp-130 (Corbi,
Rahmati et al. 2000). In knee arthroplasty Munoz et al showed decreased levels of
different immunoglobulins (IgG, IgA, and IgM) at six hours and remained
persistently low for at least three days (Munoz, Cobos et al. 2006).
Individual human beings do not respond equally to surgical trauma/stress due to
variations in soluble factors result of genetic polymorphisms in the promoters of
cytokine genes (Stuber, Klaschik et al. 2005, Johnson, Lissauer et al. 2007) as well
as other factors such as race, age, and obesity (Haider, Chang et al. 2008, Silber,
Rosenbaum et al. 2012).
A comprehensive summary of published literature describing post-operative
changes in the concentrations of different cytokines, chemokines, and DAMP, is
presented in Appendix - II, and a summary in Table 1.2. At the most frequently used
time-point, namely 24 hour, changes are summarized as follows:




87 out of 88 studies showed a consistent post-operative increase in IL-6; in one
study IL-6 was undetectable.
No increases were shown in 17% studies of IL-8, 30% studies of IL-10 and 27%
studies of IL-1RA. But in the remainder studies all were increased.
For IL-1β, IL-4, IL-12, IL-13, IL-17, IFN-γ, TNF-α, and MCP-1, most studies
showed no post-operative changes.
Decreased IL-2 was shown in 54% (7/13) studies decreased and in 4 studies
there was no change.
21
Chapter - I





Introduction
IL-5 and MIP-1α were unchanged in 50% of studies; although there were only 4
studies for IL-5 and 2 for MIP-1α.
There were only study found for IL-22 and soluble IL-6 Receptor (sIL-6R) each.
IL-22 was increased whereas decreases shown in sIL-6R levels.
For sCD14, all three studies reported postoperative increase.
HMGB-1 showed elevations in five studies; whereas one study showed no
change.
HSP-27 was studied and showed unchanged by one group. HSP-70, reported by
two groups, also showed no postoperative changes. Only one study on HSP-60
found and was decreased.
Table 1.2: Lists of investigations on changes in biomarker levels in
postoperative venous blood following major surgery
Biomarker
Type
Biomarker
Name
No. of Investigations
Postoperative Changes (1 day)
Also see Supplementary Table II
Decrease No Change Increase N.D.
IL-1β
IL-2
IL-6 *
ProIL-12
inflammatory
IL-17
Cytokines
IFN-γ
TNF-α
IL-22
IL-4
AntiIL-5
inflammatory IL-10
Cytokines
IL-13
IL-1RA
IL-8
Chemokines
MCP-1
MIP-1α
HMGB-1
DAMPs
HSP-27
HSP-60
HSP-70
Others
sIL-6R
sCD-14
3
7
2
1
5
1
1
1
1
-
21
4
5
1
6
23
4
2
12
4
3
7
7
1
1
1
2
-
3
1
87
1
0
6
1
2
2
28
8
31
2
5
3
5
1
1
1
5
2
-
Total No.
of Studies
32
13
88
8
2
7
39
1
7
4
40
4
11
40
9
2
6
1
1
2
1
3
* IL-6 is a cytokine which recently has been described as an anti-inflammatory cytokine.
Please see page no. 25
22
Chapter - I
Introduction
1.3.6. Changes in Liver
1.3.6.1. Acute Phase protein synthesis
Liver releases positive acute phase proteins (APP) after trauma. IL-6 stimulates
hepatocytes to release APPs such as C Reactive Protein (CRP), Serum Amyloid-A
(SAA), Activated Protein-C, and alpha-1-antitrypsin (Heinrich, Castell et al. 1990,
Gabay and Kushner 2001, Jawa, Anillo et al. 2011). Elevated APPs stimulate
productions of cytokine antagonists such as IL-1RA and soluble TNF-receptors,
thereby eventually resulting in immune-suppression and favouring wound healing
(Tilg, Dinarello et al. 1997, Gabay and Kushner 1999). Although APPs were initially
thought to have only pro-inflammatory activities, recent studies suggest that their
role following trauma is predominantly anti-inflammatory (Tilg, Dinarello et al.
1997, Arredouani, Kasran et al. 2005).
1.4.
Repairing Injured Tissue
During human evolution, the body has developed protective mechanisms to
repair tissues damaged by injury. These repair mechanisms involve complex
interactions between several cellular systems. Initially, there is a response involving
cells and components of the immune system. Endothelial cells helps in immediate
recruitment of additional cells from the circulation into the injured tissues through
extravasation. Subsequently, recruitment of cells from BM and spleen occurs. These
systemic effects also impact the liver hepatocytes that partake actively in the tissue
repair process by releasing APPs. These different aspects are briefly outlined below.
1.4.1. Cells activation triggers
Both local and recruited neutrophils and monocytes thus play important roles
in initial injury as well as in facilitating the tissue repair mechanisms. Neutrophils
phagocytose cellular debris and by their oxidative or proteolytic activities are
involved in removing damaged tissue (Tidball 2005, Sadik, Kim et al. 2011).
Additionally, neutrophils secrete different proteases one of whose activities is to alter
basement membrane proteins, thereby promoting cell recruitment to the injury site
(Sadik, Kim et al. 2011).
23
Chapter - I
Introduction
By their ability to secrete different growth factors and cytokines, monocytes
and macrophages play a vital role in injury and repair. Firstly, as showed by Arnold
et al (Arnold, Henry et al. 2007), recruited inflammatory monocytes in the injury site
may differentiate to other monocyte phenotypes, favouring tissue repair. In
CX3CR1/GFP+ mice whose CX3CR1 gene was replaced by a green fluorescent
protein (GFP) reporter gene, only pro-inflammatory CX3CR1lo/Ly-6Chi monocytes
(CD14hiCD16- monocytes in human) were recruited from the blood into the site of
injury. Their study also illustrated that, at the injured site, these inflammatory
CX3CR1lo/Ly-6Chi monocytes proliferated and differentiated initially into antiinflammatory CX3CR1hi/Ly-6Clo cells (CD14loCD16+ monocytes in human) and
later differentiated into F4/80hi macrophages (Arnold, Henry et al. 2007). Depletion
of monocytes and neutrophils in animals also results in a weak tissue repair process
(Teixeira, Zamuner et al. 2003). Eyerich et al (Eyerich, Eyerich et al. 2009) showed
involvement of Th22, a subset of T-cells, in the tissue repair processes where
fibroblast growth factor (FGF) expressed by Th22 cells acted synergistically with
TNF-α in epidermal repair. However, because most of these studies were carried out
in animals, the exact mechanisms in humans remain enigmatic.
1.4.2. Soluble Factors
Chemoattractants, in addition to their essential role in the recruitment of
neutrophils and monocytes to the injured site (Gillitzer and Goebeler 2001, Chen and
Nunez 2010), also have local site-specific functions. Keratinocytes express IL-8
receptors, and both in vitro and in vivo studies demonstrate that IL-8 stimulates the
migration and proliferation of keratinocytes at the injury site (Gillitzer and Goebeler
2001).
The role of different cytokines in tissue repair has also been studied (Philippou,
Maridaki et al. 2012). Briefly, DAMPs, chemokines and pro-inflammatory cytokine
such as IL-6 help the recruitment of immune cells after initiation of injury. There is
subsequent upregulation and production of anti-inflammatory cytokines such as IL10, IL-1RA, TGF-β which by inhibiting the release of pro-inflammatory cytokines,
ultimately favour tissue repair (Hogevold, Lyberg et al. 2000, Jawa, Anillo et al.
2011, Philippou, Maridaki et al. 2012). IL-6 was found to be down-regulated by the
24
Chapter - I
Introduction
secretion of IL-10, IL-1RA and sTNF-R released at the wound site resulted in IL-6
down-regulation (Ostrowski, Rohde et al. 1999).
Although in many studies IL-6 was thought to be a pro-inflammatory cytokine,
recent studies have shown two signalling pathways activated through IL-6 (Figure
1.6). The first is the so-called “classic” signalling mechanism, where by binding with
its membrane bound receptors on immune cells, IL-6 plays anti-inflammatory
activities. In contrast, the IL-6 “trans-signalling” mechanism, where IL-6 binds to a
soluble IL-6R, results in pro-inflammatory activities (Rose-John, Scheller et al.
2006, Rose-John 2012). ADAM-17 is an enzyme whose function is to shed
membrane bound IL-6R and thus converting them into sIL-6R. ADAM-17 is
therefore involved in homeostasis by balancing soluble and membrane bound forms
of IL-6R (Briso, Dienz et al. 2008, Rose-John 2012). Whether ADAM-17 levels are
altered following trauma has not yet been characterized. However, the observations
of massively elevated IL-6 and reduction in the levels of soluble IL-6 receptor at the
surgical wound site (Kristiansson, Soop et al. 1998) suggest that IL-6 at the injury
site may be predominantly anti-inflammatory, thereby favouring wound healing.
Figure 1.6: IL-6 Classical Signalling and Trans-Signalling (adopted from
Rose-John )
25
Chapter - I
Introduction
When applied to the wound site, exogenous Keratinocyte growth factor
(KGF) has role as a stromal mediator for the proliferation of epithelial cells (Finch,
Rubin et al. 1989) and therefore is involved in wound healing (Geer, Swartz et al.
2005). The tissue protective/regenerative role of KGF has been described elsewhere
(Panoskaltsis-Mortari, Taylor et al. 2000).
Recent studies showed IL-22 to be involved in tissue repair although the
mechanisms underlying its role are still unclear (Ren, Hu et al. 2010, Kumar,
Rajasekaran et al. 2013, McGee, Schmidt et al. 2013). Together with overexpression of different endogenous danger signals such as defensins and S-100
proteins at the site of injury, elevated levels of IL-22 and KGF might play synergistic
roles in tissue repair (Kumar, Rajasekaran et al. 2013)
1.4.3. MSC, the warden of tissue repair
Immunosuppression at the injured site is required to reduce tissue damage
induced by inflammation and promotes tissue remodelling. Interestingly, the
inflammatory environment helps MSCs become activated and to trigger their
immunosuppressive functions. Prockop et al has mentioned the role of MSCs in
regulating injury induced inflammation (Prockop and Oh 2012) where activated
MSC unregulated the expression of the anti-inflammatory cytokine IL-1RA.
Activated MSCs also inhibit the secretion of different inflammatory cytokines such
as IFN-γ, TNF-α, IL-1α, or IL-1β by inhibiting the NFkB pathway and secreting
TNF-α stimulated gene/protein 6 (TSG-6) (Han, Jing et al. 2012, Prockop and Oh
2012).
DAMPs, nitrous oxide (NO), and other related factors produced from the
damaged tissues, activate MSCs via the PGE2 dependant pathway (Prockop and Oh
2012). By differentiation into other cellular phenotypes such as chondrocytes and
osteoblasts, MSCs play a role in tissue remodelling. In addition, by supporting the
proliferation of fibroblasts, epithelial cells, neutrophils, and macrophages, MSC play
a predominantly positive role in tissue repair (Han, Jing et al. 2012).
Although many gaps in our understanding prevail, an outline is emerging of
multiple mechanisms that restrain inflammation and suppress systemic immunity
(Medzhitov 2008). Suppression of immunity in moments of crisis would appear to
26
Chapter - I
Introduction
have conferred no evolutionary advantage to mammals; but, without such restraints
healing would be delayed by activation of tissue destructive inflammatory cascades.
1.5.
Aggravating Factors of PTI
PTI can be aggravated by many different biological factors, some of whom are
briefly outlined below:
1.5.2. Ageing
One biological factor aggravating PTI is immune-senescence. Elderly patients
have weak immunity and are therefore more vulnerable to infections (Ginaldi, Loreto
et al. 2001, Aw, Silva et al. 2007). Zacks et al previously showed relatively slower
muscle regeneration and decreased phagocytic activities by the inflammatory cells
from the older animals (Zacks and Sheff 1982).
1.5.3. Haemorrhage
Haemorrhage is another important aggravating factor which results in
immunosuppression even without any major tissue trauma and may increase
susceptibility to acquire infections (Stephan, Kupper et al. 1987). By releasing proinflammatory cytokines IFN-γ and IL-2 in vitro with enhanced production of IL-10
by T-cells, haemorrhage may lead to PTI (Abraham and Chang 1992).
1.5.4. Blood Transfusions
Blood transfusion, a necessary step following surgery, due to major blood
loss, also aggravates PTI. Transfusion of allogeneic blood or packed red blood cells,
and pre-deposited autologous blood transfusion promote the progression of PTI
(Gharehbaghian, Haque et al. 2004, Gong, Thompson et al. 2005, Silverboard,
Aisiku et al. 2005, Karam, Tucci et al. 2009).
1.5.5. Other aggravating factors
There are many other factors that can aggravate PTI. Surgery itself is a major
trigger of physical stress (Hogan, Peter et al. 2011). In addition, factors such as presurgical medications and anaesthesia showed a direct association with the degree of
surgical stresses (Kawasaki, Ogata et al. 2007). Immune responses following trauma
is also gender specific. Males are more prone to be immunosuppressed following
27
Chapter - I
Introduction
trauma/injury than females. This may be due to the differences in hormonal activities
(Angele and Faist 2002). Use of immunosuppressant drugs such as corticosteroids,
cyclosporin-A, and anti-TNFα also depress patients‟ immunity and therefore may
increases their susceptibility to acquire infections (Hansen, Rohr et al. 1988, GeaBanacloche, Opal et al. 2004, Ali, Kaitha et al. 2013).
1.6.
Immunological consequences of PTI
The human body has developed different mechanisms to protect it from
infectious threats. However, research in the last few decades has found that following
surgical trauma, PTI represents a major risk to patients making them more vulnerable
to acquired infections (Shorr and Jackson 2005, Faruquzzaman 2011). Different
forms of nosocomial infections may occur in the immunocompromised patient in
different body compartments (Rabinowitz and Caplan 1999). But respiratory tract
infections are most frequent, followed by urinary tract infections, wound infections,
and blood stream infections (Richards, Edwards et al. 2000). Furthermore, patients
may acquire infections with multi-drug resistant bacteria that are resistant to most
antibiotics (Alanis 2005). In extreme situations, PTI may eventually lead to severe
sepsis, multiple organ failure, and death.
1.6.2. Pneumonia
Hospital-acquired pneumonia (HAP) is the most common amongst all the
nosocomial infections. Pneumonia although mostly caused by gram negative
bacteria, it can also be manifested with infections by other bacteria and viruses.
Multicentre studies on medical-surgical patients in intensive care units and on
patients who acquired infections in ICU revealed the highest episodes of pneumonia;
most of which were ventilator associated (Ponce de Leon-Rosales, Molinar-Ramos et
al. 2000, Richards, Edwards et al. 2000). Therefore, following surgery associated
immunocompromised state, patients vulnerability increases to acquire pneumonia.
1.6.3. Urinary tract infections (UTI)
This form of infection mostly affects the bladder; however can also affect
kidney, ureters, and urethra. UTI is another common infection in hospital caused by
bacteria that enter the urethra and then the bladder. UTI is the second leading cause
28
Chapter - I
Introduction
in ICU patients and it is mostly associated with the urinary catheterization (Richards,
Edwards et al. 2000). Major surgery most often requires the usage of urinary
catheters; therefore increases the chance of UTI.
1.6.4. Wound infections
Accidental injury patients may acquire wound infection immediately at the
injury site. Patients may acquire infection at the surgical wound site also after major
invasive surgery. Study on 42,509 infected surgery patients (with infections in one or
more site) stated that wound site is the most common site to acquire infections
(Horan, Culver et al. 1993).
1.6.5. Alimentary tract infections
Gastrointestinal (GI) tract is another important compartment in our body
where infections are often reported following injuries such as blunt trauma and
invasive abdominal surgeries. Infections in GI tract also can be manifested with
injuries in spleen, pancreas, and duodenum. Compared to invasive surgery, blunt
abdominal trauma reported more severe cases of abscess from alimentary infections
(Ivatury, Zubowski et al. 1988, GOINS, RODRIGUEZ et al. 1990).
1.6.6. Bloodstream Infections
Bloodstream infections are also common nosocomial infections in hospital
particularly from Staphylococcus Aureus
and coagulase-negative staphylococci.
This infection can spread in blood stream by several ways; especially intravenous
resuscitation line that is required in the trauma patients (Civetta, Hudson-Civetta et
al. 1996). Bloodstream infection may also be generated as secondary infection
followed by site specific infections, the possibility varies depending on the infection
site (Horan, Culver et al. 1993). Later consequence of this infection may lead to
sepsis. In vitro studies by Angele et al described endotoxin-induced suppression of
monocytes after trauma (Angele and Chaudry 2005).
1.6.7. Others
Another possible consequence of PTI is metastatic spread of cancer cells. As
shown in mice with mammary carcinoma, anaesthesia and blood loss are associated
with increased frequency of metastases. By decreasing the numbers and functional
29
Chapter - I
Introduction
capacity of NK cells, PTI increases the mortality and cancer recurrence in colorectal,
breast, head and neck, and lung cancer patients (Hogan, Peter et al. 2011, Veenhof,
Sietses et al. 2011).
1.7.
Treatment of PTI
Considering all the above events following sterile trauma and the resulting
immunosuppression, there has been increasing interest over the last decade in the use
of immunostimulants to prevent PTI before, during, or after surgical trauma.
Commonly used immunostimulants can be either functional nutrients or
immunotherapeutic agents.
1.7.2. Nutritional supplements
Pre-existing malnutrition has a great impact on clinical outcome, and proper
nutritional support helps to reverse PTI. Immunonutrients prescribed pre-, intra-,
and/or post-operatively can help to prevent post-surgical immunosuppression and
include: glutamine, arginine, n-acetyl cysteine, branched-chain amino acids, glucan,
nucleotides, long-chain n-3 fatty acids, antioxidant vitamins, trace elements, and
taurine (Stechmiller, Childress et al. 2004, Kudsk 2006, Calder 2007, Helminen,
Raitanen et al. 2007, Zheng, Li et al. 2007). Patients treated with probiotics may also
get substantial benefit to have healthy immunity following trauma (Klaenhammer,
Kleerebezem et al. 2012)
1.7.3. Immunotherapy
Strategies to modulate patients‟ immunity for better clinical outcome are
mostly confined to drugs to prevent overwhelming inflammatory reactions following
so-called SIRS. However, without knowing the underlying mechanisms of sepsis or
sterile trauma, treatment with drugs may aggravate and thus worsen the clinical
situations. This is reflected in the failure of many different clinical trials (Hotchkiss,
Monneret et al. 2013, Boomer, Green et al. 2014). Immunotherapeutic agents
include: anti-PD1, mifamurtide, polyinosinic-polycytidylic acid, MF-59, imiquimod,
luivac, myrrh, IDR-peptides, IL-7, rIFN-γ, IL-15, TNF-α antagonist (Scanzello,
Figgie et al. 2006, Hancock, Nijnik et al. 2012, Hotchkiss, Monneret et al. 2013,
Hutchins, Unsinger et al. 2014).
30
Chapter - I
1.8.
Introduction
Autologous Blood Transfusion; Types of transfusion
Autologous blood transfusion is the collection of blood from a person and re-
infusing back to the same person when required. This is in contrast to allogeneic
blood transfusion where blood from one or more unrelated donor is transfused to the
recipient. Autologous transfusion was initially proposed by Blundell in 1818, as a
potentially life-saving method for patients who had an immense bleeding and has
been established as a safe alternative to allogeneic transfusion since 1980 (Blundell
1819, Vamvakas and Pineda 2000).
1.8.2. Advantages of autologous transfusion
Primary concerns about allogeneic transfusions arose from the transmission
of
viral
infection
including:
hepatitis
viruses,
cytomegalovirus,
human
immunodeficiency virus (HIV), and diseases such as blood borne transmission of
variant Creutzfeldt–Jakob disease (vCJD) (Goodnough, Brecher et al. 1999,
Rosencher, Kerkkamp et al. 2003, Llewelyn, Hewitt et al. 2004, Andreas Pape
2007). To some extent viral load can be reduced by leuko-depletion of the blood.
Allogeneic transfusion can result in decreased helper T-cell counts, decreased
CD4/CD8 T-cell ratio, decreased lymphocyte responses, decreased natural killer
(NK) cell functions, reduction in delayed-type hypersensitivity, defective antigen
presentation,
decreased
productions
of
IL-2
and
IFN-γ,
and
decreased
monocyte/macrophage phagocytic functions (Eleftherios and Morris 2007,
Vamvakas, Bordin et al. 2009).
The advantages of autologous transfusion are : minimization of transfusion
transmitted infections; reduction of transfusion related non-haemolytic reactions;
avoidance of sensitization to uncommon blood types; emotional benefits to patients
who trust their own blood; and reduction in trauma-induced inflammation (Bierbaum
BE 2000, Borghi B 2000, Rosencher N 2003); absent of cross-matching
requirements; and a viable alternative for patients who refuse allogeneic transfusion
for religious reasons (e.g. Jehovah‟s Witnesses) (Dainow 1991, Newman JH 1997)
31
Chapter - I
Introduction
1.8.3. Pre-operative deposit of autologous blood
This type of transfusion involves blood collected into ACD-anticoagulant
from the patients 2-3 weeks before surgery and reinfused to the patients after
surgery. After leukodepletion, separation of red blood cells and plasma from the
whole blood units, and classifying it according to the ABO and Rh systems, blood
can be faithfully re-allocated to the donor (Schleinzer and Singbartl 1997, Ajit
Walunj 2006, Andreas Pape 2007). However, screening test of preoperative
deposited autologous blood unit is a pre-requisite prior to reinfusion likewise
allogeneic blood.
1.8.4. Normovolaemic Haemodilution
Acute normovolemic haemodilution (ANH) is the immediate pre-operative
removal of blood into ACD and concurrent infusion of plasma expander to maintain
intravascular volume. ANH is an option for patients who are able to tolerate the acute
lowering in haemoglobin concentrations and blood lost during the surgery has a
lower haematocrit thereby reducing loss of RBC. The removed blood is reinfused
during or after surgery (Schleinzer and Singbartl 1997, Ajit Walunj 2006, Andreas
Pape 2007).
1.8.5. Intra-operative salvage
Intraoperative autologous transfusion is a method whereby blood lost into the
operative field is collected sterilely, washed, filtered and then reinfused as a packed
unit of RBCs (Schleinzer and Singbartl 1997, Andreas Pape 2007). Intraoperative
blood transfusion is performed during surgical procedures where significant blood
loss is expected. Patients with multiple red blood cell antibodies or rare blood types
benefit by blood salvaged during surgery.
1.8.6. Post-operative salvage
Blood is salvaged postoperatively by collection of blood from the surgical
wound drains into a bag and re-infused with or without washing (Schleinzer and
Singbartl 1997). Postoperative salvaged blood transfusion is performed if there is
significant blood loss after the surgical procedure.
32
Chapter - I
Introduction
Blood collected into either heparin or ACD anticoagulant may be washed
with isotonic saline solutions using specialist equipment to remove the plasma
proteins; with simultaneous removal of some leukocytes and platelets (Ashworth and
Klein 2010). Only relatively pure erythrocytes are re-infused and all plasma, platelets
and leukocytes from the wash fluids is discarded.
The simplest and most widely practiced technique is to use nonanticoagulated, defibrinated salvaged blood. Postoperative blood collected under
negative pressure. After 6-8 hours this fibrinolysed blood is reinfused into the patient
through a standard 40µ blood filter.
A less commonly used vibrant technique is collecting blood into acid-citrate
dextrose (ACD) anticoagulant, the most widely used anti-coagulant. However
sodium citrate, citrate phosphate dextrose, or heparin can also be used in appropriate
proportions (Lemos and Healy 1996).
1.8.7. Advantages of postoperative salvage
Several studies confirmed that post-operative salvage blood transfusion
reduced the requirements for allogeneic blood transfusion (Gannon DM 1991, DC
Ayers 1995, Nuttall GA 1996, D. Thomas 2001, ON Nagi 2003, Steven and William
2003, Jones HW 2004, Pillonel J 2004, Horstmann, Slappendel et al. 2010, Matsuda
K 2010). Whereas, popularity of preoperative autologous blood donation has
decreased due to wastage following unexpected postponement of surgery as well as
high collection and storage costs (Etchason, Petz et al. 1995, Billote DB 2002,
Goldman M 2002, Steven and William 2003), and intra-operative salvage involves
costly tools, and skilled staff and is therefore less cost effective (Guerra JJ 1995),
postoperative salvage does not require expert workforce or additional complex
equipment, unless salvaged blood is washed, in which case specialist washing
machines (e.g. haemonatics) are required that increases the cost (Rao, Dyga et al.
2012).
Reinfusion of salvaged blood has raised some concerns. Fibrinolytic activity
in the surgical site and systemic circulation increased after re-infusion of
postoperative unwashed salvaged blood (Krohn, Reikerås et al. 2001, Matsuda K
33
Chapter - I
Introduction
2010); however such changes have not led to major clinical complications.
Complement activation and negligable erythrocyte haemolysis (<1%) after
incubating salvaged blood for 24 hours confirms its efficacy and safety (Dalén T
2002). Umlas showed that postoperatively collected red blood cells from drains have
a life span similar to normal intravenous erythrocytes (Umlas, Jacobson et al. 1993).
Febrile reactions occur in some patients (Faris PM 1991, Clements, Sculco et al.
1992, Dalén T 1996); however, Faris et al confirmed this mainly occurred in patients
who had reinfusion of blood collected between 6 and 12 hours post-operatively
(Faris PM 1991).
1.9.
Total Knee Arthroplasty (TKA) and postoperative salvaged blood
transfusion
Knee replacement, or knee arthroplasty, is a major surgical procedure to
replace the weight-bearing joint with a man-made prosthesis to relieve the pain and
disability of osteoarthritis, rheumatoid arthritis and psoriatic arthritis. With the
continuous advancement in the surgical procedure, more and more patients are
receiving the benefits of TKA (Deirmengian and Lonner 2008, Lee and Goodman
2008). As allogeneic blood transfusion transports well-known infection risks other
procedures has been introduced ranging from preoperative deposited autologous
transfusion to postoperative salvaged blood reinfusion (Peter VK 2001, Sarah,
Francesco et al. 2003, Jones HW 2004, Martin A 2006, A. Zacharopoulos 2007,
Moonen, Knoors et al. 2007, Matsuda K 2010). Postoperative salvaged blood
transfusion following knee arthroplasty is an easier, more cost effective technique,
less infection during hospital stay, safe and convenient for both doctors and patients
(Muñoz, Ariza et al. 2005, Muñoz, Slappendel et al. 2011, Muñoz, Campos et al.
2013).
In this study knee arthroplasty offered an opportunity to characterise PTI
initiated by sterile surgical trauma. Gharehbaghian et al previously reported that
arthroplasty induced post-operative depression of circulating NKp cells; this being
further exacerbated by allogeneic or pre-deposited-autologous blood transfusions
with or without leukodepletion. They also observed that when arthroplasty was
34
Chapter - I
Introduction
followed by re-infusion of unwashed ACD anti-coagulated salvaged blood collected
during the first six hours after surgery, NKp cell frequencies rose above pre-operative
levels and remained high for at least five days after surgery. Enhanced immune status
was reflected in increased Interferon-gamma (IFN-γ) synthesis and macrophage
activation,
suggesting
constituents
within
salvaged
blood
reversed
PTI
(Gharehbaghian, Haque et al. 2004, Islam, Whitehouse et al. 2011). These results
were supported by other studies showing enhanced post-operative immunity
following acute normovolaemic haemodilution involving re-infusion of freshly
collected ACD-anti-coagulated autologous venous blood (Yan, Chen et al. 2005,
Chen, Zhang et al. 2007).
35
Chapter - I
Introduction
1.10. Knowledge Gaps at the time of Project Design
During the period of study design, there were gaps or controversies in
scientific knowledge in the published literature such as:
i.
Continuous synthesis of soluble plasma factors by live cells in blood
lead to inaccurate scientific interpretations on biomarker levels
(Ayache, Panelli et al. 2006). Immediate separation of plasma from
cells in whole blood was not specified in most of the published
investigations.
ii.
PTI cannot be assessed by measuring one or two biomarkers.
However, most studies measured very few biomarkers, most with
similar functional properties such cytokines, coagulation features,
complement factors, or chemokines.
iii.
Soluble DAMPs in venous or wound site were not measured in most
studies; and interactions between DAMPs and other biomarkers are
poorly discussed.
iv.
Most studies characterized PTI in very few patients hence limiting
clinical significance of findings.
v.
Autologous blood transfusion is common in patients undergoing
elective surgery, but its effect on post-surgical immunity is poorly
studied.
vi.
The constituents of autologous salvaged blood in sterile trauma are
poorly investigated.
36
Chapter - I
Introduction
1.11. Study Aims and Hypotheses
1.11.2. Aim 1: To characterise PTI after total knee arthroplasty using biomarkers
with various functional properties including cytokines, chemokines, DAMPs,
and others.
Hypothesis 1: Postoperative levels of biomarkers with various functional
properties characterize PTI.
1.11.3. Aim 2: To characterise the effects of ACD anti-coagulated salvaged blood on
PTI.
Hypothesis 2: ACD-anticoagulated salvaged blood transfusion reverses PTI.
1.11.4. Aim 3: To analyse body‟s immunological responses at the surgical wound
site.
Hypothesis 3: Biomarker profiles in wound blood reflect in-vivo changes
within the trauma site.
1.11.5. Aim 4: To analyse and identify the immunostimulatory plasma constituents in
the ACD anti-coagulated salvaged blood.
Hypothesis 4: Immune stimulants are generated ex-vivo during collection of
salvaged blood.
37
Chapter – II
MATERIALS AND METHODS
Chapter - II
Materials and Methods
2.1. Study Design
Between 2010 and 2011 the study was established as a tri-institutional
collaboration between Institute of Technology Tralee, NUI Galway in Ireland and
University of Bristol in United Kingdom by Prof Benjamin Bradley. The study plan
involved patient recruitment, hospital data collection, sample collection, processing
and cryopreservation. This was done in Orthopaedic Research Unit, Avon
Orthopaedic centre, Southmead Hospital, University of Bristol, United Kingdom,
where the primary total knee arthroplasty patients were recruited. The second part of
this study was laboratory based research that were carried out in Immunology and
Transplant Biology Research Unit, Regenerative Medicine Institute, NUI Galway,
Ireland, and a small part of the study was also carried out in Shannon Applied
Biotechnology Centre, Institute of Technology Tralee, Ireland.
2.2. Ethical approval
This clinical study was fully approved by the local NHS research ethics
committee (Ref. No. 10/H0102/8) and informed consent was obtained from all
volunteers (Appendix-X). Patients were interviewed and informed about this study
by the respective surgeons. If they agreed to take part, the voluntary consent was
signed.
2.3. Patient Selection and Recruitment
Sample size was determined by our previous observation that 39 out of 40
patients who received salvaged blood showed evidence of reversal of PTI. A total of
43 patients undergoing primary total knee arthroplasty from the daily operation
schedule were recruited. Patients undergoing revision arthroplasty, those with preexisting infections, previous blood transfusion, malignant disease, autoimmune
disorders, and diabetes were excluded. Study patients had the following
characteristics: 70% were female and 30% male; median age was 74 (56 - 86) years;
primary diagnosis was osteoarthritis (41), rheumatoid arthritis (1), or psoriatic
arthritis (1). Non-steroidal anti-inflammatory drugs (NSAID) were given preoperatively to seven patients.
39
Chapter - II
No more than two knee arthroplasty patients were selected for each day. I
was available twenty-four hours in the hospital to process blood samples of different
time points immediately after collection, thereby avoiding the effects of storage time
on changes in the levels of different biomarkers.
2.4. Haematological and Biochemical Data
Both preoperative (before anaesthesia) and post-operative (prior to discharge
from hospital) haematological information were collected from each patient
including haemoglobin level and haematocrit (Hct), red blood cell (RBC) count,
platelets, white blood cells (WBC), neutrophils, monocytes, lymphocyte, eosinophils,
and basophils.
Biochemical parameters such as sodium, potassium, urine, and creatinine levels
were also recorded pre- and post-operatively. Clinical information such as patient
demographics, underlying disease states, surgical procedures, disease history, length
of hospital stay, drained blood volume, transfusion requirements were also
documented.
2.5. Blood Transfusion Procedure
All patients had a drain inserted subcutaneously into their wound site which
was attached to a „Dideco-797 Recovery Device‟ (Sorin Group Ltd.) through which a
rotary pump applied negative pressure. Salvaged blood was collected from the
surgical wound via a suction catheter, which has two lumens, one to aspirate blood
from the surgical wound site, and another to deliver acid citrate dextrose (ACD), to
act as anticoagulant for the blood aspired from the site of bleeding (Figure 2.1).
Anticoagulation through Dideco-797 device was carried out as a ratio of 1:12 for
ACD anti-coagulant: salvaged blood. ACD-A formulation was: acid-citratemonohydrate, 4.0 g; sodium-citrate-dihydrate, 11.0 g; glucose monohydrate, 12.25 g;
total H2O volume, 500 ml; pH 4.7 – 5.3. After six hours, or after total volume
reached 500 ml, the blood collection bag contents were re-infused via a 40 micron
blood transfusion filter (Dideco Micro- 40-Goccia. Sorin group), without further
washing or manipulation. Assignment to the two study cohorts was determined by
the volume of blood drained from the wound site within the first six hours after
surgery. If total fluid volume accumulated within six hours was greater than 175 ml.
40
Chapter - II
it was re-infused; lesser volumes being discarded, except in two patients where all
fluid was discarded for clinical reasons; these two being assigned to the nontransfused cohort. In all cases fluid salvaged beyond six hours was discarded. No
study patient received allogeneic or autologous pre-deposited blood transfusions.
Those 25 patients who received autologous salvaged blood were termed the ASBT
cohort, the median transfused volume at six hours after surgery being 360 (175-550)
ml. For comparison 18 patients received no salvaged blood transfusions and were
termed the NSBT cohort.
Figure 2.1: Dideco-797 recovery device for postoperative autologous salvaged
blood from knee joint replacement operation site. A tube (1) is inserted into the joint
space during the operation, where it removes fluid from the wound site through negative pressure
exerted by a rotary pump (2). After exiting the wound site the tube is connected to a smaller diameter
tube (3) that delivers acid citrate dextrose to the fluid from the reservoir (4). The anticoagulated fluid
is then pumped via the rotary pump (2) to the collection bag (5), which is subsequently reinfused into
the patient through another tube (6).
41
Chapter - II
2.5.1. Sampling of blood from patients
Venous blood samples were collected from each patient of both NSBT and
ASBT cohorts at two different periods; the first (hereafter termed “preoperative”)
before anaesthetic procedure and the second (hereafter termed “postoperative”)
immediately prior to discharge from the hospital, ranging between two and six days
(median day 3). Part of the preoperative blood was incubated at room temperature for
five hours for simulation studies. In the ASBT cohort, ACD anticoagulated salvaged
blood samples from the collection bag were taken at two time points, the first, one
hour after surgery (hereafter termed “wound site blood” or “WSB”) and the second,
six hours after surgery prior to reinfusion (hereafter termed “transfused salvaged
blood” or “TSB”). Preoperative samples were collected by the clinician in the
anaesthetic room of theatres at Avon Orthopaedic Centre, Southmead Hospital.
Postoperative samples were taken after transfer to designated wards. Seven patients
were excluded from this study as I could not collect the post-operative samples due
to different reasons. Two patients were discharged from the hospital without
informing the research group and five patients refused to be bled again. Blood
collections and other related procedures are described in the next sections. A
schematic diagram of the experimental design is in Fig 2.2.
42
Chapter - II
Figure 2.2: Experimental Design
43
Chapter - II
2.5.2. Blood Sample Collection, Isolation of Plasma
Venous blood samples were collected in cell preparation tube (CPT tube;
Beckton Dickenson) containing citrate anticoagulant and a blood separation system
composed of a polyester gel and a density interface forced by density gradient fluid
on top of the gel barrier. During centrifugation the gel portion moves to form a
barrier separating the mononuclear cells and plasma from other dense blood
components.
Figure 2.3: Isolation of mononuclear cells using cell preparation tube
Citrated blood tubes were centrifuged at 1800 g for 25 minutes at RT. Plasma
samples were then collected from the top of the tube without disturbing the
immediate lower portion containing the mononuclear cells (Figure 2.3). Plasma
samples were aliquoted in colour coded, pre-labelled (in cryotag) 0.5 ml Eppendorf
tubes followed by immediate freezing at -800C. Strict sterile standard operating
procedures were maintained for every step from isolation of plasma and peripheral
blood mononuclear cells (PBMC) to cryopreservation.
44
Chapter - II
2.5.3. Separation and storage of PBMC
The mononuclear cells from the CPT tubes were transferred to 15 ml tube,
washed twice with sterile PBS, and 1 ml of complete culture medium (CCM) was
added to re-suspend the cells which were then counted. Freezing medium containing
di-methyl-sulfoxide was added drop-wise to an equal volume of the cell suspension.
The cell suspension was dispensed into pre-cooled cryovials (pre-labelled) by
placing them on crushed ice. Cryovials were then placed in a sample freezing box
(MrFrosty) and stored at -800C.
2.6. Cell culture of stored PBMCs
Cryovials were thawed by placing in 370C water bath. Thawing medium was
gradually added dropwise whilst mixing gently. This cell suspension was slowly
transferred to a 15 ml tube containing complete culture medium (CCM), then mixed
gently to allow DMSO to diffuse out of the cells. Following centrifugation at 400 x g
for 10 minutes, supernatant was discarded and the pellet was washed and
resuspended in 1 ml of CCM. Number of viable cells were counted using trypan
blue.
For in vitro experiments, briefly, PBMCs from pre- and post-operative
venous blood from NSBT and ASBT cohorts were suspended in culture medium
(RPMI-1640,
10% FCS, and L-Glutamine at 2mM), and adjusted to 1x106 viable
cells/ml. Lipopolysaccharide (LPS) endotoxin was added (3 ng/ml) to half the cells.
Following incubation for three days at 370C in 5% CO2 /N2, supernatants were
collected and stored at -800C for further biomarker analysis.
2.7. Blood collection for proteomic and glycomic analysis
Part of the citrated blood was also immediately transferred to another tube named
“P100 tube (Beckton Dickenson)” to inhibit proteolysis in blood. P100 tube contains
proprietary protease inhibitor cocktails. During centrifugation, the integrated
mechanical separator in the tube moved down and placed the cells beneath it whereas
the plasma portion remained on the top of the separator (Figure 2.4). Blood plasma
was then isolated from P100 tube and aliquoted in pre-labelled Eppendorf tubes. The
aliquots were stored at -800C.
45
Chapter - II
Figure 2.4: Isolation of plasma using protease inhibitor tubes (P100 Tube)
2.8. Flow Cytometric Bead Array
Ready-to-use human flow cytometric bead array kits were selected that were
capable of measuring the following 13 cytokines simultaneously: Interleukin-1-beta
(IL-1β), IL-2, IL-4, IL-5, IL-6, IL-9, IL-10, IL-12p70, IL-13, IL-17A, IL-22, Tumour
Necrosis Factor-alpha (TNF-α), and Interferon–gamma (IFN-γ). Plasma samples
were assayed in 96 well plates and read by flow cytometer (Accuri-C6, BD)
according to manufacturer‟s protocol (BMS817FF, e-Bioscience). In brief, each
cytokine was measured by a pair of antibodies; one being a specific antibody coated
onto beads, and the other being a Biotin-conjugated secondary antibody in solution.
Plasma samples were incubated in this mix, beads were washed, and StreptavidinPhycoerythrin (PE) solution added. Cytokine-specific bead populations were
differentiated by bead size (small versus large), and by internal pre-labelling with
different intensities of fluorescent dye. Mean fluorescence intensity (MFI) of the
Biotin-Streptavidin-PE complex reflected individual cytokine levels in plasma,
quantitated from serially diluted known standards (Fig.2.5).
46
Chapter - II
Fig. 2.5(A) : Different Steps of Flow Cytometric Bead Array
Fig. 2.5(B) : Gating Strategy for Flow cytometric Bead Array Technique. There
were two sizes of beads: small (4 μm; consisting of 5 indicator populations; lower left display) and
large (5 μm; consisting of 6 indicator populations; lower right display). Bead populations were
distinguishable based on their respective APC fluorescence intensities. Cytokine concentrations were
dependent on bead PE fluorescence intensity.
47
Chapter - II
2.9.Enzyme linked immuno-sorbent assay (ELISA)
ELISA kits to measure the following: interleukin-1 beta (IL-1β), IL-2, IL-6,
IL-10, IL-12p70, IL-13, IL-17A, IFN-γ, TNF-α, IL-8, Macrophage Chemoattractant
Protein-1 (MCP-1 or CCL2), Macrophage Inflammatory Protein-1-alpha (MIP-1α or
CCL3), IL-12p70 (protein subunit 70kDa), IL-1 Receptor antagonist (IL-1RA),
Transforming Growth Factor - β1 (TGF-β1), Heat Shock Proteins (HSP-27, HSP-60,
HSP-70), Annexin-A2, Complement Factor 5a (C5a), Keratinocyte Growth factor
(KGF), soluble CD-14 (sCD-14), soluble IL-6 receptor (sIL-6R), ADAM-17, soluble
gp-130 (sgp-130) were purchased from R&D Systems, UK.
Briefly, flat-bottomed 96-well Nunc-MaxisorpTM plates were coated with
capture antibody diluted in PBS overnight. The following morning, plates were
washed in 0.05% Tween-20 in PBS. Plates were blocked in 1% BSA in PBS for 1
hour prior to being washed in 0.05% Tween-20 in PBS. Standards and pre-diluted
samples were added to the plate for 2 hours. Plates were washed in 0.05% Tween-20
in PBS followed by coating with biotin-labeled detection antibody diluted in 1%
BSA/PBS for 2 hours. Plates were washed in 0.05% Tween-20 in PBS prior to the
addition of streptavidin-horseradish peroxidase (HRP) diluted in 1% BSA in PBS for
20 minutes in the dark. Plates were washed in 0.05% Tween-20 in /PBS.
Tetramethylbenzidine substrate was added for 20 minutes. Stop solution (2N sulfuric
acid) was added and the absorbance was read at 450 nm on a Wallac 1420
Victor3TM Multilabel Counter plate reader (Perkin Elmer, Waltham, MA, US).
Finally, levels of different analytes were determined by comparison with known
standards.
High-Mobility Group Box-1 (HMGB-1) was purchased from IBL
International; Calgranulin (S-100A8/A9) and α-Defensin from Hycult Biotech; CD24, Siglec-10, and Siglec-2 from USCN Life Science Inc, USA. These ELISA kits
were used according to manufacturer‟s instructions. Briefly, pre-diluted samples and
serially diluted standards were added to the capture antibody coated ready to use 96
well plates, followed by subsequent steps as mentioned in the kit manuals.
Complete lists of all the studied biomarkers are given at Table 2.1. Crossreactivity of each measured biomarker listed in Appendix-XV.
48
Chapter - II
Table-2.1 : Details of Biomarkers Assessed.
Category
Damage
Associated
Molecular
Patterns
(DAMPs)
Name
High-Mobility Group
Box-1 (HMGB-1) a
Mobilizes immunosuppressive MSCs; (Lotfi, Eisenbacher et
al. 2011) suppresses IFN-γ secretion; enhances inhibitory Tcells;(Wild, Bergmann et al. 2012) dampens autoimmunity.
(Wild, Bergmann et al. 2012)
Calgranulin
Antimicrobial; chemotactic for leukocytes(Ryckman,
Vandal et al. 2003); protects against LPS induced septic
shock. (Goyette and Geczy 2011)
(S-100 A8/A9)
a
Heat Shock Proteins a:
and HSP-70
Upregulates IL-10; (Borges, Wieten et al. 2012, van Eden,
van Herwijnen et al. 2013) suppresses IL-1β production.
(Xie, Chen et al. 2002)
Alpha-Defensin
(α-defensin) a
Antimicrobial; increases anti-inflammatory cytokine, and
chemokine production. (Lehrer 2007)
Annexin-A2 a
Induces pro-inflammatory mediators.(Swisher, Khatri et al.
2007)
Interleukin-8
(IL-8/CXCL8) a
Neutrophil recruitment. (Kolaczkowska and Kubes 2013)
Monocyte Chemotactic
Protein-1 (MCP-1/CCL2)a
Monocyte recruitment. (Shi and Pamer 2011)
Macrophage
Inflammatory Protein-1alpha (MIP-1α/CCL3) a
Recruits immature dendritic cells; (Deshmane, Kremlev et
al. 2009)induces pyrexia. (Soares, Figueiredo et al. 2009)
Interleukin-1 beta
(IL-1β) a,b
Key mediator of inflammatory responses; essential for
resistance against pathogens; exacerbates tissue damage.
(Dinarello 2009)
Interleukin-2
(IL-2) a,b
Pleiotropic cytokine driving T-cell growth; augments NKcell activity; induces differentiation of T-reg cells.(Liao, Lin
et al. 2011)
Interleukin-6
(IL-6) a,b *
Pro-inflammatory if bound to soluble receptor (sIL-6R);
anti-inflammatory when in free form;(Rose-John 2012)
induces corticosteroid production; induces antiinflammatory acute phase proteins.(Jawa, Anillo et al. 2011)
Interleukin-12 protein
subunit-70 (IL-12p70) a,b
Induces IFN-γ production; drives activation and
differentiation of T-cells.(Gee, Guzzo et al. 2009)
Interleukin-17A
(IL-17A) b
With TNF-α enhances neutrophil chemotaxis.(Griffin,
Newton et al. 2012)
Interferon-gamma
Antiviral; induces proliferation and differentiation of
cytotoxic T-cells.(Dinarello 2000)
HSP-27, HSP-60,
Chemokines
Proinflammatory
Cytokines
Properties
(IFN-γ)
b
Tumour necrosis factor –
alpha (TNF-α) a,b
Facilitates extravasation of leukocytes to injury site;
synergizes with IL-1β to trigger inflammatory
cascades.(Dinarello 2000)
49
Chapter - II
Category
Name
Interleukin-22 (IL-22) a
Plays role in tissue regeneration (Zenewicz and Flavell
2011)
Interleukin-4
(IL-4) b
Similar functions to IL-13. (Wynn 2003)
Interleukin-5
Activates B-cells; enhances allergic responses.(Takatsu,
Kouro et al. 2009)
(IL-5)
Antiinflammatory
Cytokines
Properties
b
Interkeukin-9
(IL-9) b
Activates mast cells; promotes T-reg cells; induces TGF-β1
production; suppresses IL-12.(Noelle and Nowak 2010)
Interleukin-10
(IL-10) a,b
Synergizes with IL-4, IL-13, and TGF-β1 to suppress
inflammatory cascades induced by IL-1β and TNFα.(Dinarello 2000)
Interleukin-13
(IL-13) a,b
Pleotropic cytokine; activates Th2 cells and allergic B-cells;
potent mediator of tissue fibrosis. (Wynn 2003)
Interleukin-1 Receptor
Antagonist
(IL-1RA) a
Blocks activity of IL-1β. (Dinarello 2009)
Transforming growth
factor beta 1 (TGF-β1) a
Inhibits inflammatory cascades; (Oh and Li 2013)induces
IL-9 production by T-cells.(Noelle and Nowak 2010)
Soluble Interleukin-6
Receptor (sIL-6R) a
Binds to soluble IL-6 to trigger inflammatory cascades
through trans-stimulation. (Rose-John 2012)
sgp130 a
Binds to IL-6/IL-6R complex and therefore inhibits the
trans-stimulation (Rose-John 2012)
ADAM-17 a
Helps in shedding of membrane bound form of IL-6R into
soluble form (sIL-6R) and thus helps in trans-stimulation
(Rose-John 2012)
Soluble CD-14 a
Shows antimicrobial activities by inhibiting LPS mediated
shock (Haziot, Rong et al. 1995)
Lysozyme a
Show antimicrobial activities
CD24 a
Binds to different released DAMPs in the injury site and
helps in immunosuppressive activities (Chen, Tang et al.
2009)
Siglec-10 a
Binds to DAMPs/CD24 complex and facilitates local
immunosuppression (Chen, Tang et al. 2009)
KGF a
facilitates wound healing (Geer, Swartz et al. 2005)
C5a a
increase migration and adherence of neutrophils and
monocytes to vessels walls and many other biological
functions (Manthey, Woodruff et al. 2009)
Others
a
ELISA, b Flowcytometric Bead Array
* IL-6 is a cytokine which recently has been described as an anti-inflammatory cytokine. Please see page 25
50
Chapter - II
2.10.
Human whole blood culture assay
Human blood culture assay was performed as described by Yaqoob et al with
some modifications (Yaqoob, Newsholme et al. 1999). Briefly, preservative free
heparinised blood samples were diluted 10 times in RPMI-1640 and cultured for 0, 6,
12, 24, and 48 hours with or without lipopolysaccharide (LPS) stimulation (1 and 10
ng/ml) at 370C in 5% CO2/N2. No antibiotics were used in the culture. Supernatants
were stored in -800C. Levels of sCD14, IL-1β, and TNF-α were measured in the
supernatants by ELISA.
2.11.
Automated Liquid Handing
An automated robotic liquid handling system (JANUS Automated
Workstation; Perkin Elmer) was used to analyze the level of different cytokines
secreted by PBMCs with or without LPS stimulation. The JANUS liquid handler is
equipped with VersaTip technology that allows to equip each needle with a
disposables tip or to use it as a fixed tip. The JANUS dispensing technology is based
on liquid displacement. All aspiration and distribution steps were done by moving
internal system liquid columns. WinPREP-JANUS software coordinated all liquid
handling steps, vacuum steps and gripper movements used in the automated sample
preparation. To obtain best results, instrument settings (volumes, vacuum grade and
arm movements) were optimized. This system helped to minimize inter-assay
variations, and human error in repetitive procedures with large number of assays.
Figure 2.6 : JANUS automated liquid handling system.
51
Chapter - II
2.12.
High abundant protein depletion from blood plasma
2.12.1. Importance of Depletion
The extensive composition of plasma proteins represents the plasma
proteome. Blood circulation within different organs and tissues causes addition of
new proteins, removal of some proteins, or modification of active proteins depending
on specific physiological or pathological conditions (Liotta, Ferrari et al. 2003,
Petricoin and Liotta 2003, Petricoin and Liotta 2003, Chan, Lucas et al. 2004,
Cristea, Gaskell et al. 2004, Lathrop 2005). Blood plasma proteins can represent
most of the major categories of proteins. Plasma proteome is therefore a source to
identify diagnostic markers and therapeutic targets for many human diseases
(Anderson NL 2004, Lathrop 2005). Human „plasma proteome‟ is distinctive from
„plasma proteins‟ that provides complete identification and categorization of every
single plasma protein in particular highly specified analytical forms. Due to
alternative splicing and post-translational modifications, proteome is dynamic and
much more complex than the genome; therefore proteome is a rich source of
potential diagnostic biomarkers. Categorically blood plasma contains classical
plasma proteins (secreted by solid tissues and act in plasma), immunoglobulins,
hormones, cytokines, chemokines and other soluble mediators, temporary
passengers, tissue leakage products from dead/ damaged cells, abnormal secretions
from tumour tissues, and foreign proteins from pathogens (Omenn 2006, Johann
Schaller 2008). Classical plasma proteins comprise more than 500 true proteins, most
of them have >20 different glycosylated forms with diverse molecular mass forms of
each (degraded form from the precursor or mature form). Therefore there are more
than 50,000 different molecular forms of classical proteins in plasma (Anderson and
Anderson 2002, Johann Schaller 2008); results in 500,000 different molecular forms
of proteins or peptides due to posttranslational modification, degradation or cleavage
(Omenn 2006, Johann Schaller 2008).
Proteases and proteases inhibitors, both are present in plasma. However due
to the presence of large quantities and range of proteases that are released into the
blood by neutrophils and mononuclear phagocytes, protease inhibitors in plasma
cannot perform their activities properly (Desrochers and Weiss 1988, Boyanton and
52
Chapter - II
Blick 2002, Clark, Youngman et al. 2003). Endogenous proteolytic activity causes
protein degradations in blood if it is stored at room temperature for a prolonged time.
Therefore, long-standing contact of plasma/serum with cells in the blood specimen
results in unauthentic diagnostic results (Boyanton and Blick 2002). More
importantly, for the proteomic and glycomic analyses of plasma, it is very important
to collect, conserve and store the samples avoiding the protease related protein
degradations (Ayache, Panelli et al. 2006).
Plasma is the mostly accessible specimen in the clinical diagnosis as most
cells communicate with it. Cells also release their content into soluble form in
plasma upon damage or death (Anderson NL 2004). Twenty high-abundant proteins
in plasma together represent about 97-99% of the total proteins and the rest proteins
in plasma span a concentration of even 11 orders of magnitude (Anderson and
Anderson 2003). Albumin is the most abundant protein in human plasma which
constitutes more than half of the total plasma protein and is present at 30-50 mg/ml
concentration. The immunoglobulin heavy and light chains (the second most
abundant proteins in the plasma), along with albumin represents collectively about
80% of the total plasma proteins. In contrast, most of the proteins are secreted into
the blood stream at very low copy number; and perhaps they are the primary targets
to identify the novel markers (Adkins, Varnum et al. 2002, Merrell K 2004, Zolg and
Langen 2004, Lathrop 2005, Thadikkaran, Siegenthaler et al. 2005, Tsangaris,
Weitzdörfer et al. 2005). There is no accurate method that can detect proteins of less
than 5 or 6 orders of magnitude (Anderson and Anderson 2003, Fountoulakis,
Juranville et al. 2004). Therefore, considering the limitations in the detection
sensitivity and high abundance of few proteins, depletion of high-abundant proteins
may help to reduce the complexity of plasma for better detections of more dynamic
ranges of proteins.
2.12.2. Depletion Technique
Immuno-depletion involved selective binding of high abundant proteins from
the study samples. Multiple affinity removal spin cartridge (MARS cartridge,
Agilent Technologies) has these selected antibodies bound to its chromatographic
support which thus helps to remove 14 most abundant proteins (albumin, IgG,
53
Chapter - II
antitrypsin, IgA, transferrin, haptoglobin, fibrinogen, alpha2-macroglobulin, alpha1acid glycoprotein, IgM, apolipoprotein AI, apolipoprotein AII, complement C3, and
transthyretin) from plasma samples (Figure 2.7). Briefly, 10 μL of plasma sample
isolated from the P100 tube was diluted 20 times with buffer-A (agilent.com),
filtered (0.22-μm) to prevent clogging of spin cartridge frits, and then placed in to the
MARS cartridge. These 14 high-abundant proteins were simultaneously removed
when plasma was passed through the cartridge depleting approximately 94% of total
plasma protein. Remaining low abundance proteins were then concentrated and
desalted by using spin concentrator (>5 kDa) and stored at -800C for future
proteomic and glycomic analyses. High-abundant proteins in the elution steps were
also stored at -800C for future use.
Figure 2.7: Depletion of proteins by MARS cartridge (agilent.com)
2.13.
Quantification of Proteins (Bradford Assay)
High-abundant protein depleted plasma samples were quantitated by adding
2μl to a 96-well flat bottomed plate in duplicate. Serial dilutions of BSA were
prepared as a known standard as shown in Table 2.1. Bradford reagent (190 μl) was
added to each well. Final volume was adjusted with PBS to 200 μl/well. Absorbance
was measured at 595 nm after 5-10 minutes incubation and plasma concentration was
determined from the known standards.
54
Chapter - II
Table 2.2. Bradford protein detection assay set-up
Final protein
concentration (μg/ml)
0
10
20
30
40
50
2.14.
Volume of 1
mg/ml BSA (μl)
0
2
4
6
8
10
Volume of Bradford
reagent (μl)
190
190
190
190
190
190
Volume of
PBS (μl)
10
8
6
4
2
0
One Dimensional Gel Electrophoresis
After preparation of resolving gel and setting the plates into the unit, 3.4 ml
of resolving gel was poured in between the glass plates. One ml of water-saturated
butanol was poured on resolving gel and incubated for 40 minutes at room
temperature. The 4% stacking gel was prepared freshly. After 40 minutes the plate
was washed with water to remove butanol and dried with filter paper. Stacking gel
was poured on to the plate. The comb was then set gently and the plate was incubated
overnight at room temperature.
High abundant protein depleted plasma samples and protein standards were
prepared in pre-labelled tubes. Tubes were then placed on heating block at 950C for 5
minutes. Samples and standards added to the wells in the gel. Running buffer poured
into the chamber (outer and inner) and electrophoresis was performed at 200V for 50
minutes. The tracking dye band was observed in the gel moving forward. After
electrophoresis, the gel was placed on a staining tray and washed in 100 ml of
deionised water for 15 minutes and repeated twice. The coomassie staining solution
was then added to completely immerse the gel in the staining solution and incubated
overnight. Gel was washed with water five minutes at which point the gel was ready
for imaging by the Documentation System (Samsung ES20). Images were used to
compare the bands in different gels and wells and molecular weight of unknown
protein was measured by comparison with the bands of known protein sizes.
55
Chapter - II
2.15.
Proteomic Array
“Tandem mass tagging” (TMT) is the most recent proteomic advances;
probably the simplest with the best accuracy than other proteomic approaches. By
using a particular isobaric TMT label for each sample TMT allow identifications of
peptides depending on its relative abundance in different samples. Therefore both the
identity and relative abundances of peptide pairs can be simultaneously determined
by TMT. On the other hand, “lectin microarray” is a relatively recent and powerful
technique to investigate the affinity of different lectins to the glycoproteins in the
sample. This state of the art technique provides rapid characterization of
carbohydrates on glycoconjugates in a microarray format by simultaneous
observation of the multiple and distinct binding affinities.
This assay was performed in the proteomics laboratory in University of
Bristol using standard protocols. The procedure for proteomics (as provided by the
laboratory) is briefly outlined below:
2.15.1. TMT Labelling and cation exchange chromatography
Aliquots of 75 µg of five plasma samples (high abundant protein depleted)
were digested with trypsin (2.5µg trypsin per 100µg protein; 37°C, overnight) and
labelled with Tandem Mass Tag (TMT) six-plex reagents according to the
manufacturer‟s protocol (Thermo Fisher Scientific, Loughborough, LE11 5RG, UK).
After labelling, samples were combined in equal amounts and a 10µg aliquot was
resuspended in 5% formic acid and then desalted using SepPak cartridges according
to the manufacturer‟s instructions (Waters, Massachusetts, USA)). Eluate from the
SepPak cartridge was evaporated to dryness and resuspended in 1% formic acid prior
to analysis by nano-LC MS/MS using an LTQ-Orbitrap Velos Mass Spectrometer.
56
Chapter - II
Figure 2.8: Schematic Diagram of Tandem Mass Tagging.
2.15.2. Nano-LC Mass Spectromerty
The sample was fractionated using a Dionex Ultimate 3000 nanoHPLC
system in line with an LTQ-Orbitrap Velos mass spectrometer (Thermo Scientific).
In brief, peptides in 1% (vol/vol) formic acid were injected onto an Acclaim PepMap
C18 nano-trap column (Dionex). After washing with 0.5% (vol/vol) acetonitrile 0.1%
(vol/vol) formic acid peptides were resolved on a 250 mm × 75 μm Acclaim PepMap
C18 reverse phase analytical column (Dionex) over a 150 min organic gradient,
using 7 gradient segments (1-6% solvent B over 1min., 6-15% B over 58min., 1532%B over 58min., 32-40%B over 3min., 40-90%B over 1min., held at 90%B for
6min and then reduced to 1%B over 1min.) with a flow rate of 300 nl min−1. Solvent
A was 0.1% formic acid and Solvent B was aqueous 80% acetonitrile in 0.1% formic
acid.
Peptides were ionized by nano-electrospray ionization at 2.0kV using a
stainless steel emitter with an internal diameter of 30 μm (Thermo Scientific) and a
capillary temperature of 250°C. Tandem mass spectra were acquired using an LTQOrbitrap Velos mass spectrometer controlled by Xcalibur 2.1 software (Thermo
Scientific) and operated in data-dependent acquisition mode. The Orbitrap was set to
analyze the survey scans at 60,000 resolution (at m/z 400) in the mass range m/z 300
to 1800 and the top ten multiply charged ions in each duty cycle selected for MS/MS
57
Chapter - II
fragmentation using higher-energy collisional dissociation (HCD) with normalized
collision energy of 45%, activation time of 0.1 ms and at a resolution of 7500 within
the Orbitrap.
Charge state filtering, where unassigned precursor ions were not
selected for fragmentation, and dynamic exclusion (repeat count, 1; repeat duration,
30s; exclusion list size, 500) were used.
2.15.3. TMT Data Analysis
The raw data files were processed and quantified using Proteome Discoverer
software v1.2 (Thermo Scientific) and searched against the UniProt/SwissProt
Human database using the SEQUEST (Ver. 28 Rev. 13) algorithm.
Peptide
precursor mass tolerance was set at 10ppm, and MS/MS tolerance was set at 0.8Da.
Search criteria included oxidation of methionine (+15.9949) as a variable
modification and carbamidomethylation of cysteine (+57.0214) and the addition of
the TMT 6Plex mass tag (+229.163) to peptide N-termini and lysine as fixed
modifications. Searches were performed with full tryptic digestion and a maximum
of 1 missed cleavage was allowed. The reverse database search option was enabled
and all peptide data was filtered to satisfy false discovery rate (FDR) of 5%. The
Proteome Discoverer software generates a reverse “decoy” database from the same
protein database and any peptides passing the initial filtering parameters that were
derived from this decoy database are defined as false positive identifications. The
minimum cross-correlation factor (Xcorr) filter was readjusted for each individual
charge state separately to optimally meet the predetermined target FDR of 5% based
on the number of random false positive matches from the reverse decoy database.
Thus each data set has its own passing parameters. Quantitation was done using a
peak integration window tolerance of 0.0075Da with the integration method set as
the most confident centroid. Protein ratios presented in table X represent the median
of the raw measured peptide ratios for each protein.
58
Chapter - II
2.16.
Glycomic Array (Lectin Profiling)
A very common post-translational modification of proteins is the addition of
glycans in a process called glycosylation. Glycans are the carbohydrate portion of the
glycoconjugates, such as a glycoprotein, glycolipid, or a proteoglycan (Opdenakker,
Rudd et al. 1993). It is estimated that approximately two-third of all human proteins
are glycosylated (Apweiler, Hermjakob et al. 1999). Lectins are group of proteins
that have specific binding activity to the carbohydrate residues of glycoproteins and
glycolipids. Lectins bind to the monosaccharide portions (such as mannose,
galactose/N-acetylgalactosamine, N-acetylglucosamine, fucose, and sialic acid) on
the protein/lipid molecule with high affinity. Lectin mediates cellular recognition of
a molecule or receptor; thus play important role in the interactions of a cell with
other cells or with soluble mediators. Therefore lectins have a great involvement in
immunological events such as cell proliferation and programmed death of cells,
metastasis of tumour cells, migrations and homing of immune cells, and also
infections (Sharon 2007).
Lectin array experiment was carried out in the glycoscience core facilities in
Biosciences, NUI Galway and briefly described below:
2.16.1. Alexa-Flour Labelling of Plasma Samples
The high abundant protein depleted plasma samples were labelled with
Alexaflour-555 by following manufacturer‟s instructions. Following overnight
incubation with alexaflour, samples were dialysed with TBS overnight (TBS buffer
was changed every few hours) using the dialysis cassette with pore size of 3.5 kDa to
remove the unbound labels, and finally again dialysed with TBS-T for two hours.
Alexa-labelled plasma samples were then taken from the dialysis cassette and
quantitated for protein concentrations by measuring absorbance at 280 nm.
Additional reading at 555 nm was also taken to correct the required volume of
protein samples to be loaded in the lectin microarray slide.
59
Chapter - II
2.16.2. Microarray Preparation, Incubation, and Scanning
43 lectins were selected for the lectin microarray based on their well-known
carbohydrate binding specificities. Microarrays were printed with different lectins
(diluted to 0.5 mg mL−1 in phosphate buffered saline) on hydrogel-coated glass
slides under constant 62% humidity at 20 °C. Features were printed with 1 nL of
lectin probes on glass slides in replicates of six features per probe.
Following overnight incubation in the humidity chamber, slides were
incubated for 1 hour at RT in 100 mM ethanolamine (in 50 mM sodium borate; pH
8). The slides were washed with PBST (0.05% Tween-20 in PBS; pH 7.4) and
subsequently with PBS. Slides were then dried by centrifugation at 470g for 5 min at
room temperature and stored at 4 °C with sicative for future use. Each slide had eight
replicate sub-arrays printed on it.
The experiment was carried out in dark. Printed slides were incubated in the
cassette (Agilent Technologies). In brief, 70 μL of the labelled plasma sample
(prepared in Tris-buffered saline with 0.05% Tween-20; TBS-T) was applied to each
well of the gasket in the printed slide. Six replicate slides were incubated per lectin
microarray experiment. The microarray slide was then placed in the cassette and
incubated for 1 hour in a rotating incubator at 23 °C and 4 rpm. Slides were washed
twice with TBS-T and subsequently with TBS, dried by centrifugation, and image
was taken by a microarray scanner at the resolution of 5 μm.
2.16.3. Glycomic Data Extraction and Analysis
Raw values for intensity were extracted from the images and exported as text to an
Excel format. Following correction for background, median of six replicate spots per
array was used as single data point for subsequent graphical and statistical analysis.
The data was then normalized and presented as mean intensity with standard
deviation (SD). A final cutoff value for data filtering was then selected by 750 RFUs.
Normalization of data was performed by dividing the total intensity value for each
subarray by the median total intensity value from all the sub arrays tested. This
normalization strategy reduced the artifacts for signals above or below the normal
ranges.
60
Chapter - II
For comparative and multivariate statistical analyses, the microarray data
from each slide was normalized by dividing the total intensity of each with the
median total intensity for all the sub-arrays. Significance was tested by Student's
paired T-test (two-tailed).
For heat map analysis, the values of postop, WSB, and TSB samples were
divided by their respective levels in Preop samples for all four study patients. This
was done to avoid the individual variations between the study patients and focussing
on the effect of surgery on the local and systemic changes as well as changes in the
collected transfused blood.
61
Chapter - II
2.17.
Statistical Analyses of the Study
All statistical analyses were performed using GraphPad Prism software
version 5.0.1 (GraphPad Software, San Diego, CA, USA). Readouts from assays
were corrected for dilution factors. For simple representation of the results, figures
were expressed in Box-Whisker plots on a Log10 scale as fold-changes over preoperative blood levels. For each patient fold-changes were calculated for biomarkers
in post-operative blood, WSB and TSB samples. Pooled results were illustrated as
median inter-quartile-range (IQR). Greater than one and less than one indicated
supra-normal and sub-normal values, respectively compared to baseline preoperative levels. Missing values disqualified individual fold-calculations from
inclusion in IQRs; no attempt being made to insert estimates.
Significance was tested by non-parametric statistical tests. Postoperative
changes in different biomarkers were compared by Wilcoxon test (paired) between
Pre- and Post-operative levels in both NSBT and ASBT cohorts. Wilcoxon tests
were also performed to compare with salvaged blood such as: preop vs WSB; preop
vs TSB; and WSB vs TSB. To compare the trend of postop changes between NSBT
and ASBT cohorts, non-parametric Mann-Whitney test (unpaired) were performed
on postop fold changes between two groups. Non-parametric Mann-Whitney and
Wilcoxon tests were performed on unpaired and paired data sets as appropriate for
the in vitro PBMC studies.
Individual variations attributed to genetic polymorphisms were highlighted
by plotting post-operative against pre-operative values for each patient on a
Log10/Log10 scale or linear scale as appropriate. Similarly, individual variations in
salvaged blood were highlighted by plotting WSB and TSB values against their
respective pre-operative values on Log10/Log10 scale or linear scale as needed.
Spearman correlation was performed on the postop fold changes between a
particular biomarker (X) and the changes in all other individual biomarkers (Y) in
both NSBT and ASBT cohorts. Spearman correlations were also performed to
compare the correlation between changes in wound site (WSB) for one particular
biomarker (X) and the changes in all other biomarkers (Y) than their levels in
peripheral blood; so as for TSB vs. WSB.
62
Chapter - II
Two-way ANOVA was performed on the datasets to compare levels of
different biomarkers simultaneously for different groups and different incubation
periods in the in vitro PBMC and human whole blood culture studies.
63
CHAPTER – III
RESULTS:
Characterization of Post-traumatic Immunosuppression (PTI)
and
Assessment of the Effect of Autologous Salvaged Blood Transfusion
Chapter – III :Results
Characterization of PTI and Effects of ASBT on PTI
3.1. Patients’ Characteristics
There were no differences between the NSBT and ASBT cohorts in age,
gender and length of hospital stay (Table 3.1). Post-operative haematological
changes following surgical trauma included: decreased red cells, platelets,
lymphocytes, eosinophils and basophils; and increased neutrophils and monocytes.
Additionally haemoglobin levels as well as haematocrit values decreased
postoperatively.
Postoperative
levels
of
different
common
biochemical
measurements such as sodium, potassium, urea, and creatinine were either slightly
decreased or unchanged. However, for all the haematological and biochemical
parameters, no differences were observed between NSBT and ASBT cohorts (Table
3.1).
For all biomarkers analysed no differences were found that were attributable
to gender, diagnosis, NSAIDs, and age. No significant correlations were found
between fold-values and length of hospital stay (Appendix – IIIA). Actual values for
individual pre-operative biomarker (i.e. baseline) levels differed by several orders of
magnitude between patients, but trends in post-operative values were consistent
across group, when expressed as fold-changes over pre-operative levels. Thus major
changes were revealed between NSBT and ASBT cohorts.
There were no
noteworthy correlations observed between the fold-changes in different biomarkers
and the volume of drained blood (Appendix – IIIB). In the ASBT cohort no
significant correlations were observed between fold-values and volume of blood
salvaged (Appendix – IIIC). There were no post-operative infections or wound
healing problems in any patient, and there were no significant differences between
ASBT and NSBT cohorts in clinical outcome measures.
65
Table 3.1 - Patient Characteristics and Haematological Information
Median Age in Years
(range)
NSBT
ASBT
74 (61-86)
73(56-86)
5:13
8:17
Gender (male :
female)
NSBT &
ASBT
combined
mean-fold
Hospital Stay Median
Days (range)
4 (4-6)
change
5 (3-7)
over
pre-op.
Haematological Biomarkers: Mean ±SD (Missing Values)
Pre-op.
p
Post-op.
Pre-op.
p
Post-op.
RBC (x1012/L)
4.41±0.55(0)
***
3.54±0.46(0)
4.43±0.5(0)
***
3.55±0.44(0)
0.80±0.08
Haemoglobin(g/dl)
12.64±1.41(0)
***
10.21±1.14(0)
13.48±1.57(0)
***
10.75±1.44(0)
0.80±0.08
Haematocrit (Hct)
0.39±0.04 (0)
***
0.31±0.03 (0
0.42±0.04 (0)
***
0.33±0.04 (0)
0.79±0.08
Platelet (x109/L)
269.10±63.53(0) ** 227.78±70.62(0)
243.90±57.69(0) *** 189.56±48.69(0)
0.80±0.14
WBC (x109/ L)
7.28±1.38(0)
***
9.30±2.42(0)
7.61±2.37(0)
***
9.66±2.30(0)
1.31±0.31
Lymphocyte(x109/L)
1.91±0.75(0)
***
1.09±0.52(0)
1.85±0.75(0)
***
1.23±0.51(0)
0.64±0.20
Monocytes (x109/L)
0.61±0.24(0)
*
0.99±0.51(0)
0.56±0.19(0)
***
1.03±0.30(0)
1.84±0.79
Neutrophil (x109/L)
4.55 ± 0.88(0)
***
7.13±2.09(0)
5.00±2.11(0)
***
7.36±2.13(0)
1.62±0.52
Eosinophil (x109/L)
0.19±0.13(5)
***
0.05±0.07(5)
0.17±0.11(7)
***
0.06±0.08(7)
0.32±0.31
Basophil (x109/L)
0.04±0.03(1)
*** 0.019 ± 0.01(1)
0.03±0.01(4)
*** 0.015 ± 0.01(4)
0.55±0.27
Other Biochemical Parameters: Mean ±SD (Missing Values)
Sodium
139.9 ± 4.14 (0)
*
137.6 ± 2.28 (0)
140.3 ± 3.51 (0) *** 137.0 ± 3.55 (0)
0.98 ± 0.02
Potassium
4.51 ± 0.55 (0)
*
4.21 ± 0.27 (0)
4.33 ± 0.42 (0)
*
4.02 ± 0.33 (0)
0.94 ± 0.09
Urea
6.43 ± 1.65 (0)
*
5.16 ± 1.64 (0)
6.34 ± 1.68 (0)
NS
5.84 ± 1.82 (0)
0.90 ± 0.32
77.76 ± 19.5 (0) NS 79.24±23.85 (0)
0.98 ± 0.22
Creatinine
78.83± 18.77 (0) NS 74.94±21.54 (0)
*p= <0·05 **p= <0·001 ***p= <0·0001
66
3.2. Damage Associated Molecular Patterns (DAMPs)
Post-operative levels of the damage associated molecular patterns (DAMPs)
such as α-Defensin, HSP-70 and S100-A8/A9 were elevated whereas HMGB-1 was
decreased in both NSBT and ASBT cohorts (Fig. 3.1A & Appendix–IV). Others
HSP-27 and HSP-60 remained unchanged. Of note, increased Annexin-A2 levels
occurred after salvaged blood transfusion. Degree of postoperative reduction in
HMGB-1 was significantly higher in the NSBT cohort comparing with ASBT cohort
(Fig. 3.1A). Individual variations are shown in Figure 3.1B.
Figure 3.1: Postoperative Fold Changes in DAMPs.
(A) Levels of different DAMPs are plotted on a Log10 scale as median interquartile range (IQR) of
fold-changes relative to pre-operative values for NSBT (
), and ASBT (
) cohorts.
Significance of differences between pre-operative and post-operative values are represented by *, and
differences between NSBT and ASBT cohort fold-values are represented by +. Thus: */+ = p <0·05,
**/++ = p <0·001 and ***/+++ = p <0·0001. HMGB-1: High Mobility Group Box Protein-1; HSP:
Heat Shock Protein.
67
68
are in blue circles (
) and ASBT are in red squares (
). Units of measurement are ng/ml.
(B) Post-operative levels are plotted against pre-operative levels on a Log10/Log10 scale for each patient for DAMPs. NSBT patients
Figure 3.1: Postoperative Fold Changes in DAMPs (Continued).
Post-operative levels of pro-inflammatory cytokines, IL-1β, IL-2, IL-17A,
IFN-γ and TNF-α were suppressed in the NSBT cohort but elevated in the ASBT
cohort (Fig. 3.2A & Appendix–IV). More than 20% postoperative reductions
recorded in the levels of all these pro-inflammatory cytokines in the NSBT cohort. In
contrast, in the ASBT cohort, levels of these cytokines were elevated more than 2
folds postoperatively. In both NSBT and ASBT cohorts, as a key biomarker of
surgical trauma IL-6 was markedly elevated post-operatively (7.4±6.6 and 6.6±5.4
folds respectively), but the pro-inflammatory cytokine, IL-12p70 remained
unchanged Individual variations between patients in their levels of different proinflammatory cytokines are shown in Figure 3.2B.
Figure 3.2: Postoperative Fold Changes in Pro-inflammatory Cytokines.
(A) Levels of pro-inflammatory cytokines are plotted on a Log10 scale as median interquartile range
(IQR) of fold-changes relative to pre-operative values for NSBT (
), and ASBT (
) cohorts.
differences between NSBT and ASBT cohort fold-values are represented by +. Thus: */+ = p<0·05,
**/++ = p<0·001 and ***/+++ = p<0·0001. IL: Interleukin; IFN: Interferon; TNF: Tumour necrosis
factor.
69
70
(B) Post-operative levels are plotted against pre-operative levels on a Log10/Log10 scale for each patient for pro-inflammatory cytokines. NSBT
). Units of measurement are pg/ml.
patients are in blue circles (
Figure 3.2: Postoperative Fold Changes in Pro-inflammatory Cytokines (Continued).
In counterpoint, levels of key anti-inflammatory cytokines were either
unchanged (IL-4, IL-10, IL-13 and TGF-β1) or increased (IL-5, IL-9 and IL-1RA) in
the NSBT cohort (Fig. 3.3A & Appendix–IV). More interestingly, postoperative
levels of these anti-inflammatory cytokines were significantly decreased in the
ASBT cohort. Marked elevations in postoperative level of IL-1RA were observed in
both NSBT and ASBT cohorts. Levels of TGF-β1 remained unchanged in both study
groups. Individual variations between patients in their levels of different antiinflammatory cytokines are shown in Figure 3.3B.
Figure 3.3: Postoperative Fold Changes in Anti-inflammatory Cytokines.
(A) Levels of anti-inflammatory cytokines are plotted on a Log10 scale as median interquartile range
(IQR) of fold-changes relative to pre-operative values for NSBT (
), and ASBT (
) cohorts.
differences between NSBT and ASBT cohort fold-values are represented by +. Thus: */+ = p <0·05,
**/++ = p <0·001 and ***/+++ = p <0·0001. IL: Interleukin; TGF: Transforming growth factor.
71
72
NSBT patients are in blue circles (
). Units of measurement are pg/ml except TGF-b1 (ng/ml).
(B) Post-operative levels are plotted against pre-operative levels on a Log10/Log10 scale for each patient for anti-inflammatory cytokines.
Figure 3.3: Postoperative Fold Changes in Anti-inflammatory Cytokines (Continued).
3.5. Chemokines
Postoperative levels of IL-8 were elevated whereas MIP-1α was unchanged in
both NSBT and ASBT cohorts. Of note, increased MCP-1 levels occurred only after
salvaged blood transfusion (Fig. 3.4A & Appendix–IV). However there were no
variations in the postoperative changes in different chemokines between two study
cohorts. Individual variations between patients in their levels of different chemokines
are shown in Figure 3.4B.
Figure 3.4: Postoperative Fold
Changes in Chemokines
(A) Levels of chemokines are plotted on a
Log10 scale as median interquartile range
(IQR) of fold-changes relative to pre-operative
values for NSBT (
), and ASBT (
)
cohorts. Significance of differences between
post-operative and pre-operative values are
represented by *. Thus: * = p <0·05, ** = p
<0·001 and *** = p <0·0001.
(B)
Postoperative
plotted
against
levels
are
pre-operative
levels on a Log10/Log10 scale for
each
patient
for
different
chemokines. NSBT patients are
in blue circles (
) and ASBT
are in red squares (
). Units
of measurement are pg/ml. IL:
Interleukin;
MCP:
Monocyte
chemoattractant protein; MIP:
Macrophage
inflammatory
protein.
73
3.6. Interleukin-22 (IL-22)
Postoperative IL-22 was unchanged in the NSBT cohort but significantly
elevated in the ASBT cohort (Fig. 3.5A and Appendix–IV). Individual variations are
shown in Figure 3.5B.
Figure 3.5: Postoperative Fold Changes in IL-22.
(A) Levels of IL-22 are plotted on a Log10 scale as median interquartile range (IQR) of fold-changes
relative to pre-operative values for NSBT (
), and ASBT (
) cohorts. Significance of
differences between pre-operative and post-operative values are represented by *, and differences
between NSBT and ASBT cohort fold-values are represented by +. Thus: */+ = p <0·05, **/++ = p
<0·001 and ***/+++ = p <0·0001.
(B) Postoperative levels are plotted against pre-operative levels on a Log10/Log10 scale for each
patient for IL-22. NSBT patients are in blue circles (
). Units
of measurement are pg/ml.
74
3.7. sIL-6R (Soluble IL-6 Receptor), sgp130 (Soluble gp130) and ADAM-17 (A
Disintegrin and Metalloproteinase - domain 17)
Postoperative sIL-6R and sgp130 were significantly decreased in both NSBT
and ASBT cohorts. Whereas ADAM-17 in the NSBT cohort was unchanged, it was
marginally elevated in the ASBT cohort (Fig. 3.6A & Appendix–IV). However no
significance differences between study cohorts were detected in sIL-6R, sgp130, and
ADAM-17 levels. Individual variations are shown in Figure 3.6B.
Figure 3.6: Postoperative Fold Changes in
sIL-6R, sgp130, and ADAM-17.
(A) Levels of sIL-6R, sgp130, and ADAM-17 are
plotted on a Log10 scale as median interquartile range
(IQR) of fold-changes relative to pre-operative values
for NSBT (
), and ASBT (
) cohorts.
Significance of differences between post-operative
and pre-operative values are represented by *. Thus: *
= p <0·05, ** = p <0·001 and *** = p <0·0001.
(B) Postoperative levels are
plotted against pre-operative
levels on a Log10/Log10 scale
for each patient for sIL-6R,
sgp130,
and
ADAM-17.
NSBT patients are in blue
circles (
) and ASBT are
in red squares (
). Units
of measurement are ng/ml.
75
3.8. Soluble CD14 ( sCD14)
Postoperative sCD14 was elevated in both NSBT and ASBT cohorts (p
<0.0001) as shown in Fig. 3.7A and Appendix–IV and no difference was observed
between cohorts. Individual variations are shown in Figure 3.7B.
Figure 3.7: Postoperative Fold Changes in sCD14.
(A) Levels of sCD14 are plotted on a Log10 scale as median interquartile range (IQR) of fold-changes
), and ASBT (
differences between post-operative and pre-operative values are represented by *. Thus: * = p <0·05,
** = p <0·001 and *** = p <0·0001.
(B) Postoperative levels are plotted against pre-operative levels on a Log10/Log10 scale for each patient
for sCD14. NSBT patients are in blue circles (
). Units of
measurement are µg/ml.
76
Enzymatic activity of lysozyme was increased postoperatively in both NSBT
(p = 0.04) and ASBT (p = 0.002) cohorts (Fig. 3.8A & Appendix–IV) and no
differences were observed between cohorts. Individual variations are shown in
Figure 3.8B.
Figure 3.8: Postoperative Fold Changes in Total Lysozyme.
(A) Levels of total lysozyme are plotted on a Log10 scale as median interquartile range (IQR) of foldchanges relative to pre-operative values for NSBT (
), and ASBT (
) cohorts. Significance
of differences between post-operative and pre-operative values are represented by *. Thus: * = p
<0·05, ** = p <0·001 and *** = p <0·0001.
for total lysozyme. NSBT patients are in blue circles (
).
Units of measurement are x1000 IU/ml.
77
3.10. Heat Stable Antigen (CD-24), Sialic Acid Binding Ig-like Domain-10
(Siglec-10) and Siglec-2
Postoperative CD-24 and Siglec-10 were greatly elevated in both NSBT and
ASBT cohorts, but Siglec-2 showed no change (Fig. 3.9A & Appendix–IV).
Individual variations are shown in Figure 3.9B.
Figure 3.9: Postoperative
Fold Changes in CD-24,
Siglec-10, and Siglec-2.
(A) Levels of CD-24, Siglec-10, and
Siglec-2 are plotted on a Log10 scale
as median interquartile range (IQR) of
fold-changes relative to pre-operative
values for NSBT (
), and ASBT
(
differences between post-operative
and
pre-operative
values
are
represented by *. Thus: * = p <0·05,
** = p <0·001 and *** = p <0·0001.
(B) Postoperative levels are plotted
against pre-operative levels on a
Log10/Log10 scale for each patient for
CD-24 and Siglec-10. NSBT patients
are in blue circles (
) and ASBT
). Units of
measurement are ng/ml.
78
3.11.
Complement Split Protein – C5a
Postoperative C5a was elevated in both NSBT (p = 0.0002) and ASBT (p
<0.0001) cohorts (Fig. 3.10A and Appendix–IV). The degree of elevation in ASBT
cohort was higher than NSBT (p <0.0001). Individual variations are shown in Figure
3.10B.
Figure 3.10: Postoperative Fold Changes in C5a.
(A) Levels of C5a are plotted on a Log10 scale as median interquartile range (IQR) of fold-changes
), and ASBT (
differences between pre-operative and post-operative values are represented by *, and differences
between NSBT and ASBT cohort fold-values are represented by +. Thus: */+ = p <0·05, **/++ = p
<0·001 and ***/+++ = p <0·0001.
for C5a. NSBT patients are in blue circles (
). Units of
79
3.12.
In-vitro PBMC Culture With or Without LPS Stimulation
In absence of LPS, secretions by postoperative PBMCs were unchanged (IL-
10, IL-13 and TGF-β1) or increased (IL-1RA) equally in NSBT and ASBT cohort
(Figure 3.11A). In presence of LPS postoperative, PBMC from NSBT showed
significantly elevated levels of IL-10, IL-13, TGF-β1 and IL-1RA compared to
preoperative PBMC. However, postoperative PBMC from ASBT showed no
differences except that IL-13 was increased (Figure 3.11B).
In the absence of LPS, IL-1β was secreted at higher levels by postoperative
than preoperative PBMC in both NSBT and ASBT cohorts, and no changes occurred
in IL-6 and TNF-α (Figure 3.11C). However, addition of LPS abrogated the IL-1β
increase (Figure 3.11D). Levels of cytokines, IL- 12p70, IL-2 and IFN-γ were too
low to detect.
In the absence of LPS, no changes occurred in IL-8 release, whereas MCP-1
and MIP-1α secretion was raised in both NSBT and ASBT cohorts (Figure 3.11E).
Stimulation by LPS caused increased secretion in IL-8 in both cohorts, but abrogated
increases in MCP-1 and MIP-1α (Figure 3.11F). Individual variations are shown in
Figure 3.11.G, H, I, and J.
In absence of LPS, sCD14 (Fig 3.11.K) was secreted at higher levels by
postoperative than preoperative PBMC in both NSBT and ASBT cohorts, and were
marginally increased by LPS in the NSBT cohort (p = 0.01) as shown in Figure
3.11.K. Similarly, sIL-6R was secreted at elevated levels by postoperative PBMC in
both NSBT and ASBT cohorts irrespective of addition of LPS (Fig 3.11.M).
Individual variations are shown in Figure 3.11.L (for sCD14) and 3.11.N (for sIL6R).
80
Figure 3.11 (A-F): Biomarker Profiles of PBMCs Challenged with LPS.
Cryopreserved and thawed PBMC from NSBT and ASBT patients adjusted to 106 viable cells per
ml. and cultured with or without LPS. (A), (C) and (E): No added LPS; (B) (D) and (F): LPS
(3ng/ml.). Results are expressed as fold-changes calculated by dividing postoperative PBMC
results by their respective pre-operative PBMCs, and plotted on a Log10 scale as Median (IQR). (A)
and (B) Anti- inflammatory cytokines; (C) and (D) Pro-inflammatory cytokines; (E) and (F)
Chemokines. Post-operative values were compared with pre-operative values by one sample t-test on
the fold changes (* = p <0·05, ** = p <0·001).
81
Figure 3.11 (G-H): Biomarker Profiles of PBMCs Challenged with LPS
(Continued).
Levels of biomarkers secreted by postoperative PBMCs are plotted against pre-operative PBMC
values on Log10/Log10 scale for each patient without LPS challenge (G) or with LPS challenge (H).
). Units of measurement
are pg/ml.
82
Figure 3.11 (I-J): Biomarker Profiles of PBMCs Challenged with LPS
(Continued).
Levels of biomarkers secreted by postoperative PBMCs without or with LPS challenge are plotted
against pre-operative PBMC values on Log10/Log10 scale for each patient of NSBT (I) and ASBT (J)
cohorts. No LPS values are in violet circles (
) and LPS challenged values are in black squares
(
83
sCD14
sIL-6R
Figure 3.11 (K-N): Biomarker Profiles of PBMCs Challenged with LPS
(Continued).
Cryopreserved and thawed PBMC from NSBT and ASBT patients adjusted to 106 viable cells per
ml. and cultured with or without LPS (3 ng/ml). Results are expressed as fold-changes calculated
by dividing postoperative PBMC results by their respective pre-operative PBMCs, and plotted on a
Log10 scale as Median (IQR). (K) sCD14 and (M) sIL-6R. Significance of differences between preoperative and post-operative values are represented by *, and differences between NSBT and ASBT
cohort fold-values are represented by +. Thus: */+ = p <0·05; **/++ = p <0·001, and ***/+++ = p
<0·0001.
Levels of sCD14 and sIL-6R secreted by postoperative PBMCs are plotted against pre-operative
PBMC values for each patient without LPS challenge (L and N - upper left) or with LPS challenge (L
and N – upper right) respectively. NSBT patients are in blue circles (
) and ASBT are in red
squares (
). Levels of sCD14 and sIL-6R secreted by postoperative PBMCs without or with LPS
challenge are also plotted against pre-operative PBMC values for each patient of NSBT (L and N –
bottom left) and ASBT (L and N – bottom right) cohorts. No LPS values are in violet circles (
)
and LPS challenged values are in black squares (
84
3.13.
Summary of Postoperative Observations
Post-operative biomarkers of sterile trauma (termed common biomarkers of
sterile trauma) were divisible into two groups, the first of which were common to all
patients, and the second of which was significantly altered after anti-coagulated
salvaged blood re-infusion (termed, salvaged blood sensitive biomarkers of sterile
trauma).
3.13.1. Common Biomarkers of Sterile Trauma (Common-BST)
All patients in the NSBT and ASBT cohorts showed similar post-operative
trends in the haematological biomarkers. These included: above normal levels of
neutrophils and monocytes; and, subnormal levels of lymphocytes, eosinophils,
basophils, platelets, and red blood cells. All patients exhibited postoperative
increases in DAMPs such as HSP-70, α-Defensin and S100A8/9, and decreased
HMGB-1. All patients exhibited increases in the cytokines, IL-6 and IL-1RA; and in
the chemokine, IL-8/CXCL8. No changes were detected in post-operative levels of
HSP-27, HSP-60, TGF-β1, MIP-1α/CCL3 and IL-12p70. All patients showed
increased levels for sCD14, CD24, Siglec-10, C5a and Lysozyme, and decreased
levels for sIL-6R and sgp130. Combined mean fold-changes for all these biomarkers
are documented in Table 3.2.
85
Table 3.2 : Common Biomarkers of Sterile Trauma (Common-BST)
Marker
Change
Fold-Change (Mean ± SD)
p
Neutrophils

1·62 ± 0.53
***
Monocytes

1·84 ± 0.79
***
Lymphocytes

0·64 ± 0.20
***
Eosinophils

0·32 ± 0.32
***
Basophils

0·55 ± 0.27
***
RBC

0·80 ± 0.08
***
Platelets

0·80 ± 0.14
***
IL-6

6·94 ± 5.89
***
IL-1RA

1·33 ± 0.36
***
IL-8/CXCL8

2·02 ± 1.65
**
MIP-1α
↔
1.25 ± 0.47
NS
HMGB-1

0.57 ± 0.21
***
α-Defensin

1.18 ± 0.14
***
HSP-70

1·90 ± 0.95
***
S 100 A8/A9

3.96 ± 2.16
***
sCD14

1.63 ± 0.53
***
sIL-6R

0.87 ± 0.20
***
sgp130

0.91 ± 0.13
**
CD24

4.51 ± 1.99
***
Siglec-10

5.83 ± 3.05
***
C5a

1.73 ± 0.61
***
Lysozyme

5.83 ± 3.05
**
 = increase,  = decrease, ↔ = no change *p= 0·05 **p= 0·001
***p=  0·0001
86
3.13.2. Salvaged Blood Sensitive Biomarkers of Sterile Trauma (SBS-BST)
Biomarkers of sterile trauma that were supra-normal, normal or sub-normal in
the NSBT cohort, were significantly changed in the ASBT cohort. These included
the DAMP - Annexin-A2, which was normal in the NSBT but above normal in the
ASBT cohort. Key anti-inflammatory cytokines, IL-4, IL-5, IL-10, and IL-13 were
normal in the NSBT cohort, and IL-5 was above normal; however in the ASBT
cohort they were all sub-normal. Key pro-inflammatory cytokines, IL-1β, IL-2, IL17A, IFN-γ, and TNF-α were sub-normal in the NSBT cohort, but markedly supranormal in the ASBT cohort. ADAM-17 was normal in NSBT and supra-normal in
ASBT cohort. Mean fold-changes (± SDs) are summarized separately for NSBT and
ASBT cohorts in Table 3.3.
Table 3.3 : Salvaged Blood Sensitive Biomarkers of Sterile Trauma (SBS-BST)
NSBT Postop. Values
ASBT Postop. Values
Compared to
Compared to
Preop. Values
Preop. Values
NSBT
Versus
Biomarker
Change
Fold Change
(Mean ± SD)
Postop.
Versus
Preop
Fold Change
Change
(Mean ± SD)
p
ASBT
Postop.
Versus
p
Preop.
p
IL-1β

0·79 ± 0.79
*

2·30 ± 0.98
***
***
IL-2

0·58 ± 0.53
**

2·16 ± 1.21
***
***
IL-17A

0·80 ± 0.57
*

2·28 ± 1.61
**
**
IFN-γ

0·79 ± 1.54
*

3·15 ± 1.87
**
**
TNF-α

0·59 ± 0.53
**

3·34 ± 2.12
***
***
IL-4

1·76 ± 1.91
NS

0·77 ± 0.43
*
*
IL-5

2·39 ± 2.26
*

0·61 ± 0.35
***
***
IL-9

1·81 ± 3.25
NS

0·64 ± 0.36
**
NS
IL-10

2·03 ± 1.73
NS

0·63 ± 0.38
***
**
IL-13

1·07 ± 0.47
NS

0·72 ± 0.42
***
*
MCP-1/CCL2

1·5 ± 1.10
NS

1·52 ± 0.66
NS
NS
Annexin-A2

0·96 ± 0.13
NS

1·43 ± 0.63
**
***
ADAM-17

1.12 ± 0.47
NS

1.33 ± 0.62
*
NS
 = increase,  = decrease, ↔ = no change, NS= Not Significant. *p= 0·05 **p= 0·001 ***p=  0·0001
87
3.14.
Correlation Between Postoperative Changes in Different Biomarkers
3.14.1. NSBT
Spearman correlation analyses were performed to compare all changes in
postoperative biomarkers (Table 3.4). Some selected correlations are described
below:
1. Postoperative decrease in IL-17A correlated with decrease in IL-1β and IL-2.
2. Postoperative lymphopenia correlated with decrease in IL-1β, IL-17A, IL-2 and
IFN-γ.
3. Decreased IL-2 was inversely associated with IL-6 elevations.
4. Postoperative IL-2 was correlated with thrombocytopenia and decreased
haematocrit.
5. Decrease in sIL-6R was strongly correlated with decrease in sgp130; and
ADAM-17 was inversely correlated with decreases in sIL-6R and sgp130.
6. Postoperative increases in S100A8/A9, CD24 and Siglec-10 were strongly
correlated.
88
Table 3.4 : Spearman Correlation between changes in different biomarkers in NSBT
89
3.14.2. ASBT
Spearman correlations also performed to compare postoperative changes in
biomarkers in ASBT cohort (Table 3.5). Selected observations are described below:
1. Decrease in sIL-6R and sgp130 were strongly correlated. However ADAM-17 was
not correlated with these bio-factors.
2. Reduction in IL-10 was inversely correlated with elevation in IL-6, MCP-1, IL12p70, neopterin and CD24; and lymphopenia.
3. Elevations in both CD24 and Siglec-10 correlated with the elevated S100-A8/A9.
CD24 and Siglec-10 were very strongly correlated.
4. CD24 and Siglec-10 were inversely associated with decreased RBC counts and
haematocrit values. CD24 correlates with thrombocytopenia and lymphopenia.
90
Table 3.5 : Spearman Correlation between changes in different biomarkers in ASBT
91
CHAPTER – IV
RESULTS:
Constituents of Salvaged Blood
Chapter – IV : Results
Levels of some biomarkers were dramatically elevated in salvaged blood
plasma (both WSB and TSB) when compared with peripheral preoperative levels.
These changes are described in the next few sections and levels of individual
biomarkers in different plasma samples are given in Appendix-V.
4.1. Damage Associated Molecular Patterns (DAMPs)
Levels of DAMPs (HMGB-1, S-100-A8/A9, α-Defensin, HSP-27, HSP-60
and HSP-70) were elevated between 9 and 120-fold at the surgical wound site
compared with peripheral levels. One exception to this was Annexin-A, which was
only marginally increased. Comparison of WSB and TSB revealed that whereas
HMGB-1 was reduced either by consumption or degradation during the collection
period, Annexin-A2 was elevated in the bag. The other DAMPs remained unchanged
during the collection period (Figure 4.1A and Appendix-V). Individual variations are
shown in Figure 4.1B.
Figure 4.1 : Changes in DAMPs in Salvaged Blood
(A) Levels are plotted on a Log10 scale as median interquartile range (IQR) of fold-changes relative to
pre-operative values for WSB ( ) and TSB ( ). Significance of differences between pre-operative
and WSB or TSB values are represented by *, and differences between WSB and TSB fold-values are
represented by +. Thus: */+ = p <0.05, **/++ = p <0.001 and ***/+++ = p <0.0001. HMGB: High
mobility group box protein; HSP: Heat shock protein.
93
94
(B) Salvaged blood levels (both WSB and TSB) of DAMPs are plotted against pre-operative peripheral levels on a Log10/Log10 scale for
each patient. NSBT patients are in blue circles (
Figure 4.1 : Changes in DAMPs in Salvaged Blood (Continued)
All pro-inflammatory cytokines (IL-1β, IL-2, IL-6, IL-12p70, IL-17A, IFN-γ,
and TNF-α) were significantly elevated at the wound site and their levels continually
increased in the bag during the collection period as evidenced by comparing WSB
with TSB. In the case of IL-6, WSB contained 300 fold higher levels but more
production occurred interestingly during collection as evidenced by 2,500 fold higher
levels in TSB than in pre-operative blood (Figure 4.2A and Appendix-V). Individual
variations are shown in Figure 4.2B.
Figure 4.2 : Changes in Pro-inflammatory Cytokines in Salvaged Blood
pre-operative values for WSB ( ) and TSB ( ). Significance of differences between pre-operative
and WSB or TSB values are represented by *, and differences between WSB and TSB fold-values are
represented by +. Thus: */+ = p <0.05, **/++ = p <0.001 and ***/+++ = p <0.0001. IL: Interleukin;
IFN: Interferon; TNF: Tumour necrosis factor.
95
96
(B) Salvaged blood levels (both WSB and TSB) are plotted against pre-operative peripheral levels on a Log10/Log10 scale for each patient.
Figure 4.2 : Changes in Pro-inflammatory Cytokines in Salvaged Blood (Continued)
In WSB, normal levels of IL-5 and IL-13 were accompanied by increased
levels of IL-4, IL-9, IL-10, TGF-β1 and IL-1RA. Marginal increases occurred during
the collection period in IL-4, IL-10, TGF-β1 and IL-1RA levels whereas there were
no further changes in TSB for IL-5, IL-9 and IL-13 (Figure 4.3A and Appendix-V).
Individual variations are shown in Figure 4.3B.
Figure 4.3 : Changes in Anti-inflammatory Cytokines in Salvaged Blood
(A) Levels are plotted on a Log10 scale as median interquartile range (IQR) of fold-changes
relative to pre-operative values for WSB (
) and TSB (
). Significance of differences
between pre-operative and WSB or TSB values are represented by *, and differences
between WSB and TSB fold-values are represented by +. Thus: */+ = p <0.05, **/++ = p
<0.001 and ***/+++ = p <0.0001. IL: Interleukin; TGF: Transforming Growth Factor; IL1RA: Interleukin-1 receptor antagonist.
97
98
(B) Salvaged blood levels (both WSB and TSB) of anti-inflammatory cytokines are plotted against pre-operative peripheral levels on a
Log10/Log10 scale for each patient. NSBT patients are in blue circles (
). Units of measurement are in
pg/ml
TGF-b1
(ng/ml).
pg/mlexcept
except
TGF-β1
and IL-1RA who are in ng/ml.
Figure 4.3 : Anti-inflammatory Cytokines (Continued)
4.4. Chemokines
Chemokines (IL-8, MCP-1 and MIP-1α) were also elevated 9 to 15-fold in
WSB compared with peripheral venous blood levels. More interestingly, like the proinflammatory cytokines, further increase in chemokine levels occurred during
collection as evidenced by a further 2 to 17-fold higher levels in TSB compared to
WSB (Figure 4.4A and Appendix-V). Individual variations are shown in Figure
4.4B.
Figure 4.4 : Changes in Chemokines
in Salvaged Blood
(A) Levels are plotted on a Log10 scale
as median interquartile range (IQR) of
fold-changes relative to pre-operative
values for WSB (
) and TSB (
).
Significance of differences between
pre-operative and WSB or TSB values
are represented by *, and differences
between WSB and TSB fold-values are
represented by +. Thus: */+ = p <0.05,
**/++ = p <0.001 and ***/+++ = p
<0.0001.
(B) Salvaged blood
levels (both WSB and
TSB) of chemokines
are plotted against preoperative
peripheral
levels on a Log10/Log10
scale for each patient.
NSBT patients are in
blue circles (
) and
ASBT are in red
squares (
). Units of
measurement
are
pg/ml.
99
4.5. Interleukin-22 (IL-22) and Keratinocyte Growth Factor (KGF)
Elevations in IL-22 levels were found in WSB compared with peripheral
levels; and further increased during the collection period as shown by comparing
TSB with WSB. On the other hand, KGF levels were below the detection limit in
peripheral blood, but became easily detectable in WSB, later dropping slightly in the
bag during collection; presumably due to the dilution in the bag (Figure 4.5A and
Appendix-V). Individual variations are shown in Figure 4.5B and 4.5C.
Figure 4.5 : Changes in IL-22 and KGF in
Salvaged Blood
(A) Levels are plotted on a Log10 scale as median
interquartile range (IQR) of fold-changes relative to
pre-operative values for WSB (
) and TSB (
).
Significance of differences between pre-operative
and WSB or TSB values are represented by *, and
differences between WSB and TSB fold-values are
represented by +. Thus: */+ = p <0.05, **/++ = p
<0.001 and ***/+++ = p <0.0001.
(B) Salvaged blood levels (both WSB and TSB) of
IL-22 are plotted against pre-operative peripheral
levels on a Log10/Log10 scale for each patient. NSBT
) and ASBT are in
red squares (
(C) Peripheral and Salvaged blood levels (both WSB
and TSB) of KGF in scatter plot.
100
4.6. sIL-6R (Soluble IL-6 Receptor), sgp130 (Soluble gp130) and ADAM-17 (A
Disintegrin and Metalloproteinase - domain 17)
Levels of sIL-6R, sgp130 and ADAM-17 were significantly elevated in the
surgical wound site. However, no additional productions of sIL-6R or sgp130 were
seen, but ADAM-17 decreased slightly during the collection period (Figure 4.6A and
Appendix-V). Individual variations are shown in Figure 4.6B.
Figure 4.6 : Changes in sIL-6R,
sgp130 and ADAM-17 in Salvaged
Blood
(A) Levels are plotted on a Log10 scale as
median interquartile range (IQR) of fold-changes
relative to pre-operative values for WSB (
)
and TSB (
). Significance of differences
between pre-operative and WSB or TSB values
are represented by *, and differences between
WSB and TSB fold-values are represented by +.
Thus: */+ = p <0.05, **/++ = p <0.001 and
***/+++ = p <0.0001.
(B) Salvaged blood levels (both
WSB and TSB) of sIL-6R,
sgp130, and ADAM-17 are
plotted against pre-operative
peripheral
levels
on
a
Log10/Log10 scale for each
patient. NSBT patients are in
blue circles (
) and ASBT
). Units
of measurement are ng/ml.
101
4.7. Soluble CD14 (sCD14)
For all the study subjects, mean sCD14 levels were elevated significantly in
both WSB and TSB samples as shown in Figure 4.7A and Appendix-V. Interestingly,
a slight elevation during collection also found in TSB when compared with WSB
values (p = 0.04). Individual variations are shown in Figure 4.7B.
Figure 4.7 : Changes in sCD14 in Salvaged Blood
) and TSB (
). Significance of differences between preoperative and WSB or TSB values are represented by *, and differences between WSB and TSB foldvalues are represented by +. Thus: */+ = p <0.05, **/++ = p <0.001 and ***/+++ = p <0.0001.
(B) Salvaged blood levels (both WSB and TSB) of sCD14 are plotted against pre-operative peripheral
levels on a linear scale for each patient. NSBT patients are in blue circles (
) and ASBT are in red
squares (
). Units of measurement are µg/ml.
102
Lysozyme activity was also significantly increased at the surgical wound site,
and was further elevated in TSB samples in the bag during the six hour collection
period (TSB vs WSB) as shown in Figure 4.8A and Appendix-V. Individual
variations are shown in Figure 4.8B.
Figure 4.8 : Changes in Lysozyme in Salvaged Blood
) and TSB (
(B) Salvaged blood levels (both WSB and TSB) of lysozyme are plotted against pre-operative
peripheral levels on a linear scale for each patient. NSBT patients are in blue circles (
) and ASBT
). Units of measurement are 1000xIU/ml.
103
4.9. CD-24, Sialic Acid Binding Ig-like Domain-10 (Siglec-10) and Siglec-2
Plasma levels of soluble CD24 and Siglec-10 were elevated between 8 and 17fold respectively in WSB compared to their peripheral blood levels. However, no
additional increase occurred during the collection period (Figure 4.9A and AppendixV). A pilot study on Siglec-2 levels showed no change in salvaged blood compared
to peripheral blood level. Individual variations are shown in Figure 4.9B.
Figure 4.9 : Changes in CD24,
Siglec-10 and Siglec-2 in Salvaged
Blood
(A) Levels are plotted on a Log10 scale as
median interquartile range (IQR) of foldchanges relative to pre-operative values for
WSB (
) and TSB (
). Significance of
differences between pre-operative and WSB
or TSB values are represented by *, and
differences between WSB and TSB foldvalues are represented by +. Thus: */+ = p
<0.05, **/++ = p <0.001 and ***/+++ = p
<0.0001.
(B) Salvaged blood levels (both WSB and
TSB) of CD24 and Siglec-10 are plotted
against pre-operative peripheral levels on a
Log10/Log10 scale for each patient. NSBT
) and ASBT
). Units of
104
4.10.
Complement Protein C5a
C5a levels were also elevated in WSB. No further elevations in C5a levels
were found during collection in the bag (TSB vs WSB) as shown in Figure 4.10A
and Appendix-V. Individual variations are shown in Figure 4.10B.
Figure 4.10 : Changes in C5a in Salvaged Blood
) and TSB (
(B) Salvaged blood levels (both WSB and TSB) of C5a are plotted against pre-operative peripheral
levels on a Log10/Log10 scale for each patient. NSBT patients are in blue circles (
) and ASBT are
in red squares (
105
4.11.
Summary of Observations
Elevated levels of 33 out of 36 biomarkers were observed in salvaged blood
when expressed as fold-changes over pre-operative levels. Exceptions were IL-13
and Siglec-2 (n=3) which remained equivalent to normal pre-operative levels.
Comparisons between WSB and TSB values allowed biomarkers of the salvaged
blood transfusion effect to be divided into those that remained unchanged, termed
‘Stable-Biomarkers’, and those whose levels increased during the collection period,
and termed ‘Dynamic-Biomarkers’.
Stable-Biomarkers were assumed to have been continuously synthesised by
damaged tissue within the wound site rather than in the collection bag. These were
exemplified by massive and sustained fold-increases in: S100-A8/9, α-Defensin,
HSP-27, HSP-60, HSP-70, CD24 and Siglec-10; modest increases in IL-9, sIL-6R,
sgp130 and C5a; and also IL-5, IL-13, and Siglec-2 that remained unchanged. Mean
fold-changes for stable-biomarkers are documented in Table 4.1.
Dynamic-Biomarkers were assumed to have increased through continuous
synthesis within the bag, during collection. These were illustrated by comparing foldincreases in TSB with fold-increases in WSB. Thus significant elevation was
observed in Annexin-A2 which increased further, and HMGB-1, which was initially
elevated above normal decreased. The pro-inflammatory cytokines, IL-1β, IL-2, IL6, IL-12p70, IL-17A, IFN- and TNF-α, were significantly above normal in WSB,
but increased further in TSB. Similarly, the anti-inflammatory cytokines, IL-4, IL-10,
IL-1RA and TGF-β1 were above normal in WSB but increased further in TSB. The
chemokines, IL-8/CXCL8, MCP-1/CCL2 and MIP-1α/CCL3 were above normal in
the WSB and also increased substantially in TSB. ADAM-17 and KGF although
elevated in WSB, in the collection bag they were slightly decreased comparing with
WSB levels. Elevations in sCD14 and lysozyme in WSB was accompanied with a
further increase in TSB. IL-22 also showed similar trend of increases in TSB
likewise other pro-inflammatory cytokines. Mean fold-changes (± SDs) are
summarized separately for WSB and TSB in Table 4.1.
106
Table 4.1 : Constituents of Salvaged Blood at One Hour (WSB)and Six Hours
(TSB) After Surgery (Grey Highlight=Stable-Biomarkers)
Marker
(Number of
ASBT patients
tested)
WSB Values Compared to
Pre-operative Levels
Direction
Change
TSB Values Compared to Preoperative Levels
Fold Change
(Mean ± SD)
p
Direction
Change
Fold Change
(Mean ± SD)
p
Changes
(WSB
to TSB)
p
Pro-inflammatory Cytokines:
IL-1β (25)

2.76 ± 3.20
*

6.38 ± 6.06
***
***
IL-2 (23)

4.70 ± 4.84
***

5.18 ± 5.51
***
*
IL-6 (25)

308.5 ± 863.6
***

2495 ± 2053
***
***
IL-12p70 (23)

4.77 ± 5.96
***

4.88 ± 6.63
**
*
IL-17A (22)

4.31 ± 5.61
***

9.05 ± 9.51
***
***
IFN- (22)

8.65 ± 12.95
***

13.25 ± 13.04
***
**
TNF-α (21)

7.61 ± 7.72
***

16.16 ± 13.44
***
***
Anti-inflammatory Cytokines:
IL-4 (25)

2.27 ± 1.60
*

2.83 ± 1.83
*
*
IL-5 (25)

1.97 ± 2.43
NS

2.38 ± 3.40
*
NS
Il-9 (22)

2.82 ± 1.85
***

3.29 ± 2.51
***
NS
IL10 (25)

9.41 ± 15.89
*

10.61 ± 11.97
**
*
IL-13 (24)

0.86 ± 0.71
NS

1.09 ± 1.08
NS
NS
IL-1RA (25)

3.19 ± 1.71
***

4.40 ± 2.67
***
*
TGF-β1 (25)

8.52 ± 4.11
***

9.91 ± 4.44
***
*
IL-8/CXCL8 (25)

15.28 ± 20.13
***

194.8 ± 203.8
***
***
MCP-1/CCL2 (25)

9.24 ± 5.62
***

43.72 ± 41.65
***
***
MIP-1α/CCL3 (25)

8.87 ± 6.60
***

14.53 ± 14.01
***
***
HMGB-1(25)

17.62 ± 11.74
***

12.29 ± 5.60
***
**
Annexin-A2 (25)

1.86 ± 0.40
*

2.16 ± 0.73
**
*
S 100-A8/9 (25)

9.15 ± 5.40
***

9.65 ± 5.87
***
NS
α-Defensin (23)

27.34 ± 22.16
***

29.97 ± 20.44
***
NS
HSP-27 (25)

121.1 ± 74.76
***

104.7 ± 96.3
***
NS
HSP-60 (23)

54.36 ± 117.8
***

50.01 ± 83.94
***
NS
HSP-70 (25)

118.6 ± 59.41
***

102.1 ± 47.58
***
NS
IL-22 (16)

11.05 ± 14.81
***

14.95 ± 14.84
***
*
KGF (25)

124.5 ± 85.75
***

87.18 ± 49.21
***
***
sIL-6R (25)

2.04 ± 0.51
***

2.15 ± 0.60
***
NS
Sgp130 (24)

1.98 ± 0.51
***

2.04 ± 0.51
***
NS
ADAM-17 (25)

6.64 ± 5.18
***

5.67 ± 5.08
***
*
sCD14 (25)

1.24 ± 0.33
**

1.31 ± 0.32
***
*
Lysozyme (25)

1.33 ± 0.25
***

1.46 ± 0.26
***
*
CD24 (25)

7.81 ± 3.44
***

8.85 ± 4.45
***
NS
Siglec-10 (25)

16.73 ± 10.00
***

19.00 ± 11.89
***
NS
Siglec-2 (3)

1.63 ± 0.52
NS

1.88 ± 0.59
NS
NS
C5a (25)

3.59 ± 1.37
***

3.50 ± 1.00
*** 107 NS
Chemokines:
DAMPs:
Other Biomarkers
 = increase,  = decrease, ↔ = no change NS= Not Significant *p= 0·05 **p= 0·001 ***p=  0·0001
4.12.
Correlation on Fold Changes in Salvaged Bloods Between all different
Biomarkers
Spearman correlation analyses were performed to compare all changes in
biomarker levels in the surgical wound site (WSB/peripheral) and also in TSB during
the collection period (TSB/WSB). Some selected correlations are described below:
4.12.1. Changes in Surgical Wound Site – WSB vs Peripheral (Table 4.2)
1. Elevation in IL-6 correlated with increase in MCP-1 and MIP-1α.
2. Increased HMGB-1 was inversely correlated with elevations in IL-6, MCP-1 and
MIP-1α.
3. Elevated sIL-6R was correlated with increases in sgp130 in the surgical wound
site.
4. Increases in molecules with antimicrobial properties, such as Lysozyme, sCD14
and α-Defensins, were correlated with elevations in sgp130. Lysozyme and
sCD14 were also correlated with increased sIL-6R.
5. Elevations in S100-A8/A9, CD24 and Siglec-10 were also correlated.
4.12.2. Changes During Collection Period – TSB vs WSB (Table 4.3)
1. Further elevation in IL-6 was correlated with and those of all the studied
chemokines (IL-8, MCP-1, and MIP-1α).
2. Elevations in all the studied chemokines in TSB were also correlated.
3. Correlations also found between increases in sIL-6-R and sgp130 in the bag.
108
Table 4.2: Correlations on fold changes between different biomarkers in the surgical wound site (WSB/Peripheral)
109
Table 4.3: Correlations on fold changes between different biomarkers during the blood collection period (TSB/WSB)
110
CHAPTER – V
RESULTS:
Proteomic and Glycomic Profiling of Venous and Salvaged Blood
Chapter – V : Results
Proteomic and Glycomic Profiling
5.1. One-Dimensional Gel Electrophoresis
One-dimensional (1-D) gel electrophoresis of the highly abundant protein
depleted plasma samples showed the presence of a novel band at ~14 kDa in the
salvaged blood samples (in WSB and TSB) whereas this band was absent in both
pre- and post-operative peripheral blood samples (Fig. 5.1). This preliminary
experiment was considered sufficiently encouraging to carry out a complete
proteomic analysis of a set of samples from one individual patient. The cost of such
an analysis prevented additioal samples being analyzed.
Figure 5.1: 1-D gel electrophoresis of venous and salvaged blood plasma
samples.
112
5.2. Tandem Mass Tagging (TMT)
As mentioned earlier, the proteomic profiles of postoperative and salvaged
blood plasma samples were compared with the properative profile. All experiments
were performed using the high abundant protein depleted plasma samples from only
one patient for this TMT pilot experiment. The patient was chosen based on their
increased postoperative neopterin level, indicative of macrophage activation. A
complete total proteomic signature was obtained using the LC-MS/MS methods as
described in Chapter-II. A total of 465 proteins were identified of which 310 proteins
had a minimum of two distinct peptides. We were interested in comparing the
salvaged blood and postoperative plasma samples with the preoperative venous
blood. Out of these 310 proteins 236 were present in all plasma samples. Table 5.1
and Figure 5.2 represents the changes in proteins in different ways such as:
1. Postop vs Preop: 31% proteins were downregulated (≥2 fold), with
upregulations in 4% proteins whereas the remaining 65% were
unchanged postoperatively.
2. Wound site vs Preop: Downregulation of more than 2 fold was observed
in 19% proteins . In contrast, 22% of proteins were upregulated in the
wound site samples comparing with venous blood.
3. Transfused blood vs Preop: Among 236 proteins in all the studied plasma
samples, 53% were downregulated whereas 21% showed upregulation in
the transfused salvaged blood when compared with the peripheral
preoperative venous blood.
4. Transfused salvaged blood vs Wound site blood: Only 1% of all 236
proteins were downregulated whereas remaining 99% were unchanged
and none of the proteins showed upregulation during the collection period
in the bag in TSB when compared with the wound site blood profile.
5. Peripheral blood incubation – 5 hour vs 0 hour: When venous blood
was left on bench for five hours, of a total 236 proteins 25.8% proteins
showed downregulation whereas only 0.5% proteins were upregulated
during the incubation periods. and remaining 73.7% were unchanged.
113
Table-5.1: Summary of Mass Spectrometric Analysis of the study samples
Fold Decrease
( ≤ 2 fold)
Comparison
No Change
Fold Increase
( ≥ 2 fold)
Incomparable
proteins
≥2
peptide
hit
Single
peptide
hit
≥2
peptide
hit
Single
peptide
hit
≥2
peptide
hit
Single
peptide
hit
Preop vs. Postop
73
11
153
55
10
3
160
WSB vs. Preop
45
10
138
41
53
18
160
TSB vs. Preop
124
26
62
26
50
17
160
WSB vs. TSB
2
3
234
65
0
1
160
Preop – 5hr vs. 0hr
61
14
174
55
1
0
160
Figure 5.2: Alterations in TMT-profiles between the studied plasma samples.
Here, downregulated if ≤0.5 fold, upregulated if ≥2.0 fold, unchanged if >0.5 and <2.0 fold.
114
Compared with the preoperative venous blood plasma, in the postoperative
blood there was reduction in lipid transporting apolipoproteins, chaperone Tcomplex protein-1, clotting factors such as fibrinogen, carbon dioxide transporter
carbonic anhydrase (1 and 2), and actin binding protein prolifin-1 (Appendix-IX). On
the other hand, there was an accumulation of protease inhibitor α1-Antitrypsin, acute
phase proteins such as CRP, α1- acid glycoprotein and lipopolysaccharide binding
protein (LBP) in the postoperative plasma.
In the surgical wound site there was reduction in lipid transporting proteins
such as serum amyloid-A and apolipoproteins, chaperone T-complex protein-1,
extracellular matrix protein Fibronectin-1, and acute phase protein α1- acid
glycoprotein. On the other hand there were accumulations of glycolysis pathway
proteins such as phosphoglycerate kinase and glyceraldehyde 3 phosphate
dehydrogenase, haemoglobin subunits (α, β, γ and δ), carbon di-oxide transporter
Carbonic Anhydrase-1, actin binding protein Prolifin-1, Fibrinogens (α, β and γ),
DAMPs such as annexin-A2 and S100A4 and antimicrobial dermcidin (AppendixIX).
Reductions in CRP and dermcidin occurred during the collection period in the
bag (in TSB) compared with the initial wound site blood. In contrast following five
hour incubations of citrated venous blood in room temperature, there were reductions
in T-complex protein-1, Fibrinogens (1 and 2), apolipoproteins, and iron binding
transferrin; whereas there was an accumulation of dermcidin (Appendix-IX).
Heat map analyses were performed on fold changes in postoperative and
salvaged blood plasma samples compared with fresh preoperative venous blood
(Figure 5.3). Also, a comparison was made between freshly-collected venous blood
and an aliquot of this incubated for five hours at room temperature (RT). This was
done to simulate the incubation time incurred by the collection procedure in the bag.
For heat map analysis 236 proteins were present in all plasma samples identified with
two or more peptides. The coloured representation of the heat map analysis in Figure
5.3 clearly shows a notable difference in the proteomic profile of salvaged blood
(both WSB and TSB) compared with preoperative peripheral blood. Interestingly,
there was a very limited change during the collection period as recorded in TSB
proteomic profiles comparing with WSB. Whereas following salvaged blood
115
transfusion, postoperative blood proteomes were also hugely different from
preoperative profiles. Furthermore, incubations of venous blood for 5 hours a RT
also significantly changed the proteomic profiles of the incubated blood. The heat
map analyses was further subdivided and thus showed in three protein clusters.
Figure 5.3(A): Heat map analyses on the complete proteomic profile of the study
samples (No clustering).
116
Figure 5.3(B): Heat map analyses on individual clusters obtained from the
complete proteomic map.
117
5.3. Lectin Array
The binding specificities of lectins in the lectin microarray profile for plasma
samples are shown in Figure 5.4. Variations in responses were observed between
individual patients for lectins that bind different carbohydrate motifs. Binding
intensities were very low for many of the lectins; however SNA-I (specific for sialic
acid-α-(2/6)-Gal/GalNAc) and RCA-1 lectins which recognise underlying N-acetyllactosamine (Gal-β-(1/4)-GlcNAc, type II LacNAc) type structures, were
significantly higher in the plasma samples from most of the patients.
While comparing the degree of binding specifities among all different
samples in different patients, the following observations could be made.
1. The affinity of AIA that binds to β-Gal linked proteins was similar in all
venous and salvaged blood samples.
2. ACA, which binds to Gal/GalNAc, showed equal binding to both pre- and
post-operative venous blood plasma; however in the salvaged blood samples
binding increased more than in the preoperative sample. On the other hand,
ABL, another lectin which binds to Gal/GalNAc, binding was similar in all
venous and salvaged blood samples, but was decreased in salvaged blood in
some patients.
3. In the postoperative and salvaged blood samples, DSA’s ability to bind to Galβ-(1-3)-GalNAc was found to be unchanged compared with that in the
preoperative venous blood plasma. Binding of LEL to Gal-β-(1-3)-GalNAc
was unchanged; however there was a decreased binding in postoperative and
wound site blood plasma compared to the preoperative level.
4. Lch-A binding specificity to mannose varied between patients, thus was
unchanged or slightly decreased in postoperative and salvaged blood
compared with preoperative level.
5. Unchanged binding abilities were also found in postop and salvaged plsama
samples for WGA to bind to NeuAc. On the other hand, SNA-I, another lectin
that binds to NeuAc showed trend of increased binding in postoperative and
salvaged plasma compared with preoperative samples.
6. PHA-L, PHA-E, RCA-1, and CAA binding affinity to LacNAc were similar in
all the venous and salvaged blood plasma samples.
7. AAL that binds to fucose showed increased binding in the postop and all
salvaged plasma samples comparing with preoperatve venous plasma.
118
Figure 5.4: Lectin array data for venous and salvaged blood plasma samples
from individual patient.
119
Figure 5.4 (Continued): Lectin array data for venous and salvaged blood plasma
samples from individual patient.
120
Heat map analysis was perfomed on the normalized lectin micrarray data. For
better representation of the clustering analysis, huge individual variations in the
relative fluorescence unit (RFU) values between patients for most of the lectins was
avoided by dividing the RFU values of postoperative venous and salvaged blood
samples with the preoperative values respectively for each patient. Thus, the data
resulted in a similar baseline value of 1 for the preoperative samples of all four
patients. Using this baseline, postoperative and salvaged blood values were
calculated as the fold changes in binding specificities to particular lectins.
Cluster analysis showed mixed results with huge variations between patients
(Figure 5.5). However, compared with preoperative samples, increased binding of
SNA-I, SNA-II and AAL can be seen in most of the patients’ postoperative and
salvaged blood samples. ACA was only increased in salvaged blood samples. On the
other hand, PHA-L and LEL binding decreased in all samples compared with
preoperative levels of most patients.
Figure 5.5: Heat map analyses on lectin binding specifity of venous and salvaged
blood samples from individual patient.
121
CHAPTER – VI
DISCUSSION
Chapter - VI
Discussion
6.1. Characterization
of
Post-traumatic
Immunosuppression
(PTI)
and
Assessment of the Effect of Autologous Salvaged Blood Transfusion on PTI
Most publications on post-operative immunity, where few biomarkers were
assessed in limited numbers of patients, failed to assess more than a fraction of
immune status. This is the most extensive study to date on immune status following
major trauma. 35 biomarkers were used to measure diverse functional properties.
Similar postoperative changes were found in biomarkers with similar or linked
functional properties such as: pro-inflammatory cytokines, anti-inflammatory
cytokines, chemokines, DAMPs, antimicrobial activity and complement breakdown
products. Reinfusion of autologous salvaged blood did not alter the trends of many
biomarker groups, but with exceptions of IL-1RA/TGFβ1, anti-inflammatory
cytokines were decreased and pro-inflammatory cytokines were elevated as was
Annexin-A2.
Washing
salvaged
blood
would
remove
most
inflammatory
and
immunomodulatory contaminants. However, unwashed salvaged blood transfusion
is widely practiced in arthroplasty, and dominated by defibrinated unwashed blood
(Sarbinowski, Arvidsson et al. 2005, Muñoz, Slappendel et al. 2011). This study was
not designed to evaluate the efficacy of different salvaging procedures, duration of
surgery on immune status, or clinical outcomes such as infection. Rather this study
focused on Gharehbaghian et al’s initial findings of reversal of PTI by reinfusion of
ACD-anticoagulated unwashed salvaged blood.
With exception of changes in
natural killer cell precursor (NKp) and IFN-γ levels (Gharehbaghian, Truman et al.
2004), haematological measurements were consistent with other publications; where
changes were due to surgical trauma irrespective of salvaged blood transfusion
(Roche, Thorn et al. 1950, Laroche, Chrysanthou et al. 1992, Tsou, Peters et al. 2007,
Kimura, Shimizu et al. 2010, McDonald, Pittman et al. 2010, Albertsmeier, Quaiser
et al. 2014).
The most frequently studied biomarkers following surgical trauma in all
different investigations are the cytokines. It was revealed that IL-6 was analysed in
most of the publications and described as “pro-inflammatory” cytokine except a very
few articles (Muñoz, Muñoz et al. 2006, Rose-John, Scheller et al. 2006, Rose-John
123
Chapter - VI
Discussion
2012). However, recent observations suggest that IL-6 plays dual role and other
biomarkers such as sIL-6R, sgp130 and ADAM-17 direct IL-6 to be either pro- or
anti-inflammatory (Rose-John, Scheller et al. 2006, Briso, Dienz et al. 2008, RoseJohn 2012). Elevations in IL-6 are observed after physiological stress such as
extreme exercise (Philippou, Maridaki et al. 2012). Hence, diagnosing PTI with
elevated postoperative IL-6 alone may be misleading. Systemic changes in
postsurgical IL-1β and TNF-α remain controversial (Appendix-II). Findings of
subnormal levels of IL-2, IL-17A and IFN-γ indicate that PTI involves both the
innate and adaptive immuno-regulatory pathways. Elevation in these cytokines
strengthened the case that ACD-anticoagulated autologous salvaged blood
transfusion reversed PTI.
Following major surgery levels of anti-inflammatory cytokines were normal
(except IL-1RA and TGF-β1) as seen in NSBT cohort; whereas, decreased in ASBT,
supporting the hypothesis of reversion of PTI by salvaged blood. Elevated IL-1RA
following surgery may be associated with maintaining immune-homeostasis by
inhibiting IL-1β whereas elevation in TGF-β1 may favour wound healing. Regarding
the target sites for these key intermediaries, this study indicated that the response to
bacterial endotoxin LPS was to synthesise anti-inflammatory cytokines IL-10, TGFβ and IL-1RA. In contrast, PBMC from ASBT patients produced no increase in antiinflammatory cytokines, implying that PTI was in part due to impaired functional
capacity of immune cells against pathogens, and that ACD anti-coagulated salvaged
blood transfusion abrogated this effect. Capacity of in vitro PBMC from both NSBT
and ASBT cohorts to secrete pro-inflammatory cytokines and chemokines was
similar; even after LPS stimulation. This contrasted with in vivo plasma levels of
pro-inflammatory cytokines which were elevated in ASBT, presumably due to
increased secretion within tissues. Systemic levels of chemokines may not reliably
determine PTI. IL-8, the most frequently studied chemokine, was elevated in most
surgical investigations (Appendix-II), but in this study, was little affected by
salvaged blood transfusion.
DAMPs play an important role in post-traumatic immune-homeostasis, but
few articles have been published measuring soluble forms of DAMPs in blood.
While others showed postoperative elevations (Suda, Kitagawa et al. 2006,
124
Chapter - VI
Discussion
Giannoudis, Mallina et al. 2010, Kohno, Anzai et al. 2011, Osoegawa, Yano et al.
2011, Takahata, Ono et al. 2011), this study reported significant reductions in
HMGB-1 following total knee arthroplasty (TKA); indicating possible variations
between different assays, surgical procedures, or non-standardized sample collection.
Heat shock proteins were studied yet only in cardiac surgery where patients’ levels
were found to be unchanged (HSP27 and HSP70) or decreased (HSP-60) (Dybdahl,
Wahba et al. 2002, Pizon, Gburek et al. 2006, Szerafin, Hoetzenecker et al. 2008),
whereas this TKA study showed systemic elevations in HSP-70 and no changes in
HSP-27 and HSP-60, again indicating the possible variations between surgical
procedures. Additionally, report on levels of the soluble DAMPs, α-Defensin and
S100A8/A9 showed no differences between the study cohorts, supporting the view
that DAMPs may not be appropriate biomarkers for PTI; although reductions in
Annexin-A2 could be considered as an additional PTI marker.
Hsing et al showed elevations in the levels of IL-22 following cardiac surgery
(Hsing, Hsieh et al. 2006), whereas unchanged postoperative levels of IL-22 in the
NSBT patients in this study indicated that trauma may not be the main trigger to
release IL-22. Hence, a future study is needed to confirm the effect of surgery on IL22 following TKA. In contrast, elevations in IL-22 in ASBT patients indicated
possible enhancement of post-traumatic immunity due to salvaged blood reinfusion.
In contrast with the observations in this study, postoperative reductions in
systemic sIL-6R and sgp130 levels were reported elsewhere (Kristiansson, Soop et
al. 1998, Corbi, Rahmati et al. 2000). However, no study has been performed yet to
measure the levels of these markers in knee arthroplasty settings. In a cardiac set up,
reductions in sgp130 were reported following surgery (Corbi, Rahmati et al. 2000).
Decreased systemic sgp130 reflected similar observations in TKA. Corbi et al also
reported unchanged sIL-6R levels at different postoperative time-points following
cardiac surgery. In contrast, in this study we reported overall reductions in systemic
sIL-6R. This may indicate variations between different surgical procedures. This was
the first attempt to measure ADAM-17 levels in human blood plasma. Elevations in
ASBT but not in NSBT indicate increased shedding of membrane bound IL-6R into
sIL-6R and enhancement of immunostimulatory trans-signalling in the ASBT cohort.
125
Chapter - VI
Discussion
This is also the first report on sCD14 in TKA patients. Bastian et al reported
unchanged peripheral plasma sCD14 levels in hip replacement patients during six
postoperative days of follow up. However, their study included only seven nontransfused patients (Bastian, Tamburstuen et al. 2011). Significant postoperative
elevations were observed in sCD14 levels in all patients in this study; indicating that
surgery influences sCD14 production. This was not altered by salvaged blood
transfusion. Likewise, lysozyme studies also indicated boosting of antimicrobial
properties following major sterile trauma.
In this study elevations in sCD14 were correlated with monocytosis in vivo.
However, elevations in sCD14 productions by postoperative PBMC (in vitro)
indicated that monocytosis (in vivo) may not be the primary reason of this systemic
sCD14 elevation. However, increased ability of postoperative PBMCs to release
sCD14 may play role in this elevation. Future in vivo studies therefore are needed to
confirm these findings. Due to the use of cryopreserved PBMC, freezing-thawing
may adversely affect the survival of mature monocytes and hence affect the release
of sCD14; however, his possibility was rejected with in vitro observations using
fresh PBMCs (Appendix-VI). Only in NSBT cohort, production of sCD14 by
postoperative PBMC following LPS stimulation was found higher than without LPS.
This indicated the possibility that, after the initial insult (surgical trauma) PBMC in
the NSBT group became vulnerable to infections when a secondary insult (LPS
treatment) was applied. In contrast, postoperative PBMC in ASBT patients were
capable of tolerating the secondary LPS stress.
This is the first report measuring soluble Siglec-10 in any clinical setup; also
the first investigation measuring soluble CD24 and Siglec-10 in joint replacement
surgery. Systemic elevations in soluble forms of both CD24 and Siglec-10 were
found following major sterile trauma. Liu et al suggested plasma levels of soluble
CD24 as a rapid marker for Hepatocellular carcinoma (Liu, Yang et al. 2013) and
others suggested soluble CD24 as a marker for immunosuppression (Li, Ling et al.
2014). No differences in the systemic levels of soluble CD24 and Siglec-10 findings
between two study cohorts in this study indicated that elevations in these biomarkers
were due to the general response to surgical trauma irrespective of salvaged blood
transfusion. Unchanged systemic soluble Siglec-2 following trauma indicated no
126
Chapter - VI
Discussion
involvement of this biomarker in PTI, and strengthened the case that soluble Siglec10 may plays a vital role in immune responses following sterile trauma.
Elevations in complement split products reflected the activation of
complement pathway by enhancement of inflammatory responses (Guo and Ward
2005). A recent study reported that upregulation C5a was dependent on Factor-VII
Activating Protein (FSAP) in trauma patients (Kanse, Gallenmueller et al. 2012). In
this study, neutrophilia occurred following trauma possibly due to delayed apoptosis
which may have association with elevated C5a (Perianayagam, Balakrishnan et al.
2002), and elevated IL-6 is also associated with neutrophilia (Hashizume, Higuchi et
al. 2011). Degree of postoperative increases in both IL-6 and C5a were significantly
higher in the ASBT cohort compared with NSBT, indicating more functional ability
of circulating neutrophils in ASBT. Recent studies reported an inhibitory role of C5a
on the inflammatory responses through IL-17A/IL-23 mediated pathways (Bosmann,
Sarma et al. 2012); and also by down-regulating the TLR-4 and CD40 mediated
inflammatory pathways (Hawlisch, Belkaid et al. 2005). Therefore the impact of C5a
on posttraumatic immunity requires further mechanistic investigation.
Correlation studies between different biomarkers revealed no dramatic
insights indicating that biomarkers may have relatively independent roles in
triggering PTI. Huge individual variations between postoperative fold changes in
biomarkers in patients may lessen the degree of correlation, demanding a very large
number of study patients.
This study made a clear distinction between common and salvaged blood
sensitive biomarkers of sterile trauma. Following tissue damage at the wound site,
common biomarkers of sterile trauma may facilitate the local regeneration
mechanisms. However, to facilitate regeneration, suppression of the local
inflammatory cascades is a pre-requisite, which in turn trigger the development of
PTI. Figure 6.1 summarizes in schematic form how post-traumatic alterations in
different biomarkers influence PTI, and Figure 6.2 summarizes the scenario when
PTI was reversed by reinfusion of ACD-anticoagulated autologous salvaged blood.
In the light of these findings, we hypothesized that these salvage blood sensitive
biomarkers of sterile trauma may best reflect immune status after trauma, in contrast
127
Chapter - VI
Discussion
to common biomarkers of sterile trauma; the latter being reflecting tissue
regeneration.
Figure 6.1: Biomarkers to Assess PTI following Major Surgery
Figure 6.2: Reversion of PTI by reinfusion of salvaged blood
128
Chapter - VI
Discussion
Figure 6.3 : Radar plot of the postoperative fold changes in the biomarkers of
sterile trauma; distinguishing two distinct panels of biomarkers of sterile
trauma: common biomarkers and salvaged blood sensitive biomarkers. Here, data
represented in a Radar Plot as the postoperative fold changes in Log10 scale and the grey circle
designate no postoperative change. Biofactors represented from minimum to maximum postop
changes in NSBT cohort as shown in blue line. Red line indicates the postop fold changes in ASBT
cohort. Sensitive biomarkers were either decreased postoperatively in ASBT cohort (antiinflammatory cytokines) as shown in blue coloured boxes, or increased postoperatively in the ASBT
compared to NSBT as shown in red coloured boxes. The common biomarkers of sterile trauma were
not altered in ASBT compared to NSBT.
129
Chapter - VI
Discussion
6.2. Constituents of Salvaged Blood
In this study initial levels of biomarkers at the surgical wound site were
measured and monitored how these changed during salvaged blood collection period.
Surgery was carried out under tourniquet; therefore constituents of early salvaged
wound site blood (i.e. WSB) originated from necrotic and ischaemic tissues within
and around the surgical site.
In general, levels of most biomarkers were elevated in WSB. Subsequently,
after six hours, levels of some of these biomarkers were unchanged during the
salvation period, termed Stable-Biomarkers. Stable-Biomarkers included, S100A8/A9, α-Defensin, HSP-27, HSP-70, HSP-60, IL-5, IL-9, IL-13, sIL-6R, sgp130,
CD24, Siglec-10, Siglec-2 and C5a. These were assumed to have been continuously
synthesised exclusively in vivo within tissues at the wound site. In contrast, the
remaining biomarkers were further increased or decreased during the collection
period in the bag, and therefore termed Dynamic Biomarkers. These included,
chemokines (IL-8, MCP-1 and MIP-1α), pro-inflammatory cytokines (IL-1β, IL-2,
IL-6, IL-12p70, IL-17, IFN-γ and TNF-α), some anti-inflammatory cytokines (IL-4,
IL-10, TGF-β and IL-1RA) and others such as IL-22, KGF, ADAM-17, Annexin-A2,
sCD14 and lysozyme. These changes were assumed mainly due to the ex-vivo
synthesis by viable extravasated cells present in salvaged blood.
In contrast with pro-inflammatory cytokines, relatively low but nonetheless
significant elevations were recorded in most of anti-inflammatory cytokines;
indicating continuation of immunosuppressive events following trauma aimed at
wound healing. Relatively weak elevations in all these anti-inflammatory cytokines
in the bag, in contrast to the pro-inflammatory cytokines; support the hypothesis that
immune-stimulants were generated in the salvaged blood bag during the collection
period.
Massive elevations in different chemokines during the collection period;
indicated release by sentinel and newly recruited immune cells, and necrotic cells
drained from the surgery site. With their immediate productions at the wound site,
biomarkers that showed the highest elevations during the collection period were IL-6
(from x300 to x2500 fold), IL-8/CXCL8 (from x15 to x195 fold), MCP-1/CCL2
130
Chapter - VI
Discussion
(from x9 to x44 fold), and MIP-1α/CCL3 (from x9 to x15 fold). Correlation studies
confirmed strong associations of IL-6 with all these chemokines; indicating the
possible involvement of cytokine-chemokine interactions directing generation of
novel immunostimulatory substances.
One of the most interesting observations was massive elevation in DAMPs
with no further change during collection. DAMPs released following sterile trauma
subsequently accelerate release of chemokines that recruit immune cells to the site of
injury. Thus, increased DAMPs and chemokines would have accounted for the
neutrophilia and monocytosis. However, overall these DAMP findings indicated an
anti-inflammatory paradigm, in contrast to the SIRS paradigm (Bone, Balk et al.
1992).
Role of IL-22 in tissue regeneration has been described in different injury
models (Hsing, Hsieh et al. 2006, Zenewicz, Yancopoulos et al. 2007, Sasaki, Zhang
et al. 2011, Rendon, Li et al. 2013). In this study, elevated IL-22 at surgical wound
site supported this role and strong correlation between IL-22 and IL-17A was
consistent with the production by TH-17 cells in the injury site (Liang, Tan et al.
2006). In WSB, the correlation of IL-22 with TNF-α and chemokine IL-8 also
indicated recruitment of monocytes/neutrophils for clearance of necrotic tissues (Shi
and Pamer 2011, Kolaczkowska and Kubes 2013). Systemic changes in IL-22
showed no correlations with any other biomarkers; indicating that IL-22 may play
important role locally in triggering tissue regeneration, but not in systemic immunity.
IL-22 stimulates inflammatory gene expression and migration of keratinocytes
(Boniface, Bernard et al. 2005). Furthermore, keratinocyte growth factor (KGF)
plays role in proliferation of fibroblasts and keratinocytes, and delays differentiation
and apoptosis of cells at local surgery site; favouring wound healing (Werner, Smola
et al. 1994, Andreadis, Hamoen et al. 2001). In this study, elevated KGF indicated
possible involvement in remodelling epithelial tissue. Strong correlation of KGF at
the wound site with HMGB-1 further suggests involvement in local wound healing
mechanisms (Wild, Bergmann et al. 2012). Simulation study showed unchanged IL22 (Appendix-VII) indicating that immunostimulatory properties were generated exvivo during salvaged blood collection.
131
Chapter - VI
Discussion
In this study, slight but significant ex-vivo increases in sCD14 were possibly
due to shedding of mCD14 from activated monocytes. Simulation study further
confirmed this hypothesis with no change in incubated venous blood (AppendixVII). Local elevations in antimicrobial properties attributed to lysozyme, sCD14, αDefensins and S100A8/A9 mitigate PTI following sterile trauma. Further ex-vivo
elevations in lysozyme and sCD14 suggested salvaged blood gathered more
antimicrobial properties. In vitro whole blood culture treated with lysozyme resulted
in increased pro-inflammatory cytokine and chemokine production whereas antiinflammatory cytokines were unchanged in a time dependent manner (AppendixVIII). This indicated that, elevated lysozyme in the salvaged blood may have played
role in immunostimulation. Elevations in C5a indicated activation of complement
cascade at the wound; whereas further ex-vivo elevations supported indirect
involvement of C5a in elevating IL-6 possibly by live neutrophils (Perianayagam,
Balakrishnan et al. 2002, Hashizume, Higuchi et al. 2011).
Likewise
DAMPs,
elevated
CD24
and
Siglec-10
also
indicated
immunosuppressive events at the surgical wound site (Chen, Tang et al. 2009), and
unchanged Siglec-2 confirmed dominance of Siglec-10 within the Siglec family
glycoproteins in maintaining local immune responses. Increased CD24 and Siglec-10
in the simulation study, whereas in contrast, unchanged in TSB indicating that
salvaged blood was not accumulating immunosuppressive properties in the bag
(Appendix-VII).
132
Chapter - VI
Discussion
Figure 6.4 : Radar plot of fold changes in different biomarkers of sterile trauma
in the surgical wound site and in the transfused salvaged blood; distinguishing
two distinct panels of biomarkers in the transfused blood: stable biomarkers and
dynamic biomarkers. Data represented as fold changes in salvaged blood plasma samples
comparing with preoperative venous blood levels. Data showed in log10 scale where grey indicate
baseline with no fold change. Data sorted as minimum to maximum of the fold changes in different
biofactors. Significant changes in the wound site blood observed in most of the biomarker levels as
shown in green line. Following the six hour collection period in the bag (red line), levels of some
biomarkers were unchanged (termed as stable biomarkers) whereas the remaining biomarker levels
were changed (termed as dynamic biomarkers). Maximum changes were observed in the levels of
IL-6, IL-8, MCP-1 and MIP-1 alpha.
133
Chapter - VI
Discussion
Figure 6.5 : Radar plot of fold changes in different biomarkers of sterile trauma
in the five-hourly incubated venous blood. Changes in the biomarkers following five hour
bench incubation of preoperative venous blood was much lower comparing with changes in citrate
anticoagulated salvaged blood over five hours incubation in the bag.
134
Chapter - VI
Discussion
6.3. Diverse Roles of Interleukin-6
In this study IL-6 was included in the ex-vivo group of Dynamic-Biomarkers
in salvaged blood. However, post-operative levels in both study cohorts were equally
high; indicating reinfused IL-6 did not influence the levels due to tight homeostatic
regulation. Subnormal postoperative sIL-6R indicated that IL-6 mediated antiinflammatory activities were specially via classical signalling. Decreased systemic
sgp130 levels supported this classical signalling by blocking sIL-6R and thus
facilitating IL-6 mediated anti-inflammatory activities. ADAM-17 was unchanged in
NSBT but elevated in ASBT, indicating its potential to stimulate post-traumatic
immunity following salvaged blood transfusion. More interestingly, simultaneous
elevations in IL-6, sIL-6R, sgp130 and ADAM-17 in the surgical wound site
suggested that although pro- and anti-inflammatory events happening simultaneously
at the wound site, local immune-homeostasis favoured wound healing. Therefore,
these contentious findings indicated multifaceted interactions of IL-6 with sIL-6R
and sgp130 in maintaining immune-homeostasis following sterile trauma.
IL-6 also plays other roles favouring immune-regulation following trauma.
For example, IL-6 can be released by hepatocytes and acts as an acute phase protein
and thus play role in controlling inflammation (Jawa, Anillo et al. 2011). Another
role of IL-6 is its interactions in hypothalamus-pituitary axis to trigger release of
cortisol and favour in anti-inflammatory events (Besedovsky, del Rey et al. 1991).
IL-6 also influences the thermoregulatory centre in the hypothalamus to upregulate
the febrile response following trauma (Hamzic 2013). Furthermore, IL-6 acts as
chemo-attractant to recruit immune cells such as neutrophils to the site of injury
(Hashizume, Higuchi et al. 2011). Taken together, multifaceted and enigmatic roles
of IL-6 demand further investigations to better understand IL-6 immunobiology
following sterile trauma.
135
Chapter - VI
Discussion
6.4. Proteomic and Glycomic Profiling of Venous and Salvaged Blood
This was the first attempt to compare the effect of surgery on the proteome,
and proteomic changes in the surgical wound site. Proteomic approach in the
transfused salvaged blood also never been tested. Significant alteration in the blood
plasma proteome was found in salvaged blood when compared with the venous
blood; with very little change occurring during the collection period in the bag. This
study observed notable changes in plasma proteome of postoperative venous blood
comparing with preoperative. On the other hand, of total 43 lectins, 10 or more
showed
alterations
in
their
binding
affinities
to
the
respective
glycoproteins/glycolipids in the postoperative venous blood or salvaged blood
samples compared to preoperative.
Depletion of high abundant proteins was very important because the relative
concentrations of these proteins reduced assay sensitivity and yielded a lesser
number of proteins to be identified; in particular the low abundant proteins would
have remained undetected. The MARS cartridge was based on immuno-affinity
chromatographic principles, and did not completely remove the 14 most highly
abundant proteins. However it satisfactorily reduced the relative concentrations of
these 14 proteins and hence there were no interference in identifying and quantifying
other low molecular weight target proteins with smaller concentrations.
Systemic elevation in C-reactive protein (CRP) following surgical trauma is
widely recognized and also confirmed by this TMT study. CRP, as an acute phase
protein, may trigger immunosuppressive activities (Honsawek, Deepaisarnsakul et al.
2011). On the other hand, likewise in hip replacement (Bastian, Tamburstuen et al.
2011), postoperative elevations in lipopolysaccharide binding protein (LBP) found in
this TMT study; indicated increased antimicrobial properties following sterile
trauma.
Elevation in DAMPs such as Annexin-A2 and S100-A4 found at the surgical
wound site in the TMT study further confirmed the ELISA observations. On the
other hand, proteins associated with glycolysis/gluconeogenesis pathways also
showed elevations at the wound site; probably indicating body’s evolutionary
functional maintenance in lack of oxygen in the wound. Furthermore, increase in the
levels of coagulation factors in the TMT analysis would have facilitated the clotting
136
Chapter - VI
Discussion
mechanisms in the wound. Interestingly, increase in antimicrobial biomarkers
(dermcidin and lysozyme-C) were found at the local injury site. CRP level was
decreased in the bag during the collection period; indicating that salvaged blood was
losing its anti-inflammatory properties.
Changes in glycosylation patterns are considered as markers for different
diseases such as cancers (Ianni, Manerba et al. 2010, Fry, Afrough et al. 2011).
However, no study has yet investigated whether surgery and/or blood transfusion can
alter the glycosylation pattern. Therefore lectin microarray analysis was aimed to see
if any local or systemic changes in the binding specifities of different lectins.
Observations from four studied patients indicated that glycomic profile altered
following surgery. However, this study could not determine whether the changes
occurred due to only surgery or salvaged blood transfusion, or both.
Lectin array in this study used the immobilized forms of lectins in the slide;
which may be biologically different in human due to diverse glycoprotein
orientations in human body. At least 20 out of the 43 lectins showed some levels of
binding to the debulked plasma samples. There were some variations found between
the venous and salvaged blood plasma samples in their lectin profiles; indicating the
presence of different glycoforms in plasma. Therefore, lectin microarray study on
differetnt plasma samples from the four studied patients provided inconclusive
findings and inclusion of all TKA patients may be needed for better glycobiological
understanding.
6.5. Differences with Other’s Results
Previous studies on re-infusion of salvaged blood demonstrated either no
change or immunosuppression following surgery (Tylman, Bengtson et al. 2001,
Muñoz, Cobos et al. 2005, Munoz, Cobos et al. 2006, Muñoz, Muñoz et al. 2006).
Munoz et al investigated different subsets of NK cells and declared no differences in
post-operative NK-cell, TNF-α and IL-10 levels compared with non-transfused
patients when compared with non-anti-coagulated salvaged blood (Munoz, Cobos et
al. 2006). In-vitro LPS challenge in their study showed non-anti-coagulated salvaged
blood to be associated with an anti-inflammatory response, as reflected by
137
Chapter - VI
Discussion
unchanged IL-10 and decreased TNF-α (Muñoz, Muñoz et al. 2006). Tylman et al
also supported the above findings with increased IL-10 levels in patients who were
re-infused with non-anti-coagulated salvaged blood (Tylman, Bengtson et al. 2001).
Therefore, anti-coagulation of the reinfused blood may play vital role in modulation
of PTI. Differences between non-anticoagulated and anticoagulated salvaged blood
need to be resolved by future in-vitro and in-vivo studies. Elevated HMGB-1 was
reported in publications in different surgery patients (Suda, Kitagawa et al. 2006,
Giannoudis, Mallina et al. 2010, Osoegawa, Yano et al. 2011, Takahata, Ono et al.
2011). In contrast, in this study, postoperative reduction was found. Since there was
no study on HMGB-1 levels in knee arthroplasty, this study could not confirm the
biological significance.
More importantly, publications on the postoperative changes in different
biomarkers following surgery gave a complex scenario on identification of reliable
biomarkers to define postoperative immune status (Appendix-II). Elevations in IL-6
and IL-8 were reported in publications as the markers for inflammation; however this
study confirmed that these two biomarkers were common biomarkers of sterile
trauma and therefore may have no relevant association with modulating postsurgical
immune status. None of the studies offered extensive panels of biomarkers to
measure immune status.
6.6. Biological Insights into Effects of Sterile Trauma
6.6.1. Enhancement in Antimicrobial Properties
This study revealed postoperative elevations in the levels of different
antimicrobial proteins such as sCD14, lysozyme, S100-A8/A9 and α-Defensins.
Whereas the major role of lysozyme and sCD14 are to perform antimicrobial
activities, immediately released DAMPs at the site of injury also have their distinct
roles in other biological mechanisms. For example, S100-A8/A9 and α-Defensins, as
the danger factors released immediately after surgery, may help in the migration of
leukocytes to the site of injury (Ryckman, Vandal et al. 2003, Lehrer 2007). NETosis
is a distinct form of cell death of neutrophils, that releases intracellular compounds
(DNA, proteins or peptides) to the extracellular environment by dead or dying cells
138
Chapter - VI
Discussion
at the site of injury; thereby accelerate the anti-microbial actions (Darrah and
Andrade 2012). Therefore, despite PTI, a developed innate immune system exists
with antimicrobial properties that boosted following surgical or accidental trauma
which is aimed at preventing infections. However the level of this additional
antimicrobial defences may not be sufficient to prevent nosocomial infections due to
PTI following sterile trauma.
6.6.2. Role of CD24/Siglec-10 Axis in Immunology
Chen et al’s ground breaking observations established the mechanisms of
CD24/Siglec-10 axis in the dampening of inflammatory activities following sterile
injury (Chen, Tang et al. 2009). This study provided a means to distinguish
inflammation caused by sterile trauma from that due to sepsis. Parlato et al showed
an early and persistent reduction in CD24 expression on neutrophils from the sepsis
patients, a phenomenon that was absent both in healthy controls and sterile trauma
patients (Parlato, Souza-Fonseca-Guimaraes et al. 2014). Furthermore, in the graft
versus host disease (GvHD) patients Toubai et al recently described dampening of
inflammatory events mediated by the communications between CD24 on donor Tcells and Siglec-10 on recipient antigen presenting cells (Toubai, Hou et al. 2014).
Recent investigations indicated the important involvement of CD24/Siglec-10 in
autoimmune diseases, transplantation and other immunological complications
following injury/surgery (Chen, Chen et al. 2011, Bandala-Sanchez, Zhang et al.
2013, Liu, Yang et al. 2013, Zheng, Wu et al. 2013, Parlato, Souza-FonsecaGuimaraes et al. 2014, Toubai, Hou et al. 2014). However, none of the publications
studied the soluble forms of CD24 and Siglec-10 and therefore this is the first report
that clearly showed huge systemic elevations in soluble CD24 and Siglec-10
following sterile trauma.
Another important but yet poorly understood fact is the production of “natural
auto-antibodies (IgM)” by B1a cells. Following trauma, phagocytes may ingest
apoptotic cells and produce anti-inflammatory cytokines (IL-10, TGF-β) and
decrease secretion of pro-inflammatory cytokines (IL-1, IL-12 and TNF-α), thereby
immunosuppressing local responses (Voll, Herrmann et al. 1997, Munoz, Lauber et
al. 2010, Darrah and Andrade 2012). Neoantigens produced by different enzymes as
139
Chapter - VI
Discussion
well as by post-translational modifications, triggers autoantibody IgM production by
B1a cells. However, interestingly, recent study showed that expression of Siglec-10
on B1a cell downregulates the expansion of B1a cells and thus prevent production of
IgM (Jellusova, Wellmann et al. 2010). Therefore Siglec-10 has vital role in
alleviating the autoimmune reactions following major trauma.
With systemic
elevations in soluble CD24 and Siglec-10 following trauma, it was hypothesized that,
these two soluble biomarkers mediate immunosuppression by controlling
inflammatory monocytes/macrophages and in parallel prevent auto-immune
reactions. Figure 6.3 schematically describes these hypotheses.
Figure 6.6: Hypotheses on the immunological responses mediated by soluble
forms of CD24 and Siglec-10. CD24/Siglec-10 can play inhibitory role only if Siglec-10 has
the intracellular ITIM motif. If the Siglec-10/CD24/DAMP complex has the inhibitory ITIM motif,
this complex can pinocytose into the immune cells such as antigen presenting cells to inhibit the
NfKB mediated inflammations and hence reduce the productions of inflammatory cytokines. On the
other hand, this ITIM containing Siglec-10/CD24/DAMP complex can also pinocytose into the B1a
cells and thus limits the productions of IgM autoantibody by these cells.
140
Chapter - VI
Discussion
6.7. Clinical Significance of Findings
Reinfusion of ACD-anticoagulated autologous unwashed salvaged blood
helps not only in blood conservation by reducing the practice of allogeneic blood
transfusion, but also in boosting patients’ post-operative immunity possibly reducing
patients’
vulnerability
to
nosocomial
infections.
Identification
of
novel
immunostimulants in salvaged blood would therefore have therapeutic potential. In
contrast, as elevated Siglec-10/CD24 levels following trauma may prevent autoimmune reactions, reinfusion of salvaged blood might inhibit this natural process and
be detrimental. However, no differences between NSBT and ASBT cohorts in terms
of systemic elevations in CD24 and Siglec-10 indicated that transfusion of this blood
had very little effect on CD24/Siglec-10. Clinically, characterizing immune status of
patients would be very important prior to treating with immunostimulatory drugs to
reverse PTI.
141
Chapter - VI
Discussion
6.8. Limitations of this study
A very carefully designed study also ends up with limitations as there is no perimeter
of perfection. The major limitations in this study are as below:
a. No study on fresh immune cells: Cryopreservation of PBMC may affect the
cellular phenotypic markers and their functional abilities. Studying fresh
blood samples from patients for Flow cytometric cell surface and intracellular expressions of different immune cells could have been done
immediately after sample collection from the patient.
b. Blood Collection Time Points: Although no correlations found between
length of hospital stay and changes in different biomarkers, in this study there
was no fixed time point for postoperative blood collection. On the other hand,
collections of postoperative venous blood at 1 and 6 hours after surgery
would give better understanding on local and systemic immune responses.
c. Clinical Outcome of Patients: Measurement of postoperative clinical
outcomes such as pain score, knee score, anxiety/depression, satisfactions of
the patients and more importantly history of infections following surgery
would give better clinical picture in association with the studied
immunological changes. However, such a study would need couple of
thousand patients.
142
Chapter - VI
Discussion
6.9. Future Directions
It is possible to do some more studies using the existing plasma samples from
this project. Also, some future projects can be directed based on these current
findings by extending investigations of the specimens in hand.
6.9.1. Tandem mass tagging
TMT results on one patient from ASBT cohort showed significant alterations
in plasma proteome in both local and systemic bloods following surgery. Therefore
the study demanded investigations on the samples from all 43 study patients.
Proteomic variations between NSBT and ASBT cohorts would help in identifying the
proteins involved in the development of PTI.
6.9.2. Fractionation of plasma to test for immunostimulants
To identify the immunostimulants in transfused salvaged blood, plasma can
be fractionated into few parts by size exclusion chromatography using Fast
Performance Liquid Chromatography (FPLC). These plasma fractions can then be
tested in vitro to see which fractions have immunostimulatory properties.
Experiments would include the measurement of soluble biomarker levels as well as
cellular expressions on cells from cultured PBMC or human whole blood.
6.9.3. Inflammatory cytokine gene expressions in venous and salvaged blood
Venous and salvaged blood plasma samples can further be used to isolate
circulating RNA and microRNA for investigating the gene expressions for different
inflammatory and tissue regenerative biomarkers.
143
Chapter - VI
Discussion
My experiences in this “yet under-investigated area of research” nicely fits with the
great quote from Sir Isaac –
“I do not know what I may appear to the world; but to myself I seem to have
been only like a boy playing on the seashore, and diverting myself in now and
then finding a smoother pebble or a prettier shell than ordinary, whilst the
great ocean of truth lay all undiscovered before me” - Isaac Newton (1642-1727)
144
CHAPTER - VII
APPENDICES
Chapter - VII
Appendices
APPENDIX – I : LEVELS OF BIOMARKERS IN WOUND BLOOD
Table 7.1 : Levels of Biomarkers in Wound Blood
Here AAA = Abdominal aortic aneurysm, THA = Total Hip Arthroplasty, TKA = Total Knee Arthroplasty, CABG = Coronary Artery Bypass Grafting, CPB = Cardio
Pulmonary Bypass, h = hour. C = Complement; IL-(1β) = Interleukin-(1-beta); IL-1RA = IL-1 Receptor Antagonist; TNF = Tumour Necrosis Factor; IFN = Interferon;
MCP = Monocyte Chemotactic Protein; MIP = Macrophage Inflammatory Protein; PGE2 = Prostaglandin E2; sIL-6R = Soluble IL-6 Receptor.
INC = increase, DEC = Decrease, N.C. = No Change, N.D. = Not Detectable
146
Chapter - VII
Appendices
APPENDIX – II : LEVELS OF BIOMARKERS IN POST-OPERATIVELY FOLLOWING MAJOR SURGERY
Table 7.2 : Levels of Biomarkers Postoperatively Following Major Surgery
147
Here THA = Total Hip Arthroplasty, TKA = Total Knee Arthroplasty, TJA = Total Joint Arthroplasty, CABG = Coronary Artery Bypass Grafting, CPB = Cardio Pulmonary Bypass; D = Day, h = hour.
IL-(1β) = Interleukin-(1-beta); IL-1RA = IL-1 Receptor Antagonist; IFN = Interferon; TNF = Tumour Necrosis Factor; MCP = Monocyte Chemotactic Protein; MIP = Macrophage Inflammatory Protein;
HMGB = High Mobility Group Protein; INC = increase, DEC = Decrease, N.C. = No Change, N.D. = Not Detectable
Chapter - VII
Appendices
148
Here THA = Total Hip Arthroplasty, TKA = Total Knee Arthroplasty, TJA = Total Joint Arthroplasty, CABG = Coronary Artery Bypass Grafting, CPB = Cardio Pulmonary Bypass;
D = Day, h = hour. IL-(1β) = Interleukin-(1-beta); IL-1RA = IL-1 Receptor Antagonist; IFN = Interferon; TNF = Tumour Necrosis Factor; MCP = Monocyte Chemotactic Protein;
MIP = Macrophage Inflammatory Protein; HMGB = High Mobility Group Protein
Chapter - VII
Appendices
149
Chapter - VII
Appendices
150
Chapter - VII
Appendices
APPENDIX - III (A) : CORRELATION BETWEEN BIOMARKER LEVELS
AND LENGTH OF HOSPITAL STAY
Table 7.3 (A) : Correlation Between Biomarker Levels and Length of Hospital Stay
Hospital Stay vs.
Postop Fold Changes
Proinflammatory
Cytokines
Antiinflammatory
Cytokines
Chemokines
DAMPs
Other Factors
NSBT
ASBT
NSBT + ASBT
Drained ml
r
- 0.04
p
0.88
r
0.09
p
0.68
r
0.20
p
0.19
Age
- 0.34
0.16
0.37
0.07
0.06
0.69
IL-1β
- 0.38
0.12
- 0.04
0.85
0.07
0.66
IL-2
- 0.09
0.73
- 0.23
0.26
0.01
0.95
IL-6
0.01
0.97
- 0.09
0.66
- 0.07
0.66
IL-12p70
- 0.05
0.85
- 0.29
0.21
- 0.17
0.31
IL-17A
- 0.40
0.10
- 0.26
0.23
- 0.07
0.69
IFN-γ
- 0.21
0.54
- 0.09
0.71
0.13
0.47
TNF-α
- 0.20
0.43
0.27
0.19
0.34
0.03
IL-4
- 0.27
0.28
0.04
0.87
- 0.20
0.19
IL-5
- 0.01
0.97
0.15
0.47
- 0.12
0.45
IL-9
- 0.09
0.74
- 0.29
0.18
- 0.16
0.33
IL-10
0.73
0.00
0.26
0.21
0.20
0.21
IL-13
- 0.05
0.83
- 0.11
0.59
- 0.18
0.26
IL-1RA
0.50
0.04
0.37
0.07
0.41
0.01
TGF-β1
- 0.21
0.41
0.41
0.04
0.32
0.04
IL-8
- 0.36
0.15
0.14
0.50
0.14
0.37
MCP-1
- 0.26
0.32
- 0.12
0.56
- 0.16
0.33
MIP-1α
- 0.06
0.82
- 0.12
0.56
- 0.09
0.56
HSP-27
- 0.49
0.02
0.92
- 0.13
0.40
HSP-70
- 0.11
0.04
0.66
0.02
0.93
0.01
0.96
HSP-60
- 0.21
0.49
- 0.06
0.80
- 0.12
0.48
Annexin A2
- 0.07
0.78
- 0.06
0.77
0.06
0.69
S100-A8/A9
- 0.28
0.26
- 0.12
0.57
- 0.13
0.40
sIL-6R
0.63
0.001
0.33
0.10
0.40
0.007
Sgp130
0.14
0.57
0.11
0.60
0.15
0.32
ADAM-17
- 0.28
0.39
0.29
0.15
0.20
0.20
sCD-14
0.20
0.43
- 0.03
0.89
0.06
0.69
CD-24
- 0.13
0.61
0.21
0.30
0.12
0.45
Siglec-10
- 0.04
0.88
0.12
0.55
0.07
0.63
C5a
- 0.06
0.80
- 0.10
0.63
0.10
0.52
Lysozyme
0.02
0.95
0.21
0.31
0.20
0.19
151
Chapter - VII
Appendices
APPENDIX - III (B) : CORRELATION BETWEEN BIOMARKER LEVELS
AND DRAINED BLOOD VOLUME.
Table 7.3 (B) : Correlation Between Biomarker Levels and Drained Blood Volume
Drained Blood.Vol.ml. vs.
Postop Fold Changes
Antiinflammatory
Cytokines
Chemokines
DAMPs
Other Factors
r
ASBT
p
r
p
- 0.07
0.78
- 0.15
0.48
IL-1β
0.04
0.87
- 0.26
0.21
IL-2
0.49
0.04
0.03
0.89
IL-6
- 0.13
0.60
- 0.15
0.49
IL-12p70
- 0.16
0.54
- 0.25
0.29
IL-17A
0.15
0.56
0.15
0.49
IFN-γ
0.29
0.39
- 0.22
0.36
TNF-α
0.28
0.27
0.38
0.07
IL-4
- 0.10
0.70
0.18
0.39
IL-5
0.32
0.20
0.19
0.37
IL-9
- 0.05
0.84
0.02
0.92
IL-10
0.07
0.78
0.02
0.91
IL-13
- 0.07
0.80
- 0.22
0.30
IL-1RA
- 0.14
0.58
0.25
0.25
TGF-β1
- 0.46
0.05
0.10
0.64
IL-8
0.59
0.01
0.23
0.28
MCP-1
0.17
0.52
- 0.45
0.03
MIP-1α
0.07
0.78
- 0.24
0.26
HSP-27
0.12
0.65
0.12
0.58
HSP-70
0.14
0.59
0.05
0.80
HSP-60
- 0.19
0.53
0.48
0.02
Annexin A2
- 0.28
0.26
0.14
0.52
S100-A8/A9
- 0.26
0.29
- 0.00
0.99
sIL-6R
0.26
0.30
- 0.21
0.33
Sgp130
0.12
0.63
- 0.06
0.77
ADAM-17
0.21
0.40
0.31
0.14
sCD-14
- 0.54
0.02
- 0.12
0.62
CD-24
- 0.17
0.50
- 0.30
0.16
Siglec-10
- 0.10
0.69
- 0.08
0.72
C5a
- 0.55
0.02
- 0.09
0.66
Lysozyme
- 0.33
0.18
- 0.12
0.59
Age
Proinflammatory
Cytokines
NSBT
152
Chapter - VII
Appendices
APPENDIX – III (C) : CORRELATION BETWEEN BIOMARKER LEVELS
AND TRANSFUSED BLOOD VOLUME.
Table 7.3 (A) : Correlation Between Biomarker Levels and Transfused Blood
Volume
Proinflammatory
Cytokines
Antiinflammatory
Cytokines
Chemokines
DAMPs
Other Factors
Transfusion.Vol.ml.
vs. Postop Fold
Changes
Drained ml
Hospital Stay
Age
IL-1β
IL-2
IL-6
IL-12p70
IL-17A
IFN-γ
TNF-α
IL-4
IL-5
IL-9
IL-10
IL-13
IL-1RA
TGF-β1
IL-8
MCP-1
MIP-1α
HSP-27
HSP-70
HSP-60
Annexin A2
S100-A8/A9
sIL-6R
Sgp130
ADAM-17
sCD-14
CD-24
Siglec-10
C5a
Lysozyme
ASBT
r
0.09
0.24
- 0.15
0.10
0.08
- 0.23
- 0.45
- 0.02
- 0.25
0.27
0.34
0.20
- 0.02
0.25
0.18
0.17
0.24
- 0.01
- 0.26
0.00
0.08
- 0.03
0.13
- 0.14
- 0.16
0.07
0.09
0.20
0.14
0.007
- 0.17
0.004
- 0.42
p
0.68
0.25
0.48
0.65
0.70
0.28
0.04
0.94
0.28
0.19
0.09
0.34
0.93
0.22
0.38
0.40
0.26
0.95
0.22
1.00
0.70
0.90
0.57
0.50
0.45
0.74
0.68
0.34
0.51
0.97
0.41
0.99
0.04
153
Chapter - VII
Appendices
APPENDIX – IV : PRE- AND POST-OPERATIVE LEVELS OF DIFFERENT
BIOMARKERS (MEAN ± SD)
Table 7.4 : Pre- and Post-Operative Levels of Different Biomarkers
Biomarker Name
ProInflammatory
Cytokines
AntiInflammatory
Cytokines
Chemokines
DAMPs
Other
Biomarkers
NSBT
n
Preoperative
Postoperative
IL-1β (pg/ml)
18
128.7 ± 164.6
77.87 ± 104.2
IL-2 (pg/ml)
18
158.9 ± 159.5
IL-6 (pg/ml)
18
IL-12p70 (pg/ml)
ASBT
p
n
Preoperative
Postoperative
p
0.03
25
19.39 ± 13.25
44.73 ± 35.15
<0.0001
62.49 ± 60.48
0.005
25
21.32 ± 19.3
39.57 ± 28.77
<0.0001
7.58 ± 11.22
23.32 ± 17.73
0.0001
25
4.76 ± 4.04
19.93 ± 12.34
<0.0001
17
13.14 ± 20.85
13.11 ± 20.16
0.82
21
18.31 ± 16.57
17 ± 15.25
IL-17A (pg/ml)
18
76.12 ± 127.9
44.99 ± 79.89
0.04
23
8.64 ± 7.31
19.01 ± 24.91
0.0001
IL-22 (pg/ml)
5
361.8 ± 343.3
277.4 ± 146.6
0.06
15
55.52 ± 42.12
127.0 ± 87.0
<0.0001
IFN-γ (pg/ml)
11
723.4 ± 1135
219.9 ± 344.5
0.052
21
23.81 ± 20.08
54.66 ± 33.65
<0.0001
TNF-α (pg/ml)
18
65.52 ± 136.4
27.47 ± 55.95
0.009
24
3.67 ± 2.80
8.846 ± 6.12
<0.0001
IL-4 (pg/ml)
18
192.9 ± 232.2
204,.1 ± 183.1
0.28
25
123.3 ± 83.32
96.36 ± 73.44
0.006
IL-5 (pg/ml)
18
29.88 ± 35.59
33.82 ± 21.12
0.01
25
20.8 ± 21.23
11.86 ± 12.65
<0.0001
IL-9 (pg/ml)
15
340 ± 953.2
66.45 ± 70.14
0.60
21
20.58 ± 15.07
12.46 ± 7.28
0.0005
IL-10 (pg/ml)
18
48.66 ± 94.82
30.69 ± 42.11
0.01
25
9.9 ± 6.482
6.31 ± 5.26
0.0002
IL-13 (pg/ml)
18
143 ± 92.11
142.6 ± 91.82
0.94
24
70.89 ± 47.95
44.53 ± 32.71
0.0004
IL-1ra (pg/ml)
18
178.3 ± 100.7
210.9 ± 71.86
0.004
25
127.1 ± 23.77
174.2 ± 61.08
<0.0001
TGF-β1 (ng/ml)
18
11.53 ± 4.48
10.67 ± 1.90
0.92
25
12.63 ± 6.88
12.83 ± 2.50
0.06
IL-8 (pg/ml)
18
22.97 ± 43.08
26.72 ± 36.02
0.0006
25
26.46 ± 36.42
35.27 ± 36.59
<0.0001
MCP-1 (pg/ml)
16
147.3 ± 105.6
165.5 ± 126.3
0.12
25
131.6 ± 84.65
189.1 ± 114.6
0.0007
MIP-1α (pg/ml)
18
33.51 ± 60.29
30.74 ± 48.99
0.03
25
16.58 ± 16.87
16.42 ± 8.83
0.02
HMGB-1 (ng/ml)
17
27.92 ± 7.85
13.31 ± 5.24
<0.0001
23
41.96 ± 18.36
25.21 ± 12.02
<0.0001
HSP-27 (ng/ml)
18
3.39 ± 1.48
3.70 ± 1.50
<0.0001
25
4.15 ± 2.58
4.42 ± 1.74
<0.0001
HSP-60 (ng/ml)
14
5.65 ± 7.61
4.33 ± 5.96
0.0002
22
6.34 ± 10.15
6.61 ± 8.75
<0.0001
HSP-70 (ng/ml)
17
1.15 ± 0.53
1.86 ± 0.68
<0.0001
25
1.03 ± 0.30
1.86 ± 0.70
<0.0001
S100A8/A9 (ng/ml)
18
163 ± 87.46
521.7 ± 135.7
<0.0001
25
146 ± 71.29
455.4 ± 176.3
<0.0001
Annexin-A2(ng/ml)
18
1.95 ± 1.508
1.78 ± 1.09
<0.0001
25
2.05 ± 1.05
2.72 ± 1.26
<0.0001
α-Defensin ( ng/ml)
17
135.1 ± 91.04
150.6 ± 80.38
0.0002
23
130.9 ± 28.74
156.3 ± 35.37
<0.0001
sIL-6R (ng/ml)
18
39.95 ± 9.304
34.05 ± 8.13
0.002
25
38.91 ± 7.912
33.66 ± 9.066
0.001
sgp130 (ng/ml)
18
163.7 ± 23.89
144.7 ± 17.81
0.01
24
173.0 ± 33.01
161.3 ± 23.71
0.02
ADAM-17 (ng/ml)
18
0.97 ± 1.35
0.78 ± 0.82
0.30
25
0.37 ± 0.47
0.39 ± 0.37
0.01
sCD-14 (µg/ml)
18
1.37 ± 0.29
2.043 ± 0.56
<0.0001
25
1.107 ± 0.23
1.82 ± 0.44
<0.0001
CD-24 ng/ml)
18
125.7 ± 89.06
461.6 ± 153.0
<0.0001
25
148.9 ± 82.65
577.4 ± 275.6
<0.0001
Siglec-10 (ng/ml
18
25.23 ± 77.23
49.34 ± 70.39
<0.0001
25
7.65 ± 6.01
35.35 ± 17.82
<0.0001
Siglec-2 (ng/ml)
2
5.73 ± 1.80
8.54 ± 2.22
0.30
3
7.54 ± 4.79
10.37 ± 7.39
0.61
C5a (ng/ml)
18
17.68 ± 4.08
21.62 ± 3.51
0.0002
25
14.39 ± 6.45
28.07 ± 8.88
<0.0001
Lysozyme (IU/ml)
18
1591 ± 359.3
1745 ± 214.3
0.04
25
1689 ± 238.1
1733 ± 340.1
0.002
0.93
154
Chapter - VII
Appendices
APPENDIX – V: LEVELS OF DIFFERENT BIOMARKERS IN PERIPHERAL
AND SALVAGED BLOOD (MEAN ± SD)
Table 7.5 : Levels of Different Biomarkers in Peripheral and Salvaged Blood
n
Peripheral
WSB
TSB
p value
(WSB vs
Peripheral)
p value
(TSB vs
Peripheral)
p value
(WSB vs
TSB)
IL-1β (pg/ml)
25
19.39 ± 13.25
22.05 ± 22.72
51.16 ± 42.63
0.002
<0.0001
<0.0001
IL-2 (pg/ml)
23
23.01 ± 19.21
36.98 ± 24.07
46.91 ± 41.61
<0.0001
<0.0001
0.04
IL-6 (pg/ml)
25
4.764 ± 4.037
352.8 ± 687.9
4780 ± 3211
<0.0001
<0.0001
<0.0001
IL-12p70 (pg/ml)
23
17.4 ± 16.24
20.54 ± 12.81
17.03 ± 9.59
<0.0001
<0.0001
0.005
IL-17A (pg/ml)
22
7.75 ± 6.07
14.92 ± 13.41
26.16 ± 20.23
<0.0001
<0.0001
<0.0001
IL-22 (pg/ml)
15
48.39 ± 44.09
315.1 ± 248.7
483.1 ± 454.2
<0.0001
<0.0001
0.0001
IFN-γ (pg/ml)
22
22.8 ± 20.16
107.9 ± 257.7
146.7 ± 217.1
<0.0001
<0.0001
0.0004
TNF-α (pg/ml)
21
3.909 ± 2.76
15.14 ± 17.94
26.99 ± 19.55
<0.0001
<0.0001
<0.0001
IL-4 (pg/ml)
25
123.3 ± 83.32
148 ± 128.3
182.8 ± 161.2
0.0001
<0.0001
0.03
IL-5 (pg/ml)
25
20.8 ± 21.23
16.99 ± 18.87
20.22 ± 23.86
0.04
0.002
0.09
IL-9 (pg/ml)
22
19.7 ± 15.27
32.26 ± 30.5
39.12 ± 42.05
<0.0001
<0.0001
0.054
IL-10 (pg/ml)
25
9.9 ± 6.48
34.27 ± 38.13
41.1 ± 26.64
<0.0001
<0.0001
0.005
IL-13 (pg/ml)
24
70.89 ± 47.95
32.06 ± 36.17
42.01 ± 43.75
0.14
0.83
0.11
IL-1ra (pg/ml)
25
127.1 ± 23.77
254.1 ± 103.2
355.9 ± 240.7
<0.0001
<0.0001
0.03
TGF-β1 (ng/ml)
25
12.63 ± 6.88
64.49 ± 40.29
72.62 ± 34.71
<0.0001
<0.0001
0.002
IL-8 (pg/ml)
25
27.47 ± 36.84
128.1 ± 187.3
1012 ± 747.6
0.002
<0.0001
<0.0001
MCP-1 (pg/ml)
25
131.6 ± 84.65
770 ± 830.4
3410 ± 3488
<0.0001
<0.0001
<0.0001
MIP-1α (pg/ml)
25
16.58 ± 16.87
69.64 ± 60.32
105.3 ± 76.76
<0.0001
<0.0001
<0.0001
HMGB-1 (ng/ml)
25
4.86 ± 1.23
51.31 ± 31.52
35.58 ± 13.51
<0.0001
<0.0001
0.0006
α-Defensin (ng/ml)
23
48.16 ± 71.25
438.1 ± 381
485.4 ± 228.9
<0.0001
<0.0001
0.08
HSP-27 (ng/ml)
25
4.154 ± 2.58
258.3 ± 152.4
221.5 ± 162.7
<0.0001
<0.0001
0.10
HSP-60 (ng/ml)
23
6.192 ± 9.94
35.65 ± 21.14
37.39 ± 19.31
<0.0001
<0.0001
0.63
HSP-70 (ng/ml)
25
1.028 ± 0.30
73.55 ± 42.39
61.87 ± 27.95
<0.0001
<0.0001
0.16
S100A8/A9 (ng/ml)
25
142.1 ± 75.63
686.7 ± 385.4
713.9 ± 381.1
<0.0001
<0.0001
0.68
Annexin-A2(ng/ml)
25
2.052 ± 1.05
2.273 ± 0.93
2.531 ± 0.85
<0.0001
<0.0001
0.004
sIL-6R (ng/ml)
25
38.91 ± 7.91
30.41 ± 8.15
32.18 ± 10.43
<0.0001
<0.0001
0.31
sgp130 (ng/ml)
25
173.0 ± 33.01
208.5 ± 40.37
215.9 ± 52.28
<0.0001
<0.0001
0.15
ADAM-17 (ng/ml)
25
0.37 ± 0.47
0.63 ± 0.54
0.54 ± 0.50
<0.0001
<0.0001
0.03
sCD-14 (µg/ml)
25
1.11 ± 0.23
0.83 ± 0.16
0.89 ± 0.19
<0.0001
<0.0001
0.04
CD-24 (ng/ml)
25
148.9 ± 82.65
984.5 ± 462.0
1068 ± 463.1
<0.0001
<0.0001
0.14
Siglec-10 (ng/ml)
25
7.65 ± 6.01
92.89 ± 44.65
101.3 ± 47.53
<0.0001
<0.0001
0.11
Siglec-2 (ng/ml)
3
7.54 ± 4.79
12.53 ± 9.61
13.42 ± 7.71
0.17
0.12
0.51
C5a (ng/ml)
25
14.39 ± 6.45
18.61 ± 6.60
18.62 ± 6.87
<0.0001
<0.0001
0.46
Lysozyme (IU/ml)
25
1689 ± 238.1
1347 ± 356.4
1463 ± 341.8
<0.0001
<0.0001
0.003
KGF ( pg/ml )
25
3.55 ± 10.98
130.3 ± 80.88
94.02 ± 47.56
<0.0001
<0.0001
0.0001
Biomarker Name
ProInflammatory
Cytokines
AntiInflammatory
Cytokines
Chemokines
DAMPs
Other
Biomarkers
155
Chapter - VII
Appendices
APPENDIX – VI : IN VITRO STUDIES ON sCD14, IL-1β, AND TNF-α
PRODUCTIONS BY FRESH PBMCs AND HUMAN WHOLE BLOOD
CULTURE
(a)
PBMC
s C D - 1 4 [n g /m l]
6
C o n tr o l
L P S 3 n g /m l
4
L P S 1 0 n g /m l
2
r
h
r
8
h
4
4
2
1
2
6
h
h
r
r
0
H o u r In c u b a tio n
(b)
H u m a n W h o le B lo o d
C o n tr o l
s C D - 1 4 [n g /m l]
50
L P S 1 0 n g /m l
40
30
20
10
r
r
h
8
4
4
2
1
2
h
h
r
r
h
6
0
h
r
0
Figure 7.1: Effect of LPS addition on sCD-14 production by fresh PBMCs and
human whole blood culture. (a) Levels of sCD-14 produced by freshly separated PBMCs cultured
for different incubation periods -/+ LPS stimulation. (b) Levels of sCD-14 produced by diluted human
whole blood cultured for different incubation periods. Data shown as Mean ± SD in ng/ml in box-whisker
plot
156
Chapter - VII
Appendices
(a)
PBMC
1500
T N F -  [ p g /m l]
C o n tro l
L P S 3 n g /m l
L P S 1 0 n g /m l
1500
1000
500
C o n tro l
L P S 3 n g /m l
L P S 1 0 n g /m l
1000
500
r
h
8
4
2
1
4
h
2
6
h
r
r
h
r
h
8
2
4
2
h
h
r
r
r
h
6
1
4
H o u r In c u b a t io n
H o u r In c u b a t io n
(d)
1000
C o n tr o l
C o n tr o l
1
r
h
h
6
h
h
8
4
0
r
r
4
2
1
2
h
h
r
r
6
h
r
h
0
8
0
4
0
h
200
r
200
r
400
4
400
600
2
600
L P S 1 0 n g /m l
800
r
T N F -  [ p g /m l]
L P S 1 0 n g /m l
800
h
1000
r
(c)
IL -1  [ p g /m l]
r
0
0
2
IL -1  [ p g /m l]
(b)
PBMC
Figure 7.2: Effect of LPS addition on pro-inflammatory cytokine production by
fresh PBMCs. (a) and (b) Levels of IL-1β and TNF-α produced by freshly separated
PBMCs cultured for different incubation periods with or without treatment with LPS.
(c) and (d) Levels of IL-1β and TNF-α in the whole blood culture supernatants
following different incubation periods. Data showed as Mean ± SD in µg/ml in boxwhisker plot.
157
Chapter - VII
Appendices
APPENDIX – VII : SIMULATION OF SALVAGED BLOOD COLLECTION
CONDITIONS.
Samples of pre-operative blood from patients were collected in ACD and incubated at
room temperature for zero and five hours to simulate collection conditions for WSB
and TSB. Plasma samples were separated and cryopreserved and subsequently assayed
for biomarkers by Flow-Cytometric Bead Array and ELISA.
Figure 7.3: Simulated Salvaged Blood Results.
Fold changes plotted on a Log10 scale were derived by dividing five-hour with zero-hour results, as
follows: (a) DAMPs; (b) Chemokines; (c) Pro-inflammatory cytokines; and (d) Anti-inflammatory
cytokines. Analyses were by one sample t-test (* = p <0·05; ** = p <0·001; *** = p <0·0001).
158
Chapter - VII
Appendices
(e)
100
(g)
10
1
10]
10
F o ld C h a n g e [ L o g
10]
10
10]
(f)
1
1
0 .1
0 .1
4
7
A
D
s
A
C
D
M
1
-1
3
1
p
g
s
s
IL
IL
-6
-2
R
0
2
0 .1
(h)
(j)
(i)
10
10
10
*
1
10]
10]
10]
**
1
0 .1
1
0 .1
m
zy
a
5
L
y
s
o
C
S
ig
C
le
D
c
-1
-2
0
4
e
0 .1
Figure 7.3 (Continued): Simulated Salvaged Blood Results.
Fold changes plotted on a Log10 scale were derived by dividing five-hour with zero-hour results, as
follows: (e) IL-22; (f) sIL-6R, sgp130, and ADAM-17; (g) sCD-14; (h) CD-24 and Siglec-10; (i) C5a;
and (j) Lysozyme. Analyses were by one sample t-test (* = p <0·05; ** = p <0·001; *** = p
<0·0001).
159
Chapter - VII
Appendices
APPENDIX – VIII : EFFECT OF LYSOZYME ON PRODUCTIONS OF
DIFFERENT BIOMARKERS IN HUMAN WHOLE BLOOD CULTURE
ACD-anticoagulated human whole blood was treated with or without lysozyme (10
IU/ml)
for 0, 6, 10,
and
24
hours.
Supernatants
were
then
measured
for
different Biomarkers. And the promising antimicrobial activities of lysozyme found as
shown in the figures.
Figure 7.4: Effect of lysozyme on
production
of
different
Biomarkers in human whole
blood culture.
Levels of different Biomarkers produced
by human whole blood cultured for 0, 6,
10, and 24 hour incubation periods with or
without treatment with Lysozyme. (A)
Pro-inflammatory cytokines, (B) Antiinflammatory
Cytokines,
and
(C)
Chemokines Data showed as Mean {Log10
scale] in pg/ml.
160
Chapter - VII
Appendices
APPENDIX - IX : DETAILS OF BIOMARKERS ASSESSED BY TANDEM MASS TAGGING
Table 7.6 : Details of Biomarkers Assessed by Tandem Mass Tagging
47.93
9.01
17.78
14.95
28.96
Postop
vs.
Preop
0.33
0.38
0.68
0.38
0.42
WSB
vs.
Preop
0.95
5.32
5.50
0.89
0.61
TSB
vs.
Preop
0.72
4.42
3.99
1.06
0.41
TSB
vs.
WSB
0.76
0.83
0.73
1.19
0.72
Preop 5hr vs.
0 hr
0.44
0.66
0.74
0.30
0.57
A8K050
62.08
0.51
0.60
0.46
0.77
0.63
A8K2T4
93.35
0.88
0.60
0.43
0.70
0.68
A8K477
66.77
0.82
0.76
0.67
0.92
0.69
A8K5J8
80.15
1.03
0.59
0.41
0.75
0.49
A8K8Z4
104.65
1.21
0.39
0.25
0.64
0.63
B0AZL7
65.96
0.51
0.71
0.56
0.78
0.77
B1Q387
5.86
0.95
2.18
2.15
0.98
0.89
B2R4M6
13.20
2.18
2.51
4.79
1.91
0.84
B2R4P2
22.19
0.43
6.09
4.86
0.80
0.79
B2R582
22.52
0.56
0.73
0.59
0.81
0.55
B2R5G8
14.80
0.70
0.26
0.27
1.05
0.35
B2R6V9
83.19
0.46
0.25
0.23
0.95
0.59
Accession
MW
[kDa]
A4D2D2
A4UCS6
A4UCS8
A6XMH1
A6XND1
Description
Procollagen C-endopeptidase enhancer OS=Homo sapiens GN=PCOLCE PE=4 SV=1 - [A4D2D2_HUMAN]
Peroxiredoxin 6 (Fragment) OS=Homo sapiens PE=2 SV=1 - [A4UCS6_HUMAN]
Enolase (Fragment) OS=Homo sapiens PE=2 SV=1 - [A4UCS8_HUMAN]
Transthyretin OS=Homo sapiens PE=2 SV=1 - [A6XMH1_HUMAN]
Insulin-like growth factor binding protein 3 isoform b OS=Homo sapiens PE=2 SV=1 - [A6XND1_HUMAN]
cDNA FLJ75376, highly similar to Homo sapiens peptidoglycan recognition protein L (PGLYRP) mRNA OS=Homo sapiens
PE=2 SV=1 - [A8K050_HUMAN]
cDNA FLJ78207, highly similar to Human complement protein component C7 mRNA OS=Homo sapiens PE=2 SV=1 [A8K2T4_HUMAN]
cDNA FLJ78571, highly similar to Homo sapiens sulfhydryl oxidase mRNA OS=Homo sapiens PE=2 SV=1 [A8K477_HUMAN]
cDNA FLJ75066, highly similar to Homo sapiens complement component 1, subcomponent (C1R), mRNA OS=Homo
sapiens PE=2 SV=1 - [A8K5J8_HUMAN]
cDNA FLJ78071, highly similar to Human MHC class III complement component C6 mRNA OS=Homo sapiens PE=2 SV=1
- [A8K8Z4_HUMAN]
cDNA, FLJ79457, highly similar to Insulin-like growth factor-binding proteincomplex acid labile chain OS=Homo sapiens
PE=2 SV=1 - [B0AZL7_HUMAN]
Ferritin (Fragment) OS=Homo sapiens GN=FTL PE=3 SV=1 - [B1Q387_HUMAN]
cDNA, FLJ92148, highly similar to Homo sapiens S100 calcium binding protein A9 (calgranulin B) (S100A9), mRNA
OS=Homo sapiens PE=4 SV=1 - [B2R4M6_HUMAN]
cDNA, FLJ92164, highly similar to Homo sapiens peroxiredoxin 1 (PRDX1), mRNA OS=Homo sapiens PE=2 SV=1 [B2R4P2_HUMAN]
cDNA, FLJ92374, highly similar to Homo sapiens C-type lectin domain family 3, member B (CLEC3B), mRNA OS=Homo
sapiens PE=2 SV=1 - [B2R582_HUMAN]
Serum amyloid A protein OS=Homo sapiens PE=2 SV=1 - [B2R5G8_HUMAN]
cDNA, FLJ93141, highly similar to Homo sapiens coagulation factor XIII, A1 polypeptide (F13A1), mRNA OS=Homo
sapiens PE=2 SV=1 - [B2R6V9_HUMAN]
Here, MW = Molecular Weight; vs. = fold changes; Bold Numbers = Decreased (<2 folds); Black color numbers =Unhanged; and Highlighted Numbers = Increased (>2 folds).
161
Chapter - VII
Appendices
45.70
Postop
vs.
Preop
0.66
WSB
vs.
Preop
0.59
TSB
vs.
Preop
0.48
TSB
vs.
WSB
0.74
Preop 5hr vs.
0 hr
0.73
B2R815
48.50
0.38
0.67
0.48
0.68
0.61
B2R888
40.02
1.14
0.75
0.58
0.84
0.68
B2R950
163.77
0.42
0.53
0.42
0.78
0.40
B2R9F2
45.07
0.42
0.48
0.40
0.81
0.35
B2RA39
64.35
1.14
0.56
0.54
0.96
0.47
B2RBZ5
50.64
0.96
0.59
0.53
0.88
0.70
B2RMS9
103.30
0.68
0.65
0.51
0.79
0.45
B3KTV0
67.94
0.67
2.64
1.72
0.73
0.73
B3KWB5
36.98
0.58
0.61
0.46
0.75
0.59
B3KX75
124.68
0.67
0.69
0.54
0.82
0.76
B4DF70
B4DKJ3
B4DMS3
B4DNT5
B4DPP8
B4DUI5
20.09
77.17
120.76
30.79
46.47
22.86
0.25
0.58
2.73
0.76
0.63
0.54
4.26
1.02
1.49
0.55
0.58
4.42
3.99
0.79
1.31
0.48
0.48
2.49
0.95
0.77
0.88
0.93
0.82
0.72
0.80
0.51
1.22
0.49
0.59
0.85
B4DUV1
70.11
0.64
0.63
0.41
0.66
0.53
B4DW08
54.43
0.48
0.64
0.47
0.72
0.41
B4DWH0
32.56
0.77
0.64
0.50
0.78
0.52
Accession
MW
[kDa]
B2R701
Description
cDNA, FLJ93202, Homo sapiens protease inhibitor 16 (PI16), mRNA OS=Homo sapiens PE=2 SV=1 - [B2R701_HUMAN]
cDNA, FLJ93695, highly similar to Homo sapiens serpin peptidase inhibitor, clade A (alpha-1 antiproteinase, antitrypsin),
member 4 (SERPINA4), mRNA OS=Homo sapiens PE=2 SV=1 - [B2R815_HUMAN]
Monocyte differentiation antigen CD14 OS=Homo sapiens PE=2 SV=1 - [B2R888_HUMAN]
cDNA, FLJ94213, highly similar to Homo sapiens pregnancy-zone protein (PZP), mRNA OS=Homo sapiens PE=2 SV=1 [B2R950_HUMAN]
cDNA, FLJ94361, highly similar to Homo sapiens serine (or cysteine) proteinase inhibitor, clade A(alpha-1 antiproteinase,
antitrypsin), member 6 (SERPINA6), mRNA OS=Homo sapiens PE=2 SV=1 - [B2R9F2_HUMAN]
cDNA, FLJ94686, highly similar to Homo sapiens complement factor H-related 5 (CFHL5), mRNA OS=Homo sapiens PE=2
SV=1 - [B2RA39_HUMAN]
cDNA, FLJ95778, highly similar to Homo sapiens serpin peptidase inhibitor, clade A (alpha-1 antiproteinase, antitrypsin),
member 10 (SERPINA10), mRNA OS=Homo sapiens PE=2 SV=1 - [B2RBZ5_HUMAN]
Inter-alpha (Globulin) inhibitor H4 (Plasma Kallikrein-sensitive glycoprotein) OS=Homo sapiens GN=ITIH4 PE=2 SV=1 [B2RMS9_HUMAN]
cDNA FLJ38781 fis, clone LIVER2000216, highly similar to HEAT SHOCK COGNATE 71 kDa PROTEIN OS=Homo
sapiens PE=2 SV=1 - [B3KTV0_HUMAN]
cDNA FLJ42722 fis, clone BRAMY4000277, highly similar to Alpha-1B-glycoprotein OS=Homo sapiens PE=2 SV=1 [B3KWB5_HUMAN]
cDNA FLJ44930 fis, clone BRAMY3015549, highly similar to Neural cell adhesion molecule L1-like protein (Fragment)
OS=Homo sapiens PE=2 SV=1 - [B3KX75_HUMAN]
cDNA FLJ60461, highly similar to Peroxiredoxin-2 (EC 1.11.1.15) OS=Homo sapiens PE=2 SV=1 - [B4DF70_HUMAN]
Cartilage oligomeric matrix protein OS=Homo sapiens GN=COMP PE=2 SV=1 - [B4DKJ3_HUMAN]
cDNA FLJ59036, highly similar to von Willebrand factor OS=Homo sapiens PE=2 SV=1 - [B4DMS3_HUMAN]
cDNA FLJ60316, highly similar to Apolipoprotein-L1 OS=Homo sapiens PE=2 SV=1 - [B4DNT5_HUMAN]
cDNA FLJ53075, highly similar to Kininogen-1 OS=Homo sapiens PE=2 SV=1 - [B4DPP8_HUMAN]
Triosephosphate isomerase OS=Homo sapiens PE=2 SV=1 - [B4DUI5_HUMAN]
cDNA FLJ53207, highly similar to Homo sapiens fibulin 1 (FBLN1), transcript variant C, mRNA OS=Homo sapiens PE=2
SV=1 - [B4DUV1_HUMAN]
cDNA FLJ50886, highly similar to Aconitate hydratase, mitochondrial(EC 4.2.1.3) OS=Homo sapiens PE=2 SV=1 [B4DW08_HUMAN]
cDNA FLJ55670, highly similar to EGF-containing fibulin-like extracellularmatrix protein 1 OS=Homo sapiens PE=2 SV=1 [B4DWH0_HUMAN]
162
Chapter - VII
Appendices
Accession
MW
[kDa]
Postop
vs.
Preop
WSB
vs.
Preop
TSB
vs.
Preop
TSB
vs.
WSB
Preop 5hr vs.
0 hr
B4DYT3
65.69
0.48
0.64
0.48
0.74
0.56
B4DZ36
B4E1B3
B4E1C2
129.75
51.03
71.87
0.55
0.94
1.03
0.55
0.65
0.35
0.38
0.47
0.26
0.70
0.71
0.61
0.87
0.80
0.59
B4E1C4
46.03
0.56
0.57
0.40
0.74
0.41
B4E1D8
B4E1I8
B4E1Z4
B4E335
60.37
36.47
140.85
39.20
0.14
2.21
0.88
0.68
0.26
0.66
0.64
3.98
0.22
0.48
0.44
2.84
0.93
0.74
0.70
0.70
0.03
0.82
0.71
0.65
B7Z549
75.46
0.59
0.66
0.51
0.80
0.67
B7Z550
B7Z7A9
B7Z7M2
B7ZLF0
60.10
41.40
14.96
239.47
0.80
0.55
0.39
0.66
0.56
5.45
0.42
0.29
0.35
3.30
0.27
0.24
0.62
0.59
0.66
0.82
0.58
0.83
0.33
0.73
B8ZZW2
107.03
0.91
0.71
0.64
0.91
0.68
C0JYY2
C9J9F8
D3JV41
D6RA08
D6REX5
D9IWP9
E0D852
E3SBK4
515.24
19.65
13.70
24.10
35.09
36.23
38.49
48.77
0.75
0.51
0.41
0.66
0.50
0.74
0.84
0.44
0.43
0.61
4.76
0.59
0.64
0.39
1.21
0.58
0.38
0.53
3.41
0.46
0.55
0.34
1.17
0.40
0.87
0.86
0.72
0.72
0.82
0.94
0.93
0.69
0.66
0.44
0.43
0.53
0.57
0.58
0.85
0.51
E3UN46
12.02
0.47
0.48
0.30
0.61
0.58
E7ERU0
615.27
0.68
1.48
1.12
0.75
0.72
Description
cDNA FLJ59074, highly similar to Transcription initiation factor TFIID 105 kDa subunit OS=Homo sapiens PE=2 SV=1 [B4DYT3_HUMAN]
cDNA FLJ58441, highly similar to Attractin OS=Homo sapiens PE=2 SV=1 - [B4DZ36_HUMAN]
cDNA FLJ53950, highly similar to Angiotensinogen OS=Homo sapiens PE=2 SV=1 - [B4E1B3_HUMAN]
Kininogen 1, isoform CRA_b OS=Homo sapiens GN=KNG1 PE=2 SV=1 - [B4E1C2_HUMAN]
cDNA FLJ51179, highly similar to Vitamin K-dependent protein C (EC 3.4.21.69) OS=Homo sapiens PE=2 SV=1 [B4E1C4_HUMAN]
cDNA FLJ51597, highly similar to C4b-binding protein alpha chain OS=Homo sapiens PE=2 SV=1 - [B4E1D8_HUMAN]
cDNA FLJ54228, highly similar to Leucine-rich alpha-2-glycoprotein OS=Homo sapiens PE=2 SV=1 - [B4E1I8_HUMAN]
Complement factor B OS=Homo sapiens GN=CFB PE=2 SV=1 - [B4E1Z4_HUMAN]
cDNA FLJ52842, highly similar to Actin, cytoplasmic 1 OS=Homo sapiens PE=2 SV=1 - [B4E335_HUMAN]
cDNA FLJ56821, highly similar to Inter-alpha-trypsin inhibitor heavy chain H1 OS=Homo sapiens PE=2 SV=1 [B7Z549_HUMAN]
Complement component 8, beta polypeptide, isoform CRA_b OS=Homo sapiens GN=C8B PE=2 SV=1 - [B7Z550_HUMAN]
Phosphoglycerate kinase OS=Homo sapiens GN=PGK1 PE=2 SV=1 - [B7Z7A9_HUMAN]
cDNA FLJ51564, highly similar to Pregnancy zone protein OS=Homo sapiens PE=2 SV=1 - [B7Z7M2_HUMAN]
Fibronectin 1 OS=Homo sapiens GN=FN1 PE=2 SV=1 - [B7ZLF0_HUMAN]
GRIP and coiled-coil domain-containing protein 2 (Fragment) OS=Homo sapiens GN=GCC2 PE=4 SV=1 [B8ZZW2_HUMAN]
Apolipoprotein B (Including Ag(X) antigen) OS=Homo sapiens GN=APOB PE=4 SV=1 - [C0JYY2_HUMAN]
Coiled-coil domain-containing protein 173 (Fragment) OS=Homo sapiens GN=CCDC173 PE=4 SV=1 - [C9J9F8_HUMAN]
Thrombocidin-2 antimicrobial variant (Fragment) OS=Homo sapiens PE=4 SV=1 - [D3JV41_HUMAN]
Complement C1q subcomponent subunit B (Fragment) OS=Homo sapiens GN=C1QB PE=4 SV=1 - [D6RA08_HUMAN]
Selenoprotein P (Fragment) OS=Homo sapiens GN=SEPP1 PE=4 SV=1 - [D6REX5_HUMAN]
Beta-2-glycoprotein I (Fragment) OS=Homo sapiens PE=2 SV=1 - [D9IWP9_HUMAN]
Platelet glycoprotein Ib alpha polypeptide type 2 OS=Homo sapiens GN=GP1BA PE=4 SV=1 - [E0D852_HUMAN]
Zinc finger protein 645 OS=Homo sapiens GN=ZNF645 PE=2 SV=1 - [E3SBK4_HUMAN]
Insulin-like growth factor II transcript variant 3 isoform 1 (Fragment) OS=Homo sapiens GN=IGF2 PE=2 SV=1 [E3UN46_HUMAN]
Dystonin OS=Homo sapiens GN=DST PE=4 SV=1 - [E7ERU0_HUMAN]
163
Chapter - VII
Accession
MW
[kDa]
E7EUT5
E9PGP2
E9PLJ3
F2RM37
F5GY80
F5H6Q2
F5H6S5
F6KPG5
F8VQ14
F8VWU1
F8W1Q3
G3XAM2
H0Y509
H0YDX6
H0YL80
H3BR68
H6VRF8
I1VZV6
I2D5I8
I3L1J1
J3KRP0
K7EJ44
K7ELW0
K7ER74
K7ERI9
O43866
O60870
P00441
P00734
27.85
64.01
9.08
51.71
60.00
13.78
20.99
66.49
44.78
13.98
58.88
65.02
25.94
19.56
8.01
13.77
66.01
15.27
9.32
25.41
51.89
11.38
17.90
20.04
8.64
38.06
45.35
15.93
69.99
Appendices
Postop
vs.
Preop
0.34
0.68
0.55
0.88
0.82
0.36
0.71
0.49
0.17
0.67
0.55
0.76
1.00
0.60
0.44
0.62
1.03
0.47
0.43
0.67
0.52
0.29
0.47
0.47
0.26
0.63
0.98
0.40
0.64
WSB
vs.
Preop
3.80
0.66
4.64
0.55
0.61
5.70
0.57
0.40
0.23
0.66
0.63
0.57
0.64
0.73
5.44
3.23
0.98
13.51
0.27
0.79
0.66
6.76
4.68
0.14
0.11
0.47
2.48
3.72
0.42
TSB
vs.
Preop
3.19
0.51
3.87
0.41
0.45
4.50
0.53
0.33
0.16
0.49
0.44
0.37
0.46
0.61
4.62
2.79
1.00
11.60
0.28
0.60
0.63
4.67
4.01
0.16
0.17
0.44
2.08
3.40
0.34
TSB
vs.
WSB
0.80
0.74
0.83
0.75
0.73
0.79
0.93
0.80
0.68
0.75
0.69
0.67
0.71
0.81
0.83
0.80
1.01
0.90
1.05
0.74
0.80
0.69
0.85
1.09
1.55
0.92
0.84
0.92
0.76
Preop 5hr vs.
0 hr
0.45
0.59
0.63
0.54
0.63
0.88
0.64
1.25
0.12
0.52
0.71
0.70
0.65
0.68
0.53
0.73
1.06
0.44
0.40
0.68
0.81
0.44
1.04
0.43
0.51
0.73
0.40
1.16
0.63
Description
Glyceraldehyde-3-phosphate dehydrogenase OS=Homo sapiens GN=GAPDH PE=3 SV=1 - [E7EUT5_HUMAN]
Coagulation factor XI OS=Homo sapiens GN=F11 PE=3 SV=1 - [E9PGP2_HUMAN]
Cofilin-1 (Fragment) OS=Homo sapiens GN=CFL1 PE=4 SV=1 - [E9PLJ3_HUMAN]
Coagulation factor IX OS=Homo sapiens GN=F9 p22 PE=2 SV=1 - [F2RM37_HUMAN]
Complement component C8 beta chain OS=Homo sapiens GN=C8B PE=4 SV=1 - [F5GY80_HUMAN]
Polyubiquitin-C (Fragment) OS=Homo sapiens GN=UBC PE=4 SV=1 - [F5H6Q2_HUMAN]
Complement C1r subcomponent-like protein (Fragment) OS=Homo sapiens GN=C1RL PE=4 SV=1 - [F5H6S5_HUMAN]
Albumin (Fragment) OS=Homo sapiens PE=2 SV=1 - [F6KPG5_HUMAN]
T-complex protein 1 subunit beta OS=Homo sapiens GN=CCT2 PE=3 SV=1 - [F8VQ14_HUMAN]
Alpha-lactalbumin OS=Homo sapiens GN=LALBA PE=3 SV=1 - [F8VWU1_HUMAN]
Biotinidase OS=Homo sapiens GN=BTD PE=4 SV=1 - [F8W1Q3_HUMAN]
Complement factor I light chain OS=Homo sapiens GN=CFI PE=3 SV=1 - [G3XAM2_HUMAN]
Integral membrane protein GPR137B (Fragment) OS=Homo sapiens GN=GPR137B PE=4 SV=1 - [H0Y509_HUMAN]
CD44 antigen (Fragment) OS=Homo sapiens GN=CD44 PE=4 SV=1 - [H0YDX6_HUMAN]
Tropomyosin alpha-1 chain (Fragment) OS=Homo sapiens GN=TPM1 PE=4 SV=1 - [H0YL80_HUMAN]
Fructose-bisphosphate aldolase A (Fragment) OS=Homo sapiens GN=ALDOA PE=4 SV=1 - [H3BR68_HUMAN]
Keratin 1 OS=Homo sapiens GN=KRT1 PE=3 SV=1 - [H6VRF8_HUMAN]
Hemoglobin alpha 1 OS=Homo sapiens GN=HBA1 PE=3 SV=1 - [I1VZV6_HUMAN]
Apolipoprotein M (Fragment) OS=Homo sapiens GN=APOM PE=4 SV=1 - [I2D5I8_HUMAN]
Sex hormone-binding globulin OS=Homo sapiens GN=SHBG PE=4 SV=1 - [I3L1J1_HUMAN]
Beta-Ala-His dipeptidase OS=Homo sapiens GN=CNDP1 PE=4 SV=1 - [J3KRP0_HUMAN]
Profilin 1, isoform CRA_b OS=Homo sapiens GN=PFN1 PE=4 SV=1 - [K7EJ44_HUMAN]
Protein DJ-1 OS=Homo sapiens GN=PARK7 PE=4 SV=1 - [K7ELW0_HUMAN]
Apolipoprotein C-IV OS=Homo sapiens GN=APOC4 PE=4 SV=1 - [K7ER74_HUMAN]
Truncated apolipoprotein C-I (Fragment) OS=Homo sapiens GN=APOC1 PE=4 SV=1 - [K7ERI9_HUMAN]
CD5 antigen-like OS=Homo sapiens GN=CD5L PE=1 SV=1 - [CD5L_HUMAN]
DNA/RNA-binding protein KIN17 OS=Homo sapiens GN=KIN PE=1 SV=2 - [KIN17_HUMAN]
Superoxide dismutase [Cu-Zn] OS=Homo sapiens GN=SOD1 PE=1 SV=2 - [SODC_HUMAN]
Prothrombin OS=Homo sapiens GN=F2 PE=1 SV=2 - [THRB_HUMAN]
164
Chapter - VII
Accession
MW
[kDa]
P00738
P00742
P00747
P00915
P00918
P01008
P01009
P01011
P01023
P01024
P01031
P01034
P01834
P01860
P01871
P01876
P02042
P02452
P02647
P02649
P02652
P02656
P02671
P02675
P02679
P02741
P02743
P02745
P02747
P02748
P02760
P02763
45.18
54.70
90.51
28.85
29.23
52.57
46.71
47.62
163.19
187.03
188.19
15.79
11.60
41.26
49.28
37.63
16.05
138.86
30.76
36.13
11.17
10.85
94.91
55.89
51.48
25.02
25.37
26.00
25.76
63.13
38.97
23.50
Appendices
Postop
vs.
Preop
0.67
0.66
0.62
0.24
0.27
0.65
6.11
2.01
0.12
0.97
0.78
0.60
0.18
0.20
0.20
0.11
0.24
0.50
0.15
0.77
0.23
0.37
0.47
0.41
0.36
23.61
0.88
0.71
0.59
2.10
0.78
4.51
WSB
vs.
Preop
0.30
0.50
0.36
4.03
3.82
0.43
0.36
0.67
0.23
0.76
0.62
0.93
0.44
0.34
0.33
0.20
26.91
7.37
0.17
0.47
0.15
0.18
5.03
4.56
5.01
0.81
0.65
0.57
0.52
0.74
0.60
0.22
TSB
vs.
Preop
0.30
0.34
0.27
3.17
3.37
0.30
0.25
0.47
0.24
0.66
0.45
0.61
0.40
0.33
0.32
0.22
22.82
4.00
0.23
0.42
0.27
0.16
3.91
3.70
3.75
0.28
0.50
0.41
0.39
0.65
0.46
0.13
TSB
vs.
WSB
0.98
0.69
0.69
0.78
0.86
0.73
0.60
0.70
1.00
0.84
0.71
0.65
0.91
0.99
0.99
1.15
0.84
0.56
1.35
0.90
1.95
0.91
0.75
0.84
0.77
0.32
0.77
0.72
0.74
0.87
0.76
0.61
Preop 5hr vs.
0 hr
0.04
0.61
0.72
1.10
1.14
0.79
0.22
0.72
0.06
0.47
0.68
0.32
0.03
0.15
0.10
0.01
0.27
0.51
0.09
0.61
0.20
0.36
0.13
0.22
0.13
0.63
0.61
0.55
0.54
0.69
0.69
0.11
Description
Haptoglobin OS=Homo sapiens GN=HP PE=1 SV=1 - [HPT_HUMAN]
Coagulation factor X OS=Homo sapiens GN=F10 PE=1 SV=2 - [FA10_HUMAN]
Plasminogen OS=Homo sapiens GN=PLG PE=1 SV=2 - [PLMN_HUMAN]
Carbonic anhydrase 1 OS=Homo sapiens GN=CA1 PE=1 SV=2 - [CAH1_HUMAN]
Carbonic anhydrase 2 OS=Homo sapiens GN=CA2 PE=1 SV=2 - [CAH2_HUMAN]
Antithrombin-III OS=Homo sapiens GN=SERPINC1 PE=1 SV=1 - [ANT3_HUMAN]
Alpha-1-antitrypsin OS=Homo sapiens GN=SERPINA1 PE=1 SV=3 - [A1AT_HUMAN]
Alpha-1-antichymotrypsin OS=Homo sapiens GN=SERPINA3 PE=1 SV=2 - [AACT_HUMAN]
Alpha-2-macroglobulin OS=Homo sapiens GN=A2M PE=1 SV=3 - [A2MG_HUMAN]
Complement C3 OS=Homo sapiens GN=C3 PE=1 SV=2 - [CO3_HUMAN]
Cystatin-C OS=Homo sapiens GN=CST3 PE=1 SV=1 - [CYTC_HUMAN]
Ig kappa chain C region OS=Homo sapiens GN=IGKC PE=1 SV=1 - [IGKC_HUMAN]
Ig gamma-3 chain C region OS=Homo sapiens GN=IGHG3 PE=1 SV=2 - [IGHG3_HUMAN]
Ig mu chain C region OS=Homo sapiens GN=IGHM PE=1 SV=3 - [IGHM_HUMAN]
Ig alpha-1 chain C region OS=Homo sapiens GN=IGHA1 PE=1 SV=2 - [IGHA1_HUMAN]
Hemoglobin subunit delta OS=Homo sapiens GN=HBD PE=1 SV=2 - [HBD_HUMAN]
Collagen alpha-1(I) chain OS=Homo sapiens GN=COL1A1 PE=1 SV=5 - [CO1A1_HUMAN]
Apolipoprotein A-I OS=Homo sapiens GN=APOA1 PE=1 SV=1 - [APOA1_HUMAN]
Apolipoprotein E OS=Homo sapiens GN=APOE PE=1 SV=1 - [APOE_HUMAN]
Apolipoprotein A-II OS=Homo sapiens GN=APOA2 PE=1 SV=1 - [APOA2_HUMAN]
Apolipoprotein C-III OS=Homo sapiens GN=APOC3 PE=1 SV=1 - [APOC3_HUMAN]
Fibrinogen alpha chain OS=Homo sapiens GN=FGA PE=1 SV=2 - [FIBA_HUMAN]
Fibrinogen beta chain OS=Homo sapiens GN=FGB PE=1 SV=2 - [FIBB_HUMAN]
Fibrinogen gamma chain OS=Homo sapiens GN=FGG PE=1 SV=3 - [FIBG_HUMAN]
C-reactive protein OS=Homo sapiens GN=CRP PE=1 SV=1 - [CRP_HUMAN]
Serum amyloid P-component OS=Homo sapiens GN=APCS PE=1 SV=2 - [SAMP_HUMAN]
Complement C1q subcomponent subunit A OS=Homo sapiens GN=C1QA PE=1 SV=2 - [C1QA_HUMAN]
Complement C1q subcomponent subunit C OS=Homo sapiens GN=C1QC PE=1 SV=3 - [C1QC_HUMAN]
Complement component C9 OS=Homo sapiens GN=C9 PE=1 SV=2 - [CO9_HUMAN]
Protein AMBP OS=Homo sapiens GN=AMBP PE=1 SV=1 - [AMBP_HUMAN]
Alpha-1-acid glycoprotein 1 OS=Homo sapiens GN=ORM1 PE=1 SV=1 - [A1AG1_HUMAN]
165
Chapter - VII
Accession
MW
[kDa]
P02765
P02774
P02776
P02790
P03952
P04004
P04040
P04196
P04217
P04220
P05109
P05160
P05452
P05543
P05546
P06276
P06396
P06681
P06703
P06727
P07195
P07355
P07357
P07360
P07477
P07738
P07996
P08123
P08519
P08603
P08670
39.30
52.93
10.84
51.64
71.32
54.27
59.72
59.54
54.22
43.03
10.83
75.46
22.52
46.29
57.03
68.37
85.64
83.21
10.17
45.37
36.62
38.58
65.12
22.26
26.54
29.99
129.30
129.24
501.00
139.00
53.62
Appendices
Postop
vs.
Preop
0.47
0.74
0.55
0.71
0.41
0.81
0.46
0.40
0.72
0.15
1.06
0.51
0.52
0.56
0.48
0.43
0.57
0.80
0.44
0.48
0.53
0.60
1.00
0.96
0.70
0.55
0.65
0.77
0.83
0.67
0.56
WSB
vs.
Preop
0.57
0.58
4.27
0.62
0.49
0.49
2.84
0.46
0.61
0.30
1.08
0.56
0.66
0.65
0.58
0.65
0.69
0.64
3.85
0.61
2.02
7.27
0.63
0.68
0.93
2.55
7.73
5.24
0.60
0.49
17.31
TSB
vs.
Preop
0.43
0.43
3.08
0.43
0.37
0.40
2.46
0.41
0.45
0.32
1.63
0.39
0.52
0.43
0.42
0.44
0.56
0.49
2.96
0.47
1.55
4.38
0.45
0.59
0.78
2.41
6.12
3.08
0.43
0.34
9.23
TSB
vs.
WSB
0.73
0.70
0.72
0.70
0.76
0.76
0.80
0.84
0.73
1.05
1.50
0.65
0.78
0.66
0.74
0.68
0.80
0.73
0.77
0.76
0.79
0.61
0.73
0.84
0.84
0.93
0.86
0.59
0.68
0.70
0.54
Preop 5hr vs.
0 hr
0.52
0.74
0.61
0.83
0.52
0.53
0.98
0.47
0.78
0.06
0.72
0.53
0.60
0.77
0.53
0.80
0.59
0.75
0.74
0.49
0.46
0.73
0.69
0.68
0.66
0.83
0.79
0.74
0.57
0.63
0.72
Description
Alpha-2-HS-glycoprotein OS=Homo sapiens GN=AHSG PE=1 SV=1 - [FETUA_HUMAN]
Vitamin D-binding protein OS=Homo sapiens GN=GC PE=1 SV=1 - [VTDB_HUMAN]
Platelet factor 4 OS=Homo sapiens GN=PF4 PE=1 SV=2 - [PLF4_HUMAN]
Hemopexin OS=Homo sapiens GN=HPX PE=1 SV=2 - [HEMO_HUMAN]
Plasma kallikrein OS=Homo sapiens GN=KLKB1 PE=1 SV=1 - [KLKB1_HUMAN]
Vitronectin OS=Homo sapiens GN=VTN PE=1 SV=1 - [VTNC_HUMAN]
Catalase OS=Homo sapiens GN=CAT PE=1 SV=3 - [CATA_HUMAN]
Histidine-rich glycoprotein OS=Homo sapiens GN=HRG PE=1 SV=1 - [HRG_HUMAN]
Alpha-1B-glycoprotein OS=Homo sapiens GN=A1BG PE=1 SV=4 - [A1BG_HUMAN]
Ig mu heavy chain disease protein OS=Homo sapiens PE=1 SV=1 - [MUCB_HUMAN]
Protein S100-A8 OS=Homo sapiens GN=S100A8 PE=1 SV=1 - [S10A8_HUMAN]
Coagulation factor XIII B chain OS=Homo sapiens GN=F13B PE=1 SV=3 - [F13B_HUMAN]
Tetranectin OS=Homo sapiens GN=CLEC3B PE=1 SV=3 - [TETN_HUMAN]
Thyroxine-binding globulin OS=Homo sapiens GN=SERPINA7 PE=1 SV=2 - [THBG_HUMAN]
Heparin cofactor 2 OS=Homo sapiens GN=SERPIND1 PE=1 SV=3 - [HEP2_HUMAN]
Cholinesterase OS=Homo sapiens GN=BCHE PE=1 SV=1 - [CHLE_HUMAN]
Gelsolin OS=Homo sapiens GN=GSN PE=1 SV=1 - [GELS_HUMAN]
Apolipoprotein A-IV OS=Homo sapiens GN=APOA4 PE=1 SV=3 - [APOA4_HUMAN]
L-lactate dehydrogenase B chain OS=Homo sapiens GN=LDHB PE=1 SV=2 - [LDHB_HUMAN]
Annexin A2 OS=Homo sapiens GN=ANXA2 PE=1 SV=2 - [ANXA2_HUMAN]
Complement component C8 alpha chain OS=Homo sapiens GN=C8A PE=1 SV=2 - [CO8A_HUMAN]
Complement component C8 gamma chain OS=Homo sapiens GN=C8G PE=1 SV=3 - [CO8G_HUMAN]
Trypsin-1 OS=Homo sapiens GN=PRSS1 PE=1 SV=1 - [TRY1_HUMAN]
Bisphosphoglycerate mutase OS=Homo sapiens GN=BPGM PE=1 SV=2 - [PMGE_HUMAN]
Thrombospondin-1 OS=Homo sapiens GN=THBS1 PE=1 SV=2 - [TSP1_HUMAN]
Collagen alpha-2(I) chain OS=Homo sapiens GN=COL1A2 PE=1 SV=7 - [CO1A2_HUMAN]
Apolipoprotein(a) OS=Homo sapiens GN=LPA PE=1 SV=1 - [APOA_HUMAN]
Complement factor H OS=Homo sapiens GN=CFH PE=1 SV=4 - [CFAH_HUMAN]
Vimentin OS=Homo sapiens GN=VIM PE=1 SV=4 - [VIME_HUMAN]
166
Chapter - VII
Accession
MW
[kDa]
P08697
P09172
P09871
P0CG05
P10909
P11021
P12259
P13645
P13671
P15090
P15169
P19652
P22352
P22792
P22891
P23142
P25311
P26447
P26927
P27169
P27918
P30043
P35527
P36955
P36980
P43652
P48740
P51884
P54108
54.53
69.02
76.63
11.29
52.46
72.29
251.55
58.79
104.72
14.71
52.25
23.59
25.54
60.52
44.71
77.16
34.24
11.72
80.27
39.71
51.24
22.11
62.03
46.28
30.63
69.02
79.20
38.40
27.61
Appendices
Postop
vs.
Preop
0.71
0.60
1.04
0.12
0.43
0.59
0.66
1.15
1.18
0.35
0.79
3.94
0.81
0.76
0.67
0.96
0.78
0.32
0.80
0.23
0.56
0.38
0.90
0.76
0.79
0.51
0.77
0.61
0.56
WSB
vs.
Preop
0.25
0.65
0.63
0.26
0.64
1.23
1.03
0.69
0.51
6.66
0.64
0.23
0.77
0.60
0.58
0.81
0.58
6.33
0.57
0.57
0.51
3.58
1.59
0.65
0.57
0.53
0.57
0.69
0.56
TSB
vs.
Preop
0.20
0.48
0.47
0.27
0.52
0.94
0.84
0.63
0.42
3.69
0.44
0.16
0.60
0.44
0.40
0.66
0.46
4.48
0.44
0.74
0.40
3.05
1.44
0.47
0.39
0.38
0.42
0.52
0.40
TSB
vs.
WSB
0.75
0.76
0.75
1.07
0.80
0.76
0.86
0.94
0.72
0.55
0.70
0.73
0.78
0.71
0.71
0.81
0.77
0.71
0.76
1.22
0.76
0.88
0.95
0.74
0.70
0.69
0.72
0.77
0.70
Preop 5hr vs.
0 hr
0.49
0.84
0.63
0.01
0.27
0.68
0.38
0.71
0.67
0.33
0.71
0.13
0.63
0.67
0.55
0.58
0.77
0.81
0.67
0.16
0.71
0.81
1.30
0.75
0.62
0.77
0.68
0.70
0.75
Description
Alpha-2-antiplasmin OS=Homo sapiens GN=SERPINF2 PE=1 SV=3 - [A2AP_HUMAN]
Dopamine beta-hydroxylase OS=Homo sapiens GN=DBH PE=1 SV=3 - [DOPO_HUMAN]
Complement C1s subcomponent OS=Homo sapiens GN=C1S PE=1 SV=1 - [C1S_HUMAN]
Ig lambda-2 chain C regions OS=Homo sapiens GN=IGLC2 PE=1 SV=1 - [LAC2_HUMAN]
Clusterin OS=Homo sapiens GN=CLU PE=1 SV=1 - [CLUS_HUMAN]
78 kDa glucose-regulated protein OS=Homo sapiens GN=HSPA5 PE=1 SV=2 - [GRP78_HUMAN]
Coagulation factor V OS=Homo sapiens GN=F5 PE=1 SV=4 - [FA5_HUMAN]
Keratin, type I cytoskeletal 10 OS=Homo sapiens GN=KRT10 PE=1 SV=6 - [K1C10_HUMAN]
Complement component C6 OS=Homo sapiens GN=C6 PE=1 SV=3 - [CO6_HUMAN]
Fatty acid-binding protein, adipocyte OS=Homo sapiens GN=FABP4 PE=1 SV=3 - [FABP4_HUMAN]
Carboxypeptidase N catalytic chain OS=Homo sapiens GN=CPN1 PE=1 SV=1 - [CBPN_HUMAN]
Alpha-1-acid glycoprotein 2 OS=Homo sapiens GN=ORM2 PE=1 SV=2 - [A1AG2_HUMAN]
Glutathione peroxidase 3 OS=Homo sapiens GN=GPX3 PE=1 SV=2 - [GPX3_HUMAN]
Carboxypeptidase N subunit 2 OS=Homo sapiens GN=CPN2 PE=1 SV=3 - [CPN2_HUMAN]
Vitamin K-dependent protein Z OS=Homo sapiens GN=PROZ PE=1 SV=2 - [PROZ_HUMAN]
Fibulin-1 OS=Homo sapiens GN=FBLN1 PE=1 SV=4 - [FBLN1_HUMAN]
Zinc-alpha-2-glycoprotein OS=Homo sapiens GN=AZGP1 PE=1 SV=2 - [ZA2G_HUMAN]
Hepatocyte growth factor-like protein OS=Homo sapiens GN=MST1 PE=1 SV=2 - [HGFL_HUMAN]
Serum paraoxonase/arylesterase 1 OS=Homo sapiens GN=PON1 PE=1 SV=3 - [PON1_HUMAN]
Properdin OS=Homo sapiens GN=CFP PE=1 SV=2 - [PROP_HUMAN]
Flavin reductase (NADPH) OS=Homo sapiens GN=BLVRB PE=1 SV=3 - [BLVRB_HUMAN]
Keratin, type I cytoskeletal 9 OS=Homo sapiens GN=KRT9 PE=1 SV=3 - [K1C9_HUMAN]
Pigment epithelium-derived factor OS=Homo sapiens GN=SERPINF1 PE=1 SV=4 - [PEDF_HUMAN]
Complement factor H-related protein 2 OS=Homo sapiens GN=CFHR2 PE=1 SV=1 - [FHR2_HUMAN]
Afamin OS=Homo sapiens GN=AFM PE=1 SV=1 - [AFAM_HUMAN]
Mannan-binding lectin serine protease 1 OS=Homo sapiens GN=MASP1 PE=1 SV=3 - [MASP1_HUMAN]
Lumican OS=Homo sapiens GN=LUM PE=1 SV=2 - [LUM_HUMAN]
Cysteine-rich secretory protein 3 OS=Homo sapiens GN=CRISP3 PE=1 SV=1 - [CRIS3_HUMAN]
167
Chapter - VII
Appendices
14.54
5.05
11.36
27.73
15.99
92.28
11.28
Postop
vs.
Preop
0.66
0.34
0.57
0.48
0.55
0.43
1.19
WSB
vs.
Preop
0.11
5.59
6.03
6.53
10.18
0.66
2.63
TSB
vs.
Preop
0.09
4.10
3.92
4.03
8.82
0.42
0.87
TSB
vs.
WSB
0.82
0.72
0.65
0.63
0.85
0.65
0.33
Preop 5hr vs.
0 hr
0.49
0.68
0.69
0.69
0.53
0.53
9.19
P98160
468.53
0.93
1.13
1.25
1.11
0.93
Q03591
Q04756
Q06033
Q14476
Q14520
Q15848
Q16519
Q16610
Q1L857
Q53H26
Q59GX5
Q59HB3
Q5I6Y5
Q5JU74
Q5QTQ6
Q5SW02
Q5T123
Q5T619
Q5T985
37.63
70.64
99.79
11.03
62.63
26.40
72.42
60.64
115.40
77.03
55.68
183.46
55.01
11.36
24.64
120.54
9.37
62.30
105.15
0.41
0.55
1.51
0.77
0.59
0.72
0.46
0.57
0.77
0.09
0.81
0.53
0.73
0.72
0.72
0.56
0.50
0.37
0.53
0.50
0.62
0.70
3.42
0.62
0.72
0.50
0.53
0.61
0.15
1.16
0.50
8.48
0.64
0.64
0.65
4.85
6.83
0.65
0.30
0.49
0.59
3.01
0.42
0.57
0.44
0.39
0.43
0.14
0.86
0.38
5.38
0.47
0.52
0.60
4.11
6.76
0.51
0.60
0.82
0.77
0.88
0.69
0.80
0.83
0.71
0.70
0.93
0.74
0.75
0.61
0.75
0.80
0.92
0.85
0.99
0.79
0.44
0.68
0.68
0.46
0.28
0.67
0.35
0.63
0.78
0.04
0.68
0.49
0.69
0.71
0.72
0.64
0.75
0.47
0.66
Q5UGI6
37.26
0.97
0.53
0.39
0.76
0.64
Q5VY30
22.93
0.48
0.61
0.48
0.76
0.59
Accession
MW
[kDa]
P55056
P62328
P62805
P63104
P68871
P80108
P81605
Description
Apolipoprotein C-IV OS=Homo sapiens GN=APOC4 PE=1 SV=1 - [APOC4_HUMAN]
Thymosin beta-4 OS=Homo sapiens GN=TMSB4X PE=1 SV=2 - [TYB4_HUMAN]
Histone H4 OS=Homo sapiens GN=HIST1H4A PE=1 SV=2 - [H4_HUMAN]
14-3-3 protein zeta/delta OS=Homo sapiens GN=YWHAZ PE=1 SV=1 - [1433Z_HUMAN]
Hemoglobin subunit beta OS=Homo sapiens GN=HBB PE=1 SV=2 - [HBB_HUMAN]
Phosphatidylinositol-glycan-specific phospholipase D OS=Homo sapiens GN=GPLD1 PE=1 SV=3 - [PHLD_HUMAN]
Dermcidin OS=Homo sapiens GN=DCD PE=1 SV=2 - [DCD_HUMAN]
Basement membrane-specific heparan sulfate proteoglycan core protein OS=Homo sapiens GN=HSPG2 PE=1 SV=4 [PGBM_HUMAN]
Complement factor H-related protein 1 OS=Homo sapiens GN=CFHR1 PE=1 SV=2 - [FHR1_HUMAN]
Hepatocyte growth factor activator OS=Homo sapiens GN=HGFAC PE=1 SV=1 - [HGFA_HUMAN]
Inter-alpha-trypsin inhibitor heavy chain H3 OS=Homo sapiens GN=ITIH3 PE=1 SV=2 - [ITIH3_HUMAN]
G-gamma-hemoglobin gene from Greek HPFH mutant, . (Fragment) OS=Homo sapiens PE=2 SV=1 - [Q14476_HUMAN]
Hyaluronan-binding protein 2 OS=Homo sapiens GN=HABP2 PE=1 SV=1 - [HABP2_HUMAN]
Adiponectin OS=Homo sapiens GN=ADIPOQ PE=1 SV=1 - [ADIPO_HUMAN]
Protein S (Fragment) OS=Homo sapiens GN=PROS1 PE=2 SV=1 - [Q16519_HUMAN]
Extracellular matrix protein 1 OS=Homo sapiens GN=ECM1 PE=1 SV=2 - [ECM1_HUMAN]
Ceruloplasmin (Fragment) OS=Homo sapiens PE=2 SV=1 - [Q1L857_HUMAN]
Transferrin variant (Fragment) OS=Homo sapiens PE=2 SV=1 - [Q53H26_HUMAN]
L-plastin variant (Fragment) OS=Homo sapiens PE=2 SV=1 - [Q59GX5_HUMAN]
Apolipoprotein B variant (Fragment) OS=Homo sapiens PE=2 SV=1 - [Q59HB3_HUMAN]
Lamin A/C transcript variant 1 OS=Homo sapiens GN=LMNA PE=2 SV=1 - [Q5I6Y5_HUMAN]
Putative uncharacterized protein DKFZp761J1810 OS=Homo sapiens GN=DKFZp761J1810 PE=4 SV=1 - [Q5JU74_HUMAN]
MSTP010 OS=Homo sapiens PE=2 SV=1 - [Q5QTQ6_HUMAN]
Zinc finger MYM-type protein 1 OS=Homo sapiens GN=ZMYM1 PE=2 SV=1 - [Q5SW02_HUMAN]
SH3 domain binding glutamic acid-rich protein like 3 OS=Homo sapiens GN=SH3BGRL3 PE=2 SV=1 - [Q5T123_HUMAN]
Zinc finger protein 648 OS=Homo sapiens GN=ZNF648 PE=1 SV=1 - [ZN648_HUMAN]
Inter-alpha (Globulin) inhibitor H2 OS=Homo sapiens GN=ITIH2 PE=2 SV=1 - [Q5T985_HUMAN]
Serine/cysteine proteinase inhibitor clade G member 1 splice variant 2 (Fragment) OS=Homo sapiens GN=SERPING1 PE=2
SV=1 - [Q5UGI6_HUMAN]
Plasma retinol-binding protein(1-182) OS=Homo sapiens GN=RBP4 PE=2 SV=1 - [Q5VY30_HUMAN]
168
Chapter - VII
Appendices
83.95
71.67
24.41
35.86
7.16
33.07
Postop
vs.
Preop
0.56
0.65
0.60
0.77
0.60
0.41
WSB
vs.
Preop
0.54
0.87
0.63
0.48
0.62
5.23
TSB
vs.
Preop
0.37
0.68
0.46
0.30
0.39
4.58
TSB
vs.
WSB
0.67
0.79
0.70
0.61
0.63
0.88
Preop 5hr vs.
0 hr
0.62
0.70
0.63
0.64
0.61
0.11
Q6UXM4
31.66
0.76
0.68
0.54
0.80
1.02
Q71V99
Q7Z7Q0
Q8IZZ5
Q8TCF0
Q8TD12
Q8WY91
Q92954
Q96IY4
Q96L50
Q9BRL5
Q9H2L9
Q9H552
Q9H8A8
Q9NZP8
Q9UGM5
Q9Y5Y7
Q9Y6R7
17.96
92.28
67.69
52.90
29.35
62.85
150.98
48.39
46.69
16.50
38.02
55.12
25.94
53.46
42.03
35.19
571.64
0.44
0.68
0.54
2.03
0.57
0.45
0.74
0.64
0.55
0.56
0.54
0.64
0.39
0.93
0.48
0.62
0.84
4.37
0.50
0.56
0.77
0.72
0.37
0.73
0.52
0.62
3.79
0.68
0.56
4.24
0.60
0.68
0.66
0.70
2.85
0.30
0.39
0.63
0.44
0.20
0.53
0.37
0.37
2.78
0.53
0.43
3.25
0.48
0.51
0.57
0.51
0.65
0.59
0.74
0.81
0.64
0.52
0.73
0.72
0.60
0.72
0.78
0.76
0.80
0.79
0.79
0.87
0.75
0.66
0.63
0.58
0.49
0.83
0.49
0.67
0.64
0.64
0.75
0.59
0.66
0.89
0.63
0.62
0.61
0.69
Accession
MW
[kDa]
Q68BL8
Q6EMK4
Q6FHW3
Q6LAM1
Q6LDG4
Q6NXN2
Description
Olfactomedin-like protein 2B OS=Homo sapiens GN=OLFML2B PE=2 SV=2 - [OLM2B_HUMAN]
Vasorin OS=Homo sapiens GN=VASN PE=1 SV=1 - [VASN_HUMAN]
DF protein OS=Homo sapiens GN=DF PE=2 SV=1 - [Q6FHW3_HUMAN]
Heavy chain of factor I (Fragment) OS=Homo sapiens PE=2 SV=1 - [Q6LAM1_HUMAN]
Complement protein (Fragment) OS=Homo sapiens GN=C2 PE=4 SV=1 - [Q6LDG4_HUMAN]
Hypothetical LOC441242 OS=Homo sapiens GN=LOC441242 PE=2 SV=1 - [Q6NXN2_HUMAN]
Ficolin (Collagen/fibrinogen domain containing) 3 (Hakata antigen) OS=Homo sapiens GN=FCN3 PE=2 SV=1 [Q6UXM4_HUMAN]
Peptidyl-prolyl cis-trans isomerase OS=Homo sapiens PE=2 SV=1 - [Q71V99_HUMAN]
APOB protein OS=Homo sapiens GN=APOB PE=2 SV=1 - [Q7Z7Q0_HUMAN]
Coagulation factor XII-Mie OS=Homo sapiens PE=2 SV=1 - [Q8IZZ5_HUMAN]
LBP protein OS=Homo sapiens GN=LBP PE=2 SV=1 - [Q8TCF0_HUMAN]
Decay-accelerating factor 3 (Fragment) OS=Homo sapiens PE=2 SV=1 - [Q8TD12_HUMAN]
THAP domain-containing protein 4 OS=Homo sapiens GN=THAP4 PE=1 SV=2 - [THAP4_HUMAN]
Proteoglycan 4 OS=Homo sapiens GN=PRG4 PE=1 SV=2 - [PRG4_HUMAN]
Carboxypeptidase B2 OS=Homo sapiens GN=CPB2 PE=1 SV=2 - [CBPB2_HUMAN]
Leucine-rich repeat protein 1 OS=Homo sapiens GN=LRR1 PE=1 SV=2 - [LLR1_HUMAN]
CALM3 protein OS=Homo sapiens PE=2 SV=1 - [Q9BRL5_HUMAN]
AD034 OS=Homo sapiens PE=2 SV=1 - [Q9H2L9_HUMAN]
Keratin 8 pseudogene 11 OS=Homo sapiens GN=KRT8P11 PE=2 SV=1 - [Q9H552_HUMAN]
Selenium binding protein 1, isoform CRA_b OS=Homo sapiens GN=SELENBP1 PE=2 SV=1 - [Q9H8A8_HUMAN]
Complement C1r subcomponent-like protein OS=Homo sapiens GN=C1RL PE=1 SV=2 - [C1RL_HUMAN]
Fetuin-B OS=Homo sapiens GN=FETUB PE=1 SV=2 - [FETUB_HUMAN]
Lymphatic vessel endothelial hyaluronic acid receptor 1 OS=Homo sapiens GN=LYVE1 PE=1 SV=2 - [LYVE1_HUMAN]
IgGFc-binding protein OS=Homo sapiens GN=FCGBP PE=1 SV=3 - [FCGBP_HUMAN]
169
Chapter - VII
Appendices
APPENDIX – X : PATIENTS’ CONSENT FORM
Study Number: 1986
Patient Identification Number for this trial:
CONSENT FORM
Investigations into Immunostimulatory Properties of Anticoagulated Salvaged
Blood Retrieved from Patients Undergoing Total Knee Arthroplasty
Researcher: Prof Benjamin Bradley
Please initial box
1. I confirm that I have read and understand the information sheet
dated 2/03/2010 (version 1.1) for the above study. I have had the
opportunity to consider the information, ask questions and have had
these answered satisfactorily.
2. I understand that my participation is voluntary and that I am free to
withdraw at any time, without giving any reason, without my medical
care or legal rights being affected.
3. I understand that relevant sections of any of my medical notes and
data collected during the study may be looked at by responsible
individuals from regulatory authorities or from North Bristol NHS Trust,
where it is relevant to my taking part in this research. I give permission
for these individuals to have access to my records.
4. I agree to take part in the above study.
____________________
Name of Patient
Signature
_____________________
Name of Person taking consent
Signature
(If different from researcher)
_____________________
Researcher
__________
_______________
Date
__________
_______________
Date
__________
_______________
Date
Signature
When completed, 1 for patient; 1 for researcher site file; 1 (original) to be
kept in medical notes
Version 1.0
7/10/2009
170
Chapter - VII
Appendices
Patient Information Sheet
Investigations into Immune Stimulating Effects of the Patients own
Salvaged Blood during Total Knee Replacement
You are being invited to take part in a research study. Before you
decide it is important for you to understand why the research is being done
and what it will involve. Please take time to read the following information
carefully. Talk to others about the study if you wish.
 Part 1 tells you the purpose of this study and what will happen to you if
you take part.
 Part 2 gives you more detailed information about the conduct of the study.
Ask us if there is anything that is not clear or if you would like more
information. Take time to decide whether or not you wish to take part.
Part 1
What is the purpose of the study?
Total knee replacement has been shown to be associated with
immunosuppression postoperatively (reduced activity of the immune system
whereby your body is less able to fight infection). Hospital infections are
increasingly due to bacteria that are not susceptible to commonly used
antibiotics. After your knee replacement, it is generally routine practice for
you to be given your own blood back using a specially designed blood
salvage drainage system rather than blood from a blood bank. We have
established that one particular type of blood salvage system (the system that
collects your own blood after the operation and allows us to give this back to
you) is associated with improved immunity to levels better than that before
the operation. We hope to identify what it is in the salvaged blood that is
causing this effect and to see if we can reproduce this effect.
Why have I been chosen?
You are due to undergo a total knee replacement under the care of one of
the surgeons participating in the study. You are also an appropriate patient
for the study and for the use of the drainage system in question due to your
medical history and type of operation.
Do I have to take part?
No. It is up to you to decide whether or not to take part. If you do, you will be
given this information sheet to keep and be asked to sign a consent form.
You are still free to withdraw at any time and without giving a reason. A
decision to withdraw at any time, or a decision not to take part, will not affect
the standard of care you receive.
Version 1.1
02/03/2010
171
Chapter - VII
Appendices
What will happen to me if I take part?
You will have a blood sample taken prior to your operation, which will be
used to test your immunity preoperatively. At the end of your operation we
will use a blood salvage system to collect any blood draining from the site of
the operation. This blood salvage system is used routinely after your
operation as part of your normal care. On two occasions during the collection
period, a small sample of the blood in the system will be collected. On one
occasion after any retransfusion has taken place, prior to you leaving
hospital, we will collect another blood sample from you.
Other than the samples described above, your operation and follow up will
proceed exactly as it would have done if you had not taken part in the study.
What do I have to do?
Other than reading this form and providing consent if you do wish to take
part, the only thing that will happen to you during the course of the study is
the collection of the extra blood described above. You will not be required to
do anything else that would not normally be part of the operation.
What is the product that is being tested?
The drainage system that is being used in this study is widely used across
Europe and has been used successfully in the Avon Orthopaedic Centre for a
number of years.
What are the side effects of any treatment received when taking part?
There are no additional side effects due to your participation in the study.
What are the other possible disadvantages and risks of taking part?
No other risks or disadvantages should result from your participation.
What are the possible benefits of taking part?
We cannot promise the study will help you but the information we get might
help improve the treatment of people having a joint replacement in the future.
What happens when the research study stops?
The follow up after the samples has been analysed will be exactly the same
as for other patients having a knee replacement.
172
Chapter - VII
Appendices
What if there is a problem?
Any complaint about the way you have been dealt with during the study or
any possible harm you might suffer will be addressed. The detailed
information on this is given in Part 2.
Version 1.1
Will my taking part in the study be
02/03/2010
kept confidential?
Yes. All the information about your participation in this study will be kept
confidential. The details are included in Part 2.
Contact details
Mr Michael Whitehouse
Clinical Research Fellow
Bristol Implant Research Centre
Lower Level Avon Orthopaedic Centre
Southmead Hospital
Westbury-on-Trym
Bristol
BS9 3EJ
Tel:
0117 9505905
This completes Part 1 of the Information Sheet.
If the information in Part 1 has interested you and you are considering
participation, please continue to read the additional information in Part
2 before making any decision.
173
Chapter - VII
Appendices
APPENDIX - XI : FUNDING BODIES
Award Name
Provider
Usage of Funding
1
EMBARK Award
(RS/2011/223)
Irish Research Council
Scholarship for my monthly
stipend, consumables and
travel expenses
2
Healthcare Innovation
Partnership Award (HIPA)
(SFI09/SRC/B1794)
Science Foundation Ireland
Consumables and travel
expenses
3
TSR Strand-1 Award
(TSR/2008/TL13)
Institute of Technology
Ireland
Scholarship for my monthly
stipend, consumables and
travel expenses
4
North Bristol Orthopaedic
Trust
NHS, United Kingdom
Consumables required in
Southmead Hospital
5
(SFI PI 06/1N.1/B652)
Consumables and
Publications
6
(SFI07/SK/B1233b)
Consumables
7
Glycoscience Research
Group
NUI Galway
Lectin array experiments
174
Chapter - VII
Appendices
APPENDIX - XII: PUBLICATIONS, PRESENTATIONS and
ACHIEVEMENTS
Peer Reviewed Publications:
1. Islam N, Whitehouse M, Mehandale S, Hall M, Tierney J, O’Connell E,
Blom A, Bannister G, Ceredig R, Bradley B. 2014. Post-traumatic
Immunosuppression is Reversed by Anticoagulated Salvaged Blood
Transfusion; Deductions from studies of Immune Status after Knee
Arthroplasty. Clinical and Experimental Immunology. (In Press)
Published Abstracts:
1. Islam N, Whitehouse M, Mehandale S, Hall M, Blom A, Bannister G,
Ceredig R, Bradley B. 2012. Surgery induced Immunosuppression Syndrome
and its reversion by salvaged blood transfusion. Transfusion Alternatives in
Transfusion Medicine. 2 (2)
Ceredig R, Bradley B. 2011. Neopterin Levels Confirm Immunostimulation
by Unwashed Salvaged Blood Transfusion. Transfusion Alternatives in
Transfusion Medicine. 2 (1): 28-29.
Manuscripts in Preparation:
Ceredig R, Bradley B. Does Sterile Trauma Suppress Autoimmunity via the
Siglec-10-CD24 Pathway?
Ceredig R, Bradley B. Post-traumatic elevations in anti-microbial proteins;
Deductions from studies of Immune Status after Knee Arthroplasty.
Oral Communications:
1. Using Flow Cytometry to investigate Post-Traumatic Immunosuppression.
Irish Cytometry Society Annual Meeting, February 25, 2014, Astra Hall,
UCD, Dublin.
2. Novel observations to characterize immunosuppression after major surgical
trauma. Postgraduate Research Day, School of Medicine, NUI Galway,
Ireland. May 27, 2013 (Best Oral)
3. Systemic and peripheral immunomodulation induced by surgical trauma:
novel observations in knee arthroplasty patients. Roche-NCBES Research
Award in Life Sciences and Bioengineering, Nov 19, 2012, Galway, Ireland
4. Establishment of human whole blood assays (HWBA) as a screening model
for immunomodulatory properties by natural immunomodulators. Shannon
ABC Open Innovation Conference, April 4-5, 2012, Limerick, Ireland
175
Chapter - VII
Appendices
5. Neopterin Levels Confirm Immunostimulation by Unwashed Salvaged Blood
Transfusion. 12th NATA Conference, April 7-8, 2011, Dublin, Ireland (Best
Poster)
6. Serum adiponectin and resistin in subjects with impaired Glucose Regulation.
13th Diabetes and Endocrine Conference, 1st-4th March, 2007, Dhaka,
Bangladesh
Poster Presentations:
1. Post-traumatic Immunosuppression and its Reversal. 15th International
Congress of Immunology, Aug 22-27, 2013, Milan, Italy
2. Flow cytometric analysis of changes in cytokine profile of stored human
blood samples. Irish Cytometry Society Annual Meeting, Nov 6-7, 2012,
Galway
3. Investigation of the immuno-stimulatory properties of re-transfused
autologous blood. 2nd Annual Meeting of Irish society of Immunology, Sep
20-21, 2012, Dublin.
4. Characterization of post-traumatic immunosuppression syndrome and it’s
response to salvaged blood transfusion. Annual Meeting of Irish society of
Immunology, Sep 1-2, 2011, Galway.
5. Surgery induced Immunosuppression Syndrome and its reversion by salvaged
blood transfusion. 13th Annual NATA Symposium April 7-8, 2011,
Copenhagen, Denmark.
6. Post-traumatic immunosuppression syndrome and it’s reversal by salvaged
blood transfusion. British Society of Immunology (BSI) Congress, Dec 5-8,
2011, Liverpool, UK.
7. Neopterin Levels Confirm Immunostimulation by Salvaged Blood
Transfusion. 12th Annual NATA Symposium, April 7-8, 2011, Dublin,
Ireland (Best Poster)
Awards and Achievements:
1. Irish Research Council Award (EMBARK) for doctoral research, 2011.
2. Best Oral Award in Postgraduate Research Day, 2013, College of Medicine,
Nursing and Health Sciences, NUI Galway
3. Best Poster Award in 12th Annual NATA Symposium, 2011
4. Obtained certificates in “Laboratory Animal Science and Technique” from
The Royal College of Veterinary Surgeons, Ireland; and “Animals (Scientific
Procedures) Act 1986” from Home Office, United Kingdom.
176
Chapter - VII
Appendices
APPENDIX-XIII : ADDITIONAL RESEARCH PROJECTS
1. Feasibility of Automated Whole Blood Screening to Evaluate Novel
Immunomodulators Extracted from Natural Bye-Produce.
I had carried out a Science Foundation Ireland Healthcare Innovation Programme
jointly with Johnson and Johnson Inc in parallel with my PhD research.
Summary: Immunity involves a network of different cells that collectively
protect the body from microbial infections and cancer. However, when disordered
through disease it can cause rheumatoid arthritis, diabetes, allergy, and psoriasis, for
which we need new drugs. Nature provides a huge diversity of unique molecules
from which many drugs have been discovered. Consequently hundreds of naturalproduct extracts previously tested at Shannon ABC and shown to influence
macrophages - cells which control immunity and are at the root of these disorders.
This project was undertaken to confirm the potential value of these extracts as new
drugs by testing them on human blood cells. The main aim of this project was to
develop a human whole blood assay (HWBA) to evaluate immuno-modulatory
ingredients extracted from natural biomass derived from plant, microbial or animal
by-produce obtained from industrial, clinical or natural sources.
I studied the effect of yeast cell wall extracts (different β-Glucan extracts such as:
particulate, hydrolysate, and different fractions such as >100kDa, >3kDa and <3kDa
on immunological changes in human whole blood culture. Additionally, I also
studied the salvaged blood plasma that is known to be efficacious in patients
undergoing joint replacement surgery; albeit without knowledge of which ingredients
were responsible.
In-vitro changes in the levels of 13 cytokines and chemokines in the HWBA (e.g.
were measured following treatment of diluted human whole blood with different βGlucan samples as well as the salvaged plasma and also the positive control LPS for
different incubation periods (0, 6, 10, and 24 hours). Comparing with LPS control, I
reported productions of different inflammatory Biomarkers when treated with
different β-Glucan extracts and the elevations were also time dependent.
I optimized HWBA for kinetics, sensitivity and outcome measures with a view to
developing a low-cost rapid assay suitable for automated screening using a Biomek
FX Workstation. Genetically determined individual variations in cytokine levels
(high, intermediate and low producers) influence cytokine profiles of blood donor;
however the trend of changes in this cytokine/chemokines following treatments
remained constant.
177
Chapter - VII
Appendices
2. Investigation of the immune-stimulatory effect of natural polysaccharides on
intracellular TNF-α expression by monocytes in a human whole blood
culture
This project aimed is to screen novel naturally derived sugar compounds that
may have potential as future therapeutic immunomodulatory drugs.
Summary Post-traumatic immunosuppression is a common consequence of many
kinds of trauma (surgery or injury) leading to patients becoming more susceptible to
acquired infections. Boosting patients’ immunity therefore would be beneficial in
order to prevent them from being immunosuppressed. However, there is still a huge
shortage of drugs used to enhance post-traumatic immunity. The role of naturallyderived compounds, have been investigated as potential clinical therapeutic agents.
However, the full potential and mode of action of naturally-derived polysaccharides
(PS) in modulating different disease and pathological conditions has not been well
studied. The aim was to study the immunomodulatory effects of a panel of natural
polysaccharides derived from natural sources on two cell based model systems.
Six structurally-defined polysaccharides derived from natural sources were selected
for this study. Their activities were tested in vitro using either THP-1 monocyte cell
line or human whole blood cultures. Levels of pro-inflammatory cytokines (IL-1β,
IL-6, and TNF-α), anti-inflammatory cytokines (IL-10 and IL-13), and chemokines
(IL-8, MCP-1, and MIP-1α) in the culture supernatants were measured by ELISA.
Following a six hour incubation of heparinized blood from healthy controls, flowcytometric analysis was also used to monitor intracellular TNF-α expression by gated
monocytes using Accuri-C6 cytometer.
In a whole blood culture system, TNF-α expression increased in monocytes
following six-hour incubation with four of the six polysaccharides studied. This
would suggest an immunostimulatory role for these four polysaccharides. The two
anti-inflammatory cytokines, IL-10 and IL-13, were both undetectable in all the
samples (not shown). Increased production of all six pro-inflammatory cytokines and
chemokines by the four polysaccharides studied following incubation indicating that
these polysaccharides could boost immunity.
Therefore, stimulation of TNF-α expression by monocytes, by naturally-derived
polysaccharides indicates the potential for these molecules to be used in a clinical
setting to prevent immunosuppression. The human whole blood culture is an useful
technique to measure the effect of a compound on immunological responses and can
be used in clinical diagnosis. Polysaccharides with the immune-enhancing value need
further molecular and functional investigations prior to clinical administrations.
Poster Presentation: Flowcytometric investigation of the immune-stimulatory effect
of polysaccharides on Intracellular TNF-α expression by monocytes in a human
whole blood culture. Irish Cytometry Society Annual Meeting, Astra Hall, UCD,
Dublin, Ireland. Feb. 25, 2014. Manuscript in preparation
178
Chapter - VII
Appendices
APPENDIX – XIV : LIST OF REAGENTS
Table 7.7: List of Reagents
Reagent
Supplier
Catalogue number
Bovine serum albumin
Sigma-Aldrich
A2153
Bradford reagent
Sigma-Aldrich
B6916
Bromophenol blue
Sigma-Aldrich
B8026
Calcium chloride
Sigma-Aldrich
C8106
Dimethyl sulfoxide
Sigma-Aldrich
D2650
Dulbecco’s PBS
Gibco-Invitrogen
14190
DuoSet ELISA systems
R&D Systems
Flowcytomix Bead Array Kit
e-Bioscience (Bender
Medsystems)
HMGB-1 ELISA (1)
IBL International (developed
by Shino Corporation)
HMGB-1 ELISA (2)
USCN
SEA399Hu
α-Defensin (1)
Hycult Biotech
HK317-02
α1-Defensin (2)
USCN
SEB705Hu
S100-A8/A9
Hycult Biotech
HK325-02
CD-24
USCN
SEB143Hu
Siglec-10
USCN
SED921Hu
Siglec-2
USCN
Nunc Maxisorp ELISA Plate
Fisher Scientific
SEB144Hu
NC9229197
Ethanol
Sigma-Aldrich
E7023
Glycine
Sigma-Aldrich
G8898
HEPES
Sigma-Aldrich
H0887
Hydrochloric acid
Sigma-Aldrich
H1758
Isopropanol
Sigma-Aldrich
I9516
L-glutamine
Gibco-Invitrogen
IL-1β (DY201), IL-2 (DY202), IL-6
(DY206), IL-8 (DY208), IL-10
(DY217B), IL-12p70 (DY1270), IL-13
(DY213), IL-17 (DY317), IL-22
(DY782), IL-1RA (DY280), TNF-α
(DY210), TGF-β1 (DY240), MCP-1
(DY279), MIP-1α (DY270) sCD14
(DY383), sIL-6R (DY227), sgp130
(DY228), ADAM-17 (DY930), HSP-27
(DYC1580), HSP-60 (DYC1800), HSP70 (DYC1663), Annexin-A2
(DYC3928), C5a (DY2037), KGF
(DY251)
BMS817FF
ST51011
25030-024
179
Chapter - VII
Appendices
Reagent
Supplier
Catalogue number
Phosphate buffered saline
tablets
Sigma-Aldrich
P4417
Potassium chloride
Sigma-Aldrich
P3911
Precision Plus Protein
Biorad
RPMI-1640
Sigma-Aldrich
R0833
Sodium azide
Sigma-Aldrich
A2152
Sodium chloride
Sigma-Aldrich
S5886
Sodium dodecyl sulfate
Sigma-Aldrich
L4390
Sodium hydroxide
Sigma-Aldrich
S5881
Sulfuric acid
Sigma-Aldrich
339741
TMB ELISA substrate
Millipore
ES001
Trizma base
Sigma-Aldrich
T6066
Tween-20
Sigma-Aldrich
P1379
CPT tube
Beckton Dickenson
362753
P-100 tube
Beckton Dickenson
366456
MARS Cartridge
5188-6560
Spin filters 0.22 μm
Agilent Technologies
Buffer-A
5185-5987
Buffer-B
5185-5988
Luer-Lock adapters
5188-5249
plastic Luer-Lock syringes
5188-5250
Teflon Luer- Lock needles
5188-5253
Spin concentrator (5 kDa
MW)
5185-5991
Alexa-Fluor 555
Life Technologies
Ficoll-Hypaque
GE Healthcare
17-1440-02
Acrylamide
01709
SDS
Sigma Aldrich
Sigma Aldrich
L4390
TEMED
Sigma Aldrich
T9281
APS
Sigma Aldrich
A9164
161-0376
Foetal bovine serum (FBS)
5185-5990
A20174
180
Chapter - VII
Appendices
MEDIA/BUFFER FORMULATIONS
Complete culture media (CCM):
RPMI-1640 (89%)
Fetal bovine serum (FBS) (10%)
L-glutamine (1%)
Freezing media:
RPMI-1640 (69%)
FBS (10%)
DMSO (20%)
L-glutamine (1%)
Resolving Gel - (12.5% SDS-PAGE gel)
Acrylamide
Tris 1.5 M; pH 8.8
SDS (10%)
TEMED (1%)
APS (10%)
Stacking Gel (4%)
Acrylamide
Tris 0.5 M; pH 6.8
SDS (10%)
TEMED (1%)
APS (10%)
181
Chapter - VII
Appendices
APPENDIX – XV: CROSS-REACTIVITY OF MEASURED BIOMARKERS
Analyte
Product No.
Manufacturer
IL-1β
IL-2
IL-6
IL-12p70
IL-17A
TNF-α
IFN-γ
IL-22
IL-4
IL-5
IL-9
IL-10
IL-13
IL-1RA
TGF-β1
BMS817FF
BMS817FF
BMS817FF
BMS817FF
BMS817FF
BMS817FF
BMS817FF
BMS817FF
BMS817FF
BMS817FF
BMS817FF
BMS817FF
BMS817FF
DY280
DY240
eBioscience
eBioscience
eBioscience
eBioscience
eBioscience
eBioscience
eBioscience
eBioscience
eBioscience
eBioscience
eBioscience
eBioscience
eBioscience
R&D Systems
R&D Systems
IL-8
MCP-1
MIP-1α
HMGB-1
S100-A8/A9
α-Defensin
HSP-27
HSP-60
HSP-70
Annexin-A2
sIL-6R
sgp-130
ADAM-17
sCD14
Lysozyme
CD-24
Siglec-10
Siglec-2
C5a
IL-1β
IL-6
TNF-α
IL-10
DY208
DY279
DY270
SEA399Hu
HK325-02
SEB705Hu
DYC1580
DYC1800
DYC1663
DYC3928
DY227
DY228
DY930
DY383
E-22013
SEB143Hu
SED921Hu
SEB144Hu
DY2037
DY201
DY206
DY210
DY217B
R&D Systems
R&D Systems
R&D Systems
USCN Lifesciences
Hycult Biotech
USCN Lifesciences
R&D Systems
R&D Systems
R&D Systems
R&D Systems
R&D Systems
R&D Systems
R&D Systems
R&D Systems
Life Technologies
USCN Lifesciences
USCN Lifesciences
USCN Lifesciences
R&D Systems
R&D Systems
R&D Systems
R&D Systems
R&D Systems
IL-13
IL-12p70
IL-17
DY213
DY1270
DY317
R&D Systems
R&D Systems
R&D Systems
KGF
DY251
R&D Systems
Cross Reactivity
None
None
None
None
None
None
None
None
None
None
None
None
None
None
pTGF-β2 (0.3%); rhTGF-β1.2 (57%);
rhTGF-β2 (0.15%); rhTGF-β3
(0.96%); raTGF-β5 (1.8%)
None
None
None
None
Not tested
None
HSP-70 (0.23%)
None
HSP-27 (6.3%)
None
None
None
None
None
None
None
None
None
rRat IL-1β (3.8%); rhIL-1β ( (6.3%)
None
None
r canine IL-10 (0.3%); r equine
(5.2%); r viral IL-10 (0.6%)
None
None
Recombinant human IL-17A/F
heterodimer (21.7%)
None
182
CHAPTER – VIII
REFERENCES
Chapter - VIII
References
A. Zacharopoulos, A. A., and A. Kyriakidis (2007). "The effectiveness of reinfusion after total knee
replacement. A prospective randomised controlled study." Int Orthop 31(3): 303-308.
Abraham, E. and Y. H. Chang (1992). "Haemorrhage-induced alterations in function and cytokine
production of T cells and T cell subpopulations." Clin Exp Immunol 90(3): 497-502.
Adkins, J. N., S. M. Varnum, K. J. Auberry, R. J. Moore, N. H. Angell, R. D. Smith, D. L. Springer
and J. G. Pounds (2002). "Toward a Human Blood Serum Proteome." Molecular & Cellular
Proteomics 1(12): 947-955.
Ajit Walunj, A. B., Roger Sharpe (2006). "Autologous blood transfusion." Continuing Education in
Anaesthesia, Critical Care & Pain 6(5): 192-196.
Alanis, A. J. (2005). "Resistance to antibiotics: are we in the post-antibiotic era?" Arch Med Res
36(6): 697-705.
Alatas, O., O. Colak, B. Buyukkidan, O. Portakal and B. Tanriverdi (2001). "Soluble interleukin-2
receptor and interleukin-8 plasma levels during and after cardiopulmonary bypass: correlations with
creatine kinase and creatine kinase MB." Clin Exp Med 1(1): 13-18.
Albertsmeier, M., D. Quaiser, V. von Dossow-Hanfstingl, H. Winter, E. Faist and M. K. Angele
(2014). "Major surgical trauma differentially affects T-cells and APC." Innate Immunity.
Ali, T., S. Kaitha, S. Mahmood, A. Ftesi, J. Stone and M. S. Bronze (2013). "Clinical use of anti-TNF
therapy and increased risk of infections." Drug Healthc Patient Saf 5: 79-99.
Anderson, N. L. and N. G. Anderson (2002). "The human plasma proteome: history, character, and
diagnostic prospects." Mol Cell Proteomics 1(11): 845-867.
Anderson, N. L. and N. G. Anderson (2003). "The Human Plasma Proteome: History, Character, and
Diagnostic Prospects." Molecular & Cellular Proteomics 2(1): 50.
Anderson NL, P. M., Pieper R, Gatlin T, Tirumalai RS, Conrads TP, Veenstra TD, Adkins JN, Pounds
JG, Fagan R, Lobley A. (2004). "The human plasma proteome: a nonredundant list developed by
combination of four separate sources." Mol Cell Proteomics 3(4): 311-326.
Andersson, I., M. Tylman, J. P. Bengtson and A. Bengtsson (2001). "Complement split products and
pro-inflammatory cytokines in salvaged blood after hip and knee arthroplasty." Can J Anaesth 48(3):
251-255.
Andreadis, S. T., K. E. Hamoen, M. L. Yarmush and J. R. Morgan (2001). "Keratinocyte growth
factor induces hyperproliferation and delays differentiation in a skin equivalent model system."
FASEB J 15(6): 898-906.
Andreas Pape, O. H. (2007). "Alternatives to allogeneic blood transfusions." Best Practice & Research
Clinical Anaesthesiology 21(2): 221-239.
Andres, B. M., D. D. Taub, I. Gurkan and J. F. Wenz (2003). "Postoperative fever after total knee
arthroplasty: the role of cytokines." Clin Orthop Relat Res(415): 221-231.
Angele, M. K. and I. H. Chaudry (2005). "Surgical trauma and immunosuppression: pathophysiology
and potential immunomodulatory approaches." Langenbecks Arch Surg 390(4): 333-341.
Angele, M. K. and E. Faist (2002). "Clinical review: immunodepression in the surgical patient and
increased susceptibility to infection." Crit Care 6(4): 298-305.
Anic, D., H. Gasparovic, V. Ivancan and D. Batinic (2004). "Effects of corticosteroids on
inflammatory response following cardiopulmonary bypass." Croat Med J 45(2): 158-161.
Apweiler, R., H. Hermjakob and N. Sharon (1999). "On the frequency of protein glycosylation, as
deduced from analysis of the SWISS-PROT database." Biochim Biophys Acta 1473(1): 4-8.
184
Chapter - VIII
References
Arancibia, S. A., C. J. Beltran, I. M. Aguirre, P. Silva, A. L. Peralta, F. Malinarich and M. A.
Hermoso (2007). "Toll-like receptors are key participants in innate immune responses." Biol Res
40(2): 97-112.
Arnestad, J. P., A. Bengtsson, J. P. Bengtson, M. Tylman, H. Redl and G. Schlag (1994). "Formation
of cytokines by retransfusion of shed whole blood." Br J Anaesth 72(4): 422-425.
Arnold, L., A. Henry, F. Poron, Y. Baba-Amer, N. van Rooijen, A. Plonquet, R. K. Gherardi and B.
Chazaud (2007). "Inflammatory monocytes recruited after skeletal muscle injury switch into
antiinflammatory macrophages to support myogenesis." J Exp Med 204(5): 1057-1069.
Arredouani, M. S., A. Kasran, J. A. Vanoirbeek, F. G. Berger, H. Baumann and J. L. Ceuppens
(2005). "Haptoglobin dampens endotoxin-induced inflammatory effects both in vitro and in vivo."
Immunology 114(2): 263-271.
Ashworth, A. and A. A. Klein (2010). "Cell salvage as part of a blood conservation strategy in
anaesthesia." British Journal of Anaesthesia 105(4): 401-416.
Aw, D., A. B. Silva and D. B. Palmer (2007). "Immunosenescence: emerging challenges for an ageing
population." Immunology 120(4): 435-446.
Axford, T. C., J. A. Dearani, G. Ragno, H. MacGregor, M. A. Patel, C. R. Valeri and S. F. Khuri
(1994). "Safety and therapeutic effectiveness of reinfused shed blood after open heart surgery." Ann
Thorac Surg 57(3): 615-622.
Ayache, S., M. Panelli, F. M. Marincola and D. F. Stroncek (2006). "Effects of Storage Time and
Exogenous Protease Inhibitors on Plasma Protein Levels." American Journal of Clinical Pathology
126(2): 174-184.
Bandala-Sanchez, E., Y. Zhang, S. Reinwald, J. A. Dromey, B. H. Lee, J. Qian, R. M. Bohmer and L.
C. Harrison (2013). "T cell regulation mediated by interaction of soluble CD52 with the inhibitory
receptor Siglec-10." Nat Immunol 14(7): 741-748.
Bartels, C., J. V. Bechtel, C. Winkler and S. Horsch (1996). "Intraoperative autotransfusion in aortic
surgery: comparison of whole blood autotransfusion versus cell separation." J Vasc Surg 24(1): 102108.
Bastian, D., M. V. Tamburstuen, S. P. Lyngstadaas and O. Reikeras (2008). "Systemic and local
cytokine kinetics after total hip replacement surgery." Eur Surg Res 41(4): 334-340.
Bastian, D., M. V. Tamburstuen, S. P. Lyngstadaas and O. Reikeras (2009). "Local and systemic
chemokine patterns in a human musculoskeletal trauma model." Inflamm Res 58(8): 483-489.
Bastian, D., M. V. Tamburstuen, S. P. Lyngstadaas and O. Reikeras (2011). "LBP and sCD14 patterns
in total hip replacement surgery performed during combined spinal/epidural anaesthesia." Scand J
Clin Lab Invest 71(6): 486-491.
Bentzien, F., J. M. Brand, E. Rohrs, H. Munkel and P. Schmucker (2000). "[Mechanical
autotransfusion procedures. The effect cytokines and leukocytes on washed erythrocyte concentrate]."
Anaesthesist 49(6): 505-510.
Besedovsky, H. O., A. del Rey, I. Klusman, H. Furukawa, G. Monge Arditi and A. Kabiersch (1991).
"Cytokines as modulators of the hypothalamus-pituitary-adrenal axis." J Steroid Biochem Mol Biol
40(4-6): 613-618.
Bierbaum BE, C. J., Galante JO, Rubash HE, Tooms RE, Welch RB. (2000). "An analysis of blood
management in patients having a total hip or knee arthroplasty." J Bone Joint Surg Am 82(6): 900901.
Billote DB, G. S., Green D, Wixson RL. (2002). "A prospective, randomized study of preoperative
autologous donation for hip replacement surgery." J Bone Joint Surg Am 84(A8): 1299-1304.
185
Chapter - VIII
References
Bjornsson, G. L., L. Thorsteinsson, K. O. Gudmundsson, H. Jonsson, Jr., S. Gudmundsson and B.
Gudbjornsson (2007). "Inflammatory cytokines in relation to adrenal response following total hip
replacement." Scand J Immunol 65(1): 99-105.
Blazar, B. A., M. L. Rodrick, J. B. O'Mahony, J. J. Wood, P. Q. Bessey, D. W. Wilmore and J. A.
Mannick (1986). "Suppression of natural killer-cell function in humans following thermal and
traumatic injury." J Clin Immunol 6(1): 26-36.
Blundell, J. (1819). "Some account of a Case of Obstinate Vomiting, in which an attempt was made to
prolong Life by the Injection of Blood into the Veins." Med Chir Trans 10(Pt 2): 296–311.
Bone, R. C. (1996). "Immunologic dissonance: a continuing evolution in our understanding of the
systemic inflammatory response syndrome (SIRS) and the multiple organ dysfunction syndrome
(MODS)." Ann Intern Med 125(8): 680-687.
Bone, R. C., R. A. Balk, F. B. Cerra, R. P. Dellinger, A. M. Fein, W. A. Knaus, R. M. Schein and W.
J. Sibbald (1992). "Definitions for sepsis and organ failure and guidelines for the use of innovative
therapies in sepsis. The ACCP/SCCM Consensus Conference Committee. American College of Chest
Physicians/Society of Critical Care Medicine." Chest 101(6): 1644-1655.
Boniface, K., F. X. Bernard, M. Garcia, A. L. Gurney, J. C. Lecron and F. Morel (2005). "IL-22
inhibits epidermal differentiation and induces proinflammatory gene expression and migration of
human keratinocytes." J Immunol 174(6): 3695-3702.
Boomer, J. S., J. M. Green and R. S. Hotchkiss (2014). "The changing immune system in sepsis: Is
individualized immuno-modulatory therapy the answer?" Virulence 5(1): 45-56.
Borges, T. J., L. Wieten, M. J. van Herwijnen, F. Broere, R. van der Zee, C. Bonorino and W. van
Eden (2012). "The anti-inflammatory mechanisms of Hsp70." Front Immunol 3: 95.
Borghi B, C. A. (2000). "Incidence and risk factors for allogenic blood transfusion during major joint
replacement using an integrated autotransfusion regimen. The Rizzoli Study Group on Orthopaedic
Anaesthesia." Eur J Anaesthesiol 17(7): 411-417.
Bosmann, M., J. V. Sarma, G. Atefi, F. S. Zetoune and P. A. Ward (2012). "Evidence for antiinflammatory effects of C5a on the innate IL-17A/IL-23 axis." FASEB J 26(4): 1640-1651.
Boyanton, B. L., Jr and K. E. Blick (2002). "Stability Studies of Twenty-Four Analytes in Human
Plasma and Serum." Clin Chem 48(12): 2242-2247.
Briso, E. M., O. Dienz and M. Rincon (2008). "Cutting edge: soluble IL-6R is produced by IL-6R
ectodomain shedding in activated CD4 T cells." J Immunol 180(11): 7102-7106.
Bromander, S., R. Anckarsater, M. Kristiansson, K. Blennow, H. Zetterberg, H. Anckarsater and C. E.
Wass (2012). "Changes in serum and cerebrospinal fluid cytokines in response to non-neurological
surgery: an observational study." J Neuroinflammation 9: 242.
Burk, A. M., M. Martin, M. A. Flierl, D. Rittirsch, M. Helm, L. Lampl, U. Bruckner, G. L. Stahl, A.
M. Blom, M. Perl, F. Gebhard and M. Huber-Lang (2012). "Early complementopathy after multiple
injuries in humans." Shock 37(4): 348-354.
Burton, D., G. Nicholson and G. Hall (2004). "Endocrine and metabolic response to surgery."
Continuing Education in Anaesthesia, Critical Care & Pain 4(5): 144-147.
Buvanendran, A., J. S. Kroin, R. A. Berger, N. J. Hallab, C. Saha, C. Negrescu, M. Moric, M. S.
Caicedo and K. J. Tuman (2006). "Upregulation of prostaglandin E2 and interleukins in the central
nervous system and peripheral tissue during and after surgery in humans." Anesthesiology 104(3):
403-410.
Calder, P. C. (2007). "Immunonutrition in surgical and critically ill patients." Br J Nutr 98 Suppl 1:
S133-139.
186
Chapter - VIII
References
Cesari, F., R. Caporale, R. Marcucci, S. Caciolli, P. L. Stefano, A. Capalbo, C. Macchi, M. Vannucci,
G. F. Gensini, R. Abbate and A. M. Gori (2008). "NT-proBNP and the anti-inflammatory cytokines
are correlated with endothelial progenitor cells' response to cardiac surgery." Atherosclerosis 199(1):
138-146.
Chachkhiani, I., R. Gurlich, P. Maruna, R. Frasko and J. Lindner (2005). "The postoperative stress
response and its reflection in cytokine network and leptin plasma levels." Physiol Res 54(3): 279-285.
Chan, K., D. Lucas, D. Hise, C. Schaefer, Z. Xiao, G. Janini, K. Buetow, H. Issaq, T. Veenstra and T.
Conrads (2004). "Analysis of the human serum proteome." Clinical Proteomics 1(2): 101-225.
Chen, G., F. J. Zhang, M. Gong and M. Yan (2007). "Effect of perioperative autologous versus
allogeneic blood transfusion on the immune system in gastric cancer patients." J Zhejiang Univ Sci B
8(8): 560-565.
Chen, G. Y., X. Chen, S. King, K. A. Cavassani, J. Cheng, X. Zheng, H. Cao, H. Yu, J. Qu, D. Fang,
W. Wu, X. F. Bai, J. Q. Liu, S. A. Woodiga, C. Chen, L. Sun, C. M. Hogaboam, S. L. Kunkel, P.
Zheng and Y. Liu (2011). "Amelioration of sepsis by inhibiting sialidase-mediated disruption of the
CD24-SiglecG interaction." Nat Biotechnol 29(5): 428-435.
Chen, G. Y. and G. Nunez (2010). "Sterile inflammation: sensing and reacting to damage." Nat Rev
Immunol 10(12): 826-837.
Chen, G. Y., J. Tang, P. Zheng and Y. Liu (2009). "CD24 and Siglec-10 selectively repress tissue
damage-induced immune responses." Science 323(5922): 1722-1725.
Chen, N., W. Ma and W. D. Shen (2012). "[Influence of different frequency electroacupuncture on
serum IL-1 and IL-6 in patients with pneumonectomy]." Zhongguo Zhen Jiu 32(6): 523-526.
Civetta, J. M., J. Hudson-Civetta and S. Ball (1996). "Decreasing catheter-related infection and
hospital costs by continuous quality improvement." Crit Care Med 24(10): 1660-1665.
Clark, S., L. D. Youngman, A. Palmer, S. Parish, R. Peto and R. Collins (2003). "Stability of plasma
analytes after delayed separation of whole blood: implications for epidemiological studies."
International Journal of Epidemiology 32(1): 125-130.
Clements, D., T. Sculco, S. Burke, K. Mayer and D. Levine (1992). "Salvage and reinfusion of
postoperative sanguineous wound drainage. A preliminary report." J Bone Joint Surg Am 74(5): 646651.
Clementsen, T., C. D. Krohn and O. Reikeras (2006). "Systemic and local cytokine patterns during
total hip surgery." Scand J Clin Lab Invest 66(6): 535-542.
Clementsen, T. and O. Reikeras (2008). "Cytokine patterns after tourniquet-induced skeletal muscle
ischaemia reperfusion in total knee replacement." Scand J Clin Lab Invest 68(2): 154-159.
Corbi, P., M. Rahmati, A. Delwail, D. Potreau, P. Menu, J. Wijdenes and J. C. Lecron (2000).
"Circulating soluble gp130, soluble IL-6R, and IL-6 in patients undergoing cardiac surgery, with or
without extracorporeal circulation." Eur J Cardiothorac Surg 18(1): 98-103.
Costa, A., V. Benedetto, C. Ricci, P. Merlin, P. Borelli, E. Fadda, P. Fragapane, G. Varvello, M.
Voglino and V. De Filippis (1989). "Endocrine, hematological and immunological changes in surgical
patients undergoing general anesthesia." Ital J Surg Sci 19(1): 41-49.
Cristea, I. M., S. J. Gaskell and A. D. Whetton (2004). "Proteomics techniques and their application to
hematology." Blood 103(10): 3624-3634.
Cuenca, A. G., M. J. Delano, K. M. Kelly-Scumpia, C. Moreno, P. O. Scumpia, D. M. Laface, P. G.
Heyworth, P. A. Efron and L. L. Moldawer (2011). "A paradoxical role for myeloid-derived
suppressor cells in sepsis and trauma." Mol Med 17(3-4): 281-292.
187
Chapter - VIII
References
D. Thomas, K. W., D. Cohen, H. Hutchings (2001). "Autologous blood transfusion in total knee
replacement surgery." Br. J. Anaesth 86(5): 669-673.
Dainow, I. (1991). "The benefits of using autologous blood with the Solcotrans orthopaedic system."
Orthopedics 14(4): 473-476.
Dalén T, N. K., Engström KG. (2002). "Fever and autologous blood retransfusion after total knee
arthroplasty." Acta Orthop Scand 73(3): 321-325.
Dalén T, S. S., Thorsen K, Fredin H. (1996). "The efficacy and safety of blood reinfusion in avoiding
homologous transfusion after total knee arthroplasty." Am J Knee Surg 9(3): 117-120.
Darrah, E. and F. Andrade (2012). "NETs: the missing link between cell death and systemic
autoimmune diseases?" Front Immunol 3: 428.
DC Ayers, D. M., DM Duerr (1995). "Blood salvage after total hip arthroplasty." J Bone Joint Surg
Am 77: 1347-1351.
de Haan, J., J. Schonberger, J. Haan, W. van Oeveren and A. Eijgelaar (1993). "Tissue-type
plasminogen activator and fibrin monomers synergistically cause platelet dysfunction during
retransfusion of shed blood after cardiopulmonary bypass." J Thorac Cardiovasc Surg 106(6): 10171023.
de Mendonca-Filho, H. T., K. C. Pereira, M. Fontes, D. A. Vieira, M. L. de Mendonca, L. A. Campos
and H. C. Castro-Faria-Neto (2006). "Circulating inflammatory mediators and organ dysfunction after
cardiovascular surgery with cardiopulmonary bypass: a prospective observational study." Crit Care
10(2): R46.
Deegan, C. A., D. Murray, P. Doran, D. C. Moriarty, D. I. Sessler, E. Mascha, B. P. Kavanagh and D.
J. Buggy (2010). "Anesthetic technique and the cytokine and matrix metalloproteinase response to
primary breast cancer surgery." Reg Anesth Pain Med 35(6): 490-495.
Deirmengian, C. A. and J. H. Lonner (2008). "What's New in Adult Reconstructive Knee Surgery." J
Bone Joint Surg Am 90(11): 2556-2565.
Deng, M. C., M. Wiedner, M. Erren, T. Mollhoff, G. Assmann and H. H. Scheld (1995). "Arterial and
venous cytokine response to cardiopulmonary bypass for low risk CABG and relation to
hemodynamics." Eur J Cardiothorac Surg 9(1): 22-29.
Deshmane, S. L., S. Kremlev, S. Amini and B. E. Sawaya (2009). "Monocyte chemoattractant protein1 (MCP-1): an overview." J Interferon Cytokine Res 29(6): 313-326.
Desrochers, P. E. and S. J. Weiss (1988). "Proteolytic inactivation of alpha-1-proteinase inhibitor by a
neutrophil metalloproteinase." The Journal of clinical investigation 81(5): 1646-1650.
Di Vita, G., R. Patti, F. Fama, C. R. Balistreri, G. Candore and C. Caruso (2010). "Changes of
inflammatory mediators in obese patients after laparoscopic cholecystectomy." World J Surg 34(9):
2045-2050.
Dimopoulou, I., A. Armaganidis, E. Douka, I. Mavrou, C. Augustatou, P. Kopterides, P.
Lyberopoulos, M. Tzanela, S. E. Orfanos, E. Pelekanou, G. Kostopanagiotou, A. Macheras and E. J.
Giamarellos-Bourboulis (2007). "Tumour necrosis factor-alpha (TNFalpha) and interleukin-10 are
crucial mediators in post-operative systemic inflammatory response and determine the occurrence of
complications after major abdominal surgery." Cytokine 37(1): 55-61.
Dinarello, C. A. (2000). "Proinflammatory cytokines." Chest 118(2): 503-508.
Dinarello, C. A. (2009). "Immunological and inflammatory functions of the interleukin-1 family."
Annu Rev Immunol 27: 519-550.
188
Chapter - VIII
References
Drosos, G. I., K. S. Blatsoukas, A. Ververidis, G. Tripsianis, P. Chloropoulou, C. Iatrou, K. Kazakos
and D. A. Verettas (2012). "Blood transfusion and cytokines' changes in total knee replacement." Arch
Orthop Trauma Surg 132(10): 1505-1513.
Duchow, J., M. Ames, T. Hess and U. Seyfert (2001). "Activation of plasma coagulation by
retransfusion of unwashed drainage blood after hip joint arthroplasty: a prospective study." J
Arthroplasty 16(7): 844-849.
Dybdahl, B., A. Wahba, E. Lien, T. H. Flo, A. Waage, N. Qureshi, O. F. M. Sellevold, T. Espevik and
A. Sundan (2002). "Inflammatory Response After Open Heart Surgery: Release of Heat-Shock
Protein 70 and Signaling Through Toll-Like Receptor-4." Circulation 105(6): 685-690.
Eleftherios, C. V. and A. B. Morris (2007). "Transfusion-related immunomodulation (TRIM): An
update." Blood reviews 21(6): 327-348.
ElMokhtari, N., S. Zschernitz, S. Sebens, G. Simon-Herrmann and D. Kruger (2010). "Cardiac release
and kinetics of cytokines after elective bare metal coronary stenting." J Thromb Thrombolysis 30(4):
391-397.
Eltzschig, H. K. and P. Carmeliet (2011). "Hypoxia and inflammation." N Engl J Med 364(7): 656665.
Etchason, J., L. Petz, E. Keeler, L. Calhoun, S. Kleinman, C. Snider, A. Fink and R. Brook (1995).
"The Cost Effectiveness of Preoperative Autologous Blood Donations." New England Journal of
Medicine 332(11): 719-724.
Eyerich, S., K. Eyerich, D. Pennino, T. Carbone, F. Nasorri, S. Pallotta, F. Cianfarani, T. Odorisio, C.
Traidl-Hoffmann, H. Behrendt, S. R. Durham, C. B. Schmidt-Weber and A. Cavani (2009). "Th22
cells represent a distinct human T cell subset involved in epidermal immunity and remodeling." J Clin
Invest 119(12): 3573-3585.
Faris PM, R. M., Keating EM, Valeri CR. (1991). "Unwashed filtered shed blood collected after knee
and hip arthroplasties. A source of autologous red blood cells." J Bone Joint Surg Am 73(8): 11691178.
Farsak, B., S. Gunaydin, U. Yildiz, T. Sari and Y. Zorlutuna (2012). "Clinical evaluation of leukocyte
filtration as an alternative anti-inflammatory strategy to aprotinin in high-risk patients undergoing
coronary revascularization." Surg Today 42(4): 334-341.
Faruquzzaman (2011). "Positive associations of nosocomial infections in surgical ward with
etiological clinical factors." Bratisl Lek Listy 112(5): 273-277.
Fassbender, K., B. Gerber, U. Karrer, M. Sobieska, A. Aeschlimann and W. Muller (1993).
"Glycosylation of acute phase proteins and interleukins following hip arthroplasty. Inflammation
parameters studied in 10 patients." Acta Orthop Scand 64(2): 216-220.
Feng, Y., H. Ju, B. Yang and H. An (2008). "Effects of a selective cyclooxygenase-2 inhibitor on
postoperative inflammatory reaction and pain after total knee replacement." J Pain 9(1): 45-52.
Ferroni, P., G. Speziale, G. Ruvolo, A. Giovannelli, F. M. Pulcinelli, L. Lenti, P. Pignatelli, A. Criniti,
E. Tonelli, B. Marino and P. P. Gazzaniga (1998). "Platelet activation and cytokine production during
hypothermic cardiopulmonary bypass--a possible correlation?" Thromb Haemost 80(1): 58-64.
Filipazzi, P., R. Valenti, V. Huber, L. Pilla, P. Canese, M. Iero, C. Castelli, L. Mariani, G. Parmiani
and L. Rivoltini (2007). "Identification of a new subset of myeloid suppressor cells in peripheral blood
of melanoma patients with modulation by a granulocyte-macrophage colony-stimulation factor-based
antitumor vaccine." J Clin Oncol 25(18): 2546-2553.
Finch, P. W., J. S. Rubin, T. Miki, D. Ron and S. A. Aaronson (1989). "Human KGF is FGF-related
with properties of a paracrine effector of epithelial cell growth." Science 245(4919): 752-755.
189
Chapter - VIII
References
Flohe, S. B., S. Flohe and F. U. Schade (2008). "Invited review: deterioration of the immune system
after trauma: signals and cellular mechanisms." Innate Immun 14(6): 333-344.
Florence, N. (1999). "Florence Nightingale: Measuring Hospital Care Outcomes: Excerpts From the
Books Notes on Matters Affecting the Health, Efficiency, and Hospital Administration of the British
Army Founded Chiefly on the Experience of the Late War, and Notes on Hospitals." Oakbrook
Terrace, Ill: Joint Commission on Accreditation of Healthcare Organizations: 41–45.
Fountoulakis, M., J. F. Juranville, L. Jiang, D. Avila, D. Röder, P. Jakob, P. Berndt, S. Evers and H.
Langen (2004). "Depletion of the high-abundance plasma proteins." Amino Acids 27(3): 249-259.
Franke, A., W. Lante, V. Fackeldey, H. P. Becker, E. Kurig, L. G. Zoller, C. Weinhold and A.
Markewitz (2005). "Pro-inflammatory cytokines after different kinds of cardio-thoracic surgical
procedures: is what we see what we know?" Eur J Cardiothorac Surg 28(4): 569-575.
Franke, A., W. Lante, V. Fackeldey, H. P. Becker, C. Thode, W. D. Kuhlmann and A. Markewitz
(2002). "Proinflammatory and antiinflammatory cytokines after cardiac operation: different cellular
sources at different times." Ann Thorac Surg 74(2): 363-370; discussion 370-361.
Fry, S. A., B. Afrough, H. J. Lomax-Browne, J. F. Timms, L. S. Velentzis and A. J. C. Leathem
(2011). "Lectin Microarray Profiling of Metastatic Breast Cancers." Glycobiology.
Fujioka, S. (2001). "Increased plasma concentration of adrenomedullin during and after major
surgery." Surg Today 31(7): 575-579.
Fukunaga, K., P. Kohli, C. Bonnans, L. E. Fredenburgh and B. D. Levy (2005). "Cyclooxygenase 2
plays a pivotal role in the resolution of acute lung injury." J Immunol 174(8): 5033-5039.
Furunaga, A., H. Tsuboi, J. Sasaki, H. Itoh, A. Mikamo, H. Okada, K. Suzuki, H. Gohra, K. Hamano,
T. Katoh and et al. (1995). "[Influence of cardiopulmonary bypass on biological response]." Kyobu
Geka 48(13): 1085-1087.
Gabay, C. and I. Kushner (1999). "Acute-phase proteins and other systemic responses to
inflammation." N Engl J Med 340(6): 448-454.
Gabay, C. and I. Kushner (2001). Acute-phase Proteins. eLS, John Wiley & Sons, Ltd.
Gale, L. M. and S. R. McColl (1999). "Chemokines: extracellular messengers for all occasions?"
Bioessays 21(1): 17-28.
Gannon DM, L. A. J., Mallory TH, Vaughn BK, Finney CR, Niemcryk S. (1991). "An evaluation of
the efficacy of postoperative blood salvage after total joint arthroplasty. A prospective randomized
trial." J Arthroplasty 6(2): 109-114.
Gea-Banacloche, J. C., S. M. Opal, J. Jorgensen, J. A. Carcillo, K. A. Sepkowitz and C. Cordonnier
(2004). "Sepsis associated with immunosuppressive medications: an evidence-based review." Crit
Care Med 32(11 Suppl): S578-590.
Gee, K., C. Guzzo, N. F. Che Mat, W. Ma and A. Kumar (2009). "The IL-12 family of cytokines in
infection, inflammation and autoimmune disorders." Inflamm Allergy Drug Targets 8(1): 40-52.
Geer, D. J., D. D. Swartz and S. T. Andreadis (2005). "Biomimetic delivery of keratinocyte growth
factor upon cellular demand for accelerated wound healing in vitro and in vivo." Am J Pathol 167(6):
1575-1586.
Geiger, E. V., M. Maier, S. Schiessling, S. Wutzler, M. Lehnert, I. Marzi and D. Henrich (2013).
"Subsequent gene expression pattern in dendritic cells following multiple trauma." Langenbecks Arch
Surg 398(2): 327-333.
190
Chapter - VIII
References
Gentile, L. F., A. G. Cuenca, P. A. Efron, D. Ang, A. Bihorac, B. A. McKinley, L. L. Moldawer and
F. A. Moore (2012). "Persistent inflammation and immunosuppression: a common syndrome and new
horizon for surgical intensive care." J Trauma Acute Care Surg 72(6): 1491-1501.
Gharehbaghian, A., K. M. Haque, C. Truman, R. Evans, R. Morse, J. Newman, G. Bannister, C.
Rogers and B. A. Bradley (2004). "Effect of autologous salvaged blood on postoperative natural killer
cell precursor frequency." Lancet 363(9414): 1025-1030.
Gharehbaghian, A., C. Truman, K. M. Haque, R. Evans, R. Morse, J. Newman, G. Bannister, C.
Rogers and B. A. Bradley (2004). "Effect of autologous salvaged blood on postoperative natural killer
cell precursor frequency." Lancet 363(9414): 1025-1030.
Giannoudis, P. V., R. Mallina, P. Harwood, S. Perry, E. D. Sante and H. C. Pape (2010). "Pattern of
release and relationship between HMGB-1 and IL-6 following blunt trauma." Injury 41(12): 13231327.
Gillitzer, R. and M. Goebeler (2001). "Chemokines in cutaneous wound healing." J Leukoc Biol
69(4): 513-521.
Gilroy, D. W., P. R. Colville-Nash, D. Willis, J. Chivers, M. J. Paul-Clark and D. A. Willoughby
(1999). "Inducible cyclooxygenase may have anti-inflammatory properties." Nat Med 5(6): 698-701.
Ginaldi, L., M. F. Loreto, M. P. Corsi, M. Modesti and M. De Martinis (2001). "Immunosenescence
and infectious diseases." Microbes Infect 3(10): 851-857.
GOINS, W. A., A. RODRIGUEZ, M. JOSHI and D. JACOBS (1990). "Intra-abdominal Abscess
After Blunt Abdominal Trauma." Annals of Surgery 212(1): 60-65.
Goldman M, S. R., Long A, Gélinas S, Germain M. (2002). "Declining value of preoperative
autologous donation." Transfusion 42(7): 819-823.
Gong, M. N., B. T. Thompson, P. Williams, L. Pothier, P. D. Boyce and D. C. Christiani (2005).
"Clinical predictors of and mortality in acute respiratory distress syndrome: potential role of red cell
transfusion." Crit Care Med 33(6): 1191-1198.
Goodnough, L. T., M. E. Brecher, M. H. Kanter and J. P. AuBuchon (1999). "Transfusion Medicine
— Blood Transfusion." New England Journal of Medicine 340(6): 438-447.
Gormley, S. M., W. T. McBride, M. A. Armstrong, E. McClean, S. W. MacGowan, G. Campalani and
T. J. McMurray (2002). "Plasma and urinary cytokine homeostasis and renal function during cardiac
surgery without cardiopulmonary bypass." Cytokine 17(2): 61-65.
Gormley, S. M., W. T. McBride, M. A. Armstrong, I. S. Young, E. McClean, S. W. MacGowan, G.
Campalani and T. J. McMurray (2000). "Plasma and urinary cytokine homeostasis and renal
dysfunction during cardiac surgery." Anesthesiology 93(5): 1210-1216; discussion 1215A.
Goyette, J. and C. L. Geczy (2011). "Inflammation-associated S100 proteins: new mechanisms that
regulate function." Amino Acids 41(4): 821-842.
Griffin, G. K., G. Newton, M. L. Tarrio, D. X. Bu, E. Maganto-Garcia, V. Azcutia, P. Alcaide, N.
Grabie, F. W. Luscinskas, K. J. Croce and A. H. Lichtman (2012). "IL-17 and TNF-α sustain
neutrophil recruitment during inflammation through synergistic effects on endothelial activation." J
Immunol 188(12): 6287-6299.
Guerra JJ, C. J. (1995). "Cost effectiveness of intraoperative autotransfusion in total hip arthroplasty
surgery." Clin Orthop Relat Res 315: 212-222.
Guo, R. F. and P. A. Ward (2005). "Role of C5a in inflammatory responses." Annu Rev Immunol 23:
821-852.
191
Chapter - VIII
References
Haider, A. H., D. C. Chang, D. T. Efron, E. R. Haut, M. Crandall and E. E. Cornwell, 3rd (2008).
"Race and insurance status as risk factors for trauma mortality." Arch Surg 143(10): 945-949.
Hamzic, N. (2013). The Role of Interleukin-6 in the Febrile Response. Doctoral thesis, Linköping
University, Faculty of Health Sciences.
Han, Z., Y. Jing, S. Zhang, Y. Liu, Y. Shi and L. Wei (2012). "The role of immunosuppression of
mesenchymal stem cells in tissue repair and tumor growth." Cell Biosci 2(1): 8.
Hancock, R. E., A. Nijnik and D. J. Philpott (2012). "Modulating immunity as a therapy for bacterial
infections." Nat Rev Microbiol 10(4): 243-254.
Handel, M., J. Winkler, R. F. Hornlein, H. Northoff, P. Heeg and S. Sell (2001). "Time-related
changes of collected shed blood in autologous retransfusion after total knee arthroplasty." Arch
Orthop Trauma Surg 121(10): 557-560.
Handel, M., J. Winkler, R. F. Hornlein, H. Northoff, P. Heeg, M. Teschner and S. Sell (2001).
"Increased interleukin-6 in collected drainage blood after total knee arthroplasty: an association with
febrile reactions during retransfusion." Acta Orthop Scand 72(3): 270-272.
Hansen, B. L., N. Rohr, V. Svendsen, H. Olsen and S. A. Birkeland (1988). "Bacterial Urinary Tract
Infection in Cyclosporine-A Immunosuppressed Renal Transplant Recipients." Scandinavian Journal
of Infectious Diseases 20(4): 425-427.
Haque, A., F. Kunimoto, H. Narahara, M. Okawa, H. Hinohara, M. Kurabayashi and S. Saito (2011).
"High mobility group box 1 levels in on and off-pump cardiac surgery patients." Int Heart J 52(3):
170-174.
Hartz, R. S., J. A. Smith and D. Green (1988). "Autotransfusion after cardiac operation. Assessment
of hemostatic factors." J Thorac Cardiovasc Surg 96(1): 178-182.
Hashizume, M., Y. Higuchi, Y. Uchiyama and M. Mihara (2011). "IL-6 plays an essential role in
neutrophilia under inflammation." Cytokine 54(1): 92-99.
Hawlisch, H., Y. Belkaid, R. Baelder, D. Hildeman, C. Gerard and J. Kohl (2005). "C5a negatively
regulates toll-like receptor 4-induced immune responses." Immunity 22(4): 415-426.
Hayward, A. L., D. J. Deehan, R. M. Aspden and A. G. Sutherland (2011). "Analysis of sequential
cytokine release after ACL reconstruction." Knee Surg Sports Traumatol Arthrosc 19(10): 1709-1715.
Haziot, A., G. W. Rong, X. Y. Lin, J. Silver and S. M. Goyert (1995). "Recombinant soluble CD14
prevents mortality in mice treated with endotoxin (lipopolysaccharide)." The Journal of Immunology
154(12): 6529-6532.
Heinrich, P. C., J. V. Castell and T. Andus (1990). "Interleukin-6 and the acute phase response."
Biochem J 265(3): 621-636.
Helminen, H., M. Raitanen and J. Kellosalo (2007). "Immunonutrition in elective gastrointestinal
surgery patients." Scand J Surg 96(1): 46-50.
Henderson, R. B., J. A. Hobbs, M. Mathies and N. Hogg (2003). "Rapid recruitment of inflammatory
monocytes is independent of neutrophil migration." Blood 102(1): 328-335.
Henrich, D., M. Maier, B. Relja, P. Trendafilov, S. Schiessling, M. Wirth, T. Ottilinger, A.-K.
Nielsen, H. Wyen and I. Marzi (2009). "Significant Decline of Peripheral Myeloid Dendritic Cells
Following Multiple Trauma." Journal of Surgical Research 154(2): 239-245.
Hensler, T., H. Hecker, K. Heeg, C. D. Heidecke, H. Bartels, W. Barthlen, H. Wagner, J. R. Siewert
and B. Holzmann (1997). "Distinct mechanisms of immunosuppression as a consequence of major
surgery." Infect Immun 65(6): 2283-2291.
192
Chapter - VIII
References
Ho, C. S., J. A. Lopez, S. Vuckovic, C. M. Pyke, R. L. Hockey and D. N. Hart (2001). "Surgical and
physical stress increases circulating blood dendritic cell counts independently of monocyte counts."
Blood 98(1): 140-145.
Hogan, B. V., M. B. Peter, H. G. Shenoy, K. Horgan and T. A. Hughes (2011). "Surgery induced
immunosuppression." Surgeon 9(1): 38-43.
Hogevold, H. E., T. Lyberg, H. Kahler, E. Haug and O. Reikeras (2000). "Changes in plasma IL1beta, TNF-alpha and IL-6 after total hip replacement surgery in general or regional anaesthesia."
Cytokine 12(7): 1156-1159.
Holzheimer, R. G., R. G. Molloy, H. Gorlach, S. Wilkert and F. Hehrlein (1994). "IL-6 and TNF alpha
release in association with neutrophil activation after cardiopulmonary bypass surgery." Infection
22(1): 37-42.
Honsawek, S., B. Deepaisarnsakul, A. Tanavalee, M. Sakdinakiattikoon, S. Ngarmukos, K.
Preativatanyou and P. Bumrungpanichthaworn (2011). "Relationship of serum IL-6, C-reactive
protein, erythrocyte sedimentation rate, and knee skin temperature after total knee arthroplasty: a
prospective study." Int Orthop 35(1): 31-35.
Horan, T. C., D. H. Culver, R. P. Gaynes, W. R. Jarvis, J. R. Edwards and C. R. Reid (1993).
"Nosocomial infections in surgical patients in the United States, January 1986-June 1992. National
Nosocomial Infections Surveillance (NNIS) System." Infect Control Hosp Epidemiol 14(2): 73-80.
Horstmann, W. G., R. Slappendel, G. G. Van Hellemondt, R. M. Castelein and C. C. P. M. Verheyen
(2010). "Safety of retransfusion of filtered shed blood in 1819 patients after total hip or knee
arthroplasty." Transfusion Alternatives in Transfusion Medicine 11(2): 57-64.
Hotchkiss, R. S., G. Monneret and D. Payen (2013). "Immunosuppression in sepsis: a novel
understanding of the disorder and a new therapeutic approach." Lancet Infect Dis 13(3): 260-268.
Hsing, C. H., M. Y. Hsieh, W. Y. Chen, E. Cheung So, B. C. Cheng and M. S. Chang (2006).
"Induction of interleukin-19 and interleukin-22 after cardiac surgery with cardiopulmonary bypass."
Ann Thorac Surg 81(6): 2196-2201.
Hunter, J. (1794). A treatise on the blood, inflammation, and gun-shot wounds, Nicole, London.
Hutchins, N. A., J. Unsinger, R. S. Hotchkiss and A. Ayala (2014). "The new normal:
immunomodulatory agents against sepsis immune suppression." Trends in molecular medicine.
Ianni, M., M. Manerba, G. Di Stefano, E. Porcellini, M. Chiappelli, I. Carbone and F. Licastro (2010).
"Altered glycosylation profile of purified plasma ACT from Alzheimer's disease." Immun Ageing 7
Suppl 1: S6.
Islam, N., M. Whitehouse, S. Mehandale, M. Hall, A. Blom, G. Bannister and B. A. Bradley (2011).
"Neopterin Levels Confirm Immunostimulation by Unwashed Salvaged Blood Transfusion."
Transfusion Alternatives in Transfusion Medicine 12(1): 28-29.
Ivatury, R. R., R. Zubowski, P. Psarras, M. Nallathambi, M. Rohman and W. M. Stahl (1988). "Intraabdominal abscess after penetrating abdominal trauma." J Trauma 28(8): 1238-1243.
Jawa, R. S., S. Anillo, K. Huntoon, H. Baumann and M. Kulaylat (2011). "Analytic Review:
Interleukin-6 in Surgery, Trauma, and Critical Care: Part I: Basic Science." Journal of Intensive Care
Medicine 26(1): 3-12.
Jawa, R. S., S. Anillo, K. Huntoon, H. Baumann and M. Kulaylat (2011). "Interleukin-6 in surgery,
trauma, and critical care part II: clinical implications." J Intensive Care Med 26(2): 73-87.
Jedynak, M., B. Mroczko, A. Siemiatkowski, M. Gacko and M. Szmitkowski (2011). "Decreased
serum level of IL-12 in the course of ischemia and reperfusion during abdominal aortic surgery." Folia
Histochem Cytobiol 49(3): 465-471.
193
Chapter - VIII
References
Jellusova, J., U. Wellmann, K. Amann, T. H. Winkler and L. Nitschke (2010). "CD22 x Siglec-G
double-deficient mice have massively increased B1 cell numbers and develop systemic
autoimmunity." J Immunol 184(7): 3618-3627.
Jensen, C. M., R. Pilegaard, K. Hviid, J. D. Nielsen and H. J. Nielsen (1999). "Quality of reinfused
drainage blood after total knee arthroplasty." J Arthroplasty 14(3): 312-318.
Jiang, S., X. Liu, L. Luo, B. Qu, X. Huang, Y. Lin, S. Ye and Y. Liu (2011). "Serum levels of Th17related cytokines in Behcet disease patients after cataract surgery." Mol Vis 17: 1425-1430.
Johann Schaller, S. G., Urs Kampfer, Sofia Lejon, Christian Trachsel, Ed. (2008). Human Blood
Plasma Proteins: Structure and Function. West Sussex, John Willy & Sons Ltd.
Johnson, S. B., M. Lissauer, G. V. Bochicchio, R. Moore, A. S. Cross and T. M. Scalea (2007). "Gene
expression profiles differentiate between sterile SIRS and early sepsis." Ann Surg 245(4): 611-621.
Jones HW, S. L., White C, Goddard R, Lumley H, Kashif F, Gurusany K. (2004). "Postoperative
autologous blood salvage drains--are they useful in primary uncemented hip and knee arthroplasty? A
prospective study of 186 cases." Acta Orthop. Belg 70(5): 466-473.
Kaczmarek, A., P. Vandenabeele and D. V. Krysko (2013). "Necroptosis: the release of damageassociated molecular patterns and its physiological relevance." Immunity 38(2): 209-223.
Kaelin, W. G., Jr. and P. J. Ratcliffe (2008). "Oxygen sensing by metazoans: the central role of the
HIF hydroxylase pathway." Mol Cell 30(4): 393-402.
Kanse, S. M., A. Gallenmueller, S. Zeerleder, F. Stephan, O. Rannou, S. Denk, M. Etscheid, G.
Lochnit, M. Krueger and M. Huber-Lang (2012). "Factor VII-activating protease is activated in
multiple trauma patients and generates anaphylatoxin C5a." J Immunol 188(6): 2858-2865.
Karam, O., M. Tucci, B. J. Toledano, N. Robitaille, J. Cousineau, L. Thibault, J. Lacroix and F. Le
Deist (2009). "Length of storage and in vitro immunomodulation induced by prestorage leukoreduced
red blood cells." Transfusion 49(11): 2326-2334.
Kawahito, K., H. Adachi and T. Ino (2000). "Influence of surgical procedures on interleukin-6 and
monocyte chemotactic and activating factor responses: CABG vs. valvular surgery." J Interferon
Cytokine Res 20(1): 1-6.
Kawasaki, T., S. Fujimi, J. A. Lederer, W. J. Hubbard, M. A. Choudhry, M. G. Schwacha, K. I. Bland
and I. H. Chaudry (2006). "Trauma-hemorrhage induces depressed splenic dendritic cell functions in
mice." J Immunol 177(7): 4514-4520.
Kawasaki, T., M. Ogata, C. Kawasaki, K. Okamoto and T. Sata (2007). "Effects of epidural
anaesthesia on surgical stress-induced immunosuppression during upper abdominal surgery." Br J
Anaesth 98(2): 196-203.
Khurana, A., S. Zafar, W. Abdul, S. Mukhopadhyay and K. Mohanty (2012). "POST OPERATIVE
REDUCTION IN HAEMOGLOBIN LEVEL IS RELATED TO RISE IN TROPONIN T
FOLLOWING HIP FRACTURE SURGERY." Journal of Bone & Joint Surgery, British Volume 94B(SUPP XVII): 024.
Kim, J. C., J. K. Shim, S. Lee, Y. C. Yoo, S. Y. Yang and Y. L. Kwak (2012). "Effect of combined
remote ischemic preconditioning and postconditioning on pulmonary function in valvular heart
surgery." Chest 142(2): 467-475.
Kim, K. T., S. H. Lee, K. S. Suk and S. C. Bae (2006). "The quantitative analysis of tissue injury
markers after mini-open lumbar fusion." Spine (Phila Pa 1976) 31(6): 712-716.
Kim, T., G. J. Arnaoutakis, A. Bihorac, T. D. Martin, P. J. Hess, Jr., C. T. Klodell, C. G. Tribble, A.
A. Ejaz, L. L. Moldawer and T. M. Beaver (2011). "Early blood biomarkers predict organ injury and
resource utilization following complex cardiac surgery." J Surg Res 168(2): 168-172.
194
Chapter - VIII
References
Kimura, F., H. Shimizu, H. Yoshidome, M. Ohtsuka and M. Miyazaki (2010). "Immunosuppression
following surgical and traumatic injury." Surg Today 40(9): 793-808.
Klaenhammer, T. R., M. Kleerebezem, M. V. Kopp and M. Rescigno (2012). "The impact of
probiotics and prebiotics on the immune system." Nat Rev Immunol 12(10): 728-734.
Kleinschmidt, S., G. A. Wanner, D. Bussmann, J. P. Kremer, T. Ziegenfuss, M. D. Menger and M.
Bauer (1998). "Proinflammatory cytokine gene expression in whole blood from patients undergoing
coronary artery bypass surgery and its modulation by pentoxifylline." Shock 9(1): 12-20.
Kobayashi, M., S. Tsujitani, Y. Kurisu and N. Kaibara (2004). "Responses of cytokines and
coagulation-fibrinolytic states to surgical stress following esophagectomy." Hepatogastroenterology
51(59): 1376-1378.
Koch, A., L. Hamann, M. Schott, O. Boehm, D. Grotemeyer, M. Kurt, C. Schwenke, R. R. Schumann,
S. R. Bornstein and K. Zacharowski (2011). "Genetic variation of TLR4 influences immunoendocrine
stress response: an observational study in cardiac surgical patients." Crit Care 15(2): R109.
Kohno, T., T. Anzai, H. Shimizu, H. Kaneko, Y. Sugano, S. Yamada, T. Yoshikawa, A. Ishizaka, R.
Yozu and S. Ogawa (2011). "Impact of serum high-mobility group box 1 protein elevation on
oxygenation impairment after thoracic aortic aneurysm repair." Heart Vessels 26(3): 306-312.
Kolaczkowska, E. and P. Kubes (2013). "Neutrophil recruitment and function in health and
inflammation." Nat Rev Immunol 13(3): 159-175.
Kolaczkowska, E. and P. Kubes (2013). "Neutrophil recruitment and function in health and
inflammation." Nat Rev Immunol 13(3): 159-175.
Kotulak, T., J. Drapalova, P. Kopecky, Z. Lacinova, P. Kramar, H. Riha, I. Netuka, J. Maly, D.
Housa, J. Blaha, S. Svacina and M. Haluzik (2011). "Increased circulating and epicardial adipose
tissue mRNA expression of fibroblast growth factor-21 after cardiac surgery: possible role in
postoperative inflammatory response and insulin resistance." Physiol Res 60(5): 757-767.
Kragsbjerg, P., H. Holmberg and T. Vikerfors (1995). "Serum concentrations of interleukin-6, tumour
necrosis factor-alpha, and C-reactive protein in patients undergoing major operations." Eur J Surg
161(1): 17-22.
Kremen, J., M. Dolinkova, J. Krajickova, J. Blaha, K. Anderlova, Z. Lacinova, D. Haluzikova, L.
Bosanska, M. Vokurka, S. Svacina and M. Haluzik (2006). "Increased subcutaneous and epicardial
adipose tissue production of proinflammatory cytokines in cardiac surgery patients: possible role in
postoperative insulin resistance." J Clin Endocrinol Metab 91(11): 4620-4627.
Kristiansson, M., M. Soop, L. Saraste, K. G. Sundqvist, A. M. Suontaka and M. Blomback (1995).
"Cytokine and coagulation characteristics of retrieved blood after arthroplasty." Intensive Care Med
21(12): 989-995.
Kristiansson, M., M. Soop, K. G. Sundqvist, A. Soop, A. M. Suontaka and M. Blomback (1998).
"Local vs. systemic immune and haemostatic response to hip arthroplasty." Eur J Anaesthesiol 15(3):
260-270.
Krohn, C. D., O. Reikeras and A. O. Aasen (1999). "The cytokines IL-1beta and IL-1 receptor
antagonist, IL-2 and IL-2 soluble receptor-alpha, IL-6 and IL-6 soluble receptor, TNF-alpha and TNF
soluble receptor I, and IL10 in drained and systemic blood after major orthopaedic surgery." Eur J
Surg 165(2): 101-109.
Krohn, C. D., O. Reikerås and A. O. Aasen (1999). "The cytokines IL-1β and IL-1 receptor
antagonist, IL-2 and IL-2 soluble receptor-α, IL-6 and IL-6 soluble receptor, TNF-α and TNF soluble
receptor I, and IL10 in drained and systemic blood after major orthopaedic surgery." European Journal
of Surgery 165(2): 101-109.
195
Chapter - VIII
References
Krohn, C. D., O. Reikeras, S. Bjornsen and F. Brosstad (2000). "Tissue factor antigen and activity in
serum of postoperatively shed blood used for autologous transfusion." Blood Coagul Fibrinolysis
11(3): 219-223.
Krohn, C. D., O. Reikerås, S. Bjørnsen and F. Brosstad (2001). "Fibrinolytic activity and
postoperative salvaged untreated blood for autologous transfusion in major orthopaedic surgery."
European Journal of Surgery 167(3): 168-172.
Kudlova, M., M. Kolackova, P. Kunes, C. Andrys, K. Jankovicova, J. Mand'ak, V. Lonsky and J.
Krejsek (2007). "[Cardiac surgery operations and their influence on serum level of antiinflammatory
cytokine interleukin-10]." Cas Lek Cesk 146(1): 48-55; discussion 55-46.
Kudlova, M., P. Kunes, M. Kolackova, V. Lonsky, J. Mandak, C. Andrys, K. Jankovicova and J.
Krejsek (2007). "Lipopolysaccharide binding protein and sCD14 are not produced as acute phase
proteins in cardiac surgery." Mediators Inflamm 2007: 72356.
Kudsk, K. A. (2006). "Immunonutrition in surgery and critical care." Annu Rev Nutr 26: 463-479.
Kuhlicke, J., J. S. Frick, J. C. Morote-Garcia, P. Rosenberger and H. K. Eltzschig (2007). "Hypoxia
inducible factor (HIF)-1 coordinates induction of Toll-like receptors TLR2 and TLR6 during
hypoxia." PLoS One 2(12): e1364.
Kumar, P., K. Rajasekaran, J. M. Palmer, M. S. Thakar and S. Malarkannan (2013). "IL-22: An
Evolutionary Missing-Link Authenticating the Role of the Immune System in Tissue Regeneration." J
Cancer 4(1): 57-65.
Kvarnstrom, A., A. Schmidt, M. Tylman, M. Jacobsson and A. Bengtsson (2008). "Complement split
products and proinflammatory cytokines in intraoperatively salvaged unwashed blood during hip
replacement: comparison between heparin-coated and non-heparin-coated autotransfusion systems."
Vox Sang 95(1): 33-38.
Laroche, D., S. Chrysanthou, C. Lefrançois, J. L. Gérard, F. Dubois, M. Malet and H. Bricard (1992).
"Evidence of a progressive degranulation of basophil in subjects operated on without adverse
reaction." Agents and Actions 36(2): C199-C200.
Lathrop, J. T. H., Timothy K; Carrick, Kevin; Hammond, David J (2005). "Rarity gives a charm:
evaluation of trace proteins in plasma and serum." Expert Review of Proteomics 2(3): 393-406.
Lawrence-Brown, M. M., C. Couch, M. Halliday, M. Hellings, A. Barr and L. Marshall (1989). "Ddimer levels in blood salvage for autotransfusion." Aust N Z J Surg 59(1): 67-70.
Lee, K. and S. B. Goodman (2008). "Current state and future of joint replacements in the hip and
knee." Expert review of medical devices 5(3): 383-393.
Lehrer, R. I. (2007). "Multispecific myeloid defensins." Curr Opin Hematol 14(1): 16-21.
Lemos, M. J. and W. L. Healy (1996). "Current Concepts Review - Blood Transfusion in Orthopaedic
Operations*." The Journal of Bone & Joint Surgery 78(8): 1260-1271.
Levy, M. M., M. P. Fink, J. C. Marshall, E. Abraham, D. Angus, D. Cook, J. Cohen, S. M. Opal, J. L.
Vincent and G. Ramsay (2003). "2001 SCCM/ESICM/ACCP/ATS/SIS International Sepsis
Definitions Conference." Crit Care Med 31(4): 1250-1256.
Li, W., H. P. Ling, W. C. You, H. D. Liu, Q. Sun, M. L. Zhou, W. Shen, J. B. Zhao, L. Zhu and C. H.
Hang (2014). "Elevated Cerebral Cortical CD24 Levels in Patients and Mice with Traumatic Brain
Injury: A Potential Negative Role in Nuclear Factor Kappa B/Inflammatory Factor Pathway." Mol
Neurobiol 49(1): 187-198.
Liang, S. C., X. Y. Tan, D. P. Luxenberg, R. Karim, K. Dunussi-Joannopoulos, M. Collins and L. A.
Fouser (2006). "Interleukin (IL)-22 and IL-17 are coexpressed by Th17 cells and cooperatively
enhance expression of antimicrobial peptides." J Exp Med 203(10): 2271-2279.
196
Chapter - VIII
References
Liao, W., J. X. Lin and W. J. Leonard (2011). "IL-2 family cytokines: new insights into the complex
roles of IL-2 as a broad regulator of T helper cell differentiation." Curr Opin Immunol 23(5): 598-604.
Lin, Z. Q., T. Kondo, Y. Ishida, T. Takayasu and N. Mukaida (2003). "Essential involvement of IL-6
in the skin wound-healing process as evidenced by delayed wound healing in IL-6-deficient mice." J
Leukoc Biol 73(6): 713-721.
Lindholm, E., I. Seljeflot, E. Aune and K. A. Kirkeboen (2012). "Proinflammatory cytokines and
complement activation in salvaged blood from abdominal aortic aneurism surgery and total hip
replacement surgery." Transfusion 52(8): 1761-1769.
Liotta, L. A., M. Ferrari and E. Petricoin (2003). "Clinical proteomics: Written in blood." Nature
425(6961): 905-905.
Lisowska, B., W. Maslinski, P. Maldyk, J. Zabek and E. Baranowska (2008). "The role of cytokines in
inflammatory response after total knee arthroplasty in patients with rheumatoid arthritis." Rheumatol
Int 28(7): 667-671.
Liu, B., Y. Yang, S. Han, L. Zhang, Y. Bai and X. Fang (2013). "Plasma CD24 as a novel useful
biomarker in differentiating hepatocellular carcinoma patients from normal individuals."
Hepatogastroenterology 60(126): 1245-1250.
Liu, D. Q., Y. Lu and M. Y. Wang (2004). "[Research on the expression of some immune markers
after reamed and unreamed femoral nailing]." Zhonghua Wai Ke Za Zhi 42(12): 741-745.
Liu, Y., G. Y. Chen and P. Zheng (2009). "CD24-Siglec G/10 discriminates danger- from pathogenassociated molecular patterns." Trends Immunol 30(12): 557-561.
Llewelyn, C. A., P. E. Hewitt, R. S. G. Knight, K. Amar, S. Cousens, J. Mackenzie and R. G. Will
(2004). "Possible transmission of variant Creutzfeldt-Jakob disease by blood transfusion." The Lancet
363(9407): 417-421.
Long, H., Z. C. Lin, D. R. Situ, L. R. He, S. L. Yan, Y. B. Lin and T. H. Rong (2007). "[Cytokine
responses after lobectomy: a prospective randomized comparison of video-assisted thoracoscopic
surgery and minimal incision thoracotomy]." Ai Zheng 26(9): 991-995.
Lotfi, R., J. Eisenbacher, G. Solgi, K. Fuchs, T. Yildiz, C. Nienhaus, M. T. Rojewski and H.
Schrezenmeier (2011). "Human mesenchymal stem cells respond to native but not oxidized damage
associated molecular pattern molecules from necrotic (tumor) material." Eur J Immunol 41(7): 20212028.
Luo, J., D. Wang, H. Lan and Z. Sun (2000). "Effects of aprotinin on serum interleukin-2 and soluble
interleukin-2 receptor during cardiopulmonary bypass." J Tongji Med Univ 20(2): 143-144.
Machado, L. B., E. M. Negri, W. W. Bonafe, L. M. Santos, L. M. Sa Malbouisson and M. J. Carmona
(2011). "[Evaluation of cytokine levels and pulmonary function in patients undergoing coronary artery
bypass graft]." Rev Bras Anestesiol 61(3): 275-285.
Maier, M., E. V. Geiger, D. Henrich, C. Bendt, S. Wutzler, M. Lehnert and I. Marzi (2009). "Platelet
factor 4 is highly upregulated in dendritic cells after severe trauma." Mol Med 15(11-12): 384-391.
Maier, M., E. V. Geiger, D. Henrich, R. Ebrahimi, S. Wutzler, M. Lehnert and I. Marzi (2009).
"Apoptosis differs in dendritic cell subsets early after severe trauma." Hum Immunol 70(10): 803-808.
Maier, M., S. Wutzler, M. Bauer, P. Trendafilov, D. Henrich and I. Marzi (2008). "Altered Gene
Expression Patterns in Dendritic Cells After Severe Trauma: Implications for Systemic Inflammation
and Organ Injury." Shock 30(4): 344-351 310.1097/SHK.1090b1013e3181673eb3181674.
Majetschak, M. (2011). "Extracellular ubiquitin: immune modulator and endogenous opponent of
damage-associated molecular pattern molecules." Journal of Leukocyte Biology 89(2): 205-219.
197
Chapter - VIII
References
Majetschak, M., S. Zedler, A. Hostmann, L. T. Sorell, M. B. Patel, L. T. Novar, R. Kraft, F. Habib, M.
A. de Moya, W. Ertel, E. Faist and U. Schade (2008). "Systemic ubiquitin release after blunt trauma
and burns: association with injury severity, posttraumatic complications, and survival." J Trauma
64(3): 586-596; discussion 596-588.
Makarenkova, V. P., V. Bansal, B. M. Matta, L. A. Perez and J. B. Ochoa (2006). "CD11b+/Gr-1+
myeloid suppressor cells cause T cell dysfunction after traumatic stress." J Immunol 176(4): 20852094.
Mansilla, E., G. H. Marin, H. Drago, F. Sturla, E. Salas, C. Gardiner, S. Bossi, R. Lamonega, A.
Guzman, A. Nunez, M. A. Gil, G. Piccinelli, R. Ibar and C. Soratti (2006). "Bloodstream cells
phenotypically identical to human mesenchymal bone marrow stem cells circulate in large amounts
under the influence of acute large skin damage: new evidence for their use in regenerative medicine."
Transplant Proc 38(3): 967-969.
Manson, J., C. Thiemermann and K. Brohi (2012). "Trauma alarmins as activators of damage-induced
inflammation." Br J Surg 99 Suppl 1: 12-20.
Manthey, H. D., T. M. Woodruff, S. M. Taylor and P. N. Monk (2009). "Complement component 5a
(C5a)." Int J Biochem Cell Biol 41(11): 2114-2117.
Marik, P. E. and M. Flemmer (2012). "The immune response to surgery and trauma: Implications for
treatment." J Trauma Acute Care Surg 73(4): 801-808.
Martin A, v. S. A. (2006). "Transfusion of autologous blood from reinfusion systems in total knee
arthroplasty." Int Orthop 30(6): 541-544.
Matsuda K, N. M., Katsube S, Maezawa K, Kurosawa H. (2010). "Activation of fibrinolysis by
reinfusion of unwashed salvaged blood after total knee arthroplasty." Transfusion and Apheresis
Science 42(1): 33-37.
McDonald, B. and P. Kubes (2011). "Cellular and molecular choreography of neutrophil recruitment
to sites of sterile inflammation." J Mol Med (Berl) 89(11): 1079-1088.
McDonald, B., K. Pittman, G. B. Menezes, S. A. Hirota, I. Slaba, C. C. Waterhouse, P. L. Beck, D. A.
Muruve and P. Kubes (2010). "Intravascular danger signals guide neutrophils to sites of sterile
inflammation." Science 330(6002): 362-366.
McGee, H. M., B. A. Schmidt, C. J. Booth, G. D. Yancopoulos, D. M. Valenzuela, A. J. Murphy, S.
Stevens, R. A. Flavell and V. Horsley (2013). "IL-22 promotes fibroblast-mediated wound repair in
the skin." J Invest Dermatol 133(5): 1321-1329.
Medzhitov, R. (2008). "Origin and physiological roles of inflammation." Nature 454(7203): 428-435.
Mei, X. L., T. Guo and J. N. Zhao (2011). "[Clinical significance and expression of the inflammatory
cytokines (IL-1, IL-2, IL-6 and IL-10) in blood serum of the patients after total hip replacement]."
Zhongguo Gu Shang 24(6): 463-465.
Merrell K, S. K., Graves SW, Esplin MS, Lewis NE, Thulin CD (2004). "Analysis of low-abundance,
low-molecular-weight serum proteins using mass spectrometry." J Biomol Tech 15(4): 238-248.
Miller, A. C., R. M. Rashid and E. M. Elamin (2007). "The "T" in trauma: the helper T-cell response
and the role of immunomodulation in trauma and burn patients." J Trauma 63(6): 1407-1417.
Moonen, A. F. C. M., N. T. Knoors, J. J. Van Os, A. D. Verburg and P. Pilot (2007). "Retransfusion
of filtered shed blood in primary total hip and knee arthroplasty: a prospective randomized clinical
trial." Transfusion 47(3): 379-384.
Moselli, N. M., E. Baricocchi, D. Ribero, A. Sottile, L. Suita and F. Debernardi (2011).
"Intraoperative epidural analgesia prevents the early proinflammatory response to surgical trauma.
198
Chapter - VIII
References
Results from a prospective randomized clinical trial of intraoperative epidural versus general
analgesia." Ann Surg Oncol 18(10): 2722-2731.
Mottl-Link, S., M. Russlies, M. Klinger, M. Seyfarth, R. Ascherl and R. Gradinger (1998).
"Erythrocytes and proinflammatory mediators in wound drainage." Vox Sang 75(3): 205-211.
Munoz, L. E., K. Lauber, M. Schiller, A. A. Manfredi and M. Herrmann (2010). "The role of defective
clearance of apoptotic cells in systemic autoimmunity." Nat Rev Rheumatol 6(5): 280-289.
Muñoz, M., D. Ariza, M. Garcerán, A. Gómez and A. Campos (2005). "Benefits of postoperative shed
blood reinfusion in patients undergoing unilateral total knee replacement." Archives of Orthopaedic
and Trauma Surgery 125(6): 385-389.
Muñoz, M., A. Campos, E. Martín-Montañez and J. Pavía (2013). "Could cost-effectiveness of
postoperative shed blood salvage after elective knee arthroplasty be improved?" Transfusion 53(6):
1372-1373.
Munoz, M., A. Cobos, A. Campos, D. Ariza, E. Munoz and A. Gomez (2006). "Post-operative
unwashed shed blood transfusion does not modify the cellular immune response to surgery for total
knee replacement." Acta Anaesthesiol Scand 50(4): 443-450.
Muñoz, M., A. Cobos, A. Campos, D. Ariza, E. Muñoz and A. Gómez (2005). "Impact of
postoperative shed blood transfusion, with or without leucocyte reduction, on acute-phase response to
surgery for total knee replacement." Acta Anaesthesiol Scand 49(8): 1182-1190.
Muñoz, M., E. Muñoz, A. Navajas, A. Campos, F. Rius and A. Gómez (2006). "Impact of
postoperative unwashed shed blood retrieved after total knee arthroplasty on endotoxin-stimulated
tumor necrosis factor alpha release in vitro." Anesthesiology 104(2): 267-272.
Muñoz, M., R. Slappendel and D. Thomas (2011). "Laboratory characteristics and clinical utility of
post-operative cell salvage: washed or unwashed blood transfusion?" Blood Transfus 9(3): 248-261.
Murakami, T., H. Iwagaki, S. Saito, S. Ohtani, K. Kuroki, M. Kuinose, N. Tanaka and K. Tanemoto
(2005). "Equivalence of the acute cytokine surge and myocardial injury after coronary artery bypass
grafting with and without a novel extracorporeal circulation system." J Int Med Res 33(2): 133-149.
Murray, C. K. (2008). "Infectious disease complications of combat-related injuries." Crit Care Med
36(7 Suppl): S358-364.
Nakamura, K., T. Ueno, H. Yamamoto, Y. Iguro, K. Yamada and R. Sakata (2005). "Relationship
between cerebral injury and inflammatory responses in patients undergoing cardiac surgery with
cardiopulmonary bypass." Cytokine 29(3): 95-104.
Narita, S., N. Tsuchiya, T. Kumazawa, S. Maita, K. Numakura, T. Obara, H. Tsuruta, M. Saito, T.
Inoue, Y. Horikawa, S. Satoh and T. Habuchi (2013). "Comparison of surgical stress in patients
undergoing open versus laparoscopic radical prostatectomy by measuring perioperative serum
cytokine levels." J Laparoendosc Adv Surg Tech A 23(1): 33-37.
Newman JH, B. M., Murphy J (1997). "The clinical advantages of autologous transfusion. A
randomized, controlled study after knee replacement." J Bone Joint Surg Br 79(4): 630-632.
Noelle, R. J. and E. C. Nowak (2010). "Cellular sources and immune functions of interleukin-9." Nat
Rev Immunol 10(10): 683-687.
Notarnicola, A., L. Moretti, S. Tafuri, A. Vacca, G. Marella and B. Moretti (2011). "Postoperative
pain monitor after total knee replacement." Musculoskelet Surg 95(1): 19-24.
Nuttall GA, S. P., Oliver WC Jr, Horlocker TT, Shaughnessy WJ, Cabanela ME, Bryant S (1996).
"The predictors of red cell transfusions in total hip arthroplasties." Transfusion 36(2): 144-149.
Oh, S. A. and M. O. Li (2013). "TGF-β: Guardian of T Cell Function." J Immunol 191(8): 3973-3979.
199
Chapter - VIII
References
Ohta, A. and M. Sitkovsky (2001). "Role of G-protein-coupled adenosine receptors in downregulation
of inflammation and protection from tissue damage." Nature 414(6866): 916-920.
Omenn, G. S., Ed. (2006). Exploring the Human Plasma Proteome, John Wiley and Sons, Inc.
ON Nagi, S. K., RK Sen, YK Batra (2003). "Post operative reinfusion of unwashed, filtered drainage
blood in total knee replacement." Indian Journal of Orthopaedics 37(4): 252-256.
Opdenakker, G., P. M. Rudd, C. P. Ponting and R. A. Dwek (1993). "Concepts and principles of
glycobiology." FASEB J 7(14): 1330-1337.
Osoegawa, A., T. Yano, T. Yamanaka, T. Tagawa, F. Shoji, I. Yoshino, A. Taketomi, H. Ito, Y.
Kakeji and Y. Maehara (2011). "Plasma high-mobility group box 1 as an indicator of surgical stress."
Surg Today 41(7): 903-907.
Ostrowski, K., T. Rohde, S. Asp, P. Schjerling and B. K. Pedersen (1999). "Pro- and antiinflammatory cytokine balance in strenuous exercise in humans." J Physiol 515 ( Pt 1): 287-291.
Panoskaltsis-Mortari, A., P. A. Taylor, J. S. Rubin, A. Uren, L. A. Welniak, W. J. Murphy, C. L.
Farrell, D. L. Lacey and B. R. Blazar (2000). "Keratinocyte growth factor facilitates alloengraftment
and ameliorates graft-versus-host disease in mice by a mechanism independent of repair of
conditioning-induced tissue injury." Blood 96(13): 4350-4356.
Parlato, M., F. Souza-Fonseca-Guimaraes, F. Philippart, B. Misset, M. Adib-Conquy, J. M. Cavaillon,
S. Jacqmin, D. Journois, A. Lagrange, G. Pinot de Villechenon, N. Aissaoui, J. L. Diehl, E. Guerot,
M. Venot, O. Hamzaoui, D. Prat, B. Sztrymf, D. Annane, V. Maxime, A. Polito, L. Lecomte, E.
Bournaud, E. Audureau, L. Quinquis, A. Rouquette, C. Bruel, J. Fournier, M. Garrouste-Orgeas, C.
Gregoire, N. Lau, A. Max, B. Bouhemad, F. Ethuin, J. P. Bedos, P. Crosnier, V. Laurent, S. Merceron,
A. Pachot, V. Moucadel, C. Fitting and V. Puchois (2014). "CD24-Triggered Caspase-Dependent
Apoptosis via Mitochondrial Membrane Depolarization and Reactive Oxygen Species Production of
Human Neutrophils Is Impaired in Sepsis." J Immunol 192(5): 2449-2459.
Pasnik, J., J. A. Moll, J. Moll, A. Cywinska-Bernas, W. Mazurowski, J. Arendarczyk, A. Sysa, M.
Banasik and K. Zeman (2005). "[Alterations of procalcitonin and interleukin 6 after cardiopulmonary
bypass in children with congenital heart disease]." Pol Merkur Lekarski 19(109): 20-23.
Perianayagam, M. C., V. S. Balakrishnan, A. J. King, B. J. G. Pereira and B. L. Jaber (2002). "C5a
delays apoptosis of human neutrophils by a phosphatidylinositol 3-kinase-signaling pathway." Kidney
Int 61(2): 456-463.
Peter VK, R. M., Matthews MG. (2001). "Re-transfusion of autologous blood from wound drains: the
means for reducing transfusion requirements in total knee arthroplasty." Knee 8(4): 321-323.
Petricoin, E., III and L. A. Liotta (2003). "The Vision for a New Diagnostic Paradigm." Clin Chem
49(8): 1276-1278.
Petricoin, E. F. and L. A. Liotta (2003). "Clinical Applications of Proteomics." The Journal of
Nutrition 133(7): 2476S-2484S.
Philippou, A., M. Maridaki, A. Theos and M. Koutsilieris (2012). "Cytokines in muscle damage." Adv
Clin Chem 58: 49-87.
Pillonel J, L. S. (2004). "Trends in residual risk of transfusion-transmitted viral infections (HIV, HCV,
HBV) in France between 1992 and 2002 and impact of nucleic acid testing." Transfus Clin Biol 11(2):
81-86.
Pizon, M. T., T. Gburek, M. Pizon and K. Sztefko (2006). "Kinetics of plasma heat shock protein
HSP-70 release in coronary artery surgery: on-pump versus off-pump." Minerva Chir 61(6): 483-491.
200
Chapter - VIII
References
Ponce de Leon-Rosales, S. P., F. Molinar-Ramos, G. Dominguez-Cherit, M. S. Rangel-Frausto and V.
G. Vazquez-Ramos (2000). "Prevalence of infections in intensive care units in Mexico: a multicenter
study." Crit Care Med 28(5): 1316-1321.
Poulsen, T. D., L. W. Andersen, D. Steinbruchel, J. P. Gotze, O. S. Jorgensen and N. V. Olsen (2009).
"Two large preoperative doses of erythropoietin do not reduce the systemic inflammatory response to
cardiac surgery." J Cardiothorac Vasc Anesth 23(3): 316-323.
Presta, P., A. S. Rubino, G. Lucisano, G. F. Serraino, D. Foti, E. Gulletta, A. Renzulli and G. Fuiano
(2011). "Preoperative statins improve recovery of renal function but not by an anti-inflammatory
effect: observational study in 69 elderly patients undergoing cardiac surgery." Int Urol Nephrol 43(2):
601-609.
Prieto, M. A., S. Guash, J. C. Mendez, C. Munoz, A. Planas and G. Reyes (2013). "Does use of cell
saver decrease the inflammatory response in cardiac surgery?" Asian Cardiovasc Thorac Ann 21(1):
37-42.
Prockop, D. J. and J. Y. Oh (2012). "Mesenchymal stem/stromal cells (MSCs): role as guardians of
inflammation." Mol Ther 20(1): 14-20.
Rabinowitz, R. P. and E. S. Caplan (1999). "Management of infections in the trauma patient." Surg
Clin North Am 79(6): 1373-1383, x.
Rao, V. K., R. Dyga, C. Bartels and J. H. Waters (2012). "A cost study of postoperative cell salvage in
the setting of elective primary hip and knee arthroplasty." Transfusion 52(8): 1750-1760.
Ren, X., B. Hu and L. M. Colletti (2010). "IL-22 is involved in liver regeneration after hepatectomy."
Am J Physiol Gastrointest Liver Physiol 298(1): G74-80.
Rendon, J. L., X. Li, S. Akhtar and M. A. Choudhry (2013). "Interleukin-22 modulates gut epithelial
and immune barrier functions following acute alcohol exposure and burn injury." Shock 39(1): 11-18.
Richards, M. J., J. R. Edwards, D. H. Culver and R. P. Gaynes (2000). "Nosocomial infections in
combined medical-surgical intensive care units in the United States." Infect Control Hosp Epidemiol
21(8): 510-515.
Roche, M., G. W. Thorn and A. G. Hills (1950). "The Levels of Circulating Eosinophils and Their
Response to ACTH in Surgery." New England Journal of Medicine 242(9): 307-314.
Rock, K. L., E. Latz, F. Ontiveros and H. Kono (2010). "The sterile inflammatory response." Annu
Rev Immunol 28: 321-342.
Rose-John, S. (2012). "IL-6 trans-signaling via the soluble IL-6 receptor: importance for the proinflammatory activities of IL-6." Int J Biol Sci 8(9): 1237-1247.
Rose-John, S., J. Scheller, G. Elson and S. A. Jones (2006). "Interleukin-6 biology is coordinated by
membrane-bound and soluble receptors: role in inflammation and cancer." J Leukoc Biol 80(2): 227236.
Rosencher, N., H. E. M. Kerkkamp, G. Macheras, L. M. Munuera, G. Menichella, D. M. Barton, S.
Cremers, I. L. Abraham and O. I. For The (2003). "Orthopedic Surgery Transfusion Hemoglobin
European Overview (OSTHEO) study: blood management in elective knee and hip arthroplasty in
Europe*." Transfusion 43(4): 459-469.
Rosencher N, K. H., Macheras G, Munuera LM, Menichella G, Barton DM, Cremers S, Abraham IL
(2003). "Orthopedic Surgery Transfusion Hemoglobin European Overview (OSTHEO) study: blood
management in elective knee and hip arthroplasty in Europe." Transfusion 43(4): 459-469.
Rothenburger, M., R. Soeparwata, M. C. Deng, C. Schmid, E. Berendes, T. D. Tjan, M. J. Wilhelm,
M. Erren, D. Bocker and H. H. Scheld (2001). "Prediction of clinical outcome after cardiac surgery:
the role of cytokines, endotoxin, and anti-endotoxin core antibodies." Shock 16 Suppl 1: 44-50.
201
Chapter - VIII
References
Ryckman, C., K. Vandal, P. Rouleau, M. Talbot and P. A. Tessier (2003). "Proinflammatory
Activities of S100: Proteins S100A8, S100A9, and S100A8/A9 Induce Neutrophil Chemotaxis and
Adhesion." The Journal of Immunology 170(6): 3233-3242.
Sablotzki, A., M. Dehne, I. Welters, T. Menges, N. Lehmann, G. Gorlach, C. Osmer and G.
Hempelmann (1997). "Alterations of the cytokine network in patients undergoing cardiopulmonary
bypass." Perfusion 12(6): 393-403.
Sablotzki, A., M. G. Dehne, V. Mann, G. Gorlach, J. Muhling, B. Zickmann and G. Hempelmann
(1999). "Plasma levels of selectins and interleukins in cardiovascular surgery using cardiopulmonary
bypass." Thorac Cardiovasc Surg 47(1): 26-31.
Sablotzki, A., I. Friedrich, J. Muhling, M. G. Dehne, J. Spillner, R. E. Silber and E. Czeslik (2002).
"The systemic inflammatory response syndrome following cardiac surgery: different expression of
proinflammatory cytokines and procalcitonin in patients with and without multiorgan dysfunctions."
Perfusion 17(2): 103-109.
Sadik, C. D., N. D. Kim and A. D. Luster (2011). "Neutrophils cascading their way to inflammation."
Trends Immunol 32(10): 452-460.
Sakai, H., K. Mori, K. Suzuki, Y. Katayama and T. Matsuyama (1994). "[The clinical significance of
interleukin-6 as an inflammatory marker (the studies in patients with open heart surgery)]." Rinsho
Byori 42(11): 1151-1157.
Sakamoto, K., H. Arakawa, S. Mita, T. Ishiko, S. Ikei, H. Egami, S. Hisano and M. Ogawa (1994).
"Elevation of circulating interleukin 6 after surgery: factors influencing the serum level." Cytokine
6(2): 181-186.
Salas Millan, J., N. de Vega, J. Carmona Aurioles, S. Negri Arjona, J. Garcia-Vallejo and M. Munoz
Gomez (2001). "[Autotransfusion after cardiac surgery. Hematological, biochemical and
immunological properties of shed mediastinal blood]." Rev Esp Anestesiol Reanim 48(3): 122-130.
Sandoval, S., S. Alrawi, M. Samee, R. Satheesan, R. Raju, J. N. Cunningham and A. J. Acinapura
(2001). "A cytokine analysis of the effect of cell saver on blood in coronary bypass surgery." Heart
Surg Forum 4(2): 113-117; discussion 117-119.
Sarah, L. H., T. Francesco, A. S. Jane and N. M. Charles (2003). "Post-operative red cell salvage in
total knee replacement." 7(1): 15-17.
Sarbinowski, R., S. Arvidsson, M. Tylman, T. Oresland and A. Bengtsson (2005). "Plasma
concentration of procalcitonin and systemic inflammatory response syndrome after colorectal
surgery." Acta Anaesthesiol Scand 49(2): 191-196.
Sasajima, K., R. Futami, T. Matsutani, T. Nomura, H. Makino, H. Maruyama and M. Miyashita
(2009). "Increases in soluble tumor necrosis factor receptors coincide with increases in interleukin-6
and proteinases after major surgery." Hepatogastroenterology 56(94-95): 1377-1381.
Sasaki, J. R., Q. Zhang and M. G. Schwacha (2011). "Burn induces a Th-17 inflammatory response at
the injury site." Burns 37(4): 646-651.
Savage, C. D., G. Lopez-Castejon, A. Denes and D. Brough (2012). "NLRP3-Inflammasome
Activating DAMPs Stimulate an Inflammatory Response in Glia in the Absence of Priming Which
Contributes to Brain Inflammation after Injury." Front Immunol 3: 288.
Scanzello, C., M. Figgie, B. Nestor and S. Goodman (2006). "Perioperative Management of
Medications Used in the Treatment of Rheumatoid Arthritis." HSS Journal 2(2): 141-147.
Schaff, H. V., J. M. Hauer, W. R. Bell, T. J. Gardner, J. S. Donahoo, V. L. Gott and R. K. Brawley
(1978). "Autotransfusion of shed mediastinal blood after cardiac surgery: a prospective study." J
Thorac Cardiovasc Surg 75(4): 632-641.
202
Chapter - VIII
References
Scheingraber, S., D. Dobbert, P. Schmiedel, E. Seliger and H. Dralle (2005). "Gender-specific
differences in sex hormones and cytokines in patients undergoing major abdominal surgery." Surg
Today 35(10): 846-854.
Schilling, T., A. Kozian, M. Senturk, C. Huth, A. Reinhold, G. Hedenstierna and T. Hachenberg
(2011). "Effects of volatile and intravenous anesthesia on the alveolar and systemic inflammatory
response in thoracic surgical patients." Anesthesiology 115(1): 65-74.
Schleinzer, W. and G. Singbartl (1997). "5a The concept of autologous transfusion (CAT)." Baillière's
Clinical Anaesthesiology 11(2): 301-317.
Schmidt, H. (2000). "Autotransfusion of mediastinal blood after coronary artery bypass grafting." Dan
Med Bull 47(2): 79-93.
Schmidt, H., K. Bendtzen and P. E. Mortensen (1998). "The inflammatory cytokine response after
autotransfusion of shed mediastinal blood." Acta Anaesthesiol Scand 42(5): 558-564.
Schonberger, J. P., W. van Oeveren, J. J. Bredee, P. A. Everts, J. de Haan and C. R. Wildevuur
(1994). "Systemic blood activation during and after autotransfusion." Ann Thorac Surg 57(5): 12561262.
Sebastian, C., R. Romero, E. Olalla, C. Ferrer, J. J. Garcia-Vallejo and M. Munoz (2000).
"Postoperative blood salvage and reinfusion in spinal surgery: blood quality, effectiveness and impact
on patient blood parameters." Eur Spine J 9(6): 458-465.
Seebach, C., D. Henrich, R. Tewksbury, K. Wilhelm and I. Marzi (2007). "Number and proliferative
capacity of human mesenchymal stem cells are modulated positively in multiple trauma patients and
negatively in atrophic nonunions." Calcif Tissue Int 80(4): 294-300.
Servis, D., Z. Busic, I. Stipancic, L. Patrlj and A. Gagro (2008). "Serum cytokine changes after gastric
resection or gastrectomy for gastric cancer." Hepatogastroenterology 55(86-87): 1868-1872.
Sharon, N. (2007). "Lectins: Carbohydrate-specific Reagents and Biological Recognition Molecules."
Journal of Biological Chemistry 282(5): 2753-2764.
Shi, C. and E. G. Pamer (2011). "Monocyte recruitment during infection and inflammation." Nat Rev
Immunol 11(11): 762-774.
Shimamoto, A., S. Kanemitsu, K. Fujinaga, M. Takao, K. Onoda, T. Shimono, K. Tanaka, H. Shimpo
and I. Yada (2000). "Biocompatibility of silicone-coated oxygenator in cardiopulmonary bypass." Ann
Thorac Surg 69(1): 115-120.
Shorr, A. F. and W. L. Jackson (2005). "Transfusion practice and nosocomial infection: assessing the
evidence." Curr Opin Crit Care 11(5): 468-472.
Silber, J. H., P. R. Rosenbaum, R. R. Kelz, C. E. Reinke, M. D. Neuman, R. N. Ross, O. EvenShoshan, G. David, P. A. Saynisch, F. A. Kyle, D. W. Bratzler and L. A. Fleisher (2012). "Medical
and financial risks associated with surgery in the elderly obese." Ann Surg 256(1): 79-86.
Silverboard, H., I. Aisiku, G. S. Martin, M. Adams, G. Rozycki and M. Moss (2005). "The role of
acute blood transfusion in the development of acute respiratory distress syndrome in patients with
severe trauma." J Trauma 59(3): 717-723.
Sjogren, F. and C. Anderson (2009). "Sterile trauma to normal human dermis invariably induces
IL1beta, IL6 and IL8 in an innate response to "danger"." Acta Derm Venereol 89(5): 459-465.
Slotwinski, R., W. L. Olszewski, M. Slotkowski, G. Lech, M. Zaleska, S. M. Slotwinska and W. I.
Krasnodebski (2007). "Can the interleukin-1 receptor antagonist (IL-1ra) be a marker of antiinflammatory response to enteral immunonutrition in malnourished patients after
pancreaticoduodenectomy?" JOP 8(6): 759-769.
203
Chapter - VIII
References
Soares, D. M., M. J. Figueiredo, J. M. Martins, R. R. Machado, A. Kanashiro, D. o. C. Malvar, A. C.
Pessini, J. Roth and G. E. Souza (2009). "CCL3/MIP-1 alpha is not involved in the LPS-induced fever
and its pyrogenic activity depends on CRF." Brain Res 1269: 54-60.
Sorensen, O. E., D. R. Thapa, K. M. Roupe, E. V. Valore, U. Sjobring, A. A. Roberts, A. Schmidtchen
and T. Ganz (2006). "Injury-induced innate immune response in human skin mediated by
transactivation of the epidermal growth factor receptor." J Clin Invest 116(7): 1878-1885.
Southern, E. P., M. H. Huo, J. R. Mehta and K. J. Keggi (1995). "Unwashed wound drainage blood.
What are we giving our patients?" Clin Orthop Relat Res(320): 235-246.
Speziale, G., P. Ferroni, G. Ruvolo, K. Fattouch, F. M. Pulcinelli, L. Lenti, P. P. Gazzaniga and B.
Marino (2000). "Effect of normothermic versus hypothermic cardiopulmonary bypass on cytokine
production and platelet function." J Cardiovasc Surg (Torino) 41(6): 819-827.
Stachura, A., R. Krol, T. Poplawski, D. Michalik, S. Pomianowski, M. Jacobsson, M. Aberg and A.
Bengtsson (2011). "Transfusion of intra-operative autologous whole blood: influence on complement
activation and interleukin formation." Vox Sang 100(2): 239-246.
Stechmiller, J. K., B. Childress and T. Porter (2004). "Arginine immunonutrition in critically ill
patients: a clinical dilemma." Am J Crit Care 13(1): 17-23.
Stephan, R. N., T. S. Kupper, A. S. Geha, A. E. Baue and I. H. Chaudry (1987). "HEmorrhage without
tissue trauma produces immunosuppression and enhances susceptibility to sepsis." Archives of
Surgery 122(1): 62-68.
Steven, T. W. and W. W. William (2003). "Autologous blood transfusion after total knee arthroplasty:
A randomized, prospective study comparing predonated and postoperative salvage blood." The
Journal of arthroplasty 18(3): 243-249.
Stuber, F., S. Klaschik, L. E. Lehmann, J. C. Schewe, S. Weber and M. Book (2005). "Cytokine
promoter polymorphisms in severe sepsis." Clin Infect Dis 41 Suppl 7: S416-420.
Suda, K., Y. Kitagawa, S. Ozawa, Y. Saikawa, M. Ueda, E. Abraham, M. Kitajima and A. Ishizaka
(2006). "Serum concentrations of high-mobility group box chromosomal protein 1 before and after
exposure to the surgical stress of thoracic esophagectomy: a predictor of clinical course after
surgery?" Dis Esophagus 19(1): 5-9.
Sullivan, P. J., K. J. Sweeney, K. M. Hirpara, C. B. Malone, W. Curtin and M. J. Kerin (2009).
"Cyclical ischaemic preconditioning modulates the adaptive immune response in human limb
ischaemia-reperfusion injury." Br J Surg 96(4): 381-390.
Sun, Y. J., D. D. Song, Y. G. Diao, J. Zhou and T. Z. Zhang (2013). "Penehyclidine hydrochloride
preserves the intestinal barrier function in patients undergoing cardiopulmonary bypass." J Thorac
Cardiovasc Surg 146(1): 179-185.
Suratt, B. T., J. M. Petty, S. K. Young, K. C. Malcolm, J. G. Lieber, J. A. Nick, J. A. Gonzalo, P. M.
Henson and G. S. Worthen (2004). "Role of the CXCR4/SDF-1 chemokine axis in circulating
neutrophil homeostasis." Blood 104(2): 565-571.
Svitek, V., V. Lonsky, J. Mandak, J. Krejsek, M. Kolackova, V. Brzek, J. Kubicek, M. Volt, M.
Bartos and J. Harrer (2009). "No clear clinical benefit of using mini-invasive extracorporeal
circulation in coronary artery bypass grafting in low-risk patients." Perfusion 24(6): 389-395.
Swirski, F. K., M. Nahrendorf, M. Etzrodt, M. Wildgruber, V. Cortez-Retamozo, P. Panizzi, J.-L.
Figueiredo, R. H. Kohler, A. Chudnovskiy, P. Waterman, E. Aikawa, T. R. Mempel, P. Libby, R.
Weissleder and M. J. Pittet (2009). "Identification of Splenic Reservoir Monocytes and Their
Deployment to Inflammatory Sites." Science 325(5940): 612-616.
Swisher, J. F., U. Khatri and G. M. Feldman (2007). "Annexin A2 is a soluble mediator of
macrophage activation." J Leukoc Biol 82(5): 1174-1184.
204
Chapter - VIII
References
Szczesny, T. J., R. Slotwinski, A. Stankiewicz, B. Szczygiel, M. Zaleska and M. Kopacz (2007).
"Interleukin 6 and interleukin 1 receptor antagonist as early markers of complications after lung
cancer surgery." Eur J Cardiothorac Surg 31(4): 719-724.
Szczesny, T. J., R. Slotwinski, B. Szczygiel, A. Stankiewicz, M. Zaleska, M. Kopacz and A.
Olesinska-Grodz (2007). "Systematic mediastinal lymphadenectomy does not increase postoperative
immune response after major lung resections." Eur J Cardiothorac Surg 32(6): 868-872.
Szerafin, T., M. Brunner, A. Horvath, L. Szentgyorgyi, B. Moser, G. Boltz-Nitulescu, A. Peterffy, K.
Hoetzenecker, B. Steinlechner, E. Wolner and H. J. Ankersmit (2005). "Soluble ST2 protein in cardiac
surgery: a possible negative feedback loop to prevent uncontrolled inflammatory reactions." Clin Lab
51(11-12): 657-663.
Szerafin, T., K. Hoetzenecker, S. Hacker, A. Horvath, A. Pollreisz, P. Arpad, A. Mangold, T.
Wliszczak, M. Dworschak, R. Seitelberger, E. Wolner and H. J. Ankersmit (2008). "Heat shock
proteins 27, 60, 70, 90alpha, and 20S proteasome in on-pump versus off-pump coronary artery bypass
graft patients." Ann Thorac Surg 85(1): 80-87.
Szerafin, T., T. Niederpold, A. Mangold, K. Hoetzenecker, S. Hacker, G. Roth, M. Lichtenauer, M.
Dworschak, E. Wolner and H. J. Ankersmit (2009). "Secretion of soluble ST2 - possible explanation
for systemic immunosuppression after heart surgery." Thorac Cardiovasc Surg 57(1): 25-29.
Tablov, B., S. Stavreva, V. Tablov, I. Popov and E. Konova (2007). "[Immunological aspects of
postoperative analgesia after abdominal hysterectomy]." Akush Ginekol (Sofiia) 46(1): 18-24.
Takahata, R., S. Ono, H. Tsujimoto, S. Hiraki, A. Kimura, M. Kinoshita, H. Miyazaki, D. Saitoh and
K. Hase (2011). "Postoperative serum concentrations of high mobility group box chromosomal
protein-1 correlates to the duration of SIRS and pulmonary dysfunction following gastrointestinal
surgery." J Surg Res 170(1): e135-140.
Takatsu, K., T. Kouro and Y. Nagai (2009). "Interleukin 5 in the link between the innate and acquired
immune response." Adv Immunol 101: 191-236.
Takeda, S., K. Nakanishi, H. Ikezaki, C. Kim, A. Sakamoto, K. Tanaka and R. Ogawa (2002).
"Cardiac marker responses to coronary artery bypass graft surgery with cardiopulmonary bypass and
aortic cross-clamping." J Cardiothorac Vasc Anesth 16(4): 421-425.
Takenaka, K., E. Ogawa, H. Wada and T. Hirata (2006). "Systemic inflammatory response syndrome
and surgical stress in thoracic surgery." J Crit Care 21(1): 48-53; discussion 53-45.
Tamayo, E., F. J. Alvarez, O. Alonso, R. Bustamante, J. Castrodeza, S. Soria and C. Lajo (2009).
"Effects of simvastatin on systemic inflammatory responses after cardiopulmonary bypass." J
Cardiovasc Surg (Torino) 50(5): 687-694.
Tamayo, E., F. J. Alvarez, O. Alonso, J. Castrodeza, R. Bustamante, J. I. Gomez-Herreras, S. Florez
and R. Rodriguez (2008). "The inflammatory response to colloids and crystalloids used for pump
priming during cardiopulmonary bypass." Acta Anaesthesiol Scand 52(9): 1204-1212.
Teixeira, C. F., S. R. Zamuner, J. P. Zuliani, C. M. Fernandes, M. A. Cruz-Hofling, I. Fernandes, F.
Chaves and J. M. Gutierrez (2003). "Neutrophils do not contribute to local tissue damage, but play a
key role in skeletal muscle regeneration, in mice injected with Bothrops asper snake venom." Muscle
Nerve 28(4): 449-459.
Teixeira, R. H., L. Antonangelo, F. S. Vargas, M. L. Caramori, J. E. Afonso, Jr., M. M. Acencio, P.
M. Pego-Fernandes and F. B. Jatene (2010). "Cytokine profile in pleural fluid and serum after lung
transplantation." Transplant Proc 42(2): 531-534.
Thadikkaran, L., M. A. Siegenthaler, D. Crettaz, P.-A. Queloz, P. Schneider and J.-D. Tissot (2005).
"Recent advances in blood-related proteomics." PROTEOMICS 5(12): 3019-3034.
205
Chapter - VIII
References
Thiele, R. I., V. Daniel, G. Opelz, R. Lange, F. U. Sack, H. Jakob and S. Hagl (1998). "Circulating
interleukin-1 receptor antagonist (IL-1RA) serum levels in patients undergoing orthotopic heart
transplantation." Transpl Int 11(6): 443-448.
Tidball, J. G. (2005). "Inflammatory processes in muscle injury and repair." Am J Physiol Regul
Integr Comp Physiol 288(2): R345-353.
Tilg, H., C. A. Dinarello and J. W. Mier (1997). "IL-6 and APPs: anti-inflammatory and
immunosuppressive mediators." Immunol Today 18(9): 428-432.
Torres, A., K. Torres, T. Paszkowski, G. J. Staskiewicz and R. Maciejewski (2007). "Cytokine
response in the postoperative period after surgical treatment of benign adnexal masses: comparison
between laparoscopy and laparotomy." Surg Endosc 21(10): 1841-1848.
Toubai, T., G. Hou, N. Mathewson, C. Liu, Y. Wang, K. Oravecz-Wilson, E. Cummings, C. Rossi, R.
Evers, Y. Sun, J. Wu, S. W. Choi, D. Fang, P. Zheng, Y. Liu and P. Reddy (2014). "Siglec-G-CD24
axis controls the severity of graft-versus-host disease in mice." Blood.
Tsangaris, G., R. Weitzdörfer, D. Pollak, G. Lubec and M. Fountoulakis (2005). "The amniotic fluid
cell proteome." ELECTROPHORESIS 26(6): 1168-1173.
Tsou, C. L., W. Peters, Y. Si, S. Slaymaker, A. M. Aslanian, S. P. Weisberg, M. Mack and I. F. Charo
(2007). "Critical roles for CCR2 and MCP-3 in monocyte mobilization from bone marrow and
recruitment to inflammatory sites." J Clin Invest 117(4): 902-909.
Tylman, M., J. P. Bengtson, A. Avall, M. Hyllner and A. Bengtsson (2001). "Release of interleukin10 by reinfusion of salvaged blood after knee arthroplasty." Intensive Care Med 27(8): 1379-1384.
Umlas, J., M. S. Jacobson and S. V. Kevy (1993). "Survival and half-life of red cells salvaged after
hip and knee replacement surgery." Transfusion 33(7): 591-593.
Vamvakas, E. C., J. O. Bordin and M. A. Blajchman (2009). Immunomodulatory and
Proinflammatory Effects of Allogeneic Blood Transfusion. Rossi's Principles of Transfusion
Medicine, Wiley-Blackwell: 699-717.
Vamvakas, E. C. and A. A. Pineda (2000). "Autologous transfusion and other approaches to reduce
allogeneic blood exposure." Best Practice & Research Clinical Haematology 13(4): 533-547.
van Eden, W., M. van Herwijnen, J. Wagenaar, P. van Kooten, F. Broere and R. van der Zee (2013).
"Stress proteins are used by the immune system for cognate interactions with anti-inflammatory
regulatory T cells." FEBS Lett 587(13): 1951-1958.
van Golen, R. F., M. J. Reiniers, P. B. Olthof, T. M. van Gulik and M. Heger (2013). "Sterile
inflammation in hepatic ischemia/reperfusion injury: present concepts and potential therapeutics." J
Gastroenterol Hepatol 28(3): 394-400.
Veenhof, A. A. F. A., C. Sietses, B. M. E. Blomberg, I. M. W. Hoogstraten, M. H. G. M. Pas, W. J. H.
J. Meijerink, D. L. Peet, M. P. Tol, H. J. Bonjer and M. A. Cuesta (2011). "The surgical stress
response and postoperative immune function after laparoscopic or conventional total mesorectal
excision in rectal cancer: a randomized trial." International Journal of Colorectal Disease 26(1): 53-59.
Voll, R. E., M. Herrmann, E. A. Roth, C. Stach, J. R. Kalden and I. Girkontaite (1997).
"Immunosuppressive effects of apoptotic cells." Nature 390(6658): 350-351.
Wallace, J. L. (2006). "COX-2: a pivotal enzyme in mucosal protection and resolution of
inflammation." ScientificWorldJournal 6: 577-588.
Wang, L., W. Qin, F. Tian, G. Zhang, J. Yuan and H. Wang (2009). "Cytokine responses following
laparoscopic or open pyeloplasty in children." Surg Endosc 23(3): 544-549.
206
Chapter - VIII
References
Wang, S. Y., T. Y. Li and Y. Q. Zheng (2009). "[Effects of cardiopulmonary bypass on plasma
pentraxin-3 level and the balance of Th1/Th2 function in children with congenital heart
malformations]." Zhonghua Yi Xue Za Zhi 89(15): 1024-1027.
Warkentien, T., C. Rodriguez, B. Lloyd, J. Wells, A. Weintrob, J. R. Dunne, A. Ganesan, P. Li, W.
Bradley, L. J. Gaskins, F. Seillier-Moiseiwitsch, C. K. Murray, E. V. Millar, B. Keenan, K. Paolino,
M. Fleming, D. R. Hospenthal, G. W. Wortmann, M. L. Landrum, M. G. Kortepeter, D. R. Tribble
and f. t. I. D. C. R. P. T. I. D. O. S. Group (2012). "Invasive Mold Infections Following Combatrelated Injuries." Clinical Infectious Diseases.
Wehlin, L., J. Vedin, J. Vaage and J. Lundahl (2005). "Peripheral blood monocyte activation during
coronary artery bypass grafting with or without cardiopulmonary bypass." Scand Cardiovasc J 39(12): 78-86.
Werner, S., H. Smola, X. Liao, M. T. Longaker, T. Krieg, P. H. Hofschneider and L. T. Williams
(1994). "The function of KGF in morphogenesis of epithelium and reepithelialization of wounds."
Science 266(5186): 819-822.
Wild, C. A., C. Bergmann, G. Fritz, P. Schuler, T. K. Hoffmann, R. Lotfi, A. Westendorf, S. Brandau
and S. Lang (2012). "HMGB1 conveys immunosuppressive characteristics on regulatory and
conventional T cells." Int Immunol 24(8): 485-494.
Williams, M. R., V. Azcutia, G. Newton, P. Alcaide and F. W. Luscinskas (2011). "Emerging
mechanisms of neutrophil recruitment across endothelium." Trends Immunol 32(10): 461-469.
Wirtz, D. C., K. D. Heller, O. Miltner, K. W. Zilkens and J. M. Wolff (2000). "Interleukin-6: a
potential inflammatory marker after total joint replacement." Int Orthop 24(4): 194-196.
Witt, M., R. Claus, C. Burstein and O. Anders (2002). "TISSUE FACTOR, CYTOKINES, AND
SOLUBLE CELL SURFACE RECEPTORS IN RETRANSFUSED UNWASHED SHED BLOOD
AND IN SERUM IN TOTAL HIP REPLACEMENT." Journal of Bone & Joint Surgery, British
Volume 84-B(SUPP I): 22-b-22.
Wixson, R. L., H. C. Kwaan, S. M. Spies and A. M. Zimmer (1994). "Reinfusion of postoperative
wound drainage in total joint arthroplasty. Red blood cell survival and coagulopathy risk." J
Arthroplasty 9(4): 351-358.
Wozniak, W., M. Wierusz-Kozlowska, J. Markuszewski, K. Lesniewska and K. Wiktorowicz (2004).
"[Monitoring of IL-6 levels in patients after total hip joint replacement]." Chir Narzadow Ruchu Ortop
Pol 69(2): 121-124.
Wynn, T. A. (2003). "IL-13 effector functions." Annu Rev Immunol 21: 425-456.
Xiao, W., M. N. Mindrinos, J. Seok, J. Cuschieri, A. G. Cuenca, H. Gao, D. L. Hayden, L. Hennessy,
E. E. Moore, J. P. Minei, P. E. Bankey, J. L. Johnson, J. Sperry, A. B. Nathens, T. R. Billiar, M. A.
West, B. H. Brownstein, P. H. Mason, H. V. Baker, C. C. Finnerty, M. G. Jeschke, M. C. Lopez, M.
B. Klein, R. L. Gamelli, N. S. Gibran, B. Arnoldo, W. Xu, Y. Zhang, S. E. Calvano, G. P. McDonaldSmith, D. A. Schoenfeld, J. D. Storey, J. P. Cobb, H. S. Warren, L. L. Moldawer, D. N. Herndon, S. F.
Lowry, R. V. Maier, R. W. Davis and R. G. Tompkins (2011). "A genomic storm in critically injured
humans." J Exp Med 208(13): 2581-2590.
Xie, Y., C. Chen, M. A. Stevenson, P. E. Auron and S. K. Calderwood (2002). "Heat shock factor 1
represses transcription of the IL-1beta gene through physical interaction with the nuclear factor of
interleukin 6." J Biol Chem 277(14): 11802-11810.
Yan, M., G. Chen, L. L. Fang, Z. M. Liu and X. L. Zhang (2005). "Immunologic changes to
autologous transfusion after operational trauma in malignant tumor patients: neopterin and
interleukin-2." Journal of Zhejiang University. Science. B 6(1): 49-52.
Yaqoob, P., E. A. Newsholme and P. C. Calder (1999). "Comparison of cytokine production in
cultures of whole human blood and purified mononuclear cells." Cytokine 11(8): 600-605.
207
Chapter - VIII
References
Ye, D. X., Q. Xu, W. T. Chen, H. G. Zhu, G. C. Lin, C. H. Jiang and W. L. Qiu (2004).
"[Immunodetections and analysis of peripheral blood for patients with oral cancer undergoing
operation]." Shanghai Kou Qiang Yi Xue 13(2): 83-86.
Yoon, S. Z., I. J. Jang, Y. J. Choi, M. H. Kang, H. J. Lim, Y. J. Lim, H. W. Lee, S. H. Chang and S.
M. Yoon (2009). "Association between tumor necrosis factor alpha 308G/A polymorphism and
increased proinflammatory cytokine release after cardiac surgery with cardiopulmonary bypass in the
Korean population." J Cardiothorac Vasc Anesth 23(5): 646-650.
Zacks, S. I. and M. F. Sheff (1982). "Age-related impeded regeneration of mouse minced anterior
tibial muscle." Muscle Nerve 5(2): 152-161.
Zenewicz, L. A. and R. A. Flavell (2011). "Recent advances in IL-22 biology." Int Immunol 23(3):
159-163.
Zenewicz, L. A., G. D. Yancopoulos, D. M. Valenzuela, A. J. Murphy, M. Karow and R. A. Flavell
(2007). "Interleukin-22 but not interleukin-17 provides protection to hepatocytes during acute liver
inflammation." Immunity 27(4): 647-659.
Zheng, X., W. Wu, Y. Liu and P. Zheng (2013). Methods of use of soluble cd24 for therapy of
rheumatoid arthritis, US Patent Application US20130231464 A1.
Zheng, Y., F. Li, B. Qi, B. Luo, H. Sun, S. Liu and X. Wu (2007). "Application of perioperative
immunonutrition for gastrointestinal surgery: a meta-analysis of randomized controlled trials." Asia
Pac J Clin Nutr 16 Suppl 1: 253-257.
Zingg, U., J. Forberger, D. M. Frey, A. J. Esterman, D. Oertli, B. Beck-Schimmer and A. Zollinger
(2010). "Inflammatory response in ventilated left and collapsed right lungs, serum and pleural fluid, in
transthoracic esophagectomy for cancer." Eur Cytokine Netw 21(1): 50-57.
Zolg, J. W. and H. Langen (2004). "How Industry Is Approaching the Search for New Diagnostic
Markers and Biomarkers." Molecular & Cellular Proteomics 3(4): 345-354.
208

Title Characterization of post-traumatic - ARAN Home

Transcription

Similar documents

Pequeno resumo da história dos humanos. As próximas gerações

Hominization Proccess

The Little Blue Book - Voices for Evolving Consciousness

evolution of vasovagal syncope: is there a useful protective effect

p 10 - The Echo Of India

GURPS Dinosaurs

"Serious games", play as science and education

Prikaz primera Case report Minilaparotomy for repair of

Take a look at the full magazine feature here

Homo floresiensis

Behind the Smiles