Quantitative proteome and transcriptome changes

Transcription

Max-Planck-Institut fϋr Immunobiologie
Abteilung fϋr Zelluläre und Molekulare Immunologie
Freiburg im Breisgau
Quantitative proteome and transcriptome
changes studied in an in vitro
erythroid differentiation system
Dissertation zur Erlangung des Doktorgrades
der Fakultät fϋr Biologie
der Albert-Ludwigs-Universität
Freiburg im Breisgau
Vorgelegt 2009
Von Ravi Kumar Krovvidi
Geboren in Hyderabad, Indien
Dekan der Fakultät:
Promotionsvorsitzender:
Prof. Dr. Ad Aertsen
Prof. Dr. Eberhard Schäfer
Betreuer der Arbeit:
Referent:
Dr. Gerhard Mittler
Prof. Dr. Rudolf Grosschedl
Datum:
08 October 2009
Manuscript in preparation:
Krovvidi, R., Li, Y., Nerz, G. and Mittler, G. SILAC-based quantitative
proteomic study of erythroid differentiation in an in vitro cellular model system.
With all due respect to my parents
&
All competent teachers in my life
Abbreviations
Abbreviations
aa
BFU (E)
CFU-E
DMSO
EDTA
FACS
FCS
Fig
FDR
H1
HMBA
kDa
LC MS
MS
Me2SO
MEL
min
mRNA
M
Me2SO
MGF
OD
PAGE
PCR
PBS
RT
RT-PCR
RPLC
SDS
SFFV
TMB
µM
Amino acid
Burst-forming unit (erythrocyte)
Colony forming unit erythrocyte
Dimethyl Sulfoxide
Ethylenediamine-tetraacetate
Fluorescence Activated Cell Sorting
Fetal Calf Serum
Figure
False discovery rate
Histone 1
Hexamethylene bisacetamide
Kilo Dalton
Liquid Chromatography coupled
Mass Spectrometry
Mass Spectrometry
Dimethyl Sulfoxide
Murine erythroleukemia
minutes
messenger RNA
molar
Dimethyl Sulfoxide
Mascot Generic File
Optical Density
Poly Acryl amide Gel Electrophoresis
Polymerase Chain Reaction
Phosphate buffered saline
Reverse transcriptase
reverse transcriptase-polymerase
chain reaction
Reverse-Phase Liquid
Chromatography
Sodium Dodecyl Sulfate
Spleen focus forming virus
Tetramethyl Benzidine
micromolar
Contents
Contents
1. Summary .................................................................................... ........
1
2. Introduction
1. 0 Erythrocyte Differentiation
1.0.1 Erythroleukemia ...................................................................... 3
1.0.2 Red blood cell development in mice........................................ 3
1.0.3 Human Erythropoiesis.............................................................. 4
1.1 MEL cell system
1.1.1 Introduction.............................................................................
5
1.1.2 MEL cell maintenance and differentiation................................. 6
1.1.3 MEL cell differentiation.............................................................. 7
1.2 Quantitative Proteomics
1.2.1
Relevance of quantitative proteomics............................................... 8
1.2.2 Overview of quantitative proteomics methods........................ .9
1.2.3 SILAC based quantitative proteomics..................................... 12
1.2.4 Principle of SILAC................................................................... 12
1.2.5 Advantages of SILAC labeling ................................................. 14
1.2.6 Limitations of SILAC................................................................ 14
1.3 Mass spectrometry
1.3.1
MS based Proteomics.............................................................. 14
1.3.2
Mass spectrometers and Hybrid MS FTICR............................ 15
1.3.3
Linear-ion-trap-Fourier transform-ion cyclotron mass
spectrometer............................................................................ 16
1.3.4
3D ion traps……………………………………………………………… 17
1.3.5 2D ion traps (Finnigan LTQ)……………………………………... 19
1.3.6 Principle of FTICR-MS……………………………………………. 21
1.3.7 Strengths of FTICR-MS in proteomics applications…………… 22
1.4 Sample preparation for proteomics analysis
1.4.1 Ingel GeLC-MS…………………………………………………….. 23
1.4.2 Peptide IEF…………………………………………………………..23
1.4.2.1 Ettan TM IPGPhor TM 3 IEF……………………………………...23
1.4.2.2 OFFGEL Fractionation………………………………………….25
Contents
1.4.3 Insolution protein digestions……………………………………….26
1.4.4
Stage Tips: stop-and-go-extraction tips…………………………………26
1.5 Bioinformatics analysis / Tools
1.5.1
Peak list generation……………………………………………. 27
1.5.2
MGF file redactor……………………………………………….. 27
1.5.3
Mascot search engine ………………………….………………28
1.5.4
MSQuant…………………………………………………………. 28
1.5.5
Max Quant……………………………………………………….. 29
3. Aim of studies …………………………………………………………………31
4. Materials and Methods
4.1 Chemicals…………………………………………………………….. 32
4.2 Technical equipments……………………………………………….. 34
4.3 Cell Culture Reagents………………………………………………..36
4.4 Kits……………………………………………………………………. 36
4.5 Real-time PCR primers and probes……………………………….. 36
4.6 SDS-PAGE and Western…………………………………………….37
4.7 Peptide OFFGel Fractionation……………………………………… 38
Methods
4.8 Cell Culture…………………………………………………………… 39
4.9 TMB assay: Tetramethyl Benzidine assay………………………..39
5.0 Quantification of DNA and RNA……………………………………. 39
5.1 Preparation of total RNA and reverse transcription ………………39
5.2 Quantitative real time PCR…………………………………………..40
5.3 SDS-PAGE…………………………………………………………….41
5.4 Coomassie staining…………………………………………………...41
5.5 Silver staining………………………………………………………….41
5.6 Western blot and immunodetection………………………………....42
5.7 SDS-PAGE protein separation and In-Gel…………….……. ….....42
digestion of MEL Protein
5.8 Sub Cellular Fractionations
5.8.2 Mitochondrial Fractionation………………………………………. 43
5.8.3 Cytosol Fractionation……………………………………………………44
Contents
5.8.4 Membrane Fractionation………………………………………………..44
5.9 Peptide OffGel Separation………………………………………………45
6.0 Nanoflow LC-MS/MS……………………………………………………….46
6.1 MitoTracker ® staining………………………………………………........47
6.2 FACS Staining for Mitochondria…………………………………………47
6.3 Peak list generation………………………………………………………..48
6.4 MGF Combiner………………………………………………………………..48
6.5 Mascot search engine………………………………………………………..48
6.6 MSQuant………………………………………………………………………48
6.7 MaxQuant……………………………………………………………………..50
Results
6. 0 Results:
6.1.0
MEL cells as an in vitro model system for Murine…...................53
Erythroleukemia study
6.1.1
Maintenance of MEL cell lines……………………………………..54
6.1.2
DMSO Induction of erythroid differentiation of MEL cells ………54
6.1.3
SILAC labelling of MEL cells ………………………………………55
6.1.4
Assessment of SILAC labeling compatibility for MEL cell ………57
differentiation
6.1.5
RT-PCR analysis ………… ………………………………………...60
6.1.6
Forward and reverse SILAC labeling of MEL cells ………………61
6.1.7
Trimethyl Benzidine assay for estimating hemoglobin levels …...62
6.1.8
Subcellular fractionation of MEL cell proteome…………………..62
6.1.9
Mitochondrial protein analysis in MEL cell differentiation ………65
6.1.10
FACS analysis of mitotracker dye………………………………… 66
6.1.11
Mitochondrial staining using the Mitotracker dye of DMSO……. 67
induced MEL cells
6.1.12
Mitochondrial fractionation………………………………………… 68
6.1.12 (a)
Building and Visualizing Pathways with Pathway Studio…………….. 70
6.1.13
Cytosol fractionation…………………………………………………73
6.1.14
Nuclear fractionation………………………………………………...75
6.1.15
Membrane fractionation……………………………………………..77
6.1.16
MEL proteome combined Dataset Quantified.……………………79
Contents
6.1.17
Comparision of nano-LC MS against 2D-Gel …………………….83
6.1.18
Histone modulations during MEL cell differentiation……………..84
6.1.19
Studies on DNA changes during the induction of erythroid …….85
differentiation by DMSO in MEL cells
6.1.20
Quantile based clustering and enrichment analysis ……………..86
6.1.21
Gene expression analysis…………………………………………...93
6.1.22
MEL Proteome Versus Transcriptome analysis…………………..95
7.0
Discussion
7.1
An overview of various protein quantification labeling strategies…...101
7.2
SILAC labeling to study global protein changes during MEL………..102
Differentiation
7.3
MEL Proteome Versus transcriptome analysis………………………..107
8.0
References……………………………………………………………….109
9.0
Acknowledgements………………………………………………………..113
10.0 Curriculum Vitae…………………………………………………………..115
Summary
Summary
Erythropoiesis epitomizes highly specialized cellular differentiation and gene
regulation. It is regulated by the concerted actions of cytokine signaling
pathways and transcription factors that function as master regulators. Posttranscriptional regulatory events have been discussed to play a significant role
at the later stages of erythroid development (Weiss and dos Santos 2009).
MEL (murine erythroleukemia) cells represent the most common cellular
model system for dissecting the molecular processes involved in red blood
cell maturation. They recapitulate part of the erythroid differentiation
programme by differentiating into non-dividing hemoglobin rich cells, a stage
just before the formation of reticulocytes. In the present study we used
microarray analysis along with SILAC (stable isotope labeling of aminoacids in
cell culture) for the quantitative encoding of the MEL cell proteome in order to
monitor the changes in global gene and protein expression levels associated
with red blood cell maturation (in vitro). Our combined dataset represents the
first comprehensive description of the differentiation-induced-switch in the
MEL-cell-gene-expression programme, both at the mRNA and protein levels.
For our in-depth proteome analysis we employed subcellular fractionation
along with prefractionation techniques like peptide isoelectric focusing and
1D-SDS-PAGE, in order to reduce sample complexity for the subsequent
nanoscale LC-MS analysis. High mass accuracy data acquisition using a
linear ion trap ion-cyclotron-resonance FT hybrid mass spectrometer (LTQFTUltra) resulted in confident identification and quantification of 3774 proteins
(minimum 2 peptides matching, 1%FDR) which included known erythroid
differentiation and heme biosynthesis markers. We identified 49 novel
proteins that could play a potential role in erythroid differentiation among them
the histone methyltransferase SUV4-20H2 that is strongly upregulated during
differentiation. Proteome versus transcriptome analysis revealed 320 proteins
exclusively regulated post-transcriptionally at the protein but not at the mRNA
level. Gene Ontology (GO) analysis demonstrates these proteins to be mainly
involved in heme-binding, transport function (mitochondria), lipid and sterol
biosynthesis.
GO based hierarchial clustering analysis of the quantitative proteome and
transcriptome data indicated a strong correlation between proteome and
1
1
Summary
transcriptome changes for several pathways, namely poryphyrin metabolism,
SNARE interaction (vesicular transport), ubiquitin mediated processes, mTOR
signaling and OXPHOS.
2
2
Introduction
1.0.1 Erythroleukemia
Erythroleukemia is a neoplastic proliferation of erythroid and myeloid
precursors of the bone marrow hematopoietic stem cells. It was initially
reported by Di Guglielmo in 1917 as a syndrome composed of an association
of immature erythroid and myeloid elements and a disease characterized by a
pure normoblastic proliferation. The new WHO classification subdivides acute
erythroid-leukemias into erythroleukemia (erythroid/myeloid) and pure erythroleukemia. In simple words it can as well be defined as a cancer of the bloodforming tissues in which large numbers of immature, abnormal red blood cells
are found in the blood and bone marrow. Acute erythroleukemia accounts for
3-5% of all de novo AMLs and 20-30% of secondary leukemias. It is observed
to have a very rare occurrence in children.
1.0.2
Red blood cell development in mice.
Hematopoiesis is a developmentally regulated and tissue-specific process. It
is well established early in embryonic development and functions throughout
fetal and adult life to provide a continuous supply of mature blood cells to the
embryo, the fetus and adult. The primary sites of blood cell production change
throughout the development in most of the vertebrates. The spleen and
marrow are subsequently seeded with liver-derived cells in both mice and
humans. Bone marrow hematopoiesis is sustained throughout life, with the
marrow being a major blood-forming organ. The spleen functions as a
lymphoid organ in humans but remains as an active site of hematopoiesis in
mice throughout adulthood. The first visible sign of hematopoietic activity in
the mouse embryo is the appearance of blood islands in the developing yolk
sac at approximately day 7.5 of gestation. (Russell 1979). The erythroid cells
within the blood islands are known as embryonic or primitive erythrocytes and
differ from those found in the fetal liver and adult bone marrow in that they are
large,
nucleated,
and
produce
the
embryonic
forms
of
globin.
Primitive-hematopoiesis refers to erythropoiesis in the yolk sac. All lineages
other than primitive erythroid and early macrophage belong to the definitive
hematopoietic system. There are striking differences in the developmental
3
3
Introduction
progression of the definitive erythroid lineage in the yolk sac and fetal liver. In
the yolk sac, early stage precursors (BFU-E) can be detected before those
representing later stages of development (CFU-E), a sequence that suggests
that the lineage is establishing from an immature cell.
Human Erythropoiesis
Human erythropoiesis is a complex multistep process that involves the
differentiation
of
early
erythroid
progenitors
to
mature
erythrocytes.
Mammalian erythropoiesis is a complex process that involves many steps,
including the differentiation of early erythroid progenitors (burst-forming unitserythroid, BFU-E) via late erythroid progenitors (colony-forming unitserythroid,
CFU-E),
and
finally
morphologically
recognizable
erythroid
precursors. Nuclear condensation is a key event in the late stages of
erythropoiesis, and enucleation is the terminal step in the development of
mature-erythrocytes.
4
4
Introduction
1.1 MEL cell system
1.1.1 Introduction
MEL cells are obtained from mice infected with Friend retrovirus complex that
includes the defective SFFV and a helper friend murine leukemia virus
transformed (Ben-David, Giddens et al. 1991) blocked in the development at a
stage comparable to that of erythroid-colony forming cell, the CFU-e stage
Figure 1, these transformed cells are strongly impaired for erythroid cell
differentiation. Friend virus consists of a complex of two retro-viruses: (1)
replicative-defective spleen–focus forming virus (SFFV) and (2) a replicationcompetent, helper friend leukemia virus. These cells are characterized by two
significant genetic events, inactivation of p53 and SFFV proviral insertions at
the spi-1 locus which encodes the PU.1 transcription factor. They grow in
suspension culture and are used to study erythroid growth and cellular
differentiation mechanisms, as they retain the potential for the expression of
differentiated erythroid phenotype along with indefinite cell culture passage
cycles (Gauss, Kalkum et al. 1999)
Figure 1: Erythroid lineage developmental stages
Figure show the stages of erythroid development, hematopoietic stem cells, common myeloid
progenitors, colony forming units-erythroid, early proerythroblasts that are developing into
mature erythroblasts (source : Marks 1987). Red dotted box includes developments stages
from CFU-E to orthochromatophilic normoblasts
5
5
Introduction
1.1.2 MEL cell maintenance and differentiation
MEL cells serve as an established means of studying leukemogenesis and
the regulation of erythroid gene expression. MEL cells are also helpful in
predicting the effectiveness of hybrid polar compounds in inducing the
differentiation of transformed cell lines and fresh human tumor cells (Rifkind,
Richon et al. 1996).
SAHA, suberoylanilide-hydroxamic-acid (vorinostat
(Zolinza)), is a histone deacetylase inhibitor that reacts with and blocks the
catalytic site of these enzymes. The discovery of SAHA emerged out from the
studies carried out on DMSO induced MEL cell differentiation. SAHA has
many protein targets whose structure and function are altered by acetylation,
including chromatin-associated histones, nonhistone gene transcription
factors and proteins involved in the regulation of cell proliferation, migration
and death. In clinical trials, SAHA has shown significant anticancer activity
against both hematological and solid tumors at doses well tolerated by
patients. A new drug application was approved by the US Food and Drug
Administration for vorinostat for treatment of cutaneous T-cell lymphoma
(Marks and Breslow 2007)
MEL cells closely mimic the molecular regulatory processes of normal red
blood cell maturation and hence serve as a good model system. They
recapitulate the cellular erythroid differentiation programme by differentiating
into nondividing hemoglobin-rich cells of late erythroblast stage, when
exposed to Me2SO or a number of other inducers (Marks, P et al 1978).
1.1.3 MEL Cell differentiation
Chemically induced differentiation of MEL cells leads initially to the production
of cells that are not overtly differentiated but that are irreversibly committed to
differentiate and these committed cells do not essentially require a continuous
exposure to these inducing agents, they work as triggering agents of cellular
differentiation. Once the cells get into the commitment phase they are
destined to differentiate towards the erythroid lineage and the reversal
mechanisms are shutoff (Gusella and Housman 1976). Once the cells are
committed to differentiate they continue proliferating for about 5-6 cell
divisions before arresting in G1 stage, similar to the proliferative capacity of
6
6
Introduction
normal proerythroblasts (Housman 1976). Induction is triggered at the level of
the cell plasma membrane via a ca2+-mediated process and ends up at the
transcriptional level in the nucleus presumably via signaling mechanisms.
Induction of differentiation reduces or abrogates the cellular proliferation
potential.
Inducers like DMSO, HMBA trigger a multi-step process involving the
activation of p21, a CDK inhibitor, and hyperphosphorylation of RB, resulting
in G1 arrest in MEL cells (Macleod, Sherry et al. 1995; Zhuo, Fan et al. 1995).
Induced and transformed MEL cells acquire a phenotype characterized by
morphological changes like reduction in cell size, nuclear condensation,
disappearance of nucleoli(Tsiftsoglou and Wong 1985), but are nonenucleated. Changes are also observed in the gene expression, protein
synthesis, and metabolism. Protein synthesis is dramatically decreased.
Transport of iron, synthesis of hemoglobin chains are up-regulated along with
gradual cease of DNA replication, ribosome-synthesis, and overall protein
production(Tsiftsoglou, Pappas et al. 2003). Heme synthetic enzymes,
erythroid specific membrane proteins, suppression of rRNA synthesis occur in
a coordinated manner.
Heme-biosynthetic pathway in animals comprises of eight nuclear encoded
enzymes, distributed over two cellular compartments including the cytosol and
mitochondria.
MEL differentiation involves in vitro cell cycle withdrawl
followed by differentiation resulting in the expression of erythroid markers
including hemoglobin and other hemoproteins. Reinitiation of MEL cell
terminal differentiation appears to be controlled by an ordered program of
turning off several proto-oncogene’s. MEL cells are marked in having a
dysregulated expression of the proto-oncogene Spi-1/PU.1 and characterized
by a loss of p53 function leading to a dysregulatoin of cell-cycle control. PU.1
is known to physically interact and antagonize the erythroid differentiation
activity of the erythroid-specific transcription factor GATA-1 (Rekhtman,
Radparvar et al. 1999; Stopka, Amanatullah et al. 2005). A decrease in invitro GATA-1 DNA-binding activity is reported in the extracts of MEL cells
over-expressing PU.1 (Yamada, Kihara-Negishi et al. 1998). It was reported
that an established co-repressor, pRB, binds to a small acidic domain in the
N-terminal region of PU.1 and cooperates with PU.1 in mediating repression
7
7
Introduction
of GATA-1 leading to the inhibition of erythroid differentiation (Rekhtman,
Choe et al. 2003). The chemical inducers cause the down regulation of PU.1
levels leading to the release of GATA-1 repression and result in the onset of
differentiation programme. Erythroid lineage commitment and differentiation is
a complex process not fully understood, though several proteins like GATA-1,
EKLF, CDK’s, Myc, Myb, p27, p21, transforming growth factor-β, Smad-1,
Smad-4, and histone acetyl transferases have been identified and known to
be critical in driving the cellular differentiation. Murine erythroleukemia serves
as an excellent model system to study the cellular differentiation mechanism.
Here we use SILAC based approach to quantify the global cellular proteome
changes during the MEL cell differentiation programme.
1.2 Quantitative Proteomics
1.2.1 Relevance of quantitative proteomics
Proteins are considered to be the working horses in cellular machinery
processes. Functional genomics (using the DNA chips) based approaches are
designed in high-through put manner and they decipher the information at the
transcriptome level, yet most of the biological processes are controlled at the
protein level. Measuring the mRNA is merely a proxy for determining the
protein abundance as the steady-state levels of mature gene products are
subjected to additional levels of control, therefore mRNA and protein levels
are not always correlated. Animal microRNAs (miRNAs) regulate gene
expression by inhibiting translation and/or by inducing degradation of target
messenger RNAs, single miRNA can repress the production of hundreds of
proteins (Selbach, Schwanhausser et al. 2008). Many factors apart from
mRNA abundance determine the levels and activities of proteins, including
regulated destruction of proteins, translational and post translational
modifications. Quantitative proteomics has the potential to provide a more
accurate picture of protein-directed biological processes than the information
obtained by measuring the mRNA levels. Unfortunately it’s more challenging
to encode the proteome than the genome. Mass spectrometry has
increasingly become the method of choice for the proteome analysis.
8
8
Introduction
The aim of quantitative proteomics is to obtain quantitative information about
all proteins in a sample rather than just providing lists of proteins identified in
a certain sample. Quantitative proteomic studies yield information about
differences between samples, for example to compare samples from control
and experimental states.
1.2.2 Overview of quantitative proteomics methods
Intact protein quantification has been carried out for over 25 years by
approaches based on 1D or more mature 2D gel electrophoresis. The
approaches though “old fashioned” improvised by including more sensitive
staining methods (Rabilloud 2002), large format high resolving gels (Gauss,
Kalkum et al. 1999) and sample fractionation prior to 2DE, thereby offering
significant advantages in
resolving and investigating
the abundance of
several thousand proteins in a single sample. Systemic studies of the budding
yeast Saccharomyces cerivisae indeed revealed that typically only the most
abundant proteins can be observed by this method (Gygi, Corthals et al.
2000), suggesting the limited dynamic range of 2DE-based proteomics.
Liquid-chromatography coupled mass spectrometry (LC-MS) approaches are
used extensively in shotgun proteomics for the identification of proteins from a
variety of biological systems. The information is harnessed for protein
identifications
and
subsequent
quantifications.
Technical
challenges
associated with LC elutions and MS scan-speed limit the extent of protein
quantification and currently is an unmet technical challenge Fig 2
(Bantscheff, Schirle et al. 2007)
9
9
Introduction
Figure 2 Schematic representation of the extent of protein quantitation by MS based
quantitative proteomics. source (Bantscheff, Schirle et al. 2007)
Figure shows the schematic representation of the fraction of a proteome that can be identified
or quantified by mass-spectrometry-based proteomics. Cellular proteins span a wide range of
expression and current mass spectrometric technologies typically sample only a fraction of all
the proteins present in a sample. Due to limited data quality, only a fraction of all identified
proteins can also be reliably quantified.
Quantitative proteomics is multifaceted and with the rapidly developing
technological advancements gives a wide variety of options for quantifications
as shown in Figure 3a.Quantitative strategies involve labeling the biological
sample, Figure 3b outlines the stages of sample mixing after labeling.
10
10
Introduction
Figure 3a : Quantification strategies used in proteomics experiments
Figure represents various quantitative proteomics approaches along with their salient features
11
11
Introduction
Figure 3 b: Outline of various stages of stable isotope labels in quantitative
labeling approaches (Bantscheff, Schirle et al. 2007)
Figure shows the common quantitative mass spectrometry workflows. Boxes in blue and
yellow represent two experimental conditions. Horizontal lines indicate when samples are
combined. Dashed lines indicate points at which experimental variation and thus
quantification errors can occur.
1.2.3 SILAC based quantitative proteomics
SILAC (stable isotope labeling of aminoacids in cell culture) is a versatile
labelling tool which involves growing two populations of cells, one in a
medium that contains a ‘light’ (normal) aminoacid and other in a medium
containing a ‘heavy’ amino acid. The heavy aminoacid contains 2H instead of
H, 13C instead of 12C, or 15N instead of 14N.
1.2.4 Principle of SILAC
Cells differentially labeled by growing them in light medium with normal lysine
(Lysine-0, green color) or medium with heavy lysine (Lysine-6, red color).
Figure 4. Complete metabolic incorporation of the labeled amino acids into the
12
12
Introduction
proteins takes place after five cell doublings. The labeled amino acids are
stable during the metabolic cycles and do not interfere with biological
processes but are “encoded into the proteome”. Arginine when present at high
levels in the media is known to convert to proline and this is overcome by
monitoring the levels of L-proline to render complete MS compatibility
(Bendall, Hughes et al. 2008). SILAC labelling results in a mass shift between
the labelled and non-labelled peptides. This mass shift can be detected by a
mass spectrometer as indicated by the depicted mass spectra. When both
samples are combined, the ratio of peak intensities in the mass spectrum
reflects the relative protein abundance.
Figure 4 SILAC based experiment schematics (source Ong, S.E., and Mann, M)
Figure shows SILAC experiment design, light dish indicating in green color represents the
cells cultured in light lysine and the cells in red colored dish are cultured in labeld lysine. After
5 cell passages the cells are mixed 1:1, subjected to proteolytic digestion and analysed by LC
MS/MS
13
13
Introduction
1.2.5 Advantages of SILAC labeling
SILAC labeling does not involve chemical reactions or affinity purification
methods and so is compatible to primary cells as well. SILAC labeling does
not involve any peptide labeling steps unlike in ICAT or iTRAQ. Cells once
cycled for five passages, results up to approximately 99% label incorporation.
The labeling of cells is completely uniform and this gives the advantage of
comparing several peptides from the same protein to ensure the extent of
protein regulation. In contrast to ICAT non cysteine containing proteins can
also be quantified. The essential pre-requisite is the addition of dialysed
serum to avoid nonspecific incorporation of non-labeled aminoacids. Usage of
small amounts of dialysed serum does not significantly influence the
quantitation (Gehrmann, Hathout et al. 2004)
1.2.6 Limitations of SILAC labeling
The basic requirement of SILAC labeling is the auxotrophic condition of the
cellular system for at least one abundant amino acid to achieve an
approximate 60 % quantification of all peptides (Dreisbach, Otto et al. 2008).
SILAC-labeling of tissue samples though not possible. SILAC labeled mouse
was recently reported (Kruger, Moser et al. 2008), the mouse labeled with a
diet containing either the natural or the (13)C(6)-substituted version of lysine
over four generations and the subsequent F2 generation was found to be
completely labeled in all organs tested
1.3 Mass Spectrometry
1.3.1 MS based Proteomics
Mass spectrometry offers a quick and high-through put choice for the analysis
of complex protein samples. The notable discovery and development of
protein ionization methods, by John Fenn the winner of 2002 nobel prize in
chemistry and the availability of gene and genome sequence databases and
technical and conceptual advances altogether built the necessary foundation
for the MS biological applications to analyse protein samples from complex
mixtures. Today MS based proteomics is considered to be a rapidly
developing and an open ended endeavor. Mass spectrometry approach
involves
14
14
Introduction
a) Liquid chromatographic separation of the protein/peptide sample
b) Ionization of the analytes (proteins or peptides) by ESI or MALDI source
c) Mass analyzer that measures the m/z of the ionized analytes.
d) Detector to register the number of ions at each m/z value.
e) Bioinformatics data evaluation and scoring strategies.
Figure 5 (Aebersold and Mann 2003) outlines the various instrument
configurations and the ion sources used in various MS based proteomics
experiments
Figure 5: Experiment configurations and ion sources (Source Aebersold and Mann 2003)
Sample ionization and introduction upon electro spray ionization (ESI) and matrix-assisted
laser desorption/ionization (MALDI) upper panel cartoons. Various instrumental configurations
(a–f) with their typical ion source. a, In reflector time-of-flight (TOF) instruments, pulsed ions
travel along a flight tube as a result of their different velocities. Ion reflectron located at the
end of TOF tube compensates difference in flight times of the same m/z ions of slightly
different kinetic energies, resulting in focusing the ion packets in space and time at the
detector
b, The TOF-TOF introduces a collision cell between the two TOF tubes. Ions of interest are
selected in the first TOF section, fragmented in the collision cell, and the masses of the
fragments are separated in the second TOF section.
c, Quadrupole mass spectrometers consist of four rods called quadrupoles, that enable in
specific ion selection of defined m/z in (Q1), followed by fragmentation in a collision cell (q2),
and the fragments separated in Q3. In the linear ion trap, ions are captured in a quadruple
15
15
Introduction
section, depicted by the red dot in Q3. They are then excited via resonant electric field and the
fragments are scanned out, creating the tandem mass spectrum.
d, The quadrupole TOF instrument is a combined assembly of having triple quadruple with a
reflector TOF section. e, The (three-dimensional) ion trap captures the ions as in the case of
the linear ion trap, fragments ions of a particular m/z, and then scans out the fragments to
generate the tandem mass spectrum. f, The FT-MS instrument trap ions under a strong
magnetic field. The figure shows the combination of hybrid FT-MS configuration having the
linear ion trap for efficient isolation, fragmentation and fragment detection in the FT-MS
section.
1.3.2 Linear ion trap- Fourier transform ion cyclotron resonance - mass
spectrometer (FTICR-MS)
We used LTQ-FTICR (Ultra) hybrid mass spectrometer for all the proteomics
measurements. Till date it’s the most advanced mass spectrometer which is
capable of providing highest mass resolution, mass accuracy, sensitivity and
dynamic range. These mentioned qualities are critical for resolving individual
peptides from complex samples, and Ultra LTQFT-ICR gives a maximum
mass deviation–MMD (Zubarev and Mann 2007) of upto 0.5 ppm and a
resolution of more than 100,000 fwhm at m/z of 400 with a target ion capacity
of 1 million in the ICR cell. Space-charge effects are observed at higher fill
capacities.
.
Figure 6: Linear ion trap-FTMS cross-section view
Figure shows the schematics of hybrid mass spectrometer (LTQFT-ICR), showing 2D ion trap
and ICR mass analyzer. Hybrid MS consists of LTQ linear iontrap mass analyser followed by
ICR mass analyzer at the rear side. ICR is surrounded by a 7 Tesla magnet indicated in grez
colour (Source Thermo Electron Corporation)
16
16
Introduction
1.3.3 Ion trap mass spectrometer
There are two landmarks in the history of quadrupole ion trap. First is the
invention of the ion trap in 1953 by Wolfgang Paul and Hans Steinwedel. This
was recognized by the award of the 1989 nobel prize in physics. The second
is the discovery, announced in 1983, of the mass selective instability scan by
George S. Stafford, Jr. On these two major landmarks rests the entire filed of
ion trap mass spectrometry. Ion trap is a device that uses an oscillating
electric field to store ions in two or three dimensions. Ion traps can be
classified into two types: 3D ion traps or 2D ion traps.
1.3.4
3D ion traps
Historically, the first ion traps were 3D ion traps having a circular electrode,
with two ellipsoid caps on the top and the bottom that creates a 3D
quadrupolar field, to trap ions in a small volume. The paul trap (3D trap) is
designed to create a saddle-shaped field to trap a charged ion, but with a
quadrupole, this saddle-shaped electric field cannot be rotated about an ion in
the centre. It can only 'flap' the field up and down for this reason, the motions
of a single ion in the trap are described by the Mathieu Equations.The
advantages of the ion-trap mass spectrometer include compact size and the
ability to trap and accumulate ions to increase the signal-to-noise ratio of a
measurement.
17
17
Introduction
Figure 7: LCQ 3D-Ion trap cross-section view
Figure shows the schematic view of LCQ 3D ion trap, indicating the location of ring electrodes
and end caps (Source Thermo Electron Corporation)
Figure 8: Longitudinal cross section of iontrap
Figure represents the ion trap with its ring electrodes and end cap electrodes with ion packets
held in-between the 3D trap.
Figure 8a: RF Voltage schematics in ion trap
Figure represents the RF voltage generating a quadrupole field that maintains the ions in
stable trajectories with the ion trap
18
18
Introduction
1.3.5
2D ion traps (Finnigan LTQ)
The LTQ-2D trap is the most advanced ion traps used for performing
proteomics analysis. They can hold more ions than the linear 2D ion traps due
to the linear design of the trap, it is known for reduced space-charge
repulsions and reduced shielding effects on the mass resolution. In the 2D
trap the RF field is perpendicular to the ion entrance axis which reduces the
problems associated with ion injection in the 3D trap. This gives high value to
the 2D traps as they have a 10-15 fold improvement in ion trapping
efficiencies, faster scan speed 16.7amu/sec, higher sensitivity and higher
confidence in protein identifications. Figure 9a shows a comparative ion
storage capacity of 3D and 2D ion traps. In 2D iontrap, ions are loaded axially
and scanned or ejected out radially
Figure 9a Schematics of 3D and 2D ion traps with ion storage capacity
Figure represents the 3D-trap with ion packets held in-between the ring electrodes and the
modern 2D-traps have higher ion holding capabilities due to the RF field acting perpendicular
to the ion entrance axis.
19
19
Introduction
Figure 9b Schematics of ion entry and exit in a linear iontrap
Figure represents the schematics of linear ion trap, ions entry into the LIT is represented with
colored circles and the ions are ejected radially towards the detectors
Figure 9c Linear ion trap specifications (Thermo Electron Corporation)
Figure represents the linear ion trap specifications in the X, Y, Z dimensions
20
20
Introduction
Figure 9d Linear ion trap mass spectrometer specifications (Thermo Electron
Corporation)
Figure represents the linear ion trap, values above the red arrows indicate the sequential
vacuum levels. Electrospray ionization results in ion packets that enter into the mass
spectrometer. Ions are guided through a series of lenses to finally enter into the iontrap.
1.3.6 Principle of FTICR-MS
In a LTQFT-ICR MS
instrument,
ions that are generated from the
ESI
source enter into the ion trap mass analyzer and are transferred to ICR (Ion
cyclotron resonance) cell located in the middle of a magnetic field (7 Tesla
super conducting magnet). The injected ions move in a circular orbit in a
plane perpendicular to the magnetic field Figure 10, and are constrained to a
region by a set of trapping plates placed perpendicular to the magnetic field
axis, to which a voltage is applied. As the ions spin, they move about the
centre of the magnetic field with a rotational frequency inversely proportional
to the ion’s mass-to-charge-ratio, lighter ions spin faster than heavier ions of
the same charge. Ions are excited by sweeping a radio frequency (RF) pulse
across a set of excitation plates parallel to the magnetic field axis. Ions of
specific mass-to-charge-ratio will absorb radio frequencies that resonate with
their cyclotron frequency (the frequency at which they spin around the centre
of magnetic field). This accelerates them to a larger radius. When the ions
pass the detector plates, the ions generate an image current that can be
detected and measured. The resulting spectrum is a superimposition of every
21
21
Introduction
m/z signal, which can be deconvoluted by fourier transform to produce a
frequency spectrum, and then calibrated to produce a mass spectrum.
Figure 10: FT schematics and mass spectrum generation upon Fourier transformation
Figure represents the FT signal processing and mass spectrum generation
1.3.7 Strengths of FTICR-MS in proteomics applications
FTICR is a powerful technology for addressing important challenges currently
faced in biology, biochemistry, and medicine. Gain in selectivity and sensitivity
due to the highest possible MMA (Mass measurement accuracy) of 1 ppm is
observed with FTICR MS. Significant strengths of this hybrid mass
spectrometer are high magnetic field (7-Tesla), large penning trap diameter,
ultra cell for reduced space charge effects, digital quadrature heterodyne
detection, extended ion cooling by step wise reduction of the cell trapping
potential, low cell pressure, selected waveform inverse Fourier transform
(SWIFT) for the isolation of a narrow m/z range and multi spectra summation
and automatic gain control (AGC)
1.4 Sample preparation for proteomics analysis
1.4.1 Ingel GeLC-MS
Samples are separated on SDS-PAGE based on the molecular weight of
proteins. The resolved proteins are visualized using different staining methods
like coomassie Brilliant blue which has a sensitivity range of 0.1 – 1.0 µg of
22
22
Introduction
protein. Silver staining is also routinely used and it gives a sensitivity in the
range of 0.1-0.4 ng of protein detection and the modern fluorescent dyes like
SYPRO red, orange have lower detection limits.
The resolved protein bands are excised and subjected to proteolytic cleavage
using trypsin enzyme (most commonly used). The tryptic peptides are
extracted from the gel pieces and desalted using the offline purification
(StageTips) step which employs C18 chromatographic material. The desalted
peptides are gradient separated using the RPLC and introduced into the mass
spectrometer. This approach is commonly referred to as GeLC-MS
1.4.2 Peptide IEF
Peptide IPG isoelectric focusing involves the separation of proteins or
peptides based on their isoelectric points. IPG-IEF with immobilized pI strips
serves as an alternative fractionation technique to in-gel protein digestion in
bottom-up proteomics (Hubner, Ren et al. 2008), the higher resolving
separation power of peptide IEF allows the separation of higher abundant
peptide molecules from lower abundant ones, and allow the lower abundant
peptides to be identified by the mass spectrometer. It has an advantage of
enhancing the sensitivity of peptide detection. Peptide IEF is carried out in two
modes,
1.4.2.1 Ettan TM IPGPhor TM 3 IEF
GE-IPG Phor Figure 10a system uses onstrip peptide separation. Peptide
solution is overlaid on to an IPG strip and allowed to be absorbed into the gel
matrix in a rehydration mode. The presence of a filter paper wick at the anode
allows peptides outside the isoelectric point (pI) range of the strip to exit. This
significantly improves the quality of the data obtained at the basic end of the
strip, especially in narrow range (one pI unit) separations. The overall strategy
of IPG-IEF is represented in Figure 10 (1a) source (Cargile, Sevinsky et al. 2005).
Focused peptides from the IPG strip are extracted and analysed via nano-LC
MS/MS.
23
23
Introduction
Figure 10 a: Ettan TM IPGPhor TM 3 IEF System
Figure shows IPG fractionating unit used for carrying out peptide IEF, IPG strips are loaded
with peptide solution and are focussed to stabilize on IPG strips at their iso-electric points. It is
a common prefractionation tool used for identifying low abundant peptides/proteins
Figure 10 (1a): IPG-IEF work flow
Figure represents the work flow of IPG-IEF based shotgun approach, proteolytic peptides are
loaded onto the IPG strips and the focused peptides from the IPG strip are extracted.
Peptides are analysed in a data dependent using the LC MS/MS and database searched
1.4.2.2 OFFGEL Fractionation
24
24
Introduction
OFFGEL Fractionator from Agilent technologies Figure 10 b focuses peptides
in solution phase using a fractionation chamber having 24 wells Figure10 (2b).
Peptide solution is loaded into the sample chambers placed over the IPG
strip. Peptides start to migrate under the influence of the applied voltage
across the wells and stabilize at a ph position where the peptide molecule
surface carries no net electrical charge i.e. the isoelectric point.
Figure 10 b: OFFGEL Fractionator
Figure shows OFFGEL Fractionator used for peptide IEF, as a prefractionation tool. Peptides
are focussed insolution lodged over the IPG strip. It is a common prefractionation tool used
for identifying low abundant peptides/proteins
Figure10 (2b): OFFGEL fractionation chamber
Figure represents OFFGEL fractionation chamber placed over the IPG strip. Peptides migrate
under the applied voltage in the solution across the chambers until they reach their isoelectric points . They further stabilize in the wells, resolved peptides can be processed for MS
analysis.
25
25
Introduction
Peptide solution from the wells are processed for MS analysis. OFFGEL
fractionator differs from IPGPhor IPG-IEF in peptide extraction steps.
Peptides from IPGPhor IPG strips need to be extracted using rigorous
extraction steps, while the OFFGEL fractionates peptides in-solution phase
avoiding the harsh extraction steps.
1.4.3 Insolution protein digestions
Protein solutions for mass spectrometry analysis are routinely subjected to
insolution protein digestions using dual enzyme digestion approach. Proteins
in the solution phase are cleaved using the Lys C, which cleaves the
polypeptide chains at the lysine residues giving longer peptides under
denaturing conditions e.g. 8M urea. The peptides are further subjected to
trypsin enzyme that cleaves at lysine and arginine positions of the peptides.
The resulting tryptic peptides are fractionated using the LC separation and
analyzed on MS/MS.
1.4.4 Stage Tips : stop-and-go-extraction tips
The peptide mixtures need to be cleaned and concentrated. Millipore offers
commercial ZipTips having the C18
material for desalting purpose. New
developments in the area of proteomics include (StageTips) (Rappsilber,
Mann et al. 2007). Stop-and-go-extraction tips (StageTips) StageTips are
efficient, economical and easy to make. They can be made out of ordinary
pipette tips that are packed with circular teflon disks containing copolymerized silica beads . Each circular disk can be loaded upto 20µg of
protein digest. The fixed nature of the beads allows flexible combination of
26
26
Introduction
disks
with
different
surfaces
to
obtain
multi-functional
tips.
Figure 11: Stage-Tip vertical section overview
Figure represents the cross section of the stage-tip, tips are loaded with circular disks of C18
and sample is loaded from top, with the help of a syringe pressure is applied to the sample
making it to pass through the C18 column. Peptides bound to the column are washed and
eluted for further analysis.
Desalted and extracted peptides are introduced into the mass spectrometer
through a LC separation. (Rappsilber, Mann et al. 2007)
1.5 Bioinformatics analysis / Tools
1.5.1 Peak list generation
Spectral raw files are processed using the DTA supercharge. It is an
application for converting one or more Finnigan. RAW files to mascot search
input files in a format suitable for use with MSQuant.
1.5.2 MGF combiner
The individual peak list files generated are concatenated using the MGF
(Mascot Generic File) combiner, resulting in a mascot search input file
27
27
Introduction
1.5.3 Mascot search engine
The peak list is submitted to mascot protein search engine. It is a powerful
search engine that uses mass spectrometry data to identify proteins from
primary sequence databases. (Perkins, Pappin et al. 1999). It takes protein
modifications into consideration. It incorporates probability-based scoring,
supporting peptide mass finger print, sequence query and MS/MS ions search
and efficient decoy filter options. It is well supported to Microsoft Windows NT,
SGI Irix, Sun Solaris and DEC Unix. Snapshot of a sample result is shown in
the Figure 11 a1.
Figure 11 a1. Screenshot of mascot result page
Figure represents the snapshot of a mascot result page (html file) reported from ms/ms ions
returning with a search of 21 peptides from an in-gel tryptic digest of a complex protein
mixture. Peptides are searched against the mouse-IPI database. Labeled amino acid
modifications are indicated to the side of peptide lists.
1.5.4 MSQuant
MSQuant is used to process the raw MS spectra files to get the quantitative
information from the identified peptides. It is though automated, is flexible and
28
28
Introduction
allows scope for manual inspection of the quantified peptides. Dataprocessing with the MS Quant is highly time consuming and is associated with
high virtual memory occupancy on the computer. Its main purpose is to make
quantitation of proteins and peptides possible in the area of mass
spectrometry/proteomics. MSQuant is written in Microsoft’s .NET and thus
can only run on Windows computers that have the .NET runtime installed. MS
Quant is publicly available and can be downloaded from the following web link
http://msquant.sourceforge.net/.
1.5.5 Max Quant
MaxQuant is a quantitative proteomics software package developed by
Jurgen Cox and Mathias Mann at the Max-Planck Institute for Biochemistry,
Martinsried (Cox and Mann 2008) .
29
29
Introduction
Figure 11 a2: Overview of the computational work flow( Source (Cox, Matic et al. 2009)
It consists of five steps:
the first step—data acquisition—is performed by the vendor software of the mass
spectrometer used; in the second step, the ‘Quant.exe’ module of MaxQuant detects peak
features and quantifies peptides; in the third step, a search engine (here Mascot) associates
fragment spectra with amino acid sequences; in the fourth step, the ‘Identify.exe’ module of
MaxQuant validates and scores peptide identifications, assembles them to protein
identifications and determines protein ratios; and in the fifth step, downstream bioinformatic
analysis is performed by general purpose software (spreadsheets), statistical packages or
bioinformatic
packages.
30
30
Introduction
3. Aim of studies
MEL
(murine
erythroleukemia)
cell
differentiation
has
been
studied
extensively for more than three decades documented by more than 600
publications listed in Pubmed. However, no systems biology based studies
were carried out so far that would unravel global changes associated with cell
fate switching.
MEL cells are transformed erythroid precursors that are arrested in an
immature and proliferating state. These leukemic cells can be propagated in
cell culture and induced to terminal erythroid differentiation by treatment with
specific polar chemical inducer such as DMSO (dimethyl-sulfoxide). MEL cells
then re-enter their original erythroid programme and differentiate into nondividing hemoglobin-rich cells resembling orthochromatophilic normoblasts.
To deepen our understanding of the molecular and cellular mechanisms
underlying erythroid differentiation we aimed to establish a powerful and
robust proteomic platform that could monitor changes in protein expression
levels for thousands of proteins in a quantitative manner.
We decided to employ SILAC-labeling of MEL cells for accurate MS-based
quantification of proteome changes occurring during DMSO-induced MEL cell
differentiation. Since massive hemoglobin induction takes place in the later
stages of the MEL cell differentiation programme we decided to compare
undifferentiated against fully differentiated cells that were kept in culture for
120 hours after DMSO induction. To maximize proteome coverage we aimed
to conduct simple one-step sub-cellular fractionation experiments resulting in
sub-proteomes enriched for mitochondrial, nuclear, membrane and cytosolic
proteins. Apart from GeLC-MS we aimed to establish peptide isoelectric
focusing (IEF) as an alternative pre-fractionation approach reducing sample
complexity at the peptide level. This is important to overcome the well-known
undersampling problem inherent to the current state-of-the art MStechnologies.
In order to be able to uncover post-transcriptional regulatory events that might
be critical for differentiation we aimed to study the extent of correlation
between the transcriptome and proteome expression level changes.
Therefore, we carried out Affymetrix microarray analysis experiments.
31
31
Materials and Methods
4. Materials and Methods
4.1 Chemicals
4.2 Technical equipments
4.3 Cell culture reagents
4.4 Kits
4.5 Real-time PCR primers and probes
4.6 Antibodies
4.6 SDS-PAGE and Western
4.8 Cell Culture
4.9 TMB assay: Tetramethyl Benzidine assay
5.0 Quantification of DNA and RNA
5.1 Preparation of total RNA and reverse transcription
5.2 Quantitative real time PCR
5.3 SDS-PAGE
5.4 Coomassie staining
5.5 Silver staining
5.6 Western blot and immunodetection
5.7 One-dimensional SDS-PAGE Protein Separation and In-Gel Digest
of MEL Proteins
5.8 Peptide OffGel Separation
5.9 Nanoflow LC-MS/MS
4.1 Chemicals
Acetone
Merck
Acetonitrile
Fluka
Acrylamide
BioRad
Acrylamide-/Bisacrylamidlösung 40% (Rotiphorese Gel 40)
Roth
Acrylamide-/Bisacrylamidlösung 30% (Rotiphorese Gel 30)
Roth
Acrylamide 30% (Rotiphorese Gel A)
Roth
Actinomycine D
Sigma
Ammoniumsulfate
Merck
Ampicilline
Roth
32
32
Aprotinine
Roche
Agarose Gibco
BRL
Ammoniumchloride
Merck
Ammonium-bi-carbonate
Sigma
Agarose
Sigma
Acetonitrile
JT Baker
Acetic acid
Sigma
Acrylamide solutions
Sigma
Ammonium per oxodisulfate (APS)
Merck
Bovine serum albumin
Sigma
Bradford protein assay solution
Bromophenol blue
Sigma
Benzonase
N-Butanol
Merck
Coomassie brilliant blue G250
Sigma
Dimethyl Sulfoxide
Sigma
Dithiothreitol (DTT)
Sigma
Ethidium bromide solution
Sigma
C18 beads
Dr.Maisch
GmbH
Ethanol
JT Baker
Ethylendinitrilotetraessigsäure (EDTA)
Merck
Ethylenglycol-bis-(2-aminoethyl)-tetraessigsäure
(EGTA)
Merck
Formaldehyde solution 37%
Sigma
Glycerol
JT Baker
Glycine
JT Baker
Hyderogenperoxide
Sigma
Hepes
Sigma
Iodoacetamide
Sigma
IPG Strips
Lys C
GE Healthcare
Wako
Leupeptin
Roche
Methanol
JT Baker
33
33
MOPS
Sigma
PMSF (phenylmethansolfonylfluoride)
Roche
Pepstatin A
Roche
Protein standard (SDS-PAGE)
Fermentas
Phenol
Sigma
Sucrose
Sigma
Sodium Dodecyl sulfate
Sigma
Sodium carbonate
Sigma
Sodium fluoride
Sigma
Silver nitrate
Sigma
Sodiumthio sulfate
Sigma
Thiourea
Tris base (Trizma)
GE Healthcare
Sigma
Triton X-100
Sigma
Trizol
Sigma
TEMED
Sigma
Trypsin
Promega
Trimethyl Benzidine
Sigma
Urea
GE Healthcare
4.2 Technical equipments
Pressure bomb
Proxeon
Reverse phase columns
New
Objectives
Vortex
Heidolph
Mixer
Heidolph
Speed vac drier
Eppendorf
LTQFT MS
ThermoFisher
HPLC
GE Healthcare
Electrophoresis unit
Invitrogen
Ettan IPGphor 3 Isoelectric Focusing Unit
GE Healthcare
3100 OFFGEL Fractionator
Agilent
SDS-PAGE Gel electrophoresis
Biorad Protean
34
34
Semi-Dry Western blot chamber
Biorad
TransBlot
PCR-Thermoblock:
T3 Thermocycler, Biometra
Mastercycler, eppendorf
Thermoblock
Thermomixer comfort,
Eppendorf
pH meter:
pH 766, calimatic
Shaker
Rocky
37 0 C Incubator
Thermocenter, Salvis
Developer (Western-Blot):
Classic E.O.S., Agfa
Centrifuges and Rotors:
Beckmann OptimaTM LE-80K
Beckmann Avanti J-26Xp
Beckman Ultra TLA 100.3
Beckman HB-6 rotor
Eppendorf 5417R
Eppendorf 5424
Photometers:
eppendorf BioPhotometer
NanoDrop®, ND1000
FACS
FACS Calibur I
BD FACS AriaTM
Mass spectrometer
nanoscale-LC (MDLC nanoflow
system) coupled with 7-Tesla l
Linear ion-trap Fouriertransform ion cyclotron
resonance mass spectrometer
(LTQ-FT, Thermo Electron,
Germany) equipped with a
nanoelectrospray source
(Proxeon, Denmark)
Sonicator
Sonics vibracell
ABIPrism 7500 fast real-time
Applied Biosystems
Semi-Dry Transfer Blot SD
BioRad
Microscope
Zeiss Axio Imager Z1 with
ApoTome slider
35
35
FACSCalibur TM
BD
4.3 Cell Culture Reagents
Mitotracker ® Red CMXRos
Invitrogen
DMEM
PAA
FCS Dialysed
PAA
SILAC labeled aminoacids
Sigma
Non Labeled aminoacids
Sigma
Antibiotics
Sigma
VectaShield
Vector Labs
4.4 Kits
3100 OFFGEL Fractionator Kit
(5188-8013)
Agilent
VersoTM cDNA kit
Thermo
Qproteome TM Mitochondria isolation kit
Quiagen
Nuclei isolation kit
(Nuclei Pure Prep NUC-201)
Sigma
MitoTracker ® Probes
Invitrogen
4.5 Real-time PCR primers and probes
mGAPDH
Probe: #45
forward
reverse
5'-CCAAAACATCATCCCATCGT-3'
5'-AACTGACACGTTTGGGGTTG-3'
Globin–β
Probe: #66
forward
5'-CCGATGAAGTTGGTGGTGA-3'
reverse
5'-GCTGGGTCCAAGGGTAGAC-3'
forward
5'-TGGGTGAACTTGTACACCAACT-3'
reverse
5'-TCTGTCATGGCCCTGTCTT-3'
ERAF
36
Probe: #32
36
m β-Actin
Probe: #63
forward
5'-GGATGCAGAAGGAGATTACTGC-3'
reverse
5'-CCACCGATCCACACAGAGTA-3'
4.6 SDS-PAGE and Western
Separating gel buffer
1.5 M Tris/HCl, pH 8.8
0.4% SDS (w/v)
Stacking gel buffer
0.5 M Tris/HCl, pH 6.8
0.4% SDS (w/v)
SDS-PAGE loading buffer (2x)
150 mM Tris/HCl, pH 6.8
20% glycerol (v/v)
4% SDS (w/v)
100 mM DTT
0.001 g/mL bromphenol blue
SDS-PAGE running buffer
25 mM Tris/HCl pH 8.3
250 mM glycine
0.1% SDS (w/v)
Coomassie staining buffer
0.625 g/L Coomassie R250
90% Methanol (v/v)
10% acetic acid (v/v)
Destaining buffer
45% Methanol (v/v)
10% acetic acid (v/v)
Transfer buffer (semi-dry)
48 mM Tris/HCl, pH 8.3
20% Methanol (v/v)
39 mM glycine
0.037% SDS (w/v)
Blocking solution
PBS-T + 5% dry milk power (w/v)
Antibodies
Hexim 1
AHSP
Cdk-9 (C-20)
Aml/Runx-1
Pu.1 (T-21)
37
Ab-25388
SC-484
39000
SC-352
Abcam
Courtesy M.J. Weiss
Santa Cruz Biotechnology
Active Motif
Santa Cruz Biotechnology
37
4.7 Peptide OFFGel Fractionation
Peptide OFFGEL stock solution
(1.25X)
Glycerol Solution 6 mL
+
OFFGEL Buffer 600 μL ampholytes
pH 3-10 (Volume adjusted to 50ml
with Millipore water)
Peptide IPG Strip Rehydration Solution Peptide OFFGEL Stock Solution
(1.25X) 0.48 mL + dH20 120 μL
Methods
4.8
Cell Culture
4.9
TMB assay: Tetramethyl Benzidine assay
5.0
Quantification of DNA and RNA
5.1
Preparation of total RNA and reverse transcription
5.2
Quantitative real time PCR
5.3
SDS-PAGE
5.4
Coomassie staining
5.5
Silver staining
5.6
Western blot and immunodetection
5.7
One-dimensional SDS-PAGE Protein Separation and
In - Gel digestion of MEL Proteins
5.8
Sub-Cellular Fractionations
5.8.1
Nuclear Fractionation
5.8.2
Mitochondrial Fractionation
5.8.3
Cytosol Fractionation
5.8.4
Membrane Fractionation
5.9
Peptide OffGel Separation
6.0
Nanoflow LC-MS/MS
6.1
MitoTracker ® staining
6.2
FACS Staining for Mitochondria
38
38
4.8 Cell Culture
MEL cells were cultured in normal and labeled DMEM medium (with 10%
dialyzed FBS) containing
13
C6-arginine and 2H4-lysine plus antibiotics and the
latter cell population was stimulated to differentiate by addition of 2% (v/v)
Me2SO for 120 hours. Cells were maintained at 370 C and 5 % CO2.
4.9 TMB assay: Tetramethyl Benzidine assay
2% DMSO induced and ctrl cell pellets were washed once with PBS, and
lysed in NLS (N-Lauryl sarcosine buffer), benzonase treated and centrifuged
for 5 minutes at 12000 rpm. Supernatant collected and measured for
Hemoglobin induction levels. Standard Hemoglobin curve was plotted using
freshly made hemoglobin powder (Sigma H4131 -1G) and BSA standard
curve was plotted from BCA assay to normalize the hemoglobin induction per
mg of protein content.
5.0 Quantification of DNA and RNA
DNA and RNA were quantified measuring the OD260 with the NanoDrop,
ND1000 Spectrophotometer. The relation OD260/OD280 was used to judge
the purity of the sample.
5.1 Preparation of total RNA and reverse transcription
To isolate RNA from cell culture cells, the Total RNA Isolation Reagent
(Abgene) was used. Approximately 1 x 106 cells were centrifuged (1200 rpm,
5 min, RT). The cell pellet was resuspended in 1 mL of cold RNA isolation
reagent. After 5 minutes incubation at 4 0 C. 200 µl chloroform was added.
The mixture was vigorously vortexed and stored for 5 min at 40 C. After
centrifugation (14000 rpm, 10 min, 40 C) the upper aqueous phase, containing
the RNA, was transferred into a new eppendorf tube. The RNA was
precipitated by addition of 750 µl 2-propanol and cooled for 5 min at 40 C.
After centrifugation (14000 rpm, 10 min, 40 C) the pellet was washed one time
with 70 % ethanol. The pellet was shortly air-dried and then redissolved with
10-20 µl RNase free TE buffer. 0.5 - 1 µg of total RNA was reversed
transcribed using M-MuLV reverse transcriptase. The RNA was first incubated
39
39
with 0.5 µl random hexamers or Oligo (dT) (200ng/ µl) as primers in 12 µl for 5
min at 720 C to relax the RNA. After cooling down to 370 C, which allows the
binding of the primers, a 8 µl reaction-mixture (4 µl 5 x buffer, 10 mM dNTPs,
5 mM DTT, 1 µl reverse transcriptase, ad 8 µl H2O) was added to start the
reverse transcription. The reaction went for 50 min at 370 C and was stopped
by increasing the temperature to 720 C for 5 min. RNase inhibitors were only
used, if the mRNA amount did not exceed 0.2 µg. The gained cDNA solution
was diluted 1:1 and usually 0.5 µl was used in quantitative real-time PCR.
5.2 Quantitative real time PCR
Quantitative real time PCR was used to measure mRNA levels. In this study,
the PCR-amplification was detected by using double-dye oligonucleotides as
probe. These oligonucleotides containing a fluorophore (6-FAM) at 5' end and
a quencher (TAMRA) at the 3' end. Due to FRET (fluorescence resonance
energy transfer) the quencher reduces the fluorescence emitted by the
fluorophore. If the oligo binds specific to the amplified DNA then the
exonuclease activity of the Taq-polymerase is able to release the fluorophore,
which leads to increased fluorescence. The combination of specific primers
with a specific probe increases the specificity of the amplification signal. The
primers for the PCR amplification and the appropriate probe were selected by
using the Roche Universal Probe Library Assay Design Center.
One PCR reaction was set up in the following way:
0.5 µl Sample
5 µl Master Mix (2x Applied Biosystems)
4.2 µl H2O
0.1 µl Primer 1 (100 pmol/ µl)
0.1 µl Primer 2 (100 pmol/ µl)
0.1 µl Probe (10 µM)
The PCR reaction was run in ABIPrism 7500 machine (Applied Biosystems)
with following program:
500 C 2 min
95 0 C 15 min
950 C 15 s
600 C 1 min 40x
40
40
The analysis was done with the 7500 Fast System Software.
5.3 SDS-PAGE
Proteins were separated using the discontinuous SDS-PAGE after Laemmli
(Laemmli, 1970). A 10 % separating gel was made with 5 mL 30%
acrylamide, 3.75 mL separating gel buffer, 6.25 mL H2O, 50 µl 10% APS
(ammoniumpersulfate) and 10 µl TEMED. This solution was poured into the
prepared gel chamber and covered with butanol. The butanol was removed
when the gel became solid. The stacking gel was made with 0.65 mL 30%
acrylamide, 1.25 mL stacking gel buffer, 3.05 mL H2O, 25 µl APS and 5 µl
TEMED. The gel was placed in a SDS-PAGE apparatus containing SDSPAGE running buffer and was run with 130 V for 1.5 hours. The protein
samples were denatured for 5 min at 950 C in SDS-PAGE loading buffer. The
separated proteins were analyzed by coomassie staining, silver staining or
Western blotting.
5.4 Coomassie staining
SDS-PAGE gels were incubated for at least 4 hours with the Coomassie
staining solution and then destained by replacing the staining solution by
destaining solution. If necessary, the destaining solution was replaced several
times until a good signal to noise ratio was obtained. The gel was conserved
on Whatman paper by vacuum drying.
5.5 Silver staining
For more sensitive protein detection silver staining was performed. The gel
was first fixed for 1 hour (50% methanol (v/v), 0.01% formaldehyde (v/v), 12%
acidic acid (v/v)) then washed for 3 time 20 min with 50% ethanol (v/v). The
gel was pretreated with 0.02% Na2S2O3 (w/v) for 1 min. The gel was washed
3 times for 20 sec with H2O. The gel was now impregnated (0.2% AgNO3
(w/v), 0.02775 formaldehyde (v/v)) for 20 min. After additional 3 washing
steps with H2O for each 20 seconds, the gel was developed (6% Na2CO3
(w/v), 0.0185% formaldehyde (v/v), 0.005% Na2S2O3 (w/v)) until the protein
41
41
bands were visible. To prevent over-development the reaction was stopped
(50% methanol (v/v), 12% acidic acid (v/v)). Silver stained gels were scanned
for documentation.
5.6 Western blot and immunodetection
Proteins were transferred from the SDS-PAGE gel to a PVDF membrane by
semi-dry blotting using the Biorad TransBlot ® SD Cell. The set up was from
anode to cathode blotting paper, PVDF membrane, gel, blotting paper.
Blotting papers and PVDF membrane were moistened with transfer buffer.
The transfer took place with 125 mA per blot for 1 h and 15 min. For Western
blotting, prestained protein markers were used in the SDS-PAGE. PVDF
membranes were blocked for one hour at room temperature in PBS-T
supplemented with 5 % nonfat dry milk powder for at least 1 hour. The
membrane was washed two times with PBS-T and incubated for 2 hours with
the primary antibody in the appropriate dilution. The membrane was then
washed several times with PBS-T and incubated with a suitable secondary
antibody conjugated to horseradish peroxidase. After the secondary antibody,
the membrane was washed again several times with PBS-T. Finally, the
membrane was incubated with ECL solution and the chemiluminescence was
detected with a light sensitive film.
5.7 One-dimensional SDS-PAGE Protein Separation and In-Gel
digestion of MEL Proteins
Protein concentration of the MEL cell lysate was determined by Bradford
assay (Bio-Rad, Hercules, CA) and 100 µg of protein was applied on a 4–12%
Bis-Tris gel (Novex; Invitrogen). After staining by colloidal Coomassie
(Invitrogen), the entire gel lane was cut into 10 pieces of equal size and
subjected to in-gel tryptic digestion. Briefly, the gel pieces were destained and
washed, and, after dithiothreitol reduction and iodoacetamide alkylation, the
proteins were digested with porcine trypsin (modified sequencing grade;
Promega, Madison, WI) overnight at 37 °C. The resulting tryptic peptides were
extracted from the gel pieces with 30% acetonitrile, 0.3% trifluoroacetic acid,
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42
evaporation in a vacuum centrifuge to remove organic solvent, then desalted
and concentrated on reversed-phase C18 StageTips.
5.8 Sub Cellular Fractionations
5.8.1 Nuclear Fractionation
(Nuclear fractionation is performed using the Sigma Nuclei pure prep isolation
kit NUC-201 protocol) SILAC K4R6 labeled stimulated and unlabeled K0R0 ctrl
having equal cell counts (1 x 107 cells) are mixed and were 1X PBS washed.
Combined cell pellets were lysed in ice cold lysis buffer of 1ml volume
containing 1% protease inhibitors. Additional 9ml of lysis buffer is added and
incubated at 4
0
C on an end-over-end shaker for 10 minutes. Nuclei are
purified through 1.8 M sucrose cushion solution. 18ml of 1.8M sucrose
cushion solution is mixed with 10 ml of sample solution. In a 40 ml
ultracentrifuge tube (cat # 344058) add 10 ml of ice cold 1.8M sucrose
cushion solution and layer the 28 ml of sample solution, gently. Avoid
disturbing the cushion layer. Spin the sample at 12,700 rpm (HB-6 rotor) for
1.15 minutes at 4 0 C. Supernatant contains cytosol and other proteins. Pellet
containing the Nuclei are cleaned with 200 µl of nuclei clean storage buffer,
spin at 500 rpm for 5 minutes at
4
0
C. Nuclei are lysed in 40 µl of NLS
buffer, followed by Benzonase treatment and Sonication. Proteins are
separated on NuPage gel, coomassie stained and ingel digested with trypsin.
Peptides are stage Tip cleaned and analysed over LC MS/MS.
5.8.2 Mitochondrial Fractionation
(Mitochondrial fractionation is performed using the Quiagen Mitokit protocol)
SILAC K4R6 labeled stimulated and unlabeled K0R0 ctrl having equal cell
counts (1 x 107 cells) are mixed and were 1X PBS washed. Combined cell
pellets were lysed in ice cold lysis buffer of 2ml volume containing 1%
protease inhibitors. Cell lysis was performed by pipeting up and down using a
1ml pipette tip. Incubate for 10 min at 4
0
C on an end-over-end shaker.
Lysate is centrifuged at 1000 X g for 10 min at 4
0
C and the supernant
contains the cytosol proteins. The pellet is resuspended in 500µl ice-cold
disruption buffer containing protease inhibitors by pipeting up and down with a
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43
1ml pipette tip. Blunt-ended needle and a syringe are used to ensure
complete cell lysis and is centrifuged at 1000 X g for 10 min at 4
0
C,
supernatant contains cytosol proteins and is pooled with the supernatant
obtained in the former step. Centrifuge at 6000 X g for 10 min at 4
Supernatant
contains
microsomal
proteins
and
the
pellets
0
C.
contains
mitochondrial proteins. Wash the pellet with 1 ml of mitochondrial storage
buffer and spin at 6000 X g for 10 min at 4 0 C. Pellet containing mitochondrial
proteins is dissolved in 40 µl of NuPage buffer and proteins are separated on
NuPage SDS page, coomassie stained and ingel digested with trypsin.
Peptides are stage Tip cleaned and analysed over LC MS/MS.
5.8.3 Cytosol Fractionation
SILAC K4R6 labeled stimulated and unlabeled K0R0 ctrl having equal cell
pellets were lysed in ice cold lysis buffer of 2ml volume containing 1%
protease inhibitors. Cell lysis was performed by pipeting up and down using a
1ml pipette tip. Incubate for 10 min at 4
0
C on an end-over-end shaker.
Lysate is centrifuged at 1000 X g for 10 min at 4
0
C and the supernant
contains the cytosol proteins.
5.8.4 Membrane Fractionation
SILAC K4R6 labeled ctrl and unlabeled K0R0
stimulated having equal cell
pellets were resuspended in ice-cold sucrose buffer (255mM sucrose, 20Mm
Hepes Ph7.9, 1Mm EDTA) of 2ml volume supplemented with protease
inhibitor cocktail (Roche), transferred to a pre-chilled dounce homogenizer
and disrupted with tight pistil. Cell lysates are clarified by centrifugation at
24,223 rpm for 10 minutes, transferred to ultracentrifuge tubes and the
membranes were peletted by centrifugation at 245,000 rcf for 2 hours.
Membrane pellet was resuspended in ice-cold 100mM Na2CO3, incubated on
ice for 30 minutes with occasional vortexing, and membranes were peletted
again by centrifugation at 70,000 rcf (using the TLA 100.3 rotor) for 30
minutes. The membrane pellet was eluted with 50 µl of 500 mM Na2CO3 and
once with µl 50mM Na2CO3. 40 µl of the sample is separated on NuPage SDS
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44
page, coomassie stained and ingel digested with trypsin. Peptides are stage
Tip cleaned and analysed over LC MS/MS.
5.9 Peptide OffGel Separation
For pI-based peptide separation, the 3100 OFFGEL Fractionator and the
OFFGEL Kit pH 3–10 (both Agilent Technologies) with a 24-well setup was
used. 200 µg of protein digest is stagetip cleaned and resuspended upto 720
µl of Millipore water and is added to 2.88 ml of Peptide OFFGEL stock
solution (1.25X) and vortexed gently. IPG strips (GE Biosciences) 24 cms with
3-10 pI are thawed to room temp for 30 minutes and the protective backing is
gently removed from the IPG strip, placed in the IPG focusing tray with the gel
side up. A 24 well frame is placed on the IPG strip, 20μL of IPG strip IPG strip
rehydration buffer is added to swell the gel for 15 minutes. Wetted electrode
pads are placed at the extreme ends of the IPG strip gel surface.
After
allowing 15 minutes for reswelling of the gel load 150μL of prepared (Peptide)
OFFGEL sample (OFFGEL stock solution + sample)into each well: 24 cm
strip 24 well frame: 150μL X 24 wells = 3600μL . Place the cover seal over the
frame and press down gently on each well to secure proper fit. Reapply 10μL
of dH20 onto the electrode pads at each of the IPG gel ends. Place the tray on
the instrument platform. Pipette cover fluid (mineral oil) onto the gel strip ends
in several steps indicated as follows. When pipeting cover fluid, use caution
not to move the electrode pads. Pipette 200μL cover fluid onto the anode end
(fixed electrode) of the IPG strip. Pipette 400μL cover fluid at the cathode side
(movable electrode). After 1 minute, reapply an additional 200μL cover fluid
to both ends of the IPG strip. After 3 minutes add an additional 200μL cover
fluid onto the anode end (fixed electrode) of the IPG strip. (The cover fluid
should not extend higher than 1/2 the height of the tray grooves). Select the
fixed electrode and place the two tabs on the electrode into the slots on the
left side of the tray. Rotate the fixed electrode down into position over the
electrode pad (insert). Push down until the fixed electrode clicks into place.
Slide the tray without lifting it at the anode connector. Close the lid and start
fractionation. The sample was focused with a maximum power of 200 mW,
maximum current of 50 µA and typical voltages ranging from 500 to 4500 V
until 50 kVh was reached after 24 h. Peptide fractions were acidified by
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45
adding 10% of a solution containing 30% acetonitrile, 10% trifluoroacetic acid
and 5% acetic acid prior to Stage Tipping and MS-analysis. Peptides were
twice eluted from StageTips using 20μL 80% acetonitrile, 0.5% acetic acid,
the volume reduced to 5μL in the speed vac and acidified with 5μL of 2%
acetonitrile, 1% trifluoroacetic acid.
6.0 Nanoflow LC-MS/MS
All nanoflow LC-MS/MS experiments were done on a 7-Tesla Finnigan LTQFT mass spectrometer (Thermo Electron) equipped with a nanoelectrospray
ion
source
(Proxeon
Biosystems,
Odense,
Denmark).
The
liquid
chromatography (LC) part of the analytical system consisted of MDLC GE
Biosciences comprising a solvent degasser, a nanoflow pump, and a
thermostat micro-autosampler. Chromatographic separation of the peptides
took place in a 20-cm fused silica emitter (75µm inner diameter; Proxeon
Biosystems) packed in-house with methanol slurry of reverse-phase ReproSil-Pur C18-AQ 3µm resin (Dr. Maisch GmbH, Ammerbuch-Entringen,
Germany) at a constant pressure (50 bar) of helium. Then 4µl of the tryptic
peptide mixtures were autosampled onto the packed emitter with a flow of 500
nl/min for 30 min and then eluted with a 90-min gradient from 4–40%
acetonitrile (MeCN) in 0.5% acetic acid (AcOH) at a constant flow of 200
nl/min. The mass spectrometer was operated in the data-dependent mode to
automatically switch between MS and MS/MS acquisition. Full MS spectra
(from m/z 300–1500) were acquired in the FTICR with r=100,000 at m/z 300
(after accumulation to a target value of 10,000,000).The five most intense ions
were sequentially isolated for fragmentation in the linear ion trap by collisioninduced dissociation followed by accurate mass measurements in the FTICR.
Former target ions selected for MS/MS were dynamically excluded for 60s.
The general mass spectrometric conditions were: spray voltage, 2.3 kV, no
sheath and auxiliary gas flow, ion transfer tube temperature 120 0 C, collision
gas
pressure , 1.3 millitors, normalized collision energy using wide band
activation mode , 30% for MS2, ion selection thresholds for MS2 was 1000
counts.
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46
6.1 MitoTracker ® staining
Mitotracker ® (CMXRos) stock solution of 1mM concentration is prepared and
eth working conc
250nM is used for labeling the mitochondria of the cell.
DMSO 120hrs induced and controls MEL Cells are gently peletted and
resuspended in DMEM solution containing the dye and incubated Cells were
maintained at 370 C, 5 % CO2. for 30 minutes. Cells are peletted, 1X PBS
washed and fixed onto slides with 3.5% formaldehyde solution. Cells are
rinsed with 1X PBS three times 10 min each and nuclei were stained with
DAPI using the Vectashield mounting medium. Cells were visualized at
550nm on the Zeiss Axio Imager Z1 microscope along with ApoTome slider.
Magnification was 63 X 1.4 (Plan Apochromat objective), ApoTome slider
having 7.5 lines and is precalibrated. Camera used was Axiocam MRm in
black and white for capturing the pictures. AxioVision 4.6 software was used
for acquiring the images in ZVI file format and exported as TIFF files for
further processing. Filters used are DAPI-Filter set 49 (Excitation G365, Beam
Splitter FT395, emission BP 445/50). For Mitotracker red Filter set 43HE
(Excitation BP 500/25, Beam splitter FT570 AND Emission BP 605/70) were
used.
6.2 FACS Staining for Mitochondria
Mitotracker ® (CMXRos) stock solution of 1mM concentration is prepared and
the working conc
250nM is used for labeling the mitochondria of the cell.
DMSO 120hrs induced and control MEL Cells are gently peletted and
resuspended in DMEM solution containing the dye and incubated Cells were
maintained at 370 C, 5 % CO2. for 30 minutes. Cells are peletted, 1X PBS
washed. FACS analysis was carried out on FACSCalibur
TM
from SD in the
green channel and standard instrument setting were used. Cell-Quest
software
47
was
used
for
image
processing.
47
6.3 Peak list generation
Raw mass spectral files generated from the LTQ FT-Ultra mass spectrometer
are submitted to DTA supercharge for peak list generation. Screenshot Figure
11b shows the parameter settings used in DTASupercharge
Figure 11.b DTASuperCharge
Figure shows the DTASuperCharge, raw file is up-loaded with the browse button,
precursor tolerance of 0.1 Thompson
of the DTA supercharge v 1.01 indicating the selected parameters for peak-list
processing is shown. The output file called “mgf file” consists of peak-list of
peptides along with the peptide charge states.
6.4 MGF Combiner
The individual MGF (Mascot generic Format file) generated from DTA
supercharge are concatenated using “MGF-combiner” software to give single
MGF file.
6.5 Mascot search engine
Combined peak list is searched against the mascot search engine (version
2.1.04, Matrix science, London, UK) against a concatenated database
combining 53.236 proteins from International Protein Index mouse protein
database version 3.24, 27 commonly observed contaminants (forward
database) and the reversed sequence of all proteins (reverse database).
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48
Carbamidomethylation was set as fixed modification Figure 11 c. Variable
modifications included oxidation (M), N-acetylation (protein), Deamidated
(NQ), Lysine (D4) (K) & Arginine-13C6 (R-13C6(R). Enzyme specificity was
set to trypsin, allowing for cleavage N-terminal to proline and between
aspartic acid and proline (Olsen, Ong et al. 2004)
Figure 11c:
Mascot search engine
Figure shows the various options selected in mascot search query
Up to two missed cleavages and two labeled amino acids were allowed used.
Initial mass deviation of precursor ion and the fragment ions were up to 10
ppm and 0.5 Da respectively.
6.6 MS-Quant
Identified peptides are quantified using the MS-Quant (Version14a2.9)
quantification tool. Selected MS-Quant are as follows, sequence length range
5 amino acids, charge range 2 -5, mascot range 10 to 2000, mgf
generator
DTASuperCharge
(LTQ-FT),
raw
file
type
LTQ-FT
file
raw,
Quantification mode LysD4, Arg 13C6. Figure 11 d
49
49
Figure 11d: MSQuant page parameter settings
Figure shows parameter settings used in MSQuant data processing
6.7 MaxQuant
MaxQuant was used for automated peak-list generation and protein
quantification. The following parameters were used in the Quant.exe module.
a)Instrument: Orbitrap/FT Ultra b)SILAC doublets Lys4 and Arg6. c)
Carbamidomethylation is set as fixed modification. Variable modifications
include Oxidation (M), Pyro(N-term C), Pyro(N-term Q). Database mouse,
enzyme trypsin /P+DP, MS/MS tolerance 0.5 Da, maximum msm file size set
to 350 MB, maximum missed cleavage 2 and top MS/MS peaks per 100 Da
set to 6.
Processed raw files result in .par/.msm files. The sil0.par file
contains the SILAC state ‘light’, and no SILAC label modifications are used.
Sil1.par contains the SILAC state ‘heavy’ in this the heavy SILAC label are
treated as fixed modifications. The iso.par file contains MS/MS data from the
single isotope patterns and SILAC labels are treated as variable
modifications. The par files with its corresponding msm file are submitted to
mascot search using the mascot daemon. Mascot searched .dat files are
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50
imported into the local folder (combine folder) and further processed using the
identify.exe. A screen shot of the parameters used in the identify.exe can be
seen in Figure 11 e
Figure 11e: Identify.exe parameter settings
Figure shows the parameters used for processing the mascot result (dat) files
6.9
Gene Ontology and KEGG Enrichment Analysis-based Hierarchical
clustering
In the control against 2% Me2SOstimulated and control MEL cell study the
quantified proteome was divided into six quantiles corresponding to
percentage cutoffs of 0- 15%, 15-35%, 35-50%, 50-75% 75- 85%, and 85100%. The enrichment analysis for gene ontology (GO) biological process
and cellular component were done using the KEGG pathways. For
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51
hierarchical clustering we first collated all the categories obtained after
enrichment along with their p values and then filtered for those categories
which were at least enriched in one of the quantiles with p value > 0.05.
Categories which did not have a p value after collation in any quantile were
provided a very conservative p value of 1. This filtered p value matrix was
transformed by the function x = -log10 (p-value). Finally these x values were
transformed to z-score for each GO category by using the transformation [xmean(x)]/sd(x). These z-scores were then clustered by one-way hierarchical
clustering using “Euclidean distance” as distance function using the TIGR
MultiExperiment Viewer MeV -TM4.
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52
Results
6.1.0 MEL cells as an in vitro model system for Murine erythroleukemia
study
Murine erythroleukemia (MEL) cell lines are erythroid progenitor cells derived
from the spleen of susceptible mice infected with the Friend virus complex
SFFV (Friend, C et al 1975). The transformed cells are arrested at the
proerythroblast stage of development manifesting their leukemic potential and
can be maintained in cell culture indefinitely (Rifkind, R et al 1974).
Depending on the culture conditions 1% of MEL cell population is known to
undergo spontaneous erythroid differentiation from pronormoblasts to mature
orthochromatic normoblasts. SFFV transformed MEL cells serve as an
established model system for studying the erythroid growth and differentiation
process. In the erythroleukemic stage of SFFV infected cells, over-expression
of Spi-1/PU.1 protein is a common hallmark (Ochoa, S 1982) PU.1 is an
important regulator which activates and interacts with transcription factors like
c-JUN, GATA-1, GATA-2,C/EBP and RUNX-1 (Oikawa et al. 1999 ), (Singh H
et al 1999) and over expression of the ETS protein Spi-1/Pu.1 Spi-1 has also
been shown to immortalize erythroblasts in long-term bone marrow cultures
(Schuetze, S et al 1993). Tight control of PU.1 expression is essential for
cellular differentiation towards erythroid lineage and in preventing cells from
assuming leukemic fate (Alex Bukrinsky et al 2009). MEL cells undergo
terminal erythroid differentiation when exposed to inducers like Me2SO or
Hexamethylene-bisacetamide (HMBA) (Marks, P et al 1978), via receptormediated processes or signal transduction pathways. Differentiation period is
usually 4 to 5 days and marked by high hemoglobin expression levels giving a
characteristic red color to the cell pellet. MEL cells recapitulate the erythroid
differentiation process
Results
6.1.1 Maintenance of MEL cell lines
In order to investigate the erythroid differentiation programme murine
erythroleukemia cells (MEL) were grown in suspension culture in DMEM
medium and differentiation was induced by the addition of 2% Me2SO to cells
that were grown to a density of 2.5 x 105 cells/mL. Higher or lower cell counts
did not favor the cellular differentiation programme.
6.1.2 Me2SO Induction of erythroid differentiation of MEL cells
Prolonged culture (120 hours) of MEL cells with 2% Me2SO induction resulted
in an irreversible commitment of erythroid differentiation and >95% of cell
population were committed for irreversible differentiation. This could be
initially confirmed from the cell count by 48 hours between the control and
2% Me2SO induced MEL cell population. Once the MEL tumor cells reenter
the erythroid differentiation programme, they continue proliferating, but the
cell cycles are limited to 5-6 divisions which are then followed by arrest in G1
phase (Gusella et al 1976). The induced cells differentiation was evident from
the phenotype in having smaller and fewer cells when compared to their
counterparts, as they have limited proliferation potential upon induction. At the
completion of the day 5 the induced cell pellet phenotype is marked by a red
colored cell pellet due to high amounts of hemoglobin accumulation. The
control cell pellet, which appears as a beige colored pellet as indicated in
Figure_R1 while the induced cell pellet is red colored due to hemoglobin
accumalation
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54
Results
(a)
(b)
Figure_R1. Phenotype of MEL cell pellets (a) Ctrl and (b) Me2SO induced
Figure shows the cells pellets with and with out DMSO induction. Non-induced cell pellet (a) is
beige colored and larger size. Induced cell pellet (b) is red colored to hemoglobin
accumulation and has a smaller volume.
6.1.3 SILAC labelling of MEL cells
Stable Isotope labeling of Amino acids in Cell culture (SILAC) is a
straightforward labelling strategy for quantifying global protein expression
changes at the proteome level. SILAC based encoding of proteomes is
discussed elsewhere (Ong et al 2002). Briefly, SILAC-encoding results in
metabolic labeling of the entire proteome by heavy, non-radioactive isotopic
variants of natural amino acids, thus making labeled proteins (and therefore
tryptic peptides derived from them) distinguishable by MS analysis. The
SILAC-labeled cell populations can be mixed and analysed in one MS
experiment that allows accurate quantification of proteins from the different
cellular states (Blagoev and Mann 2006). Figure_R2 provides a schematic
overview of the experimental work followed in our experiments. Murine
erythroleukemia cells were grown in medium containing 13C6-arginine and 2H4lysine (in the presence of 10% dialyzed FCS) for upto 5 passages for
complete incorporation of the labeled amino acids. Cell growth was slightly
reduced when compared to normal non-SILAC medium, likely due to the
removal of very low molecular weight components from the dialysed serum.
The latter effect of the SILAC media affects both populations equally and is
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55
Results
therefore not influencing quantification. Cells were subjected to five passages
in SILAC medium to ensure complete metabolic labeling, which was further
evaluated by MS analysis (virtual absence of non labeled peaks”; data not
shown). The cells were additionally supplemented with 1% non-dialysed
serum and MEM non-essential amino acids since the former contains minor
traces of differentiation promoting factors, whereas the latter suppresses
arginine to proline conversion (Bendall, Hughes et al. 2008). One population
of cells was stimulated with 2% Me2SO, for 120 hours and the other noninduced one served as a control. After 120 hours equal no of cells were
collected from the control and stimulated groups and lysed with 2 % boiling
SDS. After protein estimation from cell lysates (BCA assay) equal amounts of
total
protein
were
mixed
for
further
analysis.
Figure_R2. SILAC Experiment schematic: MEL cells are adapted to SILAC media for complete
incorporation in light AA and heavy AA for 5 passages. Heavy labeled cells (indicated red colored plate)
are 2% DMSO stimulated, equal no. of cells are mixed, prefractionated and the
SILAC pairs are
resolved over FT-MS .Acquired data is subjected to bioinformatics analysis using the mascot protein
search engine and Max-Quant quantification software.
56
56
Results
6.1.4 Assessment of SILAC labeling compatibility for MEL cell
differentiation
To evaluate the compatibility of MEL cell differentiation with SILAC we
inspected the quantified proteome for known marker proteins that are involved
in MEL cell differentiation. We carried out western blot analysis Figure_R3. We
found the ETS transcription factor PU.1 levels to rapidly decline upon MEL
cell differentiation and it’s reported that enforced expression of PU.1 blocks
erythroid differentiation in cultured cells and in embryos (Delgado et.al 1998,
Quang, C.T et.al 1995, Rao, G, N et.al 1997, Rekhtman, N et.al. 1999,
Schuetze et.al 1992). Other transcription factors Ikaros, Runx 1and Prp4
levels declined during MEL differentiation indicating the cells to be less
proliferating (data not shown here). Hexim-1 protein (protein found to be
induced
upon
Hexamethylene
bis-acetamide
in
MEL
cells)
and
in
differentiating neuroblastoma cells upon retinoic acid treatment (Turano,
Napolitano et al. 2006). CDK9-L form was found up-regulated upon
differentiation and the same has been reported for MEL cell and monocyte to
macrophage differentiation (Liu and Herrmann 2005). Chemical induction of
MEL cells leads to a massive expression of alpha and beta globin. Since
accumulation of non-heme bound globins could lead to aggregation (observed
in some inherited blood disorders; beta thalassemia (Bank 2007)) the globin
chaperone alpha-hemoglobin stabilizing protein (AHSP), which represents an
erythroid-specific molecular chaperone for alpha-globin
(Otsuka, Ito et al.
2008), was found to be up-regulated upon DMSO induction. This has been
recently published during this work was still in progress. AHSP is a small
protein specifically binding to free α-globin, thereby stabilizing its structure and
limiting its toxic effects (as a monomer). Weiss and colleagues showed that
AHSP is also critical to the formation and stabilization of normal amounts of
hemoglobin. Role of AHSP is represented in the Figure_R3.a (source Journal of
clinical investigation Volume 117, No 7)
From the above results we could draw a preliminary conclusion that SILAC
labeling did not interfere with the MEL cell differentiation programme, so we
further designed a reverse SILAC labeling strategy Figure_R4 to minimize for
any systemic errors encountered during our MS based quantification
workflow.
57
57
Results
58
58
Results
Figure_R3.a (Source: Journal of clinical investigation Volume 117, No 7)
Hb formation with and without AHSP (A) In normal cells, the α-globin locus on chromosome
16 and β-globin locus on chromosome 11 produce α- and β–globin mRNA and α- and β–globin
polypeptides, respectively, which combine with the heme moiety to form Hb dimmers (HB αβ).
Two αβ dimmers combine and form the Hb tetramer HbA. AHSP stabilizes and solubulizes
newly formed and excess α- globin chains as apo- α- globin and αHb. The absence of AHSP
normally leads to a mild anemia dute to precipitated unstabilized α- chains(not shown). (B) In
β–thalassemia, there is excess α- globin due to the decreased or absent β–globin production.
The excess α-globin precipitates in the cells and on membranes and leads to ineffective
erythropoiesis in nucleated red cells in the bone marrow and red cell hemolysis in circulating
blood cells. In the absence of AHSP, the anemia is worse because of the further destabilizing
of the excess α- globin (not shown). (C) Excess of β–globin chains due to a deficiency of αglobin, and mild anemia. In α-thalassemia in the absence of AHSP, β–globin precipitates in
red cell membranes, presumably because the α–globin-AHSP complex is a required
intermediate for optimal HbA formation
Figure_R3. Western blot analysis of MEL proteome
Lysates from the control and 2% DMSO induced (120 hours) MEL cells were analysed for
hemebiosynthesis markers, lysates were separated on SDS-PAGE blotted, and probed with
antibodies as indicated.
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Results
6.1.5 RT-PCR analysis
Real time PCR analysis for β- globin and AHSP mRNA abundance was
performed in 2% Me2SO stimulated and ctrl cells. We observed β-globin
expression to be initially induced after 24 hours and robustly increased over
time until 120 hours. AHSP a molecular chaperone described earlier, its
mRNA levels are increased upon β-globin induction Figure_R3.b indicating the
erythroid differentiation process. Cycle numbers were normalized to actin.
Underneath the graph, time points are represented.
Globin: RT-PCR
40
35
30
25
St im
20
Unst im
15
10
5
0
0
24
48
72
96
120
H ou r s
AHSP: RT-PCR
35
30
25
20
Stim
15
Unstim
10
Fig 22.5 RT-PCR analysis
0
0
24
48
72
96
120
Hours
Figure_R3.b RT-PCR of globin and AHSP:
RT-PCR analysis of the genes, globin and AHSP with or without DMSO. Total RNA was
collected at the indicated time points after 2% DMSO induction. Total RNA was subjected to
RT-PCR analysis.
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Results
6.1.6 Forward and reverse SILAC labeling of MEL cells
We performed the forward and reverse labeling strategy, by stimulating the
SILAC labeled MEL cells in one group and stimulating the ctrl cells in the
other group with 2% Me2SO Figure_R4.
Figure_R4. Forward and reverse labeling schematic:
MEL cells are SILAC labeled as described in Figure_R2.For reverse labeling the un-labeled MEL cells
are 2% DMSO stimulated for 120 hours and equal no. of cells are mixed, lysed and prefractionated. The
SILAC pairs are resolved over FT-MS .Acquired data is subjected to bioinformatics analysis using the
mascot protein search engine and Max-Quant quantification software.
Equal cells are mixed, 2% SDS lysed, BCA protein estimated. Proteins were
fractionated by SDS-PAGE and peptide isoelectric focusing. For OFFGEL
peptide isoelectric focusing, insolution digestion was sequentially performed
using the endoproteinase Lys-C and trypsin, resulting peptides are
fractionated using OFFGEL peptide isoelectric focusing approach. The
peptides were extracted sequentially from the IPG strip and analyzed on
nano-LC-MS. SDS-PAGE fractionated protein lane is cut into 14 pieces, each
piece was in-gel digested using trypsin. The resulting tryptic peptides were
loaded on self made C18 stageTips (Rappsilber et al 2003) for desalting and
subsequently analyzed using nano-LC-MS to quantify the proteins from the
resulting peptide pairs.
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Results
6.1.7 Tetramethyl Benzidine assay for estimating hemoglobin levels
Induction of hemoglobin is estimated by Tetramethylbenzidine staining. TMB
is employed in the calorimetric assay for estimating the amount of
hemoglobin (Standefer and Vanderjagt 1977) in plasma. The chemical
reaction leads to the formation of hematin which catalyses the conversion of
hydrogen peroxide to water and oxygen. In this conversion benzidine is
oxidized to a chromogenic product, the intensity of the resulting color is used
as an index of hemoglobin concentration. DMSO induced MEL cells showed a
10 fold induction of hemoglobin Figure_R5
TMB ASSAY FOR TOTAL HEMOGLOBIN LEVELS
10
9
8
Hb
[µg/µl]
7
6
TMB ASSAY
5
4
3
2
1
0
Mel Control
Mel 2% DMSO
Figure_R5: TMB assay: Hemoglobin content is calculated and normalized against total
protein concentration of the cell pellet. Me2SO induced MEL cells showed higher levels of Hb
content due to the accumulation of hemoglobin compared to the uninduced cells.
6.1.8 Subcellular fractionation of MEL cell proteome
To get a deeper insight of protein changes during erythroid differentiation of
MEL proteome, we adopted a reductionist approach thereby reducing the
sample complexity and performed subcellular fractionation. We aimed at
enriching low abundant proteins and signaling complexes, henceforth isolated
mitochondrial, cytosol, nuclear and membrane fractions, quantified the
resulting proteins. The sub fractionations all together yielded a proteomics
dataset consisting of 3774 quantified proteins Figure_R6. Proteins were
included only after passing through stringent selection criteria like (a)
Minimum of two peptides per protein (2) Mass accuracy below 5 ppm (3)
Decoy filtered and (4) common contaminants like human keratins, BSA and
trypsin were excluded. Data set revealed 26% of the quantified MEL proteome
has
cytoplasmic
components,
37%
nuclear
components
and
24%
mitochondrial and 13% membrane components Figure_R7.
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Results
Max Quant-MEL proteome (3774)
MaxQuant profile of MEL 2 % Dmso stimulated proteome
8
LOG 2/1 MS quant
6
4
2
0
-2
-4
IPI Mouse
Figure_R6: MEL proteome MAX Quant processed dataset:
3374 quantified proteins from the MEL proteome are log2 represented; X-axis represents the
IPI entries and log2 values on the y-axis.
MEL Proteome SubCellular Fractionation
26%
13%
24%
37%
CytoFractionation
MitoFractionation
NuclearFractionation
Mem braneFractionation
Figure_R7 Subcellular fractionation of MEL proteome:
Pie chart represents the percentage of proteins obtained from the MEL proteome enriched in
mitochondria, nucleus, cytosol and membrane fractions. Corresponding enrichment values
are represented in the figure.
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Results
As a measure of quality check we primarily evaluated the quantified proteome
for the presence of markers known to be elevated during heme biosynthesis.
Figure_R8 represents the marker protein expression represented as Log2 fold
changes, we later correlated these proteome changes against the
corresponding mRNA fold changes.
T
Figure_R8 Hemebiosynthesis, enzymes identified using the Proteome and
Transcriptome analysis
Figure represents enzymes involved in hemebiosynthesis, log2 expression values of marker
proteins are indicated adjacent to green boxes having the proteomics observed fold changes
and the transcriptome log2 values are indicated in yellow boxes. Proteomics data is obtained
from SILAC labeled MEL proteomic analysis and transcriptome data from mRNA expression
analysis using the Affymetrix Gene 1.0 ST mouse array.
The indication for successful MEL differentiation is further marked by the
presence of hemoglobin subunit beta-1 (20 fold up-regulated), Hemoglobin
subunit beta-2 (8.7 fold up-regulated), Hba-a2 hemoglobin alpha (15.4 fold
up-regulated). The presence of hemebiosynthesis markers in the SILAC MEL
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Results
proteome suggests SILAC labeled MEL cells possess the potential to undergo
cellular differentiation.
6.1.9 Mitochondrial protein analysis in MEL cell differentiation
Mitochondrial proteins
Mitochondria are dynamic organelles essential for cellular life, death and
differentiation. Mitochondria are best known for ATP production via oxidative
phosphorylation (OXPHOS), they house myriad of other biochemical
pathways and are centres for apoptosis and ion homeostasis. Enzymatic
machinery regulating hemebiosynthesis is known to take place inside the
mitochondria Figure_R9.
Figure_R9: Source ACS Chemical biology Vol1 No 10
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Results
6.1.10 FACS analysis of Mitotracker dye
We used the mitotracker dye to assess the mitochondrial changes during
2% Me2SO induced MEL cell differentiation. Mitotracker dye upon intake by
actively respiring cells gets oxidized and binds to the thiol groups of proteins
and peptides as represented Figure_R10 a
Figure_R10a Mitotracker dye
Mitotracker is a new fixable mitochondrion-selective dye. Mitotracker probe contains a thiolreactive chloromethyl moiety. Upon intake, is sequestered in the mitochondria and reacts with
the accessible thiol groups on peptides and proteins to form an aldehyde-fixable conjugate
FACS analysis Figure_R10 b revealed the 2% Me2SO induced MEL cells had
an increase in the no of mitochondria indicating a role in erythroid
differentiation
2%
Contro
Figure_R10 b: FACS analysis of DMSO induced and ctrl MEL cells:
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Results
Mitotracker dye 250 nM working concentration is used for labeling the control and induced
MEL cells. Incubated at 370 C for 30 minutes, PBS washed and FACS analysed. Colour
coding of the peaks is indicated in the left corner
We further evaluated the mitochondrial abundance using the Apotome
microscopy Figure_R10c, and counted the mitochondrial dots. The mitodot
counting Figure_R10d from the 2% Me2SO and control cells indicated a 3 fold
increment of mitochondria in the induced population of MEL cell population
6.1.11 Mitochondrial staining using the Mitotracker dye of DMSO
induced MEL cells
Figure_R10c Mitotracker dye stained MEL cells
Control and induced MEL cells are stained with mitotracker-red and DAPI, fixed on slides and
visualized at 550 nm on the Zeiss Axio imager Z1 microscope along with ApoTome slider.
Red colored dots indicating the mitochondria can be observed. Induced cells have smaller cell
volume and more mitochondria indicated by mitodots compared to the ctrl cells having fewer
dots and larger cell volume
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Results
Mitochondrial count using the mitotracker dye
45
40
No. Of Mitochondria
35
30
25
2%
20
Ctrl
15
10
5
0
2%
Ctrl
Figure_R10d Mitotracker dye staining analysis of the mitodots:
Mitodots counting from the induced and control MEL cells visualized at 550nm on the Zeiss
Axio Imager Z1 microscope along with ApoTome slider. Data is obtained by counting 50
single MEL cells from induced and control population.
6.1.12 Mitochondrial fractionation:
We carried out subcellular fractionation to study the role of mitochondrial
proteins in MEL cell differentiation. Mitochondrial crude fractionation approach
enabled quantification of 1500 proteins and proteins are binned indicating the
fold changes Figure_R11 a. The quantified proteins were subjected to pathway
studio analysis and generated a cellular protein network Figure_R11. Proteins
were found to be concentrated in the mitochondria and directly interacting with
cytoplasmic proteins and to a lower extent with the nuclear proteins.
Additional proteins were also observed on the cell membrane and
endoplasmic reticulum having direct interactions. The pathway is built
considering the “find common targets” algorithm.
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Results
Figure_R11a: Mitochondrial proteins fold change
Histogram of mitochondrial proteins binned indicating the fold change and no of proteins per
bin
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Results
6.1.12 (a) Building and Visualizing Pathways with Pathway Studio
Pathway Studio is a windows-based application that allows researchers to
visually build pathways using a variety of methods. The software has a built-in
resource named RasNet, a database of molecular interactions based on
natural language processing of scientific abstracts in pubMed (Nikitin, Egorov
et al. 2003). Using the RasNet, a researcher can simply drag his favorite gene
product onto a new pathway diagram, and build pathway using well-known
interactions discussed in existing literature. Data input is accepted in tabdelimited format. Pathway showing the protein interactions is constructed
automatically. We evaluated the cellular distribution of the identified proteins
using the pathway studio software Figure_R12a, showing the cellular
connectivity and distribution of the identified proteins.
Figure_R12a Mitokit01 Pathway Studio profile
Figure represents the mitochondrial protein distribution, pink circles represent the proteins
located in mitochondria and cytoplasm. Pink lines indicate binding and blue lines with squared
boxes indicate the expression pattern. Cellular organelles like mitochondria, nucleus and
endoplsmatic reticulum can also be seen.
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Results
GO analysis of the isolated mitochondrial proteins Figure_R12b represents 52%
mitochondrial proteins, 32% nuclear proteins and 12% proteins from the
endoplasmatic reticulum, golgi and cytoplasm. We primarily aimed at
incrementing the proteome identifications and compromised with a medium
mitochondrial preparation that contained proteins from other organelles due to
high sensitivity of the modern MS methods (Graumann, Hubner et al. 2008).
Figure_R12b: Cellular Components of Mitochondrial proteins:
A pie chart of GO based subcellular localization of mitochondrial protein fractionation is
shown
We analysed the mitochondrial proteome for the enriched biological
processes using the Ariadne Pathway studio 6.0 and observed the upregulated proteins to be significantly enriching the cellular processes
associated with cell cycle arrest, hemebiosynthesis, and erythrocyte
differentiation Figure_R12c. The down-regulated proteins were found to enrich
the cellular processes associated with lipid biosynthesis, steroid biosynthesis
related
71
biological
processes
Figure_R12d.
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Results
Enriched Biological processes : Upregulated
Entries
5%
5%
13%
59%
18%
Hemoglobin Bisynthesis
Tetrapyrrole biosynthetic process
Hemebiosynthesis
Erythrocyte differentiation
cell cycle arrest
Figure_R12c: Enriched biological processes of up-regulated mitochondrial proteins
A pie chart of GO based enriched biological process is shown
Down regulated Biological Procesess: Downregulated Entries
1%
9%
3%
negative regulation of
megakaryocyte differentiation
negative regulation of T cell
proliferation
52%
tRNA modification
35%
steroid biosynthesis
lipid biosynthesis
Figure_R12d: Enriched biological processes of down-regulated mitochondrial proteins
A pie chart of GO based enriched biological process is shown
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Results
6.1.13 Cytosol fractionation
Cytosol proteins
Cytosol fraction obtained from Mitokit processed SILAC labeled cells, resulted
in the quantitation of1600 proteins. Fractionation is performed taking equal
cell counts from the control and 2% Me2SO stimulated cell population.
Quantitative data obtained is normalized against the total protein content. A
three fold change in total protein concentration variation is observed from the
normal and stimulated cell pellets. Max-Quant profile of the quantified proteins
is represented in Figure-R13 a.
CytoFractionation_MaxQuant
100
lOG Scale
10
1
1
93
185 277
369 461 553 645
737 829 921 1013 1105 1197 1289 1381 1473 1565
0.1
IPI
Figure_R13a: MaxQuant profile of Cytoproteins
A histogram plot representing 1600 quantified proteins obtained from cytosolic fractionation,
x-axis represents the IPI entries with log2 expression represented on y-axis
GO analysis of the cytosolic proteins Figure_R13 b indicated the major fraction
to contain the cytosolic proteins and additional proteins from nuclear and
mitochondrial compartments were observed
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Results
Cellular components CytoFractionation
proteasome core
complex (sensu
Eukaryota)
eukaryotic translation
initiation factor 3
complex
perinuclear region
chaperonin-containing Tcomplex
membrane coat
mitochondrial matrix
Arp2-3 protein complex
heterogeneous nuclear
ribonucleoprotein
complex
signalosome complex
ribosome
mitochondrion
soluble fraction
proteasome complex
(sensu Eukaryota)
cytoplasm
nucleus
ribonucleoprotein
complex
protein complex
spliceosome complex
Figure_R13b: Cellular Components of Cytoproteins
A pie chart of GO based subcellular localization of cytosol protein fractionation is shown
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Results
6.1.14 Nuclear Fractionation
Nuclear proteins
Nuclear fractionation was carried out from SILAC labeled 2% Me2SO
stimulated MEL cell population.
Nuclear fractionation resulted in the
identification and quantification of 881 proteins Figure_R14a and the binned
representation Figure_R14b. Fractionation is performed taking equal cell counts
from the control and 2% Me2SO stimulated cell population. Quantitative data
obtained is normalized against the total protein content. A three fold change in
total protein concentration variation is observed from the normal and
stimulated cell pellets. Proteins up-regulated include transcriptional activator
protein Pur-alpha protein up-regulated by 2.2 fold. Pur alpha mediates
induction of the CD11c beta 2 integrin gene promoter in monocytic cell line
U937 ((Shelley, Teodoridis et al. 2002). Transcription factor BTF3 is upregulated to 5 fold. Chromodomain-helicase-DNA-binding protein is 2 fold
down regulated, erythroid transcription factor GATA-1 levels are 1.6 fold upregulated,
prothymosin
alpha
is
modestly
down-regulated
Ratio H/L Normalized
100
Log Scale
10
1
1
82
163 244 325 406 487 568 649 730 811 892 973 1054 1135 1216 1297 1378 1459 1540 1621 1702 1783 1864 1945 2026 2107
0.1
0.01
0.001
IPI
Figure_R14a: Nuclear protein Max Quant profile
A histogram plot representing 881 quantified proteins obtained from nuclear fractionation, xaxis represents the IPI entries with log2 expression represented on y-axis
Gem-associated protein 7 is down-regulated to 4.8 fold. GO analysis of the
nuclear
fraction
represents
Figure_R14c
nuclear
and
other
cellular
compartments like ER, ribosomal, cytosolic and membrane related proteins
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Results
Nuclear Proteins (p < 0.0012)
25
20
15
10
5
0
-80
-50
-35
-25
-20
-15
-10
-5
-2
5
10
15
25
50
75
Fold Change
Figure_R14b Nuclear proteins
Figure shows the binning of nuclear proteins based on the fold change. X-axis represents fold
change and y-axis represents bins
Figure_R14c: Nuclear protein Max Quant profile
A pie chart of GO based subcellular localization of nuclear protein fractionation is shown
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Results
6.1.15 Membrane Fractionation:
Membrane proteins
Membrane fractionation enabled in the identification of 757 proteins.
fractionation is performed taking equal cell counts from the control and 2%
Me2SO stimulated cell population. In-solution digestion is performed using
Lys-C and trypsin. Peptides are OFFGel fractionated and analyzed on nanoLC MS. Quantitative data obtained is normalized against the total protein
content.
Geneset enrichment analysis of membrane proteins
endoplasmic reticulum lumen
integral to plasma membrane
proton-transporting twosector ATPase complex
Golgi stack
Genesets Enriched
membrane fraction
Golgi membrane
melanosome
endoplasmic reticulum
membrane
extracellular space
nucleosome
Golgi apparatus
membrane
integral to membrane
0.00E+00
1.00E-03
2.00E-03
3.00E-03
4.00E-03
5.00E-03
6.00E-03
7.00E-03
p - Values
Figure_R15: GSEA of membrane proteins Max Quant profile
Figure represent enriched pathways, IPI entries are analysed using Ariadne Pathway Studio.
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Results
Geneset enrichment analysis was carried out using the Ariadne pathway
studio Figure_R15, it indicated endoplsmatic reticulum, golgi membrane,
integral to plasma membrane components to be significantly enriched
We observed Tetraspanin-33 a known erythrocyte marker elevated to 11 fold.
HEM-2 a bcl2-Homolog involved in the remodeling of cytoskeleton induced to
6 fold. Erythrocyte membrane band 4.1, a 80-kD structural component of the
red blood cell (RBC) cytoskeleton. It is critical for the formation of the
spectrin/actin/protein 4.1 junctional complex (Baklouti, Huang et al. 1996), is
up-regulated to 2.7 fold. Platelet membrane glycoprotein II b, a known
erythrocyte
78
marker
levels
were
found
up-regulated
to
5
fold
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Results
6.1.16 MEL proteome combined dataset Quantified:
MEL proteome dataset obtained from the subcellular fractionations and
complete cellular lysate experiments yielded a total number of 3774 quantified
proteins. These were quantified using the Max-Quant software and the log 2
plot of the quantified proteins is represented in Figure_R16a and Figure_R16b
shows the log2-transformed protein ratios. Protein ratios have a median of 0
on the log scale (dotted red line) as expected for a 1:1 mixture and cluster
tightly around the median.
MEL-Max Quant Profile
8.00
Fold Change Log 2
6.00
4.00
2.00
0.00
-2.00 1
189 377 565 753 941 1129 1317 1505 1693 1881 2069 2257 2445 2633 2821 3009 3197 3385 3573 3761
-4.00
-6.00
-8.00
IPI
Figure_R16a: MEL complete proteome dataset Max Quant profile
A histogram plot representing 3774 quantified proteins obtained from MEL proteome, x-axis
represents the IPI entries with log2 expression represented on y-axis
The quantified proteins were probed for enriched pathways using the Ariadne
pathway studio analysis Figure_R16c and the cellular components enrichment
Figure_R16c
indicated
in
majority
nuclear,
cytosolic,
membrane
and
mitochondrial proteins along with ribosomal, splicesome complex, ER and
other soluble fraction related proteins.
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Results
Figure_R16b: Quantification of MEL proteome.
The figure shows the log2–transformed protein ratios. Protein ratios have a median of -0.05
on the log scale (dotted colored line) as expected for a 1:1 mixture and cluster tightly around
the median.
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Results
Figure_R16c: MEL complete proteome dataset enriched pathways Max-Quant profile
Figure shows the pathways enriched and p-value represented in log2 scale, several erythroid
related pathways, mitochondria and cytoskeletal remodeling pathways are found enriched
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Results
nucleus
Cellular Components
2%
1%
2% 2% 1%
4%
cytoplasm
22%
2%
Mitochondria
3%
Ribosomal
membrane
soluble fraction
18%
ribonucleoprotein complex
protein complex
20%
nucleolus
spliceosome complex
5%
outer membrane
18%
respiratory chain complex I
(sensu Eukaryota)
Figure_R16d: MEL complete proteome dataset Enriched Pathways Max Quant profile
A pie chart of GO based subcellular localization of quantified MEL proteome is shown
3774 quantified proteins were binned and it indicated the down regulated
proteins were more than the up-regulated proteins in total, suggesting the cell
to be gearing for autophagy process.
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Results
6.1.17 Comparision of nano-LC MS against 2D-Gel
We then evaluated the nanoLC-MS approach against the classical 2D-Gel
approach. A virtual 2D map Figure_R16 is constructed computing the
theoretical molecular weight and isoelectric point values of the identified
proteins using the ExPASy-compute pI/Mw tool. Dotted red box indicates the
practical boundaries of the standard 2D-gel proteomics approach and the
green box represents the complete dataset of quantified proteins by nano-LC
MS
MEL Virtual 2D Map
180000
160000
140000
Molecular Weight
120000
100000
80000
60000
40000
20000
0
0
2
4
6
8
10
12
14
pI Values
Figure_R16: Virtual: 2D map :
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Results
It was plotted by calculating the theoretical pI and molecular weight of the IPI entries using the
M. Musculus database, swissprot expasy tools. The practical boundaries of standard 2D-gel
based proteomics approach is indicated by red box and proteins identified from the nano-LC
MS approach are indicated in green box.
approach. This clearly indicates the nanoLC-MS to excel in identifying the
proteins with basic pI when compared to the classical 2D-Gel approaches.
Under representation of low and high molecular weight proteins on a 2D-map
is a well known limitation.
6.1.18 Histone modulations during MEL cell differentiation
Differentiation of metazoan cells involves dramatic changes in gene
expression patterns and proliferative capacity driven primarily by epigenetic
mechanisms (Yellajoshyula and Brown 2006). During the DMSO induced
terminal differentiation of murine erythroleukemia cells in vitro, changes in
accumulation and modification of chromatin proteins have previously been
observed (Grove and Zweidler 1984). In our study using the SILAC approach
we report the linker histones to be significantly up-regulating and the core
histone variant proportions to be modestly altered. The results are all
indicated in the table below.
IPI
Histone Variant
IPI00467914
Histone H1.0
Fold Change
5.14
Reference
(Boix
and
Ruiz-Carrillo
1992)
IPI00282848
Histone H3.2
2.25
(Lopez-Fernandez,
Lopez-Alanon et al. 1997)
IPI00623776
Histone H4
1.89
IPI00230264
H2A.X
2.15
IPI00221463
Histone H2A type-3
1.50
IPI00874492
Histone H2B type-1
1.89
(Grove
and
Zweidler
and
Zweidler
1984)
(Grove
1984)
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Results
Figure.H.1: Schematic representation of the assembly of core histones into the nucleosome
(Resource Wikipedia). H3-H4 form dimmers, tetramers and H2A-H2B dimerise, H2A-H2B
along with the H3-H4 tetramers form the Histone octamer. Linker histones implicated in the
formation of higher orders of chromatin structure and gene repression. The proposed location
of linker Histone H1 is not known from the x-ray crystal structure
Linker histone H1.0 levels were significantly up-regulated indicating chromatin
compaction stage and underlying DNA is not subjected to transcription
activation. This suggests the cells to limit their proliferation potential. We
found moderate changes in the other Histone Variant levels and needs to be
further probed. The core Histones H3, H4 and H2A, H2B levels were modestly
elevated and need to be further probed.
6.1.19
Studies on DNA changes during the induction of erythroid
differentiation by DMSO in MEL cells
DNA & Histone Levels
Acid ext r act ed pr ot eins Ct r l
Acid ext r act ed pr ot eins 2 %DMSO
Ct r l: DNA
2%: DNA
0
0.2
0.4
0.6
0.8
1
1.2
1.4
ug/ ul C onc
Figure_R17b: DNA content in DMSO stimulated and control MEL cells:
Equal cell no was used for DNA extraction from the DMSO induced and control MEL cells.
DNA amount was estimated using the nanodrop spectrophotometer. Total histone content
from acid extraction of equal cell no and quantified with BCA assay. Data shown is derived
from duplicate experiments
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Results
We estimated the total DNA content in the 2% (v/v) Me2SO induced and
control MEL cells to confirm the genome stability upon cellular differentiation
process. DNA extraction is performed from equal cell counts of DMSO
induced and non induced MEL cells. Proteins were acid extracted and
quantified. DNA content was found to be constant and it serves as a control.
Acid extracted proteins were quantified using the Bradford assay. DNA
content is found to be unchanged in both induced and control cells
Figure_R17b.
6.1.20 Quantile based distribution of MEL proteome:
To further understand the modulating proteomic events during the cellular
differentiation we distributed the entire quantified proteome into six quantiles
according the relative protein expression levels Figure_R18. Each quantile was
examined for the enriched pathways, GO based cellular components using
the DAVID-GO tool.
This approach integrates the strength of statistical
testing (taking p value as input for clustering) with the intuitive simplicity of
hierarchial clustering by automatically classifying related processes and
pathways based on the up or down-regulated protein measurements, it
provides an unbiased global portrait of representative biological functions.
(Pan, Kumar et al. 2009). Proteins significantly enriched having a fold change
of log > 2.3 on quantile based analysis using the KEGG pathways showed
enrichment of, Poryphyrin metabolism (p < 1.5E-2) resulting from the proteins
ALAD, HMBS, ALAS2 in the quantile 95-100% indicate marking of erythroid
maturation with accumulation of hemoglobin and the hemebiosynthesis
markers mentioned earlier. We also observed the enrichment of Oxidative
phosphorylation pathways (p < 8.0E-3) represented by the mitochondrial
proteins like cytochrome c oxidase indicating the role of mitochondria in
hematopoietic cell homeostasis as described by (Fontenay, Cathelin et al.
2006) through oxidative phosphorylation and mitochondrial transport proteins
for eg ABC-mitochondrial erythroid proteins 1, 2, 3, 7 and 10 are all found upregulated
indicating
strong
role
in
the
transport
functions
during
hemebiosynthesis. Glycosphingolipid biosynthesis are enriched in the quantile
75-95%
having
up-regulated
proteins
upon
DMSO
stimulation,
glycosphingolipid and glycoproteins play pivotal roles in the complex series of
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Results
events governing cell adhesion, signal transduction (Tringali, Anastasia et al.
2007) and playing a key role in cellular phenomena like cell proliferation and
differentiation. Ubiquitin mediated proteolysis pathways enrichment, indicates
the global cellular reorganization during the reticulocyte stage of erythroid
differentiation accomplished partly through programmed protein degradation
(Wefes, Mastrandrea et al. 1995) for cellular clearance of nuclei, mitochondria
and cytoskeletal proteins.
We additionally
observed the down regulated
protein entries in the lower quantile 0-15% representing the enrichment of
pathways related chronic myeloid leukemia, acute myeloid leukemia,
indicating the non-transformed state of the differentiating MEL cells, and
representing the induced cells being directed specifically towards erythroid
lineage development from proliferation state. Glycolysis and gluconeogenesis
pathways were observed in the lower quantile of 0-15% suggesting the cells
to have lowered their proliferation potential and thereby minimize the glucose
metabolism. SNARE interactions in vesicular transport pathway is found
enriched in the quantile 75-95%, suggesting the morphological changes
resulting from the sequestration of excess hemoglobin into the protrusion
along with the microfilaments (Hunt and Marshall 1981) and the cell could be
preparing for the terminal erythroid stages by clearing the nucleus and other
organelles by autophagy process.
Poryphyrin metabolism Fig 20 and
Oxidative phosphorylation pathways Fig 20 b enriched with DAVID KEGG
analysis are represented along with the proteins found in the corresponding
quantiles.
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Figure_R18 Quantile based Pathway Hierarchical clustering analysis of MEL proteome.
Figure shows the quantiles resulting from quantitative proteome comparision were separately
analysed for enriched KEGG pathways and clustered for the z-transformed p values. The
annotation on the top represents the z-transformation groups of the quantiles. The distribution
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is divided into six quantiles and represented as z1=0-15%, z2=15-35%, z3=35-50%, z4=5075%, z5=75-95% and z6=95-100%. Quantile z6 contains proteins upregulated > 2fold.
The quantile based hierarchial clustering of GO cellular components
Figure_R19 contains the proteins distributed into 6 quantiles from 0-15%, 15-
35%, 35-50%, 50-75%, 75-95% and 95-100% in the order of incrementing
protein up-regulation from lowest to the highest quantiles. The hierarchial
clustering indicated significant enrichment of components like Tetrapyrrole
biosynthetic (p < 4.7e-007) process, hexaosaminidase activity (p < 7.19e006), heme biosynthesis (p < 1.30e-005) which indicate the erythroid nature of
the differentiated cells. Mitochondrial matrix components are found in the
quantile 5, containing proteins have 2 fold up-regulation suggesting the
mitochondrial
enrichment.
Down-regulated
quantile
includes
gluconeogenesis, glycolysis and are concurrent to the pathway downregulation as in fig 18. Proteins representing the mitochondrial matrix, GTP
binding, fatty acid beta oxidation and mitochondrial electron transport chain
are found in the quantile 75-95% indicating the mitochondrial proteins to be
essential for hemebiosynthesis.
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Figure_R19 Quantile based GO-CC, hierarchical clustering analysis of MEL proteome.
Figure shows the quantiles resulting from quantitative proteome comparision analysed for
enriched GO cellular components and clustered for the z-transformed p values. The
annotation on the top represents the z-transformation groups of the quantiles. The distribution
is divided into six quantiles and represented as z1=0-15%, z2=15-35%, z3=35-50%, z4=5075%, z5=75-95% and z6=95-100%. Quantile z6 contains proteins upregulated > 2fold.
Figure_R20a : Quantile based KEGG Pathway analysis
Figure represents KEGG porphyrin metabolism pathway, red colour stars indicate enriched
proteins found in the corresponding quantiles. Enymes like Porphobilinogen,
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Hydroxymethylbilane, coprophyrinohen III, protoporyphyrinogen IX, Coproporphyrinogen IX,
protoporphyrin IX and Hemoglobin are identified
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Figure-R20b: Quantile based KEGG Pathway analysis: Oxidative Phosphorylation
Figure represents KEGG oxidative phosphorylation pathway, stars indicate enriched proteins
in the quantiles. Enzymes NADH, Flavo protein, Cytochrome bc1 complex, cytochrome c
reductase, COX 6A, 8, 4 & ATP synthase are identified.
6.1.21 Gene expression analysis
Microarray profiling was performed to monitor the gene changes during the
DMSO induced erythroid differentiation of murine erythroleukemia cells.
Microarray was performed by KFB Regensburg using the Affymetrix
GeneCHIP ® mouse whole transcript (WT) Sense Target Labeling Assay. The
analysis revealed 28816 gene expression levels to be modulating. Extensive
study to compare the MEL transcriptome and proteome has not been done so
far and we aimed to compare the proteome data with the mRNA transcript
levels during the MEL cell differentiation.
We used the quantile approach (Pan, Kumar et al. 2009)and the 22474
expressed genes were distributed into six quantiles 0-10%, 10-25%, 25-50%,
50-75%, 75-90%, 90-100% in incrementing orders of gene expression log2
values. Hierarchial clustering analysis revealed the enrichment of poryphyrin
metabolism in the quantile 90-100% indicating the up-regulation of erythroid
specific genes like ALAD, ALAS-2. Autophagy regulation genes Ulk2, Atg12,
Becn1, Ifna14, Ifna6 are found in the quantile having up-regulated genes
indicates the cells to be preparing for cellular clearance by autophagy
process. Genes related cell cycle, Acute myeloid leukemia (AML) Ccnh,
Cdkn1c, Bub1b, Cdk6, Ccna2, Chek2, Hdac2, Cdc25a, Cdc14b, Cdkn1b,
Ccnb1, Dbf4, Cdc7, Bub1, Ccnb2, Cdc6, Atm, Chek1, Cdc20, are enriched in
the quantile 0-15% having down-regulated gene entries indicating the switch
from erythroleukemic stage to erythroid differentiation pathway.
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Figure_R21 Quantile based KEGG Pathway analysis of MEL transcriptome
The quantiles resulting from quantitative proteome comparision were separately analysed for
enriched GO pathways and clustered according to z-transformed p values. The annotation on
the top represents the z-transformation groups of the quantiles. 22474 expressed genes
were distributed into six quantiles 0-10%, 10-25%, 25-50%, 50-75%, 75-90%, 90-100%.
Erythroid specific genes were found enriched in the quantile z6 (indicated as upregulated in
the figure)
6.1.22 MEL Proteome Vs Transcriptome analysis
We next aimed to compare the proteome changes against the transcriptome
changes since it would give an additional insight. Linear correlation analysis
showed a moderate correlation of (r = 0.508) Figure_R22 between the log2
transcriptome and the log2 proteomic data, as post-transcriptional events
could alter the presence of transcript. The standard deviation of the residual
values (difference between the observed and the fitted values from the linear
regression model) was calculated.
Protein expression results from the complex interplay of regulation on the
transcriptional, translational and post-translational levels due to which there is
no direct stoichometric relationship between the protein and gene expression
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Results
levels.
3373
Figure_R22: Transcriptome Vs Proteome comparision
Linear regression plot of Log2 microarray signal ratio Vs Log2 SILAC H/L ratios
mRNA is an intermediate in the process of protein synthesis and changes in
mRNA levels do not reflect absolute or relative protein level changes in
protein levels. It is not unprecedented to find a concurrence between mRNA
and protein levels using both proteomics and microarray platforms (Lu, Vogel
et al. 2007) (Futcher, Latter et al. 1999; Greenbaum, Jansen et al. 2002;
Ghaemmaghami, Huh et al. 2003; Greenbaum, Colangelo et al. 2003; Yin,
Tao et al. 2007). There is indeed a inherent technical, bioinformatic, and
biological difficulty in assigning correlations between mRNA and protein
expression, (Unwin and Whetton 2006).
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Figure_R22.a: Inliers Transcriptome Vs Proteome comparision
Figure represents log2 representation of inliers of transcriptome and proteome expression.
Inliers include a high correlation between protein and mRNA expression pattern.
Proteome to transcriptome analyses resulted in two groups a) Genes with
log2 proteome ratios <1 std dev were arbitrarily assigned as “Inliers”,
Figure_R22.a they showed a better correlation, r = 0.348 with 60 % correlation
between the transcriptome and the proteome data.
b) Genes with log2 proteome ratios >2 std dev were arbitrarily assigned as
“Outliers”, Figure_R22.b they showed a weaker correlation, r = 0.348 with 9%
correlation between the transcriptome and the proteome data.
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Figure_R22.b: Inliers Transcriptome Vs Proteome comparision
Figure represents log2 representation of outliers of transcriptome and proteome expression.
Inliers include a weak correlation between protein and mRNA expression pattern.
The weaker correlation could add additional biological information,
a) Probably due to the rates of protein degradation are higher than the rate of
mRNA degradation as these genes/transcripts are switched off b)
Translational rate fluctuation during erythroid differentiation. It would thus help
us to understand if mRNA microarray technology can predict the majority of
changes in the proteome of erythroid differentiation against the proteomic
approach.
To identify the characteristics of these outlier groups, we carried out gene
ontology enrichment analysis Figure-R22.c and found the outlier proteins were
enriched in biological processes like lipid metabolic process, sterol
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biosynthetic process, iron ion transport, aminoacid catabolic process and
mitochondrial electron transport are enriched and cellular protein metabolic
process, cellular process and translation related processes were suppressed.
Gene ontology enrichment analysis Figure_R22.d of molecular functions
showed
organelle
inner
membrane,
mitochondrial
membrane,
and
mitochondrial inner membrane and intrinsic to membrane are enriched and
ribonucleoprotein complex, ribosome, intracellular and cell cortex associated
cellular components are suppressed. Gene ontology enrichment analysis of
molecular function Figure_R22.e indicated a significant enrichment of heme
binding, protein transmembrane transporter activity.
Figure_R22.c: GO term enrichment analysis of biological processes
Figure represents a Histogram of GO based enrichment of biological processes of outlier
dataset
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Figure_R22.d: GO term enrichment analysis of cellular components
Figure represents a histogram of GO based enrichment of cellular components of outlier
dataset
Figure_R22.e: GO term enrichment analysis of molecular functions
Figure represents a histogram of GO based enrichment of molecular functions of outlier
dataset
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Discussion
Discussion
7.1 An overview of various protein quantification labeling strategies
In quantitative-proteomics based studies involving stable isotope labeling
methods there are alternative approaches to introduce the chemically identical
but mass-differentiated stable isotope tags in proteins (7, 21), broadly
classified into two groups based on the tag incorporation(a) biological
incorporation where labeling of the protein/peptide is achieved by growing
cells in media enriched in stable isotope-containing compounds as done in the
present SILAC approach ((Ong, Blagoev et al. 2002)) and (b) chemical
incorporation, which relies on the use of a derivatization agent for chemical
modification of proteins in a
site-specific manner after harvesting of the
proteins, such as in the ICAT and ITRAQ (isobaric tagging reagents)
approaches ( (Gygi, Rist et al. 1999), (Ross, Huang et al. 2004) (DeSouza,
Diehl et al. 2005)). ICAT is relatively a new technology for relative protein
quantification, it involves cysteine labeling of proteins with the ICAT reagent ,
followed by proteolytic digestion and SCX fractionation followed by MS
analysis. It does not require gel separation and thus may seem a less
laborious alternative compared with SILAC. Additionally ICAT can be applied
to any cells or even body fluids and tissue. However, the SILAC approach
offers the earliest time point to introduce isotope labels without the need of
any in vitro derivatization steps, which may induce experimental variation. In
contrast to chemical incorporation methods, e.g. the ICAT approach ((Gygi,
Rist et al. 1999)), no significant reduction in sample complexity is required in
SILAC. Therefore, using metabolic labeling potentially higher protein coverage
can be
Discussion
achieved, and the number of peptide pairs used for quantification per protein
will generally be higher. iTRAQ is a novel MS based approach for the relative
quantification of proteins, it relies on the derivatization of primary amino
groups in the intact proteins using isobaric tag for relative and absolute
quantitation. The isobaric mass design of the iTRAQ reagents enables the
peptides to have uniform mass, resulting in the proteolytic peptides to appear
as single peaks in MS scans. The MS/MS spectra contains the quantitative
information from the reporter ions. The iTRAQ reagent contains a reporter
group, a balance group and an amino-active group, which modifies the Ntermini and the lysine side chains of the peptides and upon collision induced
dissociation charged reporter group can be generated. The reporter product
ions are used to quantify their respective modified samples. Bernhard Kuster
et al have accurately quantified at 100amol level, by carefully optimizing
instrument parameters such as collision energy, activation Q, delay time, ion
isolation width, number of microscans, and number of trapped ions generated
a low m/z fragment ion intensities. (Bantscheff, Boesche et al. 2008)
7.2
SILAC labeling to study global protein changes during MEL
differentiation
In this study we used SILAC labeling to identify the global proteome changes
during the 2% (v/v) Me2SO induced MEL cell differentiation. SILAC labeling
protocol was slightly modified, (having 1% non-dialysed serum containing
minor traces of differentiation inducing factors). The modification of using nondilaysed serum did not interfere with the label incorporation and quantification.
We used MEM to significantly suppress the arginine
13
13
C6
to Proline
C5-conversion.(Bendall, Hughes et al. 2008). Since arginine is a metabolic
precursor of proline biosynthesis Figure_R23.
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Discussion
Figure_R23. Metabolic conversion of isotope-coded arginine to proline: Source
.(Bendall, Hughes et al. 2008)
The unpredictable conversion of isotopic arginine to proline creates inaccuracy in SILACbased quantitative proteomic experiments. a, a conceptual mass spectra of a non-proline
containing peptide ion from a 1:1 mixture of light and heavy labeled samples. Here the
expected Light and Heavy peptide ions have an equivalent signal. b, a spectra from a proline
containing peptide where arginine to proline conversion has occurred in the same 1:1 mixture.
The resulting heavy proline peptide signal (red) has been subtracted from the expected
‘heavy’ peptide ion signal. c, metabolic pathway outlining the inter-conversion of arginine and
proline. Isotope-coded arginine with carbon 13 (red) and nitrogen 15 (green) when used as a
synthetic precursor increases the expected mass of proline.
We aimed at identifying and quantifying the proteome changes at an in-depth
scale using a combination of subcellular fractionations, mitochondrial, nuclear,
cytoplasmic and membrane, followed by separation techniques
like SDS-
PAGE, peptide IEF and further analysed over LC-MS/MS. SILAC based
quantitative proteomics approach is proven to be significantly helpful in
comparing the expression levels of thousands of proteins in two or more
cellular states (Cox and Mann 2007; Graumann, Hubner et al. 2008), we for
the first time compared the DMSO induced proteomic changes during the
MEL cell differentiation using the SILAC approach. 2D-Gel based approaches
in the
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Discussion
past reported (Petrak, Myslivcova et al. 2007) 700 protein spots and were
able to identify 27proteins to be differentially expressed. Classic 2D-gels face
a major limitation in identifying the very basic proteins and also low and high
molecular weight proteins. Undersampling is the major bottle neck in shotgun
proteomics approach, to overcome this we used subcellular fractionation and
peptide OFFGEL prefractionation approaches. Peptide OFFGEL fractionation
approach was published to be promising in terms of identifying low abundant
proteins from complex proteome mixtures (Graumann, Hubner et al. 2008).
This approach allowed us to probe deeper into the global proteome changes
and we could quantify 3774 proteins along with all known hemebiosynthesis
markers and additionally identified 47 novel proteins. We conclude that the
combination of 1D gel electrophoresis, OFFGEL-peptide fractionation,
nanoLC-MS/MS and SILAC is an excellent method in quantifying the global
proteomics changes. Our MEL proteome dataset represents a deeper global
proteome coverage so far
We performed quantile based protein distribution and observed proteins
involved in oxidative phosphorylation, poryphyrin metabolism were upregulated, oxidative phosphorylation occurs at the inner mitochondrial
membrane (generation of proton-motive force and ATP synthesis). The
involvement of mitochondria towards cell differentiation emerges from the
study of Smith et al. demonstrating the dependence of differentiation of rat
glial oligodendrocytes precursors on redox status of cells. Shift of redox
systems to higher oxidation degree provoked differentiation of these cell
precursors into oligodendrocytes or astrocytes, whereas high reduction state
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Discussion
maintained cells in the undifferentiated state. Poryphyrin metabolism is the
key metabolic process involved in the formation of heme as the main endproduct. It includes the late mitochondrial enzymes like protoporyphyrinogen
oxidase and frrochelatase. We observed down regulation of pathways related
to AML, CML since the MEL cells upon DMSO induction loose proliferative
potential resulting in down-regulation of these cancer related pathways.
Glycolysis the source of cellular ATP and down-regulation of this biological
process indicates the cells being inactive (non-proliferative) and also delaying
the apoptotic process, Smac/DIABLO enzymes facilitate caspase activation
by neutralizing IAP (Inhibitors of apoptosis) that inhibit caspase-3,-7 and -9
(Du, Fang et al. 2000) (Verhagen, Ekert et al. 2000).
Proteomic analysis could prove more beneficial since the microarray probe
sets are fixed (Figure_R23). The probe sets on a microarray tend to be targeted
towards the 3' end of a transcript (thus potentially failing to identify truncated
or alternatively spliced forms of that transcript) they do define known
sequences and, since the complete sequencing of several genomes, probe
sets can be well characterized in terms of their target binding and crossreactivity.
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Discussion
Figure_R23. Comparision of data generated by proteomics vs transcriptomics: Source
((Unwin and Whetton 2006)
Comparison of the data generated by proteomics vs. transcriptomics (A) Transcriptomics
methods tend to have probes focused in fixed locations towards the 3’ end of the mRNA. In
the case illustrated, this technique would fail to detect the presence of the splice variant. In
addition, one of the probe sets lies in an untranslated region of the mRNA, so any change in
the level of this probe set is unlikely to result in a change in protein level. In the case of
proteomics, the peptides may be identified anywhere along the protein. Here, the presence of
a splice variant is detected by the two peptides marked ‘a’ which are specific to the long form
of the protein, and the peptide ‘b’ which is specific to the short form. By just using the labeled
peptides, the relative quantities of both the long and short variant are quantified, with data
from the ‘shared’ peptides being unreliable and discounted. Clearly, if no peptide ‘b’ had been
identified, then evidence of this form would be lost and the proteomics would just assay the
long form, as does the transcriptomics. (B) Transcriptomics does not detect post
translationally modified forms of proteins. In proteomics, the presence of either the modified
or unmodified form of a peptide (marked ‘c’) informs on the relative amount of modification, if
the ratio of this peptide is different to the ratios of other peptides from this protein.
To get added insight we studied the global mRNA patterns and performed
microarray
analysis.
Linear
regression
analysis
of
proteomics
and
transcriptome expression indicated a correlation value of r = 0.50, indicating a
low correlation. The common pathways up-regulated both in the proteomics
and transcriptome data include (1) Poryphyrin metabolism (2) SNARE
interaction in vesicular transport
(3) Ubiquitin mediated pathways (5)mTOR
signaling pathway (6) Glycan degradation (7) OXPHOS
Down-regulated pathways include (a) CML (b) AML (c) Glycolysis.(d) Purine
metabolism (e) cell cycle (f) leukocyte trans endothelial migration
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Discussion
7.3 MEL Proteome Vs Transcriptome analysis
We further looked at the overlap between gene and protein expression levels,
since the presence of protein is dependent on the presence of mRNA. It could
also be true that mRNA being an intermediate molecule in protein production
be subjected to modifications altering its stability, rate of translation, posttranslational modification etc that eventually influences the protein stability. Its
poorly understood as how many protein changes take place with out alteration
in the mRNA levels. We looked at a global level to see the extent of
correlation between the two datasets of mRNA and protein expressions. We
mapped the transcriptome transcript ID and the proteomic IPI ID to gene
symbols. Finally we got 3374 genes representing both transcriptome and
proteomic datasets. Linear correlation analysis showed a moderate
correlation between the log2 transcriptome and the log2 proteomic data,
r=0.5086. The standard deviation (SD) of the residual values (difference
between the observed and the fitted values from the linear regression model)
was calculated. Genes with log2 proteome ratios greater than two standard
deviation were arbitrarily assigned as “outliers” and genes with log2 proteome
ratios less than one standard deviation were assigned as “inliers”. “Inliers”
showed a better correlation between the transcriptome and proteomic
datasets, with r=0.8027, while “Outliers” showed a weaker correlation, having
r=0.3480.
‘Inliers’ dataset had a very high correlation between the
protein/mRNA
abundance
(66%),
while
the
‘outliers’
subgroup
was
comparably smaller (9%) and showed a weak correlation. Gene Ontology
(GO) term enrichment analysis of the outlier group indicated several proteins
involved in lipid metabolic process, sterol metabolic process, iron ion
transport, mitochondrial electron transport, indicating that these functions
have a high degree of post-transcriptional and/or post-translational regulation.
Combining the proteomic and transcriptome data will help us to expand the
identification of proteins, for e.g. we observe lipid metabolism pathway
involvement which includes fatty acid biosynthesis, fatty acid elongation in
mitochondria, fatty acid metabolism, steroid biosynthesis, glycerolipid
biosynthesis, sphingolipid metabolism, arachidonic acid metabolism etc. By
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Discussion
using directed approach of inclusion list based analysis (Jaffe, Keshishian et
al. 2008) identify the proteins involved in the above listed pathways
Outlook
Proteomics based studies reveal a valuable source of information on a large
scale and could potentially be used for hypothesis based approaches. We
consider our proteomics dataset as a novel data source that will enhance the
characterization of future proteomics based MEL cell differentiation studies,
would like to validate potential novel candidate proteins using Zebrafish model
system, to get a deeper insight into the erythroid differentiation programme.
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References
References
Aebersold, R. and M. Mann (2003). "Mass spectrometry-based proteomics." Nature
422(6928): 198-207.
Baklouti, F., S. C. Huang, et al. (1996). "Asynchronous regulation of splicing events
within protein 4.1 pre-mRNA during erythroid differentiation." Blood 87(9):
3934-41.
Bank, A. (2007). "AHSP: a novel hemoglobin helper." J Clin Invest 117(7): 1746-9.
Bantscheff, M., M. Boesche, et al. (2008). "Robust and sensitive iTRAQ
quantification on an LTQ Orbitrap mass spectrometer." Mol Cell Proteomics
7(9): 1702-13.
Bantscheff, M., M. Schirle, et al. (2007). "Quantitative mass spectrometry in
proteomics: a critical review." Anal Bioanal Chem 389(4): 1017-31.
Ben-David, Y., E. B. Giddens, et al. (1991). "Erythroleukemia induction by Friend
murine leukemia virus: insertional activation of a new member of the ets gene
family, Fli-1, closely linked to c-ets-1." Genes Dev 5(6): 908-18.
Bendall, S. C., C. Hughes, et al. (2008). "Prevention of amino acid conversion in
SILAC experiments with embryonic stem cells." Mol Cell Proteomics 7(9):
1587-97.
Blagoev, B. and M. Mann (2006). "Quantitative proteomics to study mitogenactivated protein kinases." Methods 40(3): 243-50.
Boix, J. and A. Ruiz-Carrillo (1992). "Increased histone H1(0) expression in
differentiating mouse erythroleukemia cells is related to decreased cell
proliferation." Exp Cell Res 201(2): 531-4.
Cargile, B. J., J. R. Sevinsky, et al. (2005). "Immobilized pH gradient isoelectric
focusing as a first-dimension separation in shotgun proteomics." J Biomol
Tech 16(3): 181-9.
Cox, J. and M. Mann (2007). "Is proteomics the new genomics?" Cell 130(3): 395-8.
Cox, J. and M. Mann (2008). "MaxQuant enables high peptide identification rates,
individualized p.p.b.-range mass accuracies and proteome-wide protein
quantification." Nat Biotechnol 26(12): 1367-72.
Cox, J., I. Matic, et al. (2009). "A practical guide to the MaxQuant computational
platform for SILAC-based quantitative proteomics." Nat Protoc 4(5): 698-705.
DeSouza, L., G. Diehl, et al. (2005). "Search for cancer markers from endometrial
tissues using differentially labeled tags iTRAQ and cICAT with
multidimensional liquid chromatography and tandem mass spectrometry." J
Proteome Res 4(2): 377-86.
Dreisbach, A., A. Otto, et al. (2008). "Monitoring of changes in the membrane
proteome during stationary phase adaptation of Bacillus subtilis using in vivo
labeling techniques." Proteomics 8(10): 2062-76.
Du, C., M. Fang, et al. (2000). "Smac, a mitochondrial protein that promotes
cytochrome c-dependent caspase activation by eliminating IAP inhibition."
Cell 102(1): 33-42.
Fontenay, M., S. Cathelin, et al. (2006). "Mitochondria in hematopoiesis and
hematological diseases." Oncogene 25(34): 4757-67.
Futcher, B., G. I. Latter, et al. (1999). "A sampling of the yeast proteome." Mol Cell
Biol 19(11): 7357-68.
Gauss, C., M. Kalkum, et al. (1999). "Analysis of the mouse proteome. (I) Brain
proteins: separation by two-dimensional electrophoresis and identification by
mass spectrometry and genetic variation." Electrophoresis 20(3): 575-600.
References
Gehrmann, M. L., Y. Hathout, et al. (2004). "Evaluation of metabolic labeling for
comparative proteomics in breast cancer cells." J Proteome Res 3(5): 1063-8.
Ghaemmaghami, S., W. K. Huh, et al. (2003). "Global analysis of protein expression
in yeast." Nature 425(6959): 737-41.
Graumann, J., N. C. Hubner, et al. (2008). "Stable isotope labeling by amino acids in
cell culture (SILAC) and proteome quantitation of mouse embryonic stem
cells to a depth of 5,111 proteins." Mol Cell Proteomics 7(4): 672-83.
Greenbaum, D., C. Colangelo, et al. (2003). "Comparing protein abundance and
mRNA expression levels on a genomic scale." Genome Biol 4(9): 117.
Greenbaum, D., R. Jansen, et al. (2002). "Analysis of mRNA expression and protein
abundance data: an approach for the comparison of the enrichment of features
in the cellular population of proteins and transcripts." Bioinformatics 18(4):
585-96.
Grove, G. W. and A. Zweidler (1984). "Regulation of nucleosomal core histone
variant levels in differentiating murine erythroleukemia cells." Biochemistry
23(19): 4436-43.
Gusella, J. F. and D. Housman (1976). "Induction of erythroid differentiation in vitro
by purines and purine analogues." Cell 8(2): 263-9.
Gygi, S. P., G. L. Corthals, et al. (2000). "Evaluation of two-dimensional gel
electrophoresis-based proteome analysis technology." Proc Natl Acad Sci U S
A 97(17): 9390-5.
Gygi, S. P., B. Rist, et al. (1999). "Quantitative analysis of complex protein mixtures
using isotope-coded affinity tags." Nat Biotechnol 17(10): 994-9.
Hubner, N. C., S. Ren, et al. (2008). "Peptide separation with immobilized pI strips is
an attractive alternative to in-gel protein digestion for proteome analysis."
Proteomics 8(23-24): 4862-72.
Hunt, R. C. and L. M. Marshall (1981). "Membrane protein redistribution during
differentiation of cultured human erythroleukemic cells." Mol Cell Biol 1(12):
1150-62.
Jaffe, J. D., H. Keshishian, et al. (2008). "Accurate inclusion mass screening: a bridge
from unbiased discovery to targeted assay development for biomarker
verification." Mol Cell Proteomics 7(10): 1952-62.
Kruger, M., M. Moser, et al. (2008). "SILAC mouse for quantitative proteomics
uncovers kindlin-3 as an essential factor for red blood cell function." Cell
134(2): 353-64.
Liu, H. and C. H. Herrmann (2005). "Differential localization and expression of the
Cdk9 42k and 55k isoforms." J Cell Physiol 203(1): 251-60.
Lopez-Fernandez, L. A., D. M. Lopez-Alanon, et al. (1997). "Developmental
expression of H3.3A variant histone mRNA in mouse." Int J Dev Biol 41(5):
699-703.
Lu, P., C. Vogel, et al. (2007). "Absolute protein expression profiling estimates the
relative contributions of transcriptional and translational regulation." Nat
Biotechnol 25(1): 117-24.
Macleod, K. F., N. Sherry, et al. (1995). "p53-dependent and independent expression
of p21 during cell growth, differentiation, and DNA damage." Genes Dev
9(8): 935-44.
Marks, P. A. and R. Breslow (2007). "Dimethyl sulfoxide to vorinostat: development
of this histone deacetylase inhibitor as an anticancer drug." Nat Biotechnol
25(1): 84-90.
Nikitin, A., S. Egorov, et al. (2003). "Pathway studio--the analysis and navigation of
molecular networks." Bioinformatics 19(16): 2155-7.
110
110
References
Olsen, J. V., S. E. Ong, et al. (2004). "Trypsin cleaves exclusively C-terminal to
arginine and lysine residues." Mol Cell Proteomics 3(6): 608-14.
Ong, S. E., B. Blagoev, et al. (2002). "Stable isotope labeling by amino acids in cell
culture, SILAC, as a simple and accurate approach to expression proteomics."
Mol Cell Proteomics 1(5): 376-86.
Otsuka, Y., D. Ito, et al. (2008). "Expression of alpha-hemoglobin stabilizing protein
and cellular prion protein in a subclone of murine erythroleukemia cell line
MEL." Jpn J Vet Res 56(2): 75-84.
Pan, C., C. Kumar, et al. (2009). "Comparative proteomic phenotyping of cell lines
and primary cells to assess preservation of cell type-specific functions." Mol
Cell Proteomics 8(3): 443-50.
Perkins, D. N., D. J. Pappin, et al. (1999). "Probability-based protein identification by
searching sequence databases using mass spectrometry data." Electrophoresis
20(18): 3551-67.
Petrak, J., D. Myslivcova, et al. (2007). "Proteomic analysis of erythroid
differentiation induced by hexamethylene bisacetamide in murine
erythroleukemia cells." Exp Hematol 35(2): 193-202.
Rabilloud, T. (2002). "Two-dimensional gel electrophoresis in proteomics: old, old
fashioned, but it still climbs up the mountains." Proteomics 2(1): 3-10.
Rappsilber, J., M. Mann, et al. (2007). "Protocol for micro-purification, enrichment,
pre-fractionation and storage of peptides for proteomics using StageTips." Nat
Protoc 2(8): 1896-906.
Rekhtman, N., K. S. Choe, et al. (2003). "PU.1 and pRB interact and cooperate to
repress GATA-1 and block erythroid differentiation." Mol Cell Biol 23(21):
7460-74.
Rekhtman, N., F. Radparvar, et al. (1999). "Direct interaction of hematopoietic
transcription factors PU.1 and GATA-1: functional antagonism in erythroid
cells." Genes Dev 13(11): 1398-411.
Rifkind, R. A., V. M. Richon, et al. (1996). "Induced differentiation, the cell cycle,
and the treatment of cancer." Pharmacol Ther 69(2): 97-102.
Ross, P. L., Y. N. Huang, et al. (2004). "Multiplexed protein quantitation in
Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents."
Mol Cell Proteomics 3(12): 1154-69.
Russell, E. S. (1979). "Hereditary anemias of the mouse: a review for geneticists."
Adv Genet 20: 357-459.
Selbach, M., B. Schwanhausser, et al. (2008). "Widespread changes in protein
synthesis induced by microRNAs." Nature 455(7209): 58-63.
Shelley, C. S., J. M. Teodoridis, et al. (2002). "During differentiation of the
monocytic cell line U937, Pur alpha mediates induction of the CD11c beta 2
integrin gene promoter." J Immunol 168(8): 3887-93.
Standefer, J. C. and D. Vanderjagt (1977). "Use of tetramethylbenzidine in plasma
hemoglobin assay." Clin Chem 23(4): 749-51.
Stopka, T., D. F. Amanatullah, et al. (2005). "PU.1 inhibits the erythroid program by
binding to GATA-1 on DNA and creating a repressive chromatin structure."
EMBO J 24(21): 3712-23.
Tringali, C., L. Anastasia, et al. (2007). "Modification of sialidase levels and
sialoglycoconjugate pattern during erythroid and erytroleukemic cell
differentiation." Glycoconj J 24(1): 67-79.
Tsiftsoglou, A. S., I. S. Pappas, et al. (2003). "The developmental program of murine
erythroleukemia cells." Oncol Res 13(6-10): 339-46.
111
111
References
Tsiftsoglou, A. S. and W. Wong (1985). "Molecular and cellular mechanisms of
leukemic hemopoietic cell differentiation: an analysis of the Friend system."
Anticancer Res 5(1): 81-99.
Turano, M., G. Napolitano, et al. (2006). "Increased HEXIM1 expression during
erythroleukemia and neuroblastoma cell differentiation." J Cell Physiol
206(3): 603-10.
Unwin, R. D. and A. D. Whetton (2006). "Systematic proteome and transcriptome
analysis of stem cell populations." Cell Cycle 5(15): 1587-91.
Verhagen, A. M., P. G. Ekert, et al. (2000). "Identification of DIABLO, a mammalian
protein that promotes apoptosis by binding to and antagonizing IAP proteins."
Cell 102(1): 43-53.
Wefes, I., L. D. Mastrandrea, et al. (1995). "Induction of ubiquitin-conjugating
enzymes during terminal erythroid differentiation." Proc Natl Acad Sci U S A
92(11): 4982-6.
Weiss, M. J. and C. O. dos Santos (2009). "Chaperoning erythropoiesis." Blood
113(10): 2136-44.
Yamada, T., F. Kihara-Negishi, et al. (1998). "Reduction of DNA binding activity of
the GATA-1 transcription factor in the apoptotic process induced by
overexpression of PU.1 in murine erythroleukemia cells." Exp Cell Res
245(1): 186-94.
Yellajoshyula, D. and D. T. Brown (2006). "Global modulation of chromatin
dynamics mediated by dephosphorylation of linker histone H1 is necessary for
erythroid differentiation." Proc Natl Acad Sci U S A 103(49): 18568-73.
Yin, L., Y. Tao, et al. (2007). "Proteomic and transcriptomic analysis of rice mature
seed-derived callus differentiation." Proteomics 7(5): 755-68.
Zhuo, S., S. Fan, et al. (1995). "Study of the role of retinoblastoma protein in terminal
differentiation of murine erythroleukemia cells." Proc Natl Acad Sci U S A
92(10): 4234-8.
Zubarev, R. and M. Mann (2007). "On the proper use of mass accuracy in
proteomics." Mol Cell Proteomics 6(3): 377-81.
112
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Acknowledgements
8.0 Acknowledgements
I would like to thank Dr. Gerhard Mittler for giving me this great opportunity to
work in his lab. I am highly inspired by his scientific enthusiasm, he has
always been highly informative, supportive, and maintained an encouraging
work atmosphere. I consider working in his lab to be one of the most
significant career developments so far. The finest qualities which I would carry
from him are highly organized work style, clarity in mind, novel thinking,
maintaining the laboratory standards in-par with the latest developments,
perseverance, and cool mindedness. He is my scientific mentor.
I next would like to thank Prof. Rudolf Grosschedl for all the support and the
useful critical discussions about my project. I am inspired by his pro-active
approach in discussion initiation and scientific suggestions during all the
presentation sessions at the MPI.
I sincerely thank Gabi Nerz who was very helpful in contributing to the
success of the whole project. I would like to thank all my lab members, Rudi
Engelke, Trung Ngo, Sina Pleiner for the active scientific discussions and
maintaining a good work atmosphere. Rudi Engelke was very helpful in
sharing his thoughts regarding bioinformatics data analysis and friendly
suggestions.
I would like to thank my co-supervisors Dr. Robert Schneider and Dr.
Wolfgang Schamel for their valuable scientific suggestions and periodic
discussions.
I would like to thank Chanchal kumar of Mathias Mann lab, for his inputs on
bioinformatic analysis
Acknowledgements
I would like to thank Dr. T. Brogreffe for the helpful discussions about
microarray experiments
I would like to thank our collaborator Dr. Yong Li from the FRISYS centre, for
all his support and giving me an opportunity to work with Ariadne Pathway
Studio analysis software
114
114
CV
Curriculum vitae
Ravi Kumar Krovvidi
Ph.D student (Completion early 2009)
Max Planck Institute of Immunobiolgy,
Department of Molecular and Cellular Immunology
Proteomics Division
Freiburg, Germany.
Phone (O):0049-761 5108 751
E-mail: [email protected], [email protected]
--------------------------------------------------------------------------------Date of Birth: 13 Jan 1974
Marital status: Married
Place of Birth: Hyderabad, India
CV
Current work:
Project A.
Small molecule kinase inhibitor investigation using SILAC and LTQFT MS.
Project involves identification of potential off-targets against a small molecule
inhibitor targeting Cdk9. Cells are cultured in normal and labeled medium
containing [13C6]-arginine, and [2H4]-lysine and the labeled cell lysates are
loaded onto the inhibitor matrix having the chromatographic beads coupled to
the small molecule inhibitor. The chromatography matrix without inhibitor
coupling serves as a control column. After extensive washing and
combination of the resins, the bound proteins are eluted with SDS sample
buffer, separated by 1D SDS-PAGE, and the resolved protein bands are
subjected to in-gel digestion with trypsin. Resulting tryptic peptides are
separated by nanoLC (MDLC) directly coupled online to a 7-Tesla linear iontrap Fourier-transform ion cyclotron resonance mass spectrometer (LTQFT
Ultra, Thermo fisher). Protein identification is performed using Mascot
database searching and quantification by MS Quant.
Project B.
Quantitative proteome investigation of Erythroid differentiation using Peptide
IEF prefractionation and LTQFT mass spectrometry.
Study involves a quantitative proteomics approach to identify proteins
differentially expressed during the erythroid differentiation programme in a cell
culture model system, using SILAC labeling. Peptide-isoelectric focusing is
employed as a prefractionation method along with GeLC-MS. Murine erythroid
leukemia (MEL) cells are cultured in normal and labeled DMEM medium (with
10% dialyzed FBS) containing [13C6]-arginine, and [2H4]-lysine and the latter
cell population is stimulated to differentiate by addition of 2% (v/v) DMSO for
120 hours. Equal amounts of cells are mixed and lysed in 2% (w/v) SDS.
Following protein precipitation, proteins are sequentially digested with LysC
and trypsin under denaturing conditions and the resulting peptides are
prefractionated by peptide-IEF on 18 cm IPG strips (pH 3-10). Peptides
extracted from 24 sections of the strips are analyzed by nanoLC (MDLC)
FTICR (LTQFT Ultra) mass spectrometry. Alternatively, GeLC-MS is also
used for prefractionation. Data dependent spectral acquisition, Mascot
database searching and MSQuant software are used for protein identification
and quantification.
Work and academic history:
2006 Onwards: (Pursuing Ph.D programme in proteomics and mass
spectrometry at Max Planck Institute of Immunobiolgy, Freiburg, Germany)
2002 to 2006: Worked as a Scientist in Proteomics/Mass spectrometry facility
at Dr. Reddy’s Laboratories, a leading drug discovery based pharmaceutical
company in India, Hyderabad.
116
116
CV
2001 to 2002: Worked as Project Assistant in Proteomics Core Facility in
Centre for Cellular and Molecular Biology CCMB, Hyderabad, India.
Project details: (2001 to 2002)
Project A: National cancer research project, India
Biomarker identification in oral cancer of the
indian population
Project B: Biomarker identification in Gliomas of the Indian
Population
Projects summary: (2002 to 2006)
Project
Rheumatoid Arthritis
Results
Annotated the rat serum proteome, Identified 10 differentially
expressed key marker proteins playing a major role during the
Proteomics from rat inflammation stages of arthritis progression.
Serum
Breast
cancer Annotated the human breast proteome. Identified 36 differentially
Proteomics
from expressed proteins known to be playing a major role in breast
human breast tissue cancer from a sample size of 100 patients of different stages of
cancer progression. Some of the potential proteins were taken up
further validation for biomarker development (as potential
therapeutic candidates)
Rat
hepatocytes Generated a protein database of rat hepatocytes, completely
annotated the hepatocyte proteome using the in-house and public
proteome
domain protein search engines.
Generated a protein database of toxicity related proteins for the
specific concentrations of the orally administered acetaminophen
toxicity treated rats
Toxico Proteomics
Liver
Assessment
Impurity Profiling
Small
Profiling
Molecule Analysis of small molecules (organic compounds of mass less than
500 Da) by MALDI-TOF. Work involved the characterization of
several small molecules by MS and PSD fragment spectra
Metabolite Profiling
117
Employment of MALDI-TOF as an analytical tool in quantifying the
impurities present in a compound batch.
Screening of Metabolites generated upon drug treatment in the
serum/plasma using MALDI-TOF
117
CV
Technical Expertise
•
•
•
•
•
•
•
•
•
Experience in protein sample preparation from human serum and in
vitro cell culture systems. Nuclear protein extraction from HeLa and
Jurkat cells.
Experience in peptide prefractionation using Peptide Isoelectric
focusing on IPG phor (GE Biosciences) and OFFGEL (Agilent) peptide
fractionation devices.
Biological Mass Spectrometry and Liquid chromatography. Working
regularly with MDLC (GE Biosciences) and LTQFT mass spectrometer
(Thermo fisher). Packing in-house C18 RPLC columns using Proxeon
pressure bomb. Instrument maintenance (regular calibration of the ion
trap and FT-MS, handling liquid refrigerants liquid nitrogen & helium
refilling for the 7 Tesla magnet).
Experienced in peptide sample preparation
Experience in running MALDI-TOF mass spectrometers (Micromass
and ABI MALDI-TOF, Bruker and SCIEX TOF-TOF for MS/MS
analysis).
Bioinformatics expertise: Mascot search engine, MS Quant. Protein
database Blast searching, Go annotation tools. Undergone training and
certified level II professional in Protein Molecular Modeling of Accelrys
Inc USA.
Protein 2D gel separations, DIGE basic skills, 2D gel visualization and
differential analysis using Decodon and Biorad PDQuest software.
Label free quantification: DeCyder MS software
Remote desktop communication (VNC SOFTWARE)
Academic Profile:
•
M.Sc Environmental sciences 1997
73%
Swami Ramanand Teerth Marathwada University, Nanded,
Maharashtra
• B.Sc. Microbiology
1995
58%
Osmania University, Hyderabad, A.P, India
• Intermediate
1991
70%
AP Board of intermediate, Hyderabad AP
• S.S.C
1989
74%
Board of Secondary Education, Andhra Pradesh, India
Scientific Meetings attended:
•
118
Presented a poster at the ASMS 2008 conference on
“Quantitative proteome changes during differentiation
of
murine erythroleukemia (MEL) cells assessed by SILAC
labeling and nanoLC-MS ”
118
CV
•
•
Presented a poster at the ASMS 2007 conference on “Peptide
isoelectric focusing as a first dimension separation prior to
nanoLC-MS is a powerful alternative to GeLC-MS for SILAC
based quantitative proteomics”
Presented a poster at the International Conference on
Proteomics: “Bridging the Gap between Gene Expression and
Biological Function" organized by the Gabriel Lippmann group
in Luxembourg
Publications:
1. Evidence for the association of synaptotagmin with Glutathione Stransferases: Implications for a novel function in human breast
cancer
S. Sreenatha†, K. Ravi Kumara, G. V. Reddya, B. Sridevi, D. Praveena,
S.Monikaa,S.Sudhab, M. GopalReddyc and P. Reddanna. Clinical
Biochemistry, 2005, 38:436-43.
2. Characterization of calcineurin-dependent response element binding
protein and its involvement in copper- metallothionein gene
expression in Neurospora.
Kumar KS, Ravi Kumar, Siddavattam D, Subramanyam C.
Biochem Biophys Res Commun. 2006, 345:1010-3.
3. Biodegradation of methyl parathion and p-nitrophenol: Evidence to
show Pnitrophenol 2–hydroxylase in a Gram- negative Serratia sp.
strain DS001
Pnitrophenol 2–hydroxylase in a Gram-negative Serratia sp. strain DS001
Suresh B. Pakala, Purushotam Gorla, Aleem Basha P, Ravikumar K
Rajasekhar B, Mahesh Y, Mike Merrick and Dayananda Siddavatam
Appl Microbiol Biotechnol. 2007 Jan;73 (6):1452-62.
119
119
CV
References:
1.
Dr. Gerhard Mittler
Group Leader
Department of Cellular & Molecular Immunology
Max-Planck Institute of Immunobiology
Freiburg, Germany
Email: [email protected]
2. Dr. Robert Schneider
Group Leader, Spemann Laboratory,
Freiburg, Germany
Email: [email protected]
3. Dr. Wolfgang Schamel
Group Leader, Department of Molecular Immunology
Freiburg, Germany
Email:
[email protected]
Yours Sincerely
Ravi Kumar Krovvidi
120
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Quantitative proteome and transcriptome changes

Transcription

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