CHAPTER 2 DNA based human gut microbial

CHAPTER 2
DNA based human gut microbial
diversity of Lean, Normal, Obese, and
Surgically treated-Obese Indian
Individuals
- 22 -
2.1. Introduction
World over 1.6 billion adults are overweight and 400 million adults are obese (World
Health Organization 2005). Developing nations such as India are no exception. A
recent analysis has identified an alarming prevalence of obesity in one of the metro
cities of the country [1]. Due to the associated disorders such as atherosclerosis [2],
hypertension [2,3], type 2 diabetes[4], and certain form of cancers[5], obesity has
affected life expectancy at higher magnitudes. Genetic basis of this outbreak is well
disputed, as the human genome is substantially unchanged over decades [6]. Among
the
newest
explanations,
rising
prevalence
of
contemporary
obesogenic
environmental factors [7,8] is the most accepted. These factors usually promote
uncontrolled food intake and discourage physical activity.
Gut is recognized as an organ contributing to human health and physiology due to its
role in endocrine control of food intake, digestion, and assimilation [9]. Its
contribution to energy homeostasis has been recently identified [10]. Gut hormones
such as ghrelin, PYY, and CCK effectively control appetite and satiety thereby
regulating food intake [10] and energy input. Apart from gut hormones and digestive
enzymes, it harbors a diverse array of microorganisms, which participate in digestion
and many other crucial functions such as regulation of immunity [11], stimulation of
angiogenesis [12], intestinal cell proliferation and differentiation [13], inflammatory
immune response [14], and vitamin supplementation [15]. The recent discovery of
association of gut bacteria with fat storage [16] and obesity [17] has been a milestone
in the field of human physiology. Obese gut microbiome exhibits an enhanced
efficiency to harvest energy from diet; furthermore, this phenomenon is demonstrated
to be transmissible [18]. Weight loss program in obese humans induces a comparative
increase in bacteria of phylum Bacteroidetes with significant reduction in phylum
Firmicutes [19], although, researchers failed to see such an observation in another
community [20]. It is noteworthy that, both the studies were performed
independently but with different approaches. Similar contradictory observations were
also reported from another community wherein higher weight loss displayed an
increase Bacteroides-Prevotella group with a simultaneous decline in butyrateproducing bacteria [21], however, these observations were diet dependent. Among
the existing diversity within gut microbiota, no study has yet identified any specific
bacteria augmenting or abating obesity. However, an increase in bacteria of genus
- 23 -
Bacteroides was recently correlated to excessive weight gain during pregnancy, which
is another proposed model of obesity [22].
Bariatric surgery is one of the majorly sought remedies[23] for morbid obesity. SG and
AGB are two forms of restrictive bariatric surgery, which reduce the effective volume
of stomach leading to early satiety and reduced caloric intake without affecting
absorption of the digested food [24]. Gastric bypass surgery or Roux-en-Y procedure
is another approach, which is an invasive and malabsorptive surgery. In this surgery,
a small pouch of stomach is created from fundus of the stomach, which is connected
to the mid-jejunum to form a proximal gastric pouch. The distal stomach and
proximal small intestine are thus completely bypassed. Zhang et al [25] recently
described fecal microbiota after gastric bypass anti-obesity surgery. Firmicutes were
reported to be higher in normal and obese individuals in comparison to post-gastric
bypass individuals. However, effects of SG and AGB on gut microbiota are not well
studied and such a comparison could assist to unveil a discrete relationship between
obesity and gut bacteria. Furthermore, a comparison of gut microbiota of lean
individuals with that of normal, obese, and surgically treated individuals could
substantiate the association of gut microbiota with obesity.
To identify obesity-associated bacteria, recent studies largely focused on diet-based
control models of obesity, but lean individuals and individuals who have undergone
bariatric surgery have not been properly investigated. Here, we report analysis and
comparison of 16S rRNA gene libraries based fecal microbiota from lean, normal,
obese, and treated-obese (SG and AGB) individuals. Post-identification of dominant
bacterial genera across the three groups, we quantified these genera or groups across
20 individuals classified based on BMI using real-time PCR.
2.2. Materials and Methods
Flow of the methodologies adopted is shown in the flowchart given in Figure 2.1.
2.2.1. Collection of samples
Early morning fecal samples were collected from unrelated healthy individuals (25-50
years-old) irrespective of gender (Table 2.1). Individuals were classified based on
their BMI into lean (14-19 kg/m2, n=5 with low body fat percentage, LI), normal (2024 kg/m2, n=5, N), Obese (25-53 kg/m2, n=5, O), and treated-obese (25-36 kg/m2, n=5,
- 24 -
T, Obese individuals regressing to normal BMI after restrictive type surgeries, which
primarily reduce stomach size; SG and AGB) groups. Individuals who had not used
antibiotics, pre- or probiotics within the last 3 months preceding fecal sampling and
who were not ill at the time of sampling were included in the present study. All the
fecal samples were stored at 4°C and transported to laboratory in ice. The samples
were either processed immediately or stored at -80°C until processed. All the fecal
donors volunteered for the study, which was approved by the local hospital ethical
committee of Ruby Hall Clinic, Pune.
Fecal Sample
Total DNA isolation
16S rRNA gene fragment amplification
PEG purification/gel elution
Agarose gel electrophoresis
Ligation
Agarose gel electrophoresis
Agarose gel electrophoresis
Real time PCR based
quantification
Transformation
Colony PCR
Library construction
Bioinformatics analysis
Sequencing
Figure 2.1 | Flow of methodologies adopted for the study.
- 25 -
S.
No.
Sample
ID#
Gender &
Age (in
years)$
BMI before
surgery
(kg/m2)*
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
LI1
LI2
LI3
LI4
LI5
N1
N2
N3
N4
N5
O1
O2
O3
O4
O5
T1
T2
T3
T4
T5
M27
M22
F21
M23
F24
M49
M45
M44
M28
F25
F42
F45
M41
M50
M49
F30
M42
F50
M62
F52
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
37.65
40.68
39.35
45.18
36.12
BMI at the time
of sample
collection
(kg/m2)
17.54
18.09
16.03
15.82
14.81
24.00
23.00
23.00
24.80
23.00
44.10
52.80
51.20
35.00
40.00
32.05
28.09
34.48
35.86
27.68
Body fat
percentage¥
Surgical
treatment§
Time required for
weight loss (in
days) †
17.80
18.70
21.66
12.04
19.06
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
SG
SG
SG
AGB
AGB
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
93
196
104
371
278
Table 2.1 | Details of participating individuals understudy. #Each participating individual was given a
sample ID, where LI=Lean, N=Normal, O=Obese, T=Surgically Treated-Obese. $M=Male, F=Female; the
following two digit number is age (in years). *BMI before surgery, applicable to obese individuals, who
underwent anti-obesity surgeries; NA=Not Applicable. ¥Body fat percentage was calculated using OMRON
body fat analyzer (Fig. 2.2); ND= Not Determined. †§Only applicable to Surgically Treated-Obese; AGB=
Adjustable Gastric Banding, SG=Sleeve Gastrectomy; NA=Not Applicable.
2.2.2. Assessment of BMI and leanness
To assess leanness, body fat percentage was determined using OMRON HBF-306C
hand-held body fat analyzer (OMRON HEALTHCARE INC., Illinois, USA) (Figure
2.2) as in reference 60. BMI was determined for all the individuals using the following
formula:
BMI =
Weight in kg
Height in meters square
- 26 -
Figure 2.2 | OMRON hand-held body fat analyzer. It works on the
principle of Bioelectrical Impedance. Muscles, blood vessels, and
bones due to their high water content conduct electricity easily.
However, body fat has little electric conductivity. The body fat
analyzer sends an extremely weak electrical current of 50 kHz and
500 µA through the body to determine the amount of fat tissue. This
weak electrical current is not felt during the operation of the
instrument. The Bioelectrical Impedance method safely combines
electric resistance with the distance of the electricity conducted.
Correct posture and consistent measuring conditions need to be
maintained for the best results.
2.2.3. Fecal DNA extraction
Total fecal DNA was extracted from each fecal sample using QIAamp DNA Stool
Mini Kit (Qiagen, Cat#51504). An additional step of bead beating was performed
using autoclaved silica beads of diameter 0.1, 0.2, and 0.5 mm. Purified DNA samples
were analyzed by agarose gel electrophoresis and concentrations were estimated by
Nanodrop (Thermo Scientific, USA).
2.2.4. Construction of 16S rRNA gene libraries and DNA Sequencing
To assess the total gut microbial diversity, 16S rRNA gene fragment library was
constructed for each sample individually. To avoid failure of PCR reactions due to
potential fecal inhibitors, we performed PCR amplifications for 16S rRNA gene
fragment using serially diluted DNA samples. The highest dilution producing
maximum yield of PCR product was further used. For each DNA sample five
replicates of 25 µl PCRs were set, each containing approximately 1X Thermopol Buffer
(NEB), 1 unit of Taq Polymerase (NEB), 200 µM dNTPs, 200 µM concentration of 8F-I
(5’-GGA TCC AGA CTT TGA TYM TGG CTC AI-3’) and 907R-I (5’-CCG TCA ATT
CMT TTG AGT TI-3’) primers[26]. Cycling conditions were 95°C for three minutes,
followed by 20 cycles of 95°C for ten seconds, 53°C for forty-five seconds, and 72°C
for one minute, with a final extension of 20 minutes at 72°C. Replicate PCRs were
pooled together. The resulting amplicons (approximately 900 bp) were gel-purified
using gel extraction kit (Qiagen, Cat#28704) and were cloned into pGEM-T vector
(Promega), followed by chemical transformation into E. coli DH5α (Invitrogen).
Extraction controls (no fecal material added) did not produce detectable PCR
products or colonies. For each sample, 192-384 colonies positive for the cloned
amplicons were bi-directionally sequenced with vector-specific primers (M13F 5’- 27 -
GTA AAA CGA CGG CCA G-3’ and M13R 5’-CAG GAA ACA GCT ATG AC-3’)
using BigDye™ Terminator Cycle Sequencing Ready Reaction Kit v3.1 (ABI,
Cat#4337457) in an automated 3730 DNA analyzer (ABI).
2.2.5. Sequence analysis
The flow of methodologies adopted for sequence analysis is shown in Figure 2.3.
Base-calling for each sequence read was performed using PHRED [27]. Forward &
reverse
(A)
(B)
Figure 2.3 | Flow of code for 16S rRNA gene sequence assembly and analysis. (A) Flow of the program code for
assembly of forward and reverse trace files (ab1 format) into contig and singles. (B) Flow of analysis for 16S rRNA gene
fragment library.
sequences for each clone were assembled using CAP3 [28]. Sequences were converted
to sense orientation using OrientationChecker [29], aligned in ClustalX [30], and
screened for chimeras with Mallard [29]. Non-chimeric sequences above 300 bases
were analyzed for nearest neighbors using BLAST based SEQMATCH tool at RDP-II
(version 10.9) [31] database. To represent a pictorial distribution of phyla or genera in
- 28 -
various libraries, an algorithm was developed in Visual Basic 6.0 (Programming tool
from Microsoft Corporation, USA) to produce a colored grid of blocks representing
the percentage of sequences identified in the library. The intensity of red color is
proportional to the dominance of the identified bacteria. A scale of distribution of
sequences from 0 to 100% and the color pattern is placed adjacent to every
distribution image. For estimation of OTU richness and microbial diversity, distance
matrix was generated using DNADIST from Phylip 3.68 package[32], and operational
taxonomic units (OTUs) were determined by 97% sequence similarity by the furthestneighbor method in DOTUR 1.53[33]. Non-parametric species richness estimates were
also determined using the Abundance-based coverage estimator (ACE) and Chao I
estimator at a distance of 0.03. Library comparison was performed using ∫-LIBSHUFF
[34]. All the non-chimeric sequences were deposited to NCBI’s GenBank database
(refer Appendix 1).
Phylogenetic relationship between sequences was determined by Neighbor-joining
method. Neighbor-joining tree was constructed with 1000 bootstrap and Kimura-2
parameter as a model of nucleotide substitution in MEGA4 [35].
2.2.6. Quantification of bacteria and archaea
Quantification of bacteria and archaea was performed by real-time PCR assays on
7300 Real-Time PCR system (ABI) using 2X Power SYBR green master mix (ABI,
Cat#4368708) or 2X TaqMan® Universal PCR Master Mix (ABI, Cat#4351891) for
Taqman probe based assays. Primer and probe details are given in Table 2.2. A 10fold dilution series of pCR4-TOPO vector with respective 16S rRNA gene fragment
was used to generate standard curves. All the DNA samples were assayed in
triplicates.
- 29 -
Domain/Phylum/Genus
Eubacteria*
Archaea*
Bacteroidetes*
Firmicutes*
Bacteroides†
Butyrate-producing
bacteria*
Primers
Eub338 (5’-ACT CCT ACG GGA GGC AGC AG-3’)
Eub518 (5’-ATT ACC GCG GCT GCT GG-3’)
931f (5’-AGG AAT TGG CGG GGG AGC A-3’)
m1100r (5’-BGG GTC TCG CTC GTT RCC-3’)
Cfb319 (5’-GTA CTG AGA CAC GGA CCA-3’)
Eub518 (5’-ATT ACC GCG GCT GCT GG-3’ )
Lgc353 (5’-GCA GTA GGG AAT CTT CCG-3’)
Eub518 (5’-ATT ACC GCG GCT GCT GG-3’)
HuBac566f (5’-GGG TTT AAA GGG AGC GTA GG3’)
HuBac692r (5’-CTA CAC CAC GAA TTC CGC CT-3’)
HuBac594Bhqf(5’-(FAM) TAA GTC AGT TGT GAA
AGT TTG CGG CTC (TAMRA)-3’)
BCoATscrF (5’-GCI GAI CAT TTC ACI TGG AAY
WSI TGG CAY ATG-3’)
BCoATscrR (5’-CCT GCC TTT GCA ATR TCI ACR
AAN GC-3’)
Reference
[36]
[37]
[38]
[36]
[38]
[39]
Table 2.2 | Details of the primers and probes used in this study. * SYBR green-based assay; † Taqman probe
based assay.
2.2.7. Statistical analysis
To determine correlation between BMI and various phyla, genera or gene
distribution, non-parametric Spearman's Correlation Coefficient was determined
using 2-tailed test of significance in Graphpad Prism 5.01 software. For statistical
comparison of values between two groups, unpaired t-test with Welch’s correction
was employed.
2.2.8. Proteome comparison
Proteome comparison of the dominant and available bacterial genomes, and the
human genome was carried out at the CAZy database (http://www.cazy.org).
Functional classification of the proteomes was assessed using the assigned COG terms
(http://www.ncbi.nlm.nih.gov/cog.cgi) in the available genomes for carbohydrate
metabolism
and
on
functional
proteome
classifier
from
TIGR
database
(http://www.tigr.org).
- 30 -
2.3. Results and Discussion
1
2
1 2
3
4
5 6 7 8 9 10 11 12 13
(B)
(A)
1000 bp
850 bp
~900 bp
1 2
3
4
5 6 7 8 9 10 11 12
1000 bp
850 bp
1 2
3
4
5 6 7 8
9 10 11 12 13 14 15 16
1 2
3
4
5 6 7 8
9 10 11 12 13 14 15 16
(C)
1250 bp
1000 bp
1250 bp
1000 bp
~900 bp
Figure 2.4 | Representative gel image for
isolated fecal DNA, 16S rRNA gene fragment
PCR, and Colony PCR. (A) Isolated fecal DNA.
Arrow indicates smear of bacterial genomic
~1100 bp DNA. (B) Low cycle PCR amplified 16S rRNA
gene fragments from isolated fecal community
DNA. Upper panel: Lane 1- Negative control, 21 kb plus DNA ladder (Invitrogen), 3 to 7-LI1 to
LI-5, 8 to 12 N1 to N5 and 13- E. coli genomic
DNA positive control. Lower panel: Lane 1Negative control, 2-1 kb plus DNA ladder
(Invitrogen), 3 to 7-O1 to O5, 8 to 12-T1 to T5.
(C) Colony PCR for assaying insert containing
clones. Upper panel: Lane 1-1 kb plus DNA
~1100 bp ladder (Invitrogen), 2 to 6- clones from LI-1, 7
to 10- clones from N1 and 11-15- clones from
O1. Lower panel: Lane 1-1 kb plus DNA ladder
(Invitrogen), 2 to 6- clones from T1, 7 to 10clones from LI-2 and 11-16- clones from O2.
2.3.1. Clone libraries & Classification of clones
Representative gel images are presented in Figure 2.4 for the extracted DNA quality
(A), 16S rRNA gene fragment PCR amplification (B), and confirmation of inserts in
some of the 16S rRNA gene libraries in pGEM-T vector (C). 192-384 clones were
sequenced and analyzed from each sample in lean, normal, obese, and treated-obese
groups. Low quality sequence trace files were automatically discarded and more than
50% of the sequence trace files assembled into contigs in most of the libraries.
Sequences failing to assemble with the counterpart reverse sequence were identified
as singlets. Table 2.3 shows sequence assembly details. Further analysis was carried
out with the identified non-chimeric and long sequences (>300 bases). Otherwise,
- 31 -
chimeric and smaller sequences were discarded. Identification and classification of
sequences was done using SEQMATCH tool at database RDP-II. All the taxonomic
assignments were confirmed by phylogenetic analysis (Appendix 3).
S. No.
Library
Singlets∫
Assembled Contigs†
Total Sequences‡
1
LI1
19
274
293
2
LI2
51
180
231
3
LI3
19
284
303
4
LI4
52
174
226
5
LI5
41
205
246
6
N1
36
131
167
7
N2
12
133
145
8
N3
23
136
159
9
N4
9
172
181
10
N5
11
161
172
11
O1
45
81
126
12
O2
16
162
178
13
O3
28
108
136
14
O4
31
102
133
15
O5
24
76
100
16
T1
6
180
186
17
T2
9
171
180
18
T3
13
156
169
19
T4
13
159
172
20
T5
15
154
169
Total Sequences
3672
Table 2.3 | Sequence assembly summary. The table represents summary of sequence assembly carried out
using the bioinformatics pipeline shown in Figure 2.3A. ∫Sequences (either forward or reverse) which failed to
assemble with its other partner due to low sequence quality or short length; †Formed by assembly of forward
and reverse sequences for a clone; ‡Sum of Contigs and Singlets.
2.3.2. Estimation of OTU richness & Library comparison
As evident from Table 2.4, human gut microbial community shows a great species
richness.
Total number of OTUs in each library was estimated using the non-
parametric estimators Chao1 and ACE utilizing the DOTUR program. Both the
estimators project a high OTU number in each library. So an additional sampling or
deeper sequencing would be needed to capture the difference between the observed
number of OTUs and the Chao1 or ACE estimates as shown by the rarefaction curves
(Figure 2.5). No rarefaction curve reached an asymptote and despite of a higher
sequence number in some libraries such as the lean ones, the number of OTUs
continued to increase at 3% (species level). These observations warrant the need of
- 32 -
additional sampling to determine the existing microbial diversity in the microbial
community of human intestine. Diversity indices such as Shannon index and
Libraries
Lean
Normal
Obese
TreatedObese
LI-1
LI-2
LI-3
LI-4
LI-5
N1
N2
N3
N4
N5
O1
O2
O3
O4
O5
T1
T2
T3
T4
T5
Observed
OTU's
30
53
63
26
66
37
25
22
36
63
29
44
25
22
50
33
55
46
78
43
Richness estimators
ACE
Chao I
60.48
45.60
76.82
64.88
120.10
103.10
57.74
42.50
118.10
122.10
64.31
54.00
35.54
29.00
78.49
67.33
60.87
58.67
111.00
107.00
45.43
38.75
63.50
63.00
58.63
46.00
22.36
22.00
270.80
186.70
59.96
63.00
250.80
211.00
74.08
69.00
172.10
137.40
91.97
80.50
Diversity indices
Shannon (H)
Simpson (1-D)
2.5020
0.1212
3.3013
0.0636
3.0537
0.0937
2.4989
0.1088
3.5338
0.0463
3.2777
0.0403
2.6200
0.1153
1.0658
0.6467
2.8924
0.0919
3.6737
0.0369
2.8442
0.0775
3.0723
0.0918
2.1740
0.2353
2.6563
0.1047
3.6865
0.0217
2.6815
0.1145
3.1267
0.0809
2.8881
0.1189
3.8024
0.0475
2.8849
0.1043
Table 2.4 | Distance-based OTUs, Species richness estimates, and Diversity indices. OTU grouping is
reported at 0.03 level, which refers to 3% distance and corresponds to the bacterial species level.
- 33 -
Figure
2.5
|
Rarefaction curves
calculated with the
program DOTUR.
Simpson’s diversity indices were also determined. No trend or correlation was
observed between the bacterial diversity and BMI or a particular group, which is
contradictory to an earlier report [19], wherein obesity is reported to be associated
with reduced bacterial diversity.
Library comparison was done using ∫-LIBSHUFF to determine whether differences in
16S rDNA sequence libraries are due to underlying variability in the microbial
populations or any artifacts of population sub-sampling. Most of the libraries were
significantly different from each other (P<0.05) (Table 2.5) and the data showed that
each individual has a unique gut microbial community in agreement with previous
reports about the individuality of human gut microbiota. Interestingly, except T1, all
the surgically treated-obese individuals exhibited bacterial sequences from lean,
normal and obese groups, which is described by insignificant P value (P>0.05,
highlighted in Table 2.5). Individual T1, who has undergone Surgical Gastrectomy
just 93 days prior to sample collection, displayed a unique gut microbiota. Therefore,
according to the libshuff analysis, surgery-mediated weight loss displays an
associated microbiota change especially towards the individuals of lean and normal
group. These individuals showed bacterial sequences from obese group too, as no
- 34 -
individual has regressed to normalcy completely (refer Table 2.1) and were classified
in the moderate obesity class at the time of sample collection. SG and AGB effectively
reduced stomach volume thereby reducing the food intake and regulation of gut
hormones expression. Therefore, the role of gut hormones and the amount of food
ingested cannot be ignored in determining the host-gut microbiota profile and
obesity.
- 35 -
LI1
LI2
LI3
LI4
LI5
N1
N2
N3
N4
N5
O1
O2
O3
O4
O5
T1
T2
T3
T4
T5
LI1
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
1.000
0
1.000
0
1.000
0
1.000
0
LI2
0.003
6
0.000
0
0.000
1
0.000
0
0.000
0
0.024
6
0.002
6
0.013
3
0.000
0
0.000
0
0.000
0
0.102
3
0.000
0
0.000
0
0.000
0
1.000
0
1.000
0
1.000
0
1.000
0
LI3
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.006
3
0.000
0
0.952
2
0.000
0
0.999
7
1.000
0
1.000
0
1.000
0
LI4
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
9
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.999
4
1.000
0
1.000
0
1.000
0
LI5
0.005
8
0.000
0
0.000
0
0.000
0
0.134
2
0.402
7
0.000
8
0.001
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
1
0.000
0
0.999
6
1.000
0
1.000
0
1.000
0
N1
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.024
9
0.000
9
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.980
2
1.000
0
1.000
0
1.000
0
N2
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.282
4
0.004
3
0.000
0
0.000
0
0.000
0
0.008
9
0.000
0
0.000
0
0.000
0
0.850
8
1.000
0
1.000
0
1.000
0
N3
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
9
0.000
0
0.000
0
0.000
0
0.500
8
1.000
0
1.000
0
1.000
0
N4
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.014
1
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
1.000
0
1.000
0
1.000
0
1.000
0
N5
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
5
0.000
5
0.000
1
0.000
6
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
1.000
0
1.000
0
1.000
0
1.000
0
O1
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.024
4
0.000
5
0.000
0
0.000
0
0.168
1
0.000
0
0.000
0
0.000
0
0.999
0
1.000
0
1.000
0
1.000
0
O2
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
2
0.000
0
0.000
0
0.000
0
0.181
1
0.000
0
0.363
4
0.000
0
1.000
0
1.000
0
1.000
0
1.000
0
O3
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
1
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.908
5
1.000
0
1.000
0
1.000
0
O4
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
1
0.000
0
0.000
0
0.002
6
0.000
0
1.000
0
1.000
0
1.000
0
1.000
0
O5
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
1
0.000
1
0.000
7
0.000
6
0.000
0
0.000
0
0.000
0
0.031
4
0.000
0
0.000
5
0.990
0
1.000
0
1.000
0
1.000
0
T1
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
1
0.000
0
0.317
0
0.413
4
1.000
0
1.000
0
1.000
0
T2
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
1.000
0
1.000
0
1.000
0
T3
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.003
5
1.000
0
1.000
0
T4
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
1.000
0
1.000
0
T5
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
0.000
0
1.000
0
1.000
0
-
- 36 -
Table 2.5 | ∫-LIBSHUFF comparison of 16S rRNA gene fragment libraries. The values in the table represent P values for ΔCXY of homologous library X and heterologous
library Y (lower triangle) and ΔCYX of homologous library Y and heterologous library X (upper triangle). Two libraries are distinct if both pair wise comparisons (ΔCXY and
ΔCYX) are statistically significant. If ΔCXY is statistically significant (in the lower triangle of the matrix) but ΔCYX is not (in the upper triangle), then library Y is a subset of
library X. Library X is a subset of library Y in the opposite situation. Insignificant P values are highlighted grey.
- 37 -
Library/Total
Total nonchimeric
sequences
Percent BLAST hits
with Culturable &
Unculturable bacterial
reference sequences (at
RDP-II)
Percent BLAST hits
with Culturable & typed
bacterial isolate
reference sequences (at
RDP II)
>97%£
<97%¥
>97%
<97%
L1
282
279
3
220
62
L2
218
211
7
160
58
L3
291
291
0
192
100
L4
218
217
1
159
59
L5
Total(L)
236
1245
227
9
20
154
82
361
N1
95
94
1
91
4
N2
98
96
2
88
10
N3
140
136
4
130
10
N4
162
160
2
146
16
N5
Total(N)
166
661
154
12
21
80
86
126
O1
100
98
2
88
12
O2
153
147
6
126
27
O3
102
100
2
98
4
O4
91
88
3
70
21
O5
Total(O)
71
517
63
8
21
40
31
95
T1
178
176
2
161
17
T2
175
161
14
139
36
T3
164
157
7
139
25
T4
166
135
31
43
123
T5
Total(T)
Total Sequences
(L+N+O+T)
164
847
150
14
68
140
24
225
3270
Table 2.6 | BLAST hit summary. The table represents summary of SEQMATCH tool based percentage BLAST hits
to the deposited unculturable and culturable bacterial isolate sequences at RDP-II. £Sequence homology above 97%
denotes similarity at species level. ¥Sequence homology below 97% denotes novel species.
- 38 -
Figure 2.6 | Phylum based comparison. The heat map describes the percentage distribution of fecal
microbiota across the subjects understudy. Individuals are represented in top portion of the map and the
obtained phyla are indicated at the extreme left.
2.3.3. Comparative microbial composition at Phylum, Genus, and Species level
Microbial composition was determined using SEQMATCH tool at RDP-II database.
SEQMATCH-based BLAST hit summary is presented in Table 2.6. Phylum based
comparison is shown in Figure 2.6. At phylum level, all the individuals exhibited a
dominance of bacteria of phyla, Bacteroidetes and Firmicutes, which corroborates with
earlier reports [17,19,40]. Additionally, phyla such as Proteobacteria and Actinobacteria
were also recorded but in less numbers. Some of the sequences referred to as
“unclassified bacteria” remained unidentified, which could be putative novel bacteria. At
phylum level, no defined distribution of these bacteria was seen based on the percentage
distribution of sequences (Figure 2.6). However, RT-PCR based quantification of
Firmicutes 16S rRNA gene copies demonstrates a positive correlation (Figure 2.7,
P=0.0285, Spearman’s r=0.4895) with BMI, which substantiated an earlier report [19].
- 39 -
Figure 2.7 | Real-time PCR based
assessment of the dominant phyla.
The graph presents real-time PCR
based distribution of Bacteroidetes
(P=0.7886, Spearman’s r=0.06403) and
Firmicutes (P =0.0373, Spearman’s
r=0.4684) 16S gene copies are
normalized to the quantity of input
DNA and BMI of the individuals
understudy. Linear regression is also
shown.
Differences in bacterial distribution were clearly illustrated by the comparison of 16S
rRNA gene libraries at genus level (Figure 2.8). All the individuals exhibited a variety of
genera. Although, Prevotella genus dominated the libraries, bacteria of genus Bacteroides
were seen to be dominant in obese and SG treated-obese individuals in comparison to
normal and lean except L3 (For representative closest Culturable isolate BLAST hits,
please refer Appendix 2). This was confirmed by means of RT-PCR (Figure 2.9), which
demonstrated a positive correlation between Bacteroides distribution and BMI. These
well-known bacteria have numerous beneficial and adverse effects on human health [41].
Their large genomes encompass an enriched set of enzymatic machinery for digestion of
indigestible carbohydrates [39]. These are efficient polysaccharide (cellulose, -
- 40 -
Figure 2.8 | Genus based comparison. The heat map describes the percentage distribution of fecal microbiota across the subjects
understudy. The intensity of red color represents the extent of distribution.
- 41 -
Figure 2.9 | Real-time PCR based
assessment of Bacteroides genus.
The graph presents real-time PCR
based percentage distribution of
Bacteroides (P =0.0020 Spearman’s
r=0.6491) 16S gene copies and BMI
of the individuals understudy.
Linear regression is also shown.
Table 2.7 | Utilization of polysaccharide substrates among Bacteroides spp. (Adopted from reference 62)
- 42 -
Figure 2.10|Proteome involved in Carbohydrate metabolism. Large numbers of glycoside hydrolases are present
in the bacteria of genus Bacteroides. L.=Lactobacillus, B.=Bacteroides, Bi.=Bifidobacterium, and H.=Homo.
hemicellulose, starch and pectin) foragers (refer Table 2.7 and Figure 2.10). Surprisingly,
some Firmicutes also show an enriched set of glycoside hydrolases, e.g., Eubacterium
rectale [18]. However, we did not find dominance of this bacterium in any of the obese
individuals under study. Interestingly, Bacteroides were earlier correlated to weight gain
and diabetes in pregnant women [22] and in diabetes prone rats [42], respectively. These
bacteria are also shown to be associated with ulcerative colitis [42,43]. Moreover, some of
the Bacteroides species produce faecapentaenes [44], which is a potent in vitro mutagen.
Butyrate-producing bacteria such as Faecalibacterium were seen in low but comparatively
higher percentages in the normal gut in comparison to the others. These bacteria actively
participate in butyrate synthesis, which acts as an energy source for colonocytes and is
known to exhibit beneficial effects on gut health [45]. Faecalibacterium prausnitzii is shown
to have anti-inflammatory properties and epidemiology of Crohn’s disease [46]. Reduced
levels of this bacterium and lower levels of butyrate in the obese may put them at risk of
inflammatory disorders of GIT.
Some lean and almost all the obese individuals exhibited dominant presence of the
bacteria of family Ruminococcaceae, which remain unclassified and unidentified. Many
- 43 -
other species belonging to genera, "Lachnospiraceae Incertae Sedis", Veillonella, Streptococcus,
Ruminococcus, Porphyromonas, Parabacteroides, Mitsuokella, Megamonas, Eubacterium, and
Coprococcus were also found in the individuals understudy but in low abundance. For the
list of identified closest Culturable reference bacterial isolate sequences please refer
Appendix 4.
2.3.4.
Archaeal distribution
To assess the distribution of archaea in the groups understudy, RT-PCR based assay was
carried out. As reported earlier [25,47], a significantly high archaeal density was observed
in obese individuals, however, treated-obese showed a significant decrease (Figure 2.11).
Figure 2.11|Distribution of archaeal 16S rRNA gene
copies among the groups under study. The graph
presents real-time PCR based distribution of Archaeal
16S rRNA gene copies in the groups understudy. Linear
regression is also shown (*P=0.0341 #P=0.0596).
Normal and lean individuals showed a very low Archaeal DNA amplification with some
individuals showing no amplification at all. Methanobrevibacter smithii, a predominant
methanogenic archaeon of human gut [48], produces elevated amounts of SCFAs
(especially acetate and propionate) in consortium with Bacteroides thetaiotaomicron. This
disturbs energy balance leading to increased adiposity in mice [47]. Especially,
propionate is gluconeogenic [49] and acetate stimulates hepatic de novo lipogenesis [47].
Such a phenomenon can also be speculated in the human subjects understudy. In the
obese individuals understudy, the dominance of H 2 producing and polysaccharide
- 44 -
foraging bacterial species such as Prevotella [50,51] and Bacteroides [25,47], along with
methanogenic archaea results in increased glucose and SCFA supply, thereby leading to
perturbations in the host metabolism. Elevated glucose levels with increased adiposity
are renowned hallmarks of obesity [5,25]. Comparatively lower occurrences of bacteria of
genus, Bacteroides and domain, Archaea in lean, normal, and treated-obese individuals
indicate a reversal of above discussed alteration in metabolism, thereby leading to a
normal or restoring BMI.
2.4. Conclusions
1. Human gut of Indian origin is dominated by the bacteria of Phyla, Bacteroidetes
and Firmicutes.
2. Every individual has a unique and personalized microbiota.
3. At phylum level, Firmicutes seem to correlate with BMI.
4. Obese individuals show an increased prevalence of bacteria of genus, Bacteroides
and domain, archaea. Treated-obese individuals with regressing BMI, who have
undergone surgical intervention, exhibited reduced Bacteroides sequences and
archaeal population.
5. Based on diversity indices, there seems to be no difference in the microbial
diversity among the groups under study.
6. Bacteria of Genus Prevotella dominated the gut microbial diversity in the
individuals understudy with a variable percentage of other bacterial species
found in human gut.
- 45 -
7. Many sequences in each library remained unclassified at various taxonomic
levels, which indicate existence of novel bacteria, bacterial genera, and bacterial
species in the Indian gut.
2.5. References
1. Deepa M, Farooq S, Deepa R, Manjula D, Mohan V: Prevalence and significance
of generalized and central body obesity in an urban Asian Indian population in
Chennai, India (CURES: 47). Eur J Clin Nutr 2009, 63: 259-267.
2. Berenson GS, Srinivasan SR, Bao W, Newman WP, III, Tracy RE, Wattigney WA:
Association between multiple cardiovascular risk factors and atherosclerosis in
children and young adults. The Bogalusa Heart Study. N Engl J Med 1998, 338:
1650-1656.
3. Goran MI, Gower BA: Abdominal obesity and cardiovascular risk in children.
Coron Artery Dis 1998, 9: 483-487.
4. Arslanian SA: Type 2 diabetes mellitus in children: pathophysiology and risk
factors. J Pediatr Endocrinol Metab 2000, 13 Suppl 6: 1385-1394.
5. Haslam DW, James WP: Obesity. Lancet 2005, 366: 1197-1209.
6. Eaton SB, Konner M, Shostak M: Stone agers in the fast lane: chronic
degenerative diseases in evolutionary perspective. Am J Med 1988, 84: 739-749.
7. Hill JO, Peters JC: Environmental contributions to the obesity epidemic. Science
1998, 280: 1371-1374.
8. Egger G, Swinburn B: An "ecological" approach to the obesity pandemic. BMJ
1997, 315: 477-480.
9. Gosman GG, Katcher HI, Legro RS: Obesity and the role of gut and adipose
hormones in female reproduction. Hum Reprod Update 2006, 12: 585-601.
- 46 -
10. Murphy KG, Bloom SR: Gut hormones and the regulation of energy
homeostasis. Nature 2006, 444: 854-859.
11. Rautava S, Isolauri E: The development of gut immune responses and gut
microbiota: effects of probiotics in prevention and treatment of allergic disease.
Curr Issues Intest Microbiol 2002, 3: 15-22.
12. Stappenbeck TS, Hooper LV, Gordon JI: Developmental regulation of intestinal
angiogenesis by indigenous microbes via Paneth cells. Proc Natl Acad Sci U S A
2002, 99: 15451-15455.
13. Rakoff-Nahoum S, Paglino J, Eslami-Varzaneh F, Edberg S, Medzhitov R:
Recognition of commensal microflora by toll-like receptors is required for
intestinal homeostasis. Cell 2004, 118: 229-241.
14. Noverr MC, Huffnagle GB: Does the microbiota regulate immune responses
outside the gut? Trends Microbiol 2004, 12: 562-568.
15. Stevens CE, Hume ID: Contributions of microbes in vertebrate gastrointestinal
tract to production and conservation of nutrients. Physiol Rev 1998, 78: 393-427.
16. Backhed F, Ding H, Wang T, Hooper LV, Koh GY, Nagy A et al.: The gut
microbiota as an environmental factor that regulates fat storage. Proc Natl Acad
Sci U S A 2004, 101: 15718-15723.
17. Ley RE, Backhed F, Turnbaugh P, Lozupone CA, Knight RD, Gordon JI: Obesity
alters gut microbial ecology. Proc Natl Acad Sci U S A 2005, 102: 11070-11075.
18. Turnbaugh PJ, Ley RE, Mahowald MA, Magrini V, Mardis ER, Gordon JI: An
obesity-associated gut microbiome with increased capacity for energy harvest.
Nature 2006, 444: 1027-1031.
19. Ley RE, Turnbaugh PJ, Klein S, Gordon JI: Microbial ecology: human gut
microbes associated with obesity. Nature 2006, 444: 1022-1023.
- 47 -
20. Duncan SH, Lobley GE, Holtrop G, Ince J, Johnstone AM, Louis P et al.: Human
colonic microbiota associated with diet, obesity and weight loss. Int J Obes
(Lond) 2008, 32: 1720-1724.
21. Nadal I, Santacruz A, Marcos A, Warnberg J, Garagorri M, Moreno LA et al.:
Shifts in clostridia, bacteroides and immunoglobulin-coating fecal bacteria
associated with weight loss in obese adolescents. Int J Obes (Lond) 2009, 33: 758767.
22. Collado MC, Isolauri E, Laitinen K, Salminen S: Distinct composition of gut
microbiota during pregnancy in overweight and normal-weight women. Am J
Clin Nutr 2008, 88: 894-899.
23. Choban PS, Jackson B, Poplawski S, Bistolarides P: Bariatric surgery for morbid
obesity: why, who, when, how, where, and then what? Cleve Clin J Med 2002, 69:
897-903.
24. Schneider BE, Mun EC: Surgical management of morbid obesity. Diabetes Care
2005, 28: 475-480.
25. Zhang H, DiBaise JK, Zuccolo A, Kudrna D, Braidotti M, Yu Y et al.: Human gut
microbiota in obesity and after gastric bypass. Proc Natl Acad Sci U S A 2009, 106:
2365-2370.
26. Ben-Dov E, Shapiro OH, Siboni N, Kushmaro A: Advantage of using inosine at
the 3' termini of 16S rRNA gene universal primers for the study of microbial
diversity. Appl Environ Microbiol 2006, 72: 6902-6906.
27. Ewing B, Hillier L, Wendl MC, Green P: Base-calling of automated sequencer
traces using phred. I. Accuracy assessment. Genome Res 1998, 8: 175-185.
28. Huang X, Madan A: CAP3: A DNA sequence assembly program. Genome Res
1999, 9: 868-877.
- 48 -
29. Ashelford KE, Chuzhanova NA, Fry JC, Jones AJ, Weightman AJ: New screening
software shows that most recent large 16S rRNA gene clone libraries contain
chimeras. Appl Environ Microbiol 2006, 72: 5734-5741.
30. Larkin MA, Blackshields G, Brown NP, Chenna R, McGettigan PA, McWilliam H
et al.: Clustal W and Clustal X version 2.0. Bioinformatics 2007, 23: 2947-2948.
31. Cole JR, Chai B, Farris RJ, Wang Q, Kulam SA, McGarrell DM et al.: The
Ribosomal Database Project (RDP-II): sequences and tools for high-throughput
rRNA analysis. Nucleic Acids Res 2005, 33: D294-D296.
32. Felsenstein J.: PHYLIP - Phylogeny Inference Package (Version 3.2). Cladistics
1989, 5: 164-166.
33. Schloss PD, Handelsman J: Introducing DOTUR, a computer program for
defining operational taxonomic units and estimating species richness. Appl
Environ Microbiol 2005, 71: 1501-1506.
34. Schloss PD, Larget BR, Handelsman J: Integration of microbial ecology and
statistics: a test to compare gene libraries. Appl Environ Microbiol 2004, 70: 54855492.
35. Tamura K, Dudley J, Nei M, Kumar S: MEGA4: Molecular Evolutionary Genetics
Analysis (MEGA) software version 4.0. Mol Biol Evol 2007, 24: 1596-1599.
36. Fierer N, Jackson JA, Vilgalys R, Jackson RB: Assessment of soil microbial
community structure by use of taxon-specific quantitative PCR assays. Appl
Environ Microbiol 2005, 71: 4117-4120.
37. Einen J, Thorseth IH, Ovreas L: Enumeration of Archaea and Bacteria in seafloor
basalt using real-time quantitative PCR and fluorescence microscopy. FEMS
Microbiol Lett 2008, 282: 182-187.
38. Layton A, McKay L, Williams D, Garrett V, Gentry R, Sayler G: Development of
Bacteroides 16S rRNA gene TaqMan-based real-time PCR assays for estimation
- 49 -
of total, human, and bovine fecal pollution in water. Appl Environ Microbiol 2006,
72: 4214-4224.
39. Flint HJ, Bayer EA, Rincon MT, Lamed R, White BA: Polysaccharide utilization
by gut bacteria: potential for new insights from genomic analysis. Nat Rev
Microbiol 2008, 6: 121-131.
40. Martin Dworkin (Editor-in-Chief) SFERK-HSESE: The Prokaryotes-A Handbook
on the Biology of Bacteria-Third Edition-Volume 7: Proteobacteria: Delta and
Epsilon Subclasses. Deeply Rooting Bacteria. Springer Science; 2006.
41. Wexler HM: Bacteroides: the good, the bad, and the nitty-gritty. Clin Microbiol
Rev 2007, 20: 593-621.
42. Roesch LF, Lorca GL, Casella G, Giongo A, Naranjo A, Pionzio AM et al.: Cultureindependent identification of gut bacteria correlated with the onset of diabetes
in a rat model. ISME J 2009, 3: 536-548.
43. Lucke K, Miehlke S, Jacobs E, Schuppler M: Prevalence of Bacteroides and
Prevotella spp. in ulcerative colitis. J Med Microbiol 2006, 55: 617-624.
44. Huycke MM, Gaskins HR: Commensal bacteria, redox stress, and colorectal
cancer: mechanisms and models. Exp Biol Med (Maywood ) 2004, 229: 586-597.
45. Flint HJ, Duncan SH, Scott KP, Louis P: Interactions and competition within the
microbial community of the human colon: links between diet and health.
Environ Microbiol 2007, 9: 1101-1111.
46. Sokol H, Pigneur B, Watterlot L, Lakhdari O, Bermudez-Humaran LG, Gratadoux
JJ et al.: Faecalibacterium prausnitzii is an anti-inflammatory commensal
bacterium identified by gut microbiota analysis of Crohn disease patients. Proc
Natl Acad Sci U S A 2008, 105: 16731-16736.
47. Samuel BS, Gordon JI: A humanized gnotobiotic mouse model of host-archaealbacterial mutualism. Proc Natl Acad Sci U S A 2006, 103: 10011-10016.
- 50 -
48. Eckburg PB, Bik EM, Bernstein CN, Purdom E, Dethlefsen L, Sargent M et al.:
Diversity of the human intestinal microbial flora. Science 2005, 308: 1635-1638.
49. Yost WM: Gluconeogenesis in ruminants: propionic acid production from a
high-grain diet fed to cattle. 1977.
50. Avgustin G, Wallace RJ, Flint HJ: Phenotypic diversity among ruminal isolates
of Prevotella ruminicola: proposal of Prevotella brevis sp. nov., Prevotella
bryantii sp. nov., and Prevotella albensis sp. nov. and redefinition of Prevotella
ruminicola. Int J Syst Bacteriol 1997, 47: 284-288.
51. Wood J: Estimation of the relative abundance of different Bacteroides and
Prevotella ribotypes in gut samples by restriction enzyme profiling of PCRamplified 16S rRNA gene sequences. 1998.
- 51 -