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 Table of Contents  
Year : 2021  |  Volume : 14  |  Issue : 1  |  Page : 42-47

Analysis of gut bacterial community composition in obese and lean Indian participants by denaturing gradient gel electrophoresis

1 Department of Microbiology, All India Institute of Medical Sciences, New Delhi, India
2 Department of Dietetics, All India Institute of Medical Sciences, New Delhi, India
3 Division of Vector Borne Diseases, National Institute for Research in Tribal Health, Jabalpur, Madhya Pradesh, India
4 School of Life Science, Jawaharlal Nehru University, New Delhi, India

Date of Submission29-Aug-2020
Date of Acceptance26-Nov-2020
Date of Web Publication09-Feb-2021

Correspondence Address:
Prof. Rama Chaudhry
Department of Microbiology, All India Institute of Medical Sciences, New Delhi
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/kleuhsj.kleuhsj_273_20

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Background: Human gut microbiota consists of variety of microbes which play vital role in the host development, physiology and homeostasis. Alteration in gut microbial composition or dysbiosis may lead to various diseases and lifestyle disorders including obesity. Since gut microbiota varies with differences in dietary habits and geographical locations therefore studies are required to look into the gut microbial diversity in Indian obese and lean subjects whose dietary habits and geography are different from the western world. Therefore, the present study was conducted to assess the microbial diversity in obese and lean Indian subjects.
Materials and Methods: Subjects with similar dietary habits were enrolled in the study. Fecal samples were collected from each individuals and DNA was extracted. Polymerase chain reaction (PCR) was performed to amplify the 16S ribosomal RNA gene (V1-V5 region) followed by GC clamp PCR (V3 region) for the same. PCR products were run on Denaturing Gradient Gel Electrophoresis (DGGE) system and DGGE sketches were analyzed in Gel Compar II version 6.6 software (Applied maths, Belgium) to assess the gut microbial diversity.
Results and Conclusion: The results showed variation in the gut microbial profiles among obese and lean individuals and revealed more microbial diversity in the lean as compared to obese individuals. These observations indicate that BMI is a contributing factor for the difference in gut bacterial profile of obese and lean subjects and that support the role of gut microbiota in obesity.

Keywords: Body mass index, denaturing gradient gel electrophoresis, gut microbiota, Indian population, lean, obese

How to cite this article:
Bahadur T, Chaudhry R, Bamola VD, Chutani AM, Verma AK, Paul J. Analysis of gut bacterial community composition in obese and lean Indian participants by denaturing gradient gel electrophoresis. Indian J Health Sci Biomed Res 2021;14:42-7

How to cite this URL:
Bahadur T, Chaudhry R, Bamola VD, Chutani AM, Verma AK, Paul J. Analysis of gut bacterial community composition in obese and lean Indian participants by denaturing gradient gel electrophoresis. Indian J Health Sci Biomed Res [serial online] 2021 [cited 2021 Apr 16];14:42-7. Available from: https://www.ijournalhs.org/text.asp?2021/14/1/42/308962

  Introduction Top

Obesity is one of the serious public health issues globally, and recent estimates reported that more than 500 million people are affected by obesity. The prevalence of obesity is increasing not only in a developed country but also in developing country including India.[1] The microorganism living in the human body is termed microbiota. The large intestine of humans contains the maximum number of microbiota.[2] Different physiological functions, energy balance, and some metabolic activity are regulated by human gut microbiota and alteration to the composition of human gut microbiota may lead to different metabolic diseases and lifestyle disorders including obesity.[3] Human gut microbiota also influences the metabolic potential and energy yield from the diet.[4],[5] Microbial population which convert hemicelluloses, cellulose, resistant starch (indigestible components of the host diet), and plant polysaccharides into short-chain fatty acids (SCFAs) and sugars get anaerobic environment within the host for the utilization of host polysaccharides as a substrate.[6],[7] Difference in body weight may be due to physical activity, dietary patterns, and gut microbiota. Studies have reported that lifestyle factors such as geographic site, age, and diet can influence the alteration in gut microbiota.[8],[9] Gut microbiota transplantation from obese mice in the recipient mice resulting in the better weight gain as compared to the gut microbiota transplantation from lean donors.[10]

Human gut containing huge diversity of bacterial communities and hence its richness is complicated to the study. Mostly, cultivation-based analysis has been used which though not provide exact impression of the bacteria present in the human gut as the majority of the bacteria is very complicated to cultivate on the media, and this procedure as a result gives only fast-growing agents. 16S rRNA sequence-based molecular methods such as denaturing gradient gel electrophoresis (DGGE), terminal restriction fragment length polymorphism, quantitative polymerase chain reaction, fluorescent in situ hybridization, and high-throughput sequencing technologies are also used to illustrate human gut microbiota.[11] DGGE generates the best profile of gut microbiota in terms of resolution and species richness by using V3 region of the 16S rRNA gene universal primer;[12] therefore, in this study, DGGE has been used to assess gut bacterial composition in obese and lean Indian participants.

  Materials and Methods Top

Subject enrollment

Healthy human volunteers were enrolled in this study. The study was conducted at All India Institute of Medical Sciences, New Delhi, India, after ethical clearance which was obtained from All India Institute of Medical Sciences, Ansari Nagar New Delhi. Institutional Ethical Committee with Ref no- IESC/T-143/01.03.2013. Promising risks related with this study and procedures were explained to all volunteers, and informed consent was obtained before the assessment. The inclusion criteria for obese and lean individuals were according to the WHO guideline for Asian Indians. The body mass index (BMI) cutoff for obese individuals was more than 25 (≥25 kg/m2) and for lean individuals <22.9 (18.5–22.9 kg/m2). The exclusion criteria to enroll participants were as follows: (i) history of intake of probiotic in the past 8 weeks, (ii) history of intake of antibiotics in the past 8 weeks, (iii) history of intake of steroid, and (iv) history of any known gastrointestinal disorder.

Fecal samples collection and DNA isolation

Fecal samples from the 20 participants (10 obese and 10 lean participants) were collected in the sterile container and stored at 4°C until further processing. All fecal samples were processed for DNA isolation. Total DNA was extracted from the stool samples by using QIAamp DNA Stool Mini Kit (Qiagen) with slight improvisation in manufacturer instructions to increase the DNA yield. The quality and quantity of DNA were checked by Nanodrop (TECAN Nano quant). The extracted DNA from the stool samples of each subject was used for DGGE analysis.

Polymerase chain reaction amplification of the 16S rRNA gene

The universal primer sequences (27F - AGA GTT TGA TCC TGG CTC AG; 907R – CCG TCA ATT CCT TTR AGT TT) targeting the V1-V5 region of 16S rRNA gene, with yielding a band size of 880 bp were used to amplify the stool DNA samples.[13] The PCR was performed in a 50 μl of reaction mixture containing 2.5 mM/μl PCR buffer, 0.5 mM/μl MgCl2, 1.6 mM/μl dNTPs, 0.2 units/μl of Taq polymerase, 0.2 μM/μl each of forward – reverse primers, and 50 ng of template DNA. Thermal Cycler – Applied Biosystems Veriti was used in this study. The program consisted of one cycle of initial denaturation 95°C for 5 min, 29 cycles of denaturation 95°C for 1 min, annealing 55°C for 1 min, and extension 72°C for 90 s and a final extension step of 72°C for 5 min. PCR products were run on 1.5% agarose gel, stained with ethidium bromide and documented with Bio-RAD Gel DOC EZ System [Figure 1]. All nonspecific amplifications were eliminated by separation and purification of 880 bp bands from agarose gel followed by DNA extraction by using QIA quick gel extraction kit.
Figure 1: Agarose gel picture showing amplified 16S rRNA gene of the obese and lean stool DNA. Lane L is 1kb DNA ladder, Lane + C is C. difficile ATCC 9689, Lane –C is Negative control, Lane B1 –B10 are amplified product of Obese and L1- L10 are amplified product of lean participants genomic DNA

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Polymerase chain reaction amplification for denaturing gradient gel electrophoresis

Nested PCR was performed with GC clamp targeting V3 region of 16S r RNA gene to amplify the 200 bp fragment for DGGE analysis. The primer sequences were HDA-1-GC (5-CGCCCGGGGCGCGCCCCGG GCGGGGCGGGGGCACGGGGGGACTCCTA CGGGAGGCAGCAGT-3) and HDA-2 (5-GTATTACCGCGGCTGCTGGCAC-3).[14] The PCR was performed in a 25 μl of reaction mixture containing 2.5mM/μl PCR buffer, 0.5 mM/μl MgCl2, 1.6 mM/μl dNTPs, 0.2 μM/μl each of forward and reverse primers, 0.2 units/μl of Taq polymerase, and 25 ng of 16S rDNA purified PCR product as template DNA. The program consisted of one cycle of initial denaturation 95°C for 4 min, 29 cycles of denaturation 95°C for 30 s, annealing 56°C for 30 s, and extension 72°C for 45s. This was followed by a final extension step of 72°C for 5 min. Agarose gel electrophoresis picture is mentioned in [Figure 2].
Figure 2: Agarose gel picture with GC Clamp amplified product of V3 region from the obese and lean 16S rRNA gene amplified product. Lane L is 100bp + DNA ladder, Lane B1 –B10 are GC Clamp amplified product of obese and L1-L10 GC Clamp amplified product of lean

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Denaturing gradient gel electrophoresis

The DGGE was carried out in 8% acrylamide: bisacrylamide (37.5:1) gel using 40% and 80% denaturing gradient in DGGE-20001-220 system (C.B.S. Scientific, USA). Forty percent denaturing solution contained 40% acrylamide/Bis (37.5:1) 15.0 ml, 2.0 ml of 50X TAE, 40% deionized formamide (Sigma Aldrich, USA) 16.0 ml, 7M urea 16.8 g, and double distilled water was used. Eighty percent denaturing solution contained 40% acrylamide/Bis (37.5:1) 15.0 ml, 2.0 ml of 50X TAE, 40% deionized formamide 32.0 ml, 7M urea 33.6 g and double-distilled water. The system was assembled according to the C.B.S. Scientific Manual. In each denaturation solution, 10% of APS (Ammonium persulfate) (200 μl) and 13 μl TEMED were added. The gel was casted using these solutions, and DGGE gradient was formed. The 500–600 ng of DNA samples was loaded, and the electrophoresis was run in 1X TAE buffer at the constant temperature of 60°C for 18 h at 40V. The gel was stained for 15 min with EtBr on rocker and then destained with 1X TAE. The gel was visualized under ultraviolet (UV) transilluminator, and image was recorded on gel documentation system (Bio-Rad, USA) [Figure 3]. The number of DGGE bands in each sample was defined as the number of operational taxonomic units (OTUs).
Figure 3: Denaturing gradient gel electrophoresis profiles of the V3 regions of the 16S rRNA gene. Gel picture (B1-B10) representing obese and (L1-L10) is representing lean participants. Band number (a-j) and (a1-e1) were excised and sequenced. Dendrogram constructed with Unweighted Pair Group Method with the Arithmetic average, viewing the similarity were performed using GelCompar II version 6.6 (Applied Maths, Belgium) among obese (B1-B10) and lean (L1-L10) participants

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Ten dominant bands were separated from each group with a sterile scalpel from the polyacrylamide gel under UV transilluminator, and each band was purified in mili Q water at 4°C overnight by diffusion. This purified gel DNA product was used as template for the re-amplification by using the same GC clamp primer and condition. These PCR products were sequenced.

Denaturing gradient gel electrophoresis gel images and sequence analysis

Comparative analysis of DGGE bands pattern was performed using Gel Compar II version 6.6 software (Applied Maths, Belgium). Group of bands was allocated automatically by the software. A Unweighted Pair Group Method with Arithmetic Mean (UPGMA) dendrogram of the fingerprints was prepared using Pearson correlations in Gelcompar II, and the data were exported and analyzed.[15] Cluster analysis was done on the DGGE bands to compare the differences between obese and lean gut microbial profiles. In DGGE profiles, it was assumed that each individual band refers to one microbial taxon, also referred to as a phylotype or OTU. NCBI Genbank data base, Blast search program (http://www.ncbi.nlm.nih.gov/blast/) was used to analyze the similarity between 16S rRNA gene sequences. Molecular evolutionary genetics analysis (MEGA4) software (Developers- Pennsylvania State University USA) was used to construct the Bootstrap consensus phylogenetic tree by using UPGMA as shown in [Figure 4].
Figure 4: Bootstrap consensus phylogenetic tree were constructed by using UPGMA method of MEGA4 software from 15 DGGE band nucleotide sequences

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Statistical analysis

Statistical analysis of the data was performed in Stata 11.1 software, (College Station, Texas: StataCorp LP) and independent test was performed to compare the difference in the number of DGGE bands between obese and lean group. P < 0.05 was considered statistically significant.

  Results Top

Denaturing gradient gel electrophoresis gel bands analysis

DGGE gives a semi-quantitative and rapid qualitative visual image of microbial DNA. DNA fragments having similar length can be separated using this technique. In this study, mean BMI of obese participants was 33.8 ± 3.6, and BMI of lean participants was 21.4 ± 1.5. BMI and weight of obese participants was significantly high as compared to lean subject (P < 0.05). However, there was no significant difference in the height of the participants of both groups. In this study, DGGE pattern of each sample revealed several bands with different intensities at different positions. DGGE analysis of the V3 region showed average no of bands 19.4 ± 2.0 in the obese group and 25.7 ± 2.6 in lean group, and DGGE bands were significantly more in lean compared to obese with (P < 0.05) [Table 1].
Table 1: Statistical analysis of body mass index, height, and number of denaturing gradient gel electrophoresis bands

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Same species of bacteria were represented by many bands in the DGGE gel. On the basis of number of bands, dominant human gut microbiota of the obese and lean groups was analyzed [Figure 3]. The results revealed that the diversity in the lean groups was significantly more as compared with obese groups (P < 0.05). Dendrogram was created based on the investigation of cluster from DGGE sketches. Differences and similarity in bacterial community configuration were analyzed with UPGMA average clustering algorithm and dice coefficient by using Gel Compar II version 6.6 (Applied Maths, Belgium). Each subject showed unique DGGE profile; however, bands similarity was also present between participants. DGGE fingerprint of V3 region and band sequencing results is shown in Figure 3 and [Table 2]. In this study, all ten DGGE band sequences of the obese group were passed in the quality check, and only five DGGE band sequences of the lean group were passed the quality check. Blast match analysis of DGGE band sequences with NCBI database showed minimum of 81% and maximum 100% identity. Two bands were identified as Collinsella aerofaciens and Paraclostridium bifermentans strain, and two bands were identified as uncultured bacterium, and other was identified as uncultured bacterium at Phylum family and species level.
Table 2: Denaturing gradient gel electrophoresis bands sequencing results of obese (a to j) and lean (a1to e1) fecal DNA (V3 region of 16SrRNA gene)

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Sequences with names and accession numbers were matched from GenBank. Clostridium was clustered in one group, Uncultured Victivallis sp, Collinsella aerofaciens, and Uncultured Prevotella were clustered in different group. Uncultured Ruminococcaceae bacterium, uncultured Eubacterium sp., Dialister sp, uncultured Mitsuokella sp., and two uncultured bacteroidetes were clustered separately.

  Discussion Top

In this study, DGGE was used to examine the human gut microbial diversity in obese and lean participants. Clustering analysis (power of statistical tools) was used for the detection of small variations. Each lane of DGGE gel was representing a bacterial fingerprint of fecal sample, and each band was corresponding to a single bacterial species. Dominant bands were excised from DGGE gel, purified and sequenced for the identification of bacterial species. One band was common in all the participants of both the groups and that was sequenced as uncultured Prevotella species. All the recruited participants were similar dietary habits ovo-lacto-vegetarian diet. Wu et al. in 2011[1] also reported that family Prevotella is more abundant in the gut of humans on vegan, ovo-lacto-vegetarian diets, and long-term fiber intake. Another study also showed that the Indian population has carbohydrate rich diet, so their gut bacterial profile found enriched with the members of family Prevotellaceae designated as carbohydrate-metabolizing bacteria.[16] Asian Microbiome project and another studies from Indonesia and Thailand also suggested the predominance of Prevotella.[17]

DNA-based DGGE fingerprints and phylogenetic tree analysis represent a remarkable separation of the bands. However, in phylogenetic analysis, the DNA fingerprints did not exactly cluster on the basis of BMI, although bands B1, B4, B5, B6, B8, and B9 clustered together in the obese group and band L3, L7, L9, L10 in the lean group. Band B2, B3 and L2, L4 was clustered separately from the above clusters. However, band B7, B10, L1, L5, L6, and L8 were clustered in between them. In this study, most of the identified microorganisms were indexed as uncultured bacteria. Microbiota plays a crucial role with host health and nutrition.[18],[19] Microbiota may help in food supplementation and digestion in the intestine.[20] Several studies reported that intestinal microbiota can be influenced by several factors such as diets, genetics, age, and existing environment.[21],[22] Complex carbohydrate such as dietary fiber is fermented by the gut microbiota in to (SCFAs: acetate, butyrate, and propionate), which contribute 10% of the total nutritional energy deliver in humans.[23] Lucas López et al. in 2017[24] reported that the function and composition of human gut microbiome are associated with obesity. Butyrate and other SCFAs are considered as direct anti-inflammatory effect in the gut.

Conventional molecular technique, i.e., PCR DGGE fingerprinting, can be used for gut microbiome study and predominant microbiota can be identified by DGGE.[25] In visual comparison technique, DGGE is one of the best techniques for the identification of microbial communities and that is effectively applied to observe the diversity of microbiota.[26] In this study, DGGE band sequence represents gut microbial diversity of Firmicutes, Bacteroidetes, Actinobacteria, and Lentisphaerae in fecal DNA sample. The sequences were confirmed at genus and species level with the accession number. Multiple sequence alignments of DGGE band sequence were prepared with Clustalw. Phylogenetic tree was constructed by UPGMA from nucleotide sequences using MEGA program version 4.[27] In this study, participants of the both obese and lean group showed difference in gut microbiota profile even all the participants were Indian vegetarian, residing in New Delhi, India, for last 5–6 years thus sharing similar environment and geographical condition. The results revealed that individuals with less BMI having more gut microbiota as compared to individuals with more BMI. Therefore, these results are indicating that BMI of the enrolled participants was a contributing factor for the difference in gut microbiota and that is supporting an associated of BMI with the composition of human gut microbiota.

  Conclusion Top

We investigate the gut bacterial profile of obese and lean individuals by DGGE. We observed a significant difference in the gut bacterial profile of obese and lean participants. The results of the study revealed that individuals with less BMI having more gut bacteria as compared to individuals with more BMI. These observations indicate that BMI is a contributing factor for the difference in the gut bacterial profile of obese and lean particiants, and that support the role of gut microbiota in obesity. However, large-scale next-generation sequencing-based studies are required to explore the full spectrum of gut microbiota in obese and lean participants in the Indian population.


The authors acknowledged financial support in the form of Senior Research Fellowship (SRF) to Dr. Tej Bahadur by the Indian Council of Medical Research (ICMR) New Delhi, Government of India (ICMR file number 80/686/2011-ECD-1) and Institute fellowship from All India Institute of Medical Sciences (AIIMS), New Delhi.

Financial support and sponsorship


Conflicts of interest

There are no conflicts of interest.

  References Top

Wu GD, Chen J, Hoffmann C, Bittinger K, Chen YY, Keilbaugh SA, et al. Linking long-term dietary patterns with gut microbial enterotypes. Science 2011;334:105-8.  Back to cited text no. 1
Turnbaugh PJ, Ley RE, Hamady M, Fraser-Liggett CM, Knight R, Gordon JI. The human microbiome projects. Nature 2007;449:804-10.  Back to cited text no. 2
Blaut M. Ecology and physiology of the intestinal tract. Curr Top Microbiol Immunol 2013;358:247-72. [doi: 10.1007/82_2011_192].  Back to cited text no. 3
Tremaroli V, Bäckhed F. Functional interactions between the gut microbiota and host metabolism. Nature 2012;489:242-9.  Back to cited text no. 4
Turnbaugh PJ, Ley RE, Mahowald MA, Magrini V, Mardis ER, Gordon JI, et al. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature 2006;444:1027-31.  Back to cited text no. 5
Bäckhed 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 USA 2004;101:15718-23.  Back to cited text no. 6
Bäckhed F, Ley RE, Sonnenburg JL, Peterson DA, Gordon JI. Host-bacterial mutualism in the human intestine. Science 2005;307:1915-20.  Back to cited text no. 7
Wu GD, Lewis JD. Analysis of the human gut microbiome and association with disease. Clin Gastroenterol Hepatol 2013;11:774-7.  Back to cited text no. 8
David LA, Maurice CF, Carmody RN, Gootenberg DB, Button JE, Wolfe BE, et al. Diet rapidly and reproducibly alters the human gut microbiome. Nature 2014;505:559-63.  Back to cited text no. 9
Bäckhed F, Manchester JK, Semenkovich CF, Gordon JI. Mechanisms underlying the resistance to diet-induced obesity in germ-free mice. Proc Natl Acad Sci U S A 2007;104:979-84.  Back to cited text no. 10
Francavilla R, Calasso M, Calace L, Siragusa S, Ndagijimana M, Vernocchi P, et al. Effect of lactose on gut microbiota and metabolome of infants with cow's milk allergy. Pediatr Allergy Immunol 2012;23:420-7.  Back to cited text no. 11
Yu Z, Morrison M. Comparisons of different hypervariable regions of rrs genes for use in fingerprinting of microbial communities by PCR-denaturing gradient gel electrophoresis. Appl Environ Microbiol 2004;70:4800-6.  Back to cited text no. 12
Lane D J. 16S/23S rRNA sequencing. In: Stackebrandt E, Goodfellow M, editors. Nucleic acid Techniques in Bacterial Systematics. Chichester, United Kingdom: John Wiley and Sons; 1991. p. 115-75.  Back to cited text no. 13
Walter J, Tannock GW, Tilsala-Timisjarvi A, Rodtong S, Loach DM, Munro K, et al. Detection and identification of gastrointestinal Lactobacillus species by using denaturing gradient gel electrophoresis and species specific PCR primers. Appl Environ Microbiol 2000;66:297-303.  Back to cited text no. 14
McCune B, Grace J, Urban D. Analysis of Ecological Communities. USA: MjM Software Design; 2002.  Back to cited text no. 15
Rho JH, Wright DP, Christie DL, Clinch K, Richard HF, Roberton AM. A novel mechanism for desulfation of mucin: Identification and cloning of a mucin-desulfating glycosidase (sulfoglycosidase) from Prevotella strain RS2. J Bacteriol 2005;187:1543-51.  Back to cited text no. 16
Nakayama J, Koichi W, Jiang J, Matsuda K, Chao SH, Haryono P, et al. Diversity in gut bacterial community of school-age children in Asia. Sci Rep 2015;5:8397.  Back to cited text no. 17
Burr G, Galtin D, Ricke S. Microbial ecology of the GI tract of fis hand the potential application of prebiotiocs and probiotics in finfish aquaculture. J World Aquacult Soc 2005;36:425-36.  Back to cited text no. 18
Verschuere L, Rombaut G, Sorgeloos P, Verstraete W. Probiotics bacteria as biological control agents in aquaculture. Microbiol Mol Biol Rev 2000;64:655-71.  Back to cited text no. 19
Sakata T. Microflora in the digestive tract of fish and shell fish. In: Lessel R, editor. Microbiology in Poikiloterms. Amsterdam: Elsevier; 1990. p. 171-6.  Back to cited text no. 20
Mueller S, Saunier K, Hanisch C, Norin E, Alm L, Midtvedt T, et al. Differences in fecal microbiota in different European study populations in relation to age, gender, and country: A cross-sectional study. Appl Environ Microbiol 2006;72:1027-33.  Back to cited text no. 21
Xu X, Xu P, Ma C, Tang J, Zhang X. Gut microbiota, host health, and polysaccharides. Biotechnol Adv 2013;31:318-37.  Back to cited text no. 22
Clarke SF, Murphy EF, Nilaweera K, Ross PR, Shanahan F, O'Toole PW, et al. The gut microbiota and its relationship to diet and obesity: New insights. Gut Microbes 2012;3:186-202.  Back to cited text no. 23
Lucas López R, Grande Burgos MJ, Gálvez A, Pérez Pulido R. The human gastrointestinal tract and oral microbiota in infammatory bowel disease: a state of the science review. APMIS 2017;125:3-10.  Back to cited text no. 24
Keijser BJ, Zaura E, Huse SM, van der Vossen JM, Schuren FH, Montijn RC, et al. Pyrosequencing analysis of the oral microflora of healthy adults. J Dent Res 2008;87:1016-20.  Back to cited text no. 25
Ledder RG, Gilbert P, Huws SA, Aarons L, Ashley MP, Hull PS, et al. Molecular analysis of the subgingival microbiota in health and disease. Appl Environ Microbiol 2007;73:516-23.  Back to cited text no. 26
Tamura K, Dudley J, Nei M, Kumar S. MEGA4: Molecular evolutionary genetics analysis (MEGA) software version 4.0. Mol Biol Evol 2007;24:1596-9.  Back to cited text no. 27


  [Figure 1], [Figure 2], [Figure 3], [Figure 4]

  [Table 1], [Table 2]


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