- Original article
- Open Access
Similarities and differences in gut microbiome composition correlate with dietary patterns of Indian and Chinese adults
AMB Express volume 8, Article number: 104 (2018)
The interaction between diet and gut microbiota, and ultimately their link to health, has turned into the concentration of huge research. However, this relationship still needs to be fully characterized, particularly in case of the Asian population. We compared the fecal bacterial diversity and composition of healthy Indian and Chinese adults, ages 22–35 years, using next-generation sequencing analysis on IlluminaHiSeq 2500 platform. Our analysis revealed unique community structure, dominant Firmicutes, Actinobacteria and underrepresented Bacteroides, of Indian and Chinese gut bacteria. This community structure closely matched with the gut bacterial composition of the Russian population. Therefore, we hypothesized that enrichment of these bacterial clades is supported by high consumption of starch-rich diet such as rice, potato, refined grains. The dominance of genus Bifidobacterium due to carbohydrate-rich diet is another notable feature of this study. Moreover, Indian gut bacteria are significantly represented by Bacteroidetes (p = 0.001) and Prevotella (p = 0.002) in contrast to Chinese, which could be associated with whole grains and plant-based vegetarian diet of Indians. The gut bacterial population of Indian adults were as diverse as Chinese adults (p > 0.1), but significant difference was noticed in gut bacterial composition and relative abundance between two populations (R = 0.625, p < 0.005). Partial least squares discriminant analysis and non-metric multidimensional scaling plots showed dietary habit wise clustering of subjects. Thus, the present work confirms an important role of diet in determining gut bacterial composition. LEfse analysis revealed genera Prevotella, Megasphaera, Catenibacterium, Lactobacillus, Ruminococcus and species Prevotella copri, Lactobacillus ruminis as the potential biomarkers of diet.
The human gut is a host of trillions of bacteria. The entire gut microbiota is estimated to contain 150-fold more genes than our host genome (Vacharaksa and Finlay 2010). Tremendous progress has been made in linking human gut microbiota with health and disease (Imhann et al. 2018; Qin et al. 2012; Zhao 2013). Imbalance in normal gut microbiota has been associated with inflammatory and metabolic disorders including inflammatory bowel disease (Frank et al. 2007), irritable bowel syndrome (Jeffery et al. 2012), and obesity (Claesson et al. 2012). Therefore, an understanding of what constitutes a health-promoting or disease-promoting microbial group has turned into the concentration of huge research. Gut microbiota composition varies among individuals within and between communities (Conlon and Bird 2015). Several factors such as diet, geography, host genetics and physiology, and drug usage, influence gut microbial composition (Lozupone et al. 2012; Simon and Gorbach 1984; Yatsunenko et al. 2012) but diet has been considered as the most prominent factor amongst all (Chakraborti 2015; Clarke et al. 2012; Harakeh et al. 2016; Lagier et al. 2012; Zhang et al. 2014). Moreover, diet is simplest to modulate and provides the easiest route for therapeutic intervention (Wu et al. 2011). Recent studies have linked diet and microbiome with health (Cardona et al. 2013; Donovan 2017; Singh et al. 2017). Changes in gut microbiota reported in experimental animals fed a high fat diet that induces obesity (Lecomte et al. 2015; Mullin 2010; Murphy et al. 2010). Moreover, controlled diets consist of non-digestible carbohydrates gave to overweight men actuate remarkable changes in certain dominant species, although the responses vary among subjects (Walker et al. 2011).
Over the last 2 decades, microbiome analysis of fecal samples using culture-independent methods, such as high-throughput DNA sequencing has emerged as a non-invasive tool to study nutrition and health (Khanna and Tosh 2014). The development of tools has enabled researchers to explore the interaction between diet and gut microbiota, but this relationship still needs to be fully characterized, especially in case of the Asian population. Research in this area is still at preliminary stages and most of the studies have been performed on European and American population. The diets of Asians vary markedly within the continent and differ substantially from those of other continents. In particular, two most populated countries India and China have unique diet profile. Moreover, India and China together accounted for 15% of world’s total obese population and placed immediately after the US with 13% obese population (Ng et al. 2014). Therefore, it becomes important to characterize the gut microbiota of people from these two countries to further comprehend the correlation between dietary components and the profile of gut bacteria and, eventually, their link to health and disease such as obesity.
In this study, we aimed to (1) characterize the prevalent bacterial taxa to define the community structure in gut samples from healthy Indian and Chinese adults, (2) compare the bacterial diversity distribution within and between the two groups studied, and (3) discover possible relationship of gut bacteria with diet and identify gut bacterial biomarkers of diet which distinguish Indian and Chinese adults. The gut bacterial profiling of 16 healthy Asian adults, including 11 Indians and 5 Chinese, obtained using next-generation sequencing. We further explored the influence of dietary habits on the composition of gut microbiota.
Materials and methods
Recruitment of volunteers
A total of 16 healthy adults, including 11 Indians and 5 Chinese, were recruited for the current study (Additional file 1: Table S1). All the volunteers were university students, ages 22–35, studying in Singapore for past 1–3 years. Healthy individuals without any gastrointestinal disorder and who did not use any antibiotics, laxatives or other drugs known to influence gastrointestinal function in the 3 months before the study, were selected. The written informed consent forms and standard questionnaire were taken from the volunteers. Food Frequency Questionnaire (FFQ) was used to recall food diary (Additional file 2). Ethical approval was granted by Nanyang Technological University—Institutional Review Board, Singapore.
All participants were asked to refrain from smoking, eating, drinking for at least 1–2 h prior to samples collection. Study participants were provided with two different containers: a sterile pot and a 50 mL sterile centrifuge tube. The volunteers were asked to transfer fresh feces from the sterile pot to the tube immediately after defecation. The samples were anonymized in the same order with the questionnaire (e.g. IN1, IN2…IN11 for Indians and CHI, CH2…CH5 for Chinese). Samples were homogenized, 10 g of feces were taken in 50 ml falcon tube and centrifuged (50,000×g at 10 °C for 2 h). The fecal water was removed and samples were stored at − 80 °C freezer prior to DNA extraction.
0.25 g of frozen fecal sample was used for total genome DNA extraction. DNA was extracted using Mo Bio Powersoil DNA isolation kit (Qiagen). The concentration of DNA sample was measured using NanoDrop and purity was monitored on 1% agarose gel. The concentration of DNA sample was diluted to 1 ng/µL in sterile water.
For each sample, 16S rRNA genes of V4 region were amplified. The primer set corresponding to primers 515F-806R with the barcode was used for amplification. All PCR reactions were performed with Phusion high fidelity master mix (New England Biolabs). The same volume of loading buffer containing SYBR green was mixed with PCR products. Gel electrophoresis with 2% agarose was performed for detection. Samples with the bright main strip between 400 and 450 bp were selected for facilitating experiments. PCR products were mixed in equidensity ratios and afterward purified with Qiagen gel extraction kit (Qiagen, Germany).
Library preparation and sequencing
Sequencing and analysis were performed by NovogeneAIT (Singapore). Sequencing libraries were generated using TrueSeq DNA PCR-free sample preparation kit (Illumina, USA) according to manufacturer’s recommendations and index codes were added. The library quality was assessed on the Qubit@ 2.0 Fluorometer (Thermo Scientific) and Agilent Bioanalyzer 2100 system. Then, sequencing was performed on an Illumina HiSeq 2500 platform and 250 bp paired-end reads were generated.
Samples were assigned paired-end reads and reads were merged using FLASH (Magoč and Salzberg 2011). Quality filtering of raw tags was performed according to QIIME quality-controlled process. UCHIME (Edgar et al. 2011) algorithm was used to detect and remove chimera sequences, finally, effective tags were obtained. The Uparse software was used to analyse sequences. Same OTUs were assigned to sequences with ≥ 97% similarity. The representative sequence for each OTU was screened for further annotation. For each representative sequence, the Greengenes database was used based on RDP classifier algorithm to annotate taxonomic information. PyNAST software (Version 1.2) (Caporaso et al. 2010) was used to execute multiple sequence alignment against the “Core Set” dataset in the Greengenes database. Thus, phylogenetic relationship of different OTUs and the distinction of the prevailing species in various samples (groups), were contemplated. Normalization of OTUs abundance information was conducted using a standard of sequence number corresponding to the sample with the least sequences.
Alpha diversity was analysed using, Observed-species, Chao1, ACE, Goods-coverage, Shannon, and Simpson diversity indices. Beta diversity analysis, based on both weighted and unweighted unifrac distances were performed to evaluate compositional heterogeneity among bacterial communities. Unweighted Pair-group Method with Arithmetic Means (UPGMA) hierarchical clustering was performed to interpret the distance matrix using average linkage. All these analyses were conducted by QIIME software (Version 1.7.0) and R software (Version 2.15.3). Non-metric multidimensional scaling (NMDS) based on Bray–Curtis dissimilarity was applied with Analysis of similarities (ANOSIM) and Multi-response permutation procedure (MRPP) to analyse the difference in microbial composition among subjects and test the significance of the difference (Ramette 2007).
Metastats analysis (Paulson et al. 2011), two tailed t test, Wilcoxon two-sample test and Pearson correlation (Xia et al. 2015) were performed. Metastats identifies the differentially abundant feature in metagenomics dataset via nonparametric t test, Fisher’s exact test and False discovery rate (FDR). LEfSe [linear discriminant analysis (LDA) effect size] (Segata et al. 2011) was applied to gut bacterial profiling data to identify bacterial biomarkers differentiating Indians and Chinese based on their dietary habits. The LDA > 4.0 was set as the threshold for selection of features. MetaboAnalyst 3.0 (Xia et al. 2015) was used to perform partial least square discriminant analysis (PLS-DA) to see the separation between gut bacterial profiles of subjects based on their dietary habits.
To compare the bacterial composition of GI tracts of healthy Asian adults, we collected fresh fecal samples from 16 adults aged 22–35 years old together with food frequency questionnaire. The diet of Indian cohorts mainly consists of whole wheat, rice, lentils, legumes, green vegetables, fruits and dairy products. Apart from this other whole grains, ghee, white flour, and fast food are also substantially included in the Indian diet. Their Chinese counterpart consumed a diet high in animal fat and protein in addition to carbohydrate and vegetables. Chinese food includes rice, noodles, beans, refined grains, white flour, peanut oil, sea food, fish, a lot of variety of meat and animal fat such as lard.
Taxonomic differences and similarities between gut microbiota of Indian and Chinese adults
Amplicon sequencing on IlluminaHiSeq 2500 platform covering the V4 region of bacterial 16S rRNA of 16 fecal DNA samples representing 11 Indian and 5 Chinese produced on an average of 85,752 high quality reads (average read length 413.12 nt) in each sample (Additional file 3: Table S2). These reads were clustered at 97% similarity threshold into 7223 unique OTUs (388–521 per subject). The classification tree of particularly concerned gut bacteria (top 10 genera for each sample) from both Indian and Chinese samples is shown in Fig. 1. The four major bacterial phyla detected were Firmicutes, Actinobacteria, Bacteroidetes and Proteobacteria, in agreement with previous studies reporting these phyla contributed to the majority of human gut bacteria (Bäckhed et al. 2005; Qin et al. 2010). The dominant taxa at all the taxonomic levels were found to be same in both the groups. The preponderant bacteria were belonging to Firmicutes (phylum), Clostridia (class), Clostridiales (order), Lachnospiraceae (family), Bifidobacterium (genus) at different taxonomic levels.
The 10 major bacterial clades from the gut bacterial profiles of both the groups at various taxon levels including phylum, class, order, family, genus, and species, are represented in Fig. 2. Metastats analysis and t test were performed to determine species with significant variation between groups (p < 0.05) (Additional file 4: Table S3 and Additional file 6: Figure S1). As shown in Additional file 6: Figure S1a, Bacteroidetes (p = 0.001) and Cyanobacteria (p = 0.003) were significantly higher in Indian group at the phylum level. Particularly, the abundance of Bacteroidetes in Indians (16.39%) was almost 4 times higher as compared to Bacteroidetes in Chinese (4.27%). The abundance of Firmicutes (68.08%), Actinobacteria (25.48%), in Chinese were not significantly different and slightly higher, as compared to Firmicutes (60.5%), Actinobacteria (20.57%) in Indians. It is noticeable that Firmicutes has outnumbered Bacteroidetes in both the populations (Fig. 2a).
The bacterial classes that showed a significant difference (p < 0.05) between two groups were Bacteroidia, Bacilli, Gammaproteobacteria, and Chloroplast. The bacterial orders (p < 0.05) accounting for significant variation between Indians and Chinese were found to be Bacteroidales, Lactobacillales, Bacillales, Streptophyta. The bacterial families (p < 0.05) Prevotellaceae, Lactobacillaceae, Leuconostocaceae, Carnobacteriaceae showed significantly higher abundance in Indians whereas Porphyromonadaceae was higher in Chinese.
The bacterial genera (p < 0.05) Prevotella, Megasphaera, Catenibacterium, Lactobacillus, Mitsuokella, Carnobacterium, Lachnospira were significantly higher in Indians as compared to Chinese. The genera, Bifidobacterium (15.2%) and Prevotella (13.07%) were almost equally dominant in Indian population (Fig. 2e). Prevotella was dominant in 5 Indian subjects whereas rest of the 6 subjects were dominated by Bifidobacterium. As shown in Fig. 2e, the two most abundant genera in Chinese were Bifidobacterium (20.90%) and Blautia (13.19%). Previous studies on different populations have reported that human gut microbiome could be classified based on one of the four distinct communities, namely Prevotella, Bacteroides, Clostridiales, and Bifidobacterium (Gorvitovskaia et al. 2016). In our study, both the populations were dominated by Bifidobacterium and Clostridiales but Prevotella in Indians (13.07%) is highly abundant as opposed to very low percentage of Prevotella in Chinese (0.58%) (Fig. 2e). Thus, we can conclude that Prevotella is a potential biomarker which distinguishes Indian cohorts from Chinese.
To better understand if Chinese and Indians can be separated based on their gut bacterial profiles, hierarchical cluster analysis and partial least squares discriminant analysis (PLS-DA) were performed at all taxa levels (Fig. 3a, b). PLS-DA plot showed clustering of individuals based on dietary habits. Leave-one-out-cross-validation of PLS-DA plot gave R2 = 96.56 and Q2 = 68.39 which represent variance and predictive capability respectively. We could see differences at all taxonomic levels but the gut bacterial composition at genus level showed the marked difference between two groups. The abundance distribution of dominant 35 genera among all samples was displayed in the heatmap (Fig. 3a). All the Indians clustered together and showed a clear separation from Chinese samples, thus dietary habits played an important role in clustering of the individuals based on the gut bacterial profiles.
Microbiota from Indian adults is as diverse as microbiota from Chinese adults
Alpha diversity is widely used for the analysis of microbial community diversity. It reflects the richness and diversity of microbial community by using a series of statistical indices, species accumulation curve, and species richness curve. As can be seen from Fig. 4, individual rarefaction and rank abundance curves revealed that the Indian adults exhibited similar diversity, including evenness, as the Chinese adults. Alpha diversity between two groups was further compared using observed species, goods coverage, Chao1, ACE, Shannon and Simpson diversity indices. The box plots and tabulated description of statistical indices of alpha diversity are shown in Additional file 5: Table S4 and Additional file 6: Figure S2. Overall, the gut bacterial profiles of Indians and Chinese had the similar value of these statistical indices, as calculated using two-tailed t test with unequal variance and Wilcoxon two-sample test, the p > 0.1 for all the indices.
Gut microbiota composition and relative abundance differ between healthy Indian and Chinese adults
The alpha diversity indices were found to be similar in both the groups, now to estimate the extent of variation between gut bacterial profiles of Indians and Chinese, we proceeded to analyse beta diversity using unweighted and weighted unifrac distances. The unweighted unifrac distance is calculated according to phylogenetic relationships of OTUs and weighted unifrac distance is based on the OTU’s abundance information. The beta diversity indices were significantly different (p < 0.02) between Indian and Chinese groups, as shown by two-tailed t test and Wilcoxon two-sample test. It reveals that two groups can be distinguished based on both community composition and relative abundance of gut bacteria. The beta diversity indices box plots which describe differences of species diversity between groups are shown in Fig. 5b. Box plots also directly reflect the mean value, the degree of dispersion, the maximum and minimum value which describes intragroup species diversity. Furthermore, the unweighted pair-group method with arithmetic means (UPGMA), using unweighted and weighted unifrac distances, was applied to study the similarities among the subjects. UPGMA hierarchical clustering produced distinct clusters which distinguished Indian adults and Chinese adults (Fig. 5c).
Non-metric multi-dimensional scaling (NMDS) plot of the gut bacterial profiling data indicates the tendency of dietary habit wise grouping of subjects (Fig. 5a). The stress factor value is < 0.2, which confirms the reliability of NMDS analysis results. As shown in Fig. 5a, most of the Indian samples were grouped closely and showed clear separation from Chinese samples, which were more dispersed. The significance of differences was confirmed by the test of analysis of similarity (ANOSIM) (R = 0.620.5, p = 0.001), and multi-response permutation procedures (MRPP) (p = 0.003). R > 0.5 implies that separation between groups is good and intergroup variation is significantly greater than intragroup variations.
LEfse analysis identified gut bacterial biomarkers of diet
Our analysis has revealed a noteworthy contrast in gut bacterial profiles of Indian and Chinese adults. It suggests that different dietary habits could be the major drivers towards distinction in gut microbiota. This led us to examine gut bacterial biomarkers of diet. LEfse [linear discriminant analysis (LDA) effect size] analysis, which couples statistical significance with biological consistency and effect size estimation, detected 15 bacterial clades (LDA score > 4) showing marked differences between Indian and Chinese adults (Fig. 6a). The histogram with LDA scores and cladogram are shown in Fig. 6a, b. In the histogram, green colour represents the taxa found to be more abundant in Indian adults and red colour represents the taxa more abundant in Chinese adults.
The discriminating features at higher taxonomic levels are unlikely to be an effective biomarker, as they incorporate a diversity of genera and species. In this regard, genera Prevotella, Megasphaera, Catenibacterium, Lactobacillus, Ruminococcus and species Prevotella copri, Lactobacillus ruminis can potentially serve as biomarkers of diet to distinguish Indian and Chinese adults.
The colours in the cladogram represent the branch of the phylogenetic tree more significantly represents a certain group. In this case, Indians are significantly represented by Bacteroidetes.
Long-term diet has been noted as one of the significant factors linked to gut bacterial composition in US and European subjects (De Filippo et al. 2010; Wu et al. 2011). In our study, the unique community structure, dominated by Firmicutes and Actinobacteria and underrepresented Bacteroides, of Indian and Chinese gut bacteria closely matches with the gut bacterial composition of eastern Russia and rural regions consuming starch-rich diet (Tyakht et al. 2013). It is known that a large portion of bacteria belongs to Firmicutes and Actinobacteria, are nutritionally specialized towards starch (Martens et al. 2011). Presumably, the unique bacterial community is due to high consumption of starch-rich food such as rice, oatmeal or other breakfast cereals, potatoes, refined grains, white flour, noodles, tortillas, beans, in Indian and Chinese adults.
The Firmicutes to Bacteroidetes (F/B) ratio was 14:1 in Chinese adults and the ratio was 3.7:1 in the Indian adults (Additional file 6: Figure S3). Previous studies have reported that high (F/B) ratio and higher abundance of Actinobacteria are associated with obesity (Clarke et al. 2012; Ley et al. 2006; Turnbaugh et al. 2009). The high abundance of Firmicutes and Actinobacteria (Additional file 6: Figure S4) in Indians (81.07%) and Chinese (93.56%) justifies the study carried out by Ng et al. (2014) in which China and India are placed at second and third position respectively after the US in the world’s most obese population list. Indian and Chinese together accounted for 15% of world’s obese people with 46 million and 30 million obese population respectively.
Another notable feature of our study was the dominance of genus Bifidobacterium in gut bacteria of both the communities. Bifidobacteria have been reported to confer protection against pathogens and maintain immune system and exertion of nutritional effects to the intestinal cells and the host (Turroni et al. 2014; Ventura et al. 2012). The high abundance of Bifidobacterium has previously been noted as a particular feature in Asian children, rural Indian tribes, and rural Russian population (Dehingia et al. 2015; Nakayama et al. 2015; Tyakht et al. 2013). It could be conceived that carbohydrate-based Asian diet drives the colonization of Bifidobacteria.
Dominance of Bacteroidetes, Prevotella, and Lactobacillus in gut bacteria of Indians is consistent with their dietary habits
We found that Bacteroidetes and Prevotella are significantly dominant in Indians as compared to that of Chinese. These bacterial clades have previously been reported to be more prevalent in the populations consuming vegan or vegetarian diet (De Filippis et al. 2016; De Filippo et al. 2010). Prevotella is also known to be associated with indigestible carbohydrate-rich diet, particularly grains such as whole wheat, barley (Kovatcheva-Datchary et al. 2015; Wu et al. 2011). US American and Europe population consuming high animal fat and protein diet had shown a significantly lower abundance of Bacteroidetes and Prevotella, as compared to Egyptian and rural African population which consumed the vegetarian diet rich in carbohydrate and dietary fibers (De Filippo et al. 2010; Shankar et al. 2017). Prevotella are known degraders of xylan and other fibrous polysaccharides (Dodd et al. 2011) which justifies their presence in Indian group where a large fraction of diet is comprised of vegetables and whole grains. It could be inferred that differential abundance of Bacteroidetes and Prevotella is consistent with the difference between Chinese and Indian diet. Prevotella is very well known as an important biomarker of diet (Gorvitovskaia et al. 2016).
In our study, Lactobacillus was identified as another important biomarker of diet. Past reports have suggested that dairy associated microbes such as Lactobacillus can survive the transit through the digestive system (David et al. 2014). It implies that the significantly higher abundance of Lactobacillus in Indians could be due to their high intake of milk products (including cheese or paneer, curd, buttermilk and other dairy products) and fermented products containing Lactobacillus.
Catenibacterium, Megasphaera, and Mitsuokella were also found to be enriched in Indian gut. Higher abundance of these three genera was noted in Egyptian children when compared to US children (Shankar et al. 2017). Similarly, Catenibacterium and Mitsuokella were found only in Bangladeshi children but were absent in the gut of US children (Lin et al. 2013) and they have been previously linked to the abundances of Prevotella (Noguera-Julian et al. 2016; Shankar et al. 2017) In present study also, Prevotella was found to be in positive correlation with Catenibacterium, Megasphaera, Mitsuokella (r = 0.562–0.90). In comparison to Indians, starch-degrading genera, namely, Ruminococcus, and Blautia, were more abundant in the stools of Chinese, feasibly due to the high preponderance of starch as a dietary polysaccharide in their diet. It concurs with De Filippis et al. (2016) who previously reported a lower abundance of Prevotella and higher abundance of Ruminococcus in Omnivores. In addition to this, mucin degrading genus Akkermansia which has previously been associated with non-vegetarian (Ruengsomwong et al. 2016) was detected in all Chinese samples whereas only 3 out of 11 Indian samples have shown its presence. Prevotella has shown a negative correlation with Ruminococcus, Blautia, Dorea, Megamonas Bacteroides (−(0.587–0.797)), while a positive correlation with Faecalibacterium Dialister, Lactobacillus (0.641–0.775). The correlation of Prevotella with these bacteria is in agreement with previously reported results (Dehingia et al. 2015; Nakayama et al. 2015).
Overall this study deepens our understanding of gut microbiota composition of healthy adults from two largest Asian countries, India and China. In spite of the fact that, the prevalent gut bacteria were similar to those found in other populaces, we discovered some exceptional community structure also. This information expands our knowledge of healthy human microbiome ecology and serves as a reference point for future epidemiological investigations and translational applications. Moreover, we examined the correlations among gut bacterial community. We further suggested the possible link between diet and gut bacterial composition. We identified bacterial biomarkers of diet, which are very well known for their interaction with diet, distinguishing Indian and Chinese adults. However other hidden factors such as host physiology and genetics may also interact with their microbiota. Thus, Knowledge from this study could aid future research aim to modulate human gut microbiota, with probiotics or synbiotic dietary supplementations, which may provide new approaches to control the diet-microbiota-human health interactions.
Bäckhed F, Ley RE, Sonnenburg JL, Peterson DA, Gordon JI (2005) Host-bacterial mutualism in the human intestine. Science 307(5717):1915–1920. https://doi.org/10.1126/science.1104816
Caporaso JG, Bittinger K, Bushman FD, DeSantis TZ, Andersen GL, Knight R (2010) PyNAST: a flexible tool for aligning sequences to a template alignment. Bioinformatics 26(2):266–267. https://doi.org/10.1093/bioinformatics/btp636
Cardona F, Andres-Lacueva C, Tulipani S, Tinahones FJ, Queipo-Ortuno MI (2013) Benefits of polyphenols on gut microbiota and implications in human health. J Nutr Biochem 24(8):1415–1422. https://doi.org/10.1016/j.jnutbio.2013.05.001
Chakraborti CK (2015) New-found link between microbiota and obesity. World J Gastrointest Pathophysiol 6(4):110–119. https://doi.org/10.4291/wjgp.v6.i4.110
Claesson MJ, Jeffery IB, Conde S, Power SE, O’Connor EM, Cusack S, Harris HMB, Coakley M, Lakshminarayanan B, O’Sullivan O, Fitzgerald GF, Deane J, O’Connor M, Harnedy N, O’Connor K, O’Mahony D, van Sinderen D, Wallace M, Brennan L, Stanton C, Marchesi JR, Fitzgerald AP, Shanahan F, Hill C, Ross RP, O’Toole PW (2012) Gut microbiota composition correlates with diet and health in the elderly. Nature 488(7410):178–184. https://doi.org/10.1038/nature11319
Clarke SF, Murphy EF, Nilaweera K, Ross PR, Shanahan F, O’Toole PW, Cotter PD (2012) The gut microbiota and its relationship to diet and obesity: new insights. Gut Microbes 3(3):186–202. https://doi.org/10.4161/gmic.20168
Conlon MA, Bird AR (2015) The impact of diet and lifestyle on gut microbiota and human health. Nutrients 7(1):17–44. https://doi.org/10.3390/nu7010017
David LA, Maurice CF, Carmody RN, Gootenberg DB, Button JE, Wolfe BE, Ling AV, Devlin AS, Varma Y, Fischbach MA, Biddinger SB, Dutton RJ, Turnbaugh PJ (2014) Diet rapidly and reproducibly alters the human gut microbiome. Nature 505(7484):559–563. https://doi.org/10.1038/nature12820
De Filippis F, Pellegrini N, Vannini L, Jeffery IB, La Storia A, Laghi L, Serrazanetti DI, Di Cagno R, Ferrocino I, Lazzi C, Turroni S, Cocolin L, Brigidi P, Neviani E, Gobbetti M, O’Toole PW, Ercolini D (2016) High-level adherence to a Mediterranean diet beneficially impacts the gut microbiota and associated metabolome. Gut 65(11):1812–1821. https://doi.org/10.1136/gutjnl-2015-309957
De Filippo C, Cavalieri D, Di Paola M, Ramazzotti M, Poullet JB, Massart S, Collini S, Pieraccini G, Lionetti P (2010) Impact of diet in shaping gut microbiota revealed by a comparative study in children from Europe and rural Africa. Proc Natl Acad Sci USA 107(33):14691–14696. https://doi.org/10.1073/pnas.1005963107
Dehingia M, Devi KT, Talukdar NC, Talukdar R, Reddy N, Mande SS, Deka M, Khan MR (2015) Gut bacterial diversity of the tribes of India and comparison with the worldwide data. Sci Rep. https://doi.org/10.1038/srep18563
Dodd D, Mackie RI, Cann IKO (2011) Xylan degradation, a metabolic property shared by rumen and human colonic Bacteroidetes. Mol Microbiol 79(2):292–304. https://doi.org/10.1111/j.1365-2958.2010.07473.x
Donovan SM (2017) Introduction to the special focus issue on the impact of diet on gut microbiota composition and function and future opportunities for nutritional modulation of the gut microbiome to improve human health. Gut Microbes 8(2):75–81. https://doi.org/10.1080/19490976.2017.1299309
Edgar RC, Haas BJ, Clemente JC, Quince C, Knight R (2011) UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27(16):2194–2200. https://doi.org/10.1093/bioinformatics/btr381
Frank DN, Amand ALS, Feldman RA, Boedeker EC, Harpaz N, Pace NR (2007) Molecular-phylogenetic characterization of microbial community imbalances in human inflammatory bowel diseases. Proc Natl Acad Sci USA 104(34):13780–13785. https://doi.org/10.1073/pnas.0706625104
Gorvitovskaia A, Holmes SP, Huse SM (2016) Interpreting Prevotella and Bacteroides as biomarkers of diet and lifestyle. Microbiome. https://doi.org/10.1186/s40168-016-0160-7
Harakeh SM, Khan I, Kumosani T, Barbour E, Almasaudi SB, Bahijri SM, Alfedul SM, Ajabnoor GMA, Azhar EI (2016) Gut microbiota: a contributing factor to obesity. Front Cell Infect Microbiol. https://doi.org/10.3389/fcimb.2016.00095
Imhann F, Vila AV, Bonder MJ, Fu JY, Gevers D, Visschedijk MC, Spekhorst LM, Alberts R, Franke L, van Dullemen HM, Ter Steege RWF, Huttenhower C, Dijkstra G, Xavier RJ, Festen EAM, Wijmenga C, Zhernakova A, Weersma RK (2018) Interplay of host genetics and gut microbiota underlying the onset and clinical presentation of inflammatory bowel disease. Gut 67(1):108–119. https://doi.org/10.1136/gutjnl-2016-312135
Jeffery IB, O’Toole PW, Ohman L, Claesson MJ, Deane J, Quigley EMM, Simren M (2012) An irritable bowel syndrome subtype defined by species-specific alterations in faecal microbiota. Gut 61(7):997–1006. https://doi.org/10.1136/gutjnl-2011-301501
Khanna S, Tosh PK (2014) A clinician’s primer on the role of the microbiome in human health and disease. Mayo Clin Proc 89(1):107–114. https://doi.org/10.1016/j.mayocp.2013.10.011
Kovatcheva-Datchary P, Nilsson A, Akrami R, Lee YS, De Vadder F, Arora T, Hallen A, Martens E, Bjorck I, Backhed F (2015) Dietary fiber-induced improvement in glucose metabolism is associated with increased abundance of Prevotella. Cell Metab 22(6):971–982. https://doi.org/10.1016/j.cmet.2015.10.001
Lagier JC, Million M, Hugon P, Armougom F, Raoult D (2012) Human gut microbiota: repertoire and variations. Front Cell Infect Microbiol. https://doi.org/10.3389/fcimb.2012.00136
Lecomte V, Kaakoush NO, Maloney CA, Raipuria M, Huinao KD, Mitchell HM, Morris MJ (2015) Changes in gut microbiota in rats fed a high fat diet correlate with obesity-associated metabolic parameters. PLoS ONE 10(5):e0126931. https://doi.org/10.1371/journal.pone.0126931
Ley R, Turnbaugh P, Klein SI, Gordon J (2006) Microbial ecology: human gut microbes associated with obesity. Nature 444(7122):1022–1023. https://doi.org/10.1038/4441022a
Lin A, Bik EM, Costello EK, Dethlefsen L, Haque R, Relman DA, Singh U (2013) Distinct distal gut microbiome diversity and composition in healthy children from Bangladesh and the United States. PLoS ONE 8(1):e53838. https://doi.org/10.1371/journal.pone.0053838
Lozupone CA, Stombaugh JI, Gordon JI, Jansson JK, Knight R (2012) Diversity, stability and resilience of the human gut microbiota. Nature 489(7415):220–230. https://doi.org/10.1038/nature11550
Magoč T, Salzberg SL (2011) FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27(21):2957–2963. https://doi.org/10.1093/bioinformatics/btr507
Martens EC, Lowe EC, Chiang H, Pudlo NA, Wu M, McNulty NP, Abbott DW, Henrissat B, Gilbert HJ, Bolam DN, Gordon JI (2011) Recognition and degradation of plant cell wall polysaccharides by two human gut symbionts. PLoS Biol 9(12):e1001221. https://doi.org/10.1371/journal.pbio.1001221
Mullin GE (2010) High-fat diet determines the composition of the murine gut microbiome independently of obesity. Nutr Clin Pract 25(3):310–311. https://doi.org/10.1177/0884533610368714
Murphy EF, Cotter PD, Healy S, Marques TM, O’Sullivan O, Fouhy F, Clarke SF, O’Toole PW, Quigley EM, Stanton C, Ross PR, O’Doherty RM, Shanahan F (2010) Composition and energy harvesting capacity of the gut microbiota: relationship to diet, obesity and time in mouse models. Gut 59(12):1635–1642. https://doi.org/10.1136/gut.2010.215665
Nakayama J, Watanabe K, Jiang JH, Matsuda K, Chao SH, Haryono P, La-ongkham O, Sarwoko MA, Sujaya IN, Zhao L, Chen KT, Chen YP, Chiu HH, Hidaka T, Huang NX, Kiyohara C, Kurakawa T, Sakamoto N, Sonomoto K, Tashiro K, Tsuji H, Chen MJ, Leelavatcharamas V, Liao CC, Nitisinprasert S, Rahayu ES, Ren FZ, Tsai YC, Lee YK (2015) Diversity in gut bacterial community of school-age children in Asia. Sci Rep. https://doi.org/10.1038/srep08397
Ng M, Fleming T, Robinson M, Thomson B, Graetz N, Margono C, Mullany EC, Biryukov S, Abbafati C, Abera SF, Abraham JP, Abu-Rmeileh NME, Achoki T, AlBuhairan FS, Alemu ZA, Alfonso R, Ali MK, Ali R, Guzman NA, Ammar W, Anwari P, Banerjee A, Barquera S, Basu S, Bennett DA, Bhutta Z, Blore J, Cabral N, Nonato IC, Chang JC, Chowdhury R, Courville KJ, Criqui MH, Cundiff DK, Dabhadkar KC, Dandona L, Davis A, Dayama A, Dharmaratne SD, Ding EL, Durrani AM, Esteghamati A, Farzadfar F, Fay DFJ, Feigin VL, Flaxman A, Forouzanfar MH, Goto A, Green MA, Gupta R, Hafezi-Nejad N, Hankey GJ, Harewood HC, Havmoeller R, Hay S, Hernandez L, Husseini A, Idrisov BT, Ikeda N, Islami F, Jahangir E, Jassal SK, Jee SH, Jeffreys M, Jonas JB, Kabagambe EK, Khalifa SEAH, Kengne AP, Khader YS, Khang YH, Kim D, Kimokoti RW, Kinge JM, Kokubo Y, Kosen S, Kwan G, Lai T, Leinsalu M, Li Y, Liang X, Liu S, Logroscino G, Lotufo PA, Lu Y, Ma J, Mainoo NK, Mensah GA, Merriman TR, Mokdad AH, Moschandreas J, Naghavi M, Naheed A, Nand D, Narayan KMV, Nelson EL, Neuhouser ML, Nisar MI, Ohkubo T, Oti SO, Pedroza A, Prabhakaran D, Roy N, Sampson U, Seo H, Sepanlou SG, Shibuya K, Shiri R, Shiue I, Singh GM, Singh JA, Skirbekk V, Stapelberg NJC, Sturua L, Sykes BL, Tobias M, Tran BX, Trasande L, Toyoshima H, van de Vijver S, Vasankari TJ, Veerman JL, Velasquez-Melendez G, Vlassov VV, Vollset SE, Vos T, Wang C, Wang X, Weiderpass E, Werdecker A, Wright JL, Yang YC, Yatsuya H, Yoon J, Yoon SJ, Zhao Y, Zhou M, Zhu S, Lopez AD, Murray CJL, Gakidou E (2014) Global, regional, and national prevalence of overweight and obesity in children and adults during 1980–2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet 384:766–781. https://doi.org/10.1016/S0140-6736(14)60460-8
Noguera-Julian M, Rocafort M, Guillen Y, Rivera J, Casadella M, Nowak P, Hildebrand F, Zeller G, Parera M, Bellido R, Rodriguez C, Carrillo J, Mothe B, Coll J, Bravo I, Estany C, Herrero C, Saz J, Sirera G, Torrela A, Navarro J, Crespo M, Brander C, Negredo E, Blanco J, Guarner F, Calle ML, Bork P, Sonnerborg A, Clotet B, Paredes R (2016) Gut microbiota linked to sexual preference and HIV infection. Ebiomedicine 5:135–146. https://doi.org/10.1016/j.ebiom.2016.01.032
Paulson JN, Pop M, Bravo HC (2011) Metastats: an improved statistical method for analysis of metagenomic data. Genome Biol 12(Suppl 1):P17. https://doi.org/10.1186/gb-2011-12-s1-p17
Qin J, Li R, Raes J, Arumugam M, Burgdorf KS, Manichanh C, Nielsen T, Pons N, Levenez F, Yamada T, Mende DR, Li J, Xu J, Li S, Li D, Cao J, Wang B, Liang H, Zheng H, Xie Y, Tap J, Lepage P, Bertalan M, Batto JM, Hansen T, Le Paslier D, Linneberg A, Nielsen HB, Pelletier E, Renault P, Sicheritz-Ponten T, Turner K, Zhu H, Yu C, Li S, Jian M, Zhou Y, Li Y, Zhang X, Li S, Qin N, Yang H, Wang J, Brunak S, Doré J, Guarner F, Kristiansen K, Pedersen O, Parkhill J, Weissenbach J, Meta HITC, Bork P, Ehrlich SD, Wang J (2010) A human gut microbial gene catalogue established by metagenomic sequencing. Nature 464(7285):59–65. https://doi.org/10.1038/nature08821
Qin JJ, Li YR, Cai ZM, Li SH, Zhu JF, Zhang F, Liang SS, Zhang WW, Guan YL, Shen DQ, Peng YQ, Zhang DY, Jie ZY, Wu WX, Qin YW, Xue WB, Li JH, Han LC, Lu DH, Wu PX, Dai YL, Sun XJ, Li ZS, Tang AF, Zhong SL, Li XP, Chen WN, Xu R, Wang MB, Feng Q, Gong MH, Yu J, Zhang YY, Zhang M, Hansen T, Sanchez G, Raes J, Falony G, Okuda S, Almeida M, LeChatelier E, Renault P, Pons N, Batto JM, Zhang ZX, Chen H, Yang RF, Zheng WM, Li SG, Yang HM, Wang J, Ehrlich SD, Nielsen R, Pedersen O, Kristiansen K (2012) A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature 490(7418):55–60. https://doi.org/10.1038/nature11450
Ramette A (2007) Multivariate analyses in microbial ecology. FEMS Microbiol Ecol 62(2):142–160. https://doi.org/10.1111/j.1574-6941.2007.00375.x
Ruengsomwong S, La-Ongkham O, Jiang JH, Wannissorn B, Nakayama J, Nitisinprasert S (2016) Microbial community of healthy thai vegetarians and non-vegetarians, their core gut microbiota, and pathogen risk. J Microbiol Biotechnol 26(10):1723–1735. https://doi.org/10.4014/jmb.1603.03057
Segata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, Huttenhower C (2011) Metagenomic biomarker discovery and explanation. Genome Biol 12(6):R60. https://doi.org/10.1186/gb-2011-12-6-r60
Shankar V, Gouda M, Moncivaiz J, Gordon A, Reo NV, Hussein L, Paliy O (2017) Differences in gut metabolites and microbial composition and functions between Egyptian and US children are consistent with their diets. mSystems 2(1):e00169-16. https://doi.org/10.1128/mSystems.00169-16
Simon GL, Gorbach SL (1984) Intestinal flora in health and disease. Gastroenterology 86(1):174–193
Singh RK, Chang HW, Yan D, Lee KM, Ucmak D, Wong K, Abrouk M, Farahnik B, Nakamura M, Zhu TH, Bhutani T, Liao W (2017) Influence of diet on the gut microbiome and implications for human health. J Transl Med 15(1):73. https://doi.org/10.1186/s12967-017-1175-y
Turnbaugh PJ, Hamady M, Yatsunenko T, Cantarel BL, Duncan A, Ley RE, Sogin ML, Jones WJ, Roe BA, Affourtit JP, Egholm M, Henrissat B, Heath AC, Knight R, Gordon JI (2009) A core gut microbiome in obese and lean twins. Nature 457(7228):480–484. https://doi.org/10.1038/nature07540
Turroni F, Ventura M, Butto LF, Duranti S, O’Toole PW, Motherway MO, van Sinderen D (2014) Molecular dialogue between the human gut microbiota and the host: a Lactobacillus and Bifidobacterium perspective. Cell Mol Life Sci 71(2):183–203. https://doi.org/10.1007/s00018-013-1318-0
Tyakht AV, Kostryukova ES, Popenko AS, Belenikin MS, Pavlenko AV, Larin AK, Karpova IY, Selezneva OV, Semashko TA, Ospanova EA, Babenko VV, Maev IV, Cheremushkin SV, Kucheryavyy YA, Shcherbakov PL, Grinevich VB, Efimov OI, Sas EI, Abdulkhakov RA, Abdulkhakov SR, Lyalyukova EA, Livzan MA, Vlassov VV, Sagdeev RZ, Tsukanov VV, Osipenko MF, Kozlova IV, Tkachev AV, Sergienko VI, Alexeev DG, Govorun VM (2013) Human gut microbiota community structures in urban and rural populations in Russia. Nat Commun. https://doi.org/10.1038/ncomms3469
Vacharaksa A, Finlay BB (2010) Gut microbiota: metagenomics to study complex ecology. Curr Biol 20(13):R569–R571. https://doi.org/10.1016/j.cub.2010.05.020
Ventura M, Turroni F, Motherway MO, MacSharry J, van Sinderen D (2012) Host-microbe interactions that facilitate gut colonization by commensal bifidobacteria. Trends Microbiol 20(10):467–476. https://doi.org/10.1016/j.tim.2012.07.002
Walker AW, Ince J, Duncan SH, Webster LM, Holtrop G, Ze XL, Brown D, Stares MD, Scott P, Bergerat A, Louis P, McIntosh F, Johnstone AM, Lobley GE, Parkhill J, Flint HJ (2011) Dominant and diet-responsive groups of bacteria within the human colonic microbiota. ISME J 5(2):220–230. https://doi.org/10.1038/ismej.2010.118
Wu GD, Chen J, Hoffmann C, Bittinger K, Chen YY, Keilbaugh SA, Bewtra M, Knights D, Walters WA, Knight R, Sinha R, Gilroy E, Gupta K, Baldassano R, Nessel L, Li HZ, Bushman FD, Lewis JD (2011) Linking long-term dietary patterns with gut microbial enterotypes. Science 334(6052):105–108. https://doi.org/10.1126/science.1208344
Xia J, Sinelnikov IV, Han B, Wishart DS (2015) MetaboAnalyst 3.0—making metabolomics more meaningful. Nucleic Acids Res 43(W1):W251–W257. https://doi.org/10.1093/nar/gkv380
Yatsunenko T, Rey FE, Manary MJ, Trehan I, Dominguez-Bello MG, Contreras M, Magris M, Hidalgo G, Baldassano RN, Anokhin AP, Heath AC, Warner B, Reeder J, Kuczynski J, Caporaso JG, Lozupone CA, Lauber C, Clemente JC, Knights D, Knight R, Gordon JI (2012) Human gut microbiome viewed across age and geography. Nature 486(7402):222–227. https://doi.org/10.1038/nature11053
Zhang JC, Guo Z, Lim AAQ, Zheng Y, Koh EY, Ho DL, Qiao JM, Huo DX, Hou QC, Huang WQ, Wang LF, Javzandulam C, Narangerel C, Jirimutu Menghebilige, Lee YK, Zhang HP (2014) Mongolians core gut microbiota and its correlation with seasonal dietary changes. Sci Rep. https://doi.org/10.1038/srep05001
Zhao LP (2013) The gut microbiota and obesity: from correlation to causality. Nat Rev Microbiol 11(9):639–647. https://doi.org/10.1038/nrmicro3089
Experiment planning and design: AJ, XHL and WNC; analysis: AJ; recruitment of subjects and questionnaire: AJ; fecal sample collection and DNA extraction: AJ; data analysis: AJ and WNC; paper construction: AJ, XHL and WNC. All authors read and approved the final manuscript.
The authors would like to thank the Nanyang Environment and Water Research Institute (NEWRI), Singapore and the Interdisciplinary Graduate School (IGS), Nanyang Technological University, Singapore for the award of research scholarship to Abhishek Jain and the support for this research.
The authors declare that they have no competing interests.
Availability of data and materials
The 16S rRNA gene sequencing data in the paper have been deposited in the National Omics Data Encyclopaedia (http://www.biosino.org/node/index) under accession number NODEP00371763.
Consent for publication
The written informed consent forms were taken from the volunteers.
Ethics approval and consent to participate
Ethical approval was granted by Nanyang Technological University—Institutional Review Board, Singapore. The written informed consent forms were taken from the volunteers.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Sample information on age, gender and ethnicity.
Food Frequency Questionnaire (FFQ).
The summarized information of amplicon sequencing of 16 fecal DNA samples.
Metastats analysis showed gut bacterial clades with significant variation between Indian and Chinese adults.
Alpha diversity indices.
Between groups T test analysis. Figure S2. Between group variation of alpha diversity indices. Figure S3. Firmicutes to Bacteroidetes (F/B) ratio in Indian and Chinese adults. Figure S4. Relative abundance of Firmicutes and Actinobacteria.
About this article
Cite this article
Jain, A., Li, X.H. & Chen, W.N. Similarities and differences in gut microbiome composition correlate with dietary patterns of Indian and Chinese adults. AMB Expr 8, 104 (2018). https://doi.org/10.1186/s13568-018-0632-1
- Next-generation sequencing
- Gut microbiota