Introduction

The microbes in our bodies collectively make up to 100 trillion cells and they encode 100-fold more unique genes than our own genome1. The abundant and diverse microbiota in the gastrointestinal tract plays an important role in host physiology, including immunomodulation, maintenance of the gut barrier and involvement in nutrient metabolism2. The functions of microbiota in the gastrointestinal tract are largely determined by the composition.

Different geographic origins of humans may result in diverse compositions of gut microbiota, due to distinctive life environments, genetic background or dietary habits. De Filippo et al. (2010)3 found that children living in rural Burkina Faso had a higher bacterial diversity and a lower Firmicutes/Bacteroidetes (F/B) ratio than those of Italian children. In addition, genera Xylanibacter and Prevotella, which are related to cellulose and xylan hydrolysis, were exclusively present in gut microbiota of Burkina Faso children3. Similar research performed by Lin et al. (2013)4 revealed that children living in Bangladesh showed a higher bacterial diversity and different community composition as compared to children living in the United States of America. An across-country survey performed by Yatsunenko et al. (2012)5 also showed that there were significant differences in the phylogenetic composition of fecal microbiota among individuals living in three different countries (Venezuela, Malawi and United States of America) and the authors attributed the differences to different geographic origins and age. In contrast, Arumugam et al. (2011)6 found no significant structural changes of gut microbiota of subjects from 6 different countries and no effects of host properties such as body mass index, age, or gender on gut microbiota structure. The conflicting results from various studies highlight the necessity that a wider range of human living in different areas should be studied to characterize the global extent of human-associated microbial diversity4,7,8 and reflect the compounding effects of different life environment, genetic background and dietary habit. The dietary factor has been recognized to have a dominant role in different gut microbiota3. As for genetic background, Khachatryan et al. (2008)9 was the first to show that a gene (MEFV) mutation could affect gut microbiota in humans. Subsequently, Goodrich et al. (2014)10 compared microbiota in more than 1,000 fecal samples obtained from the TwinsUK population and identified many microbial taxa whose abundances were influenced by host genetics. In addition, environmental change also results in altered gut microbiota. For example, studies have shown that short exposure to high-altitude can alter the composition of gut microbiota11,12.

Tibet is a typical high altitude area in China and has special environment conditions, such as low barometric pressure, low temperature, low humidity and high radiation. The population in Tibet is mainly composed of Tibetans and Chinese Han. Indigenous Tibetans have been living at high altitude for many years. The hypoxia of altitude (hypobaric hypoxia) would thus have exerted substantial evolutionary selection pressure. Tibetans adapt well to the hypoxia environment because of their different genotypes formed by long time natural selection, called genetic adaptation13. Recently, genetic studies conducted on the hypoxia adaptation of Tibetans have found differences in several genes compared with lowland Han14,15,16. For example, hypoxia-related genes EPAS1 and EGLN1 were suggested to be responsible for genetic adaptation of high-altitude hypoxia in Tibetan14,15,16,17. However, whether similar genetic changes have occurred to affect gut microbiota is not clear. Tibetans also have different diet habits, culture and lifestyles from Chinese Han18,19. By using quantitative polymerase chain reaction (qPCR), Kowk et al. (2014)8 compared 10 major bacterial groups from the 4 dominant phyla of the common core gut microbiota between Chinese Han and Tibetans and found different fecal bacterial composition and a weak difference in structure. However, due to technical limitations, qPCR used by Kowk et al. (2014)8 is not suitable to study comprehensive changes of microbiota. Therefore, one objective of this study was to compare different ethnic origins (Han and Tibetan) on gut microbiota by 454 bar-code pyrosequencing.

Individuals from low-altitude populations (Han) suffer from a number of potential diseases specifically related to the low levels of oxygen, when they move to live at high altitude20. Unlike Tibetan, Chinese Han who recently migrated and are living in Tibet may adapt to the hypoxia environment in different ways. A series of physiological changes may take place such as ventilation, cardiac function, oxygen delivery, muscle structure, oxygen consumption and metabolism21,22. Short exposure to high-altitude could affect the composition of gut microbiota11,12. However, whether the physiological changes in the Chinese Han’s acclimatization to high altitude are accompanied by changes of gut microbiota is still unknown. So another objective of this study was to investigate the effect of altitude on gut microbiota by comparing Chinese Han living at low altitude to those living at high altitude.

Results

Characteristics of pyrosequencing results

In total, 442,737 raw reads were obtained from all 35 fecal samples. After filtering, 294,698 high-quality sequences were produced, with an average of 6923 ± 1191 reads per sample. The total number of OTUs at the 97% similarity level was 22,913 (Table 1 and Table S1). The Shannon-Wiener curve of all samples already reached a plateau at this sequencing depth (Fig. 1), suggesting that the sequencing was deep enough. Good’s coverage estimations revealed that 80.3% to 91.0% of the species were obtained in all of the samples. Estimators of the community richness (Chao) and the diversity (Shannon index) are summarized in Table 1. There were no statistically significant differences in the community richness estimator (Chao) and the diversity estimator (Shannon index) among three groups (Table 1).

Table 1 Summary of pyrosequencing data.
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Figure 1
figure 1

Rarefaction curves and Shannon-Wiener curves of each sample.

Shannon-Wiener curves (A) and rarefaction curves (B) were all calculated at the 97% similarity level with pyrosequencing data in microbiota from groups of LH, HH and T. LH = Chinese Han living in Shaanxi; HH = Chinese Han living in Tibet; T = Tibetans.

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Taxonomy-based comparisons of fecal microbiota at the phylum and genus levels among three groups

Overall microbiota compositions for each group at the phylum and class levels are shown in Fig. 2. There were 14 phyla and 21 classes in the fecal samples. The dominant phyla of all groups were Firmicutes, Bacteroidetes and Proteobacteria while the dominant classes of all groups were Clostridia, Bacteroidia and Erysipelotrichi. At the phylum level, the most predominant phylum was Firmicutes, contributing 75.31%, 90.45% and 92.77% of the fecal microbiota in LH, HH and T groups respectively, followed by Bacteroidetes, contributing 23.59%, 7.98% and 4.69% respectively. The microbial composition varied greatly among individuals. Relative abundances of the phyla were analyzed by using a metastats test which either incorporates a paired t-test or uses a paired Wilcoxon rank sum test23 and are shown in Table 2. Firmicutes was significantly more abundant in the fecal microbiota of HH and T groups than that in the LH group (p = 0.022 and 0.006, respectively), while Bacteroidetes was significantly less abundant in the fecal microbiota of HH and T groups than that in the LH group (p = 0.017 and 0.001, respectively). Actinobacteria differed between LH and HH groups (p = 0.044). Cyanobacteria and Fusobacteria exhibited significant differences between HH and T groups (p = 0.042 and 0.037, respectively), while Verrucomicrobia differed between LH and T groups (p = 0.027).

Table 2 Relative abundance at the phylum level and statistical significance among three groups.
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Figure 2
figure 2

Relative abundance of bacterial phyla (A) and classes (B) in fecal microbiota of three groups.

LH = Chinese Han living at the lowland; HH = Chinese Han living in Tibet; T = Tibetans.

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Relative abundances of major genera detected and statistical analyses among three groups are shown in Fig. 3. There were 120 genera in fecal samples. At the genus level, the relative abundances of 13 genera were significantly higher in the HH group than in the LH group (p < 0.05), including Acidaminococcus, Actinomyces, Blautia, Butyricimonas, Clostridium, Desulfovibrio, Helicobacter, Leuconostoc, Peptostreptococcaceae Incertae Sedis, Prevotellaceae uncultured, Prevotella, RC9 gut group and Rhodococcus. On the other hand, 3 genera were significantly lower in the HH group than in the LH group (p < 0.05), including Butyricimonas, Oscillospira and Sutterella. The relative abundances of 16 genera were significantly higher in HH group than in T group (p < 0.05), including Actinomyces, Alistipes, Bacteroides, Barnesiella, Blautia, Clostridium, Enterococcus, Faecalibacterium, Gordonibacter, Helicobacter, Holdemania, Mitsuokella, Peptostreptococcaceae Incertae Sedis, Rhodococcus, Ruminococcaceae Incertae Sedis and Staphylococcus. On the other hand, 5 genera were significantly lower in the HH group than in the T group (p < 0.05), including Allisonella, Christensenella, Oribacterium, Pseudobutyrivibrio and Solobacterium. The relative abundances of 6 genera were significantly higher in the LH group than in the T group (p < 0.05), including Alistipe, Bacteroides, Barnesiella, Prevotella, Staphylococcus and Sutterella. On the other hand, 8 genera were significantly lower in the LH group than in the T group (p < 0.05), including Butyrivibrio, Catenibacterium, Lactobacillus, Leuconostoc, Oscillibacter, Prevotellaceae uncultured, Slackia and Solobacterium.

Figure 3
figure 3

Heatmap of the distribution of the relative abundance of the genera.

(A) Taxonomic classification of the genera. (B) Genera detected and statistical analysis among three groups (Metastats for the sequence count data). LH = Chinese Han living in Shaanxi; HH = Chinese Han living in Tibet; T = Tibetans. Significance: NS > 0.05, *≤0.05; **≤0.01.

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Beta diversity of gut microbiota among three groups with multivariate statistics analysis

A weighted UniFrac principal coordinates analysis (PCoA) was performed to compare the overall structure of gut microbiota of all samples, based on the relative abundance of OTUs (at a 97% similarity level). There was an obvious separation of HH and T groups, while individuals were scattered for the LH group. Pco1, Pco2 and Pco3 accounted for 22%, 12% and 10% of the total variations, respectively (Fig. 4).

Figure 4
figure 4

Weighted UniFrac principal coordinates analysis (PCoA) plots based on weighted UniFrac metric.

LH = Chinese Han living in Shaanxi (green dots); HH = Chinese Han living in Tibet (red dots); T = Tibetans (blue dots).

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Other analysis methods (Jacaard, Bray-curtis and unweighted UniFrac) for the beta-diversity produced similar results (Figure S1).

Following the PCoA analysis, the statistical analyses showed that microbiota composition was significantly affected by ethnicity with various distance matrixes, but altitude significantly affected the microbiota composition only in weighted UniFrac, unweighted UniFrac and Abund_Jaccard analyses (Table S2). These results are in general agreement with those from the canonical analysis of principle coordinates (CAP) (Fig. 5). However, ethnicity and altitude only accounted for 8.5% of the total variations, indicating that many other factors have effects on the microbiota composition among three groups.

Figure 5
figure 5

Canonical analysis of principal coordinates (CAP) of human gut microbiota with different ethnicity and altitude.

CAP1 was the ethnic axis and CAP2 was the altitude axis. ANOVA test showed significant influence of the two constraints (p = 0.001). LH = Chinese Han living in Shaanxi; HH = Chinese Han living in Tibet; T = Tibetans.

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Real-time qPCR

Analysis by real-time qPCR showed total bacterial numbers per 10 ng DNA significantly differed between LH and HH groups (p = 0.023). However, total bacterial numbers per 10 ng DNA did not differ between LH and T groups or between HH and T groups (Fig. 6A). Next, the prevalence of the two most abundant phyla of commensal bacteria in the gut, Bacteroidetes and Firmicutes, were assessed among the three groups. Firmicutes dominance was less in the LH group than in the HH and T groups (p = 0.013) (Fig. 6B). In contrast to the Firmicutes dominance, the Bacteroidetes dominance was more in the LH group than in the HH and T groups (p = 0.021) (Fig. 6C). Furthermore, the proportional representation of two phyla was significantly shifted in the HH and T groups, in favor of Firmicutes (p = 0.017) (Fig. 6D).

Figure 6
figure 6

Relative abundance of total bacteria per 10 ng DNA (A), ratios of Firmicutes:total bacteria (B), ratios of Bacteroides:total bacteria (C) and ratios of Firmicutes:Bacteroides (D) in the fecal samples of three groups.a,b

Different lower-case letters indicate significant differences between groups. LH = Chinese Han living in Shaanxi; HH = Chinese Han living in Tibet; T = Tibetans.

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Discussion

Our results have provided additional evidence that the geographic origin contributes to shaping human gut microbiota, as previously reported3,4,5. Our study focused on the differences of gut microbiota in human living at high altitude (Tibet) and low altitude (Shaanxi) as well as two ethnic groups (Tibetan and Chinese Han). Study subjects were carefully selected, such that indigenous Tibetans had been living in Tibet for generations, Chinese Han living at high altitude had been living in Tibet more than 4 years and Chinese Han living at low altitude never went to Tibet. Meantime, our participants were within narrow ranges of age (between 18 to 25 years old) and body mass index (BMI) (between 17.8 to 25.1) and all participants were males. Overall, we found different fecal microbiota compositions among the three groups, but no significant differences in the diversity.

The gut microbiota of Tibetans was compositionally distinct from Chinese Han living at high or low altitude in this study. Firmicutes and Bacteroidetes are two prominent phyla in gut microbiota of humans and have received much attention. Our results showed that Tibetans had a high relative abundance of Firmicutes and a low relative abundance of Bacteroidetes compared with Chinese Han living at low altitude. However, there was no difference between Tibetans and Chinese Han living at the same high altitude area. These results from pyrosequencing were further verified by qPCR. Interestingly, these changes of the Firmicutes and Bacteroidetes at high altitude were similar to the shift observed in patients with obesity24. However, most of the study subjects in our study had normal BMI according to the standards25. In contrast, Kwok et al. (2014)8 found Chinese Han had a significantly higher F/B ratio than Tibetans. The high ratio of F/B was in response to a high-fat diet and did not affect energy harvesting26. A previous large-scale survey revealed the existence of obvious differences in dietary intake between Chinese Han and Tibetans18, which was also observed in our study (Table S3). From the questionnaire we found that the frequency for intake of high fat foods such as buttered tea, cheese and meat for Tibetans was higher than that of the Han people. This may explain a high ratio of F/B in Tibetans compared with Chinese Han living in Shaanxi. Nevertheless, the higher ratio of F/B in the HH group than in the LH group could be caused by unknown factors other than high-fat diets, since the two groups of people had similar dietary habits (Table S3).

Compared with the LH group, the changes of the Firmicutes and Bacteroidetes in the T group were mainly due to significant decreases in genera Bacteroides (4.2% vs 0.6%) and Prevotella (16.7% vs 3.8%) and a significant increase in genus Catenibacterium (0% vs 4.3%). Furet et al. (2010)27 found that Bacteroides and Prevotella were negatively correlated with energy intake. Tibetans have higher energy intakes than Chinese Han (13.7 MJ/d vs 11.1 MJ/d)18. This may explain low levels of Bacteroides and Prevotella observed in Tibetans. In addition, Tibetans had a high level of Pseudobutyrivibrio (26.8%). The metabolites of genus Pseudobutyrivibrio are mainly formate, butyrate and lactate28, but the physiological function of Pseudobutyrivibrio as a part of the human microbiome is not yet known. While both T and HH groups had a high ratio of F/B, the HH group was mainly due to significant increases in genera Faecalibacterium (21.3% vs 13.4%) and Bacteroides (6.1% vs 0.6%) and a significant decrease in genus Pseudobutyrivibrio (6.2% vs 26.8%) in comparison with the T group. Several genome-wide studies confirmed that several genes showed significantly different genotype frequencies between Tibetans and Chinese Han14,15,16. The gene differences can result in changing the composition of gut microbiota9,10. Therefore, genetic and dietary factors may explain the differences in the composition of gut microbiota between Tibetans and Chinese Han living in the same high altitude area.

In order to further investigate the environmental factors at high altitude on compositions of gut microbiota, we also compared the gut microbiota between Chinese Han living at low altitude or at high altitude. The total bacteria of Chinese Han in the HH group were significantly decreased in comparison with the LH group. The decreased microbial load could weaken the intestinal ecosystem stability29. This result suggests that the intestinal health of immigrants living in Tibet might be easily compromised. The HH group had a higher ratio of F/B than the LH group. These differences were mainly due to a significant decrease in Prevotella (2.2% vs 16.7%). Prevotella is associated with consumption of a diet rich in carbohydrates30. However, Chinese Han living at low or high altitude have similar diet habits and genetic background. Therefore, environmental factors at high altitude are thought to have a certain effect on gut microbiota.

In our study, we found significant differences in genera Clostridium, Desulfovibrio, Bacteroides, Lactobacillus and Prevotella among the three groups. These genera of microbiota are all associated with production of short-chain fatty acids (SCFAs)3,31. Humans living at high altitude have high energy demands32 and pulmonary hypertension33. Metabolites produced by microbiota play important roles in host health by being involved in host metabolism34. Microbiota can use non-digestible carbohydrates in the colon and produce SCFAs, mainly acetate, propionate and butyrate34. SCFAs produced by the gut microbiota not only provide energy, but also decrease blood pressure via olfactory receptor 78 and G protein couple receptor 4135. Different genera of microbiota can produce different metabolites. Although propionate exerts a hypotensive effect34, the effects of different SCFAs produced by microbiota on blood pressure are still unknown. Our results suggest that gut microbiota potentially influences healthy status by modulating energy harvest and blood pressure response to hypoxia environment at high altitude.

In summary, our study revealed that humans living at high altitude (Tibetans and Chinese Han) had a high abundance of Firmicutes and a low abundance of Bacteroidete. However, these differences were due to different genus compositions. Compared with the LH group, the changes of the Firmicutes and Bacteroidetes in the HH group were mainly due to a significant decrease in Prevotella. However, in the T group these changes were primarily attributable to significant decreases in Bacteroides and Prevotella and a significant increase in Catenibacterium. Whether these changes of genera could affect adaption to high altitude needs further large-scale studies, taking socioeconomic status, sanitation and exercise intensity into consideration.

Experimental procedures

Sampling

We enrolled 12 healthy Tibetan adults (T group) and 12 healthy Han adults (HH group) living in Tibet (Nyingchi, Tibet, with an average altitude of 3100 meters above sea level) and 11 healthy Han adults (LH group) living in Shaanxi (Yangling, Shaanxi, with an average altitude of 450 meters above sea level) for this study. The individuals were all males within a narrow range of age (18–25 years old). They did not have bowel diseases or metabolic diseases and had not taken any antibiotics or probiotics in the three months before the sampling dates. All Tibetan participants were living in Tibet for generations and never left Tibet. Chinese Han in the HH group had lived in Tibet for more than 4 years. Detailed information of each participant is shown in Table 3. The fecal sample was collected from each subject. Fecal samples were frozen immediately after sampling and stored at −80 °C. This study was approved by the Ethics Committee of Agricultural and Animal Husbandry College of Tibet University (Nyingchi, Tibet). Informed consent was provided by all individuals before participation. All experiments were performed in accordance with approved guidelines and regulations.

Table 3 Basic information of experimental subjects.
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DNA extraction

Genomic DNA was extracted from fecal samples by using the E.Z.N.A. stool DNA Kit (Omega, USA) according to the manufacturer’s instructions. The amount of DNA was determined by Nanodrop ND-2000 (Nanodrop, USA). Integrity and size of DNA were checked by 1% (w/v) agarose gel electrophoresis. All DNA samples were stored at −20 °C until further processing.

Pyrosequencing

The V1-V3 region of the 16S ribosomal RNA (rRNA) gene from each DNA sample was amplified using the bacterial universal forward primer 27F: 5′- AGAGTTTGATCCTGGCTCAG-3′ and the reverse primer 533R: 5′-TTACCGCGGCTGCTGGCAC-3′ containing the A and B sequencing adaptors. The cycling parameters were as follows: 2 min initial denaturation at 95 °C; 25 cycles of denaturation at 95 °C (30 s), annealing at 55 °C (30 s), elongation at 72 °C (30 s); and final extension at 72 °C for 5 min. Three separate PCR reactions of each sample were pooled for pyrosequencing. The PCR products were separated by 2% agarose gel electrophoresis and purified by using the AxyPrepDNA Gel extraction kit (Axygen, USA). Amplicon DNA concentrations were qualified by using a Quant-iTTM PicoGreen double-stranded DNA assay (Invitrogen, Germany). For sequencing, amplicons from each reaction were mixed in equal amounts based on concentrations and subjected to an emulsion PCR as recommended by 454 Life Sciences by using Roche GS FLX Titanium emPCR Kits (Roche, Switzerland). Using the Sequencing Method Manual XLR70 kit (Roche, Switzerland), sequencing was performed on the Roche Genome Sequencer GS-FLX Titanium platform by the Majorbio Bio-Pharm Technology (Shanghai, China).

Valid sequences were sorted into different samples according to barcodes. The programs Seqcln (http://sourceforge.net/projects/seqclean) and Mothur (http://www.mothur.org/wiki/Main_Page) were used for removing low quality sequences (i) that did not perfectly match the PCR primer at the beginning of a read (allowing for 2 errors), (ii) that did not match a barcode and PCR primer at the end of a read (pairwise identity less than 95%), (iii) that were shorter than 200 base pair in length, (iv) that contained ambiguous and homologous nucleotides, or (v) sequence quality score was less than 25 (using minimum average quality score of 25, maximum number of N’s = 0, minimum sequence length = 200).

The high-quality sequences were aligned in accordance with the SILVA alignment (http://www.arb-silva.de/) and similar sequences with a minimum pairwise identity of 97% were clustered into operational taxonomic units (OTUs) using the Mothur program (http://www.mothur.org/wiki/Cluster). OTUs that reached a 97% similarity level were used for diversity (Shannon), richness (Chao), Good’s coverage and rarefaction curve analyses using the Mothur program. Taxonomical assignments of OTUs exhibiting 97% similarity were performed using the Mothur (http://www.mothur.org/wiki/Classify.seqs) which uses a RDP Naive Bayesian classifier36 to calculate the probability of a sequence from a given taxonomy in accordance with SSU rRNA database of SILVA 111 at an 80% confidence level. For each sample, the abundance of bacterial phyla was expressed as the percentage of total sequences. The beta-diversity was performed with QIIME v1.80 by choosing various distance measures37. The Adonis test in the R package “vegan” was conducted to test the significance of constrained factors on microbial community38. The canonical analysis of principle coordinates (CAP)39 was performed by the R package “vegan”. All plots were constructed using the R software. Testing for significantly different phyla and genus among three groups were performed using the Metastats software (http://metastats.cbcb.umd.edu/). The number of permutations used to calculate the P value (significance threshold = 0.05, false discovery rate threshold = 0.2) was set to 1000.

Confirmation of pyrosequencing data by quantitative real-time PCR

In order to further confirm the pyrosequencing data, real-time qPCR reactions were performed as previously described40 in a volume of 25 μL containing 10 ng of fecal DNA, but the annealing temperature was changed to 60 °C. A plasmid containing a total bacteria, Firmicutes and Bacteroidetes 16S rRNA gene from a clone library was diluted from 1 × 103 to 1 × 109 (copies/ml) to construct standard curves for the detection of total bacteria, Firmicutes and Bacteroidetes. PCR reactions were performed with CFX96 system (Bio-rad, USA) using SYBR Premix Ex TaqII (Takara, Dalian, China). The data are represented as the mean ±SD of triplicate qPCR values.

Additional Information

Accession number: The sequence information in this paper has been deposited in the GenBank Sequence Read Archive with accession number SRP035344.

How to cite this article: Li, L. and Zhao, X. Comparative analyses of fecal microbiota in Tibetan and Chinese Han living at low or high altitude by barcoded 454 pyrosequencing. Sci. Rep. 5, 14682; doi: 10.1038/srep14682 (2015).