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, 16 (11), e2005396
eCollection

Gut Microbiome Transition Across a Lifestyle Gradient in Himalaya

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Gut Microbiome Transition Across a Lifestyle Gradient in Himalaya

Aashish R Jha et al. PLoS Biol.

Abstract

The composition of the gut microbiome in industrialized populations differs from those living traditional lifestyles. However, it has been difficult to separate the contributions of human genetic and geographic factors from lifestyle. Whether shifts away from the foraging lifestyle that characterize much of humanity's past influence the gut microbiome, and to what degree, remains unclear. Here, we characterize the stool bacterial composition of four Himalayan populations to investigate how the gut community changes in response to shifts in traditional human lifestyles. These groups led seminomadic hunting-gathering lifestyles until transitioning to varying levels of agricultural dependence upon farming. The Tharu began farming 250-300 years ago, the Raute and Raji transitioned 30-40 years ago, and the Chepang retain many aspects of a foraging lifestyle. We assess the contributions of dietary and environmental factors on their gut-associated microbes and find that differences in the lifestyles of Himalayan foragers and farmers are strongly correlated with microbial community variation. Furthermore, the gut microbiomes of all four traditional Himalayan populations are distinct from that of the Americans, indicating that industrialization may further exacerbate differences in the gut community. The Chepang foragers harbor an elevated abundance of taxa associated with foragers around the world. Conversely, the gut microbiomes of the populations that have transitioned to farming are more similar to those of Americans, with agricultural dependence and several associated lifestyle and environmental factors correlating with the extent of microbiome divergence from the foraging population. The gut microbiomes of Raute and Raji reveal an intermediate state between the Chepang and Tharu, indicating that divergence from a stereotypical foraging microbiome can occur within a single generation. Our results also show that environmental factors such as drinking water source and solid cooking fuel are significantly associated with the gut microbiome. Despite the pronounced differences in gut bacterial composition across populations, we found little differences in alpha diversity across lifestyles. These findings in genetically similar populations living in the same geographical region establish the key role of lifestyle in determining human gut microbiome composition and point to the next challenging steps of determining how large-scale gut microbiome reconfiguration impacts human biology.

Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Sampling locations and habitats of the Himalayan populations in Nepal.
(A) Map displaying the geographical locations of sampled villages in southern Nepal (altitudes <1,000 m above sea level, latitude 26.97–29.15 °N). The Tharu are geographically most distant from the Raute and Raji and reside closer to the Chepang. (B) Habitats of each population. From top left in a clockwise direction, the remote Chepang village, the Raute village, the Tharu harvesting rice, and the Raji village. The census population sizes of the Raute, Raj, Chepang, and Tharu are 650, 3,758, 48,476, and 1.5 million, respectively.
Fig 2
Fig 2. CA based on survey questionnaires and parasite assessment in the Himalayan populations.
(A) First two dimensions of the CA and the amount of variation explained are shown. Each circle represents an individual, and colors represent the populations. (B) Distribution of populations along the primary CA1 axis shows patterns of separation by lifestyles. Chepang foragers (red) and Tharu farmers (blue) are on two extreme ends of CA1. In between the two are the Raute (yellow) and Raji (cyan), the two communities that are transitioning from foraging to farming. (C) Factors in gold are those that have more than expected eigenvalues and thus contribute most to the top two dimensions in the CA. The data underlying this figure can be found in S4 Table. CA, correspondence analysis.
Fig 3
Fig 3. Gut microbiome compositions show gradients corresponding to lifestyles.
(A) PCoA of the unweighted UniFrac distances of the 16S rRNA data colored by populations. Each dot represents an individual, and colors indicate the populations. Chepang foragers (red), Raute (yellow), and Raji (cyan) communities that are transitioning from foraging to farming; Tharu farmers (blue); and Americans (orange). (B) Distributions of populations along the PCoA1 axis show patterns of separation by lifestyles. (C) Gut microbial composition of the Himalayan populations represented by the primary dimension of the unweighted UniFrac distance (PCoA1) strongly correlates with lifestyle differences represented by the top dimension of the corresponding analysis performed on the survey data (CA1, Spearman’s Rho = 0.44 and P value = 0.001). Correlation between CA2 and PCoA1 was not statistically significant. The data underlying this figure can be found in S1 Data. CA, correspondence analysis; PCoA, Principal Coordinates Analysis; rRNA, ribosomal RNA.
Fig 4
Fig 4. Alpha diversity across lifestyles.
Rarefaction curves showing two commonly used measures of alpha diversity—species richness (top) and Shannon’s H (bottom) calculated by subsampling 10–6,500 reads per sample. No significant differences in species richness was detected between the five study populations at a lower depth of 3,000 reads per sample, which included all 64 samples, or at 6,500 reads per sample, which included 61 samples. Shannon’s H was significantly lower in the Tharu relative to the Americans at both rarefaction depths. No differences in any of these two alpha diversity metrics were observed between the Chepang, Raji, Raute, and the Americans. Population labels are colored to indicate the range of different lifestyles (red, foragers; yellow and cyan, former foragers; blue, farmers; orange, industrialists). The data underlying this figure can be found in S1 Data.
Fig 5
Fig 5. Distinctions in the gut microbiome across lifestyles.
(A) Phyla with most significant differences in abundances between the five populations. Abundances of Firmicutes, Verrucomicrobia, and Actinobacteria reflect gradients of traditional industrialized lifestyles. Proteobacteria distinguishes rural Himalayan populations from the Americans. (B) Heatmap displaying 52 genera with significantly different abundance across the five populations. Bars on the top represent the grouping of individuals in the heatmap columns by their populations or lifestyles. Genera labels in rows are colored by their phylum. Purple, Actinobacteria; dark blue, Bacteroidetes; light red, Elusimicrobia; orange, Firmicutes; light blue, Proteobacteria; magenta, Spirochaetes; light pink, Tenericutes; brown, Verrucomicrobia. Heatmap colors reflect relative abundances of each genus. Among the Himalayan populations, Ruminobacter, Campylobacter, unknown Veillonellaceae genus, Bulleidia, Weissella, Treponema, Barnesiella, Odoribacter, Alistipes, and Bifidobacterium differed significantly. The data underlying this figure can be found in S6 and S7 Tables.
Fig 6
Fig 6. Microbial coabundance networks across lifestyles.
(A) Visualization of the occurrence patterns of bacterial genera using the Fruchterman–Reingold force–directed layout algorithm. Nodes (circles) represent bacterial genera, node colors represent the seven CAGs, and node sizes represent genus abundance. Only the most dominant genera in each CAG are labeled. Edges represent the significant and positive correlations between genera. Members of the red, blue, and yellow CAGs are tightly correlated to one another and mostly negatively correlated with the members of cyan, magenta, gold, and green CAGs. Labels with “x__unk” indicate taxa with unknown classification level. (B) The relative proportions of these CAGs vary across the lifestyle gradient. Chepang foragers show elevated proportion of the magenta CAG, which is dominated by Prevotella, Succinivibrio, Ruminobacter, and Treponema. This CAG decreases in the Raute, Raji, and Tharu farmers with concurrent increase in the blue CAG, which is dominated by Bacteroides, Faecalibacterium, and Bifidobacterium. The American gut is dominated by the blue CAG and highly depleted of the magenta CAG. The data underlying this figure can be found in S1 Data. c, class; CAG, coabundance group; f, family; g, genus; o, order.
Fig 7
Fig 7. Gut microbiome composition is associated with environmental factors in Himalaya.
(A) The two primary CCA axes and the proportion of constrained variance they explain are shown. Triangles represent individuals and circles represent genera. Individuals and genera are color coded by their respective drinking water sources and phyla. Drinking water and cooking fuel contributed most to CCA1, and sisnu (nettles) contributed most to CCA2. Genera labeled in grey contribute to the top two CCA axes. Among these, Fusobacterium and Treponema were significantly associated with drinking water. (B) PCoA of the unweighted UniFrac distances. Each dot represents an individual, colors indicate the two drinking water sources, and shapes represent different populations. Gut microbiomes of the Chepang (circles) and Raute (diamonds) who drink water from rivers and streams vary significantly from those of the Raji (squares) and Tharu (triangles). Statistical significance was assessed using PERMANOVA using the 10 variables that differentiate their lifestyles (P value = 0.0001). The data underlying this figure can be found in S1 Data and S4 Table. CCA, canonical correspondence analysis; PCoA, Principal Coordinates Analysis; PERMANOVA, permutational multivariate analysis of variance.

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References

    1. Nicholson JK, Holmes E, Kinross J, Burcelin R, Gibson G, Jia W, et al. Host-gut microbiota metabolic interactions. Science (80-). 2012; 10.1126/science.1223813 - DOI - PubMed
    1. Belkaid Y, Hand TW. Role of the microbiota in immunity and inflammation. Cell. 2014. 10.1016/j.cell.2014.03.011 - DOI - PMC - PubMed
    1. van Nood E, Vrieze A, Nieuwdorp M, Fuentes S, Zoetendal EG, de Vos WM, et al. Duodenal Infusion of Donor Feces for Recurrent Clostridium difficile. N Engl J Med. 2013;368: 407–415. 10.1056/NEJMoa1205037 - DOI - PubMed
    1. Wu GD, Chen J, Hoffmann C, Bittinger K, Chen Y, Keilbaugh SA, et al. Linking Long-Tem Dietary Patterns with Gut Microbial Enterotypes. Science (80-). 2011;334: 105–109. 10.1126/science.1208344 - DOI - PMC - PubMed
    1. David LA, Maurice CF, Carmody RN, Gootenberg DB, Button JE, Wolfe BE, et al. Diet rapidly and reproducibly alters the human gut microbiome. Nature. 2013; 10.1038/nature12820 - DOI - PMC - PubMed

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