The digestive system is home to a complex community of microbes—known as the gut microbiome—that contributes to our health and wellbeing by digesting food, producing essential vitamins, and preventing the growth of harmful bacteria. The recent development of rapid genome sequencing techniques has made it much easier to identify the species of microbes found in the gut microbiome, and how this microbiome's composition varies between individuals.

Studies in humans and other primates suggest that direct contact during social interactions may alter the composition of the gut microbiome in an individual. This could explain why there is a strong association between social interactions and health in humans and other social animals. However, similarities in the gut microbiomes of individuals within a social group could also be due to a shared diet or a common environment. The information collected during long-term studies of wild primates offers an opportunity to analyze and assess the influence of diet, environment and social interaction on the gut microbiome.

Here, Tung et al. studied the gut microbiomes of 48 wild baboons belonging to two different social groups in Amboseli, Kenya. Using a technique called shotgun metagenomic sequencing, they sequenced DNA extracted from samples of feces collected from individual baboons. The sequence data revealed that an individual's social group and social network can predict the species found in its gut microbiome. This remained the case even when other factors—such as diet, kinship, and shared environments—were taken into account.

Tung et al.'s findings suggest that direct physical contact during social interactions may be important in transmitting gut microbiomes between members of the same social group. However, scientists still don't know whether this exchange is good or bad for the health of the baboons. Future work will try to understand whether baboons benefit from acquiring gut microbes from their group members, and if the gut microbes of some social groups are better than others.

Vertebrate intestines are home to thousands of bacterial species that exert profound effects on their hosts: they train the immune system, produce vitamins, help resist pathogens, and contribute substantially to daily energy acquisition (Bergman, 1990; Turnbaugh et al., 2006; Hooper et al., 2012; Bengmark, 2013; Morgan et al., 2013). In humans, inter-individual variation in gut microbiome composition has repeatedly been linked to major health concerns, including obesity, diabetes, cancer, heart disease, and autoimmune disorders (e.g., Turnbaugh et al., 2009; Hooper et al., 2012; Bengmark, 2013; Iida et al., 2013; Koeth et al., 2013; Viaud et al., 2013).

However, despite its importance, large gaps remain in our understanding of the forces that shape gut microbiome composition. Among the least understood but potentially most significant such forces are the effects of host social interactions. From an evolutionary perspective, social effects on the gut microbiome may be an underappreciated consequence of group living, associated with both fitness costs and benefits (Lombardo, 2008; Archie and Theis, 2011; Ezenwa et al., 2012; Montiel-Castro et al., 2013). For example, co-housing in lab mice promotes the transmission of bacterial communities that contribute to inflammatory bowel disease, implicating social relationships in microbiome-associated disease risk (Garrett et al., 2010). In bumblebees, socially transmitted gut bacteria protect against a widespread and virulent gut parasite, suggesting that socially mediated microbial transmission can also confer powerful benefits (Koch and Schmid-Hempel, 2011). If social interactions predict gut microbiome composition in free-living vertebrates as well, this link could help explain the strong association between social interactions and health in highly social species (e.g., Berkman and Syme, 1979; House et al., 1988; Sapolsky, 2004; Holt-Lunstad et al., 2010).

A handful of recent studies in humans and other primates provide circumstantial evidence for social effects on the gut microbiome (Degnan et al., 2012; Kinross and Nicholson, 2012; Yatsunenko et al., 2012; Song et al., 2013). For instance, in wild chimpanzees, social group membership predicts the identity and abundance of gut microbes, while kinship, age, and sex do not (Degnan et al., 2012). In humans, shared residence predicts gut microbiome similarity (Kinross and Nicholson, 2012; Yatsunenko et al., 2012; Song et al., 2013). To date, these effects have largely been attributed to shared diets, as members of the same household or social group tend to consume similar foods in similar proportions (Claesson et al., 2011; Kinross and Nicholson, 2012; Yatsunenko et al., 2012). However, social relationships could also shape gut microbiomes more directly, via transmission from shared environments (Lax et al., 2014) or during physical contact.

Differentiating between these mechanisms requires fine-grained data on social interactions, shared environments, and diet. Such complementary data sets are rare, but are frequently collected in long-term primate field studies. Here, we leveraged one such study, on the intensively studied Amboseli baboons of Kenya (Alberts and Altmann, 2012), to test whether social group structure and social interactions within groups predict either the taxonomic or the functional composition of the gut microbiome. Like humans, baboons are highly social, group-living primates. Members of the same social group travel together, consume similar foods, and drink from the same water sources. Within social groups, individuals selectively engage in frequent affiliative grooming interactions, which solidify social bonds and have the potential to mediate bacterial transmission. Within this context, we addressed three central questions. We first asked (i) does social group membership predict gut microbiome composition, as shown for humans and chimpanzees (Degnan et al., 2012; Kinross and Nicholson, 2012; Yatsunenko et al., 2012; Song et al., 2013)? We then asked two novel questions that have not been addressed in prior studies: (ii) within social groups, do rates of social interactions (captured here by grooming-based social networks) predict gut microbiome similarity after accounting for dietary patterns, shared environments, and kinship? And (iii) which bacterial species, with what lifestyle traits, are most likely to be socially transmitted, both between and within social groups?

We generated shotgun metagenomic data for the distal gut using fecal samples from 48 members of two baboon social groups (‘Mica's group’ or ‘Viola's group’; one sample per individual; Supplementary file 1). Together, these individuals represented almost complete sampling (92%) of the adult members of both groups. Fecal samples were collected during a single 1-month timespan to minimize developmental, temporal, and seasonal heterogeneity. During this time, Mica's group and Viola's group exploited adjacent home ranges, with the center of each range separated by just a few kilometers (Figure 1A).

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Using the program MetaPhlAn 2.0 (Segata et al., 2012), we identified 925 bacterial and archaeal taxa to the species level and quantified their relative abundance (364 ± 150 s.d. species per sample; Figure 1—figure supplement 1; Supplementary files 2, 3; see Supplementary files 3, 4 for parallel results using a de novo assembly approach). We also identified and quantified the relative abundance of 9013 microbial-encoded enzyme orthologs using the HUMAnN pipeline (mean ± SD = 2746 ± 560 KEGG orthologs per sample; Abubucker et al., 2012; Figure 1—figure supplement 3; Supplementary file 5). The taxa we found comprised a typical primate gut microbiota, dominated by the phyla Firmicutes (mean ± SD = 42.2% ± 8.4%), Proteobacteria (13.0% ± 2.8%), Actinobacteria (9.4% ± 4.6%), and Bacteroidetes (7.3% ± 2.4%) (Figure 1—figure supplement 1, 5; Supplementary file 6). In some samples, especially those in Viola's group, we also detected a large contribution from the phylum Spirochaetes, consistent with findings from other primates, ancient humans, and modern-day human hunter-gatherers (De Filippo et al., 2010; Tito et al., 2012; Schnorr et al., 2014).

Grooming networks predicted gut microbiome composition within groups

Despite few detectable differences in diet, unidentified environmental differences between Mica's group and Viola's group could explain the differences in gut microbiome composition we observed. To test whether social contacts per se predicted gut microbiome composition, we turned to fine-grained data on within-group grooming interactions. Grooming is by far the most common form of physical contact in baboons. Importantly, the strength of grooming relationships between pairs of individuals in the same social group varies considerably, despite the fact that all members of a social group travel together and use the same resource base.

To test whether physical contact predicted gut microbiome composition, we constructed grooming networks for each social group, using all grooming interactions observed in the year prior to and during microbiome sampling (Figure 2A,B). We found that, in both groups, closer grooming partners harbored more similar communities of gut bacteria (Mantel test between Bray–Curtis microbiome dissimilarity matrices and social network matrices: Mica's group r = −0.257, p = 3.0 × 10⁻⁴; Viola's group r = −0.173, p = 8.0 × 10⁻⁴; Figure 2C,D). This pattern was not driven by host genetic effects: although female relatives have stronger grooming bonds, controlling for pairwise relatedness still produced strong support for a relationship between grooming and taxonomic composition for Viola's group (partial Mantel test controlling for kinship: r = −0.148, p = 2.0 × 10⁻³), and a consistent trend in Mica's group (partial Mantel test controlling for kinship: r = −0.163, p = 0.060). Interestingly, extending this analysis to the level of enzyme gene orthologs suggested that close grooming partners also have functionally more similar gut microbiomes. Grooming networks predicted variation in within-group enzyme gene ortholog abundance for Mica's group (partial Mantel test controlling for kinship: r = −0.22, p = 0.014), but not Viola's group (partial Mantel test controlling for kinship: r = −0.051, p = 0.166).

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Despite the relative homogeneity of diet within social groups, our results could still be explained by a diet-related mechanism if close grooming partners consumed more similar diets. Alternatively, close social partners might experience similar environmental exposures if they used more similar microenvironments in the group's home range. We tested these possibilities directly, focusing on adult females for whom diet composition and spatial proximity data were routinely collected (N = 11 females in Mica's group and N = 20 females in Viola's group). Grooming network proximity also predicted microbiota composition in this restricted data set (Mantel tests: Mica's group: r = −0.328, p = 9.0 × 10⁻³; Viola's group: r = −0.228, p = 2.6 × 10⁻³), and remained a significant predictor of microbiota composition after accounting for dietary similarity (partial Mantel test controlling for dietary similarity: Mica's group p = 0.020; Viola's group: p = 0.005) and spatial proximity (partial Mantel test controlling for spatial proximity for Mica's group p = 0.039; Viola's group: p = 0.005). Additionally, we found no evidence that close social partners consumed more similar diets (Mantel tests: Mica's group: Mantel r = −0.200, p = 0.080; Viola's group: Mantel r = 0.0942, p = 0.876).

Socially structured bacteria tended to be anaerobic and non-spore-forming

We next investigated which bacterial species were associated with the strong signature of social structure in our data set. To identify these ‘socially structured’ species, we focused on the 327 most prevalent species in our data set (i.e., those found in ≥50% of samples). Using a mixed effects model controlling for age, sex, read depth, and host genetic relatedness, we identified 64 species (19.6%, using a 10% false discovery rate) that were significantly differentially abundant in the two social groups. We performed a complementary analysis, using a test of spatial autocorrelation, to investigate whether close grooming partners exhibited similar bacterial abundances within social groups as well (due to the larger sample size, we performed these tests in Viola's group; see ‘Materials and methods’). Among the same set of 327 prevalent species, we found 51 species (15.6%, 10% false discovery rate) for which proximity within the group's grooming network significantly predicted abundance (Supplementary file 8). Interestingly, 15 species were significantly socially structured both between groups and within social networks—more species than expected by chance (hypergeometric test, p = 0.020).

We next conducted an enrichment analysis to test whether the set of significantly socially structured species contained some taxonomic groups more often than by chance. We found that socially structured species were phylogenetically non-random at both between-group and within social network levels of analysis (Figure 3A,B). Moreover, at both levels of analysis, similar taxonomic groups were significantly enriched for socially structured species (red asterisks on Figure 3), including the phylum Actinobacteria; the families Bifidobacteriaceae, Coriobacteriaceae, and Veillonellaceae; and the genus Bifidobacterium, a group of Gram-negative bacteria that has been linked to beneficial health effects in humans (Servin, 2004; Gronlund et al., 2007; Turroni et al., 2008). The striking similarities between the two levels of analysis suggest that common underlying mechanisms—mediated by direct social contact rather than diet or general physical proximity—account for both between-group differences and grooming network effects within groups.

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Finally, we extended our enrichment analysis to test whether the set of socially structured species was enriched for particular bacterial lifestyles. We reasoned that, if socially structured species depend on direct transmission between baboons, as our data suggest, they should be less likely than other species to persist outside of a host. Thus, we predicted that socially structured species would tend to be anaerobic and unable to produce spores. To test these predictions, we turned to information about bacterial lifestyles available in the Genomes OnLine Database (Pagani et al., 2012), using both species-level (n = 138) and genus-level (n = 299) traits (see ‘Materials and methods’ for trait assignment criteria). We found that socially structured species were consistently enriched (relative to all species or genera tested) for an anaerobic, non-spore forming lifestyle (Figure 3C; hypergeometric tests for socially structured species between groups, species level traits: p = 0.017; socially structured species within group, species level traits: p = 0.067; socially structured species between groups, genus level traits: p = 0.036; socially structured species within group, genus level traits: p = 0.040). For instance, 17% of the species in this analysis differed significantly in abundance between social groups; however 32% of anaerobic and non-spore forming species were significantly socially structured. Notably, no species that were both aerobic and spore-forming were socially structured at the level of social groups or social networks, except for one case in the genus-level analysis.

Taken together, our results provide strong evidence that social interactions directly affect the composition of the gut microbiome in wild baboons. To our knowledge, this study is the first to test whether rates of interaction within cohabiting groups, as opposed to between groups or households, explain variation in the gut microbiome. Specifically, we found that an individual's contacts in a grooming-based social network, as well as its membership in a given social group, were highly predictive of its gut microbiome composition at both the species and genic levels. Unlike prior studies, we were able to exclude kinship, shared diet, and shared environment as the basis for our observations. Our results are thus unique among studies to date in the degree to which they implicate direct, affiliative physical contact as a determinant of gut microbiome composition in natural populations. Our data also provide the first evidence in vertebrates that social effects on the microbiome extend to its functional composition. These findings lend important support to the hypothesis that social interactions play a role in the health-related consequences of variation in gut microbiome composition (e.g., Turnbaugh et al., 2009; Hooper et al., 2012; Bengmark, 2013; Iida et al., 2013; Koeth et al., 2013; Viaud et al., 2013), with potentially important consequences for the evolution of sociality (Lombardo, 2008; Archie and Theis, 2011; Ezenwa et al., 2012; Montiel-Castro et al., 2013).

Thus, our results highlight the importance of socially mediated transmission in shaping gut microbiomes. However, unlike some prior studies in mice and bumblebees (Garrett et al., 2010; Koch and Schmid-Hempel, 2011), baboons are not coprophagic, raising a question about the mechanisms that facilitate gut microbial transfer between social partners. One possibility is that the duration and intimacy of grooming bouts, which include frequent hand-to-mouth contact, may be important in exposing baboons to the gut bacteria of their grooming partners. Furthermore, some grooming bouts, especially those directed from adult males to estrous females, concentrate heavily on the ano-genital region, increasing the probability of fecal-oral transfer. Such close contact may be especially important in the transmission of anaerobic, non-spore-forming species, as these bacteria are not thought to persist for long periods of time outside of a host (Wilson, 2008). However, some relatively hardy bacterial species may also be transmitted via social contact (VanderWaal et al., 2013), and recent modeling efforts suggest that fecal-oral transmission can be highly efficient in socially structured host populations, even when transmission is indirectly mediated through the soil (Nunn et al., 2011).

Interestingly, our observations suggest that social partners not only share more similar sets of gut microbes, but also similar abundances of individual microbial species. One explanation for this pattern is that when bacteria from a host colonize a social partner, they arrive pre-adapted to occupy the available gut microbial niches in their new host (Walter and Ley, 2011). Specifically, because members of a single bacterial species can have markedly different gene contents, a given member of a gut microbial species may perform different functions in different hosts (Walter and Ley, 2011; Costello et al., 2012). However, if social partners transmit bacteria with similar capabilities to each other, these bacteria may serve similar functions in both hosts and thus be found in similar abundances. This hypothesis could be further tested by assessing if bacterial species isolated from social partners tend to represent shared strains that perform similar biological functions.

In humans, affiliative physical contact (e.g., hugging, kissing, holding hands) is common and may provide a similar route through which close social partners transmit gut bacteria. In addition, surfaces in human homes may act as reservoirs for household-specific bacterial communities (Lax et al., 2014), possibly facilitating social transmission through intermediate surfaces. Future work, in both humans and animals, will be important to establishing the relative importance and generality of socially mediated transmission. In particular, population genetic studies have the potential to directly map the genetic structure of microbiome-associated species onto the social structure of host populations to test whether close social partners tend to share genetically more similar bacterial populations than non-partners (e.g., VanderWaal et al., 2013). Fine-grained studies of how gut microbial communities change in social species, before and after perturbations to their social networks, will also be important for understanding the time scales on which social transmission of microbes act. Such efforts would also contribute an important longitudinal perspective. Our power to identify associations between social relationships and microbiome composition in this study was probably facilitated by our sampling scheme, which eliminated the contribution of temporal or seasonal effects. More comprehensive long-term studies will be valuable for placing these effects in context, alongside concomitant changes in season, diet, and resource use.

In humans, variation in the taxonomic and genic composition of the microbiome is increasingly linked to health issues, such as obesity and autoimmune disorders (e.g., Turnbaugh et al., 2009; Hooper et al., 2012; Bengmark, 2013; Koeth et al., 2013). Health and survival in social species (including humans and baboons) are also strongly associated with social relationships (Berkman and Syme, 1979; House et al., 1988; e.g., Sapolsky, 2004; Silk et al., 2009; Holt-Lunstad et al., 2010; Silk et al., 2010; Archie et al., 2014). However, few studies have connected these two observations. By highlighting the strong relationship between microbiome composition and social networks, our findings indicate the importance of further research in this area. One of the most important unanswered questions is whether social network-mediated microbiome sharing produces net fitness benefits or costs for hosts. Previous research on fecal-oral or social network-mediated transmission has focused almost exclusively on pathogens or parasites. Microbiome studies have the potential to broaden this perspective to include species with beneficial effects. Indeed, while we found several socially structured taxa that have been associated with pathogenic effects (e.g., Fusobacterium spp, Campylobacter ureolyticus), we found several other bacteria thought to be beneficial to hosts. For example, members of the phylum Actinobacteria, especially the genus Bifidobacterium, are commonly thought to have probiotic effects in humans due to their role in complex carbohydrate digestion, pathogen inhibition, and vitamin production (Servin, 2004; Gronlund et al., 2007; Turroni et al., 2008). Understanding the balance between social transmission of pathogenic vs commensal or beneficial bacteria thus promises to provide valuable new insight into the link between disease risk and the evolution of sociality.

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Author details

1. Jenny Tung

   1. Department of Evolutionary Anthropology, Duke University, Durham, United States
   2. Department of Biology, Duke University, Durham, United States
   3. Institute of Primate Research, National Museums of Kenya, Nairobi, Kenya
   4. Duke Population Research Institute, Duke University, Durham, United States

   Contribution

   JT, Conception and design, Analysis and interpretation of data, Drafting or revising the article

   For correspondence

   jt5@duke.edu

   Competing interests

   The authors declare that no competing interests exist.

2. Luis B Barreiro

   Department of Pediatrics, Sainte-Justine Hospital Research Centre, University of Montreal, Montreal, Canada

   Contribution

   LBB, Conception and design, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

3. Michael B Burns

   1. Department of Genetics, Cell Biology, and Development, University of Minnesota, Minneapolis, United States
   2. Department of Ecology, Evolution, and Behavior, University of Minnesota, Minneapolis, United States

   Contribution

   MBB, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

4. Jean-Christophe Grenier

   Department of Pediatrics, Sainte-Justine Hospital Research Centre, University of Montreal, Montreal, Canada

   Contribution

   J-CG, Analysis and interpretation of data

   Competing interests

   The authors declare that no competing interests exist.

5. Josh Lynch

   1. Department of Genetics, Cell Biology, and Development, University of Minnesota, Minneapolis, United States
   2. Department of Ecology, Evolution, and Behavior, University of Minnesota, Minneapolis, United States

   Contribution

   JL, Analysis and interpretation of data

   Competing interests

   The authors declare that no competing interests exist.

6. Laura E Grieneisen

   Department of Biological Sciences, University of Notre Dame, Notre Dame, United States

   Contribution

   LEG, Acquisition of data, Analysis and interpretation of data

   Competing interests

   The authors declare that no competing interests exist.

7. Jeanne Altmann

   1. Institute of Primate Research, National Museums of Kenya, Nairobi, Kenya
   2. Department of Ecology and Evolutionary Biology, Princeton University, Princeton, United States

   Contribution

   JA, Acquisition of data

   Competing interests

   The authors declare that no competing interests exist.

8. Susan C Alberts

   1. Department of Biology, Duke University, Durham, United States
   2. Institute of Primate Research, National Museums of Kenya, Nairobi, Kenya

   Contribution

   SCA, Acquisition of data

   Competing interests

   The authors declare that no competing interests exist.

9. Ran Blekhman

   1. Department of Genetics, Cell Biology, and Development, University of Minnesota, Minneapolis, United States
   2. Department of Ecology, Evolution, and Behavior, University of Minnesota, Minneapolis, United States

   Contribution

   RB, Conception and design, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

10. Elizabeth A Archie

    1. Institute of Primate Research, National Museums of Kenya, Nairobi, Kenya
    2. Department of Biological Sciences, University of Notre Dame, Notre Dame, United States

    Contribution

    EAA, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

    For correspondence

    earchie@nd.edu

    Competing interests

    The authors declare that no competing interests exist.

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