Universal gut microbial relationships in the gut microbiome of wild baboons

  1. Kimberly E Roche
  2. Johannes R Bjork
  3. Mauna R Dasari
  4. Laura Grieneisen
  5. David Jansen
  6. Trevor J Gould
  7. Laurence R Gesquiere
  8. Luis B Barreiro
  9. Susan C Alberts
  10. Ran Blekhman
  11. Jack A Gilbert
  12. Jenny Tung
  13. Sayan Mukherjee
  14. Elizabeth A Archie

  • Curated by eLife

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    This fundamental work reports an analysis of microbial abundance similarities among individuals over time in a longitudinal wild baboon cohort from Amboseli, Kenya. The authors provide compelling evidence that there are remarkably consistent dynamic associations over time in microbial abundances between baboons, despite individual baboons having individualized microbial signatures. The authors further identify universal microbial associations that appear to go beyond the studied baboon cohort, extending to human microbiomes. This study adopts a novel powerful statistical approach to analyzing longitudinal microbial dynamics at the individual level, which will likely make this work become a key reference study in the field of microbial ecology.

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Abstract

Ecological relationships between bacteria mediate the services that gut microbiomes provide to their hosts. Knowing the overall direction and strength of these relationships is essential to learn how ecology scales up to affect microbiome assembly, dynamics, and host health. However, whether bacterial relationships are generalizable across hosts or personalized to individual hosts is debated. Here, we apply a robust, multinomial logistic-normal modeling framework to extensive time series data (5534 samples from 56 baboon hosts over 13 years) to infer thousands of correlations in bacterial abundance in individual baboons and test the degree to which bacterial abundance correlations are ‘universal’. We also compare these patterns to two human data sets. We find that, most bacterial correlations are weak, negative, and universal across hosts, such that shared correlation patterns dominate over host-specific correlations by almost twofold. Further, taxon pairs that had inconsistent correlation signs (either positive or negative) in different hosts always had weak correlations within hosts. From the host perspective, host pairs with the most similar bacterial correlation patterns also had similar microbiome taxonomic compositions and tended to be genetic relatives. Compared to humans, universality in baboons was similar to that in human infants, and stronger than one data set from human adults. Bacterial families that showed universal correlations in human infants were often universal in baboons. Together, our work contributes new tools for analyzing the universality of bacterial associations across hosts, with implications for microbiome personalization, community assembly, and stability, and for designing microbiome interventions to improve host health.

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  1. Version published to 10.7554/elife.83152 on eLife
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    Author Response

    Reviewer #1 (Public Review):

    In this work, Roche et al. study a 13-year long time series of microbiome samples from wild baboons from Kenya. The data used in this work challenge a previous finding from the same authors that temporal dynamics in microbiome changes are largely individualized. Using a multinomial logistic-normal modeling approach, the authors detect that co-variance in temporal dynamics in microbial pair-wise associations among individuals occurs more frequently between relatives. Furthermore, the authors identify that microbial phylogenetic proximity is associated with consistent co-abundance changes over time and that their metric of universal microbial relationships is robust across hosts and is detected even in human longitudinal data. The authors conduct a thorough statistical revision of publicly available results, highlighting this time (e.g. compared to Björk et al, doi: 10.1038/s41559-022-01773-4) the consistently shared microbial properties between individuals, rather that the individual microbial signatures highlighted in their previous work.

    Thank you for this summary. We would like to briefly clarify that we do not see the current work as inconsistent with our prior finding in Björk et al. that microbiome taxonomic compositions are idiosyncratic and asynchronized. However, this new analysis, which focuses on abundance correlations between pairs of taxa, indicates that the personalized compositions and dynamics we observed in Björk et al. are probably not attributable to personalized microbiome ecologies. In other words, Björk et al. showed that microbial taxa found in the guts of different baboons can be quite distinct (and remain so over time, giving rise to semi-stable individual signatures). The current study shows that, despite this taxonomic individuality, the correlations between pairs of microbes in the baboon gut are often quite consistent. To give a basic example, hot weather and ice cream, when observed, are often observed together (positively correlated), but while some places have a lot of both, some have little of either. This idea is discussed in more detail below (see response R6) and in the revised Discussion section (lines 572 to 586).

    Strengths:

    This work is foundational in its compelling effort to generate a rigorous method to evaluate coabundance dynamics in longitudinal microbiome data. The approach taken will likely inspire developments that will sharpen the capacity to extract co-varying microbial features, taking into account seasonality, diet, age, relatedness, and more. To the best of my understanding, their hierarchical model integrated into the Gaussian process to analyze microbial dynamics is reasonably robust and they clearly explain the implementation. Furthermore, this work introduces and defines the concept of a universality score for microbial taxon pairs. Overall, the work presented is clear and convincing and provides tools for the community to benefit from both methods and results. Furthermore, conceptually, this work stresses the value of consistent and shared microbial dynamics in groups, which enriches our understanding of host-associated microbial ecology, otherwise understood to be largely dependent on external fluctuations.

    Weakness:

    It is not entirely clear the extent to which the presented results revise, refute, or support the previously published analysis performed by the authors on the same dataset (doi: 10.1038/s41559-022-01773-4), which was more focused on individuality.

    We agree the relationship between Björk et al. and the current manuscript was unclear in our original submission. We now elucidate the relationship between these papers in the Discussion (lines 572 to 586). Briefly, Björk et al. found that microbiome taxonomic compositions are idiosyncratic and asynchronized. The current analysis finds that pairwise bacterial abundance correlations are predominantly shared and not highly personalized. We think the most likely explanation is that, as mentioned by Reviewer 2 below, the current analyses do not account for the role that environmental gradients play in the gut. If these environments differ asynchronously across hosts, it could lead to shared abundance correlations, but individualized microbiome compositions and individualized single-taxon dynamics. We discuss this possibility and other potential explanations in the revised Discussion (lines 572 to 586).

    Reviewer #2 (Public Review):

    The authors of this paper identify a knowledge gap in our understanding of the generalizability of ecological associations of gut bacteria across hosts. Theoretically, it is possible that ecological associations between bacteria are consistent within a host organism but differ between hosts, or that they are universal across hosts and their environmental gradients. The authors utilize longitudinal data with a unique temporal resolution, on Amboseli baboons, 56 individuals who were sampled for gut microbiome hundreds of times over a decade. This data allows disentangling ecological dynamics within and across individuals in a way that as far as I know has never been done before. The authors show that ecological relationships among baboon gut bacteria, measure through a correlation based on covariation, are largely universal (similar within and across host individuals) and that the most universally covarying taxa are almost always positively associated with each other. They also compare these results with two sets of human data, finding similar patterns in one human data set but not in the other.

    The main aim of this paper is to establish whether gut microbial ecologies are universal across hosts, and this the authors generally show to be true in a thorough and convincing way. However, some re-assessment or re-assurance on the solidity of their chosen method of estimating co-variation would be needed to fully assess the robustness of subsequent results. Specifically, the authors measure the correlation between microbial taxa from data on their abundance co-variation across samples. While necessary steps have been taken to validate the estimates across spurious correlations due to the compositional nature and autocorrelation structures present in the data, I worry that the sparsity of the data might influence the estimation of positive and negative correlations in a slightly different manner. There exist more microbial taxa than samples in the data and some taxa are present in as few as 20% of the samples, meaning that the covariation data will have a large amount of 0-0 pairs. I worry that the abundance of 0-0 pairs in the data might inflate the measures of positive co-variation, making taxa seem highly positively correlated in abundance when they in fact are missing from many samples. Of course, mutual absence is also a form of biologically meaningful covariation but taking the larger number of taxa than samples and the inability of sequencing technology to detect all low-abundance taxa in a sample, I am currently not convinced that all of the 0-0 pairs are modeled as a realistic and balanced way as a continuum of the other non-zero co-variation between taxa in the data. This may become problematic when positive and negative relationships are compared: The authors state that even though most associations between taxa were negative, the most universally correlated taxa pairs (taxa pairs with strongest correlations in abundance both within and between hosts) were enriched in positive associations. It may be possible that this is influenced by the fact that zero inflation in the data lends more weight to positive links than negative links. Whether these universal positive correlations are driven by positive non-zero abundance covariation or just 0-0 links in the data is currently unclear.

    Thank you for pointing out this weakness in our original analyses. As described in response R1 above, your hunch was correct: zero inflation biased our correlation patterns such that taxa pairs with a high frequency of joint zero observations (i.e., where both members of the pair had very low or zero abundances) tended to be positively correlated (Fig. R1). Consequently, as you suggested, zero inflation in the data lent more weight to positive links than negative links in our data set. To address this problem in the revised manuscript, we now restrict our analyses to taxon pairs whose joint zero-abundance observations were less than 5% of all samples across hosts (pairs to the left of the dashed vertical line in Fig. R1 above). We also restricted our analyses to taxa observed in at least 50% of all samples. The first of these criteria was the most restrictive. As described above, our new filtering procedure retained 1,878 of the original 7,750 ASV-ASV pairs; 57 of the original 66 phylum-phylum pairs; and 473 of the original 666 class/order/family-level pairs.

    Another additional result that would benefit from a more clear context is the result that taxa correlation patterns were more similar between phylogenetically close taxa and between genetically close host individuals. The former notion is to be expected if taxa abundances are driven by environmental (or host physiology-related) selective forces that favor bacteria with similar phenotypes. This yields more support to the idea that covariation is environmentally driven rather than driven by the ecological network of the bacteria themselves, and this could be more clearly emphasized. The latter notion of covariation being more similar in genetically related hosts is currently impossible to disentangle from the notion that covariation patterns were more similar with individuals harboring a more similar baseline microbiome composition since microbiome composition and genetic relatedness were apparently correlated. To understand if something about relatedness was actually influential over correlation pattern similarity, one would need to model that effect on top of the baseline similarity effect. Currently, it is not clear if this was done or not.

    We agree that shared responses to environmental gradients within hosts—especially immune profiles and pH—could explain both of these findings. These ideas are now described in the Discussion in lines 559 to 562.

    We also now report partial Mantel tests to control for baseline similarity in microbiome composition when testing for shared microbial correlation patterns among genetic relatives. Controlling for baseline similarity had little effect on the results, and we now report the statistics for this partial Mantel (Fig. 5B; Table S7; r2=0.009; partial Mantel p-value=0.002). See lines 391-392.

    The authors also slightly overemphasize the generalizability of their results to humans, taking that only one of the human data sets they compare their results to, shows similar patterns. While they mention that the other human data set (that was not similar in patterns to theirs) was different in some key aspects (sampling frequency was much higher), the other human data set was also dissimilar to the other two (it only contained infants, not adults). Furthermore, to back up the statement that higher sampling frequency would be the reason this data set had dissimilar covariation between taxa, one would need to show that the temporal variation in this data set was different from the baboon one and show that these covariation patterns were sensitive to timescale by subsampling either data to create mock data sets with different sampling frequency and see how this would change the inference of ecological associations.

    We have revised the text to tone down the generalizability of our results to humans. For instance, the abstract (line 58) now states that “universality in baboons was similar to that in human infants, and stronger than one data set from human adults” but does not state that our results are generalizable to humans.

    We also considered sub-sampling the data set from Johnson et al., from daily to monthly scales, but unfortunately that data set is only 17 days long, so doing so is impossible. This is now stated in the Discussion in line 619, which states, “However, without the ability to subsample Johnson et al. [7] to monthly scales (this data set is only 17 days long), it is impossible to test this prediction.”

    To the extent that the results are robust, particularly regarding to the main result of the universality of gut microbial ecological associations, the impact of this paper is not small. This question has never been so thoroughly and convincingly addressed, and the results as they stand have the power to strongly influence the expectations of gut microbial ecology across many different systems. Moreover, as the authors point out, evidence for universal gut microbial ecology is important for the future development of probiotics. An important point here, underemphasized by the authors, is that universal gut microbe ecologies will allow specific interventions that use gut microbe ecology to manipulate emergent community properties of microbiomes to be more beneficial for the host, rather than just designing compositional cocktails that should fit all. In addition to the main finding of this study, the unique data set and the methods developed as part of this study (e.g. the universality score, the enrichment measures, the model of log-ratio dynamics, the assessment of covariation from time-ordered abundance trajectories) will doubtlessly be translatable to many other studies in the future.

    Thank you for these suggestions. We now mention these implications in the introduction (line 82-84) and in the discussion in lines 537-539 and line 630.

    Reviewer #3 (Public Review):

    This is a well-executed study, offering thorough analysis and insightful interpretations. It is wellwritten, and I find the conclusions interesting, important, and well-supported.

    Thank you for your supportive comments.

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  3. eLife

    eLife assessment

    This fundamental work reports an analysis of microbial abundance similarities among individuals over time in a longitudinal wild baboon cohort from Amboseli, Kenya. The authors provide compelling evidence that there are remarkably consistent dynamic associations over time in microbial abundances between baboons, despite individual baboons having individualized microbial signatures. The authors further identify universal microbial associations that appear to go beyond the studied baboon cohort, extending to human microbiomes. This study adopts a novel powerful statistical approach to analyzing longitudinal microbial dynamics at the individual level, which will likely make this work become a key reference study in the field of microbial ecology.

  4. eLife

    Reviewer #1 (Public Review):

    In this work, Roche et al. study a 13-year long time series of microbiome samples from wild baboons from Kenya. The data used in this work challenge a previous finding from the same authors that temporal dynamics in microbiome changes are largely individualized. Using a multinomial logistic-normal modeling approach, the authors detect that co-variance in temporal dynamics in microbial pair-wise associations among individuals occurs more frequently between relatives. Furthermore, the authors identify that microbial phylogenetic proximity is associated with consistent co-abundance changes over time and that their metric of universal microbial relationships is robust across hosts and is detected even in human longitudinal data. The authors conduct a thorough statistical revision of publicly available results, highlighting this time (e.g. compared to Björk et al, doi: 10.1038/s41559-022-01773-4) the consistently shared microbial properties between individuals, rather that the individual microbial signatures highlighted in their previous work.

    Strengths:
    This work is foundational in its compelling effort to generate a rigorous method to evaluate co-abundance dynamics in longitudinal microbiome data. The approach taken will likely inspire developments that will sharpen the capacity to extract co-varying microbial features, taking into account seasonality, diet, age, relatedness, and more. To the best of my understanding, their hierarchical model integrated into the Gaussian process to analyze microbial dynamics is reasonably robust and they clearly explain the implementation. Furthermore, this work introduces and defines the concept of a universality score for microbial taxon pairs.
    Overall, the work presented is clear and convincing and provides tools for the community to benefit from both methods and results. Furthermore, conceptually, this work stresses the value of consistent and shared microbial dynamics in groups, which enriches our understanding of host-associated microbial ecology, otherwise understood to be largely dependent on external fluctuations.

    Weakness:
    It is not entirely clear the extent to which the presented results revise, refute, or support the previously published analysis performed by the authors on the same dataset (doi: 10.1038/s41559-022-01773-4), which was more focused on individuality.

  5. eLife

    Reviewer #2 (Public Review):

    The authors of this paper identify a knowledge gap in our understanding of the generalizability of ecological associations of gut bacteria across hosts. Theoretically, it is possible that ecological associations between bacteria are consistent within a host organism but differ between hosts, or that they are universal across hosts and their environmental gradients. The authors utilize longitudinal data with a unique temporal resolution, on Amboseli baboons, 56 individuals who were sampled for gut microbiome hundreds of times over a decade. This data allows disentangling ecological dynamics within and across individuals in a way that as far as I know has never been done before. The authors show that ecological relationships among baboon gut bacteria, measure through a correlation based on covariation, are largely universal (similar within and across host individuals) and that the most universally covarying taxa are almost always positively associated with each other. They also compare these results with two sets of human data, finding similar patterns in one human data set but not in the other.

    The main aim of this paper is to establish whether gut microbial ecologies are universal across hosts, and this the authors generally show to be true in a thorough and convincing way. However, some re-assessment or re-assurance on the solidity of their chosen method of estimating co-variation would be needed to fully assess the robustness of subsequent results. Specifically, the authors measure the correlation between microbial taxa from data on their abundance co-variation across samples. While necessary steps have been taken to validate the estimates across spurious correlations due to the compositional nature and autocorrelation structures present in the data, I worry that the sparsity of the data might influence the estimation of positive and negative correlations in a slightly different manner. There exist more microbial taxa than samples in the data and some taxa are present in as few as 20% of the samples, meaning that the covariation data will have a large amount of 0-0 pairs. I worry that the abundance of 0-0 pairs in the data might inflate the measures of positive co-variation, making taxa seem highly positively correlated in abundance when they in fact are missing from many samples. Of course, mutual absence is also a form of biologically meaningful covariation but taking the larger number of taxa than samples and the inability of sequencing technology to detect all low-abundance taxa in a sample, I am currently not convinced that all of the 0-0 pairs are modeled as a realistic and balanced way as a continuum of the other non-zero co-variation between taxa in the data. This may become problematic when positive and negative relationships are compared: The authors state that even though most associations between taxa were negative, the most universally correlated taxa pairs (taxa pairs with strongest correlations in abundance both within and between hosts) were enriched in positive associations. It may be possible that this is influenced by the fact that zero inflation in the data lends more weight to positive links than negative links. Whether these universal positive correlations are driven by positive non-zero abundance covariation or just 0-0 links in the data is currently unclear.
    Another additional result that would benefit from a more clear context is the result that taxa correlation patterns were more similar between phylogenetically close taxa and between genetically close host individuals. The former notion is to be expected if taxa abundances are driven by environmental (or host physiology-related) selective forces that favor bacteria with similar phenotypes. This yields more support to the idea that covariation is environmentally driven rather than driven by the ecological network of the bacteria themselves, and this could be more clearly emphasized. The latter notion of covariation being more similar in genetically related hosts is currently impossible to disentangle from the notion that covariation patterns were more similar with individuals harboring a more similar baseline microbiome composition since microbiome composition and genetic relatedness were apparently correlated. To understand if something about relatedness was actually influential over correlation pattern similarity, one would need to model that effect on top of the baseline similarity effect. Currently, it is not clear if this was done or not.

    The authors also slightly overemphasize the generalizability of their results to humans, taking that only one of the human data sets they compare their results to, shows similar patterns. While they mention that the other human data set (that was not similar in patterns to theirs) was different in some key aspects (sampling frequency was much higher), the other human data set was also dissimilar to the other two (it only contained infants, not adults). Furthermore, to back up the statement that higher sampling frequency would be the reason this data set had dissimilar covariation between taxa, one would need to show that the temporal variation in this data set was different from the baboon one and show that these covariation patterns were sensitive to timescale by subsampling either data to create mock data sets with different sampling frequency and see how this would change the inference of ecological associations.

    To the extent that the results are robust, particularly regarding to the main result of the universality of gut microbial ecological associations, the impact of this paper is not small. This question has never been so thoroughly and convincingly addressed, and the results as they stand have the power to strongly influence the expectations of gut microbial ecology across many different systems. Moreover, as the authors point out, evidence for universal gut microbial ecology is important for the future development of probiotics. An important point here, under-emphasized by the authors, is that universal gut microbe ecologies will allow specific interventions that use gut microbe ecology to manipulate emergent community properties of microbiomes to be more beneficial for the host, rather than just designing compositional cocktails that should fit all. In addition to the main finding of this study, the unique data set and the methods developed as part of this study (e.g. the universality score, the enrichment measures, the model of log-ratio dynamics, the assessment of covariation from time-ordered abundance trajectories) will doubtlessly be translatable to many other studies in the future.

  6. eLife

    Reviewer #3 (Public Review):

    This is a well-executed study, offering thorough analysis and insightful interpretations. It is well-written, and I find the conclusions interesting, important, and well-supported.

  7. Version published to 10.1101/2022.08.20.504530v2 on bioRxiv
  8. Version published to 10.1101/2022.08.20.504530v1 on bioRxiv