Statistically learning the functional landscape of microbial communities

Skwara, Abigail; Gowda, Karna; Yousef, Mahmoud; Diaz-Colunga, Juan; Raman, Arjun S.; Sanchez, Alvaro; Tikhonov, Mikhail; Kuehn, Seppe

doi:10.1038/s41559-023-02197-4

Cited by 20 publications

(21 citation statements)

References 49 publications

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“…Our data suggest that given a model that is sufficiently predictive of strain phenotype, considering higher-order microbial interactions may not be necessary for microbial community design. This observation is consistent with recent work showing that phenotypes of microbial communities can predicted and learned from considering just first-order and pairwise interaction terms ( 36 , 37 ). In addition, the utility of our models does not generalize beyond strains that are not phylogenetically represented in our strain bank.…”

Section: Resultssupporting

confidence: 93%

An evolution-based framework for describing human gut bacteria

Doran,

Chen,

Giba

et al. 2023

Preprint

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The human gut microbiome contains many bacterial strains of the same species (‘strain-level variants’). Describing strains in a biologically meaningful way rather than purely taxonomically is an important goal but challenging due to the genetic complexity of strain-level variation. Here, we measured patterns of co-evolution across >7,000 strains spanning the bacterial tree-of-life. Using these patterns as a prior for studying hundreds of gut commensal strains that we isolated, sequenced, and metabolically profiled revealed widespread structure beneath the phylogenetic level of species. Defining strains by their co-evolutionary signatures enabled predicting their metabolic phenotypes and engineering consortia from strain genome content alone. Our findings demonstrate a biologically relevant organization to strain-level variation and motivate a new schema for describing bacterial strains based on their evolutionary history.One Sentence SummaryDescribing bacterial strains in the human gut by a statistical model that captures their evolutionary history provides insight into their biology.

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Section: Resultssupporting

confidence: 93%

An evolution-based framework for describing human gut bacteria

Doran,

Chen,

Giba

et al. 2023

Preprint

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“…12 ). A likely driving force behind our results for the target function of K. pneumoniae suppression is that in contrast to the apparent complexity of microbial ecosystems, profoundly low-dimensional representations of structure-function relationships exist and can be discovered in a facile manner by placing statistical patterns of phenomenology before biological understanding—an emerging viewpoint that has been the subject of some recent efforts in microbiome studies and has rapidly found immense success in the form of deep-learning models at other scales of biology, namely synthetic protein design 15, 20, 21, 50–54 . Following this we note that our approach does not consider mechanisms of action at any scale nor compositional information about natural microbiomes and their associated functions.…”

Section: Discussionmentioning

confidence: 99%

Statistical design of a synthetic microbiome that clears a multi-drug resistant gut pathogen

Oliveira,

Pandey,

Lee

et al. 2024

Preprint

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Microbiomes perform critical functions across many environments on Earth1–3. However, elucidating principles of their design is immensely challenging4–7. Using a diverse bank of human gut commensal strains and clearance of multi-drug resistantKlebsiella pneumoniaeas a target, we engineered a functional synthetic microbiome using a process that was agnostic to mechanism of action, bacterial interactions, or compositions of natural microbiomes. Our strategy was a modified ‘Design-Build-Test-Learn’ approach (‘DBTL+’) coupled with statistical inference that learned design principles by considering only the strain presence-absence of designed communities. In just a single round of DBTL+, we converged on a generative model ofK. pneumoniaesuppression. Statistical inference performed on our model identified 15 strains that were key for community function. Combining these strains into a community (‘SynCom15’) suppressedK. pneumoniaeacross unrelatedin vitroenvironments and matched the clearance ability of a whole stool transplant in a pre-clinically relevant mouse model of infection. Considering metabolic profiles of communities instead of strain presence-absence yielded a poor generative model, demonstrating the advantage of using strain presence-absence for deriving principles of community design. Our work introduces the concept of ‘statistical design’ for engineering synthetic microbiomes, opening the possibility of synthetic ecology more broadly.

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“…Increasing experimental evidence indicates that quantifying the interactions between a trio of species allows for the accurate prediction of complex microbial communities dynamics (Friedman et al . 2017; Skwara et al . 2023; Ishizawa et al .…”

Section: Discussionmentioning

confidence: 99%

“…This study utilizes small community motifs, to study the role of TIMs on communities. Increasing experimental evidence indicates that quantifying the interactions between a trio of species allows for the accurate prediction of complex microbial communities dynamics (Friedman et al 2017;Skwara et al 2023;Ishizawa et al 2024). As such, we believe the TIMs observed in our three species motifs, will be informative of the dynamics in more species-rich communities.…”

Section: Incorporating Trophic Interaction Modifications To Better Un...mentioning

confidence: 99%

Food web context modifies predator foraging and weakens trophic interaction strength

Lemmen,

Pennekamp

2024

Preprint

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Trophic interaction modifications (TIM) are widespread in natural systems and occur when a third species indirectly alters the strength of a trophic interaction. Past studies have focused on documenting the existence and relative magnitude of TIMs, however the underlying processes and long-term consequences often remain elusive. To address this gap, we experimentally quantified the density-dependent effect of a third species on a predator's functional response to identify the processes impacted by, and consequences of TIMs. To do so we conducted short-term experiments with two ciliate communities each composed of a predator, prey, and non-consumable 'modifier' species. In both communities, increasing modifier density weakened the trophic interaction strength, due to a negative effect on the predator's search clearance rate, however the magnitude of the effect differed with prey species identity. Using parameters estimated from our experimental observation we simulated long-term dynamics and observed quantitative differences between models that account for TIMs or include only pairwise interactions. Our study is a clear demonstration that TIMs are important to understand and predict community dynamics and highlights the need to extend past pairs of focal species to understand the consequences of species interactions in communities.

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Statistically learning the functional landscape of microbial communities

Cited by 20 publications

References 49 publications

An evolution-based framework for describing human gut bacteria

An evolution-based framework for describing human gut bacteria

Statistical design of a synthetic microbiome that clears a multi-drug resistant gut pathogen

Food web context modifies predator foraging and weakens trophic interaction strength

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