Higher-Order Interaction between Species Inhibits Bacterial Invasion of a Phototroph-Predator Microbial Community

Mickalide, Harry; Kuehn, Seppe

doi:10.1016/j.cels.2019.11.004

Cited by 118 publications

(124 citation statements)

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“…Interestingly, the main driver of taxonomic variability among replicates was the dominant member of the respirator group (a sub-dominant species). Amelioration of competition between two fermenter strains in the presence of one (but not the other) dominant respirator points to the subtle role that high-order interactions may play in community assembly (Billick and Case 1994;Mickalide and Kuehn 2019;Sanchez 2019;Sanchez-Gorostiaga et al 2019;Senay et al 2019;Levine et al 2017;Grilli et al 2017) . This is also in line with previous observations of the potential importance of sub-dominant bacteria in shaping the composition of microbial communities ) .…”

Section: Discussionmentioning

confidence: 99%

Metabolic rules of microbial community assembly

Estrela

Vila

et al. 2020

Preprint

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To develop a quantitative theory that can predict how microbiomes assemble, and how they respond to perturbations, we must identify which descriptive features of microbial communities are reproducible and predictable, which are unpredictable, and why. The emergent metagenomic structure of communities is often quantitatively convergent in similar habitats, with highly similar fractions of the metagenome being devoted to the same metabolic pathways. By contrast, the species-level taxonomic composition is often highly variable even in replicate environments. The mechanisms behind these patterns are not yet understood. By studying the self-assembly of hundreds of communities in replicate, synthetic habitats, we show that the reproducibility of microbial community assembly reflects an emergent metabolic structure, which is quantitatively predictable from first-principles, genome-scale metabolic models. Taxonomic variability within functional groups arises through multistability in population dynamics, and the species-level community composition is predictably governed by the mutual competitive exclusion of two sub-dominant strains. Our findings provide a mechanistic bridge between microbial community structure at different levels of organization, and show that the evolutionary conservation of metabolic traits, both in terms of growth responses and niches constructed, can be leveraged to quantitatively predict the taxonomic and metabolic structure of microbial communities.

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Section: Discussionmentioning

confidence: 99%

Metabolic rules of microbial community assembly

Estrela

Vila

et al. 2020

Preprint

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“…One approach has been to engineer communities from the bottom-up, by mixing species with known functionality towards a desired community function (Gilbert et al 2003;Regot et al 2011;Minty et al 2013;Smith et al 2013;Wang et al 2016;Eng and Borenstein 2019) . An important challenge of this approach is the combinatorial explosion in the number of interactions in the community, with high-order interactions posing significant challenges for the predictability of even mid-size consortia (Gould et al 2018;Mickalide and Kuehn 2019;Sanchez-Gorostiaga et al 2019;Senay et al 2019) . In addition, ecological and evolutionary processes can also act against the engineered community function (Goldman and Brown 2009) .…”

Section: Introductionmentioning

confidence: 99%

Artificially selecting microbial communities using propagule strategies

Chang

Osborne

Bajić

et al. 2020

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Artificial selection is a promising approach to manipulate the function of microbial communities. Here, we report the outcome of two artificial selection experiments at the microbial community level. Both experiments used "propagule" strategies, in which a set of the best-performing communities are used as the inocula to form a new generation of communities. In both cases, the selected communities are compared to a control treatment where communities are randomly selected. The first experiment used a defined set of strains as the starting inoculum, and the function under selection was the amylolytic activity of the consortia. The second experiment used a diverse set of natural communities as the inoculum, and the function under selection was the cross-feeding potential of the resulting communities towards a reference bacterial strain. In both experiments, the selected communities reached a higher mean and a higher maximum function than the control. In the first experiment this is caused by a decline in function of the control, rather than an improvement of the selected line. In the second experiment, the strong response of the mean is caused by the large initial variance in function across communities, and is the immediate consequence of the spread of the top-performing community in the starting group, whose function does not increase. Our results are in agreement with basic expectations of artificial selection theory, pointing out some of the limitations of community-level selection experiments which can inform the design of future studies.

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“…In contrast, 59 experiments based on the cheese rind microbiome found significant differences in the 60 genes required for a non-native E. coli species to persist in a multi-species bacterial 61 community compared to predictions from pairwise coexistence with community 62 members [49]. A closed ecosystem consisting of one species each of algae, bacteria, and 63ciliates exhibited a strong non-pairwise interaction, in which the bacterium is abundant 64 in the presence of each of the algae or ciliate alone, but is subject to strong predation in 65 the three-species system [50].…”

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confidence: 99%

“…ciliates exhibited a strong non-pairwise interaction, in which the bacterium is abundant 64 in the presence of each of the algae or ciliate alone, but is subject to strong predation in 65 the three-species system [50].…”

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confidence: 99%

“…Inferring and quantifying indirect interactions in natural ecosystems is, however, 352 challenging, calling for subtle and model-dependent statistical tests [41,42,66]. systems involving macroscopic organisms [67][68][69][70][71], as well as microorganisms [32,50] 356 have uncovered significant indirect interactions. However, some studies of microbial 357 communities have found weak or negligible higher order interactions [47,48], including 358 one study examining combinations of species introduced to the C. elegans gut [31].…”

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confidence: 99%

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Quantifying multi-species microbial interactions in the larval zebrafish gut

Sundarraman

et al. 2020

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These authors contributed equally to this work. AbstractThe microbial communities resident in animal intestines are composed of multiple species that together play important roles in host development, health and disease. Due to the complexity of these communities and the difficulty of characterizing them in situ, the determinants of microbial composition remain largely unknown. Further, it is unclear for many multi-species consortia whether their species-level makeup can be predicted based on an understanding of pairwise species interactions, or whether higher-order interactions are needed to explain emergent compositions. To address this, we examine commensal intestinal microbes in larval zebrafish, initially raised germ-free to allow introduction of controlled combinations of bacterial species. Using a dissection and plating assay, we demonstrate the construction of communities of one to five bacterial species and show that the outcomes from the two-species competitions fail to predict species abundances in more complex communities. With multiple species present, inter-bacterial interactions become weaker and more cooperative, suggesting that higher-order interactions in the vertebrate gut may stabilize complex communities. 5 immune regulation [5-10] and a wide range of diseases [11][12][13][14][15][16][17][18][19][20]. 6 Despite the importance of intestinal communities, the determinants of their 7 composition remain largely unknown. A growing number of studies map the effects of 8 external perturbations, such as antibiotic drugs [21, 22] and dietary fiber [23] and 9 fat [24, 25] on the relative abundance of gut microbial species. Intrinsic inter-microbial 10interactions, however, are especially challenging to measure and are important not only 11 for shaping community composition in the absence of perturbations but also for 12 propagating species-specific perturbations to the rest of the intestinal ecosystem. 13The considerable majority of studies of the gut microbiota have been performed on 14 naturally assembled microbiomes by sequencing DNA extracted from fecal samples, an 15May 28, 2020 1/23approach that provides information about the microbial species and genes present in the 16 gut, but that imposes several limitations on the inference of inter-species interactions. 17The high diversity of natural intestinal communities, and therefore the low abundance 18 of any given species among the multitude of its fellow residents, implies that stochastic 19 fluctuations in each species' abundance will be large, easily masking true biological 20 interactions. The accuracy of inference is considerably worse if only relative, rather than 21 absolute, abundance data is available [26-29], as is typically the case in 22 sequencing-based studies. Finally, we note that fecal sampling assesses only the 23 microbes that have exited the host, which may not be representative of the intestinal 24 community [30]. 25 An alternative approach to using DNA sequencing and naturally assembled 26 host-microbiota systems is to build suc...

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Higher-Order Interaction between Species Inhibits Bacterial Invasion of a Phototroph-Predator Microbial Community

Cited by 118 publications

References 75 publications

Metabolic rules of microbial community assembly

Metabolic rules of microbial community assembly

Artificially selecting microbial communities using propagule strategies

Quantifying multi-species microbial interactions in the larval zebrafish gut

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