Lotka-Volterra pairwise modeling fails to capture diverse pairwise microbial interactions

Momeni, Babak; Xie, Li; Shou, Wenying

doi:10.7554/elife.25051

Cited by 233 publications

(281 citation statements)

References 75 publications

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“…It is possible that higher‐order interactions significantly influence community dynamics in lower dimensional multi‐species assemblages, such as three‐member consortia. Previous work showed that pairwise phenomenological models of low‐dimensional assemblages (2–3 species) trained on an interval of time of Monod‐based community models failed in some cases to predict future dynamic behaviors (Momeni et al , ). The mechanistic models considered in this study involved a limited number of metabolites, whereas numerous metabolites likely mediate microbial interactions.…”

Section: Discussionmentioning

confidence: 99%

Deciphering microbial interactions in synthetic human gut microbiome communities

Venturelli

Carr

Fisher

et al. 2018

Molecular Systems Biology

414

420

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The ecological forces that govern the assembly and stability of the human gut microbiota remain unresolved. We developed a generalizable model‐guided framework to predict higher‐dimensional consortia from time‐resolved measurements of lower‐order assemblages. This method was employed to decipher microbial interactions in a diverse human gut microbiome synthetic community. We show that pairwise interactions are major drivers of multi‐species community dynamics, as opposed to higher‐order interactions. The inferred ecological network exhibits a high proportion of negative and frequent positive interactions. Ecological drivers and responsive recipient species were discovered in the network. Our model demonstrated that a prevalent positive and negative interaction topology enables robust coexistence by implementing a negative feedback loop that balances disparities in monospecies fitness levels. We show that negative interactions could generate history‐dependent responses of initial species proportions that frequently do not originate from bistability. Measurements of extracellular metabolites illuminated the metabolic capabilities of monospecies and potential molecular basis of microbial interactions. In sum, these methods defined the ecological roles of major human‐associated intestinal species and illuminated design principles of microbial communities.

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Section: Discussionmentioning

confidence: 99%

Deciphering microbial interactions in synthetic human gut microbiome communities

Venturelli

Carr

Fisher

et al. 2018

Molecular Systems Biology

414

420

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“…In addition, as donor and acceptor bacteria were not in physical contact in our assay, additional contactmediated interactions (54) may affect in vivo interactions. Moreover, higher-order interactions between more than two bacteria could potentially affect community stability (20), and the complexity of such interactions may limit the predictability of simple pairwise models (55). However, a recent study found that pairwise interactions largely predict the population dynamics of larger communities (56).…”

Section: Discussionmentioning

confidence: 99%

Interaction networks, ecological stability, and collective antibiotic tolerance in polymicrobial infections

Vos

Zagórski

McNally

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

158

207

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Polymicrobial infections constitute small ecosystems that accommodate several bacterial species. Commonly, these bacteria are investigated in isolation. However, it is unknown to what extent the isolates interact and whether their interactions alter bacterial growth and ecosystem resilience in the presence and absence of antibiotics. We quantified the complete ecological interaction network for 72 bacterial isolates collected from 23 individuals diagnosed with polymicrobial urinary tract infections and found that most interactions cluster based on evolutionary relatedness. Statistical network analysis revealed that competitive and cooperative reciprocal interactions are enriched in the global network, while cooperative interactions are depleted in the individual host community networks. A population dynamics model parameterized by our measurements suggests that interactions restrict community stability, explaining the observed species diversity of these communities. We further show that the clinical isolates frequently protect each other from clinically relevant antibiotics. Together, these results highlight that ecological interactions are crucial for the growth and survival of bacteria in polymicrobial infection communities and affect their assembly and resilience. microbiology | systems biology | antibiotics | infection

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“…The null expectation from a simple model of interactionthe generalized Lotka-Volterra modelis that an obligate species will always benefit from an increase in growth rate of its nutrient provider (Vandermeer and Boucher, 1978;Boucher et al, 1982). More recent phenomenological theory that combines the positive effects of crossfeeding with the negative effects of competition suggest that cross-feeding systems can be destabilized by the addition of cross-fed nutrients (Estrela et al, 2012;Müller et al, 2014;Hoek et al, 2016;Momeni et al, 2017). This discrepancy is because in the recent models, the net impact that one species has on the other can switch from positive to negative as the system approaches a shared carrying capacity.…”

Section: Introductionmentioning

confidence: 99%

A shared limiting resource leads to competitive exclusion in a cross‐feeding system

Hammarlund

Chacón

Harcombe

2019

Environmental Microbiology

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Summary Species interactions and coexistence are often dependent upon environmental conditions. When two cross-feeding bacteria exchange essential nutrients, the addition of a cross-fed nutrient to the environment can release one species from its dependence on the other. Previous studies suggest that continued coexistence depends on relative growth rates: coexistence is maintained if the slower-growing species is released from its dependence on the other, but if the faster-growing species is released, the slower-growing species will be lost (a hypothesis that we call “feed the faster grower” or FFG). Using invasion-from-rare experiments with two reciprocally cross-feeding bacteria, genome-scale metabolic modeling, and classical ecological models, we explored the potential for coexistence when one cross-feeder became independent. We found that whether nutrient addition shifted an interaction from mutualism to commensalism or parasitism depended on whether the nutrient that limited total growth was required by one or both species. Parasitism resulted when both species required the growth-limiting resource. Importantly, coexistence was only lost when the interaction became parasitism, and the obligate species had a slower growth rate. Under these restricted conditions, the FFG hypothesis applied. Our results contribute to a mechanistic understanding of how resources can be manipulated to alter interactions and coexistence in microbial communities.

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Lotka-Volterra pairwise modeling fails to capture diverse pairwise microbial interactions

Cited by 233 publications

References 75 publications

Deciphering microbial interactions in synthetic human gut microbiome communities

Deciphering microbial interactions in synthetic human gut microbiome communities

Interaction networks, ecological stability, and collective antibiotic tolerance in polymicrobial infections

A shared limiting resource leads to competitive exclusion in a cross‐feeding system

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