Microbial coexistence through chemical-mediated interactions

Niehaus, Lori; Boland, Ian; Liu, Minghao; Chen, Kevin; Fu, David; Henckel, Catherine; Chaung, Kaitlin; Miranda, Suyen Espinoza; Dyckman, Samantha; Crum, Matt; Dedrick, Sandra; Shou, Wenying; Momeni, Babak

doi:10.1101/358481

Cited by 12 publications

(23 citation statements)

References 90 publications

(93 reference statements)

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“…In this case, one would isolate an individual with the desirable interaction value, culture it, and add it back to the original community in much larger numbers. Interaction values can also be modified by environmental factors such as temperature, pH, and chemicals [33][34][35][36][37][38][39][40]. For example one could use a drug that targets a protein responsible for mediating a particular interspecies interaction.…”

Section: Introductionmentioning

confidence: 99%

Theoretical guidelines for editing ecological communities

Nguyen

Vural

2020

Preprint

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Having control over species abundances and community resilience is of great interest for experimental, agricultural, industrial and conservational purposes. Here, we theoretically explore the possibility of manipulating ecological communities by modifying pairwise interactions. Specifically, we establish which interaction values should be modified, and by how much, in order to alter the composition or resilience of a community towards a favorable direction. While doing so, we also take into account the experimental difficulties in making such modifications by including in our optimization process, a cost parameter, which penalizes large modifications. In addition to prescribing what changes should be made to interspecies interactions given some modification cost, our approach also serves to establish the limits of community control, i.e. how well can one approach an ecological goal at best, even when not constrained by cost.

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Section: Introductionmentioning

confidence: 99%

Theoretical guidelines for editing ecological communities

Nguyen

Vural

2020

Preprint

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“…modify and react to their environment [19][20][21][22][23] . The higher the nutrient concentrations the microbes have access to the stronger they can metabolize and hence the stronger they can modify the environment.…”

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confidence: 99%

Strength of species interactions determines biodiversity and stability in microbial communities

Ratzke

Barrere

Gore

2019

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Organisms -especially microbes -tend to live in complex communities. While some of these ecosystems are very bio-diverse, others aren't 1-3 , and while some are very stable over time others undergo strong temporal fluctuations 4,5 . Despite a long history of research and a plethora of data it is not fully understood what sets biodiversity and stability of ecosystems 6,7 . Theory as well as experiments suggest a connection between species interaction, biodiversity, and stability of ecosystems [8][9][10][11][12][13] , where an increase of ecosystem stability with biodiversity could be observed in several cases 7,9,14 . However, what causes these connections remains unclear. Here we show in microbial ecosystems in the lab that the concentrations of available nutrients can set the strength of interactions between bacteria. At high nutrient concentrations, extensive microbial growth leads to strong chemical modifications of the environment, causing more negative interactions between species. These stronger interactions exclude more species from the community -resulting in a loss of biodiversity. At the same time, these stronger interactions also decrease the stability of the microbial communities, providing a mechanistic link between species interaction, biodiversity and stability.

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“…Modeling microbial metabolic processes at an intermediate level is appropriate to fit the population dynamics data. Inspired by the classical MacArthur's CR models 19 and many followups 13,[20][21][22] , we propose to integrate CR models with a coarse-grained yet mechanistic description of cell metabolism. Metabolic reactions can be broadly classified as catabolic and anabolic, where 6 catabolic reactions break down complex substrates from culture media into smaller metabolic intermediates that can be used to build up biomass components by anabolic reactions.…”

Section: Resultsmentioning

confidence: 99%

“…This is because, as explained above, the inferred value of : (0.33) is much greater than that of ∆ 0 (0.12). The remaining diversity of the phase space structure is primarily driven by the dose-dependent effect of acetate 24 : it serves as a 13 nutrient for the acetate specialist at low concentration but becomes inhibitory to growth of both strains when abundant ( Supplementary Fig. 1).…”

Section: Environmental Changes To Nutrients Can Reverse the Sign Of Mmentioning

confidence: 99%

“…Any model can potentially yield insights 11 , but the complexity of most models so far has been either too high for parameterization, or too low to shed light on cellular mechanisms. Microbial processes may be modelled across a range of details: At the low end of this spectrum of details we have population dynamic models such as generalized Lotka-Volterra (gLV) 12 and Consumer-Resource (C-R) models 13 , which treat each organism as a 'black-box' at the cellular level. For example, C-R models assume a linear or Monod dependence of microbial growth on resource uptake kinetics.…”

Section: Introductionmentioning

confidence: 99%

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Modeling microbial cross-feeding at intermediate scale portrays community dynamics and species coexistence

Liao

Wang

Maslov

et al. 2020

Preprint

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16Social interaction between microbes can be described at many levels of details, ranging from the 17 biochemistry of cell-cell interactions to the ecological dynamics of populations. Choosing the best 18 level to model microbial communities without losing generality remains a challenge. Here we 19 propose to model cross-feeding interactions at an intermediate level between genome-scale 20 metabolic models of individual species and consumer-resource models of ecosystems, which is 21 suitable to empirical data. We applied our method to three published examples of multi-strain 22Significance statement 32 The behavior of complex multispecies communities such as the human microbiome is hard to 33 predict by its composition alone. Our efforts to engineer such communities would benefit from 34 mechanistic models that accurately describe how microbes exchange metabolites with each other 35 and how their environment shapes these exchanges. But what is the most appropriate level of 36 details to model microbial interaction? We propose an intermediate level to model metabolic 37 exchanges that accurately describes population dynamics and stability of microbial communities. 38We demonstrate this approach by constraining models with experimental data from three 39 laboratory communities with increasing levels of complexity. Each model allows us to predict 40 metabolic byproduct leakage fractions as well as how external perturbations such as nutrient 41 variations or addition of antibiotics impact those communities. Our work paves the way to model 42 real-world applications including precise engineering of the microbiome to improve human health. 43 4 of their experimental data. We also thank Dr. Jinyuan Yan for proofreading early drafts. This work 462 was supported by NIH grants U01 AI124275 and R01 AI137269-01 to J.B.X. The funders had no 463 role in study design, data collection and analysis, decision to publish, or preparation of the 464 manuscript. 465 466 Data availability 467 The simulation data that support the conclusions of this study are available from authors upon 468 reasonable request. 469 Code availability470 23The source codes for simulations of the three cross-feeding communities are available from 471 https://github.com/liaochen1988/coarse-grained-ecology-models-for-microbial-community. 472

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Microbial coexistence through chemical-mediated interactions

Cited by 12 publications

References 90 publications

Theoretical guidelines for editing ecological communities

Theoretical guidelines for editing ecological communities

Strength of species interactions determines biodiversity and stability in microbial communities

Modeling microbial cross-feeding at intermediate scale portrays community dynamics and species coexistence

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