Skip to main content
  • ASM Journals
    • Antimicrobial Agents and Chemotherapy
    • Applied and Environmental Microbiology
    • Clinical Microbiology Reviews
    • Clinical and Vaccine Immunology
    • EcoSal Plus
    • Infection and Immunity
    • Journal of Bacteriology
    • Journal of Clinical Microbiology
    • Journal of Microbiology & Biology Education
    • Journal of Virology
    • mBio
    • Microbiology and Molecular Biology Reviews
    • Microbiology Resource Announcements
    • Microbiology Spectrum
    • Molecular and Cellular Biology
    • mSphere
    • mSystems
  • Log in
  • My alerts
  • My Cart

Main menu

  • Home
  • Articles
    • Latest Articles
    • Special Issues
    • COVID-19 Special Collection
    • Editor's Picks
    • Special Series: Sponsored Minireviews and Video Abstracts
    • Archive
  • Topics
    • Applied and Environmental Science
    • Ecological and Evolutionary Science
    • Host-Microbe Biology
    • Molecular Biology and Physiology
    • Novel Systems Biology Techniques
    • Early-Career Systems Microbiology Perspectives
  • For Authors
    • Getting Started
    • Submit a Manuscript
    • Scope
    • Editorial Policy
    • Submission, Review, & Publication Processes
    • Organization and Format
    • Errata, Author Corrections, Retractions
    • Illustrations and Tables
    • Nomenclature
    • Abbreviations and Conventions
    • Publication Fees
    • Ethics
  • About the Journal
    • About mSystems
    • Editor in Chief
    • Board of Editors
    • For Reviewers
    • For the Media
    • For Librarians
    • For Advertisers
    • Alerts
    • RSS
    • FAQ
  • ASM Journals
    • Antimicrobial Agents and Chemotherapy
    • Applied and Environmental Microbiology
    • Clinical Microbiology Reviews
    • Clinical and Vaccine Immunology
    • EcoSal Plus
    • Infection and Immunity
    • Journal of Bacteriology
    • Journal of Clinical Microbiology
    • Journal of Microbiology & Biology Education
    • Journal of Virology
    • mBio
    • Microbiology and Molecular Biology Reviews
    • Microbiology Resource Announcements
    • Microbiology Spectrum
    • Molecular and Cellular Biology
    • mSphere
    • mSystems

User menu

  • Log in
  • My alerts
  • My Cart

Search

  • Advanced search
mSystems
publisher-logosite-logo

Advanced Search

  • Home
  • Articles
    • Latest Articles
    • Special Issues
    • COVID-19 Special Collection
    • Editor's Picks
    • Special Series: Sponsored Minireviews and Video Abstracts
    • Archive
  • Topics
    • Applied and Environmental Science
    • Ecological and Evolutionary Science
    • Host-Microbe Biology
    • Molecular Biology and Physiology
    • Novel Systems Biology Techniques
    • Early-Career Systems Microbiology Perspectives
  • For Authors
    • Getting Started
    • Submit a Manuscript
    • Scope
    • Editorial Policy
    • Submission, Review, & Publication Processes
    • Organization and Format
    • Errata, Author Corrections, Retractions
    • Illustrations and Tables
    • Nomenclature
    • Abbreviations and Conventions
    • Publication Fees
    • Ethics
  • About the Journal
    • About mSystems
    • Editor in Chief
    • Board of Editors
    • For Reviewers
    • For the Media
    • For Librarians
    • For Advertisers
    • Alerts
    • RSS
    • FAQ
Special Issue Perspective | Ecological and Evolutionary Science

The Power of Metabolism for Predicting Microbial Community Dynamics

Jeremy M. Chacón, William R. Harcombe
Jeremy M. Chacón
aEcology, Evolution, and Behavior, University of Minnesota, St. Paul, Minnesota, USA
bBiotechnology Institute, University of Minnesota, St. Paul, Minnesota, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
William R. Harcombe
aEcology, Evolution, and Behavior, University of Minnesota, St. Paul, Minnesota, USA
bBiotechnology Institute, University of Minnesota, St. Paul, Minnesota, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for William R. Harcombe
DOI: 10.1128/mSystems.00146-19
  • Article
  • Info & Metrics
  • PDF
Loading

ABSTRACT

Quantitative understanding and prediction of microbial community dynamics are an outstanding challenge. We test the hypothesis that metabolic mechanisms provide a foundation for accurate prediction of dynamics in microbial systems. In our research, metabolic models have been able to accurately predict species interactions, evolutionary trajectories, and response to perturbation in simple synthetic consortia. However, metabolic models have many constraints and often serve best as null models to identify additional processes at play. We anticipate that major advances in metabolic systems biology will involve scaling bottom-up approaches to complex communities and expanding the processes that are incorporated in a metabolic perspective. Ultimately, cellular metabolism will inform predictive ecology that enables precision management of microbial systems.

mSystems® vol. 4, no. 3, is a special issue sponsored by Illumina.

PERSPECTIVE

Precise prediction and management of microbial community dynamics are a goal of personalized medicine, industrial biotechnology, and environmental management. A fundamental aspect of this challenge is understanding how community composition and function emerge from lower-level information like species' metabolism. Metabolism determines the nutrients that a cell needs to survive and reproduce as well as the compounds that a cell excretes into its environment. This basic information leads to the hypothesis that an understanding of metabolism and the nutrient environment can allow for prediction of bacterial phenotypes, species interactions, and emergent community properties. Since modern genomics and systems biology have provided us with the ability to predict species’ metabolisms from sequence data, we can now rigorously test this hypothesis. In this Perspective, we will use examples from our research to argue that understanding species’ metabolism does in fact allow us to accurately predict microbial interactions, evolution, and community dynamics. However, we will also discuss important exceptions, where metabolic predictions failed. We’ll conclude by discussing what future research we think should be prioritized to reduce the limitations currently faced by metabolic systems biology.

We use several tools to quantitatively predict, and rigorously test, how microbial community dynamics emerge from intracellular metabolism. Primarily, we use dynamic genome-scale metabolic modeling to predict optimal microbial physiology from cells’ intracellular metabolic networks and the resources available in the environment (1, 2). This method quantitatively predicts the multispecies ecosystem dynamics which emerge from cells’ underlying metabolic capabilities by iterating through optimization of strain-specific models and updating the environment through time (1). We frequently supplement these genome-scale models with simpler Monod models (e.g., reference 3) to investigate general principles about how metabolites structure microbial systems, as well as to include nonmetabolic phenomena. We focus our predictions on systems comprised of some combination of the model organisms Escherichia coli, Salmonella enterica, and Methylobacterium extorquens. We engineered a strain of each species so that we can toggle interactions from obligate cross-feeding mutualism to competition simply by changing the nutrients provided. Furthermore, we have curated genome-scale metabolic models of each strain, which reduces the chance that metabolic predictions will fail due to annotation errors.

We have found that metabolism is sufficient to predict species interactions and equilibrium community composition in different nutrient environments. Genome-scale metabolic modeling was able to predict a priori the environment-specific interdependency of our 3-species mutualism, as well as the species ratios to which the community converged (1). Metabolic modeling was also instrumental in understanding how changes in available nutrients impact interactions and maintenance of diversity in cross-feeding systems. Models highlighted that as dependency on other species decreased, community stability depended on growth rates and which nutrients became limiting (3). Metabolism also drives the impact that spatial location has on bacterial interactions. For example, we were able to show that the rate of nutrient uptake strongly influences the scale over which colonies interact (4). The degree to which a colony competed only with neighbors increased predictably with increasing nutrient uptake rates and decreasing nutrient diffusion. The success of these simple explorations is promising for our ability to predict community dynamics. However, failures have also been informative. Quantitative divergence in the expected scale of competition allowed us to identify autoinhibition by toxic waste products (4). Incorporation of this unexpected self-poisoning as a modifier of growth metabolism was critical for understanding dynamics in the system. Toxicity as a modifier of metabolism is likely to be a common theme in microbial communities (5).

Beyond ecology, a metabolic approach has allowed us to make successful predictions about the evolutionary trajectory of metabolically interdependent species. Game theory and other approaches provide powerful theory for predicting the evolution of social interactions (6). However, this theory tends to provide qualitative predictions that rely on assumed costs and benefits. By incorporating metabolism, it is possible to quantify fitness effects based on stoichiometric tradeoffs and to predict the mechanistic basis of adaptation. For example, metabolic mechanisms were useful for understanding how adding an exploiter impacted selection for cooperation in a bipartite mutualism (7). Metabolic modeling predicted that addition of the exploiter in a structured environment would increase selection for cooperation, by increasing the variance in nutrient concentrations. Social evolutionary theory predicted the opposite: that adding an exploiter would make cooperation an unfit strategy. The metabolic models ultimately proved more accurate and helped us understand the mechanisms underlying experimentally observed evolution. Dynamic, genome-scale metabolic modeling has also proven capable of predicting the genetic basis of adaptation. We computationally predicted the knockouts that would have the greatest impact on species' fitness in a mutualism (8) and then subsequently observed that one of the most consequential knockouts repeatedly evolved in long-term experiments (9). As an important caveat, the observed mutation was one of several solutions predicted to be equally optimal. The fact that we repeatedly observed the same solution versus a mixture of equally optimal solutions suggests that some important biological constraints such as gene regulation were missing from the model.

Metabolism has also proven to be surprisingly useful for predicting how communities will respond to nonmetabolic perturbations. A metabolic approach predicts that different types of metabolic interactions should generate different types of response to a given perturbation. If species are engaged in nutrient competition, then inhibition of a single species tends to increase the abundance of competitors, altering species ratios but having little impact on total biomass (8). In contrast, metabolic interdependencies tend to constrain species ratios such that inhibition of any species reduces the abundance of all interdependent members of the community. In one experiment, we challenged our three-species synthetic community with antibiotics in environments that either caused competition or required interdependency (10). When the species were competing for metabolites, ecosystem productivity was not greatly affected until the antibiotic concentration was high enough to kill all species. When the species were metabolically interdependent, however, the growth of all species was limited by the most drug-sensitive member (the “weakest link”). This growth limitation also occurred when we challenged an E. coli/S. enterica interdependency with an S. enterica bacteriophage. However, initial metabolic predictions were not supported when we used a phage that killed E. coli. Instead, we saw an increase in abundance of metabolically dependent S. enterica (11). A closer examination revealed a metabolic explanation: phage lysis released nutrients in the environment which the metabolic partner could then scavenge, reminiscent of the “viral shunt” which cycles nutrients in marine food webs (12). We did not predict this impact of phage a priori, though modifying our models to include the released nutrients allowed for the experimental results to be qualitatively recapitulated with a metabolic approach. On the one hand, the iteration between model and experiment has proven to be a valuable tool for reconciling deviations from metabolic theory with the underlying metabolic framework. On the other hand, such iteration is intensive and will assuredly be harder as we move to more complex communities.

Our work has found a metabolic perspective to be a successful predictor of some, but not all, dynamics in small, well-defined consortia. Importantly, even when predictions were incorrect they served as a useful null model that helped us to identify processes that could be incorporated to explain observed community dynamics. It should be noted, however, that the power to make useful predictions in our system is strongly influenced by using well-curated models in environments with defined nutrients. Looking forward, we envision two essential tasks. First, we need to improve methods for using bottom-up approaches for predicting dynamics in complex communities. In part, this will involve improving the accuracy, and therefore predictive power, of metabolic models for uncharacterized species, for example, through better methods to gap-fill and predict biomass composition (13). Additionally, we are likely to need hybrid approaches that allow metabolic prediction in the context of uncertain environments. Incorporation of phenomenological information, for example, by measuring net ecological interactions (14), may be required when detailed metabolic information is unavailable. A second major task will be to continue to enlarge our vision of metabolism. We predict that transformative research will come from developing a mechanistic understanding of how extrametabolic processes influence metabolism. We have found that incorporating nonmetabolic phenomena (e.g., phage and toxicity) in terms of their metabolic effects paved the way to accurate predictions. Optimistically, continued research may find that nonmetabolic phenomena can be boxed into a few categories of metabolic effects, perhaps such as those which alter the metabolic rates and capabilities of cells (e.g., regulation, toxins, temperature, and pH) and those whose effects alter the nutrient environments (e.g., lytic phage and hosts). Work has begun in this direction, but there is still much to be done (15). A central goal should be to determine major categories of metabolic modifiers, predict the information computationally, and test these predictions in controlled and more complex communities. In this vision, the metabolic requirements, abilities, and by-products of species are the essential heart of predictive ecology, with all other phenomena acting as modifiers.

ACKNOWLEDGMENTS

We thank Beth Adamowicz, Lisa Fazzino, and Sarah Hammarlund for helpful feedback.

This work was supported by NIH grant GM121498.

FOOTNOTES

    • Received February 25, 2019.
    • Accepted April 24, 2019.
  • Copyright © 2019 Chacón and Harcombe.

This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.

REFERENCES

  1. 1.↵
    1. Harcombe WR,
    2. Riehl W,
    3. Dukovski I,
    4. Granger BR,
    5. Betts A,
    6. Lang AH,
    7. Bonilla G,
    8. Kar A,
    9. Leiby N,
    10. Mehta P,
    11. Marx CJ,
    12. Segre D
    . 2014. Predicting the spatio-temporal dynamics of microbial community metabolism. Cell Rep 7:1104–1115. doi:10.1016/j.celrep.2014.03.070.
    OpenUrlCrossRefPubMed
  2. 2.↵
    1. Orth JD,
    2. Thiele I,
    3. Palsson BØ
    . 2010. What is flux balance analysis? Nat Biotechnol 28:245–248. doi:10.1038/nbt.1614.
    OpenUrlCrossRefPubMedWeb of Science
  3. 3.↵
    1. Hammarlund SP,
    2. Chacón JM,
    3. Harcombe WR
    . 2019. A shared limiting resource leads to competitive exclusion in a cross-feeding system. Environ Microbiol 21:759–771. doi:10.1111/1462-2920.14493.
    OpenUrlCrossRef
  4. 4.↵
    1. Chacón JM,
    2. Möbius W,
    3. Harcombe WR
    . 2018. The spatial and metabolic basis of colony size variation. ISME J 12:669. doi:10.1038/s41396-017-0038-0.
    OpenUrlCrossRef
  5. 5.↵
    1. LaSarre B,
    2. McCully AL,
    3. Lennon JT,
    4. McKinlay JB
    . 2017. Microbial mutualism dynamics governed by dose-dependent toxicity of cross-fed nutrients. ISME J 11:337–348. doi:10.1038/ismej.2016.141.
    OpenUrlCrossRef
  6. 6.↵
    1. Estrela S,
    2. Libby E,
    3. Van Cleve J,
    4. Débarre F,
    5. Deforet M,
    6. Harcombe WR,
    7. Peña J,
    8. Brown SP,
    9. Hochberg ME
    . 2019. Environmentally mediated social dilemmas. Trends Ecol Evol 34:6–18. doi:10.1016/j.tree.2018.10.004.
    OpenUrlCrossRef
  7. 7.↵
    1. Harcombe WR,
    2. Betts A,
    3. Shapiro J,
    4. Marx CM
    . 2016. Adding biotic complexity alters the metabolic benefits of mutualism. Evolution 70:1871–1881. doi:10.1111/evo.12973.
    OpenUrlCrossRef
  8. 8.↵
    1. Chubiz L,
    2. Granger BR,
    3. Segre D,
    4. Harcombe WR
    . 2015. Species interactions differ in their genetic robustness. Front Microbiol 6:271. doi:10.3389/fmicb.2015.00271.
    OpenUrlCrossRefPubMed
  9. 9.↵
    1. Harcombe WR,
    2. Chacón JM,
    3. Adamowicz EM,
    4. Chubiz LM,
    5. Marx CJ
    . 2018. Evolution of bidirectional mutualism from byproduct consumption. Proc Natl Acad Sci U S A 115:12000–12004. doi:10.1073/pnas.1810949115.
    OpenUrlAbstract/FREE Full Text
  10. 10.↵
    1. Adamowicz EM,
    2. Flynn J,
    3. Hunter RC,
    4. Harcombe WR
    . 2018. Cross-feeding modulates antibiotic tolerance in bacterial communities. ISME J 12:2723–2735. doi:10.1038/s41396-018-0212-z.
    OpenUrlCrossRef
  11. 11.↵
    1. Fazzino L,
    2. Anisman J,
    3. Chacón JM,
    4. Heineman R,
    5. Harcombe WR
    . 2019. Lytic bacteriophage have diverse indirect effects in a synthetic cross-feeding community. bioRxiv doi:10.1101/560037.
    OpenUrlAbstract/FREE Full Text
  12. 12.↵
    1. Weitz JS,
    2. Wilhelm SW
    . 2012. Ocean viruses and their effects on microbial communities and biogeochemical cycles. F1000 Biol Rep 4:17. doi:10.3410/B4-17.
    OpenUrlCrossRef
  13. 13.↵
    1. Machado D,
    2. Andrejev S,
    3. Tramontano M,
    4. Patil KR
    . 2018. Fast automated reconstruction of genome-scale metabolic models for microbial species and communities. Nucleic Acids Res 46:7542–7553. doi:10.1093/nar/gky537.
    OpenUrlCrossRefPubMed
  14. 14.↵
    1. Friedman J,
    2. Higgins LM,
    3. Gore J
    . 2017. Community structure follows simple assembly rules in microbial microcosms. Nat Ecol Evol 1:109. doi:10.1038/s41559-017-0109.
    OpenUrlCrossRefPubMed
  15. 15.↵
    1. O’Brien EJ,
    2. Lerman JA,
    3. Chang RL,
    4. Hyduke DR,
    5. Palsson BØ
    . 2013. Genome-scale models of metabolism and gene expression extend and refine growth phenotype prediction. Mol Syst Biol 9:693. doi:10.1038/msb.2013.52.
    OpenUrlAbstract/FREE Full Text
PreviousNext
Back to top
Download PDF
Citation Tools
The Power of Metabolism for Predicting Microbial Community Dynamics
Jeremy M. Chacón, William R. Harcombe
mSystems Jun 2019, 4 (3) e00146-19; DOI: 10.1128/mSystems.00146-19

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
Print
Alerts
Sign In to Email Alerts with your Email Address
Email

Thank you for sharing this mSystems article.

NOTE: We request your email address only to inform the recipient that it was you who recommended this article, and that it is not junk mail. We do not retain these email addresses.

Enter multiple addresses on separate lines or separate them with commas.
The Power of Metabolism for Predicting Microbial Community Dynamics
(Your Name) has forwarded a page to you from mSystems
(Your Name) thought you would be interested in this article in mSystems.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
The Power of Metabolism for Predicting Microbial Community Dynamics
Jeremy M. Chacón, William R. Harcombe
mSystems Jun 2019, 4 (3) e00146-19; DOI: 10.1128/mSystems.00146-19
del.icio.us logo Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo
  • Top
  • Article
    • ABSTRACT
    • PERSPECTIVE
    • ACKNOWLEDGMENTS
    • FOOTNOTES
    • REFERENCES
  • Info & Metrics
  • PDF

KEYWORDS

metabolism
antibiotics
bacteriophage
ecology
evolution
genome-scale modeling
systems biology

Related Articles

Cited By...

About

  • About mSystems
  • Author Videos
  • Board of Editors
  • Policies
  • Overleaf Pilot
  • For Reviewers
  • For the Media
  • For Librarians
  • For Advertisers
  • Alerts
  • RSS
  • FAQ
  • Permissions
  • Journal Announcements

Authors

  • ASM Author Center
  • Submit a Manuscript
  • Author Warranty
  • Types of Articles
  • Getting Started
  • Ethics
  • Contact Us

Follow #mSystemsJ

@ASMicrobiology

       

 

ASM Journals

ASM journals are the most prominent publications in the field, delivering up-to-date and authoritative coverage of both basic and clinical microbiology.

About ASM | Contact Us | Press Room

 

ASM is a member of

Scientific Society Publisher Alliance

Copyright © 2021 American Society for Microbiology | Privacy Policy | Website feedback

Online ISSN: 2379-5077