Resource Competition: When communities collide

  1. Jason Merritt
  2. Seppe Kuehn  Is a corresponding author
  1. University of Illinois at Urbana-Champaign, United States

In an unending process that Charles Darwin called “the struggle for existence,” all organisms compete for survival. Studies of microbes, which live in complex communities that contain hundreds of interacting species, have taught us much about the dynamics of this struggle (Vetsigian et al., 2011). Moreover, we have recently learned that entire microbial communities can behave as cohesive structures, responding to change and challenge as if each community was a single organism. In the case of fecal transplants, for example, when a healthy microbial community is introduced into the bowel of a sick patient, we often observe the new community outcompeting the resident community responsible for the disease (Khoruts et al., 2010).

But why and how does one of these microbial communities outcompete another? What makes them behave in a cohesive way, instead of collapsing into their constituent species under the pressure of such competition? Now, in eLife, Mikhail Tikhonov of Harvard University reports how a simple modeling framework can help us to understand what happens when microbial communities collide (Tikhonov, 2016).

One way to understand how one microbial community competes with another is to take a “bottom-up” approach and describe the interactions between each species in the community. Within a single community, however, many chemical, physical and ecological processes happen simultaneously. Given the sheer number of processes at work when two communities compete, predicting the outcome of a competition event from the bottom up is daunting. Even so, sophisticated computational models may provide a route toward making such predictions (Klitgord and Segrè, 2010; Freilich et al., 2011).

Another way to explore the mysteries of this competition is to look at the problem from a “top-down” perspective. Here the idea is to use simplified mathematical models that focus on large-scale changes to look for statistical or organizational principles that shape communities (O'Dwyer et al., 2012; Hekstra and Leibler, 2012).

Tikhonov takes a top-down approach and builds on a classic ecological model proposed by the late Robert MacArthur almost 50 years ago (MacArthur, 1969). In the model, environments are uniform and contain multiple resources. The species that populate these environments are distinguished only by the resources they can use and by the price they pay for using those resources. Each species may consume one or many resources, although consuming more resources comes at an increasing cost. Critically, the model permits no cooperation between species: they can only compete with each other for resources.

When an environment is first populated by a random set of organisms in Tikhonov’s model, the population achieves an equilibrium that is determined by the different species present in the community. You might expect that the species with the lowest nutrient costs for reproduction would come to dominate the community at equilibrium. But because the growth of each species depends on the other species present in the community, things turn out differently. For example, a species that can grow efficiently on a resource for which there is high demand from other species will ultimately be forced to share that resource, thus slowing its growth. However, a specialized species that can grow and reproduce slowly on a little-used resource will eventually flourish.

Tikhonov’s model shows that the community dynamics modeled in this way do not optimize the fitness of individuals, but instead optimize the community “fitness”, as measured by the ability of the entire population to consume resources fully: the more thoroughly a community exhausts the available resources, the more fit it is (Figure 1). A similar idea has been proposed previously for predicting the outcome of competition between species (Tilman, 1982).

Community collision and cohesion.

Two groups of randomly chosen species are separately grown in environments with various resources, yielding two distinct communities. A "high fitness" community (A, blue) consumes nearly all available resources, and a "low fitness" community (B, red) uses less of the available resources. Note that fitness is defined by the ability to consume resources; therefore otherwise low-performing individuals can form a high-fitness community as long as they consume all the resources in the environment. When species from both groups are grown together in a new environment, species from the low fitness community are more likely to go extinct, resulting in the cohesion of the high fitness community even when the interactions between species in the high-fitness community are purely competitive.

With this framework in place, Tikhonov carried out simulations in which two initial populations of different species are seeded in entirely separate environments and allowed to evolve to equilibrium. When that is achieved, the species from both environments are mixed together and allowed to equilibrate once more, and the resulting composition of the combined community is examined.

Unexpectedly, the fitness of individual species does not reliably predict which species survive and thrive during this coalescence process. Instead, the overall fitness of each initial community is a much better predictor of the composition of the final combined community. For example, a community composed of low-performing species that exhaustively depletes resources in its environment outcompetes a community of high-performing species that uses resources poorly. Tikhonov shows that this is a direct consequence of interactions that cause organisms to influence their environment because this alters the fitness of individual species regardless of how well they perform in a random environment. For example, if a community contains a species that drives a specific resource to very low levels, the presence of this species `constructs’ an environment for other species in the community where this resource level is low. Thus, remarkably, communities cohere even when all the species in the community selfishly compete with each other. At present it is not known if the mechanism proposed by Tikhonov for community cohesion is at play in real-world processes like fecal transplants, but this is an important avenue for future work.

Microbial ecologists have long used the metaphor of the community as an individual (Shapiro, 1998). Tikhonov’s model makes this metaphor mathematically exact. In particular, the changing abundances of different species in the community act in the same way as regulated metabolic pathways act within a single organism as it responds to changes in the availability of resources.

Though conventional logic might lead us to assume that cohesive communities arise from cooperative interactions, Tikhonov’s model forces us to think again, reminding us of the critical role that theory can play in helping us understand systems as complex as microbial communities. In the future, with sequencing data now available on microbial communities in virtually any setting, our search for the signatures of community cohesion during competition will be guided by theory.

References

    1. MacArthur R
    (1969) Species packing, and what competition minimizes
    Proceedings of the National Academy of Sciences of the United States of America 64:1369–1371.
    https://doi.org/10.1073/pnas.64.4.1369
  1. Book
    1. Tilman D
    (1982)
    Resource Competition and Community Structure
    Princeton University Press.

Article and author information

Author details

  1. Jason Merritt

    Department of Physics, University of Illinois at Urbana-Champaign, Urbana, United States
    Competing interests
    The authors declare that no competing interests exist.
  2. Seppe Kuehn

    Department of Physics, University of Illinois at Urbana-Champaign, Urbana, United States
    For correspondence
    seppe@illinois.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4130-6845

Publication history

  1. Version of Record published: July 15, 2016 (version 1)

Copyright

© 2016, Merritt et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 2,257
    views
  • 230
    downloads
  • 1
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Jason Merritt
  2. Seppe Kuehn
(2016)
Resource Competition: When communities collide
eLife 5:e18753.
https://doi.org/10.7554/eLife.18753

Further reading

    1. Computational and Systems Biology
    2. Genetics and Genomics
    Weichen Song, Yongyong Shi, Guan ning Lin
    Tools and Resources

    We propose a new framework for human genetic association studies: at each locus, a deep learning model (in this study, Sei) is used to calculate the functional genomic activity score for two haplotypes per individual. This score, defined as the Haplotype Function Score (HFS), replaces the original genotype in association studies. Applying the HFS framework to 14 complex traits in the UK Biobank, we identified 3619 independent HFS–trait associations with a significance of p < 5 × 10−8. Fine-mapping revealed 2699 causal associations, corresponding to a median increase of 63 causal findings per trait compared with single-nucleotide polymorphism (SNP)-based analysis. HFS-based enrichment analysis uncovered 727 pathway–trait associations and 153 tissue–trait associations with strong biological interpretability, including ‘circadian pathway-chronotype’ and ‘arachidonic acid-intelligence’. Lastly, we applied least absolute shrinkage and selection operator (LASSO) regression to integrate HFS prediction score with SNP-based polygenic risk scores, which showed an improvement of 16.1–39.8% in cross-ancestry polygenic prediction. We concluded that HFS is a promising strategy for understanding the genetic basis of human complex traits.

    1. Computational and Systems Biology
    Qianmu Yuan, Chong Tian, Yuedong Yang
    Tools and Resources

    Revealing protein binding sites with other molecules, such as nucleic acids, peptides, or small ligands, sheds light on disease mechanism elucidation and novel drug design. With the explosive growth of proteins in sequence databases, how to accurately and efficiently identify these binding sites from sequences becomes essential. However, current methods mostly rely on expensive multiple sequence alignments or experimental protein structures, limiting their genome-scale applications. Besides, these methods haven’t fully explored the geometry of the protein structures. Here, we propose GPSite, a multi-task network for simultaneously predicting binding residues of DNA, RNA, peptide, protein, ATP, HEM, and metal ions on proteins. GPSite was trained on informative sequence embeddings and predicted structures from protein language models, while comprehensively extracting residual and relational geometric contexts in an end-to-end manner. Experiments demonstrate that GPSite substantially surpasses state-of-the-art sequence-based and structure-based approaches on various benchmark datasets, even when the structures are not well-predicted. The low computational cost of GPSite enables rapid genome-scale binding residue annotations for over 568,000 sequences, providing opportunities to unveil unexplored associations of binding sites with molecular functions, biological processes, and genetic variants. The GPSite webserver and annotation database can be freely accessed at https://bio-web1.nscc-gz.cn/app/GPSite.