When is microbial cross-feeding evolutionarily stable?
Bibliographic record
Abstract
Abstract Cross-feeding, a phenomenon in which organisms share metabolites, is frequently observed in microbial communities across the natural world. One of the most common forms is waste-product cross-feeding, a unidirectional interaction in which the waste products of one microbe support the growth of another. Despite its ubiquity, it is not well-understood why waste-product cross-feeding persists when a single organism could in principle perform both the producer and consumer role. To address this question, we first analyze cross-feeding evolution in a minimal model of microbial metabolism. The model describes multi-step extraction of energy from a substrate in a simple but thermodynamically correct formulation. Surprisingly, we find that cross-feeding is never evolutionarily stable in this model. By analyzing models with more complex growth functions, we identify a novel mechanism for the evolutionary stability of waste-product cross-feeding, namely, generalized intracellular metabolite toxicity. Such toxicity arises because, in excess, the same intracellular metabolites that cells require for metabolism can be detrimental to growth (e.g., due to osmotic stress). We show that some but not all forms of such toxicity can lead to evolutionarily stable consortia of microbes that cross-feed waste products. This stability results from the potential of such consortia to divide the burden of toxic metabolites among a larger population, allowing them to perform their collective metabolism more efficiently than non-cross-feeders. More generally, we predict that growth penalties that scale nonlinearly with intracellular metabolite levels promote cross-feeding. We find that this mechanism for cross-feeding evolutionary stability implies nontrivial population dynamics, such as a discontinuity in population biomass at the onset of cross-feeding. Significance statement The chemical reactions performed by microbes have large impacts on our world: from nitrification within the nitrogen cycle to the breakdown of fiber in animal digestive tracts. A striking commonality in many of these processes is that the chemical reactions are a collective effort, with the complete reaction subdivided between many microbes. This phenomenon is known as cross-feeding, and its origins are poorly understood. Understanding the eco-evolutionary forces promoting cross-feeding have the potential to not only enhance our understanding of natural ecosystems, but also improve our ability to engineer such distributed reactions in biotechnology. Here, we develop mathematical theory for the evolution of a common type of cross-feeding and provide predictions for what metabolic and environmental conditions promote this behavior.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".