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Experimental core: asymmetric, partner-dependent selection

In swimming-agar competitions (n = 5 per treatment), E. coli motility was favoured with a motile S. enterica partner (w = +0.84 ± 0.23, p = 0.043) but selected against with a non-motile partner (w = −0.88 ± 0.16, p = 0.014; Tukey Δw = 1.720, p < 0.001). S. enterica motility was favoured with both partners (+2.41 ± 0.67 and +1.98 ± 0.11), with no significant difference between partner conditions (Δw = 0.436, p = 0.75) [paper text].

Well-mixed liquid assays isolated the cost of motility, which did not differ between partner conditions (E. coli Δs = 0.024, p = 0.47). The spatial benefit for E. coli with a non-motile partner collapsed to +0.021 ± 0.016 per generation (p = 0.23), versus +0.154 to +0.434 in the five other treatments; benefit/cost ratios ranged 1.5–2.9 where motility was favoured, but only 0.21 for E. coli + non-motile S. enterica. Thus the reversal reflects loss of spatial benefit, not an increased cost.

Model-based mechanism

A 1D reaction–diffusion consumer-resource model (undirected motility; Monod kinetics; empirically derived parameters) qualitatively recapitulated all four experimental outcomes and identifies three determinants of motility selection: (1) the metabolite accumulation ratio (ϖ = production/consumption) — scarce cross-fed metabolites create exploitable depletion-front gradients; (2) the number of limiting resources — co-limited E. coli requires lactose and methionine colocalised, making its motility benefit partner-dependent (a synthetic single-resource E. coli is favoured regardless of partner); and (3) partner motility and growth-rate asymmetry (ς = 0.23), which controls where metabolite production occurs. Metabolites diffuse ~10× faster than cells (ϱ ≈ 0.1), bounding beneficial dispersal below ϱ ≈ 0.3.

Strengths, limitations, falsification

Strengths: clean cost–benefit decomposition (liquid vs agar), mirrored by a mechanistic model, with sensitivity analyses showing results hold across four orders of magnitude of ϖ where ϖMϖA > 1, plus grid-convergence checks. Limitations: single species pair and single geometry; 1D model vs 2D radial experiment; chemotaxis excluded; ϖM relies on an effective (not directly measured) methionine production value; n = 5 with multiple comparisons. The central claim would weaken if replicated agar competitions with a non-motile S. enterica partner failed to disfavour motile E. coli (w ≥ 0). Context: findings align with independent evolution experiments showing cross-feeding preserves motility that uniform external supply erodes, supporting a broad biogenic-vs-uniform resource framing.

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Study Novelty

Extends dispersal-evolution theory (which predicted disfavour in obligate mutualisms) to diffusible-benefit cross-feeding, adding metabolite scarcity and co-limitation rules; prior work (Rosazza 2026; Wang 2026) touched adjacent questions, making this a substantial but not revolutionary advance.

Scientific Quality

Clean factorial design, Holm-Bonferroni corrections, cost-benefit decomposition, model sensitivity and grid-convergence checks. Weaknesses: n=5, effective (assumed) ϖM parameter, 1D model vs 2D experiment, single species pair.

Study Generality

The three-rule framework (accumulation ratio, co-limitation, partner spatial behaviour) is conceptually general to any diffusibly mediated mutualism, but empirically tested in only one E. coli–S. enterica system and one geometry.

Study Usefulness

Provides a predictive framework for when motility evolves in structured microbial communities, relevant to synthetic consortium design and interpreting motility polymorphisms; immediate practical application is limited to lab-model systems.

Study Reproducibility

Full strain construction, media recipes, statistics, and code (github.com/naven22/motility_evolution_mutualism) are reported; some model parameters are assumed rather than measured, and no raw data accession is stated.

Explanatory Depth

Mechanistic, quantitative link from metabolite accumulation ratios and co-limitation through spatial gradients to selection outcomes, with falsifiable in-silico perturbations (flipped ϖ, synthetic single-resource E. coli) that confirm the causal chain.