Evolving a plant-beneficial bacterium in soil vs. nutrient-rich liquid culture has contrasting effects on in-soil fitness
Bibliographic record
Abstract
ABSTRACT Inoculation of plant-beneficial microbes into agricultural soils can improve crop growth, but such outcomes depend on microbial survival. Here, we assessed how exposure to prior environmental conditions impacts microbial in-soil fitness, particularly focusing on incubation in liquid culture as an unavoidable phase of inoculant production and on pre-incubation in target soils as a potential method to improve performance. We conducted experimental evolution on a phosphorus-solubilizing bacterial species, Priestia megaterium , in (i) soil only, (ii) liquid media only, and (iii) soil followed by liquid media, using population metagenomic sequencing to track mutations over time. Several typical in vitro evolutionary phenomena were observed in liquid media-incubated populations, including clonal interference, genetic hitchhiking, and mutation parallelism between replicate populations, particularly in the sporulation transcription factor spo0A . Liquid media-incubated populations also developed a clear fitness reduction in soil compared to the ancestral isolate. However, soil-incubated populations grew slowly, experienced far fewer generations despite longer absolute time, and accumulated minimal mutational changes. Correspondingly, soil-incubated populations did not display improved survival compared to the ancestral isolate in their target soils, though there did appear to be minor fitness reductions in unfamiliar soil. This work demonstrates that adaptation to liquid media and/or a native soil can impact bacterial fitness in new soil and that bacterial evolution in more complex real-world habitats does not closely resemble bacterial evolution in liquid media. IMPORTANCE Innovative solutions are needed to address emerging challenges in agriculture while reducing its environmental footprint. Management of soil microbiomes could contribute to this effort, as plant growth-promoting microorganisms provide key ecosystem services that support crops. Yet, inoculating beneficial microbes into farm soils yields unreliable results. We require a greater knowledge of the ecology of these taxa to improve their functioning in sustainable agroecosystems. In this report, we demonstrate that exposure to laboratory media and lingering adaptation to another soil can negatively impact the in-soil survival of a phosphorus-solubilizing bacterial species. We go further to highlight the underlying mutations that give rise to these patterns. These insights can be leveraged to improve our understanding of how soil-dwelling beneficial microorganisms adapt to different evolutionary pressures.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".