A Global Metabolic Shift Is Linked to Salmonella Multicellular Development
Bibliographic record
Abstract
Bacteria can elaborate complex patterns of development that are dictated by temporally ordered patterns of gene expression, typically under the control of a master regulatory pathway. For some processes, such as biofilm development, regulators that initiate the process have been identified but subsequent phenotypic changes such as stress tolerance do not seem to be under the control of these same regulators. A hallmark feature of biofilms is growth within a self-produced extracellular matrix. In this study we used metabolomics to compare Salmonella cells in rdar colony biofilms to isogenic csgD deletion mutants that do not produce an extracellular matrix. The two populations show distinct metabolite profiles. Even though CsgD controls only extracellular matrix production, metabolite signatures associated with cellular adaptations associated with stress tolerances were present in the wild type but not the mutant cells. To further explore these differences we examine the temporal gene expression of genes implicated in biofilm development and stress adaptations. In wild type cells, genes involved in a metabolic shift to gluconeogenesis and various stress-resistance pathways exhibited an ordered expression profile timed with multicellular development even though they are not CsgD regulated. In csgD mutant cells, the ordered expression was lost. We conclude that the induction of these pathways results from production of, and growth within, a self produced matrix rather than elaboration of a defined genetic program. These results predict that common physiological properties of biofilms are induced independently of regulatory pathways that initiate biofilm formation.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".