The small membrane protein YohP induces membrane depolarization and ppGpp accumulation in <i>Escherichia coli</i>
Bibliographic record
Abstract
Summary Small membrane proteins represent an abundant and ubiquitous class of proteins that are often up-regulated when cells encounter unfavorable conditions, yet details about their exact function are largely missing. In bacteria, these proteins consist of typically less than 50 amino acids and contain a single transmembrane domain, but lack any detectable catalytic activity. Thus, the benefit of producing these proteins during stress conditions is unknown. In the current study we used a multidisciplinary approach to determine the function of the 27 amino acid long protein YohP in E. coli . Our proteomics approach revealed that YohP production leads to an up-regulation of proteins involved in membrane protection and to a down-regulation of many enzymes involved in key metabolic processes, such as nucleotide biosynthesis. Further biochemical characterizations revealed increased cardiolipin content in the membrane, a partial dissipation of the membrane potential and reduced membrane fluidity in YohP-containing membranes. Finally, our data show that YohP production induces the stringent response and leads to elevated levels of (p)ppGpp. Overall, our data indicate that the YohP-induced proteome and membrane changes initiate a state of metabolic silencing that protects E. coli against stress and helps to conserve cellular resources.
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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".