Unraveling the effects of static magnetic field stress on cytosolic proteins of <i>Salmonella</i> by using a proteomic approach
Bibliographic record
Abstract
The present study investigated the adaptation of Salmonella enterica subsp. enterica serovar Hadar to static magnetic field (SMF) exposure (200 mT, 9 h). The proteomic analysis provides an overview of potentially important cytosolic proteins that Salmonella needs to regulate to survive and adapt to magnetic stress. Via 2-dimensional electrophoresis and liquid chromatography tandem mass spectrometry, we compared cytosolic proteomes before and after exposure to magnetic field. A total of 35 proteins displaying more than a 2-fold change were differentially expressed in exposed cells, among which 25 were upregulated and 10 were downregulated. These proteins can be classified mainly into 6 categories: (i) proteins involved in metabolic pathways of carbohydrates, (ii) chaperones and proteins produced in response to oxidative stress, (iii) proteins involved in energy homeostasis, (iv) elongation factors (EF-Tu and EF-Ts), (v) proteins involved in motility, and (vi) proteins involved in molecules transport. Many of the presented observations could be explained, while some represent still-unknown mechanisms. In addition, this study reveals 5 hypothetical proteins. It seems that the stress response to SMF (200 mT) is essentially set up to avoid oxidative damages, with the overexpression of proteins directly involved in oxidative stress response and metabolic switches to counteract oxidative stress. Interestingly, several proteins induced under SMF exposure are found to overlap with those induced by other stresses, such as heat shock and starvation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".