Autoclaving and long-term storage deplete glutamine sources in complex media and affect bacterial phenotypes
Bibliographic record
Abstract
Abstract Autoclave sterilization is the most common method for sterilizing reagents and media in biology. However, the effects of heat-induced loss or modification of complex medium components on bacterial growth and phenotypes remain poorly understood. Here, we investigated the impact of autoclaving on glutamine sources in bacterial complex media using an Escherichia coli Δ glnA mutant, which requires exogenous glutamine for growth. Δ glnA exhibited impaired growth in LB medium after autoclaving compared with non-autoclaved LB, whereas its residual growth indicated the presence of heat-stable glutamine sources. Growth assays and HPLC quantification revealed that free glutamine in LB decreased from 58 µM to 12 µM upon autoclaving, while heat-stable glutamine sources remained at 117 µM. Similar growth defects were observed for Δ glnA in autoclaved BHI, TSB, and M17 media compared with their non-autoclaved counterparts. Long-term storage of LB at room temperature for 24 weeks also reduced Δ glnA growth regardless of autoclaving, compared with freshly prepared LB. Furthermore, supplementation of glutamine sources into glutamine-deficient MHB medium enhanced biofilm formation by Pseudomonas aeruginosa . Collectively, these results demonstrate that autoclaving and storage reduce glutamine sources in complex media, thereby influencing bacterial growth and phenotypes.
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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 | 0.001 |
| 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".