Whole-Genome Transcriptional Analysis of Escherichia coli during Heat Inactivation Processes Related to Industrial Cooking
Bibliographic record
Abstract
Escherichia coli K-12 was grown to the stationary phase, for maximum physiological resistance, in brain heart infusion (BHI) broth at 37°C. Cells were then heated at 58°C or 60°C to reach a process lethality value \[\mathbf{\left(}{{\mathit{F}}^{\mathit{o}}}_{\mathbf{70}}^{\mathbf{10}}\mathbf{\right)} \] of 2 or 3 or to a core temperature of 71°C (control industrial cooking temperature). Growth recovery and cell membrane integrity were evaluated immediately after heating, and a global transcription analysis was performed using gene expression microarrays. Only cells heated at 58°C with F(o) = 2 were still able to grow on liquid or solid BHI broth after heat treatment. However, their transcriptome did not differ from that of bacteria heated at 58°C with F(o) = 3 (P value for the false discovery rate [P-FDR] > 0.01), where no growth recovery was observed posttreatment. Genome-wide transcriptomic data obtained at 71°C were distinct from those of the other treatments without growth recovery. Quantification of heat shock gene expression by real-time PCR revealed that dnaK and groEL mRNA levels decreased significantly above 60°C to reach levels similar to those of control cells at 37°C (P < 0.0001). Furthermore, despite similar levels of cell inactivation measured by growth on BHI media after heating, 132 and 8 genes were differentially expressed at 71°C compared to 58°C and 60°C at F(o) = 3, respectively (P-FDR < 0.01). Among them, genes such as aroA, citE, glyS, oppB, and asd, whose expression was upregulated at 71°C, may be worth investigating as good biomarkers for accurately determining the efficiency of heat treatments, especially when cells are too injured to be enumerated using growth media.
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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 | 0.001 |
| 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".