An Effective Method for Quantifying RNA Expression of IbsC‐SibC, a Type I Toxin‐Antitoxin System in <i>Escherichia coli</i>
Bibliographic record
Abstract
Toxin and antitoxin (TA) systems are small genetic modules consisting of a toxin protein and an RNA or protein antitoxin. It is difficult to study their functions in a large part due to the lack of effective methods to study toxin RNAs, which usually exist at exceptionally low levels. Herein, we describe a sensitive reverse transcription quantitative PCR (RT-qPCR) method that is able to quantitate such RNA species. The method was directed at detection of the toxin mRNA of the ibsC-sibC TA pair, and its high specificity was validated by sequencing. The approach was used to determine relative expression of the IbsC and SibC RNAs at different cell-growth phases; this revealed an expression pattern that cannot be explained by the prevailing notion of growth stasis by the toxin and rescue by the antitoxin. The usefulness of the method was further showcased by the determination of average cellular copy numbers of the IbsC-SibC RNAs in wild-type E. coli cells and RNA abundance in E. coli cells engineered with extra copies of the ibsC-sibC genes. With a robust method to quantitate cellular small RNAs at very low concentrations, we are now equipped to study the expression of TA systems under different conditions to gain useful insights about their functions.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".