The primary transcriptome, small RNAs, and regulation of antimicrobial resistance in <i>Acinetobacter baumannii</i>
Bibliographic record
Abstract
A BSTRACT We present the first high-resolution determination of transcriptome architecture in the priority pathogen Acinetobacter baumannii . Pooled RNA from 16 laboratory conditions was used for differential RNA-seq (dRNA-seq) to identify 3731 transcriptional start sites (TSS) and 110 small RNAs, including the first identification in A. baumannii of sRNAs encoded at the 3’ end of coding genes. Most sRNAs were conserved among sequenced A. baumannii genomes, but were only weakly conserved or absent in other Acinetobacter species. Single nucleotide mapping of TSS enabled prediction of −10 and −35 RNA polymerase binding sites and revealed an unprecedented base preference at position +2 that hints at an unrecognized transcriptional regulatory mechanism. To apply functional genomics to the problem of antimicrobial resistance, we dissected the transcriptional regulation of the drug efflux pump responsible for chloramphenicol resistance, craA . The two craA promoters were both down-regulated >1000-fold when cells were shifted to nutrient limited medium. This conditional down-regulation of craA expression renders cells sensitive to chloramphenicol, a highly effective antibiotic for the treatment of multidrug resistant infections. An online interface that facilitates open data access and visualization is provided as “AcinetoCom” ( http://bioinf.gen.tcd.ie/acinetocom/ ).
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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 | 0.001 |
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".