A response regulator protein with antar domain, AvnR, in Acinetobacter baumannii ATCC 17978 impacts its virulence and amino acid metabolism
Bibliographic record
Abstract
Acinetobacter baumannii, a Gram-negative coccobacillus, is notorious for its involvement in opportunistic infections around the world. Its resistance to antibiotics makes treatment of infections challenging. In this study, we describe a novel response regulator protein, AvnR (A1S_2006) that regulates virulence-related traits in A. baumannii ATCC17978. Sequence analysis suggests that AvnR is a CheY-like response regulator and contains the RNA-binding ANTAR (AmiR and NasR transcription anti-termination regulators) domain. We show that AvnR plays a role in regulating biofilm formation (on glass and plastic surfaces), surface motility, adhesion to A549 cells as well as in nitrogen metabolism in A. baumannii . RNA-Seq analysis revealed that avnR deletion results in altered expression of more than 150 genes (116 upregulated and 42 downregulated). RNA-Seq data suggest that altered biofilm formation and surface motility observed in the avnR deletion mutant is likely mediated by previously unknown pathways. Of note, was the altered expression of genes predicted to be involved in amino acid transport and metabolism in avnR deletion mutant. Biolog phenotypic array showed that deletion of avnR hampered A. baumannii ATCC17978’s ability to metabolize various nitrogen sources, particularly that of glutamic acid, serine, histidine, aspartic acid, isoleucine and arginine. Taken together our data show that AvnR, the first ANTAR protein described in A. baumannii, affects virulence phenotypes as well as its ability to metabolize nitrogen sources.
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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) | 0.001 | 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 | 0.001 |
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".