ChIP-seq analysis of the LuxR-type regulator VjbR reveals novel insights into the Brucella virulence gene expression network
Bibliographic record
Abstract
LuxR-type transcription factors control diverse physiological functions necessary for bacterial adaptation to environmental changes. In the intracellular pathogen Brucella, the LuxR homolog VjbR has been shown to regulate the expression of virulence factors acting at early stages of the intracellular infection and, directly or indirectly, hundreds of additional genes. However, the precise determination of VjbR direct targets has so far proved elusive. Here, we performed chromatin immunoprecipitation of VjbR followed by next-generation sequencing (ChIP-seq). We detected a large amount of VjbR-binding sites distributed across the Brucella genome and determined a markedly asymmetric binding consensus motif, an unusual feature among LuxR-type regulators. RNA-seq analysis performed under conditions mimicking the eukaryotic intracellular environment revealed that, among all loci associated to VjbR-binding, this regulator directly modulated the expression of only a subset of genes encoding functions consistent with an intracellular adaptation strategy for survival during the initial stages of the host cell infection. Other VjbR-binding events, however, showed to be dissociated from transcription and may require different environmental signals to produce a transcriptional output. Taken together, our results bring new insights into the extent and functionality of LuxR-type-related transcriptional networks.
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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 | 0.000 |
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".