Transcriptomic dissection of the horizontally acquired response regulator EsrB reveals its global regulatory roles in the physiological adaptation and activation of T3SS and the cognate effector repertoire in<i>Edwardsiella piscicida</i>during infection toward turbot
Bibliographic record
Abstract
Edwardsiella piscicida is the leading pathogen threatening worldwide aquaculture industries. The 2-component system (TCS) EsrA-EsrB is essential for the pathogenesis of this bacterium. However, little is known about the regulon and regulatory mechanism of EsrA-EsrB or about the factors that mediate the interaction of TCS with bacterial hosts. Here, our RNA-seq analysis indicated that EsrB strongly induces type III and type VI secretion systems (T3/T6SS) expression and that it modulates the expression of both physiology- and virulence-associated genes in E. piscicida grown in DMEM. EsrB binds directly to a highly conserved 18-bp DNA motif to regulate the expression of T3SS and other genes. EsrB/DMEM-activated genes include 3 known and 6 novel T3SS-dependent effectors. All these effector genes are highly induced by EsrB during the late stage of in vivo infection in fish. Furthermore, although in vivo colonization by the bacterium relies on EsrB and T3/T6SS expression, it does not require the expression of individual effectors other than EseJ. The mutant lacking these 9 effectors showed significant defects in in vivo colonization and virulence toward turbot, and, more importantly, a high level of protection against challenges by wild-type E. piscicida, suggesting that it may represent a promising live attenuated vaccine. Taken together, our data demonstrate that EsrB plays a global regulatory role in controlling physiologic responses and the expression of T3SS and its cognate effector genes. Our findings will facilitate further work on the mechanism of molecular pathogenesis of this bacterium during infection.
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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 | 0.001 |
| 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".