Whole-Genome Expression Profiling Defines the HrpL Regulon of <i>Pseudomonas syringae</i> pv. <i>tomato</i> DC3000, Allows de novo Reconstruction of the Hrp <i>cis</i> Element, and Identifies Novel Coregulated Genes
Bibliographic record
Abstract
Pseudomonas syringae pv. tomato DC3000 is a model pathogen of tomato and Arabidopsis that uses a hypersensitive response and pathogenicity (Hrp) type III secretion system (T3SS) to deliver virulence effector proteins into host cells. Expression of the Hrp system and many effector genes is activated by the HrpL alternative sigma factor. Here, an open reading frame-specific whole-genome microarray was constructed for DC3000 and used to comprehensively identify genes that are differentially expressed in wild-type and deltahrpL strains. Among the genes whose differential regulation was statistically significant, 119 were upregulated and 76 were downregulated in the wild-type compared with the deltahrpL strain. Hierarchical clustering revealed a subset of eight genes that were upregulated particularly rapidly. Gibbs sampling of regions upstream of HrpL-activated operons revealed the Hrp promoter as the only identifiable regulatory motif and supported an iterative refinement involving real-time polymerase chain reaction testing of additional HrpL-activated genes and refinements in a hidden Markov model that can be used to predict Hrp promoters in P. syringae strains. This iterative bioinformatic-experimental approach to a comprehensive analysis of the HrpL regulon revealed a mix of genes controlled by HrpL, including those encoding most type III effectors, twin-arginine transport (TAT) substrates, other regulatory proteins, and proteins involved in the synthesis or metabolism of phytohormones, phytotoxins, and myo-inositol. This analysis provides an extensively verified, robust method for predicting Hrp promoters in P. syringae genomes, and it supports subsequent identification of effectors and other factors that likely are important to the host-specific virulence of P. syringae.
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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".