Bioinformatics-Enabled Identification of the HrpL Regulon and Type III Secretion System Effector Proteins of <i>Pseudomonas syringae</i> pv. <i>phaseolicola</i> 1448A
Bibliographic record
Abstract
The ability of Pseudomonas syringae pv. phaseolicola to cause halo blight of bean is dependent on its ability to translocate effector proteins into host cells via the hypersensitive response and pathogenicity (Hrp) type III secretion system (T3SS). To identify genes encoding type III effectors and other potential virulence factors that are regulated by the HrpL alternative sigma factor, we used a hidden Markov model, weight matrix model, and type III targeting-associated patterns to search the genome of P. syringae pv. phaseolicola 1448A, which recently was sequenced to completion. We identified 44 high-probability putative Hrp promoters upstream of genes encoding the core T3SS machinery, 27 candidate effectors and related T3SS substrates, and 10 factors unrelated to the Hrp system. The expression of 13 of these candidate HrpL regulon genes was analyzed by real-time polymerase chain reaction, and all were found to be upregulated by HrpL. Six of the candidate type III effectors were assayed for T3SS-dependent translocation into plant cells using the Bordetella pertussis calmodulin-dependent adenylate cyclase (Cya) translocation reporter, and all were translocated. PSPPH1855 (ApbE-family protein) and PSPPH3759 (alcohol dehydrogenase) have no apparent T3SS-related function; however, they do have homologs in the model strain P. syringae pv. tomato DC3000 (PSPTO2105 and PSPTO0834, respectively) that are similarly upregulated by HrpL. Mutations were constructed in the DC3000 homologs and found to reduce bacterial growth in host Arabidopsis leaves. These results establish the utility of the bioinformatic or candidate gene approach to identifying effectors and other genes relevant to pathogenesis in P. syringae genomes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".