Hijacking host microPEP: pathogens modulate the microRNA-microPEP loop to promote infection
Bibliographic record
Abstract
ABSTRACT The partners of an ecological association tend to copy the biological system of their hosts. We hypothesized that microorganisms, particularly pathogens, have acquired the ability to express short peptides (pathoPEPs) homologous to host microPEPs (miPEPs) thus modulating the expression of the corresponding host microRNA (miRNA) and the function of miRNA-targeted genes. The pathosystem involving interactions between Brassica napus and its pathogen Plasmodiophora brassicae was studied. Using in silico analysis and ribosomal profiling, we identified three putative pathoPEPs produced by P. brassicae and their targeted plant miRNA genes. A link between the level of infection of B. napus by P. brassicae and the expression of pathoPEPs and their targeted miRNA genes was found, with the expression of the latter two being inversely related. Finally, we identified differential expression and translation of genes predicted to be targets of pathoPEP-regulated miRNAs. These genes, involved in auxin pathway, immune defense, root architecture or carbohydrate metabolism, are thought to enable P. brassicae , through its pathoPEPs, to hijack plant’s metabolic pathways (hormonal pathways, sugar synthesis, root morphology), thereby facilitating its invasion. Using computational in silico approaches, the involvement of miPEPs from plant pathogens as a host post-transcriptional regulatory pathway is described herein for the first time.
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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) | 0.002 | 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".