Comparative Genomics Analysis of Three Species of Root-Lesion Nematodes, <i>Pratylenchus</i> spp., Suggests an Intricate Evolutionary Origin of Effector Genes
Bibliographic record
Abstract
Root-lesion nematodes of the genus Pratylenchus, which includes over 100 species, are among the most damaging plant-parasitic nematodes, affecting a wide range of crops globally. Their migration in and out of roots causes mechanical damage and necrosis, leading to significant yield losses worldwide. In this study, we generated high-quality genome assemblies for three Pratylenchus species, P. penetrans, P. crenatus, and P. neglectus, isolated from potato fields across Canada. Using in silico analyses, we performed comprehensive genome annotation, comparative gene family analysis, and life-stage-specific gene expression profiling to investigate candidate genes likely involved in host interactions. Horizontal gene transfer (HGT) events were also predicted using the Alienness vs Predictor tool, based on protein homology comparisons and phylogeny between metazoan and non-metazoan taxa. These analyses revealed unique genomic structures, expansions of effector genes, and putative HGT events that may contribute to parasite adaptability. Notably, in P. crenatus and P. penetrans, the diversification and expansion of effector repertoires, combined with species-specific HGT candidates, could suggest evolutionary adaptations to support a broad host range. In contrast, the more compact effectorome of P. neglectus points to a parasitic strategy based on broad acting effectors. Although these findings provide an initial genome-scale view of the molecular toolkit used by these nematodes, they are based on computational predictions and await functional validation. This study lays a foundation for future research into the molecular mechanisms underlying parasitism, host adaptation, and nematode evolution.
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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 | 0.001 | 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".