Decoding the Arsenal: Protist Effectors and Their Impact on Photosynthetic Hosts
Bibliographic record
Abstract
Interactions between various microbial pathogens including viruses, bacteria, fungi, oomycetes, and their plant hosts have traditionally been the focus of phytopathology. In recent years, a significant and growing interest in the study of eukaryotic microorganisms not classified among fungi or oomycetes has emerged. Many of these protists establish complex interactions with photosynthetic hosts, and understanding these interactions is crucial in understanding the dynamics of these parasites within traditional and emerging types of farming, including marine aquaculture. Many phytopathogenic protists are biotrophs with complex polyphasic life cycles, which makes them difficult or impossible to culture, a fact reflected in a wide gap in the availability of comprehensive genomic data when compared to fungal and oomycete plant pathogens. Furthermore, our ability to use available genomic resources for these protists is limited by the broad taxonomic distance that these organisms span, which makes comparisons with other genomic datasets difficult. The current rapid progress in genomics and computational tools for the prediction of protein functions and interactions is revolutionizing the landscape in plant pathology. This is also opening novel possibilities, specifically for a deeper understanding of protist effectors. Tools like AlphaFold2 enable structure-based function prediction of effector candidates with divergent protein sequences. In turn, this allows us to ask better biological questions and, coupled with innovative experimental strategies, will lead into a new era of effector research, especially for protists, to expand our knowledge on these elusive pathogens and their interactions with photosynthetic hosts. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.009 |
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".