Plants versus Fungi and Oomycetes: Pathogenesis, Defense and Counter-Defense in the Proteomics Era
Bibliographic record
Abstract
Plant-fungi and plant-oomycete interactions have been studied at the proteomic level for many decades. However, it is only in the last few years, with the development of new approaches, combined with bioinformatics data mining tools, gel staining, and analytical instruments, such as 2D-PAGE/nanoflow-LC-MS/MS, that proteomic approaches thrived. They allow screening and analysis, at the sub-cellular level, of peptides and proteins resulting from plants, pathogens, and their interactions. They also highlight post-translational modifications to proteins, e.g., glycosylation, phosphorylation or cleavage. However, many challenges are encountered during in planta studies aimed at stressing details of host defenses and fungal and oomycete pathogenicity determinants during interactions. Dissecting the mechanisms of such host-pathogen systems, including pathogen counter-defenses, will ensure a step ahead towards understanding current outcomes of interactions from a co-evolutionary point of view, and eventually move a step forward in building more durable strategies for management of diseases caused by fungi and oomycetes. Unraveling intricacies of more complex proteomic interactions that involve additional microbes, i.e., PGPRs and symbiotic fungi, which strengthen plant defenses will generate valuable information on how pathosystems actually function in nature, and thereby provide clues to solving disease problems that engender major losses in crops every year.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".