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Record W4320481704 · doi:10.1093/plcell/koad035

Reeling in countless effectors (RICE): time-course transcriptomics of rice blast disease reveal an expanded effector repertoire for<i>Magnaporthe oryzae</i>

2023· letter· en· W4320481704 on OpenAlexaff
Bradley Laflamme

Bibliographic record

VenueThe Plant Cell · 2023
Typeletter
Languageen
FieldAgricultural and Biological Sciences
TopicPlant-Microbe Interactions and Immunity
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBiologyBlast diseaseEffectorRepertoireMagnaportheOryza sativaPlant disease resistanceBotanyMagnaporthe griseaCell biologyGeneticsGene

Abstract

fetched live from OpenAlex

Call them “empty carbs” if you want, but we owe them immensely: the major grass crops (maize, wheat, and rice) collectively account for about half of the global caloric intake. Unfortunately, our global dependance on grasses has inadvertently promoted some of the most destructive fungal phytopathogens, such as Magnaporthe oryzae, the causal agent of rice blast disease. The life cycle of M. oryzae occurs in three main steps spores land on rice plants, specialized cells called appressoria to penetrate host tissues, and then the pathogens thrive, spreads, and kill its host to feed before sporulating to spread further (Wilson and Talbot 2009). However, there remain several gaps in our knowledge about how the pathogen's virulence strategies evolve over the course of this dynamic infection cycle. The secretion of “effector” virulence proteins in host cells is known to underlie these virulence traits to some extent, but M. oryzae, like most fungal pathogens, has only had a portion of its effector repertoire revealed and appreciably studied in the context of its highly varied lifestyle. In this issue, Yan et al. (2023) take a holistic view of the M. oryzae-rice interaction using RNA-seq and offer the most comprehensive picture yet painted of the M. oryzae effector repertoire. While RNA-seq has previously been used to study M. oryzae disease development (Shimizu et al. 2019), Yan et al. made several adjustments to improve the resolution of fungal gene expression across the pathogen's multifaceted infection cycle. This included the use of multiple infection methods, two cultivars of varying sensitivity to M. oryzae, and eight time points covering a range of the most important stages in disease development (see Fig. 1). Schematic representation of the eight stages of M. oryzae infection used in this study, highlighting the gradual penetration, proliferation, and sporulation of M. oryzae over a 144-h period. The distinct gene co-expression modules relevant to each stage of infection are noted beside each timepoint. Reprinted from Yan et al. (2023), Figure 2B. Across a six-day infection time course, the group identified ten modules of co-expressed fungal genes that frequently corresponded to key phases in the pathogen's life cycle, such as appressorium development, necrotrophy, or conidiation. These modules captured not only many of the well-characterized genes involved in specific physiological processes (e.g. genes involved in appressorium development), but also the broader metabolic shifts that are known to occur over the course of M. oryzae infection, such as the switch toward secondary metabolism in the later necrotrophic stages. Having established the stage-specific transcriptome of infecting M. oryzae, Yan et al. then set their sights on elucidating the entire effector repertoire of M. oryzae—that is, all the predicted secreted proteins that might be expressed throughout infection, which may interfere with host defenses and/or aid in an infection. They identified a whopping 863 differentially accumulated transcripts encoding predicted secreted proteins, with 546 being annotated as effectors. Based on structural predictions with AlphaFold and ChimeraX (Seong and Krasileva 2021), these 546 effectors were split into hundreds of structural clusters, suggesting their involvement in a wide range of biological processes. Several clusters had interesting expression signatures across the infection time course. For example, the well-studied MAX (Magnaporthe oryzae avirulence and ToxB-like) effectors, as well as putative ADP-ribosyltransferase effectors, both appeared to be upregulated only during certain stages of biotrophic development, suggesting these structurally distinct effectors have overlapping roles in virulence. Yan et al. further characterized many of these putative effectors with an impressive array of functional assays. This included building effector-GFP fusions to assess subcellular localization and stage-specificity, validating the secretion of several effectors in planta, and showing that one novel effector, Mep1, confers a fitness advantage to M. oryzae during rice colonization. These experiments functionally validate the transcriptomic approach for the discovery of fungal effectors, while hinting at the hundreds of other possible functional pathways involved. Standing alongside recent insights into the structure and evolution of fungal effectors (Seong and Krasileva 2023), this study by Yan et al. should invigorate fungal phytopathologists, who are becoming more and more invested in identifying and functionally cataloging effectors. The structural and regulatory complexity of fungal effectors has long made their study difficult relative to those of bacteria, but it is exciting to see that advances in genomics, transcriptomics, and structural biology may see fungi quickly catch up to the prokaryotes.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0110.011
Insufficient payload (model declined to judge)0.0020.003

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.

Opus teacher head0.019
GPT teacher head0.215
Teacher spread0.196 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations1
Published2023
Admission routes1
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