Gene co-expression analysis reveals conserved and distinct gene networks between resistant and susceptible Lens ervoides challenged by hemibiotrophic and necrotrophic pathogens
Bibliographic record
Abstract
As field crops are likely to be challenged by multiple pathogens during their development, the investigation of broad-spectrum resistance in the host is of great interest for crop genetic enhancement. In this study, we attempted to address this question by adopting a weighed gene co-expression approach to study the temporal transcriptome dynamics of resistant and susceptible recombinant inbred lines (RILs) derived from an intraspecific Len ervoides cross during the infection process with either the necrotrophic pathogens Ascochyta lentis or Stemphylium botryosum, or the hemibiotrophic pathogen Colletotrichum lentis. By comparing networks of resistant and susceptible RILs, seven network module pairs were found to possess high correlation coefficients (R > 0.70) and large number of overlapping genes (n > 100). The conserved co-regulation of genes in these network module pairs were involved in plant cell wall synthesis, cell division, cytoskeleton organization, and protein ubiquitin related processes and appeared to be common disease responses against these pathogens. On the other hand, we also identified eight modules with low correlation between resistance and susceptibility networks. Among those, a stronger gene co-expression in R-genes and small RNA processes in the resistant hosts may be enhancing L. ervoides resistance against A. lentis, C. lentis, and S. botryosum, whereas the higher level of synergistic regulation in the synthesis of arginine and glutamine and phospholipid and glycerophospholipids in the susceptible hosts may contribute to increased susceptibility in L. ervoides.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".