Transcriptomic analysis provides insight into defensive strategies in response to continuous cropping in strawberry (Fragaria × ananassa Duch.) plants
Bibliographic record
Abstract
BACKGROUND: Strawberries are an important economic fruit crop world-wide. In strawberry cultivation, continuous cropping (CC) can seriously threaten yield and quality. However, our understanding of the gene expression changes in response to CC and during subsequent defense processes is limited. In this study, we analyzed the impact of CC on the transcriptome of strawberry roots using RNA-Seq technology to elucidate the effect of CC and the subsequent molecular changes. RESULTS: We found that CC significantly affects the growth of strawberry plants. The transcriptome analysis identified 136 differentially expressed genes (DEGs), including 49 up-regulated and 87 down-regulated DEGs. A Gene Ontology (GO) analysis indicated that the up-regulated DEGs were mainly assigned to defense-related GO terms, and most down-regulated DEGs were assigned to nutrient-related GO terms. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed that the responsive DEGs were classified in a large number of important biological pathways, such as phenylalanine metabolism, starch and sucrose metabolism, phenylpropanoid biosynthesis, glutathione metabolism and plant-pathogen interaction. We also found that four WRKY transcription factors and three peroxidase genes involved in plant defense pathways were up-regulated in the roots of strawberry plants subjected to CC. CONCLUSION: Several unigenes involved in plant defense processes, such as CNGCs, WRKY transcription factors, PR1, and peroxidase genes with highly variable expression levels between non-CC and CC treatments may be involved in the regulation of CC in strawberry. These results indicate that strawberry roots reallocate development resources to defense mechanisms in response to CC. This study will further deepen our understanding of the fundamental regulatory mechanisms of strawberry resource reallocation in response to CC.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".