Transcriptional regulation of photosynthesis under heat stress in poplar
Bibliographic record
Abstract
Abstract Background Photosynthesis has been recognized as a complicated process that is modulated through the intricate regulating network at transcriptional level. However, its underlying mechanism at molecular level under heat stress remains to be understood. Analysis of the adaptive response and regulatory networks of trees to heat stress will expand our understanding of thermostability in perennial plants. In this study, we used a multi-gene network to investigate the regulatory pathway under heat stress, as constructed by a multifaceted approach of combining time-course RNA-seq, regulatory motif enrichment, and expression-trait association analysis. Results By analyzing changes in the transcriptome under heat stress, we identified 77 key photosynthetic genes, of which 97.4% (75 genes) were down-regulated, and these results conformed to the decreased photosynthesis measured values. According to analysis of regulating motif enrichment, these 77 differentially expressed genes (DEGs) had common vital light-responsive elements involved in photosynthesis. When integrating all the differential expressed genes, 5 co-expressed gene modules (1,548 genes) were identified to be significantly correlated with 4 photosynthesis-related traits. Thus, based on this, a three-layered gene regulatory network (GRN) was established, which had included 77 photosynthetic genes (in the bottom layer), 40 TFs/miRNAs (in the second layer), as well as 20 TFs/miRNAs (in the top layer), using a backward elimination random forest (BWERF) algorithm. Importantly, 6 miRNAs and 4 TFs were found to be key regulators in this regulatory pathway, emphasizing the significant roles of TFs/miRNAs in affecting photosynthetic traits. The results imply a functional role for these key genes in mediating photosynthesis under heat stress, demonstrating the potential of combining time-course transcriptome-based regulatory pathway construction, cis-elements enrichment analysis, and expression-trait association approaches to dissect complex genetic networks. Conclusions The heat-responsive pathway in regulating photosynthesis is a multi-layered complex network which is co-controlled by TFs and miRNAs. Our work not only imply a functional role for these key genes in mediating photosynthesis responding to abiotic stress in poplar, but demonstrate time-course transcriptome-based regulatory network construction will facilitate further the genetic network and key nodes examining in plants.
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 | 0.000 | 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) | 0.001 | 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".