De novo transcriptome sequencing of Isaria cateniannulata and comparative analysis of gene expression in response to heat and cold stresses
Bibliographic record
Abstract
Isaria cateniannulata is a very important and virulent entomopathogenic fungus that infects many insect pest species. Although I. cateniannulata is commonly exposed to extreme environmental temperature conditions, little is known about its molecular response mechanism to temperature stress. Here, we sequenced and de novo assembled the transcriptome of I. cateniannulata in response to high and low temperature stresses using Illumina RNA-Seq technology. Our assembly encompassed 17,514 unigenes (mean length = 1,197 bp), in which 11,445 unigenes (65.34%) showed significant similarities to known sequences in NCBI non-redundant protein sequences (Nr) database. Using digital gene expression analysis, 4,483 differentially expressed genes (DEGs) were identified after heat treatment, including 2,905 up-regulated genes and 1,578 down-regulated genes. Under cold stress, 1,927 DEGs were identified, including 1,245 up-regulated genes and 682 down-regulated genes. The expression patterns of 18 randomly selected candidate DEGs resulting from quantitative real-time PCR (qRT-PCR) were consistent with their transcriptome analysis results. Although DEGs were involved in many pathways, we focused on the genes that were involved in endocytosis: In heat stress, the pathway of clathrin-dependent endocytosis (CDE) was active; however at low temperature stresses, the pathway of clathrin-independent endocytosis (CIE) was active. Besides, four categories of DEGs acting as temperature sensors were observed, including cell-wall-major-components-metabolism-related (CWMCMR) genes, heat shock protein (Hsp) genes, intracellular-compatible-solutes-metabolism-related (ICSMR) genes and glutathione S-transferase (GST). These results enhance our understanding of the molecular mechanisms of I. cateniannulata in response to temperature stresses and provide a valuable resource for the future investigations.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".