Midgut transcriptomic responses to dengue and chikungunya viruses in the vectors Aedes albopictus and Aedes malayensis
Bibliographic record
Abstract
Dengue (DENV) and chikungunya (CHIKV) viruses are among the most preponderant arboviruses. Although primarily transmitted through the bite of Aedes aegypti mosquitoes, Aedes albopictus and Aedes malayensis are competent vectors and have an impact on arbovirus epidemiology. Here, to fill the gap in our understanding of the molecular interactions between secondary vectors and arboviruses, we used transcriptomics to profile the whole-genome responses of A. albopictus to CHIKV and of A. malayensis to CHIKV and DENV at 1 and 4 days post-infection (dpi) in midguts. In A. albopictus, 1793 and 339 genes were significantly regulated by CHIKV at 1 and 4 dpi, respectively. In A. malayensis, 943 and 222 genes upon CHIKV infection, and 74 and 69 genes upon DENV infection were significantly regulated at 1 and 4 dpi, respectively. We reported 81 genes that were consistently differentially regulated in all the CHIKV-infected conditions, identifying a CHIKV-induced signature. We identified expressed immune genes in both mosquito species, using a de novo assembled midgut transcriptome for A. malayensis, and described the immune architectures. We found the JNK pathway activated in all conditions, generalizing its antiviral function to Aedines. Our comprehensive study provides insight into arbovirus transmission by multiple Aedes vectors.
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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".