Transcriptomic analysis of Macrobrachium rosenbergii (giant fresh water prawn) post-larvae in response to M. rosenbergii nodavirus (MrNV) infection: de novo assembly and functional annotation
Bibliographic record
Abstract
BACKGROUND: Macrobrachium rosenbergii, is one of a major freshwater prawn species cultured in Southeast Asia. White tail disease (WTD), caused by Macrobrachium rosenbergii nodavirus (MrNV), is a serious problem in farm cultivation and is responsible for up to 100% mortality in the post larvae stage. Molecular data on how M. rosenbergii post-larvae launches an immune response to an infection with MrNV is not currently available. We therefore compared the whole transcriptomic sequence of M. rosenbergii post-larvae before and after MrNV infection. RESULTS: of 1308 bp. The assembled transcriptome was successfully annotated against the NCBI non-redundant arthropod database (33.75%), UniProt database (26.73%), Gene Ontology (GO) (18.98%), Evolutionary Genealogy of Genes: Non-supervised Orthologous Groups (EggNOG) (20.88%), and Kyoto Encyclopedia of Genes and Genome pathway (KEGG) (20.46%). GO annotations included immune system process, signaling, response to stimulus, and antioxidant activity. Differential abundance analysis using EdgeR showed 2413 significantly up-regulated genes and 3125 significantly down-regulated genes during the infection of MrNV. CONCLUSIONS: This study reported a highly complete transcriptome from the post-larvae stage of giant river prawn, M. rosenbergii. Differential abundant transcripts during MrNV infection were identified and validated by qPCR, many of these differentially abundant transcripts as key players in antiviral immunity. These include known members of the innate immune response with the largest expression change occurring in the M. rosenbergii post-larvae after MrNV infection such as antiviral protein, C-type lectin, prophenol oxidase, caspase, ADP ribosylation factors, and dicer.
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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) | 0.001 | 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 | 0.001 |
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".