Sequence analysis of a normalized cDNA library of Mytilus edulis hemocytes exposed to Vibrio splendidus LGP32 strain
Bibliographic record
Abstract
In the past decades, reports on bivalves' pathogens and associated mortalities have steadily increased. To face pathogenic micro-organisms, bivalves rely on innate defenses established in hemocytes which are essentially based on phagocytosis and cytotoxic reactions. As a step towards a better understanding of the molecular mechanisms involved in the mussel Mytilus edulis innate immune system, we constructed and sequenced a normalized cDNA library specific to M. edulis hemocytes unchallenged (control) and challenged with Vibrio splendidus LGP32 strain for 2, 4 and 6 h. A total of 1,024,708 nucleotide reads have been generated using 454 pyrosequencing. These reads have been assembled and annotated into 19,622 sequences which we believe cover most of the M. edulis hemocytes transcriptome. These sequences were successfully assigned to biological process, cellular component, and molecular function Gene Ontology (GO) categories. Several transcripts related to immunity and stress such as some fibrinogen related proteins and Toll-like receptors, the complement C1qDC, some antioxidant enzymes and antimicrobial peptides have already been identified. In addition, Toll-like receptors signaling pathways and the lysosome and apoptosis mechanisms were compared to KEGG reference pathways. As an attempt for large scale RNA sequencing, this study focuses on identifying and annotating transcripts from M. edulis hemocytes regulated during an in vitro experimental challenge with V. splendidus. The bioinformatic analysis provided a reference transcriptome, which could be used in studies aiming to quantify the level of transcripts using high-throughput analysis such as RNA-Seq.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".