The salivary transcriptome of<i>Limnobdella mexicana</i>(Annelida: Clitellata: Praobdellidae) and orthology determination of major leech anticoagulants
Bibliographic record
Abstract
Bloodfeeding requires several adaptations that allow the parasite to feed efficiently. Leeches and other hematophagous animals have developed different mechanisms to inhibit hemostasis, one of the main barriers imposed by their hosts. Limnobdella mexicana is a member of the leech family Praobdellidae, a family of host generalists known for their preference to attach on mucosal membranes of mammals, such as those in nasopharyngeal cavities, bladders and ocular orbits. Previous studies have hypothesized a positive relationship between diversity of anticoagulants and diversity of hosts in bloodfeeding leeches. However, orthology determination of putative anticoagulants and the lack of standardization of sequencing effort and method hinder comparisons between publicly available transcriptomes generated in different laboratories. In the present study, we examine the first transcriptome of a praobdellid leech and identify 15 putative anticoagulants using a phylogeny-based inference approach, amino-acid conservation, Pfam domains and BLAST searches. Our phylogenetic analyses suggest that the ancestral leech was able to inhibit factor Xa and that some hirudins that have been reported in previous studies on leech anticoagulants may not be orthologous with the archetypal hirudin.
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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".