Among-Population Differentiation in the Tapeworm Proteome through Prediction of Excretory/Secretory and Transmembrane Proteins in <i>Schistocephalus solidus</i>
Bibliographic record
Abstract
Background: Parasites secrete and excrete a variety of molecules evolve to help establish and sustain infections within hosts. Parasite adaptation to their host may lead to between-population divergence in these excretory and secretory products (ESPs), but few studies have tested for intraspecific variation in helminth proteomes. Methods: . We used an ultra-performance liquid chromatography-mass spectrometry protocol to characterize the ESP and whole-body proteome of S. solidus. Specifically, we characterized the proteome of S. solidus at the plerocercoid stage from wild caught stickleback from three lakes on Vancouver Island (British Columbia, Canada) and one lake in Alaska (United States). We tested for differences in proteome composition among the four populations and specifically between ESPs and body tissue. Results: Overall, we identified 1362 proteins in the total proteome of S. solidus, with 542 of the 1362 proteins detected exclusively in the ESPs. Of the ESP proteins, we found signaling peptides and transmembrane proteins that were previously not detected or characterized in S. solidus. We also found protein spectrum counts greatly varied between all lake populations. Conclusions: These population-level differences were observed in both ESP and tissue types. Our study suggests that S. solidus can excrete and secrete a wide range of proteins which are distinct among populations. These differences might reflect plastic responses to host genotype differences, or evolved adaptations by Schistocephalus to different local host populations.
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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 | 0.001 | 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".