The Phenotypic and Molecular Characterization of Immune Response Variation in Wild Marine Threespine Stickleback (Gasterosteus aculeatus)
Bibliographic record
Abstract
Recognition of immune regulation through molecular and phenotypic analysis is critical for understanding and preventing disease. This study examines immune gene expression and phenotypic immune responses in marine threespine stickleback (Gasterosteus aculeatus) from two coastal environments—Barkley Sound and Bayne’s Sound (British Colombia, Canada) — highlighting their potential as indicators of immune stress. Using phenotypic sampling observations and molecular qPCR analyses, I investigated the relationship between infection status, immune gene expression, and environmental pressures. The RBPL13a housekeeping gene exhibited consistent expression across phenotypes, validating its use as a normalization standard. Results revealed heightened expression of FoxP3a in phenotypically symptomatic fish, particularly in Bayne’s Sound, where increased potential pathogen prevalence correlated with elevated immune activation. Notably, no significant differences in mean immune gene expression were observed between phenotypically symptomatic and wild-type (WT) fish across locations, suggesting location-specific immune regulatory mechanisms. These findings highlight the role of local adaptation in immune responses shaped by environmental pressures and pathogen exposure. By leveraging marine threespine stickleback as a model, this research provides insights into host-pathogen dynamics, marine ecosystem health, and fish population resilience in the face of anthropogenic and climate-induced stressors. This work contributes to conservation strategies by identifying molecular and phenotypic markers of immune stress, emphasizing the broader implications for ecosystem health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".