RSCA genotyping of MHC for high-throughput evolutionary studies in the model organism three-spined stickleback Gasterosteus aculeatus
Bibliographic record
Abstract
BACKGROUND: In all jawed vertebrates, highly polymorphic genes of the major histocompatibility complex (MHC) encode antigen presenting molecules that play a key role in the adaptive immune response. Their polymorphism is composed of multiple copies of recently duplicated genes, each possessing many alleles within populations, as well as high nucleotide divergence between alleles of the same species. Experimental evidence is accumulating that MHC polymorphism is a result of balancing selection by parasites and pathogens. In order to describe MHC diversity and analyse the underlying mechanisms that maintain it, a reliable genotyping technique is required that is suitable for such highly variable genes. RESULTS: We present a genotyping protocol that uses Reference Strand-mediated Conformation Analysis (RSCA), optimised for recently duplicated MHC class IIB genes that are typical for many fish and bird species, including the three-spined stickleback, Gasterosteus aculeatus. In addition we use a comprehensive plasmid library of MHC class IIB alleles to determine the nucleotide sequence of alleles represented by RSCA allele peaks. Verification of the RSCA typing by cloning and sequencing demonstrates high congruency between both methods and provides new insight into the polymorphism of classical stickleback MHC genes. Analysis of the plasmid library additionally reveals the high resolution and reproducibility of the RSCA technique. CONCLUSION: This new RSCA genotyping protocol offers a fast, but sensitive and reliable way to determine the MHC allele repertoire of three-spined sticklebacks. It therefore provides a valuable tool to employ this highly polymorphic and adaptive marker in future high-throughput studies of host-parasite co-evolution and ecological speciation in this emerging model organism.
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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| 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".