Estimating chronic wasting disease susceptibility in cervids using real-time quaking-induced conversion
Bibliographic record
Abstract
In mammals, susceptibility to prion infection is primarily modulated by the host’s cellular prion protein (PrPC) sequence. In the sheep scrapie model, a graded scale of susceptibility has been established both in vivo and in vitro based on PrPC amino acids 136, 154 and 171, leading to global breeding programmes to reduce the prevalence of scrapie in sheep. Chronic wasting disease (CWD) resistance in cervids is often characterized as decreased prevalence and/or protracted disease progression in individuals with specific alleles; at present, no PrPC allele conferring absolute resistance in cervids has been identified. To model the susceptibility of various naturally occurring and hypothetical cervid PrPC alleles in vitro, we compared the amplification rates and amyloid extension efficiencies of eight distinct CWD isolates in recombinant cervid PrPC substrates using real-time quaking-induced conversion. We hypothesized that the in vitro conversion characteristics of these isolates in cervid substrates would correlate to in vivo susceptibility – permitting susceptibility prediction for the rare alleles found in nature. We also predicted that hypothetical alleles with multiple resistance-associated codons would be more resistant to in vitro conversion than natural alleles with a single resistant codon. Our studies demonstrate that in vitro conversion metrics align with in vivo susceptibility, and that alleles with multiple amino acid substitutions, each influencing resistance independently, do not necessarily contribute additively to conversion resistance. Importantly, we found that the naturally occurring whitetail deer QGAK substrate exhibited the slowest amplification rate among those evaluated, suggesting that further investigation of this allele and its resistance in vivo is warranted.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".