Supplementary Material for: Activity, Expression and Genetic Variation of Canine β-Defensin 103: A Multifunctional Antimicrobial Peptide in the Skin of Domestic Dogs
Bibliographic record
Abstract
The skin functions as more than a physical barrier to infection. Epithelial cells of the skin can synthesize antimicrobial peptides, including defensins, which exhibit direct antimicrobial activity. Here we characterize the expression pattern, genetic variation and activity of the major β-defensin expressed in canine skin, canine β-defensin 103 (CBD103). The gene encoding CBD103 exhibits two forms of polymorphism: a common 3-basepair deletion allele and a gene copy-number variation. Golden retrievers and Labrador retrievers were the only breeds that encoded the variant allele of CBD103, termed CBD103ΔG23. Both these breeds also exhibited a CBD103 gene copy-number polymorphism that ranged from 2 to 4 gene-copies per diploid genome. Recombinant CBD103 and CBD103ΔG23, as well as the human ortholog human β-defensin 3 (hBD3) and hBD3ΔG23, showed potent and comparable antimicrobial killing against both methicillin-susceptible and methicillin-resistant Staphylococcus pseudintermedius. Skin biopsy specimens from dogs with atopic dermatitis revealed CBD103 expression levels similar to those in healthy controls and comparable at lesional and nonlesional sites. This expression pattern in dogs differs from the previously reported reduced expression of the human ortholog in atopic dermatitis. Overall, the similarities of CBD103 and its human ortholog reported here support the notion that the domestic dog may serve as a valuable model for studying β-defensin biology in the skin.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.718 | 0.238 |
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".