Novel Secreted Antigens of Mycobacterium paratuberculosis as Serodiagnostic Biomarkers for Johne's Disease in Cattle
Bibliographic record
Abstract
Johne's disease is a chronic gastroenteritis of cattle caused by Mycobacterium avium subsp. paratuberculosis that afflicts 40% of dairy herds worldwide. M. avium subsp. paratuberculosis-infected cattle can remain asymptomatic for years while transmitting the pathogen via fecal contamination and milk. Current serodiagnosis with enzyme-linked immunosorbent assays (ELISAs) fails to detect asymptomatic M. avium subsp. paratuberculosis-infected cattle due to the use of poorly defined antigens and knowledge gaps in our understanding of M. avium subsp. paratuberculosis components eliciting pathogen-specific immune responses. We set out to (i) define a subset of proteins that contain putative antigenic targets and (ii) screen these antigen pools for immunogens relevant in detecting infection. To accomplish our first objective, we captured and resolved M. avium subsp. paratuberculosis-secreted proteins using a 2-step fractionation method and reverse-phase liquid chromatography to identify 162 unique proteins, of which 66 had not been previously observed in M. avium subsp. paratuberculosis culture filtrates. Subsequent screening of M. avium subsp. paratuberculosis-secreted proteins showed four antigens, of which one or more reacted on immunoblotting with individual serum samples from 35 M. avium subsp. paratuberculosis-infected cows. Moreover, these novel antigens reacted with sera from 6 low M. avium subsp. paratuberculosis shedders and 3 fecal-culture-positive cows labeled as ELISA seronegative. The specificity of these antigens was demonstrated using negative-control sera from uninfected calves (n = 5) and uninfected cows (n = 5), which did not react to any of these antigens in immunoblotting. As three of the four antigens are novel, their characterization and incorporation into an ELISA-based format will aid in detecting asymptomatic cattle in early or subclinical stages of disease.
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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 | 0.000 | 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) | 0.000 | 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".