Localization of proteins in the cell wall of Mycobacterium avium subsp. paratuberculosis K10 by proteomic analysis
Bibliographic record
Abstract
Mycobacterium avium subsp. paratuberculosis is a pathogen which causes a debilitating chronic enteritis in ruminants. Unfortunately, the mechanisms that control M. avium subsp. paratuberculosis persistence during infection are poorly understood and the key steps for developing Johne's disease remain elusive. A proteomic analysis approach, based on one dimensional polyacrylamide gel electrophoresis (SDS-PAGE) followed by LC-MS/MS, was used to identify and characterize the cell wall associated proteins of M. avium subsp. paratuberculosis K10 and an cell surface enzymatic shaving method was used to determine the surface-exposed proteins. 309 different proteins were identified, which included 101 proteins previously annotated as hypothetical or conserved hypothetical. 38 proteins were identified as surface-exposed by trypsin treatment. To categorize and analyze these proteomic data on the proteins identified within cell wall of M. avium subsp. paratuberculosis K10, a rational bioinformatic approach was followed. The analyses of the 309 cell wall proteins provided theoretical molecular mass and pI distributions and determined that 18 proteins are shared with the cell surface-exposed proteome. In short, a comprehensive profile of the M. avium subsp. paratuberculosis K10 cell wall subproteome was created. The resulting proteomic profile might become the foundation for the design of new preventive, diagnostic and therapeutic strategies against mycobacterial diseases in general and M. avium subsp. paratuberculosis in particular.
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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".