Mycobacteriophage cell binding proteins for the capture of mycobacteria
Bibliographic record
Abstract
Slow growing Mycobacterium avium subsp. paratuberculosis (MAP) causes a deadly condition in cattle known as Johne's disease where asymptomatic carriers are the major source of disease transmission. MAP was also shown to be associated with chronic Crohn's disease in humans. Mycobacterium smegmatis is a model mycobacterium that can cause opportunistic infections in a number of human tissues and, rarely, a respiratory disease. Currently, there are no rapid, culture-independent, reliable and inexpensive tests for the diagnostics of MAP or M. smegmatis infections. Bacteriophages are viruses producing a number of proteins that effectively and specifically recognize the cell envelopes of their bacterial hosts. We demonstrate that the mycobacterial phage L5 minor tail protein Gp6 and lysin Gp10 are useful tools for the rapid capture of mycobacteria. Immobilized Gp10 was able to bind both MAP and M. smegmatis cells whereas Gp6 was M. smegmatis specific. Neither of the 2 proteins was able to capture E. coli, salmonella, campylobacter or Mycobacterium marinum cells. Gp6 was detected previously as a component of the phage particle and shows no homology to proteins with known function. Therefore, electrospray ionization mass spectrometry was used to determine whether recombinant Gp6 could bind to a number of chemically synthesized fragments of mycobacterial surface glycans. These findings demonstrate that mycobacteriophage proteins could be used as a pathogen capturing platform that can potentially improve the effectiveness of existing diagnostic methods.
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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 | 0.001 | 0.001 |
| 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".