Optimized Bioorthogonal Non-canonical Amino Acid Tagging to Identify Serotype-Specific Biomarkers in Verotoxigenic <i>Escherichia coli</i>
Bibliographic record
Abstract
According to Canada’s Food Report Card 2016, there are 4 million foodborne illnesses acquired each year in the nation alone. The leading causes of foodborne illness are pathogenic bacteria such as shigatoxigenic/verotoxigenic Escherichia coli (STEC/VTEC) and Listeria monocytogenes . Most current detection methods used to identify these bacterial pathogens are limited in their validity since they are not specific to detecting metabolically active organisms, potentially generating false-positive results from non-living or non-viable bacteria. Previously, our lab developed an optimized bioorthogonal non-canonical amino acid tagging (BONCAT) method which allows for the labeling of translationally active wild-type pathogenic bacteria. Incorporation of homopropargyl glycine (HPG) into the cellular surfaces of bacteria allows for protein tagging using the bioorthogonal alkyne handle to report on the presence of pathogenic bacteria. Here, we use proteomics to identify more than 400 proteins differentially detected by BONCAT between at least two of five different VTEC serotypes. These findings pave the way for future examination of these proteins as biomarkers in BONCAT-utilizing assays.
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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 | 0.001 |
| 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".