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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.001 | 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 teacher head, 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".