Rapid, Sensitive, and Specific Escherichia coli H Antigen Typing by Matrix-Assisted Laser Desorption Ionization–Time of Flight-Based Peptide Mass Fingerprinting
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Bibliographic record
Abstract
Matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) has gained popularity in recent years for rapid bacterial identification, mostly at the genus or species level. In this study, a rapid method to identify the Escherichia coli flagellar antigen (H antigen) at the subspecies level was developed using a MALDI-TOF MS platform with high specificity and sensitivity. Flagella were trapped on a filter membrane, and on-filter trypsin digestion was performed. The tryptic digests of each flagellin then were collected and analyzed by MALDI-TOF MS through peptide mass fingerprinting. Sixty-one reference strains containing all 53 H types and 85 clinical strains were tested and compared to serotyping designations. Whole-genome sequencing was used to resolve conflicting results between the two methods. It was found that DHB (2,5-dihydroxybenzoic acid) worked better than CHCA (α-cyano-4-hydroxycinnamic acid) as the matrix for MALDI-TOF MS, with higher confidence during protein identification. After method optimization, reference strains representing all 53 E. coli H types were identified correctly by MALDI-TOF MS. A custom E. coli flagellar/H antigen database was crucial for clearly identifying the E. coli H antigens. Of 85 clinical isolates tested by MALDI-TOF MS-H, 75 identified MS-H types (88.2%) matched results obtained from traditional serotyping. Among 10 isolates where the results of MALDI-TOF MS-H and serotyping did not agree, 60% of H types characterized by whole-genome sequencing agreed with those identified by MALDI-TOF MS-H, compared to only 20% by serotyping. This MALDI-TOF MS-H platform can be used for rapid and cost-effective E. coli H antigen identification, especially during E. coli outbreaks.
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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 | 0.002 | 0.001 |
| 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 it