Rapid, Sensitive, and Specific Escherichia coli H Antigen Typing by Matrix-Assisted Laser Desorption Ionization–Time of Flight-Based Peptide Mass Fingerprinting
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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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".