MS-H: A Novel Proteomic Approach to Isolate and Type the E. coli H Antigen Using Membrane Filtration and Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS)
Bibliographic record
Abstract
Serotyping is the long-standing gold standard method to determine E. coli H antigens; however, this method requires a panel of H-antigen specific antibodies and often culture-based induction of the H-antigen flagellar motility. In this study, a rapid and accurate method to isolate and identify the Escherichia coli (E. coli) H flagellar antigen was developed using membrane filtration and liquid chromatography-tandem mass spectrometry (LC-MS/MS). Flagella were isolated from pure culture, digested with trypsin, and then subjected to LC-MS/MS using one of two systems (Agilent-nano-LC-QSTAR XL or Proxeon-nano-LC-LTQ-Orbitrap XL). The resulting peptide sequence data were searched against a custom E. coli flagella/H antigen database. This approach was evaluated using flagella isolated from reference E. coli strains representing all 53 known H antigen types and 41 clinical E. coli strains. The resulting LC-MS/MS classifications of H antigen types (MS-H) were concordant with the known H serogroup for all 53 reference types, and of 41 clinical isolates tested, 38 (92.7%) were concordant with the known H serogroup. MS-H clearly also identified two clinical isolates (4.9%) that were untypeable by serotyping. Notably, successful detection and classification of flagellar antigens with MS-H did not generally require induction of motility, establishing this proteomic approach as more rapid and cost-effective than traditional methods, while providing equitable specificity for typing E. coli H antigens.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".