Identification and quantification of virulence factors of enterotoxigenic Escherichia coli by high-resolution melting curve quantitative PCR
Bibliographic record
Abstract
Diagnosis of enterotoxigenic E. coli (ETEC) associated diarrhea is complicated by the diversity of E.coli virulence factors. This study developed a multiplex quantitative PCR assay based on high-resolution melting curves analysis (HRM-qPCR) to identify and quantify genes encoding five ETEC fimbriae related to diarrhea in swine, i.e. K99, F41, F18, F6 and K88. Five fimbriae expressed by ETEC were amplified in multiple HRM-qPCR reactions to allow simultaneous identification and quantification of five target genes. The assay was calibrated to allow quantification of the most abundant target gene, and validated by analysis of 30 samples obtained from piglets with diarrhea and healthy controls, and comparison to standard qPCR detection. The five amplicons with melting temperatures (Tm) ranging from 74.7 ± 0.06 to 80.5 ± 0.15 °C were well-separated by HRM-qPCR. The area of amplicons under the melting peak correlated linearly to the proportion of the template in the calibration mixture if the proportion exceeded 4.8% (K88) or <1% (all other amplicons). The suitability of the method was evaluated using 30 samples from weaned pigs aged 6–7 weeks; 14 of these animals suffered from diarrhea in consequence of poor sanitary conditions. Genes encoding fimbriae and enterotoxins were quantified by HRM-qPCR and/or qPCR. The multiplex HRM-qPCR allowed accurate analysis when the total gene copy number of targets was more than 1 × 10 5 / g wet feces and the HRM curves were able to simultaneously distinguish fimbriae genes in the fecal samples. The relative quantification of the most abundant F18 based on melting peak area was highly correlated ( P < 0.001; r 2 = 0.956) with that of individual qPCR result but the correlation for less abundant fimbriae was much lower. The multiplex HRM assay identifies ETEC virulence factors specifically and efficiently. It correctly indicated the predominant fimbriae type and additionally provides information of presence/ absence of other fimbriae types and it could find broad applications for pathogen diagnosis.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".