Identification of Virulence Genes Linked with Diarrhea Due to Atypical Enteropathogenic <i>Escherichia coli</i> by DNA Microarray Analysis and PCR
Bibliographic record
Abstract
The role of atypical enteropathogenic Escherichia coli (EPEC) in childhood diarrhea is controversial. The aim of the present study was to search for genes linked with diarrhea in atypical EPEC strains from a case-control study among Norwegian children. Using DNA microarray analysis, genomic DNAs from strains isolated from children with (n = 37) and without (n = 20) diarrhea were hybridized against 242 different oligonucleotide probes specific for 182 virulence genes or markers from all known E. coli pathotypes. PCR was performed to test the strains for seven putative virulence genes not included in the microarray panel. The OI-122 gene efa1/lifA was the gene with the strongest statistical association with diarrhea (P = 0.0008). Other OI-122 genes (set/ent, nleB, and nleE) and genes with other locations (lpfA, paa, ehxA, and ureD) were also associated with diarrheal disease. The phylogenetic marker gene yjaA was negatively associated with diarrhea (P = 0.0004). Atypical EPEC strains could be classified in two main virulence groups based on their content of OI-122, lpfA, and yjaA genes. Among children with diarrhea, atypical EPEC isolates belonging to virulence group I (OI-122 and lpfA positive, yjaA negative) were the most common, while the majority of isolates from healthy children were classified as virulence group II strains (OI-122 negative, lpfA and yjaA positive; P < 0.001). In conclusion, using DNA microarray analysis to determine the virulence gene profile of atypical EPEC isolates, several genes were found to be significantly associated with diarrhea. Based on their composition of virulence genes, the majority of strains could be classified in two virulence groups, of which one was seen mainly in children with diarrhea.
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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 | 0.000 | 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) | 0.001 | 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".