Genomic and Bioinformatic Insights Into <i>Enterococcus faecalis</i> From Retail Meats in Nigeria
Bibliographic record
Abstract
Enterococcus faecalis is a commensal and opportunistic pathogen increasingly recognized for its antimicrobial resistance (AMR) and zoonotic potential. This study employs whole‐genome sequencing (WGS) to characterize E. faecalis isolates from retail meat samples, focusing on antimicrobial resistance genes (ARGs), virulence determinants, mobile genetic elements, and phylogenomic relationships. Fifty raw meat samples, including chicken ( n = 18), beef ( n = 17), and turkey ( n = 15), were collected from retail markets in Akungba‐Akoko, Nigeria. Confirmed isolates underwent antimicrobial susceptibility testing and WGS‐based genomic analysis. Ten E. faecalis isolates were recovered, predominantly from chicken. All exhibited resistance to clindamycin, erythromycin, and tetracycline. Dominant AMR genes included aac(6 ′ )-aph(2 ″ ), ant(6)-Ia , lsa(A) , erm(B) , tet(M) , and tet(L) . Plasmid replicons rep9c and repUS43 were associated with sequence types ST477 and ST16, respectively. MGEs such as IS3, IS6, IS256, and IS1380 colocalized with resistance and virulence genes. Phylogenomic analysis revealed two major lineages (ST477 and ST16) and indicated geographic clustering across African isolates. The co‐occurrence of multidrug resistance, virulence factors, and MGEs in foodborne E. faecalis poses a public health concern due to the risk of horizontal gene transfer and zoonotic spread. These findings support the need for strengthened genomic surveillance and AMR control strategies in food systems, particularly within low‐ and middle‐income countries.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".