Shared resistome and mobilome signatures across One Health Escherichia coli sources
5 affiliations
- 1Antimicrobial Research Unit, School of Health Sciences, University of KwaZulu-Nata l, Durban, South Africa
- 2Sequencing Core Facility, Division of the National Health Laboratory Service, National Institute for Communicable Diseases, Johannesburg, South Africa
- 3Environmental Research Foundation, Durban, South Africa
- 4School of Pharmacy, University of Jordan, Amman, Jordan
- 5Department of Pathobiology, University of Guelph, Guelph, ON, Canada
Abstract
Background: Antimicrobial resistance (AMR) is a major One Health challenge facilitated by the persistence and dissemination of antibiotic resistance genes (ARGs) and mobile genetic elements (MGEs) across interconnected human, animal, and environmental compartments. Despite growing recognition of environmental reservoirs in AMR ecology, integrated resistome and mobilome studies across One Health interfaces remain limited in Africa. This study applied shotgun metagenomic sequencing to culture-derived pools of multidrug-resistant (MDR) E. coli to characterize resistome composition and mobilome signatures across human, animal, and environmental sources in uMgungundlovu District, South Africa
Methods: A total of 539 MDR E. coli isolates recovered from human, food-animal, wastewater, and other environmental sources were grouped into 20 source-specific pools. ARGs, plasmid-associated sequences, and MGE-associated sequences were annotated against the Comprehensive Antibiotic Resistance Database, ACLAME, and MetaStorm MGE database, respectively. Analyses included alpha diversity, Bray–Curtis dissimilarity, clustering, ordination, and permutational multivariate analysis of variance (PERMANOVA); MetaCompare prioritized pools using ARG–MGE–pathogen co-occurrence patterns.
Results: Across the 20 pools, 247 distinct ARGs were detected, of which 86 (35%) occurred in at least one pool from each of the three sectors. Antibiotic inactivation and target protection were the most highly represented resistance mechanisms. ARG richness, Shannon diversity, and Simpson diversity did not differ significantly among sectors. Hierarchical clustering and ordination demonstrated substantial cross-sector overlap and within-sector heterogeneity. Nevertheless, PERMANOVA identified a significant but modest association between source sector and resistome composition (pseudo-F = 1.585, R 2 = 0.157, p = 0.022), with no significant evidence of unequal multivariate dispersion (F 2,17 = 2.498, p = 0.086). Of 1,012 plasmid-associated features, 713 (70%) occurred in at least one pool from each sector, while 232 of 975 MG E-associated features (24%) were shared. Exploratory random forest classification did not significantly outperform classification under permuted source labels (p = 0.053).
Conclusion: These findings reveal modest sector-associated resistome structuring alongside substantial cross-sector overlap in ARG, plasmid-associated, and MGE-associated signatures. Although the pooled data do not establish direct transmission, active mobility, or physical ARG–MGE linkage, they identify shared resistome patterns and source groups that warrant targeted, isolate-resolved and linkage-aware investigation within integrated One Health surveillance.
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BibTeX
@article{nnah2026shared,
title = {{Shared resistome and mobilome signatures across One Health Escherichia coli sources}},
author = {Eberechi Phoebe Nnah and Arshad Ismail and Akebe Luther King Abia and Sabiha Essack and Daniel Gyamfi Amoako},
journal = {Frontiers in Bioinformatics},
year = {2026},
month = oct,
doi = {10.3389/fbinf.2026.1930054},
url = {https://doi.org/10.3389/fbinf.2026.1930054},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Shared resistome and mobilome signatures across One Health Escherichia coli sources
AU - Eberechi Phoebe Nnah
AU - Arshad Ismail
AU - Akebe Luther King Abia
AU - Sabiha Essack
AU - Daniel Gyamfi Amoako
PY - 2026
DA - 2026/10/21
JO - Frontiers in Bioinformatics
DO - 10.3389/fbinf.2026.1930054
UR - https://doi.org/10.3389/fbinf.2026.1930054
AB - Background: Antimicrobial resistance (AMR) is a major One Health challenge facilitated by the persistence and dissemination of antibiotic resistance genes (ARGs) and mobile genetic elements (MGEs) across interconnected human, animal, and environmental compartments. Despite growing recognition of environmental reservoirs in AMR ecology, integrated resistome and mobilome studies across One Health interfaces remain limited in Africa. This study applied shotgun metagenomic sequencing to culture-derived pools of multidrug-resistant (MDR) E. coli to characterize resistome composition and mobilome signatures across human, animal, and environmental sources in uMgungundlovu District, South Africa Methods: A total of 539 MDR E. coli isolates recovered from human, food-animal, wastewater, and other environmental sources were grouped into 20 source-specific pools. ARGs, plasmid-associated sequences, and MGE-associated sequences were annotated against the Comprehensive Antibiotic Resistance Database, ACLAME, and MetaStorm MGE database, respectively. Analyses included alpha diversity, Bray–Curtis dissimilarity, clustering, ordination, and permutational multivariate analysis of variance (PERMANOVA); MetaCompare prioritized pools using ARG–MGE–pathogen co-occurrence patterns. Results: Across the 20 pools, 247 distinct ARGs were detected, of which 86 (35%) occurred in at least one pool from each of the three sectors. Antibiotic inactivation and target protection were the most highly represented resistance mechanisms. ARG richness, Shannon diversity, and Simpson diversity did not differ significantly among sectors. Hierarchical clustering and ordination demonstrated substantial cross-sector overlap and within-sector heterogeneity. Nevertheless, PERMANOVA identified a significant but modest association between source sector and resistome composition (pseudo-F = 1.585, R 2 = 0.157, p = 0.022), with no significant evidence of unequal multivariate dispersion (F 2,17 = 2.498, p = 0.086). Of 1,012 plasmid-associated features, 713 (70%) occurred in at least one pool from each sector, while 232 of 975 MG E-associated features (24%) were shared. Exploratory random forest classification did not significantly outperform classification under permuted source labels (p = 0.053). Conclusion: These findings reveal modest sector-associated resistome structuring alongside substantial cross-sector overlap in ARG, plasmid-associated, and MGE-associated signatures. Although the pooled data do not establish direct transmission, active mobility, or physical ARG–MGE linkage, they identify shared resistome patterns and source groups that warrant targeted, isolate-resolved and linkage-aware investigation within integrated One Health surveillance.
SN - 2673-7647
ER -