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Спутники
РН
SpaceX
Роскосмос
NASA
СпутникиРНSpaceXРоскосмосNASABackground Antimicrobial Resistance (AMR) is a major global health challenge, particularly in Africa, where antibiotic misuse, limited surveillance systems, and unregulated access to antimicrobials accelerate resistance. Carbapenem-resistant E. coli, especially those resistant to imipenem, represent a critical clinical concern due to limited treatment options. This study aims to identify potential drug targets in imipenem-resistant E. coli isolates using a four-phase integrative framework that combines interactive GIS-based surveillance analysis, genomic data analysis, subtractive genomics, and druggability assessment within a One Health context. Methods Phase I involved surveillance data analysis and the development of interactive Geographical Information System (GIS) dashboards to examine resistance patterns for Ampicillin, Minocycline, and Imipenem across North America and Africa using ATLAS (human) and Vet-LIRN (veterinary) datasets, with Chi-square statistical comparisons. Phase II focused on genomic analysis of resistant and susceptible isolates, including quality control, mapping to a reference genome, SNP identification, and annotation of non-synonymous mutations associated with resistance. Phase III applied subtractive genomics to identify pathogen-specific, essential bacterial proteins by excluding proteins homologous to human, dog, and gut microbiota sequences. Phase IV assessed druggability through homology with DrugBank targets, alongside physicochemical characterization, subcellular localization, transmembrane analysis, and structural prediction using AlphaFold. Results GIS-based analysis revealed geographic variation in resistance patterns, with higher Ampicillin resistance in Africa (80%) compared to North America (55%), while imipenem resistance remained low but present. Resistance trends were comparable between human and veterinary datasets (p > 0.05). Genomic analysis identified 337 non-synonymous proteins associated with imipenem resistance, which were reduced to 68 prioritized candidate targets. Druggability assessment identified 29 proteins with similarity to DrugBank targets, collectively interacting with over 1,000 drugs, particularly within transport-related and metabolic protein groups. Conclusions This study demonstrates that integrating surveillance, genomics, and druggability assessment provides a robust framework for identifying actionable targets in imipenem-resistant E. coli. The findings highlight the potential of drug repurposing and offer a transferable One Health–aligned approach for AMR research in data-limited settings.