Antimicrobial resistance (AMR) is a growing problem in African cattle production systems, posing a threat to human and animal health and the associated economic value chain. However, there is a poor understanding of the resistomes in small-holder cattle breeds in East African countries. This study aims to examine the distribution of Antimicrobial Resistance Genes (ARGs) in Kenya, Tanzania, and Uganda cattle using a metagenomics approach. We used the SqueezeMeta-Abricate (assembly-based) pipeline to detect ARGs and benchmarked this approach using the Centifuge-AMRplusplus (read-based) pipeline to evaluate its efficiency. Our findings reveal a significant number of ARGs of critical medical and economic importance in all three countries, including resistance to drugs of last resort such as carbapenems, suggesting the presence of highly virulent and antibiotic-resistant bacterial pathogens (ESKAPE) circulating in East Africa. Shared ARGs such as aph(6)-id (aminoglycoside phosphotransferase), tet (tetracycline resistance gene), sul2 (sulfonamide resistance gene) and cfxA_gen (betalactamase gene) were detected. Assembly-based methods revealed fewer ARGs compared to read-based methods, indicating the sensitivity and specificity of read-based methods in resistome characterization. Our findings call for further surveillance to estimate the intensity of the antibiotic resistance problem and wider resistome classification. Effective management of livestock and antibiotic consumption is crucial in minimizing antimicrobial resistance and maximizing productivity, making these findings relevant to stakeholders, agriculturists, and veterinarians in East Africa and Africa at large.

The human gut virome is a critical yet understudied component of the microbiome that shapes microbial community structure and host-microbe interactions. However, most existing human gut virome reference databases have been constructed predominantly from populations in high-income countries, resulting in the substantial underrepresentation of African populations. To help address this disparity, we developed the Kenyan Human Gut Virome Catalogue (KHGVC), the first comprehensive human gut virome resource for Kenya and the first country-specific human gut virome catalogue from Africa. Using a standardized viromics pipeline applied to 626 fecal metagenomes spanning infants and adults across three Kenyan counties, we reconstructed 116,968 viral operational taxonomic units (vOTUs). Cross-catalogue comparisons revealed extensive novelty where 65.6% of KHGVC's vOTUs larger than 10 kb lacked matches in five major human gut virome databases, and 95% remained unique relative to the Unified Human Gut Virome (UHGV). Temperate bacteriophages accounted for ~ 70% of vOTUs, supporting a major role for lysogeny in gut ecosystem stability. Functional annotation assigned putative roles to ~ 27% of predicted viral proteins, primarily structural and replication-associated functions. Application of KHGVC revealed pronounced age-dependent virome structuring in which infant viromes were less diverse and enriched in Bifidobacterium-infecting phages, including Bifidobacterium longum, whereas adult viromes exhibited greater diversity and expansion of Prevotella-associated phages. Together, the KHGVC substantially expands known human gut viral diversity and provides a foundational reference for Kenyan and African virome research. The KHGVC can be accessed freely through a publicly available interactive web interface (https://igmr.org/software/kenyavirocat).

The global increase in carbapenem-resistant Klebsiella pneumoniae (CRKP) has undoubtedly changed the management of infectious diseases. Meropenem resistance in K. pneumoniae is a response to bacterial strategies, these includes production of carbapenemases (KPC, NDM, OXA-48), a reduction in membrane permeability through mutation of porin (OmpK35/36), and genomic plasticity via gene amplification and/or plasmid exchange. This study developed a machine learning model that targets “Very Major Errors” (VMEs) and predicts antimicrobial resistance (AMR) for prognosis. Genomic datasets (6000) were retrieved from the Bacterial and Viral Bioinformatics Resource Center (BV-BRC) database and preprocessed. Gene-based feature engineering was performed using Kleborate, and a Random Forest model was trained and deployed via an interactive R Shiny web application. Internal validation of the tool PredKlebAMR was done using a stratified 80/20 split of the training dataset, and a 5-fold cross-validation was implemented to optimize the Random Forest hyperparameters. While benchmarking against ResFinder 4.1 and AMRFinderPlus 4.2.7 was done using an independent external cohort consisting of three-tiered datasets from the National Center for Biotechnology Information (NCBI) database. PredKlebAMR, trained using 5,942 K. pneumoniae genomes (38 Kleborate-derived predictors), achieved a strong performance on a held-out global test set (accuracy 91.92%, sensitivity 94.67%, specificity 89.47%; Kappa 0.839), with key predictors including fluoroquinolone mutations, high-risk sequence types, KPC-2/KPC-3, and OmpK35/36 porin changes. When benchmarked against AMRFinderPlus 4.2.7 and ResFinder 4.1, PredKlebAMR delivered a higher sensitivity and fewer VMEs in the global cohort (95.45% vs 90.91% | 86.36% sensitivity; VMEs 1 vs 2 | 3), the African cohort (95.56% vs 91.11% | 82.2% sensitivity; VMEs 2 vs 4 | 8), and the Thailand cohort (95.62% vs 92.5% | 63.12% sensitivity; VMEs 7 vs 12 | 59) with statistically significant sensitivity improvement in Africa (McNemar’s p = 0.041). PredKlebAMR, with its high sensitivity to VMEs, and via an interactive dashboard (https://igmr.org/software/predklebamr) that translates predictions into biological rationale, will support clinicians in making life-changing decisions while managing patients.