Biology subjects

Kibwana, U.

Publications and source records attributed to Kibwana, U..

3 recordsLinked to original sources

bioRxiv2026

Learning and forecasting shared evolutionary pathways to multi-drug resistance across global pathogens

Infections with bacteria which have evolved multi-drug resistance (MDR) cause millions of deaths worldwide. Large-scale efforts are gathering genotypic and phenotypic data on MDR bacteria, but methods for learning the structure, diversity, and predictors of evolutionary pathways to MDR have yet to take full advantage of these data. Here, we use evolutionary accumulation modelling (EvAM), an emerging class of machine learning methods with roots in cancer progression, to infer these evolutionary pathways across ESKAPEE pathogens (seven bacterial species that dominate health burdens), using a database of over 635k genotyped phenotypic observations from around the world. We identify global patterns in MDR evolutionary pathways, remarkably shared across multiple ESKAPEE species. Species-specific deviations from these stereotypical pathways are connected with geographical and demographic covariates, facilitating predictions of future MDR evolution. We verify these predictions with several hundred new phenotypes from ESKAPEE samples spanning decades of clinical infections in sub-Saharan Africa, demonstrating the capacity to forecast future MDR evolution from these inferred shared pathways.

evolutionary biology↗

bioRxiv2025

Global diversity, predictors, and predictions of AMR evolutionary pathways in Klebsiella pneumoniae

Antimicrobial resistance (AMR) is a substantial and growing global health burden. Understanding, and predicting, its evolution in specific pathogens will help responses across scales from individual patient cases to large-scale policy. Here, we use global data on AMR features, predicted from 47k Klebsiella pneumoniae genomes, with hypercubic transition path sampling to infer the evolutionary pathways by which AMR features in K. pneumoniae (KpAMR) are acquired across 102 countries, territories and areas. We identify "globally consistent" evolutionary behaviours that hold across countries, and "globally divergent" behaviours including carbapenem and fluoroquinolone resistance that vary across countries. We show how these divergent dynamics covary both with public health superregion and drug use policy, and reveal competing evolutionary pathways within and between countries. Using newly-sequenced data across several decades from sub-Saharan Africa, we show that this inferred global roadmap of KpAMR evolution successfully predicts prospective evolutionary dynamics. Together, we hope that the ability to characterize and predict evolutionary dynamics of AMR acquisition, connected to socio-economic and drug policy predictors, will help strengthen our understanding of AMR evolution worldwide. SignificanceAntimicrobial resistance (AMR) occurs when microbial pathogens evolve resistance to the drugs we use to treat them. Our understanding of bacterial genomes and how they confer AMR is constantly expanding through beautiful and powerful work establishing large-scale global datasets. Here, we use emerging machine learning approaches with this genomic data to reveal the evolutionary dynamics that have generated AMR characters in a particular pathogen, Klebsiella pneumoniae (Kp), and how these dynamics are influenced by geography and drug use across the globe. This "natural history" of AMR in Kp makes predictions about which characters will evolve next for a given bacterium, and we validate these predictions with newly-sequenced data from clinical isolates from Africa, providing both past and prospective descriptions of AMR in Kp.

evolutionary biology↗

bioRxiv2019

Multi-drug resistance bacteria predict mortality in blood stream infection in a tertiary setting in Tanzania

BackgroundBlood-stream infections (BSIs) are serious and life-threatening infections associated with high mortality and morbidity. In resource limited settings, there is paucity of data on predictors of outcome in patients with BSI. This study aimed at examining the predictors of mortality in patients with BSI as well as bacteria causing BSI.\n\nMethods and MaterialsThis was cross-sectional study conducted in Muhimbili National Hospital between April and May 2018. Blood culture results from all inpatients at clinical microbiology laboratory were recorded and clinical information were retrieved retrospectively from the files. Bacteria from positive blood culture were identified and antimicrobial susceptibility was performed.\n\nResultsThe overall prevalence of BSI was 11.4% (46/402), with case fatality rate of 37%. There was significant high rate of BSI in patient who had died compared to those survived p= 0.008. Gram-negative bacteria (74%) were the common cause of BSI, with predominance of Enterobacteriaceae (22), followed by Pseudomonas aeruginosa (11). Majority (70.5%) of the bacteria isolated from patients with BSI were multi-drug resistant. Forty six percent of Pseudomonas aeruginosa were resistance to meropenem. Sixty eight percent 68.2% (15/22) of Enterobacteriaceae were ESBL producers. Carbapenemases production were detected in 27% (3/11) of Pseudomonas aeruginosa and in one Proteus mirabillis. Forty percent (40%) of Staphylococcus aureus were methicillin resistant Staphylococcus aureus (MRSA). Positive blood culture (cOR 7.4, 95%CI 1.24 - 43.83, p 0.03) and admission in ICU cOR 4 (95%CI 1.7 - 9.41, p 0.001) were independent factors for mortality in suspected BSI. Isolation of Multi-drug resistant bacteria was independent predictor for mortality in confirmed BSI (cOR 7.4, 95%CI 1.24 - 43.83, p 0.03).\n\nConclusionThe prevalence of BSI was 11.4%, with majority of bacteria in BSI were MDR. Positive blood culture and MDR were predictors for mortality.

microbiology↗