Biology subjects

Aga, O. N. L.

Publications and source records attributed to Aga, O. N. L..

3 recordsLinked to original sources

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↗

bioRxiv2024

Identifying parsimonious pathways of accumulation and convergent evolution from binary data

How stereotypical, and hence predictable, are evolutionary and accumulation dynamics? Here we consider processes - from genome evolution to cancer progression - involving the irreversible accumulation of binary features (characters), which can be modelled as Markov processes on a hypercubic transition network. We seek subgraphs of such networks that can generate a given set of paired before-after observations and minimize a topological cost function, involving criteria on out-branching which are interpretable in terms of biological parsimony. A transition network supporting a single, deterministic dynamic pathway is maximally simple and lowest cost, and branches (corresponding to possibly different next steps) increase cost, particularly if these branches are "deep", occurring at early stages in the dynamics. In this sense, the lowest-cost subgraph measures how stereotypical the evolutionary or accumulation process is, and also identifies good start points for likelihood-based inference. The problem is solvable in polynomial time for cross-sectional observations by building on an existing method due to Gutin, and we provide a polynomial-time estimate in the more general case of pairs of observed states. We use this approach to define a "stereotypy index" reflecting the extent of evolutionary predictability. We demonstrate use cases in the evolution of antimicrobial resistance, organelle genomes, and cancer progression, and provide a software implementation at https://github.com/StochasticBiology/hyperDAGs.

evolutionary biology↗

bioRxiv2024

HyperTraPS-CT: Inference and prediction for accumulation pathways with flexible data and model structures

Accumulation processes, where many potentially coupled features are acquired over time, occur throughout the sciences, from evolutionary biology to disease progression, and particularly in the study of cancer progression. Existing methods for learning the dynamics of such systems typically assume limited (often pairwise) relationships between feature subsets, cross-sectional or untimed observations, small feature sets, or discrete orderings of events. Here we introduce HyperTraPS-CT (Hypercubic Transition Path Sampling in Continuous Time) to compute posterior distributions on continuous-time dynamics of many, arbitrarily coupled, traits in unrestricted state spaces, accounting for uncertainty in observations and their timings. We demonstrate the capacity of HyperTraPS-CT to deal with cross-sectional, longitudinal, and phylogenetic data, which may have no, uncertain, or precisely specified sampling times. HyperTraPS-CT allows positive and negative interactions between arbitrary subsets of features (not limited to pairwise interactions), supporting Bayesian and maximum-likelihood inference approaches to identify these interactions, consequent pathways, and predictions of future and unobserved features. We also introduce a range of visualisations for the inferred outputs of these processes and demonstrate model selection and regularisation for feature interactions. We apply this approach to case studies on the accumulation of mutations in cancer progression and the acquisition of anti-microbial resistance genes in tuberculosis, demonstrating its flexibility and capacity to produce predictions aligned with applied priorities.

bioinformatics↗