Biology subjects

Helekal, D.

Publications and source records attributed to Helekal, D..

4 recordsLinked to original sources

bioRxiv2022

Estimating the fitness cost and benefit of antimicrobial resistance from pathogen genomic data

Increasing levels of antibiotic resistance in many bacterial pathogen populations is a major threat to public health. Resistance to an antibiotic provides a fitness benefit when the bacteria is exposed to this antibiotic, but resistance also often comes at a cost to the resistant pathogen relative to susceptible counterparts. We lack a good understanding of these benefits and costs of resistance for many bacterial pathogens and antibiotics, but estimating them could lead to better use of antibiotics in a way that reduces or prevents the spread of resistance. Here, we propose a new model for the joint epidemiology of susceptible and resistant variants, which includes explicit parameters for the cost and benefit of resistance. We show how Bayesian inference can be performed under this model using phylogenetic data from susceptible and resistant lineages and that by combining data from both we are able to disentangle and estimate the resistance cost and benefit parameters separately. We applied our inferential methodology to several simulated datasets to demonstrate good scalability and accuracy. We analysed a dataset of Neisseria gonorrhoeae genomes collected between 2000 and 2013 in the USA. We found that two unrelated lineages resistant to fluoroquinolones shared similar epidemic dynamics and resistance parameters. Fluoroquinolones were abandoned for the treatment of gonorrhoea due to increasing levels of resistance, but our results suggest that they could be used to treat a minority of around 10% of cases without causing resistance to grow again.

evolutionary biology↗

bioRxiv2022

Distinguishing imported cases from locally acquired cases within a geographically limited genomic sample of an infectious disease

The ability to distinguish imported cases from locally acquired cases has important consequences for the selection of public health control strategies. Genomic data can be useful for this, for example using a phylogeographic analysis in which genomic data from multiple locations is compared to determine likely migration events between locations. However, these methods typically require good samples of genomes from all locations, which is rarely available. Here we propose an alternative approach that only uses genomic data from a location of interest. By comparing each new case with previous cases from the same location we are able to detect imported cases, as they have a different genealogical distribution than that of locally acquired cases. We show that, when variations in the size of the local population are accounted for, our method has good sensitivity and excellent specificity for the detection of imports. We applied our method to data simulated under the structured coalescent model and demonstrate relatively good performance even when the local population has the same size as the external population. Finally, we applied our method to several recent genomic datasets from both bacterial and viral pathogens, and show that it can, in a matter of seconds or minutes, deliver important insights on the number of imports to a geographically limited sample of a pathogen population.

evolutionary biology↗

bioRxiv2022

Simulating structurally variable Nuclear Pore Complexes for Microscopy

MotivationThe Nuclear Pore Complex (NPC) is the only passageway for macromolecules between nucleus and cytoplasm, and one of localization microscopys most important reference standards: it is massive and stereotypically arranged. The average architecture of NPC proteins has been resolved with pseudo-atomic precision, however observed NPC heterogeneities evidence a high degree of divergence from this average. Single Molecule Localization Microscopy (SMLM) images NPCs at protein-level resolution, whereupon image analysis software studies NPC variability. However the true picture of NPC variability is unknown. In quantitative image analysis experiments, it is thus difficult to distinguish intrinsically high SMLM noise from true variability of the underlying structure. ResultsWe introduce CIR4MICS ("ceramics", Configurable, Irregular Rings FOR MICroscopy Simulations), a pipeline that creates artificial datasets of structurally variable synthetic NPCs based on architectural models of the true NPC. Users can select one or more N- or C-terminally tagged NPC proteins, and simulate a wide range of geometric variations. We also represent the NPC as a spring-model such that arbitrary deforming forces, of user-defined magnitudes, simulate irregularly shaped variations. We provide an open-source simulation pipeline, as well as reference datasets of simulated human NPCs. Accompanying ground truth annotations allow to test the capabilities of image analysis software and facilitate a side-by-side comparison with real data. We demonstrate this by synthetically replicating a geometric analysis of real NPC radii and reveal that a wide range of simulated variability parameters can lead to observed results. Our simulator is therefore valuable to benchmark and develop image analysis methods, as well as to inform experimentalists about the requirements of hypothesis-driven imaging studies. AvailabilityCode: https://github.com/uhlmanngroup/cir4mics. Simulated data is available at BioStudies (Accession number S-BSST1058). Contacttheiss@ebi.ac.uk Supplementary informationSupplementary data are available at

bioinformatics↗

bioRxiv2021

Bayesian inference of clonal expansions in a dated phylogeny

Microbial population genetics models often assume that all lineages are constrained by the same population size dynamics over time. However, many neutral and selective events can invalidate this assumption, and can contribute to the clonal expansion of a specific lineage relative to the rest of the population. Such differential phylodynamic properties between lineages result in asymmetries and imbalances in phylogenetic trees that are sometimes described informally but which are difficult to analyse formally. To this end, we developed a model of how clonal expansions occur and affect the branching patterns of a phylogeny. We show how the parameters of this model can be inferred from a given dated phylogeny using Bayesian statistics, which allows us to assess the probability that one or more clonal expansion events occurred. For each putative clonal expansion event we estimate their date of emergence and subsequent phylodynamic trajectories, including their long-term evolutionary potential which is important to determine how much effort should be placed on specific control measures. We demonstrate the applicability of our methodology on simulated and real datasets.

evolutionary biology↗