Biology subjects

Helekal, D.

Publications and source records attributed to Helekal, D..

9 recordsLinked to original sources

bioRxiv2026

Fluoroquinolone resistance-conferring gyrA variants alter the fitness cost and potentiate the resistance of the zoliflodacin resistance mutation gyrB D429N in Neisseria gonorrhoeae

Neisseria gonorrhoeae is a major public health concern due to its high global prevalence and rapid evolution of antibiotic resistance. A first-in-class topoisomerase inhibitor, zoliflodacin (a spiropyrimidinetrione) recently received FDA approval for treatment of gonorrhea, but its potential for cross-resistance with another topoisomerase inhibitor, the fluoroquinolone antibiotic ciprofloxacin, remains poorly understood. Here, we investigated how genetic diversity in the fluoroquinolone target gyrA influences the resistance and fitness effects of the zoliflodacin resistance mutation gyrBD429N. We constructed an isogenic panel of N. gonorrhoeae to determine how the resistance and fitness effects of the gyrBD429N mutation are modulated by the most common ciprofloxacin resistance-associated variants in gyrA. In the presence of gyrBD429N, the zoliflodacin minimum inhibitory concentration (MIC) was 2-4-fold higher in strains that also contained ciprofloxacin resistance-associated gyrA alleles, and the gyrBD429N mutation reciprocally increased ciprofloxacin MICs of these strains 3-6-fold. Fitness cost of the gyrBD429N mutation varied from modest to severe across gyrA backgrounds, with the largest cost in ciprofloxacin resistant gyrA91F/95G and gyrA91F/95N backgrounds and comparatively minimal cost in the ciprofloxacin resistant gyrA91F/95A background. These results demonstrate the capacity for epistatic interactions among resistance-associated gyrA and gyrB mutations, underscoring the need for genomic surveillance to monitor high-risk combinations of resistance determinants as new therapies are deployed.

microbiology↗

bioRxiv2026

Diversity and evolution of a phase-variable multi-locus antigen in Neisseria gonorrhoeae

Neisseria gonorrhoeae is a sexually transmitted bacterial pathogen that deploys multiple mechanisms to evade the immune system, including rapid variation in surface antigens. One of the most abundant and diverse antigens is the Opacity (Opa) protein, a surface protein that mediates gonococcal attachment to host receptors. Studies of Opa diversity and evolution have been limited by the inability of short-read sequencing to resolve the multiple copies of opa in each genome, preventing a comprehensive understanding of antigenic variation for vaccine design and immunology studies. We assembled a dataset of 219 complete genomes from diverse clinical isolates using long-read sequencing and developed bioinformatics and phylogenetics tools to assess opa variation quantitatively. Each genome had on average 7 distinct opa alleles at 9 to 12 opa loci, and almost all isolates had at least one pair of identical or near-identical opa genes. Fewer opa genes were in frame, and thus inferred to be expressed, than expected due to chance. While genomic distance between isolates correlated with overall opa allele sequence similarity, opa genes were on average 74 times more diverse than the rest of the genome. One opa locus evolved more rapidly than the other loci. There was little evidence that interspecies recombination contributed to N. gonorrhoeae opa diversity. Our findings reveal a continuously evolving opa repertoire that leads to diverse opa alleles even in closely related strains and indicate that there are likely unknown biological factors modulating opa expression. Author SummaryThe rising levels of antibiotic resistance in Neisseria gonorrhoeae make controlling the spread of this sexually transmitted pathogen a public health priority. N. gonorrhoeae rapidly varies surface proteins to evade recognition by the human adaptive immune system. Understanding how these proteins evolve may help us design better vaccines and control measures for curbing the spread of gonorrhea. One of the most abundant surface proteins is the Opacity protein (Opa), which helps N. gonorrhoeae bind to host cells upon colonization. Research efforts to understand the evolution of Opa have been limited because it is encoded by multiple genes in the genome that are not resolved by short-read sequencing technologies. Here, we resolved the genes that encode Opa using a dataset of 132 publicly available complete genomes and 87 genomes that we completed using long-read sequencing of diverse clinical isolates. We found that Opa evolves rapidly to generate different versions of the protein in the same isolate, but very few of these protein versions appear expressed. We also found evidence that there may be other, uncharacterized mechanisms that control how these proteins evolve over longer timescales.

evolutionary biology↗

bioRxiv2025

Zoliflodacin and gepotidacin cross-resistance in Neisseria gonorrhoeae

Resistance to zoliflodacin, a first-in-class antibiotic for gonorrhea treatment, can occur through gyrBD429N, but this mutations impact on fitness and resistance to other topoisomerase targeting drugs, including gepotidacin, have been unclear. Here, we show that gyrBD429N confers cross-resistance to gepotidacin in some clinical isolates and that its fitness effect varies with strain back-ground. These findings inform strategies for introducing the new topoisomerase inhibitors into clinical use and for surveillance of resistance.

microbiology↗

bioRxiv2025

Quantifying the impact of genetic determinants of antibiotic resistance on bacterial lineage dynamics

The dynamics of antimicrobial resistance in bacterial populations are informed by the fitness impact of genetic determinants of resistance and antibiotic pressure. However, estimates of real-world fitness impact have been lacking. To address this gap, we developed a hierarchical Bayesian phylodynamic model to quantify contributions of resistance determinants to strain success in a 20-year collection of Neisseria gonorrhoeae isolates. Fitness contributions varied with antibiotic use, and genetic pathways to phenotypically identical resistance conferred distinct fitness effects. These findings were supported by in vitro and experimental infection competition. Quantifying these fitness contributions to lineage dynamics reveals opportunities for investigation into other genetic and environmental drivers of fitness. This work thus establishes a method for linking pathogen genomics and antibiotic use to define factors shaping ecological trends.

microbiology↗

bioRxiv2023

Inference of multiple mergers while dating a pathogen phylogeny

The vast majority of pathogen phylogenetic studies do not consider the possibility of multiple merger events being present, where a single node of the tree leads to more than two descendent branches. These events are however likely to occur when studying a relatively small population or if there is high variability in the reproductive chances. Here we consider the problem of detecting the presence of multiple mergers in the context of dating a phylogeny, that is determining the date of each of the nodes. We use the Lambda-coalescent theory as a modelling framework and show how Bayesian inference can be efficiently performed using a Billera-Holmes-Vogtmann space embedding and a customised Markov Chain Monte Carlo sampling scheme. We applied this new analysis methodology to a large number of simulated datasets to show that it is possible to infer if and when multiple merger events occurred, and that the phylogenetic dating is improved as a result of taking this information into account. We also analysed real datasets of Vibrio cholerae and Mycobacterium tuberculosis to demonstrate the relevance of our approach to real pathogen evolutionary epidemiology. We have implemented our new methodology in a R package which is freely available at https://github.com/dhelekal/MMCTime.

evolutionary biology↗

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↗