Biology subjects

Hicks, A. L.

Publications and source records attributed to Hicks, A. L..

6 recordsLinked to original sources

bioRxiv2023

Transposon-sequencing across multiple Mycobacterium abscessus isolates reveals significant functional genomic diversity among strains

Mycobacterium abscessus (Mab) is a clinically significant pathogen and a highly genetically diverse species due to its large accessory genome. The functional consequence of this diversity remains unknown mainly because, to date, functional genomic studies in Mab have been primarily performed on reference strains. Given the growing public health threat of Mab infections, understanding the functional genomic differences among Mab clinical isolates can provide more insight into how its genetic diversity influences gene essentiality, clinically relevant phenotypes, and importantly, potential drug targets. To determine the functional genomic diversity among Mab strains, we conducted transposon-sequencing (TnSeq) on 21 genetically diverse clinical isolates, including 15 M. abscessus subsp. abscessus isolates and 6 M. abscessus subsp. massiliense isolates, cataloging all the essential and non-essential genes in each strain. Pan-genome analysis revealed a core set of 3845 genes and a large accessory genome of 11,507. We identified 259 core essential genes across the 21 clinical isolates and 425 differentially required genes, representing [~]10% of the Mab core genome. We also identified genes whose requirements were sub-species, lineage, and isolate-specific. Finally, by correlating TnSeq profiles, we identified 19 previously uncharacterized genetic networks in Mab. Altogether, we find that Mab clinical isolates are not only genetically diverse but functionally diverse as well.

microbiology↗

bioRxiv2020

Increased power from bacterial genome-wide association conditional on known effects identifies Neisseria gonorrhoeae macrolide resistance mutations in the 50S ribosomal protein L4

The emergence of resistance to azithromycin complicates treatment of N. gonorrhoeae, the etiologic agent of gonorrhea. Population genomic analyses of clinical isolates have demonstrated that some azithromycin resistance remains unexplained after accounting for the contributions of known resistance mutations in the 23S rRNA and the MtrCDE efflux pump. Bacterial genome-wide association studies (GWAS) offer a promising approach for identifying novel resistance genes but must adequately address the challenge of controlling for genetic confounders while maintaining power to detect variants with lower effect sizes. Compared to a standard univariate GWAS, conducting GWAS conditioned on known resistance mutations with high effect sizes substantially reduced the number of variants that reached genome-wide significance and identified a G70D mutation in the 50S ribosomal protein L4 (encoded by the gene rplD) as significantly associated with increased azithromycin minimum inhibitory concentrations ({beta} = 1.03, 95% CI [0.76, 1.30]). The role and prevalence of these rplD mutations in conferring macrolide resistance in N. gonorrhoeae had been unclear. Here, we experimentally confirmed our GWAS results, identified other resistance-associated mutations in RplD, and showed that in total these RplD binding site mutations are prevalent (present in 5.42% of 4850 isolates) and geographically and temporally widespread (identified in 21/65 countries across two decades). Overall, our findings demonstrate the utility of conditional associations for improving the performance of microbial GWAS and advance our understanding of the genetic basis of macrolide resistance in a prevalent multidrug-resistant pathogen.

microbiology↗

bioRxiv2020

Targeted surveillance strategies for efficient detection of novel antibiotic resistance variants

Genotype-based diagnostics for antibiotic resistance represent a promising alternative to empiric therapy, reducing inappropriate and ineffective antibiotic use. However, because such assays infer resistance phenotypes based on the presence or absence of known genetic markers, their utility will wane in response to the emergence of novel resistance. Maintenance of these diagnostics will therefore require surveillance designed to ensure early detection of novel resistance variants, but efficient strategies to do so remain to be defined. Here, we evaluate the efficiency of targeted sampling approaches informed by patient and pathogen characteristics in detecting genetic variants associated with antibiotic resistance or diagnostic escape in Neisseria gonorrhoeae, focusing on this pathogen because of its high burden of disease, the imminent threat of treatment resistance, and the use and ongoing development of genotype-based diagnostics. We show that incorporating patient characteristics, such as demographics, geographic regions, or anatomical sites of isolate collection, into sampling approaches is not a reliable strategy for increasing variant detection efficiency. In contrast, sampling approaches informed by pathogen characteristics, such as genomic diversity and genomic background, are significantly more efficient than random sampling in identifying genetic variants associated with antibiotic resistance and diagnostic escape.

genomics↗

bioRxiv2020

Increased antibiotic susceptibility in Neisseria gonorrhoeae through adaptation to the cervical environment

Neisseria gonorrhoeae is an urgent public health threat due to rapidly increasing incidence and antibiotic resistance. In contrast with the trend of increasing resistance, clinical isolates that have reverted to susceptibility regularly appear, prompting questions about which pressures compete with antibiotics to shape gonococcal evolution. Here, we used genome-wide association on the largest collection of N. gonorrhoeae isolates to date (n=4852) to identify loss-of-function (LOF) mutations in the efflux pump mtrCDE operon as a mechanism of increased antibiotic susceptibility and demonstrate that these mutations are overrepresented in cervical isolates relative to urethral isolates (odds ratio (OR) = 3.74, 95% CI [1.98-6.70]). In support of a model in which pump expression incurs a fitness cost in this niche, cervical isolates were also enriched relative to urethral isolates in LOF mutations in the mtrCDE activator mtrA (OR = 8.60, 95% CI [4.96-14.57]) and in farA, a subunit of the FarAB efflux pump (OR = 6.25, 95% CI [3.90-9.83]). In total, approximately 2 in 5 cervical isolates (42.6%) contained a LOF mutation in either the efflux pump components mtrC or farA or the activator mtrA. Our findings extend beyond N. gonorrhoeae to other Neisseria: mtrC LOF mutations are rare (<1%) in the primarily nasopharyngeal-colonizing N. meningitidis in a collection of 14,798 genomes but enriched in a heterosexual urethritis-associated lineage (8.6%, p = 9.90x10-5), indicating that efflux pump downregulation contributes broadly to the adaptation of pathogenic Neisseria to the female urogenital tract. Overall, our findings highlight the impact of integrating microbial population genomics with host metadata and demonstrate how host environmental pressures can lead to increased antibiotic susceptibility.

microbiology↗

bioRxiv2019

Quantifying the surveillance required to sustain genetic marker-based antibiotic resistance diagnostics

Diagnostics that minimize the time to selection of an appropriate antibiotic treatment represent an important strategy in addressing the challenge of antimicrobial resistance (AMR). Among this class of diagnostics, the use of pathogen genotype to predict AMR phenotype has been facilitated by advances in rapid sequencing platforms. A longstanding objection to this approach, however, is that the emergence of novel resistance mechanisms will inevitably lead to a decline in the sensitivity of these diagnostics. Here, we show that while the sensitivities of some genetic markers of resistance remain stably high, sensitivities of other markers rapidly decline, as expected, due to the emergence of novel resistance variants. We then present a simple mathematical framework that defines the sampling and phenotypic testing rates needed for early detection of novel resistance variants and thus demonstrate how surveillance can help maintain the sensitivity and utility of sequence-based AMR diagnostics.\n\nOne sentence summaryTargeted sampling strategies are necessary for early detection of novel resistance mechanisms and sustainability of genotype-based detection of novel resistance mechanisms and sustainability of genotype-based diagnostics.

microbiology↗

bioRxiv2019

Evaluation of parameters affecting performance and reliability of machine learning-based antibiotic susceptibility testing from whole genome sequencing data

Prediction of antibiotic resistance phenotypes from whole genome sequencing data by machine learning methods has been proposed as a promising platform for the development of sequence-based diagnostics. However, there has been no systematic evaluation of factors that may influence performance of such models, how they might apply to and vary across clinical populations, and what the implications might be in the clinical setting. Here, we performed a meta-analysis of seven large Neisseria gonorrhoeae datasets, as well as Klebsiella pneumoniae and Acinetobacter baumannii datasets, with whole genome sequence data and antibiotic susceptibility phenotypes using set covering machine classification, random forest classification, and random forest regression models to predict resistance phenotypes from genotype. We demonstrate how model performance varies by drug, dataset, resistance metric, and species, reflecting the complexities of generating clinically relevant conclusions from machine learning-derived models. Our findings underscore the importance of incorporating relevant biological and epidemiological knowledge into model design and assessment and suggest that doing so can inform tailored modeling for individual drugs, pathogens, and clinical populations. We further suggest that continued comprehensive sampling and incorporation of up-to-date whole genome sequence data, resistance phenotypes, and treatment outcome data into model training will be crucial to the clinical utility and sustainability of machine learning-based molecular diagnostics.\n\nAuthor SummaryMachine learning-based prediction of antibiotic resistance from bacterial genome sequences represents a promising tool to rapidly determine the antibiotic susceptibility profile of clinical isolates and reduce the morbidity and mortality resulting from inappropriate and ineffective treatment. However, while there has been much focus on demonstrating the diagnostic potential of these modeling approaches, there has been little assessment of potential caveats and prerequisites associated with implementing predictive models of drug resistance in the clinical setting. Our results highlight significant biological and technical challenges facing the application of machine learning-based prediction of antibiotic resistance as a diagnostic tool. By outlining specific factors affecting model performance, our findings provide a framework for future work on modeling drug resistance and underscore the necessity of continued comprehensive sampling and reporting of treatment outcome data for building reliable and sustainable diagnostics.

microbiology↗