Biology subjects

Glascock, A. L.

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

4 recordsLinked to original sources

bioRxiv2024

Simultaneous detection of pathogens and antimicrobial resistance genes with the open source, cloud-based, CZ ID pipeline

Antimicrobial resistant (AMR) pathogens represent urgent threats to human health, and their surveillance is of paramount importance. Metagenomic next generation sequencing (mNGS) has revolutionized such efforts, but remains challenging due to the lack of open-access bioinformatics tools capable of simultaneously analyzing both microbial and AMR gene sequences. To address this need, we developed the Chan Zuckerberg ID (CZ ID) AMR module, an open-access, cloud-based workflow designed to integrate detection of both microbes and AMR genes in mNGS and whole-genome sequencing (WGS) data. It leverages the Comprehensive Antibiotic Resistance Database and associated Resistance Gene Identifier software, and works synergistically with the CZ ID short-read mNGS module to enable broad detection of both microbes and AMR genes. We highlight diverse applications of the AMR module through analysis of both publicly available and newly generated mNGS and WGS data from four clinical cohort studies and an environmental surveillance project. Through genomic investigations of bacterial sepsis and pneumonia cases, hospital outbreaks, and wastewater surveillance data, we gain a deeper understanding of infectious agents and their resistomes, highlighting the value of integrating microbial identification and AMR profiling for both research and public health. We leverage additional functionalities of the CZ ID mNGS platform to couple resistome profiling with the assessment of phylogenetic relationships between nosocomial pathogens, and further demonstrate the potential to capture the longitudinal dynamics of pathogen and AMR genes in hospital acquired bacterial infections. In sum, the new AMR module advances the capabilities of the open-access CZ ID microbial bioinformatics platform by integrating pathogen detection and AMR profiling from mNGS and WGS data. Its development represents a critical step toward democratizing pathogen genomic analysis and supporting collaborative efforts to combat the growing threat of AMR.

microbiology↗

bioRxiv2023

Evolutionary genomics identifies host-directed therapeutics to treat intracellular bacterial infections

Obligate intracellular bacteria shed essential biosynthetic pathways during their evolution towards host dependency, providing an opportunity for host-directed therapeutics. Using Rickettsiaceae as a model, we employed a novel computational pipeline called PoMeLo to systematically compare this cytosolic family of bacteria to the related Anaplasmataceae, which reside in a membrane-bound vacuole in the host cell. We identified 20 metabolic pathways that have been lost since the divergence of Anaplasmataceae and Rickettsiaceae, corresponding to the latters change to a cytosolic niche. We hypothesized that drug inhibition of these host metabolic pathways would reduce the levels of metabolites available to the bacteria, thereby inhibiting bacterial growth. We tested 22 commercially available inhibitors for 14 of the identified pathways and found that the majority (59%) reduced bacterial growth at concentrations that did not induce host cell cytotoxicity. Of these, 5 inhibitors with an IC50 under 5 M were tested to determine whether their mode of inhibition was bactericidal or bacteriostatic. Both mycophenolate mofetil, an inhibitor of inosine-5-monophosphate dehydrogenase in the purine biosynthesis pathway, and roseoflavin, an analog of riboflavin, displayed bactericidal activity. A complementary unbiased mass spectrometry-based metabolomics approach identified 14 pathways impacted by Rickettsia infection based on alterations in metabolite levels. Strikingly, 11 of these (79%) overlapped with those identified by our computational predictions. These in vitro validation studies support the feasibility of a novel evolutionary genomics-guided approach for host-directed antibiotic drug development against obligate pathogens. ImportanceMany pathogens have evolved to acquire essential metabolites from their host cell, while in turn shedding their own biosynthetic capacities. This leads to an interesting dilemma: on one hand, reduced genomes allow pathogens to save energy and replicate more quickly, while on the other hand, they become more dependent on the host cell for survival. This vulnerability can be exploited by identifying and therapeutically inhibiting the host pathways that are essential for pathogen survival. The significance of our research is in predicting the precise pathways lost during a pathogens evolutionary adaptation to parasitism and validating these predictions through targeted in vitro growth assays and an unbiased metabolomic survey of the host-pathogen interface.

genomics↗

bioRxiv2023

PoMeLo: a systematic computational approach to predicting metabolic loss in pathogen genomes

BackgroundGenome streamlining, the process by which genomes become smaller and encode fewer genes over time, is a common phenomenon among pathogenic bacteria. This reduction is driven by selection for faster replication and minimized energy expenditure in a nutrientrich environment. As pathogens evolve to become more reliant on the host, metabolic genes and resulting capabilities are lost in favor of siphoning metabolites from the host. Characterizing genome streamlining, gene loss, and pathway degradation can be useful in assessing pathogen metabolic dependency on host metabolism and identifying potential targets for host-directed therapeutics. ResultsPoMeLo (Predictor of Metabolic Loss) is a novel evolutionary genomics-guided computational approach for identifying metabolic gaps in the genomes of pathogenic bacteria. PoMeLo leverages a centralized public database of high-quality genomes and annotations and allows the user to compare an unlimited number of genomes across individual genes and pathways. PoMeLo runs locally using user-friendly prompts in a matter of minutes and generates tabular and visual outputs for users to compare predicted metabolic capacity between groups of bacteria and individual species. Each pathway is assigned a Predicted Metabolic Loss (PML) score to assess the magnitude of genome streamlining. Optionally, PoMeLo places results in an evolutionary context by including phylogenetic relationships in visual outputs. It can also initially compute phylogenetically-weighted mean genome sizes to identify genome streamlining events. Here we describe PoMeLo and demonstrate its use in identifying metabolic gaps in genomes of pathogenic Treponema species. ConclusionsPoMeLo represents an advance over existing methods for identifying metabolic gaps in genomic data, allowing comparison across large numbers of genomes and placing the resulting data in a phylogenetic context. PoMeLo is freely available for academic and nonacademic use at https://github.com/czbiohub-sf/pomelo.

bioinformatics↗

bioRxiv2020

Unique roles of vaginal Megasphaera phylotypes in reproductive health

The composition of the human vaginal microbiome has been extensively studied and is known to influence reproductive health. However, the functional roles of individual taxa and their contributions to negative health outcomes have yet to be well characterized. Here, we examine two vaginal bacterial taxa grouped within the genus Megasphaera that have been previously associated with bacterial vaginosis (BV) and pregnancy complications. Phylogenetic analyses support the classification of these taxa as two distinct species. These two phylotypes, Megasphaera phylotype 1 (MP1) and Megasphaera phylotype 2 (MP2), differ in genomic structure and metabolic potential, suggestive of differential roles within the vaginal environment. Further, these vaginal taxa show evidence of genome reduction and changes in DNA base composition, which may be common features of host dependence and/or adaptation to the vaginal environment. In a cohort of 3,870 women, we observed that MP1 has a stronger positive association with bacterial vaginosis whereas MP2 was positively associated with trichomoniasis. MP1, in contrast to MP2 and other common BV-associated organisms, was not significantly excluded in pregnancy. In a cohort of 52 pregnant women, MP1 was both present and transcriptionally active in 75.4% of vaginal samples. Conversely, MP2 was largely absent in the pregnant cohort. This study provides insight into the evolutionary history, genomic potential and predicted functional role of two clinically relevant vaginal microbial taxa.

microbiology↗