Biology subjects

Kurtz, Z.

Publications and source records attributed to Kurtz, Z..

3 recordsLinked to original sources

bioRxiv2022

Precision Discovery of Novel Inhibitors of Human Cancer Target HsMetAP1 from Vast Unexplored Metagenomic Diversity

Microbial natural products have long been a rich source of human therapeutics. While the chemical diversity encoded in the genomes of microbes is large, this modality has waned as fermentation-based discovery methods have suffered from rediscovery, inefficient scaling, and incompatibility with target-based discovery paradigms. Here, we leverage a metagenomic partitioning strategy to sequence soil microbiomes at unprecedented depth and quality. We then couple these data with target-focused, in silico search strategies and synthetic biology to discover multiple novel natural product inhibitors of human methionine aminopeptidase-1 (HsMetAP1), a validated oncology target. For one of these, metapeptin B, we demonstrate sub-micromolar potency, strong selectivity for HsMetAP1 over HsMetAP2 and elucidate structure-activity relationships. Our approach overcomes challenges of traditional natural product methods, accesses vast, untapped chemical diversity in uncultured microbes, and demonstrates computationally-enabled precision mining of modulators of human proteins.

microbiology↗

bioRxiv2021

Glycosuria alters uropathogenic Escherichia coli global gene expression and virulence

Uropathogenic Escherichia coli (UPEC) is the principal etiology of more than half of urinary tract infections (UTI) in humans with diabetes mellitus. Epidemiological data and studies in mouse model of ascending UTI have elucidated various host factors responsible for increasing the susceptibility of diabetic hosts to UPEC-UTI. In contrast, the nature of alterations in UPEC physiology mediated by diabetic urinary microenvironment and the contributions of altered UPEC physiology in shaping UPEC-UTI pathogenesis in diabetes have not been examined. Our central hypothesis is that glycosuria directly induces urinary virulence of UPEC. We compared virulence characteristics and gene expression in human UPEC strains UTI89 (cystitis) and CFT073 (pyelonephritis) exposed for 2h, in vitro to human urine either in the presence or absence of glycosuria (600mg/dl glucose). Compared to control UPEC exposed to nutrient-rich culture medium LB, glycosuria-exposed UPEC exhibited significant increase in biofilm formation and reduction in the hemagglutination of Guinea pig erythrocytes (a surrogate measure of type 1 piliation). In addition, analysis of UTI89 transcriptome by RNA sequencing revealed that 2h-long, in vitro exposure to glycosuria also significantly alters expression of virulence and metabolic genes central to urinary virulence of UPEC. In summary, our results provide novel insights into how glycosuria-mediated early changes in UPEC fitness may facilitate UTI pathogenesis in the diabetic urinary microenvironment. IMPORTANCEUropathogenic Escherichia coli (UPEC) is an important causative agent of urinary tract infections in diabetic humans. We examined the effects of in vitro exposure to glycosuria (presence of glucose in urine) on the virulence and gene expression by UPEC. Our results show that glycosuria rapidly (in 2h) alters UPEC gene expression, induces biofilm formation, and suppresses hemagglutination. These results offer a novel insight into the pathogenesis of UPEC in the urinary tract.

microbiology↗

bioRxiv2018

Normalization methods for microbial abundance data strongly affect correlation estimates

Consistent estimation of associations in microbial genomic survey count data is fundamental to microbiome research. Technical limitations, including compositionality, low sample sizes, and technical variability, obstruct standard application of association measures and require data normalization prior to estimating associations. Here, we investigate the interplay between data normalization and microbial association estimation by a comprehensive analysis of statistical consistency. Leveraging the large sample size of the American Gut Project (AGP), we assess the consistency of the two prominent linear association estimators, correlation and proportionality, under different sample scenarios and data normalization schemes, including RNA-seq analysis work flows and log-ratio transformations. We show that shrinkage estimation, a standard technique in high-dimensional statistics, can universally improve the quality of association estimates for microbiome data. We find that large-scale association patterns in the AGP data can be grouped into five normalization-dependent classes. Using microbial association network construction and clustering as examples of exploratory data analysis, we show that variance-stabilizing and log-ratio approaches provide for the most consistent estimation of taxonomic and structural coherence. Taken together, the findings from our reproducible analysis workflow have important implications for microbiome studies in multiple stages of analysis, particularly when only small sample sizes are available.

bioinformatics↗