Warren, A. S. | bioRxiv Science

bioRxiv2022

Physiological niche informs evolution of metabolic function and corresponding drug targets of pathobionts

Treatment of infections with traditional antimicrobials has become difficult due to the growing antimicrobial resistance crisis, necessitating the development of innovative approaches for deeply understanding pathogen function. Here, we generated a collection of genome-scale metabolic network reconstructions to gain insight into evolutionary drivers of metabolic function. We determined physiological location is a major driver of evolution of metabolic function. We observed that stomach-associated pathobionts had the most unique metabolic phenotypes and identified three essential genes unique to stomach pathobionts across diverse phylogenetic relationships. We demonstrate that inhibition of one such gene, thyX, inhibited growth of stomach- specific pathobionts exclusively, indicating possible physiological niche-specific targeting. This pioneering approach is the first step to using unique metabolic signatures to inform targeted antimicrobial therapies. One sentence summaryA data-driven approach to drug target discovery through metabolic signatures of diverse pathogens conserved across body-sites.

systems biology↗

bioRxiv2017

Panaconda: Application of pan-synteny graph models to genome content analysis

MotivationWhole-genome alignment and pan-genome analysis are useful tools in understanding the similarities and differences of many genomes in an evolutionary context. Here we introduce the concept of pan-synteny graphs, an analysis method that combines elements of both to represent conservation and change of multiple prokaryotic genomes at an architectural level. Pan-synteny graphs represent a reference free approach for the comparison of many genomes and allows for the identification of synteny, insertion, deletion, replacement, inversion, recombination, missed assembly joins, evolutionary hotspots, and reference based scaffolding.\n\nResultsWe present an algorithm for creating whole genome multiple sequence comparisons and a model for representing the similarities and differences among sequences as a graph of syntenic gene families. As part of the pan-synteny graph creation, we first create a de Bruijn graph. Instead of the alphabet of nucleotides commonly used in genome assembly, we use an alphabet of gene families. This de Bruijn graph is then processed to create the pan-synteny graph. Our approach is novel in that it explicitly controls how regions from the same sequence and genome are aligned and generates a graph in which all sequences are fully represented as paths. This method harnesses previous computation involved in protein family calculation to speed up the creation of whole genome alignment for many genomes. We provide the software suite Panaconda, for the calculation of pan-synteny graphs given annotation input, and an implementation of methods for their layout and visualization.\n\nAvailabilityPanaconda is available at https://github.com/aswarren/pangenome_graphs and datasets used in examples are available at https://github.com/aswarren/pangenome_examples\n\nContactAndrew Warren anwarren@vt.edu

bioinformatics↗