Biology subjects

Evans, P.

Publications and source records attributed to Evans, P..

5 recordsLinked to original sources

bioRxiv2018

Divergent methyl-coenzyme M reductase genes in a deep-subseafloor Archaeoglobi

The methyl-coenzyme M reductase (MCR) complex is a key enzyme in archaeal methane generation and has recently been proposed to also be involved in the oxidation of short-chain hydrocarbons including methane, butane and potentially propane. The number of archaeal clades encoding the MCR complex continues to grow, suggesting that this complex was inherited from an ancient ancestor, or has undergone extensive horizontal gene transfer. Expanding the representation of MCR-encoding lineages through metagenomic approaches will help resolve the evolutionary history of this complex. Here, a near-complete Archaeoglobi metagenome-assembled genome (MAG; rG16) was recovered from the deep subseafloor along the Juan de Fuca Ridge flank that encodes two divergent McrABG operons similar to those found in Candidatus Bathyarchaeota and Candidatus Syntrophoarchaeum MAGs. rG16 is basal to members of the class Archaeoglobi, and encodes the genes for {beta}-oxidation, potentially allowing an alkanotrophic metabolism similar to that proposed for Ca. Syntrophoarchaeum. rG16 also encodes a respiratory electron transport chain that can potentially utilize nitrate, iron, and sulfur compounds as electron acceptors. As the first Archaeoglobi with the MCR complex, rG16 extends our understanding of the evolution and distribution of novel MCR encoding lineages among the Archaea.

microbiology↗

bioRxiv2018

Genetic variant pathogenicity prediction trained using large-scale disease specific clinical sequencing datasets

Recent advances in DNA sequencing technologies have expanded our understanding of the molecular underpinnings for several genetic disorders, and increased the utilization of genomic tests by clinicians. Given the paucity of evidence to assess each variant, and the difficulty of experimentally evaluating a variants clinical significance, many of the thousand variants that can be generated by clinical tests are reported as variants of unknown clinical significance. However, the creation of population-scale variant databases can significantly improve clinical variant interpretation. Specifically, pathogenicity prediction for novel missense variants can now utilize features describing regional variant constraint. Constrained genomic regions are those that have an unusually low variant count in the general population. Several computational methods have been introduced to capture these regions and incorporate them into pathogenicity classifiers, but these methods have yet to be compared on an independent clinical variant dataset. Here we introduce one variant dataset derived from clinical sequencing panels, and use it to compare the ability of different genomic constraint metrics to determine missense variant pathogenicity. This dataset is compiled from 17,071 patients surveyed with clinical genomic sequencing for cardiomyopathy, epilepsy, or RASopathies. We further utilize this dataset to demonstrate the necessity of disease-specific classifiers, and to train PathoPredictor, a disease-specific ensemble classifier of pathogenicity based on regional constraint and variant level features. PathoPredictor achieves an average precision greater than 90% for variants from all 99 tested disease genes while approaching 100% accuracy for some genes. Accumulation of larger clinical variant datasets and their utilization to train existing pathogenicity metrics can significantly enhance their performance in a disease and gene-specific manner.

genomics↗

bioRxiv2018

Neutrophil microvesicles drive atherosclerosis by delivering miR-155 to atheroprone endothelium.

Neutrophils have been implicated in the pathogenesis of atherosclerosis, a lipid-driven disease of arteries, but they are seldom found in atherosclerotic plaques. To resolve this longstanding paradox, we investigated whether neutrophil-derived microvesicles may influence arterial pathophysiology. Clinical and pre-clinical studies revealed that levels of circulating neutrophil microvesicles were enhanced by exposure to a high fat diet, a known risk factor for atherosclerosis. Neutrophil microvesicles accumulated at disease-prone regions of arteries that are exposed to complex flow patterns, and they promoted vascular inflammation and atherosclerosis in a murine model. Using cultured endothelial cells exposed to disturbed flow, it was demonstrated that neutrophil microvesicles promoted inflammatory gene expression by delivering a microRNA (miR-155) that enhanced NF-{kappa}B activation. Similary, neutrophil microvesicles increased miR-155 and enhanced NF-{kappa}B at disease-prone sites of disturbed flow in arteries of mice. We conclude that delivery of microvesicles carrying miR-155 to disease-prone regions of arteries provides a novel mechanism by which neutrophils contribute to vascular inflammation and atherogenesis.

cell biology↗

bioRxiv2018

ExomeSlicer: a resource for the development and validation of exome-based clinical panels

Exome-based panels (exome slices) are becoming the preferred diagnostic strategy in clinical laboratories, especially for genetically heterogeneous disorders. The advantages of this approach include enabling frequent updates to gene content without the need for re-designing, reflexing to exome analysis bioinformatically without requiring additional sequencing, and streamlining laboratory operation by using established exome kits and protocols. Despite their increasing use, there are currently no guidelines or appropriate resources to support their clinical implementation. Here, we highlight principles and important considerations for the clinical development and validation of exome-based panels, guided by clinical data from a diagnostic epilepsy panel using this approach. We also present a novel, publically accessible web-based resource, ExomeSlicer, and demonstrate its clinical utility in predicting gene-specific and exome-wide technically challenging regions that are not amenable to Next Generation Sequencing (NGS), and that might significantly lead to increased post hoc Sanger fill in burden. Using this tool, we also characterize > 2000 low complexity, GC-rich and/or high homology, regions across the exome that can be a source of false positive or false negative variant calls thus potentially leading to misdiagnoses in tested patients.

genomics↗

bioRxiv2017

RNA-Seq 2G: Online Analysis Of Differential Gene Expression With Comprehensive Options Of Statistical Methods

RNA-seq has become the most prevalent technology for measuring genome-wide gene expression, but the best practices for processing and analysing RNA-seq data are still an open question. Many statistical methods have been developed to identify genes differentially expressed between sample groups from RNA-seq data. These methods differ by their data distribution assumptions, choice of statistical test, and computational resource requirements. Over 25 methods of differential expression detection were validated and made available through a user-friendly web portal, RNA-seq 2G. All methods are suitable for analysing differential gene expression between two groups of samples. They commonly use a read count matrix derived from RNA-seq data as input and statistically compare groups for each gene. The web portal uses a Shiny app front-end and is hosted by a cloud-based server provided by Amazon Web Service. The comparison of methods showed that the data distribution assumption is the major determinant of differences between methods. Most methods are more likely to find that longer genes are differentially expressed, which substantially impacts downstream gene set-level analysis. Combining results from multiple methods can potentially diminish this bias. RNA-seq 2G makes the analysis of RNA-seq data more accessible and efficient, and is freely available at http://rnaseq2g.awsomics.org.

bioinformatics↗