Biology subjects

Patrick, R.

Publications and source records attributed to Patrick, R..

3 recordsLinked to original sources

bioRxiv2022

Quantitative trait and transcriptome analysis of genetic complexity underpinning cardiac interatrial septation in mice using an advanced intercross line

Unlike single-gene mutations leading to Mendelian conditions, common human diseases are likely emergent phenomena arising from multilayer, multiscale and highly interconnected interactions. Atrial and ventricular septal defects are the most common forms of cardiac congenital anomalies in humans. Atrial septal defects (ASD) show an open communication between left and right atria postnatally, potentially resulting in serious hemodynamic consequences if untreated. A milder form of atrial septal defect, patent foramen ovale (PFO), exists in about one quarter of the human population, strongly associated with ischaemic stroke and migraine. The anatomic liabilities and genetic and molecular basis of atrial septal defects remain unclear. Here, we advance our previous analysis of atrial septal variation through quantitative trait locus (QTL) mapping of an advanced intercross line (AIL) established between the inbred QSi5 and 129T2/SvEms mouse strains, that show extremes of septal phenotypes. Analysis resolved 37 unique septal QTL with high overlap between QTL for distinct septal traits. Whole genome sequencing of parental strains identified high confidence candidate deleterious variants, including in known human congenital heart disease genes, whereas transcriptome analysis of developing septa revealed networks involving ribosome, nucleosome, mitochondrial and extracellular matrix biosynthesis underlying septal variation. Analysis of variant architecture across different gene features, including enhancers and promoters, provided evidence for involvement of non-coding as well as protein coding variants. Our study provides the first high resolution picture of genetic complexity and network liability underlying common congenital heart disease, with relevance to human ASD and PFO.

genetics↗

bioRxiv2021

Social hosts evade predation but have deadlier parasites

Parasites exploit hosts to replicate and transmit, but overexploitation kills host and parasite (1): predators may shift this cost-benefit balance by consuming hosts (2-4) or changing host behavior, but the strength of these effects remains unclear. Modeling both, we find a primary, strong effect: hosts group to defend against predators (5), increasing parasite transmission, thus multiple infections, and therefore favoring more exploitative, virulent, parasites (6). Indeed, among 18 Trinidadian Gyrodactyus spp. parasite lines, those collected from high predation guppy populations were more virulent in common garden than those from low predation populations. Our model accurately predicted this result when parametrized with our experimentally demonstrated virulence-transmission trade-off, implicating the behavioral effects of predation. Broadly, our results indicate that reduced social contact selects against parasite virulence. One-Sentence SummaryOur theory and data show predators cause increased host social grouping; the resulting transmission favors parasite virulence.

ecology↗

bioRxiv2019

Sierra: Discovery of differential transcript usage from polyA-captured single-cell RNA-seq data

High-throughput single-cell RNA-seq (scRNA-seq) is a powerful tool for studying gene expression in single cells. Most current scRNA-seq bioinformatics tools focus on analysing overall expression levels, largely ignoring alternative mRNA isoform expression. We present a computational pipeline, Sierra, that readily detects differential transcript usage from data generated by commonly used polyA-captured scRNA-seq technology. We validate Sierra by comparing cardiac scRNA-seq cell-types to bulk RNA-seq of matched populations, finding significant overlap in differential transcripts. Sierra detects differential transcript usage across human peripheral blood mononuclear cells and the Tabula Muris, and 3UTR shortening in cardiac fibroblasts. Sierra is available at https://github.com/VCCRI/Sierra.

bioinformatics↗