Biology subjects

Chesler, E. J.

Publications and source records attributed to Chesler, E. J..

5 recordsLinked to original sources

bioRxiv2018

Integration of heterogeneous functional genomics data in gerontology research identifies genes and pathway underlying aging across species

Understanding the biological mechanisms behind aging, lifespan and healthspan is becoming increasingly important as the proportion of the world's population over the age of 65 grows, along with the cost and complexity of their care. BigData oriented approaches and analysis methods for integrative functional genomics enable current and future bio-gerontologists to synthesize, distill and interpret vast, heterogeneous data. GeneWeaver is an analysis system for integration of data that allows investigators to store, search, and analyze immense amounts of data including user-submitted experimental data, data from primary publications, and data in other databases. Aging related genome-wide gene sets from primary publications were curated into this system in concert with data from other model-organism and aging-specific databases, and used in several application using GeneWeavers analysis tools. For example, we identified Cd63 as a frequently represented gene among aging-related genome-wide results. To evaluate the role of Cd63 in aging, we performed RNAi knockdown of the C. elegans ortholog, tsp-7, demonstrating that this manipulation is capable of extending lifespan. The tools in GeneWeaver enable aging researchers to make new discoveries into the associations between the genes, normal biological processes, and diseases that affect aging, healthspan, and lifespan.

bioinformatics↗

bioRxiv2018

Genetic mapping in Diversity Outbred mice identifies a Trpa1 variant influencing late phase formalin response

Identification of genetic variants that influence susceptibility to chronic pain is key to identifying molecular mechanisms and targets for effective and safe therapeutic alternatives to opioids. To identify genes and variants associated with chronic pain, we measured late phase response to formalin injection in 275 male and female Diversity Outbred (DO) mice genotyped for over 70 thousand SNPs. One quantitative trait locus (QTL) reached genome-wide significance on chromosome 1 with a support interval of 3.1 Mb. This locus, Nociq4 (nociceptive sensitivity inflammatory QTL 4; MGI:5661503), harbors the well-known pain gene Trpa1 (transient receptor potential cation channel, subfamily A, member 1). Trpa1 is a cation channel known to play an important role in acute and chronic pain in both humans and mice. Analysis of DO founder strain allele effects revealed a significant effect of the CAST/EiJ allele at Trpa1, with CAST/EiJ carrier mice showing an early, but not late, response to formalin relative to carriers of the seven other inbred founder alleles (A/J, C57BL/6J, 129S1/SvImJ, NOD/ShiLtJ, NZO/HlLtJ, PWK/PhJ, and WSB/EiJ). We characterized possible functional consequences of sequence variants in Trpa1 by assessing channel conductance, Trpa1/Trpv1 interactions, and isoform expression. The phenotypic differences observed in CAST/EiJ relative to C57BL/6J carriers were best explained by Trpa1 isoform expression differences, implicating a splice junction variant as the causal functional variant. This study demonstrates the utility of advanced, high-precision genetic mapping populations in resolving specific molecular mechanisms of variation in pain sensitivity.

bioinformatics↗

bioRxiv2018

Systems genetic discovery of host-microbiome interactions reveals mechanisms of microbial involvement in disease

The role of the microbiome in health and disease involves complex networks of host genetics, genomics, microbes and environment. Identifying the mechanisms of these interactions has remained challenging. Systems genetics in the laboratory mouse enables data-driven discovery of network components and mechanisms of host-microbial interactions underlying multiple disease phenotypes. To examine the interplay among the whole host genome, transcriptome and microbiome, we mapped quantitative trait loci and correlated the abundance of cecal mRNA, luminal microflora, physiology and behavior in incipient strains of the highly diverse Collaborative Cross mouse population. The relationships that are extracted can be tested experimentally to ascribe causality among host and microbe in behavior and physiology, providing insight into disease. Application of this strategy in the Collaborative Cross population revealed experimentally validated mechanisms of microbial involvement in models of autism, inflammatory bowel disease and sleep disorder.\n\neTOC BlurbHost genetic diversity provides a variable selection environment and physiological context for microbiota and their interaction with host physiology. Using a highly diverse mouse population Bubier et al. identified a variety of host, microbe and potentially disease interactions.\n\nHighlights* 18 significant species-specific QTL regulating microbial abundance were identified\n* Cis and trans eQTL for 1,600 cecal transcripts were mapped in the Collaborative Cross\n* Sleep phenotypes were highly correlated with the abundance of B.P. Odoribacter\n* Elimination of sleep-associated microbes restored normal sleep patterns in mice.

genetics↗

bioRxiv2018

Robust Mouse Tracking in Complex Environments using Neural Networks

The ability to track animals accurately is critical for behavioral experiments. For video-based assays, this is often accomplished by manipulating environmental conditions to increase contrast between the animal and the background, in order to achieve proper foreground/background detection (segmentation). However, as behavioral paradigms become more sophisticated with ethologically relevant environments, the approach of modifying environmental conditions offers diminishing returns, particularly for scalable experiments. Currently, there is a need for methods to monitor behaviors over long periods of time, under dynamic environmental conditions, and in animals that are genetically and behaviorally heterogeneous. To address this need, we developed a state-of-the-art neural network-based tracker for mice, using modern machine vision techniques. We test three different neural network architectures to determine their performance on genetically diverse mice under varying environmental conditions. We find that an encoder-decoder segmentation neural network achieves high accuracy and speed with minimal training data. Furthermore, we provide a labeling interface, labeled training data, tuned hyperparameters, and a pre-trained network for the mouse behavior and neuroscience communities. This general-purpose neural network tracker can be easily extended to other experimental paradigms and even to other animals, through transfer learning, thus providing a robust, generalizable solution for biobehavioral research.

animal behavior and cognition↗

bioRxiv2017

Large-scale discovery of mouse transgenic integration sites reveals frequent structural variation and insertional mutagenesis

Transgenesis has been a mainstay of mouse genetics for over 30 years, providing numerous models of human disease and critical genetic tools in widespread use today. Generated through the random integration of DNA fragments into the host genome, transgenesis can lead to insertional mutagenesis if a coding gene or essential element is disrupted, and there is evidence that larger scale structural variation can accompany the integration. The insertion sites of only a tiny fraction of the thousands of transgenic lines in existence have been discovered and reported due in part to limitations in the discovery tools. Targeted Locus Amplification (TLA) provides a robust and efficient means to identify both the insertion site and content of transgenes through deep sequencing of genomic loci linked to specific known transgene cassettes. Here, we report the first large-scale analysis of transgene insertion sites from 40 highly used transgenic mouse lines. We show that the transgenes disrupt the coding sequence of endogenous genes in half of the lines, frequently involving large deletions and/or structural variations at the insertion site. Furthermore, we identify a number of unexpected sequences in some of the transgenes, including undocumented cassettes and contaminating DNA fragments. We demonstrate that these transgene insertions can have phenotypic consequences, which could confound certain experiments, emphasizing the need for careful attention to control strategies. Together, these data show that transgenic alleles display a high rate of potentially confounding genetic events, and highlight the need for careful characterization of each line to assure interpretable and reproducible experiments.

genetics↗