Biology subjects

Chesler, E. J.

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

25 records · Page 2Linked to original sources

bioRxiv2019

Noninvasive sleep monitoring in large-scale screening of knock-out mice reveals novel sleep-related genes

Sleep is a critical process that is well-conserved across mammalian species, and perhaps most animals, yet its functions and underlying mechanisms remain poorly understood. Identification of genes and pathways that can influence sleep may shed new light on these functions. Genomic screens enable the detection of previously unsuspected molecular processes that influence sleep. In this study, we report results from a large-scale phenotyping study of sleep-wake parameters for a population of single-gene knockout mice. Sleep-wake parameters were measured using a high throughput, non-invasive piezoelectric system called PiezoSleep. Knockout mice generated on a C57BL6/N (B6N) background were monitored for sleep and wake parameters for five days. By analyzing data from over 6000 mice representing 343 single gene knockout lines, we identified 122 genes influencing traits like sleep duration and bout length that have not been previously implicated in sleep, including those that affect sleep only during a specific circadian phase. PiezoSleep also allows assessment of breath rates during sleep and this was integrated as a supplemental tool in identifying aberrant physiology in these knockout lines. Sex differences were evident in both normal and altered sleep behavior. Through a combination of genetic and phenotypic associations, and known QTLs for sleep, we propose a set of candidate genes playing specific roles in sleep. The high \"hit rate\" demonstrates that many genes can alter normal sleep behaviors through a variety of mechanisms. Further investigation of these genes may provide insight into the pathways regulating sleep, functional aspects of sleep, or indirect potentially pathological processes that alter normal sleep.

neuroscience↗

bioRxiv2018

Reference trait analysis reveals correlations between gene expression and quantitative traits in disjoint samples

Systems genetic analysis of complex traits involves the integrated analysis of genetic, genomic, and disease related measures. However, these data are often collected separately across multiple study populations, rendering direct correlation of molecular features to complex traits impossible. Recent transcriptome-wide association studies (TWAS) have harnessed gene expression quantitative trait loci (eQTL) to associate unmeasured gene expression with a complex trait in genotyped individuals, but this approach relies primarily on strong eQTLs. We propose a simple and powerful alternative strategy for correlating independently obtained sets of complex traits and molecular features. In contrast to TWAS, our approach gains precision by correlating complex traits through a common set of continuous phenotypes instead of genetic predictors, and can identify transcript-trait correlations for which the regulation is not genetic. In our approach, a set of multiple quantitative \"reference\" traits is measured across all individuals, while measures of the complex trait of interest and transcriptional profiles are obtained in disjoint sub-samples. A conventional multivariate statistical method, canonical correlation analysis, is used to relate the reference traits and traits of interest in order to identify gene expression correlates. We evaluate power and sample size requirements of this methodology, as well as performance relative to other methods, via extensive simulation and analysis of a behavioral genetics experiment in 258 Diversity Outbred mice involving two independent sets of anxiety-related behaviors and hippocampal gene expression. After splitting the dataset and hiding one set of anxiety-related traits in half the samples, we identified transcripts correlated with the hidden traits using the other set of anxiety-related traits and exploiting the highest canonical correlation (R = 0.69) between the trait datasets. We demonstrate that this approach outperforms TWAS in identifying associated transcripts. Together, these results demonstrate the validity, reliability, and power of the reference trait method for identifying relations between complex traits and their molecular substrates.\n\nAUTHOR SUMMARYSystems genetics exploits natural genetic variation and high-throughput measurements of molecular intermediates to dissect genetic contributions to complex traits. An important goal of this strategy is to correlate molecular features, such as transcript or protein abundance, with complex traits. For practical, technical, or financial reasons, it may be impossible to measure complex traits and molecular intermediates on the same individuals. Instead, in some cases these two sets of traits may be measured on independent cohorts. We outline a method, reference trait analysis, for identifying molecular correlates of complex traits in this scenario. We show that our method powerfully identifies complex trait correlates across a wide range of parameters that are biologically plausible and experimentally practical. Furthermore, we show that reference trait analysis can identify transcripts correlated to a complex trait more accurately than approaches such as TWAS that use genetic variation to predict gene expression. Reference trait analysis will contribute to furthering our understanding of variation in complex traits by identifying molecular correlates of complex traits that are measured in different individuals.

genomics↗

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↗