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Philip VM, He H, Saul MC, Dickson PE, Bubier JA, Chesler EJ. Gene expression genetics of the striatum of Diversity Outbred mice. Sci Data 2023; 10:522. [PMID: 37543624 PMCID: PMC10404230 DOI: 10.1038/s41597-023-02426-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 07/28/2023] [Indexed: 08/07/2023] Open
Abstract
Brain transcriptional variation is a heritable trait that mediates complex behaviors, including addiction. Expression quantitative trait locus (eQTL) mapping reveals genomic regions harboring genetic variants that influence transcript abundance. In this study, we profiled transcript abundance in the striatum of 386 Diversity Outbred (J:DO) mice of both sexes using RNA-Seq. All mice were characterized using a behavioral battery of widely-used exploratory and risk-taking assays prior to transcriptional profiling. We performed eQTL mapping, incorporated the results into a browser-based eQTL viewer, and deposited co-expression network members in GeneWeaver. The eQTL viewer allows researchers to query specific genes to obtain allelic effect plots, analyze SNP associations, assess gene expression correlations, and apply mediation analysis to evaluate whether the regulatory variant is acting through the expression of another gene. GeneWeaver allows multi-species comparison of gene sets using statistical and combinatorial tools. This data resource allows users to find genetic variants that regulate differentially expressed transcripts and place them in the context of other studies of striatal gene expression and function in addiction-related behavior.
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Affiliation(s)
- Vivek M Philip
- The Jackson Laboratory for Mammalian Genetics, Bar Harbor, ME, 04605, USA
| | - Hao He
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA
| | - Michael C Saul
- The Jackson Laboratory for Mammalian Genetics, Bar Harbor, ME, 04605, USA
| | - Price E Dickson
- Department of Biomedical Sciences, Joan C. Edwards School of Medicine Marshall University, Huntington, WV, 25703, USA
| | - Jason A Bubier
- The Jackson Laboratory for Mammalian Genetics, Bar Harbor, ME, 04605, USA
| | - Elissa J Chesler
- The Jackson Laboratory for Mammalian Genetics, Bar Harbor, ME, 04605, USA.
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2
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Jabarin R, Netser S, Wagner S. Beyond the three-chamber test: toward a multimodal and objective assessment of social behavior in rodents. Mol Autism 2022; 13:41. [PMID: 36284353 PMCID: PMC9598038 DOI: 10.1186/s13229-022-00521-6] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 10/06/2022] [Indexed: 12/31/2022] Open
Abstract
MAIN: In recent years, substantial advances in social neuroscience have been realized, including the generation of numerous rodent models of autism spectrum disorder. Still, it can be argued that those methods currently being used to analyze animal social behavior create a bottleneck that significantly slows down progress in this field. Indeed, the bulk of research still relies on a small number of simple behavioral paradigms, the results of which are assessed without considering behavioral dynamics. Moreover, only few variables are examined in each paradigm, thus overlooking a significant portion of the complexity that characterizes social interaction between two conspecifics, subsequently hindering our understanding of the neural mechanisms governing different aspects of social behavior. We further demonstrate these constraints by discussing the most commonly used paradigm for assessing rodent social behavior, the three-chamber test. We also point to the fact that although emotions greatly influence human social behavior, we lack reliable means for assessing the emotional state of animals during social tasks. As such, we also discuss current evidence supporting the existence of pro-social emotions and emotional cognition in animal models. We further suggest that adequate social behavior analysis requires a novel multimodal approach that employs automated and simultaneous measurements of multiple behavioral and physiological variables at high temporal resolution in socially interacting animals. We accordingly describe several computerized systems and computational tools for acquiring and analyzing such measurements. Finally, we address several behavioral and physiological variables that can be used to assess socio-emotional states in animal models and thus elucidate intricacies of social behavior so as to attain deeper insight into the brain mechanisms that mediate such behaviors. CONCLUSIONS: In summary, we suggest that combining automated multimodal measurements with machine-learning algorithms will help define socio-emotional states and determine their dynamics during various types of social tasks, thus enabling a more thorough understanding of the complexity of social behavior.
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Affiliation(s)
- Renad Jabarin
- Sagol Department of Neurobiology, Faculty of Natural Sciences, University of Haifa, Haifa, Israel.
| | - Shai Netser
- Sagol Department of Neurobiology, Faculty of Natural Sciences, University of Haifa, Haifa, Israel
| | - Shlomo Wagner
- Sagol Department of Neurobiology, Faculty of Natural Sciences, University of Haifa, Haifa, Israel
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3
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Goyal P, van Leeuwen JL, Muijres FT. Bumblebees land rapidly by intermittently accelerating and decelerating toward the surface during visually guided landings. iScience 2022; 25:104265. [PMID: 35521517 PMCID: PMC9065724 DOI: 10.1016/j.isci.2022.104265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 03/19/2022] [Accepted: 04/12/2022] [Indexed: 11/18/2022] Open
Abstract
Many flying animals parse visual information to control their landing, whereby they can decelerate smoothly by flying at a constant radial optic expansion rate. Here, we studied how bumblebees (Bombus terrestris) use optic expansion information to control their landing, by analyzing 10,005 landing maneuvers on vertical platforms with various optic information, and at three dim light conditions. We showed that bumblebees both decelerate and accelerate during these landings. Bumblebees decelerate by flying at a constant optic expansion rate, but they mostly accelerate toward the surface each time they switched to a new, often higher, optic expansion rate set-point. These transient acceleration phases allow bumblebees to increase their approach speed, and thereby land rapidly and robustly, even in dim twilight conditions. This helps explain why bumblebees are such robust foragers in challenging environmental conditions. The here-proposed sensorimotor landing control system can serve as bio-inspiration for landing control in unmanned aerial vehicles. Bumblebees land by intermittently decelerating and accelerating toward a surface Acceleration and deceleration phases result from a single visual-motor controller The accelerations toward the surface allow bees to maximize their landing speed Bumblebees adjust their sensorimotor control response to fly slower in dim light
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Affiliation(s)
- Pulkit Goyal
- Experimental Zoology Group, Wageningen University and Research, P.O. Box 338, 6700 AH, Wageningen, the Netherlands
| | - Johan L van Leeuwen
- Experimental Zoology Group, Wageningen University and Research, P.O. Box 338, 6700 AH, Wageningen, the Netherlands
| | - Florian T Muijres
- Experimental Zoology Group, Wageningen University and Research, P.O. Box 338, 6700 AH, Wageningen, the Netherlands
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Swanzey E, O'Connor C, Reinholdt LG. Mouse Genetic Reference Populations: Cellular Platforms for Integrative Systems Genetics. Trends Genet 2021; 37:251-265. [PMID: 33010949 PMCID: PMC7889615 DOI: 10.1016/j.tig.2020.09.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Revised: 08/28/2020] [Accepted: 09/02/2020] [Indexed: 02/07/2023]
Abstract
Interrogation of disease-relevant cellular and molecular traits exhibited by genetically diverse cell populations enables in vitro systems genetics approaches for uncovering the basic properties of cellular function and identity. Primary cells, stem cells, and organoids derived from genetically diverse mouse strains, such as Collaborative Cross and Diversity Outbred populations, offer the opportunity for parallel in vitro/in vivo screening. These panels provide genetic resolution for variant discovery and functional characterization, as well as disease modeling and in vivo validation capabilities. Here we review mouse cellular systems genetics approaches for characterizing the influence of genetic variation on signaling networks and phenotypic diversity, and we discuss approaches for data integration and cross-species validation.
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Affiliation(s)
| | - Callan O'Connor
- The Jackson Laboratory, Bar Harbor, ME, USA; Tufts Graduate School of Biomedical Sciences, Boston, MA, USA
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Parker CC, Lusk R, Saba LM. Alcohol Sensitivity as an Endophenotype of Alcohol Use Disorder: Exploring Its Translational Utility between Rodents and Humans. Brain Sci 2020; 10:E725. [PMID: 33066036 PMCID: PMC7600833 DOI: 10.3390/brainsci10100725] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Revised: 10/06/2020] [Accepted: 10/09/2020] [Indexed: 12/21/2022] Open
Abstract
Alcohol use disorder (AUD) is a complex, chronic, relapsing disorder with multiple interacting genetic and environmental influences. Numerous studies have verified the influence of genetics on AUD, yet the underlying biological pathways remain unknown. One strategy to interrogate complex diseases is the use of endophenotypes, which deconstruct current diagnostic categories into component traits that may be more amenable to genetic research. In this review, we explore how an endophenotype such as sensitivity to alcohol can be used in conjunction with rodent models to provide mechanistic insights into AUD. We evaluate three alcohol sensitivity endophenotypes (stimulation, intoxication, and aversion) for their translatability across human and rodent research by examining the underlying neurobiology and its relationship to consumption and AUD. We show examples in which results gleaned from rodents are successfully integrated with information from human studies to gain insight in the genetic underpinnings of AUD and AUD-related endophenotypes. Finally, we identify areas for future translational research that could greatly expand our knowledge of the biological and molecular aspects of the transition to AUD with the broad hope of finding better ways to treat this devastating disorder.
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Affiliation(s)
- Clarissa C. Parker
- Department of Psychology and Program in Neuroscience, Middlebury College, Middlebury, VT 05753, USA
| | - Ryan Lusk
- Department of Pharmaceutical Sciences, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA;
| | - Laura M. Saba
- Department of Pharmaceutical Sciences, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA;
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Thomas AL, Evans LM, Nelsen MD, Chesler EJ, Powers MS, Booher WC, Lowry CA, DeFries JC, Ehringer MA. Whole-Genome Sequencing of Inbred Mouse Strains Selected for High and Low Open-Field Activity. Behav Genet 2020; 51:68-81. [PMID: 32939625 DOI: 10.1007/s10519-020-10014-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Accepted: 08/21/2020] [Indexed: 02/09/2023]
Abstract
We conducted whole-genome sequencing of four inbred mouse strains initially selected for high (H1, H2) or low (L1, L2) open-field activity (OFA), and then examined strain distribution patterns for all DNA variants that differed between their BALB/cJ and C57BL/6J parental strains. Next, we assessed genome-wide sharing (3,678,826 variants) both between and within the High and Low Activity strains. Results suggested that about 10% of these DNA variants may be associated with OFA, and clearly demonstrated its polygenic nature. Finally, we conducted bioinformatic analyses of functional genomics data from mouse, rat, and human to refine previously identified quantitative trait loci (QTL) for anxiety-related measures. This combination of sequence analysis and genomic-data integration facilitated refinement of previously intractable QTL findings, and identified possible genes for functional follow-up studies.
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Affiliation(s)
- Aimee L Thomas
- Department of Integrative Physiology, University of Colorado Boulder, Boulder, CO, USA.,Institute for Behavioral Genetics, University of Colorado Boulder, 447 UCB, Boulder, CO, USA
| | - Luke M Evans
- Department of Ecology and Evolutionary Biology, University of Colorado Boulder, Boulder, CO, USA.,Institute for Behavioral Genetics, University of Colorado Boulder, 447 UCB, Boulder, CO, USA
| | - Michaela D Nelsen
- Department of Integrative Physiology, University of Colorado Boulder, Boulder, CO, USA
| | | | - Matthew S Powers
- Department of Integrative Physiology, University of Colorado Boulder, Boulder, CO, USA.,Institute for Behavioral Genetics, University of Colorado Boulder, 447 UCB, Boulder, CO, USA
| | - Winona C Booher
- Department of Integrative Physiology, University of Colorado Boulder, Boulder, CO, USA.,Institute for Behavioral Genetics, University of Colorado Boulder, 447 UCB, Boulder, CO, USA
| | - Christopher A Lowry
- Department of Integrative Physiology, University of Colorado Boulder, Boulder, CO, USA.,Center for Microbial Exploration, University of Colorado Boulder, Boulder, CO, USA.,Center for Neuroscience, University of Colorado Boulder, Boulder, CO, USA.,Department of Physical Medicine and Rehabilitation and Center for Neuroscience, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - John C DeFries
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA.,Institute for Behavioral Genetics, University of Colorado Boulder, 447 UCB, Boulder, CO, USA
| | - Marissa A Ehringer
- Department of Integrative Physiology, University of Colorado Boulder, Boulder, CO, USA. .,Center for Neuroscience, University of Colorado Boulder, Boulder, CO, USA. .,Institute for Behavioral Genetics, University of Colorado Boulder, 447 UCB, Boulder, CO, USA.
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7
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Reid RW, Ferrier JW, Jay JJ. Automated gene data integration with Databio. BMC Res Notes 2020; 13:195. [PMID: 32238171 PMCID: PMC7110638 DOI: 10.1186/s13104-020-05038-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2020] [Accepted: 03/20/2020] [Indexed: 02/04/2023] Open
Abstract
Objective Although sequencing and other high-throughput data production technologies are increasingly affordable, data analysis and interpretation remains a significant factor in the cost of -omics studies. Despite the broad acceptance of findable, accessible, interoperable, and reusable (FAIR) data principles which focus on data discoverability and annotation, data integration remains a significant bottleneck in linking prior work in order to better understand novel research. Relevant and timely information discovery is difficult for increasingly multi-disciplinary projects when scientists cannot easily keep up with work across multiple fields. Computational tools are necessary to accurately describe data contents, and empower linkage to existing resources without prior knowledge of the various database resources. Results We developed the Databio tool, accessible at https://datab.io/, to automate data parsing, identifier detection, and streamline common tasks to provide a point-and-click approach to data manipulation and integration in life sciences research and translational medicine. Databio uses fast real-time data structures and a data warehouse of 137 million identifiers, with automated heuristics to describe data provenance without highly specialized knowledge or bioinformatics training.
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Affiliation(s)
- Robert W Reid
- Department of Bioinformatics and Genomics, College of Computing and Informatics, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC, 28223, USA.,North Carolina Research Campus, 150 N Research Campus Dr, Kannapolis, NC, 28081, USA
| | - Jacob W Ferrier
- Department of Bioinformatics and Genomics, College of Computing and Informatics, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC, 28223, USA
| | - Jeremy J Jay
- Department of Bioinformatics and Genomics, College of Computing and Informatics, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC, 28223, USA. .,North Carolina Research Campus, 150 N Research Campus Dr, Kannapolis, NC, 28081, USA.
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8
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Salvatore JE, Han S, Farris SP, Mignogna KM, Miles MF, Agrawal A. Beyond genome-wide significance: integrative approaches to the interpretation and extension of GWAS findings for alcohol use disorder. Addict Biol 2019; 24:275-289. [PMID: 29316088 PMCID: PMC6037617 DOI: 10.1111/adb.12591] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2017] [Revised: 11/20/2017] [Accepted: 11/26/2017] [Indexed: 12/16/2022]
Abstract
Alcohol use disorder (AUD) is a heritable complex behavior. Due to the highly polygenic nature of AUD, identifying genetic variants that comprise this heritable variation has proved to be challenging. With the exception of functional variants in alcohol metabolizing genes (e.g. ADH1B and ALDH2), few other candidate loci have been confidently linked to AUD. Genome-wide association studies (GWAS) of AUD and other alcohol-related phenotypes have either produced few hits with genome-wide significance or have failed to replicate on further study. These issues reinforce the complex nature of the genetic underpinnings for AUD and suggest that both GWAS studies with larger samples and additional analysis approaches that better harness the nominally significant loci in existing GWAS are needed. Here, we review approaches of interest in the post-GWAS era, including in silico functional analyses; functional partitioning of single nucleotide polymorphism heritability; aggregation of signal into genes and gene networks; and validation of identified loci, genes and gene networks in postmortem brain tissue and across species. These integrative approaches hold promise to illuminate our understanding of the biological basis of AUD; however, we recognize that the main challenge continues to be the extremely polygenic nature of AUD, which necessitates large samples to identify multiple loci associated with AUD liability.
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Affiliation(s)
- Jessica E. Salvatore
- Department of Psychology; Virginia Commonwealth University; Richmond VA USA
- Virginia Institute for Psychiatric and Behavioral Genetics; Virginia Commonwealth University; Richmond VA USA
| | - Shizhong Han
- Department of Psychiatry; University of Iowa; Iowa City IA USA
- Department of Psychiatry and Behavioral Sciences; Johns Hopkins School of Medicine; Baltimore MD USA
| | - Sean P. Farris
- Waggoner Center for Alcohol and Addiction Research; The University of Texas at Austin; Austin TX USA
| | - Kristin M. Mignogna
- Virginia Institute for Psychiatric and Behavioral Genetics; Virginia Commonwealth University; Richmond VA USA
| | - Michael F. Miles
- Department of Pharmacology and Toxicology; Virginia Commonwealth University; Richmond VA USA
| | - Arpana Agrawal
- Department of Psychiatry; Washington University School of Medicine; Saint Louis MO USA
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Ellens KW, Christian N, Singh C, Satagopam VP, May P, Linster CL. Confronting the catalytic dark matter encoded by sequenced genomes. Nucleic Acids Res 2017; 45:11495-11514. [PMID: 29059321 PMCID: PMC5714238 DOI: 10.1093/nar/gkx937] [Citation(s) in RCA: 52] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2017] [Accepted: 10/03/2017] [Indexed: 01/02/2023] Open
Abstract
The post-genomic era has provided researchers with a deluge of protein sequences. However, a significant fraction of the proteins encoded by sequenced genomes remains without an identified function. Here, we aim at determining how many enzymes of uncertain or unknown function are still present in the Saccharomyces cerevisiae and human proteomes. Using information available in the Swiss-Prot, BRENDA and KEGG databases in combination with a Hidden Markov Model-based method, we estimate that >600 yeast and 2000 human proteins (>30% of their proteins of unknown function) are enzymes whose precise function(s) remain(s) to be determined. This illustrates the impressive scale of the ‘unknown enzyme problem’. We extensively review classical biochemical as well as more recent systematic experimental and computational approaches that can be used to support enzyme function discovery research. Finally, we discuss the possible roles of the elusive catalysts in light of recent developments in the fields of enzymology and metabolism as well as the significance of the unknown enzyme problem in the context of metabolic modeling, metabolic engineering and rare disease research.
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Affiliation(s)
- Kenneth W Ellens
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, L-4362 Esch-sur-Alzette, Luxembourg
| | - Nils Christian
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, L-4362 Esch-sur-Alzette, Luxembourg
| | - Charandeep Singh
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, L-4362 Esch-sur-Alzette, Luxembourg
| | - Venkata P Satagopam
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, L-4362 Esch-sur-Alzette, Luxembourg
| | - Patrick May
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, L-4362 Esch-sur-Alzette, Luxembourg
| | - Carole L Linster
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, L-4362 Esch-sur-Alzette, Luxembourg
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