Genetic and molecular network analysis of behavior

The potential of integrating human and mouse discovery platforms to advance our understanding of cardiometabolic diseases

Cardiometabolic diseases encompass a range of interrelated conditions that arise from underlying metabolic perturbations precipitated by genetic, environmental, and lifestyle factors. While obesity, dyslipidaemia, smoking, and insulin resistance are major risk factors for cardiometabolic diseases, individuals still present in the absence of such traditional risk factors, making it difficult to determine those at greatest risk of disease. Thus, it is crucial to elucidate the genetic, environmental, and molecular underpinnings to better understand, diagnose, and treat cardiometabolic diseases. Much of this information can be garnered using systems genetics, which takes population-based approaches to investigate how genetic variance contributes to complex traits. Despite the important advances made by human genome-wide association studies (GWAS) in this space, corroboration of these findings has been hampered by limitations including the inability to control environmental influence, limited access to pertinent metabolic tissues, and often, poor classification of diseases or phenotypes. A complementary approach to human GWAS is the utilisation of model systems such as genetically diverse mouse panels to study natural genetic and phenotypic variation in a controlled environment. Here, we review mouse genetic reference panels and the opportunities they provide for the study of cardiometabolic diseases and related traits. We discuss how the post-GWAS era has prompted a shift in focus from discovery of novel genetic variants to understanding gene function. Finally, we highlight key advantages and challenges of integrating complementary genetic and multi-omics data from human and mouse populations to advance biological discovery.

Progressions on the Coexistence of Neuronal and Glial Precursor Cells in the Cerebral Ventricular Zone

Heterogeneity is defined as the quality or state of being diverse in character or content. This article summarizes the natural progression from my studies, reported in the first issue of the Journal of Neuroscience, that identified molecular heterogeneity in precursor cells of the developing primate cerebral cortex to the current state in which differences defined at the molecular, cellular, circuit, and systems levels are building data encyclopedias. The emphasis on heterogeneity has impacted many contributors in the field of developmental neuroscience, who have led a quest to determine the extent to which there is diversity, when it appears developmentally, and what heritable and nonheritable factors mediate nervous system assembly and function. Since the appearance of the article on progenitor cell heterogeneity in the inaugural issue of the Journal of Neuroscience, there have been continuous advances in technologies and data analytics that are contributing to a much better understanding of the origins of neurobiological and behavioral heterogeneity.

Genetic Dissection of Variation in Hippocampal Intra- and Infrapyramidal Mossy Fibers in the Mouse

This chapter describes the genetic analysis of a morphometric neuroanatomic trait. We used the extended BXD family of recombinant inbred mouse strains with the intent to analyze the genetic bases of heritable differences in hippocampal neurocircuitry and to identify Quantitative Trait Loci that underlie these variations. A detailed description of a GeneNetwork analysis is provided using data for the intra- and infrapyramidal mossy fiber (IIPMF) terminal fields which are strongly correlated with spatial navigation/radial maze learning.

Multidimensional Genetic Analysis of Repeated Seizures in the Hybrid Mouse Diversity Panel Reveals a Novel Epileptogenesis Susceptibility Locus

Epilepsy has many causes and comorbidities affecting as many as 4% of people in their lifetime. Both idiopathic and symptomatic epilepsies are highly heritable, but genetic factors are difficult to characterize among humans due to complex disease etiologies. Rodent genetic studies have been critical to the discovery of seizure susceptibility loci, including Kcnj10 mutations identified in both mouse and human cohorts. However, genetic analyses of epilepsy phenotypes in mice to date have been carried out as acute studies in seizure-naive animals or in Mendelian models of epilepsy, while humans with epilepsy have a history of recurrent seizures that also modify brain physiology. We have applied a repeated seizure model to a genetic reference population, following seizure susceptibility over a 36-d period. Initial differences in generalized seizure threshold among the Hybrid Mouse Diversity Panel (HMDP) were associated with a well-characterized seizure susceptibility locus found in mice: Seizure susceptibility 1. Remarkably, Szs1 influence diminished as subsequent induced seizures had diminishing latencies in certain HMDP strains. Administration of eight seizures, followed by an incubation period and an induced retest seizure, revealed novel associations within the calmodulin-binding transcription activator 1, Camta1. Using systems genetics, we have identified four candidate genes that are differentially expressed between seizure-sensitive and -resistant strains close to our novel Epileptogenesis susceptibility factor 1 (Esf1) locus that may act individually or as a coordinated response to the neuronal stress of seizures.

Systems Genetic Analysis in GeneNetwork.org

Genome-wide association studies (GWAS) have emerged as a powerful tool to identify alleles and molecular pathways that influence susceptibility to psychiatric disorders and other diseases. Forward genetics using mouse mapping populations allows for a complementary approach that provides rigorous genetic and environmental control. In this unit, we describe techniques and tools that reduce the technical burden traditionally associated with genetic mapping in mice and enhance their translational utility to human psychiatric disorders. We provide guidance on choosing the appropriate mapping population, discuss the importance of phenotype, and offer detailed instructions on using the Web-based resource GeneNetwork to aid neuroscientists in better understanding the mechanisms through which genes influence behavior. We believe that the continued development of mouse mapping populations, genetic tools, bioinformatics resources, and statistical methodologies should remain a parallel strategy by which to investigate the genetic and environmental underpinnings of psychiatric disorders and other diseases in humans. © 2017 by John Wiley & Sons, Inc.

Effects of selection for ethanol preference on gene expression in the nucleus accumbens of HS-CC mice

Previous studies on changes in murine brain gene expression associated with the selection for ethanol preference have used F₂ intercross or heterogeneous stock (HS) founders, derived from standard laboratory strains. However, these populations represent only a small proportion of the genetic variance available in Mus musculus. To investigate a wider range of genetic diversity, we selected mice for ethanol preference using an HS derived from the eight strains of the collaborative cross. These HS mice were selectively bred (four generations) for high and low ethanol preference. The nucleus accumbens shell of naive S₄ mice was interrogated using RNA sequencing (RNA-Seq). Gene networks were constructed using the weighted gene coexpression network analysis assessing both coexpression and cosplicing. Selection targeted one of the network coexpression modules (greenyellow) that was significantly enriched in genes associated with receptor signaling activity including Chrna7, Grin2a, Htr2a and Oprd1. Connectivity in the module as measured by changes in the hub nodes was significantly reduced in the low preference line. Of particular interest was the observation that selection had marked effects on a large number of cell adhesion molecules, including cadherins and protocadherins. In addition, the coexpression data showed that selection had marked effects on long non-coding RNA hub nodes. Analysis of the cosplicing network data showed a significant effect of selection on a large cluster of Ras GTPase-binding genes including Cdkl5, Cyfip1, Ndrg1, Sod1 and Stxbp5. These data in part support the earlier observation that preference is linked to Ras/Mapk pathways.

Integrative Functional Genomics for Systems Genetics in GeneWeaver.org

The abundance of existing functional genomics studies permits an integrative approach to interpreting and resolving the results of diverse systems genetics studies. However, a major challenge lies in assembling and harmonizing heterogeneous data sets across species for facile comparison to the positional candidate genes and coexpression networks that come from systems genetic studies. GeneWeaver is an online database and suite of tools at www.geneweaver.org that allows for fast aggregation and analysis of gene set-centric data. GeneWeaver contains curated experimental data together with resource-level data such as GO annotations, MP annotations, and KEGG pathways, along with persistent stores of user entered data sets. These can be entered directly into GeneWeaver or transferred from widely used resources such as GeneNetwork.org. Data are analyzed using statistical tools and advanced graph algorithms to discover new relations, prioritize candidate genes, and generate function hypotheses. Here we use GeneWeaver to find genes common to multiple gene sets, prioritize candidate genes from a quantitative trait locus, and characterize a set of differentially expressed genes. Coupling a large multispecies repository curated and empirical functional genomics data to fast computational tools allows for the rapid integrative analysis of heterogeneous data for interpreting and extrapolating systems genetics results.

GeneNetwork: A Toolbox for Systems Genetics

The goal of systems genetics is to understand the impact of genetic variation across all levels of biological organization, from mRNAs, proteins, and metabolites, to higher-order physiological and behavioral traits. This approach requires the accumulation and integration of many types of data, and also requires the use of many types of statistical tools to extract relevant patterns of covariation and causal relations as a function of genetics, environment, stage, and treatment. In this protocol we explain how to use the GeneNetwork web service, a powerful and free online resource for systems genetics. We provide workflows and methods to navigate massive multiscalar data sets and we explain how to use an extensive systems genetics toolkit for analysis and synthesis. Finally, we provide two detailed case studies that take advantage of human and mouse cohorts to evaluate linkage between gene variants, addiction, and aging.

Genetic Networks in Mouse Retinal Ganglion Cells

Retinal ganglion cells (RGCs) are the output neuron of the eye, transmitting visual information from the retina through the optic nerve to the brain. The importance of RGCs for vision is demonstrated in blinding diseases where RGCs are lost, such as in glaucoma or after optic nerve injury. In the present study, we hypothesize that normal RGC function is transcriptionally regulated. To test our hypothesis, we examine large retinal expression microarray datasets from recombinant inbred mouse strains in GeneNetwork and define transcriptional networks of RGCs and their subtypes. Two major and functionally distinct transcriptional networks centering around Thy1 and Tubb3 (Class III beta-tubulin) were identified. Each network is independently regulated and modulated by unique genomic loci. Meta-analysis of publically available data confirms that RGC subtypes are differentially susceptible to death, with alpha-RGCs and intrinsically photosensitive RGCs (ipRGCs) being less sensitive to cell death than other RGC subtypes in a mouse model of glaucoma.

Initial locomotor sensitivity to cocaine varies widely among inbred mouse strains

Initial sensitivity to psychostimulants can predict subsequent use and abuse in humans. Acute locomotor activation in response to psychostimulants is commonly used as an animal model of initial drug sensitivity and has been shown to have a substantial genetic component. Identifying the specific genetic differences that lead to phenotypic differences in initial drug sensitivity can advance our understanding of the processes that lead to addiction. Phenotyping inbred mouse strain panels are frequently used as a first step for studying the genetic architecture of complex traits. We assessed locomotor activation following a single, acute 20 mg/kg dose of cocaine (COC) in males from 45 inbred mouse strains and observed significant phenotypic variation across strains indicating a substantial genetic component. We also measured levels of COC, the active metabolite, norcocaine and the major inactive metabolite, benzoylecgonine, in plasma and brain in the same set of inbred strains. Pharmacokinetic (PK) and behavioral data were significantly correlated, but at a level that indicates that PK alone does not account for the behavioral differences observed across strains. Phenotypic data from this reference population of inbred strains can be utilized in studies aimed at examining the role of psychostimulant-induced locomotor activation on drug reward and reinforcement and to test theories about addiction processes. Moreover, these data serve as a starting point for identifying genes that alter sensitivity to the locomotor stimulatory effects of COC.

Systems genetic analysis of hippocampal neuroanatomy and spatial learning in mice

Variation in hippocampal neuroanatomy correlates well with spatial learning ability in mice. Here, we have studied both hippocampal neuroanatomy and behavior in 53 isogenic BXD recombinant strains derived from C57BL/6J and DBA/2J parents. A combination of experimental, neuroinformatic and systems genetics methods was used to test the genetic bases of variation and covariation among traits. Data were collected on seven hippocampal subregions in CA3 and CA4 after testing spatial memory in an eight-arm radial maze task. Quantitative trait loci were identified for hippocampal structure, including the areas of the intra- and infrapyramidal mossy fibers (IIPMFs), stratum radiatum and stratum pyramidale, and for a spatial learning parameter, error rate. We identified multiple loci and gene variants linked to either structural differences or behavior. Gpc4 and Tenm2 are strong candidate genes that may modulate IIPMF areas. Analysis of gene expression networks and trait correlations highlight several processes influencing morphometrical variation and spatial learning.

FASTKD2 is associated with memory and hippocampal structure in older adults

Memory impairment is the cardinal early feature of Alzheimer's disease, a highly prevalent disorder whose causes remain only partially understood. To identify novel genetic predictors, we used an integrative genomics approach to perform the largest study to date of human memory (n=14 781). Using a genome-wide screen, we discovered a novel association of a polymorphism in the pro-apoptotic gene FASTKD2 (fas-activated serine/threonine kinase domains 2; rs7594645-G) with better memory performance and replicated this finding in independent samples. Consistent with a neuroprotective effect, rs7594645-G carriers exhibited increased hippocampal volume and gray matter density and decreased cerebrospinal fluid levels of apoptotic mediators. The MTOR (mechanistic target of rapamycin) gene and pathways related to endocytosis, cholinergic neurotransmission, epidermal growth factor receptor signaling and immune regulation, among others, also displayed association with memory. These findings nominate FASTKD2 as a target for modulating neurodegeneration and suggest potential mechanisms for therapies to combat memory loss in normal cognitive aging and dementia.

A genome wide association study links glutamate receptor pathway to sporadic Creutzfeldt-Jakob disease risk

We performed a genome-wide association (GWA) study in 434 sporadic Creutzfeldt-Jakob disease (sCJD) patients and 1939 controls from the United Kingdom, Germany and The Netherlands. The findings were replicated in an independent sample of 1109 sCJD and 2264 controls provided by a multinational consortium. From the initial GWA analysis we selected 23 SNPs for further genotyping in 1109 sCJD cases from seven different countries. Five SNPs were significantly associated with sCJD after correction for multiple testing. Subsequently these five SNPs were genotyped in 2264 controls. The pooled analysis, including 1543 sCJD cases and 4203 controls, yielded two genome wide significant results: rs6107516 (p-value=7.62x10-9) a variant tagging the prion protein gene (PRNP); and rs6951643 (p-value=1.66x10-8) tagging the Glutamate Receptor Metabotropic 8 gene (GRM8). Next we analysed the data stratifying by country of origin combining samples from the pooled analysis with genotypes from the 1000 Genomes Project and imputed genotypes from the Rotterdam Study (Total n=12967). The meta-analysis of the results showed that rs6107516 (p-value=3.00x10-8) and rs6951643 (p-value=3.91x10-5) remained as the two most significantly associated SNPs. Rs6951643 is located in an intronic region of GRM8, a gene that was additionally tagged by a cluster of 12 SNPs within our top100 ranked results. GRM8 encodes for mGluR8, a protein which belongs to the metabotropic glutamate receptor family, recently shown to be involved in the transduction of cellular signals triggered by the prion protein. Pathway enrichment analyses performed with both Ingenuity Pathway Analysis and ALIGATOR postulates glutamate receptor signalling as one of the main pathways associated with sCJD. In summary, we have detected GRM8 as a novel, non-PRNP, genome-wide significant marker associated with heightened disease risk, providing additional evidence supporting a role of glutamate receptors in sCJD pathogenesis.

Genetics of gene expression in CNS

Transcriptome studies have revealed a surprisingly high level of variation among individuals in expression of key genes in the CNS under both normal and experimental conditions. Ten-fold variation is common, yet the specific causes and consequences of this variation are largely unknown. By combining classic gene mapping methods-family linkage studies and genomewide association-with high-throughput genomics, it is now possible to define quantitative trait loci (QTLs), single-gene variants, and even single SNPs and indels that control gene expression in different brain regions and cells. This review considers some of the major technical and conceptual challenges in analyzing variation in expression in the CNS with a focus on mRNAs, rather than noncoding RNAs or proteins. At one level of analysis, this work has been highly successful, and we finally have techniques that can be used to track down small numbers of loci that control expression in the CNS. But at a higher level of analysis, we still do not understand the genetic architecture of gene expression in brain, the consequences of expression QTLs on protein levels or on cell function, or the combined impact of expression differences on behavior and disease risk. These important gaps are likely to be bridged over the next several decades using (1) much larger sample sizes, (2) more powerful RNA sequencing and proteomic methods, and (3) novel statistical and computational models to predict genome-to-phenome relations.

Genetic variation of the cutaneous HPA axis: an analysis of UVB-induced differential responses

Mammalian skin incorporates a local equivalent of the hypothalamic-pituitary-adrenal (HPA) axis that is critical in coordinating homeostatic responses against external noxious stimuli. Ultraviolet radiation B (UVB) is a skin-specific stressor that can activate this cutaneous HPA axis. Since C57BL/6 (B6) and DBA/2J (D2) strains of mice have different predispositions to sensorineural pathway activation, we quantified expression of HPA axis components at the gene and protein levels in skin incubated ex vivo after UVB or sham irradiation. Urocortin mRNA was up-regulated after all doses of UVB with a maximum level at 50 mJ/cm(2) after 12h for D2 and at 200 mJ/cm(2) after 24h for B6. Proopiomelanocortin mRNA was enhanced after 6h with the peak after 12h and at 200 mJ/cm(2) for both genotypes of mice. ACTH levels in tissue and media increased after 24h in B6 but not in D2. UVB stimulated β-endorphin expression was higher in D2 than in B6. Melanocortin receptor 2 mRNA was stimulated by UVB in a dose-dependent manner, with a peak at 200 mJ/cm(2) after 12h for both strains. The expression of Cyp11a1 mRNA - a key mitochondrial P450 enzyme in steroidogenesis, was stimulated at all doses of UVB irradiation, with the most pronounced effect after 12-24h. UVB radiation caused, independently of genotype, a dose-dependent increase in corticosterone production in the skin, mainly after 24h of histoculture. Thus, basal and UVB stimulated expression of the cutaneous HPA axis differs as a function of genotype: D2 responds to UVB earlier and with higher amplitude than B6, while B6 shows prolonged (up to 48 h) stress response to a noxious stimulus such as UVB.