An informatics approach to systems neurogenetics

Integration of heterogeneous functional genomics data in gerontology research to find 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 enable current and future bio-gerontologists to synthesize, distill and interpret vast, heterogeneous data from functional genomics studies of aging. 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 applied to several questions in genrontology using. 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.

Systems Genetics Analysis of Iron and Its Regulation in Brain and Periphery

In this contribution, we demonstrate the utility of the systems genetics-systems biology approach to the study of iron regulation while employing a comprehensive database. We describe our work in iron regulation in the brain and periphery under normal iron and iron-restricted dietary conditions in the BXD family of recombinant inbred mouse strains. Using multiple measures, we showed wide variation among the strains in the effect of being fed an iron-restricted diet for 100 days in every measure from brain and from the periphery. All data were entered into GeneNetwork ( www.genenetwork.org ), a database that contains genotypic, phenotypic, and gene expression data (Rosen et al., Methods Mol Biol 401:287-303, 2007). Using this resource, we were able to ask the following four questions concerning possible candidate genes underlying our measures: (1) what is the range of response for each of the measures? (2) Does the pattern of variability show continuous (additive genetic) or discrete (Mendelian) distribution across strains? (3) Are there genetic markers that are associated with the variability in the measures? (4) Are there genes in near the markers that contain associated allelic differences, and whose expression is related to the variability in the measures? Other questions that we could address include: (5) what is the association among the measures between the sexes? (6) What is the association among the measures, e.g., is liver iron status under the diets related to brain iron? (7) What is the relationship between our measures and other phenotypic parameters-i.e., is there an association between our brain iron measures and neurochemical phenotypes extant in the database? And finally, (8) are there gene networks that underlie single or combined measures?

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.

Initial genetic dissection of serum neuroactive steroids following chronic intermittent ethanol across BXD mouse strains

Neuroactive steroids modulate alcohol's impact on brain function and behavior. Ethanol exposure alters neuroactive steroid levels in rats, humans, and some mouse strains. We conducted an exploratory analysis of the neuroactive steroids (3α,5α)-3-hydroxypregnan-20-one (3α,5α-THP), (3α,5α)-3,21-dihydroxypregnan-20-one (3α,5α-THDOC), and pregnenolone across 126-158 individuals and 19 fully inbred strains belonging to the BXD family, which were subjected to air exposure, or chronic intermittent ethanol (CIE) exposure. Neuroactive steroids were measured by gas chromatography-mass spectrometry in serum following five cycles of CIE or air exposure (CTL). Pregnenolone levels in CTLs range from 272 to 578 pg/mL (strain variation of 2.1 fold with p = 0.049 for strain main effect), with heritability of 0.20 ± 0.006 (SEM), whereas in CIE cases values range from 304 to 919 pg/mL (3.0-fold variation, p = 0.007), with heritability of 0.23 ± 0.005. 3α,5α-THP levels in CTLs range from 375 to 1055 pg/mL (2.8-fold variation, p = 0.0007), with heritability of 0.28 ± 0.01; in CIE cases they range from 460 to 1022 pg/mL (2.2-fold variation, p = 0.004), with heritability of 0.23 ± 0.005. 3α,5α-THDOC levels in CTLs range from 94 to 448 pg/mL (4.8-fold variation, p = 0.002), with heritability of 0.30 ± 0.01, whereas levels in CIE cases do not differ significantly. However, global averages across all BXD strains do not differ between CTL and CIE for any of the steroids. 3α,5α-THDOC levels were lower in females than males in both groups (CTL -53%, CIE -55%, p < 0.001). Suggestive quantitative trait loci are identified for pregnenolone and 3α,5α-THP levels. Genetic variation in 3α,5α-THP was not correlated with two-bottle choice ethanol consumption in CTL or CIE-exposed animals. However, individual variation in 3α,5α-THP correlated negatively with ethanol consumption in both groups. Moreover, strain variation in neuroactive steroid levels correlated with numerous behavioral phenotypes of anxiety sensitivity accessed in GeneNetwork, consistent with evidence that neuroactive steroids modulate anxiety-like behavior.

A Genetic Screen Identifies Hypothalamic Fgf15 as a Regulator of Glucagon Secretion

The counterregulatory response to hypoglycemia, which restores normal blood glucose levels to ensure sufficient provision of glucose to the brain, is critical for survival. To discover underlying brain regulatory systems, we performed a genetic screen in recombinant inbred mice for quantitative trait loci (QTL) controlling glucagon secretion in response to neuroglucopenia. We identified a QTL on the distal part of chromosome 7 and combined this genetic information with transcriptomic analysis of hypothalami. This revealed Fgf15 as the strongest candidate to control the glucagon response. Fgf15 was expressed by neurons of the dorsomedial hypothalamus and the perifornical area. Intracerebroventricular injection of FGF19, the human ortholog of Fgf15, reduced activation by neuroglucopenia of dorsal vagal complex neurons, of the parasympathetic nerve, and lowered glucagon secretion. In contrast, silencing Fgf15 in the dorsomedial hypothalamus increased neuroglucopenia-induced glucagon secretion. These data identify hypothalamic Fgf15 as a regulator of glucagon secretion.

A Next Generation Multiscale View of Inborn Errors of Metabolism

Inborn errors of metabolism (IEM) are not unlike common diseases. They often present as a spectrum of disease phenotypes that correlates poorly with the severity of the disease-causing mutations. This greatly impacts patient care and reveals fundamental gaps in our knowledge of disease modifying biology. Systems biology approaches that integrate multi-omics data into molecular networks have significantly improved our understanding of complex diseases. Similar approaches to study IEM are rare despite their complex nature. We highlight that existing common disease-derived datasets and networks can be repurposed to generate novel mechanistic insight in IEM and potentially identify candidate modifiers. While understanding disease pathophysiology will advance the IEM field, the ultimate goal should be to understand per individual how their phenotype emerges given their primary mutation on the background of their whole genome, not unlike personalized medicine. We foresee that panomics and network strategies combined with recent experimental innovations will facilitate this.

Genetic variability to diet-induced hippocampal dysfunction in BXD recombinant inbred (RI) mouse strains

Evidence has emerged suggesting that diet-induced obesity can have a negative effect on cognitive function. Here, we exploited a mouse genetic reference population to look for the linkage between these two processes on a genome-wide scale. The focus of this report is to determine whether the various BXD RI strains exhibited different behavioral performance and hippocampal function under high fat dietary (HFD) condition. We quantified genetic variation in body weight gain and consequent influences on behavioral tests in a cohort of 14 BXD strains of mice (8-12 mice/strain, n = 153), for which we have matched data on gene expression and neuroanatomical changes in the hippocampus. It showed that BXD66 was the most susceptible, whereas BXD77 was the least susceptible strain to dietary influences. The performance of spatial reference memory tasks was strongly correlated with body weight gain (P < 0.05). The obesity-prone strains displayed more pronounced spatial memory defects compared to the obesity-resistant strains. These abnormalities were associated with neuroinflammation, synaptic dysfunction, and neuronal loss in the hippocampus. The biological relevance of DSCAM gene polymorphism was assessed using the trait correlation analysis tool in Genenetwork. Furthermore, a significant strain-dependent gene expression difference of DSCAM was detected in the hippocampus of obese BXD strains by real-time quantitative PCR. In conclusion, a variety of across-strain hippocampal alterations and genetic predispositions to diet-induced obesity were found in a set of BXD strains. The obesity-prone and obesity-resistant lines we have identified should be highly useful to study the molecular genetics of diet-induced cognitive decline.

Impulsivity and comorbid traits: a multi-step approach for finding putative responsible microRNAs in the amygdala

Malfunction of synaptic plasticity in different brain regions, including the amygdala plays a role in impulse control deficits that are characteristics of several psychiatric disorders, such as ADHD, schizophrenia, depression and addiction. Previously, we discovered a locus for impulsivity (Impu1) containing the neuregulin 3 (Nrg3) gene, of which the level of expression determines levels of inhibitory control. MicroRNAs (miRNAs) are potent regulators of gene expression, and have recently emerged as important factors contributing to the development of psychiatric disorders. However, their role in impulsivity, as well as control of Nrg3 expression or malfunction of the amygdala, is not well established. Here, we used the GeneNetwork database of BXD mice to search for correlated traits with impulsivity using an overrepresentation analysis to filter for biologically meaningful traits. We determined that inhibitory control was significantly correlated with expression of miR-190b, -28a, -340, -219a, and -491 in the amygdala, and that the overrepresented correlated traits showed a specific pattern of coregulation with these miRNAs. A bioinformatics analysis identified that miR-190b, by targeting an Nrg3-related network, could affect synaptic plasticity in the amygdala, targeting bot impulsive and compulsive traits. Moreover, miR-28a, -340, -219a, and possibly -491 could act on synaptic function by determining the balance between neuronal outgrowth and differentiation. We propose that these miRNAs are attractive candidates of regulation of amygdala synaptic plasticity, possibly during development but also in maintaining the impulsive phenotype. These results can help us to better understand mechanisms of synaptic dysregulation in psychiatric disorders.

Innate immune network in the retina activated by optic nerve crush

PURPOSE

Innate immunity plays a role in many diseases, including glaucoma and AMD. We have used transcriptome profiling in the mouse to identify a network of genes involved in innate immunity that is present in the normal retina and that is activated by optic nerve crush (ONC).

METHODS

Using a recombinant inbred (RI) mouse strain set (BXD, C57BL/6 crossed with DBA/2J mice), we generate expression datasets (Illumina WG 6.2 arrays) in the normal mouse retina and 2 days after ONC. The normal dataset is constructed from retinas from 80 mouse strains and the ONC dataset is constructed from 62 strains. These large datasets are hosted by GeneNetwork.org, along with a series of powerful bioinformatic tools.

RESULTS

In the retina datasets, one intriguing network involves transcripts associated with the innate immunity. Using C4b to interrogate the normal dataset, we can identify a group of genes that are coregulated across the BXD RI strains. Many of the genes in this network are associated with the innate immune system, including Serping1, Casp1, C3, Icam1, Tgfbr2, Cfi, Clu, C1qg, Aif1, and Cd74. Following ONC, the expression of these genes is upregulated, along with an increase in coordinated expression across the BXD strains. Many of the genes in this network are risk factors for AMD, including C3, EFEMP1, MCDR2, CFB, TLR4, HTA1, and C1QTNF5.

CONCLUSIONS

We found a retina-intrinsic innate immunity network that is activated by injury including ONC. Many of the genes in this network are risk factors for retinal disease.

Systems genetics of the lateral septal nucleus in mouse: heritability, genetic control, and covariation with behavioral and morphological traits

The lateral septum has strong efferent projections to hypothalamic and midbrain regions, and has been associated with modulation of social behavior, anxiety, fear conditioning, memory-related behaviors, and the mesolimbic reward pathways. Understanding natural variation of lateral septal anatomy and function, as well as its genetic modulation, may provide important insights into individual differences in these evolutionarily important functions. Here we address these issues by using efficient and unbiased stereological probes to estimate the volume of the lateral septum in the BXD line of recombinant inbred mice. Lateral septum volume is a highly variable trait, with a 2.5-fold difference among animals. We find that this trait covaries with a number of behavioral and physiological phenotypes, many of which have already been associated with behaviors modulated by the lateral septum, such as spatial learning, anxiety, and reward-seeking. Heritability of lateral septal volume is moderate (h(2) = 0.52), and much of the heritable variation is caused by a locus on the distal portion of chromosome (Chr) 1. Composite interval analysis identified a secondary interval on Chr 2 that works additively with the Chr 1 locus to increase lateral septum volume. Using bioinformatic resources, we identified plausible candidate genes in both intervals that may influence the volume of this key nucleus, as well as associated behaviors.

Genetic, morphometric, and behavioral factors linked to the midsagittal area of the corpus callosum

The corpus callosum is the main commissure connecting left and right cerebral hemispheres, and varies widely in size. Differences in the midsagittal area of the corpus callosum (MSACC) have been associated with a number of cognitive and behavioral phenotypes, including obsessive-compulsive disorders, psychopathy, suicidal tendencies, bipolar disorder, schizophrenia, autism, and attention deficit hyperactivity disorder. Although there is evidence to suggest that MSACC is heritable in normal human populations, there is surprisingly little evidence concerning the genetic modulation of this variation. Mice provide a potentially ideal tool to dissect the genetic modulation of MSACC. Here, we use a large genetic reference panel – the BXD recombinant inbred line – to dissect the natural variation of the MSACC. We estimated the MSACC in over 300 individuals from nearly 80 strains. We found a 4-fold difference in MSACC between individual mice, and a 2.5-fold difference among strains. MSACC is a highly heritable trait (h² = 0.60), and we mapped a suggestive QTL to the distal portion of Chr 14. Using sequence data and neocortical expression databases, we were able to identify eight positional and plausible biological candidate genes within this interval. Finally, we found that MSACC correlated with behavioral traits associated with anxiety and attention.

Using genome-wide expression profiling to define gene networks relevant to the study of complex traits: from RNA integrity to network topology

Postgenomic studies of the function of genes and their role in disease have now become an area of intense study since efforts to define the raw sequence material of the genome have largely been completed. The use of whole-genome approaches such as microarray expression profiling and, more recently, RNA-sequence analysis of transcript abundance has allowed an unprecedented look at the workings of the genome. However, the accurate derivation of such high-throughput data and their analysis in terms of biological function has been critical to truly leveraging the postgenomic revolution. This chapter will describe an approach that focuses on the use of gene networks to both organize and interpret genomic expression data. Such networks, derived from statistical analysis of large genomic datasets and the application of multiple bioinformatics data resources, potentially allow the identification of key control elements for networks associated with human disease, and thus may lead to derivation of novel therapeutic approaches. However, as discussed in this chapter, the leveraging of such networks cannot occur without a thorough understanding of the technical and statistical factors influencing the derivation of genomic expression data. Thus, while the catch phrase may be "it's the network … stupid," the understanding of factors extending from RNA isolation to genomic profiling technique, multivariate statistics, and bioinformatics are all critical to defining fully useful gene networks for study of complex biology.

Genetic analysis of the neurosteroid deoxycorticosterone and its relation to alcohol phenotypes: identification of QTLs and downstream gene regulation

BACKGROUND

Deoxycorticosterone (DOC) is an endogenous neurosteroid found in brain and serum, precursor of the GABAergic neuroactive steroid (3α,5α)-3,21-dihydroxypregnan-20-one (tetrahydrodeoxycorticosterone, THDOC) and the glucocorticoid corticosterone. These steroids are elevated following stress or ethanol administration, contribute to ethanol sensitivity, and their elevation is blunted in ethanol dependence.

METHODOLOGY/PRINCIPAL FINDINGS

To systematically define the genetic basis, regulation, and behavioral significance of DOC levels in plasma and cerebral cortex we examined such levels across 47 young adult males from C57BL/6J (B6)×DBA/2J (D2) (BXD) mouse strains for quantitative trait loci (QTL) and bioinformatics analyses of behavior and gene regulation. Mice were injected with saline or 0.075 mg/kg dexamethasone sodium salt at 8:00 am and were sacrificed 6 hours later. DOC levels were measured by radioimmunoassay. Basal cerebral cortical DOC levels ranged between 1.4 and 12.2 ng/g (8.7-fold variation, p<0.0001) with a heritability of ∼0.37. Basal plasma DOC levels ranged between 2.8 and 12.1 ng/ml (4.3-fold variation, p<0.0001) with heritability of ∼0.32. QTLs for basal DOC levels were identified on chromosomes 4 (cerebral cortex) and 14 (plasma). Dexamethasone-induced changes in DOC levels showed a 4.4-fold variation in cerebral cortex and a 4.1-fold variation in plasma, but no QTLs were identified. DOC levels across BXD strains were further shown to be co-regulated with networks of genes linked to neuronal, immune, and endocrine function. DOC levels and its responses to dexamethasone were associated with several behavioral measures of ethanol sensitivity previously determined across the BXD strains by multiple laboratories.

CONCLUSIONS/SIGNIFICANCE

Both basal and dexamethasone-suppressed DOC levels are positively correlated with ethanol sensitivity suggesting that the neurosteroid DOC may be a putative biomarker of alcohol phenotypes. DOC levels were also strongly correlated with networks of genes associated with neuronal function, innate immune pathways, and steroid metabolism, likely linked to behavioral phenotypes.

Evaluating gene expression in C57BL/6J and DBA/2J mouse striatum using RNA-Seq and microarrays

C57BL/6J (B6) and DBA/2J (D2) are two of the most commonly used inbred mouse strains in neuroscience research. However, the only currently available mouse genome is based entirely on the B6 strain sequence. Subsequently, oligonucleotide microarray probes are based solely on this B6 reference sequence, making their application for gene expression profiling comparisons across mouse strains dubious due to their allelic sequence differences, including single nucleotide polymorphisms (SNPs). The emergence of next-generation sequencing (NGS) and the RNA-Seq application provides a clear alternative to oligonucleotide arrays for detecting differential gene expression without the problems inherent to hybridization-based technologies. Using RNA-Seq, an average of 22 million short sequencing reads were generated per sample for 21 samples (10 B6 and 11 D2), and these reads were aligned to the mouse reference genome, allowing 16,183 Ensembl genes to be queried in striatum for both strains. To determine differential expression, ‘digital mRNA counting’ is applied based on reads that map to exons. The current study compares RNA-Seq (Illumina GA IIx) with two microarray platforms (Illumina MouseRef-8 v2.0 and Affymetrix MOE 430 2.0) to detect differential striatal gene expression between the B6 and D2 inbred mouse strains. We show that by using stringent data processing requirements differential expression as determined by RNA-Seq is concordant with both the Affymetrix and Illumina platforms in more instances than it is concordant with only a single platform, and that instances of discordance with respect to direction of fold change were rare. Finally, we show that additional information is gained from RNA-Seq compared to hybridization-based techniques as RNA-Seq detects more genes than either microarray platform. The majority of genes differentially expressed in RNA-Seq were only detected as present in RNA-Seq, which is important for studies with smaller effect sizes where the sensitivity of hybridization-based techniques could bias interpretation.

The NeuroDevNet Neuroinformatics Core

The field of neuroinformatics has expanded dramatically during the past decade building on the development of new technologies in brain research as well as in computing. The activities are diverse, from data management and standardization that has become essential due to the large amount of data generated and the needs to share it, to the development of sophisticated software necessary for the analyses and visualization of such data. NeuroDevNet is a Canadian initiative, funded by the Networks of Centres of Excellence, devoted to the study of brain development with the goal to translate this knowledge into improved diagnosis, prevention and treatment of neurodevelopmental disorders. The NeuroDevNet Neuroinformatics Core is dedicated to helping researchers across the network with their data management, standardization and sharing, as well as to implement innovative solutions to facilitate their research. It is an essential component to NeuroDevNet, enabling active collaboration across the country and optimizing this unique endeavor.

High-throughput behavioral phenotyping in the expanded panel of BXD recombinant inbred strains

Genetic reference populations, particularly the BXD recombinant inbred (BXD RI) strains derived from C57BL/6J and DBA/2J mice, are a valuable resource for the discovery of the bio-molecular substrates and genetic drivers responsible for trait variation and covariation. This approach can be profitably applied in the analysis of susceptibility and mechanisms of drug and alcohol use disorders for which many predisposing behaviors may predict the occurrence and manifestation of increased preference for these substances. Many of these traits are modeled by common mouse behavioral assays, facilitating the detection of patterns and sources of genetic coregulation of predisposing phenotypes and substance consumption. Members of the Tennessee Mouse Genome Consortium (TMGC) have obtained phenotype data from over 250 measures related to multiple behavioral assays across several batteries: response to, and withdrawal from cocaine, 3,4-methylenedioxymethamphetamine; "ecstasy" (MDMA), morphine and alcohol; novelty seeking; behavioral despair and related neurological phenomena; pain sensitivity; stress sensitivity; anxiety; hyperactivity and sleep/wake cycles. All traits have been measured in both sexes in approximately 70 strains of the recently expanded panel of BXD RI strains. Sex differences and heritability estimates were obtained for each trait, and a comparison of early (N = 32) and recent (N = 37) BXD RI lines was performed. Primary data are publicly available for heritability, sex difference and genetic analyses using the MouseTrack database, and are also available in GeneNetwork.org for quantitative trait locus (QTL) detection and genetic analysis of gene expression. Together with the results of related studies, these data form a public resource for integrative systems genetic analysis of neurobehavioral traits.

Evidence for epigenetic interactions for loci on mouse chromosome 1 regulating open field activity

The expression of motor activity levels in response to novel situations is under complex genetic and environmental control. Several genetic loci have been implicated in the regulation of this behavioral phenotype, but their relationship to epigenetic and epistatic interactions is relatively unknown. Here, we report on a quantitative trait locus (QTL) on mouse chromosome 1 for novelty-induced motor activity in the open field, using chromosome substitution strains derived from a high active host strain (C57BL/6J) and a low active donor strain (A/J). The QTL for open field (horizontal distance moved) peaked at the location of Kcnj9, however, QTL detection was initially masked by an interplay of both grandparent genetic origin and genetic co-factors influencing behavior on chromosome 1. Our findings indicate that epigenetic interactions can play an important role in the identification of behavioral QTLs and must be taken into consideration when applying behavioral genetic strategies.