RCFGL: Rapid Condition adaptive Fused Graphical Lasso and application to modeling brain region co-expression networks.

Inferring gene co-expression networks is a useful process for understanding gene regulation and pathway activity. The networks are usually undirected graphs where genes are represented as nodes and an edge represents a significant co-expression relationship. When expression data of multiple (p) genes in multiple (K) conditions (e.g., treatments, tissues, strains) are available, joint estimation of networks harnessing shared information across them can significantly increase the power of analysis. In addition, examining condition-specific patterns of co-expression can provide insights into the underlying cellular processes activated in a particular condition. Condition adaptive fused graphical lasso (CFGL) is an existing method that incorporates condition specificity in a fused graphical lasso (FGL) model for estimating multiple co-expression networks. However, with computational complexity of O(p2K log K), the current implementation of CFGL is prohibitively slow even for a moderate number of genes and can only be used for a maximum of three conditions. In this paper, we propose a faster alternative of CFGL named rapid condition adaptive fused graphical lasso (RCFGL). In RCFGL, we incorporate the condition specificity into another popular model for joint network estimation, known as fused multiple graphical lasso (FMGL). We use a more efficient algorithm in the iterative steps compared to CFGL, enabling faster computation with complexity of O(p2K) and making it easily generalizable for more than three conditions. We also present a novel screening rule to determine if the full network estimation problem can be broken down into estimation of smaller disjoint sub-networks, thereby reducing the complexity further. We demonstrate the computational advantage and superior performance of our method compared to two non-condition adaptive methods, FGL and FMGL, and one condition adaptive method, CFGL in both simulation study and real data analysis. We used RCFGL to jointly estimate the gene co-expression networks in different brain regions (conditions) using a cohort of heterogeneous stock rats. We also provide an accommodating C and Python based package that implements RCFGL.

The regulatory landscape of multiple brain regions in outbred heterogeneous stock rats.

Heterogeneous Stock (HS) rats are a genetically diverse outbred rat population that is widely used for studying genetics of behavioral and physiological traits. Mapping Quantitative Trait Loci (QTL) associated with transcriptional changes would help to identify mechanisms underlying these traits. We generated genotype and transcriptome data for five brain regions from 88 HS rats. We identified 21 392 cis-QTLs associated with expression and splicing changes across all five brain regions and validated their effects using allele specific expression data. We identified 80 cases where eQTLs were colocalized with genome-wide association study (GWAS) results from nine physiological traits. Comparing our dataset to human data from the Genotype-Tissue Expression (GTEx) project, we found that the HS rat data yields twice as many significant eQTLs as a similarly sized human dataset. We also identified a modest but highly significant correlation between genetic regulatory variation among orthologous genes. Surprisingly, we found less genetic variation in gene regulation in HS rats relative to humans, though we still found eQTLs for the orthologs of many human genes for which eQTLs had not been found. These data are available from the RatGTEx data portal (RatGTEx.org) and will enable new discoveries of the genetic influences of complex traits.

Peptide-Spectrum Match Validation with Internal Standards (P-VIS): Internally-Controlled Validation of Mass Spectrometry-Based Peptide Identifications.

Liquid chromatography-tandem mass spectrometry is an increasingly powerful tool for studying proteins in the context of disease. As technological advances in instrumentation and data analysis have enabled deeper profiling of proteomes and peptidomes, the need for a rigorous, standardized approach to validate individual peptide-spectrum matches (PSMs) has emerged. To address this need, we developed a novel and broadly applicable workflow: PSM validation with internal standards (P-VIS). In this approach, the fragmentation spectrum and chromatographic retention time of a peptide within a biological sample are compared with those of a synthetic version of the putative peptide sequence match. Similarity measurements obtained for a panel of internal standard peptides are then used to calculate a prediction interval for valid matches. If the observed degree of similarity between the biological and the synthetic peptide falls within this prediction interval, then the match is considered valid. P-VIS enables systematic and objective assessment of the validity of individual PSMs, providing a measurable degree of confidence when identifying peptides by mass spectrometry.

Unsupervised discovery of phenotype-specific multi-omics networks.

MOTIVATION: Complex diseases often involve a wide spectrum of phenotypic traits. Better understanding of the biological mechanisms relevant to each trait promotes understanding of the etiology of the disease and the potential for targeted and effective treatment plans. There have been many efforts towards omics data integration and network reconstruction, but limited work has examined the incorporation of relevant (quantitative) phenotypic traits. RESULTS: We propose a novel technique, sparse multiple canonical correlation network analysis (SmCCNet), for integrating multiple omics data types along with a quantitative phenotype of interest, and for constructing multi-omics networks that are specific to the phenotype. As a case study, we focus on miRNA-mRNA networks. Through simulations, we demonstrate that SmCCNet has better overall prediction performance compared to popular gene expression network construction and integration approaches under realistic settings. Applying SmCCNet to studies on chronic obstructive pulmonary disease (COPD) and breast cancer, we found enrichment of known relevant pathways (e.g. the Cadherin pathway for COPD and the interferon-gamma signaling pathway for breast cancer) as well as less known omics features that may be important to the diseases. Although those applications focus on miRNA-mRNA co-expression networks, SmCCNet is applicable to a variety of omics and other data types. It can also be easily generalized to incorporate multiple quantitative phenotype simultaneously. The versatility of SmCCNet suggests great potential of the approach in many areas. AVAILABILITY AND IMPLEMENTATION: The SmCCNet algorithm is written in R, and is freely available on the web at https://cran.r-project.org/web/packages/SmCCNet/index.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Investigating RNA expression profiles altered by nicotinamide mononucleotide therapy in a chronic model of alcoholic liver disease.

BACKGROUND: Chronic alcohol consumption is a significant cause of liver disease worldwide. Several biochemical mechanisms have been linked to the initiation and progression of alcoholic liver disease (ALD) such as oxidative stress, inflammation, and metabolic dysregulation, including the disruption of NAD+/NADH. Indeed, an ethanol-mediated reduction in hepatic NAD+ levels is thought to be one factor underlying ethanol-induced steatosis, oxidative stress, steatohepatitis, insulin resistance, and inhibition of gluconeogenesis. Therefore, we applied a NAD+ boosting supplement to investigate alterations in the pathogenesis of early-stage ALD. METHODS: To examine the impact of NAD+ therapy on the early stages of ALD, we utilized nicotinamide mononucleotide (NMN) at 500 mg/kg intraperitoneal injection every other day, for the duration of a Lieber-DeCarli 6-week chronic ethanol model in mice. Numerous strategies were employed to characterize the effect of NMN therapy, including the integration of RNA-seq, immunoblotting, and metabolomics analysis. RESULTS: Our findings reveal that NMN therapy increased hepatic NAD+ levels, prevented an ethanol-induced increase in plasma ALT and AST, and changed the expression of 25% of the genes that were modulated by ethanol metabolism. These genes were associated with a number of pathways including the MAPK pathway. Interestingly, our analysis revealed that NMN treatment normalized Erk1/2 signaling and prevented an induction of Atf3 overexpression. CONCLUSIONS: These findings reveal previously unreported mechanisms by which NMN supplementation alters hepatic gene expression and protein pathways to impact ethanol hepatotoxicity in an early-stage murine model of ALD. Overall, our data suggest further research is needed to fully characterize treatment paradigms and biochemical implications of NAD+-based interventions.

Relationship between nocturnal blood pressure patterns and end organ damage and diastolic dysfunction in heart transplant recipients.

BACKGROUND: We assessed the relationship between circadian blood pressure (BP) patterns and clinical outcomes in a contemporary cohort of adult heart transplant recipients. METHODS: This retrospective, cross-sectional study included adult heart transplant recipients at least 6 months post-transplant. Ambulatory BP measurements were recorded over 24 hours. Nondippers were defined as a decline in average nighttime BP ≤ 10% compared with daytime. Primary outcomes were the presence of end organ damage, that is, microalbuminuria, chronic kidney disease, and/or left ventricular hypertrophy. Secondary outcomes were measures of diastolic dysfunction (ie, mitral valve deceleration time, e/e', E/A, and isovolumetric relaxation time), microalbumin/creatinine ratio, eGFR, interventricular septal thickness, and left ventricular posterior wall thickness. RESULTS: Of 30 patients, 53.3% (n = 16) were systolic nondippers and 40% (n = 12) were diastolic nondippers. Diastolic nondippers had three times higher urine microalbumin/creatinine ratios than diastolic dippers (P = .03). Systolic nondippers had 16.3% lower mitral valve deceleration time (P = .05) than systolic dippers, while diastolic nondippers had 20.4% higher e/e' (P = .05) than diastolic dippers. There were no significant relationships between BP dipping status and any of the primary outcomes. CONCLUSIONS: These data suggest that systolic and diastolic nondipping BP patterns are associated with subclinical kidney damage and diastolic dysfunction in heart transplant recipients.

The Role of A Priori-Identified Addiction and Smoking Gene Sets in Smoking Behaviors.

INTRODUCTION: Smoking is a leading cause of death, and genetic variation contributes to smoking behaviors. Identifying genes and sets of genes that contribute to risk for addiction is necessary to prioritize targets for functional characterization and for personalized medicine. METHODS: We performed a gene set-based association and heritable enrichment study of two addiction-related gene sets, those on the Smokescreen Genotyping Array and the nicotinic acetylcholine receptors, using the largest available GWAS summary statistics. We assessed smoking initiation, cigarettes per day, smoking cessation, and age of smoking initiation. RESULTS: Individual genes within each gene set were significantly associated with smoking behaviors. Both sets of genes were significantly associated with cigarettes per day, smoking initiation, and smoking cessation. Age of initiation was only associated with the Smokescreen gene set. Although both sets of genes were enriched for trait heritability, each accounts for only a small proportion of the single nucleotide polymorphism-based heritability (2%-12%). CONCLUSIONS: These two gene sets are associated with smoking behaviors, but collectively account for a limited amount of the genetic and phenotypic variation of these complex traits, consistent with high polygenicity. IMPLICATIONS: We evaluated evidence for the association and heritable contribution of expert-curated and bioinformatically identified sets of genes related to smoking. Although they impact smoking behaviors, these specifically targeted genes do not account for much of the heritability in smoking and will be of limited use for predictive purposes. Advanced genome-wide approaches and integration of other 'omics data will be needed to fully account for the genetic variation in smoking phenotypes.

Quantifying Competition among Mitochondrial Protein Acylation Events Induced by Ethanol Metabolism.

Mitochondrial dysfunction is one of many key factors in the etiology of alcoholic liver disease (ALD). Lysine acetylation is known to regulate numerous mitochondrial metabolic pathways, and recent reports demonstrate that alcohol-induced protein acylation negatively impacts these processes. To identify regulatory mechanisms attributed to alcohol-induced protein post-translational modifications, we employed a model of alcohol consumption within the context of wild type (WT), sirtuin 3 knockout (SIRT3 KO), and sirtuin 5 knockout (SIRT5 KO) mice to manipulate hepatic mitochondrial protein acylation. Mitochondrial fractions were examined by label-free quantitative HPLC-MS/MS to reveal competition between lysine acetylation and succinylation. A class of proteins defined as "differential acyl switching proteins" demonstrate select sensitivity to alcohol-induced protein acylation. A number of these proteins reveal saturated lysine-site occupancy, suggesting a significant level of differential stoichiometry in the setting of ethanol consumption. We hypothesize that ethanol downregulates numerous mitochondrial metabolic pathways through differential acyl switching proteins. Data are available via ProteomeXchange with identifier PXD012089.

Systems genetic analysis of brown adipose tissue function.

Brown adipose tissue (BAT) has been suggested to play an important role in lipid and glucose metabolism in rodents and possibly also in humans. In the current study, we used genetic and correlation analyses in the BXH/HXB recombinant inbred (RI) strains, derived from Brown Norway (BN) and spontaneously hypertensive rats (SHR), to identify genetic determinants of BAT function. Linkage analyses revealed a quantitative trait locus (QTL) associated with interscapular BAT mass on chromosome 4 and two closely linked QTLs associated with glucose oxidation and glucose incorporation into BAT lipids on chromosome 2. Using weighted gene coexpression network analysis (WGCNA) we identified 1,147 gene coexpression modules in the BAT from BXH/HXB rats and mapped their module eigengene QTLs. Through an unsupervised analysis, we identified modules related to BAT relative mass and function. The Coral4.1 coexpression module is associated with BAT relative mass (includes Cd36 highly connected gene), and the Darkseagreen coexpression module is associated with glucose incorporation into BAT lipids (includes Hiat1, Fmo5, and Sort1 highly connected transcripts). Because multiple statistical criteria were used to identify candidate modules, significance thresholds for individual tests were not adjusted for multiple comparisons across modules. In summary, a systems genetic analysis using genomic and quantitative transcriptomic and physiological information has produced confirmation of several known genetic factors and significant insight into novel genetic components functioning in BAT and possibly contributing to traits characteristic of the metabolic syndrome.

Voluntary exposure to a toxin: the genetic influence on ethanol consumption.

Ethyl alcohol is a toxin that, when consumed at high levels, produces organ damage and death. One way to prevent or ameliorate this damage in humans is to reduce the exposure of organs to alcohol by reducing alcohol ingestion. Both the propensity to consume large volumes of alcohol and the susceptibility of human organs to alcohol-induced damage exhibit a strong genetic influence. We have developed an integrative genetic/genomic approach to identify transcriptional networks that predispose complex traits, including propensity for alcohol consumption and propensity for alcohol-induced organ damage. In our approach, the phenotype is assessed in a panel of recombinant inbred (RI) rat strains, and quantitative trait locus (QTL) analysis is performed. Transcriptome data from tissues/organs of naïve RI rat strains are used to identify transcriptional networks using Weighted Gene Coexpression Network Analysis (WGCNA). Correlation of the first principal component of transcriptional coexpression modules with the phenotype across the rat strains, and overlap of QTLs for the phenotype and the QTLs for the coexpression modules (module eigengene QTL) provide the criteria for identification of the functionally related groups of genes that contribute to the phenotype (candidate modules). While we previously identified a brain transcriptional module whose QTL overlapped with a QTL for levels of alcohol consumption in HXB/BXH RI rat strains and 12 selected rat lines, this module did not account for all of the genetic variation in alcohol consumption. Our search for QTL overlap and correlation of coexpression modules with phenotype can, however, be applied to any organ in which the transcriptome has been measured, and this represents a holistic approach in the search for genetic contributors to complex traits. Previous work has implicated liver/brain interactions, particularly involving inflammatory/immune processes, as influencing alcohol consumption levels. We have now analyzed the liver transcriptome of the HXB/BXH RI rat panel in relation to the behavioral trait of alcohol consumption. We used RNA-Seq and microarray data to construct liver transcriptional networks, and identified a liver candidate transcriptional coexpression module that explained 24% of the genetic variance in voluntary alcohol consumption. The transcripts in this module focus attention on liver secretory products that influence inflammatory and immune signaling pathways. We propose that these liver secretory products can interact with brain mechanisms that affect alcohol consumption, and targeting these pathways provides a potential approach to reducing high levels of alcohol intake and also protecting the integrity of the liver and other organs.

Interindividual Regulation of the Breast Cancer Resistance Protein/ABCG2 Transporter in Term Human Placentas.

The breast cancer resistance protein (BCRP/ABCG2) is a maternally-facing efflux transporter that regulates the placental disposition of chemicals. Transcription factors and gene variants are important regulatory factors that influence transporter expression. In this study, we sought to identify the genetic and transcriptional mechanisms underlying the interindividual expression of BCRP mRNA and protein across 137 term placentas from uncomplicated pregnancies. Placental expression of BCRP and regulatory transcription factor mRNAs was measured using multiplex-branched DNA analysis. BCRP expression and ABCG2 genotypes were determined using Western blot and Fluidigm Biomark genetic analysis, respectively. Placentas were obtained from a racially and ethnically diverse population, including Caucasian (33%), African American (14%), Asian (14%), Hispanic (15%), and mixed (16%) backgrounds, as well as unknown origins (7%). Between placentas, BCRP mRNA and protein varied up to 47-fold and 14-fold, respectively. In particular, BCRP mRNA correlated significantly with known transcription factor mRNAs, including nuclear factor erythroid 2-related factor 2 and aryl hydrocarbon receptor. Somewhat surprisingly, single-nucleotide polymorphisms (SNPs) in the ABCG2 noncoding regions were not associated with variation in placental BCRP mRNA or protein. Instead, the coding region polymorphism (C421A/Q141K) corresponded with 40%-50% lower BCRP protein in 421C/A and 421A/A placentas compared with wild types (421C/C). Although BCRP protein and mRNA expression weakly correlated (r = 0.25, P = 0.040), this relationship was absent in individuals expressing the C421A variant allele. Study results contribute to our understanding of the interindividual regulation of BCRP expression in term placentas and may help to identify infants at risk for increased fetal exposure to chemicals due to low expression of this efflux protein.

Unsupervised, Statistically Based Systems Biology Approach for Unraveling the Genetics of Complex Traits: A Demonstration with Ethanol Metabolism.

BACKGROUND: A statistical pipeline was developed and used for determining candidate genes and candidate gene coexpression networks involved in 2 alcohol (i.e., ethanol [EtOH]) metabolism phenotypes, namely alcohol clearance and acetate area under the curve in a recombinant inbred (RI) (HXB/BXH) rat panel. The approach was also used to provide an indication of how EtOH metabolism can impact the normal function of the identified networks. METHODS: RNA was extracted from alcohol-naïve liver tissue of 30 strains of HXB/BXH RI rats. The reconstructed transcripts were quantitated, and data were used to construct gene coexpression modules and networks. A separate group of rats, comprising the same 30 strains, were injected with EtOH (2 g/kg) for measurement of blood EtOH and acetate levels. These data were used for quantitative trait loci (QTL) analysis of the rate of EtOH disappearance and circulating acetate levels. The analysis pipeline required calculation of the module eigengene values, the correction of these values with EtOH metabolism rates and acetate levels across the rat strains, and the determination of the eigengene QTLs. For a module to be considered a candidate for determining phenotype, the module eigengene values had to have significant correlation with the strain phenotypic values and the module eigengene QTLs had to overlap the phenotypic QTLs. RESULTS: Of the 658 transcript coexpression modules generated from liver RNA sequencing data, a single module satisfied all criteria for being a candidate for determining the alcohol clearance trait. This module contained 2 alcohol dehydrogenase genes, including the gene whose product was previously shown to be responsible for the majority of alcohol elimination in the rat. This module was also the only module identified as a candidate for influencing circulating acetate levels. This module was also linked to the process of generation and utilization of retinoic acid as related to the autonomous immune response. CONCLUSIONS: We propose that our analytical pipeline can successfully identify genetic regions and transcripts which predispose a particular phenotype and our analysis provides functional context for coexpression module components.

Knockout of the Gsta4 Gene in Male Mice Leads to an Altered Pattern of Hepatic Protein Carbonylation and Enhanced Inflammation Following Chronic Consumption of an Ethanol Diet.

BACKGROUND: Glutathione S-transferase A4-4 (GSTA4) is a key enzyme for removal of toxic lipid peroxidation products such as 4-hydroxynonenal (4-HNE). In this study, we examined the potential role of GSTA4 on protein carbonylation and progression of alcoholic liver disease by examining the development of liver injury in male wild-type (WT) SV/J mice and SV/J mice lacking functional GSTA4 (GSTA4-/- mice). METHODS: Adult male WT and GSTA4-/- mice were fed chow (N = 10 to 12) or high-fat Lieber-DeCarli liquid diets containing up to 28% calories as ethanol (EtOH) (N = 18 to 20) for 116 days. At the end of the study, half of the EtOH-fed mice were acutely challenged with an EtOH binge (3 g/kg given intragastrically) 12 hours before sacrifice. Carbonylation of liver proteins was assessed by immunohistochemical staining for 4-HNE adduction and by comprehensive liquid chromatography-tandem mass spectrometry (LC-MS/MS) of purified carbonylated proteins. RESULTS: Chronic EtOH intake significantly increased hepatic 4-HNE adduction and protein carbonylation, including carbonylation of ribosomal proteins. EtOH intake also resulted in steatosis and increased serum alanine aminotransferase. Hepatic infiltration with B cells, T cells, and neutrophils and mRNA expression of pro-inflammatory cytokines tumor necrosis factor (TNF)α and interferon (IFN)γ was modest in WT mice. However, an EtOH binge increased hepatic necrosis, hepatic cell proliferation, and expression of TNFα mRNA (p < 0.05). EtOH treatment of GSTA4-/- mice increased B-cell infiltration and increased mRNA expression of TNFα and IFNγ and of matrix remodeling markers MMP9, MMP13, and Col1A1 (p < 0.05). GSTA4-/- mice exhibited panlobular rather than periportal distribution of 4-HNE-adducted proteins and increased overall 4-HNE staining after EtOH binge. Comprehensive LC-MS of carbonylated proteins identified 1,022 proteins of which 189 were unique to the GSTA4-/- group. CONCLUSIONS: These data suggest long-term adaptation to EtOH in WT mice does not occur in GSTA4-/- mice. Products of lipid peroxidation appear to play a role in inflammatory responses due to EtOH. And EtOH effects on B-cell infiltration and autoimmune responses may be secondary to formation of carbonyl adducts.

Model based heritability scores for high-throughput sequencing data.

BACKGROUND: Heritability of a phenotypic or molecular trait measures the proportion of variance that is attributable to genotypic variance. It is an important concept in breeding and genetics. Few methods are available for calculating heritability for traits derived from high-throughput sequencing. RESULTS: We propose several statistical models and different methods to compute and test a heritability measure for such data based on linear and generalized linear mixed effects models. We also provide methodology for hypothesis testing and interval estimation. Our analyses show that, among the methods, the negative binomial mixed model (NB-fit), compound Poisson mixed model (CP-fit), and the variance stabilizing transformed linear mixed model (VST) outperform the voom-transformed linear mixed model (voom). NB-fit and VST appear to be more robust than CP-fit for estimating and testing the heritability scores, while NB-fit is the most computationally expensive. CP-fit performed best in terms of the coverage of the confidence intervals. In addition, we applied the methods to both microRNA (miRNA) and messenger RNA (mRNA) sequencing datasets from a recombinant inbred mouse panel. We show that miRNA and mRNA expression can be a highly heritable molecular trait in mouse, and that some top heritable features coincide with expression quantitative trait loci. CONCLUSIONS: The models and methods we investigated in this manuscript is applicable and extendable to sequencing experiments where some biological replicates are available and the environmental variation is properly controlled. The CP-fit approach for assessing heritability was implemented for the first time to our knowledge. All the methods presented, as well as the generation of simulated sequencing data under either negative binomial or compound Poisson mixed models, are provided in the R package HeritSeq.

Uncovering the liver's role in immunity through RNA co-expression networks.

Gene co-expression analysis has proven to be a powerful tool for ascertaining the organization of gene products into networks that are important for organ function. An organ, such as the liver, engages in a multitude of functions important for the survival of humans, rats, and other animals; these liver functions include energy metabolism, metabolism of xenobiotics, immune system function, and hormonal homeostasis. With the availability of organ-specific transcriptomes, we can now examine the role of RNA transcripts (both protein-coding and non-coding) in these functions. A systems genetic approach for identifying and characterizing liver gene networks within a recombinant inbred panel of rats was used to identify genetically regulated transcriptional networks (modules). For these modules, biological consensus was found between functional enrichment analysis and publicly available phenotypic quantitative trait loci (QTL). In particular, the biological function of two liver modules could be linked to immune response. The eigengene QTLs for these co-expression modules were located at genomic regions coincident with highly significant phenotypic QTLs; these phenotypes were related to rheumatoid arthritis, food preference, and basal corticosterone levels in rats. Our analysis illustrates that genetically and biologically driven RNA-based networks, such as the ones identified as part of this research, provide insight into the genetic influences on organ functions. These networks can pinpoint phenotypes that manifest through the interaction of many organs/tissues and can identify unannotated or under-annotated RNA transcripts that play a role in these phenotypes.

Influence of sex on genetic regulation of "drinking in the dark" alcohol consumption.

The ILSXISS (LXS) recombinant inbred (RI) panel of mice is a valuable resource for genetic mapping studies of complex traits, due to its genetic diversity and large number of strains. Male and female mice from this panel were used to investigate genetic influences on alcohol consumption in the "drinking in the dark" (DID) model. Male mice (38 strains) and female mice (36 strains) were given access to 20% ethanol during the early phase of their circadian dark cycle for four consecutive days. The first principal component of alcohol consumption measures on days 2, 3, and 4 was used as a phenotype (DID phenotype) to calculate QTLs, using a SNP marker set for the LXS RI panel. Five QTLs were identified, three of which included a significant genotype by sex interaction, i.e., a significant genotype effect in males and not females. To investigate candidate genes associated with the DID phenotype, data from brain microarray analysis (Affymetrix Mouse Exon 1.0 ST Arrays) of male LXS RI strains were combined with RNA-Seq data (mouse brain transcriptome reconstruction) from the parental ILS and ISS strains in order to identify expressed mouse brain transcripts. Candidate genes were determined based on common eQTL and DID phenotype QTL regions and correlation of transcript expression levels with the DID phenotype. The resulting candidate genes (in particular, Arntl/Bmal1) focused attention on the influence of circadian regulation on the variation in the DID phenotype in this population of mice.

Quantitative trait locus mapping of acute functional tolerance in the LXS recombinant inbred strains.

BACKGROUND: We previously reported that acute functional tolerance (AFT) to the hypnotic effects of alcohol was significantly correlated with drinking in the dark (DID) in the LXS recombinant inbred panel, but only in mice that had been pretreated with alcohol. Here, we have conducted quantitative trait locus (QTL) mapping for AFT. DNA sequencing of the progenitor ILS and ISS strains and microarray analyses were also conducted to identify candidate genes and functional correlates. METHODS: LXS mice were given either saline or alcohol (5 g/kg) on day 1 and then tested for loss of righting reflex AFT on day 2. QTLs were mapped using standard procedures. Two microarray analyses from brain were conducted: (i) naïve LXS mice and (ii) an alcohol treatment time course in the ILS and ISS. The full genomes of the ILS and ISS were sequenced to a depth of approximately 30×. RESULTS: A significant QTL for AFT in the alcohol pretreatment group was mapped to distal chromosome 4; numerous suggestive QTLs were also mapped. Preference drinking and DID have previously been mapped to the chromosome 4 locus. The credible interval of the significant chromosome 4 QTL spanned 23 Mb and included 716 annotated genes of which 150 had at least 1 nonsynonymous single nucleotide polymorphism or small indel that differed between the ILS and ISS; expression of 48 of the genes was cis-regulated. Enrichment analysis indicated broad functional categories underlying AFT, including proteolysis, transcription regulation, chromatin modification, protein kinase activity, and apoptosis. CONCLUSIONS: The chromosome 4 QTL is a key region containing possibly pleiotropic genes for AFT and drinking behavior. Given that the region contains many viable candidates and a large number of the genes in the interval fall into 1 or more of the enriched functional categories, we postulate that many genes of varying effect size contribute to the observed QTL effect.

Is the alcohol deprivation effect genetically mediated? Studies with HXB/BXH recombinant inbred rat strains.

BACKGROUND: Two features of alcohol addiction that have been widely studied in animal models are relapse drinking following periods of alcohol abstinence and the escalation of alcohol consumption after chronic continuous or intermittent alcohol exposure. The genetic contribution to these phenotypes has not been systematically investigated. METHODS: HXB/BXH recombinant inbred (RI) rat strains were given access to alcohol sequentially as follows: alcohol (10%) as the only fluid for 1 week; alcohol (10%) and water in a 2-bottle choice paradigm for 7 weeks ("pre-alcohol deprivation effect [ADE] alcohol consumption"); 2 weeks of access to water only (alcohol deprivation); and 2 weeks of reaccess to 10% alcohol and water ("post-ADE alcohol consumption"). The periods of deprivation and reaccess to alcohol were repeated 3 times. The ADE was defined as the amount of alcohol consumed in the first 24 hours after deprivation minus the average daily amount of alcohol consumed in the week prior to deprivation. Heritability of the phenotypes was determined by analysis of variance, and quantitative trait loci (QTLs) were identified. RESULTS: All strains showed increased alcohol consumption, compared to the predeprivation period, in the first 24 hours after each deprivation (ADE). Broad-sense heritability of the ADEs was low (ADE1, 9.1%; ADE2, 26.2%; ADE3, 16.3%). Alcohol consumption levels were relatively stable over weeks 2 to 7. Post-ADE alcohol consumption levels consistently increased in some strains and were decreased or unchanged in others. Heritability of pre- and post-ADE alcohol consumption was high and increased over time (week 2, 38.5%; week 7, 51.1%; week 11, 56.8%; week 15, 63.3%). QTLs for pre- and post-ADE alcohol consumption were similar, but the strength of the QTL association with the phenotype decreased over time. CONCLUSIONS: In the HXB/BXH RI rat strains, genotypic variance does not account for a large proportion of phenotypic variance in the ADE phenotype (low heritability), suggesting a role of environmental factors. In contrast, a large proportion of the variance across the RI strains in pre- and post-ADE alcohol consumption is due to genetically determined variance (high heritability).

Genome-Wide Association Study and transcriptome analysis reveals a complex gene network that regulates opsin gene expression and cell fate determination in Drosophila R7 photoreceptor cells.

Background: An animal's ability to discriminate between differing wavelengths of light (i.e., color vision) is mediated, in part, by a subset of photoreceptor cells that express opsins with distinct absorption spectra. In Drosophila R7 photoreceptors, expression of the rhodopsin molecules, Rh3 or Rh4, is determined by a stochastic process mediated by the transcription factor spineless. The goal of this study was to identify additional factors that regulate R7 cell fate and opsin choice using a Genome Wide Association Study (GWAS) paired with transcriptome analysis via RNA-Seq. Results: We examined Rh3 and Rh4 expression in a subset of fully-sequenced inbred strains from the Drosophila Genetic Reference Panel and performed a GWAS to identify 42 naturally-occurring polymorphisms-in proximity to 28 candidate genes-that significantly influence R7 opsin expression. Network analysis revealed multiple potential interactions between the associated candidate genes, spineless and its partners. GWAS candidates were further validated in a secondary RNAi screen which identified 12 lines that significantly reduce the proportion of Rh3 expressing R7 photoreceptors. Finally, using RNA-Seq, we demonstrated that all but four of the GWAS candidates are expressed in the pupal retina at a critical developmental time point and that five are among the 917 differentially expressed genes in sevenless mutants, which lack R7 cells. Conclusions: Collectively, these results suggest that the relatively simple, binary cell fate decision underlying R7 opsin expression is modulated by a larger, more complex network of regulatory factors. Of particular interest are a subset of candidate genes with previously characterized neuronal functions including neurogenesis, neurodegeneration, photoreceptor development, axon growth and guidance, synaptogenesis, and synaptic function.

Genetic background and sex influence somatosensory sensitivity and oxycodone analgesia in the Hybrid Rat Diversity Panel.

Opioid use disorder (OUD) is an ongoing public health concern in the United States, and relatively little work has addressed how genetic background contributes to OUD. Understanding the genetic contributions to oxycodone-induced analgesia could provide insight into the early stages of OUD development. Here, we present findings from a behavioral phenotyping protocol using several inbred strains from the Hybrid Rat Diversity Panel. Our behavioral protocol included a modified "up-down" von Frey procedure to measure inherent strain differences in the sensitivity to a mechanical stimulus on the hindpaw. We also performed the tail immersion assay, which measures the latency to display tail withdrawal in response to a hot water bath. Initial withdrawal thresholds were taken in drug-naïve animals to record baseline thermal sensitivity across the strains. Oxycodone-induced analgesia was measured after administration of oxycodone over the course of 2 h. Both mechanical and thermal sensitivity are shaped by genetic factors and display moderate heritability (h2 = 0.23-0.40). All strains displayed oxycodone-induced analgesia that peaked at 15-30 min and returned to baseline by 2 h. There were significant differences between the strains in the magnitude and duration of their analgesic response to oxycodone, although the heritability estimates were quite modest (h2 = 0.10-0.15). These data demonstrate that genetic background confers differences in mechanical sensitivity, thermal sensitivity, and oxycodone-induced analgesia.