Identification of candidate genes for nicotine withdrawal in C57BL/6J × DBA/2J recombinant inbred mice.

Nicotine is the reinforcing ingredient in tobacco. Following chronic exposure, sudden cessation of nicotine use produces negative symptoms of withdrawal that contribute to dependence. The molecular mechanisms underlying nicotine withdrawal behaviors, however, are poorly understood. Using recombinant inbred mice, chronic nicotine was delivered by minipump and withdrawal induced using mecamylamine. Somatic signs of withdrawal, and anxiety-like behavior using elevated plus maze, were then assessed. Interval mapping was used to identify associations between genetic variation and withdrawal behaviors, and with basal gene expression. Differential gene expression following nicotine exposure and withdrawal was also assessed in progenitor mice using microarrays. Quantitative trait loci mapping identified chromosome intervals with significant genetic associations to somatic signs of withdrawal or withdrawal-induced anxiety-like behavior. Using bioinformatics, and association with basal gene expression in nucleus accumbens, we implicated Rb1, Bnip3l, Pnma2, Itm2b, and Kif13b as candidate genes for somatic signs of withdrawal, and Galr1, which showed trans-regulation from a region of chromosome 14 that was associated with somatic signs of withdrawal. Candidate genes within the chromosome 9 region associated with anxiety-like withdrawal behavior included Dixdc1, Ncam1, and Sorl1. Bioinformatics identified six genes that were also significantly associated with nicotine or alcohol traits in recent human genome-wide association studies. Withdrawal-associated somatic signs and anxiety-like behavior had strong non-overlapping genetic associations, respectively, with regions of chromosome 14 and chromosome 9. Genetic, behavioral and gene expression correlations, and bioinformatics analysis identified several candidate genes that may represent novel molecular targets for modulating nicotine withdrawal symptoms.

Characterizing the longitudinal course of symptoms and functioning in bipolar disorder.

BACKGROUND: The course of Bipolar Disorder (BD) is highly variable, with marked inter and intra-individual differences in symptoms and functioning. In this study, we identified illness trajectories across major clinical domains that could have etiological, prognostic, and therapeutic relevance. METHODS: Using the Systematic Treatment Enhancement Program for Bipolar Disorder (STEP-BD) study, we performed univariate and multivariate trajectory modeling of depressive symptoms, manic symptoms, and psychosocial functioning. Multinomial regression was performed to identify baseline variables associated with poor outcome trajectories. RESULTS: Depressive symptoms predominated, with most subjects being found in trajectories characterized by various degrees of depressive symptoms and 13% of subjects being classified in a poor outcome 'persistently depressed' trajectory. Most subjects experienced few manic symptoms, although approximately 10% of subjects followed a trajectory of persistently manic symptoms. Trajectory analysis of psychosocial functioning showed impairment in most of the sample, with little improvement during follow up. Multi-trajectory analyses highlighted significant impairment in subjects with persistently mixed and persistently depressed trajectories of illness. In general, poor outcome trajectories were marked by lower educational attainment, higher unemployment and disability, and a greater likelihood of adverse clinical features (rapid cycling and suicide attempts) and comorbid diagnoses (anxiety disorders, PTSD, and substance abuse/dependence disorders). CONCLUSIONS: Subjects with BD can be classified into several trajectories of clinically relevant domains that are prognostically relevant and show differing degrees of associations with a broad range of negative clinical risk factors. The highest level of psychosocial disability was found in subjects with chronic mixed and depressive symptoms, who show limited improvement despite guideline-based treatment.

Cross-species alcohol dependence-associated gene networks: Co-analysis of mouse brain gene expression and human genome-wide association data.

Genome-wide association studies on alcohol dependence, by themselves, have yet to account for the estimated heritability of the disorder and provide incomplete mechanistic understanding of this complex trait. Integrating brain ethanol-responsive gene expression networks from model organisms with human genetic data on alcohol dependence could aid in identifying dependence-associated genes and functional networks in which they are involved. This study used a modification of the Edge-Weighted Dense Module Searching for genome-wide association studies (EW-dmGWAS) approach to co-analyze whole-genome gene expression data from ethanol-exposed mouse brain tissue, human protein-protein interaction databases and alcohol dependence-related genome-wide association studies. Results revealed novel ethanol-responsive and alcohol dependence-associated gene networks in prefrontal cortex, nucleus accumbens, and ventral tegmental area. Three of these networks were overrepresented with genome-wide association signals from an independent dataset. These networks were significantly overrepresented for gene ontology categories involving several mechanisms, including actin filament-based activity, transcript regulation, Wnt and Syndecan-mediated signaling, and ubiquitination. Together, these studies provide novel insight for brain mechanisms contributing to alcohol dependence.

Beyond genome-wide significance: integrative approaches to the interpretation and extension of GWAS findings for alcohol use disorder.

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.

Integrative Analysis of Genetic, Genomic, and Phenotypic Data for Ethanol Behaviors: A Network-Based Pipeline for Identifying Mechanisms and Potential Drug Targets.

Complex behavioral traits, such as alcohol abuse, are caused by an interplay of genetic and environmental factors, producing deleterious functional adaptations in the central nervous system. The long-term behavioral consequences of such changes are of substantial cost to both the individual and society. Substantial progress has been made in the last two decades in understanding elements of brain mechanisms underlying responses to ethanol in animal models and risk factors for alcohol use disorder (AUD) in humans. However, treatments for AUD remain largely ineffective and few medications for this disease state have been licensed. Genome-wide genetic polymorphism analysis (GWAS) in humans, behavioral genetic studies in animal models and brain gene expression studies produced by microarrays or RNA-seq have the potential to produce nonbiased and novel insight into the underlying neurobiology of AUD. However, the complexity of such information, both statistical and informational, has slowed progress toward identifying new targets for intervention in AUD. This chapter describes one approach for integrating behavioral, genetic, and genomic information across animal model and human studies. The goal of this approach is to identify networks of genes functioning in the brain that are most relevant to the underlying mechanisms of a complex disease such as AUD. We illustrate an example of how genomic studies in animal models can be used to produce robust gene networks that have functional implications, and to integrate such animal model genomic data with human genetic studies such as GWAS for AUD. We describe several useful analysis tools for such studies: ComBAT, WGCNA, and EW_dmGWAS. The end result of this analysis is a ranking of gene networks and identification of their cognate hub genes, which might provide eventual targets for future therapeutic development. Furthermore, this combined approach may also improve our understanding of basic mechanisms underlying gene x environmental interactions affecting brain functioning in health and disease.