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

The genetical genomic path to understanding why rats and humans consume too much alcohol

Background

At the invitation of the Journal, we are providing a summary of our published work that has followed the publication in 2009 of our manuscript entitled "Genetical Genomic Determinants of Alcohol Consumption in Rats and Humans". Our initial premise, which has been maintained throughout, is that knowledge regarding gene transcription would greatly enhance GWAS of alcohol-related phenotypes. We chose to concentrate our studies on the quantitative phenotype of alcohol consumption since high levels of alcohol consumption are a prerequisite for the development of alcohol use disorder (AUD). We also structured our studies to focus on "predisposition" to higher levels of alcohol consumption. We defined predisposition as a genetic structure and transcriptional pattern that is inherent in an organism and present prior to exposure to an environmental stimulus that engenders a physiological/behavioral response. In studies using humans, this interest in predisposition usually requires prolonged periods of cohort follow-up. On the other hand, studies with animals can use resources such as panels of recombinant inbred (RI) animals (in our case, the HXB/BXH rat panel) to capture the transcriptional landscape of animals not exposed to alcohol and compare this transcriptional landscape to levels of alcohol consumption collected from a different cohort of animals that are the same age, have an identical genetic composition, and are raised in an identical environment. The other benefit is that the stable genetic structure of inbred strains allows for a chronological expansion of information on these animals. This characteristic of the HXB/BXH RI rats allowed us to add important information as technology and analytical methods developed over time.

Methods findings and conclusions

Our initial studies relied on hybridization arrays for RNA quantification in brain, an initial set of polymorphic markers for the rat genome, and a standard behavioral (b)QTL analysis for alcohol consumption. What we added to the conceptual basis for analysis and interpretation was the calculation of transcript expression (e)QTLs and the requirements that: 1. the eQTL overlapped the location of the bQTL; and 2. the transcript levels were significantly correlated with the quantitative levels of alcohol consumption across rat strains. These criteria were used to identify genes (transcripts) as "candidate" contributors to the alcohol consumption phenotype. We soon realized that the search for candidate genes as unique determinants of a complex trait is irrational, since these phenotypes are best characterized by differences in genetic networks. Therefore, we incorporated Weighted Gene Coexpression Network Analysis (WGCNA) in our further work. We also realized the limitations of hybridization arrays for breadth of transcriptome coverage and quantification, and in the more current work used total RNA-Seq-derived data for characterizing nearly all of the brain transcriptome. Finally, we participated in the efforts for whole genome sequencing of the strains of the HXB/BXH panel, generating an extensive new panel of markers for remapping of the QTLs. We also realized that the biological determinants of a behavioral phenotype do not have to reside in brain and, by examining the liver transcriptome, we found that the gut-liver-brain axis was, in part, involved in predisposition to higher levels of free-choice alcohol consumption. In all, from the first exploration of the genetical genomics of the alcohol consumption phenotype, to the current status of our work, the function of the brain immune system, with emphasis on microglia and astrocytes, even prior to the animal being offered alcohol, has emerged as a most significant genetic contributor to the amount of alcohol an animal will consume on a daily basis. Particularly prominent was a cluster of inflammasome (NLRP3)-modulating transcripts (P2rx4, Ift81, Oas1b, Txnip) and a long noncoding transcript, "Lrap" that repeatedly appeared within a gene coexpression module associated with alcohol consumption levels. Interestingly, data from post-mortem tissue from brain of humans suffering from AUD also indicates a hyperactive neuroimmune function. The data from studies with animals may indicate that neuroimmune hyperactivity may be a trait rather than a state marker for AUD.

Aptardi predicts polyadenylation sites in sample-specific transcriptomes using high-throughput RNA sequencing and DNA sequence

Annotation of polyadenylation sites from short-read RNA sequencing alone is a challenging computational task. Other algorithms rooted in DNA sequence predict potential polyadenylation sites; however, in vivo expression of a particular site varies based on a myriad of conditions. Here, we introduce aptardi (alternative polyadenylation transcriptome analysis from RNA-Seq data and DNA sequence information), which leverages both DNA sequence and RNA sequencing in a machine learning paradigm to predict expressed polyadenylation sites. Specifically, as input aptardi takes DNA nucleotide sequence, genome-aligned RNA-Seq data, and an initial transcriptome. The program evaluates these initial transcripts to identify expressed polyadenylation sites in the biological sample and refines transcript 3'-ends accordingly. The average precision of the aptardi model is twice that of a standard transcriptome assembler. In particular, the recall of the aptardi model (the proportion of true polyadenylation sites detected by the algorithm) is improved by over three-fold. Also, the model-trained using the Human Brain Reference RNA commercial standard-performs well when applied to RNA-sequencing samples from different tissues and different mammalian species. Finally, aptardi's input is simple to compile and its output is easily amenable to downstream analyses such as quantitation and differential expression.

Genome-to-phenome research in rats: progress and perspectives

Because of their relatively short lifespan (<4 years), rats have become the second most used model organism to study health and diseases in humans who may live for up to 120 years. First-, second- and third-generation sequencing technologies and platforms have produced increasingly greater sequencing depth and accurate reads, leading to significant advancements in the rat genome assembly during the last 20 years. In fact, whole genome sequencing (WGS) of 47 strains have been completed. This has led to the discovery of genome variants in rats, which have been widely used to detect quantitative trait loci underlying complex phenotypes based on gene, haplotype, and sweep association analyses. DNA variants can also reveal strain, chromosome and gene functional evolutions. In parallel, phenome programs have advanced significantly in rats during the last 15 years and more than 10 databases host genome and/or phenome information. In order to discover the bridges between genome and phenome, systems genetics and integrative genomics approaches have been developed. On the other hand, multiple level information transfers from genome to phenome are executed by differential usage of alternative transcriptional start (ATS) and polyadenylation (APA) sites per gene. We used our own experiments to demonstrate how alternative transcriptome analysis can lead to enrichment of phenome-related causal pathways in rats. Development of advanced genome-to-phenome assays will certainly enhance rats as models for human biomedical research.

Alcohol Sensitivity as an Endophenotype of Alcohol Use Disorder: Exploring Its Translational Utility between Rodents and Humans

Alcohol use disorder (AUD) is a complex, chronic, relapsing disorder with multiple interacting genetic and environmental influences. Numerous studies have verified the influence of genetics on AUD, yet the underlying biological pathways remain unknown. One strategy to interrogate complex diseases is the use of endophenotypes, which deconstruct current diagnostic categories into component traits that may be more amenable to genetic research. In this review, we explore how an endophenotype such as sensitivity to alcohol can be used in conjunction with rodent models to provide mechanistic insights into AUD. We evaluate three alcohol sensitivity endophenotypes (stimulation, intoxication, and aversion) for their translatability across human and rodent research by examining the underlying neurobiology and its relationship to consumption and AUD. We show examples in which results gleaned from rodents are successfully integrated with information from human studies to gain insight in the genetic underpinnings of AUD and AUD-related endophenotypes. Finally, we identify areas for future translational research that could greatly expand our knowledge of the biological and molecular aspects of the transition to AUD with the broad hope of finding better ways to treat this devastating disorder.

Networking in Biology: The Hybrid Rat Diversity Panel

One of the most fruitful resources for systems genetic studies of nonhuman mammals is a panel of inbred strains that exhibits significant genetic diversity between strains but genetic stability (isogenicity) within strains. These characteristics allow for fine mapping of complex phenotypes (QTLs) and provide statistical power to identify loci which contribute nominally to the phenotype. This type of resource also allows the planning and performance of investigations using the same genetic backgrounds over several generations of the test animals. Often, rats are preferred over mice for physiologic and behavioral studies because of their larger size and more distinguishable anatomy (particularly for their central nervous system). The Hybrid Rat Diversity Panel (HRDP) is a panel of inbred rat strains, which combines two recombinant inbred panels (the HXB/BXH, 30 strains; the LEXF/FXLE, 34 strains and 35 more strains of inbred rats which were selected for genetic diversity, based on their fully sequenced genomes and/or thorough genotyping). The genetic diversity and statistical power of this panel for mapping studies rivals or surpasses currently available panels in mouse. The genetic stability of this panel makes it particularly suitable for collection of high-throughput omics data as relevant technology becomes available for engaging in truly integrative systems biology. The PhenoGen website ( http://phenogen.org ) is the repository for the initial transcriptome data, making the raw data, the processed data, and the analysis results, e.g., organ-specific protein coding and noncoding transcripts, isoform analysis, expression quantitative trait loci, and co-expression networks, available to the research public. The data sets and tools being developed will complement current efforts to analyze the human transcriptome and its genetic controls (the Genotype-Tissue Expression Project (GTEx)) and allow for dissection of genetic networks that predispose to particular phenotypes and gene-by-environment interactions that are difficult or even impossible to study in humans. The HRDP is an essential population for exploring truly integrative systems genetics.

Integrating Genetic and Gene Co-expression Analysis Identifies Gene Networks Involved in Alcohol and Stress Responses

Although the link between stress and alcohol is well recognized, the underlying mechanisms of how they interplay at the molecular level remain unclear. The purpose of this study is to identify molecular networks underlying the effects of alcohol and stress responses, as well as their interaction on anxiety behaviors in the hippocampus of mice using a systems genetics approach. Here, we applied a gene co-expression network approach to transcriptomes of 41 BXD mouse strains under four conditions: stress, alcohol, stress-induced alcohol and control. The co-expression analysis identified 14 modules and characterized four expression patterns across the four conditions. The four expression patterns include up-regulation in no restraint stress and given an ethanol injection (NOE) but restoration in restraint stress followed by an ethanol injection (RSE; pattern 1), down-regulation in NOE but rescue in RSE (pattern 2), up-regulation in both restraint stress followed by a saline injection (RSS) and NOE, and further amplification in RSE (pattern 3), and up-regulation in RSS but reduction in both NOE and RSE (pattern 4). We further identified four functional subnetworks by superimposing protein-protein interactions (PPIs) to the 14 co-expression modules, including γ-aminobutyric acid receptor (GABA) signaling, glutamate signaling, neuropeptide signaling, cAMP-dependent signaling. We further performed module specificity analysis to identify modules that are specific to stress, alcohol, or stress-induced alcohol responses. Finally, we conducted causality analysis to link genetic variation to these identified modules, and anxiety behaviors after stress and alcohol treatments. This study underscores the importance of integrative analysis and offers new insights into the molecular networks underlying stress and alcohol responses.