Whole brain and brain regional coexpression network interactions associated with predisposition to alcohol consumption

Determinants of ecological footprint in European countries: Fresh insight from Bayesian model averaging for panel data analysis

This study examines the determinants of the ecological footprint of production in European countries from 1992 to 2020. Using partial and semipartial correlation analyses and Bayesian Model Averaging (BMA) approach for the first time, the research identifies key variables affecting ecological footprint. Using Bayesian methods, posterior inclusion probabilities (PIPs) were calculated for each variable's coefficient estimates, revealing their relative importance. Biocapacity, energy consumption, industrialization, financial development, life expectancy, and globalization displayed notably high PIPs, indicating their strong influence on the ecological footprint. In addition, the study employs cointegration tests to examine the long-run relationship between ecological footprint and explanatory variables. The results indicate significant cointegration between these variables across panels, supported by various test statistics. In the Weighted Pooled DOLS estimation, biocapacity, energy consumption, and life expectancy significantly influence the ecological footprint, while industrialization, financial development, and globalization exert a comparatively smaller impact. Researchers and policymakers should consider these determinants for effective sustainable development planning. These findings underscore the intricate interplay of factors shaping the ecological footprint and offer insights for effective policy interventions towards sustainable development.

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.

Do gene expression findings from mouse models of cocaine use recapitulate human cocaine use disorder in reward circuitry?

Animal models of drug use have investigated possible mechanisms governing human substance use traits for over 100 years. Most cross-species research on drug use/addiction examines behavioral overlap, but studies assessing neuromolecular (e.g. RNA) correspondence are lacking. Our study utilized transcriptome-wide data from the hippocampus and ventral tegmental area (VTA)/midbrain from a total of 35 human males with cocaine use disorder/controls and 49 male C57BL/6J cocaine/saline administering/exposed mice. We hypothesized differential expressed genes and systems of co-expressed genes (gene networks) would show appreciable overlap across mouse cocaine self-administration and human cocaine use disorder. We found modest, but significant relationships between differentially expressed genes associated with cocaine self-administration (short access) and cocaine use disorder within reward circuitry. Differentially expressed genes underlying models of acute cocaine exposure (cocaine), context re-exposure and cocaine + context re-exposure were not consistently associated with human CUD across brain regions. Investigating systems of co-expressed genes, we found several validated gene networks with weak to moderate conservation between cocaine/saline self-administering mice and disordered cocaine users/controls. The most conserved hippocampal and VTA gene networks demonstrated substantial overlap (2029 common genes) and included both novel and previously implicated targets for cocaine use/addiction. Lastly, we conducted (expression-based) phenome-wide association studies of the nine common hub genes across conserved gene networks. Common hub genes were associated with dopamine/serotonin function, cocaine self-administration and other relevant mouse traits. Overall, our study pinpointed and characterized conserved brain-related RNA patterns across mouse cocaine self-administration and human cocaine use disorder. We offer recommendations for future research and add to the dialogue surrounding pre-clinical animal research for human disease.

Genetic Architecture and Molecular Neuropathology of Human Cocaine Addiction

We integrated genomic and bioinformatic analyses, using data from the largest genome-wide association study of cocaine dependence (CD; n = 6546; 82.37% with CD; 57.39% male) and the largest postmortem gene-expression sample of individuals with cocaine use disorder (CUD; n = 36; 51.35% with CUD; 100% male). Our genome-wide analyses identified one novel gene (NDUFB9) associated with the genetic predisposition to CD in African-Americans. The genetic architecture of CD was similar across ancestries. Individual genes associated with CD demonstrated modest overlap across European-Americans and African-Americans, but the genetic liability for CD converged on many similar tissue types (brain, heart, blood, liver) across ancestries. In a separate sample, we investigated the neuronal gene expression associated with CUD by using RNA sequencing of dorsal-lateral prefrontal cortex neurons. We identified 133 genes differentially expressed between CUD case patients and cocaine-free control subjects, including previously implicated candidates for cocaine use/addiction (FOSB, ARC, KCNJ9/GIRK3, NR4A2, JUNB, and MECP2). Differential expression analyses significantly correlated across European-Americans and African-Americans. While genes significantly associated with CD via genome-wide methods were not differentially expressed, two of these genes (NDUFB9 and C1qL2) were part of a robust gene coexpression network associated with CUD involved in neurotransmission (GABA, acetylcholine, serotonin, and dopamine) and drug addiction. We then used a "guilt-by-association" approach to unravel the biological relevance of NDUFB9 and C1qL2 in the context of CD. In sum, our study furthers the understanding of the genetic architecture and molecular neuropathology of human cocaine addiction and provides a framework for translating biological meaning into otherwise obscure genome-wide associations.SIGNIFICANCE STATEMENT Our study further clarifies the genetic and neurobiological contributions to cocaine addiction, provides a rapid approach for generating testable hypotheses for specific candidates identified by genome-wide research, and investigates the cross-ancestral biological contributions to cocaine use disorder/dependence for individuals of European-American and African-American ancestries.

Variation among intact tissue samples reveals the core transcriptional features of human CNS cell classes

It is widely assumed that cells must be physically isolated to study their molecular profiles. However, intact tissue samples naturally exhibit variation in cellular composition, which drives covariation of cell-class-specific molecular features. By analyzing transcriptional covariation in 7221 intact CNS samples from 840 neurotypical individuals representing billions of cells, we reveal the core transcriptional identities of major CNS cell classes in humans. By modeling intact CNS transcriptomes as a function of variation in cellular composition, we identify cell-class-specific transcriptional differences in Alzheimer’s disease, among brain regions, and between species. Among these, we show that PMP2 is expressed by human but not mouse astrocytes and significantly increases mouse astrocyte size upon ectopic expression in vivo, causing them to more closely resemble their human counterparts. Our work is available as an online resource (http://oldhamlab.ctec.ucsf.edu/) and provides a generalizable strategy for determining the core molecular features of cellular identity in intact biological systems.

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.

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.

Rat Genome and Model Resources

Rats remain a major model for studying disease mechanisms and discovery, validation, and testing of new compounds to improve human health. The rat’s value continues to grow as indicated by the more than 1.4 million publications (second to human) at PubMed documenting important discoveries using this model. Advanced sequencing technologies, genome modification techniques, and the development of embryonic stem cell protocols ensure the rat remains an important mammalian model for disease studies. The 2004 release of the reference genome has been followed by the production of complete genomes for more than two dozen individual strains utilizing NextGen sequencing technologies; their analyses have identified over 80 million variants. This explosion in genomic data has been accompanied by the ability to selectively edit the rat genome, leading to hundreds of new strains through multiple technologies. A number of resources have been developed to provide investigators with access to precision rat models, comprehensive datasets, and sophisticated software tools necessary for their research. Those profiled here include the Rat Genome Database, PhenoGen, Gene Editing Rat Resource Center, Rat Resource and Research Center, and the National BioResource Project for the Rat in Japan.

Effect of vertical sleeve gastrectomy on alcohol consumption and preferences in dietary obese rats and mice: A plausible role for altered ghrelin signaling

Vertical sleeve gastrectomy (VSG) and Roux-en-Y gastric bypass (RYGB) are the most common surgical options for the treatment of obesity and metabolic disorder. Whereas RYGB may result in greater and more durable weight loss, recent clinical and pre-clinical studies in rats have raised concerns that RYGB surgery may increase risk for alcohol use disorder (AUD). In contrast, recent clinical reports suggest a lesser risk for AUD following VSG, although no preclinical studies have been done to confirm that. Therefore, the present study sought to determine the effects of VSG on ethanol intake and preferences in rodent models using protocols similar to those previously used in animal studies for RYGB. Male Sprague Dawley rats and male C57B6 mice were made obese on a high fat diet (60%kcal from fat) and received VSG or no surgery (controls). All animals then were given access to increasing concentrations of ethanol (2%, 4%, 6%, and 8%), presented for few days each. Compared to controls, VSG rats consumed significantly less of 2, 6 and 8% ethanol and showed significantly reduced preferences to 6 and 8% ethanol over water. VSG mice also displayed reduced intake and preference for 6 and 8% ethanol solutions. After a two-week period of forced abstinence, 8% ethanol was reintroduced and the VSG rats and mice continued to exhibit reduced consumption and less preference for ethanol. Regarding the underlying mechanism, we hypothesized that the removal of the ghrelin producing part of the stomach in the VSG surgery is a possible contributor to the observed reduced ethanol preference. To test for functional changes at the ghrelin receptors, the VSG and control rats were given IP injections of acyl-ghrelin (2.5nmol and 5nmol) prior to ethanol access. Neither concentration of ghrelin resulted in a significant increase in 8% ethanol consumption of VSG or control subjects. Next, the rats were given IP injections of the ghrelin receptor antagonist, JMV (2.5mg/kg body weight). This dose induced a significant reduction in 8% ethanol consumption in the VSG group, but no effect on ethanol intake in the controls. While ghrelin injection was uninformative, increased sensitivity to subthreshold doses of the ghrelin receptor antagonist may indicate reduced ghrelin signaling following VSG. Overall, these findings suggest that bariatric patients with increased susceptibility to AUD may benefit from receiving VSG instead of RYGB surgery, and that changes in ghrelin signaling, at least in part, may play a role in the differential AUD risks between the two most commonly performed bariatric surgical procedures.

Genetic regulation of gene expression in the epileptic human hippocampus

Epilepsy is a serious and common neurological disorder. Expression quantitative loci (eQTL) analysis is a vital aid for the identification and interpretation of disease-risk loci. Many eQTLs operate in a tissue- and condition-specific manner. We have performed the first genome-wide cis-eQTL analysis of human hippocampal tissue to include not only normal (n = 22) but also epileptic (n = 22) samples. We demonstrate that disease-associated variants from an epilepsy GWAS meta-analysis and a febrile seizures (FS) GWAS are significantly more enriched with epilepsy-eQTLs than with normal hippocampal eQTLs from two larger independent published studies. In contrast, GWAS meta-analyses of two other brain diseases associated with hippocampal pathology (Alzheimer's disease and schizophrenia) are more enriched with normal hippocampal eQTLs than with epilepsy-eQTLs. These observations suggest that an eQTL analysis that includes disease-affected brain tissue is advantageous for detecting additional risk SNPs for the afflicting and closely related disorders, but not for distinct diseases affecting the same brain regions. We also show that epilepsy eQTLs are enriched within epilepsy-causing genes: an epilepsy cis-gene is significantly more likely to be a causal gene for a Mendelian epilepsy syndrome than to be a causal gene for another Mendelian disorder. Epilepsy cis-genes, compared to normal hippocampal cis-genes, are more enriched within epilepsy-causing genes. Hence, we utilize the epilepsy eQTL data for the functional interpretation of epilepsy disease-risk variants and, thereby, highlight novel potential causal genes for sporadic epilepsy. In conclusion, an epilepsy-eQTL analysis is superior to normal hippocampal tissue eQTL analyses for identifying the variants and genes underlying epilepsy.

Interhemispheric gene expression differences in the cerebral cortex of humans and macaque monkeys

Handedness and language are two well-studied examples of asymmetrical brain function in humans. Approximately 90% of humans exhibit a right-hand preference, and the vast majority shows left-hemisphere dominance for language function. Although genetic models of human handedness and language have been proposed, the actual gene expression differences between cerebral hemispheres in humans remain to be fully defined. In the present study, gene expression profiles were examined in both hemispheres of three cortical regions involved in handedness and language in humans and their homologues in rhesus macaques: ventrolateral prefrontal cortex, posterior superior temporal cortex (STC), and primary motor cortex. Although the overall pattern of gene expression was very similar between hemispheres in both humans and macaques, weighted gene correlation network analysis revealed gene co-expression modules associated with hemisphere, which are different among the three cortical regions examined. Notably, a receptor-enriched gene module in STC was particularly associated with hemisphere and showed different expression levels between hemispheres only in humans.

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.

Voluntary wheel running reduces voluntary consumption of ethanol in mice: identification of candidate genes through striatal gene expression profiling

Hedonic substitution, where wheel running reduces voluntary ethanol consumption, has been observed in prior studies. Here, we replicate and expand on previous work showing that mice decrease voluntary ethanol consumption and preference when given access to a running wheel. While earlier work has been limited mainly to behavioral studies, here we assess the underlying molecular mechanisms that may account for this interaction. From four groups of female C57BL/6J mice (control, access to two-bottle choice ethanol, access to a running wheel, and access to both two-bottle choice ethanol and a running wheel), mRNA-sequencing of the striatum identified differential gene expression. Many genes in ethanol preference quantitative trait loci were differentially expressed due to running. Furthermore, we conducted Weighted Gene Co-expression Network Analysis and identified gene networks corresponding to each effect behavioral group. Candidate genes for mediating the behavioral interaction between ethanol consumption and wheel running include multiple potassium channel genes, Oprm1, Prkcg, Stxbp1, Crhr1, Gabra3, Slc6a13, Stx1b, Pomc, Rassf5 and Camta2. After observing an overlap of many genes and functional groups previously identified in studies of initial sensitivity to ethanol, we hypothesized that wheel running may induce a change in sensitivity, thereby affecting ethanol consumption. A behavioral study examining Loss of Righting Reflex to ethanol following exercise trended toward supporting this hypothesis. These data provide a rich resource for future studies that may better characterize the observed transcriptional changes in gene networks in response to ethanol consumption and wheel running.

Circuit-wide Transcriptional Profiling Reveals Brain Region-Specific Gene Networks Regulating Depression Susceptibility

Depression is a complex, heterogeneous disorder and a leading contributor to the global burden of disease. Most previous research has focused on individual brain regions and genes contributing to depression. However, emerging evidence in humans and animal models suggests that dysregulated circuit function and gene expression across multiple brain regions drive depressive phenotypes. Here, we performed RNA sequencing on four brain regions from control animals and those susceptible or resilient to chronic social defeat stress at multiple time points. We employed an integrative network biology approach to identify transcriptional networks and key driver genes that regulate susceptibility to depressive-like symptoms. Further, we validated in vivo several key drivers and their associated transcriptional networks that regulate depression susceptibility and confirmed their functional significance at the levels of gene transcription, synaptic regulation, and behavior. Our study reveals novel transcriptional networks that control stress susceptibility and offers fundamentally new leads for antidepressant drug discovery.

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.

ppcor: An R Package for a Fast Calculation to Semi-partial Correlation Coefficients

Lack of a general matrix formula hampers implementation of the semi-partial correlation, also known as part correlation, to the higher-order coefficient. This is because the higher-order semi-partial correlation calculation using a recursive formula requires an enormous number of recursive calculations to obtain the correlation coefficients. To resolve this difficulty, we derive a general matrix formula of the semi-partial correlation for fast computation. The semi-partial correlations are then implemented on an R package ppcor along with the partial correlation. Owing to the general matrix formulas, users can readily calculate the coefficients of both partial and semi-partial correlations without computational burden. The package ppcor further provides users with the level of the statistical significance with its test statistic.

Polymorphisms in early neurodevelopmental genes affect natural variation in alcohol sensitivity in adult drosophila

BACKGROUND

Alcohol abuse and alcoholism are significant public health problems, but the genetic basis for individual variation in alcohol sensitivity remains poorly understood. Drosophila melanogaster presents a powerful model system for dissecting the genetic underpinnings that determine individual variation in alcohol-related phenotypes. We performed genome wide association analyses for alcohol sensitivity using the sequenced, inbred lines of the D. melanogaster Genetic Reference Panel (DGRP) together with extreme QTL mapping in an advanced intercross population derived from sensitive and resistant DGRP lines.

RESULTS

The DGRP harbors substantial genetic variation for alcohol sensitivity and tolerance. We identified 247 candidate genes affecting alcohol sensitivity in the DGRP or the DGRP-derived advanced intercross population, some of which met a Bonferroni-corrected significance threshold, while others occurred among the top candidate genes associated with variation in alcohol sensitivity in multiple analyses. Among these were candidate genes associated with development and function of the nervous system, including several genes in the Dopamine decarboxylase (Ddc) cluster involved in catecholamine synthesis. We found that 58 of these genes formed a genetic interaction network. We verified candidate genes using mutational analysis, targeted gene disruption through RNAi knock-down and transcriptional profiling. Two-thirds of the candidate genes have been implicated in previous Drosophila, mouse and human studies of alcohol-related phenotypes.

CONCLUSIONS

Individual variation in alcohol sensitivity in Drosophila is highly polygenic and in part determined by variation in evolutionarily conserved signaling pathways that are associated with catecholamine neurotransmitter biosynthesis and early development of the nervous system.

The sequenced rat brain transcriptome--its use in identifying networks predisposing alcohol consumption

A quantitative genetic approach, which involves correlation of transcriptional networks with the phenotype in a recombinant inbred (RI) population and in selectively bred lines of rats, and determination of coinciding quantitative trait loci for gene expression and the trait of interest, has been applied in the present study. In this analysis, a novel approach was used that combined DNA-Seq data, data from brain exon array analysis of HXB/BXH RI rat strains and six pairs of rat lines selectively bred for high and low alcohol preference, and RNA-Seq data (including rat brain transcriptome reconstruction) to quantify transcript expression levels, generate co-expression modules and identify biological functions that contribute to the predisposition of consuming varying amounts of alcohol. A gene co-expression module was identified in the RI rat strains that contained both annotated and unannotated transcripts expressed in the brain, and was associated with alcohol consumption in the RI panel. This module was found to be enriched with differentially expressed genes from the selected lines of rats. The candidate genes within the module and differentially expressed genes between high and low drinking selected lines were associated with glia (microglia and astrocytes) and could be categorized as being related to immune function, energy metabolism and calcium homeostasis, as well as glial-neuronal communication. The results of the present study show that there are multiple combinations of genetic factors that can produce the same phenotypic outcome. Although no single gene accounts for predisposition to a particular level of alcohol consumption in every animal model, coordinated differential expression of subsets of genes in the identified pathways produce similar phenotypic outcomes.

DATABASE

The datasets supporting the results of the present study are available at http://phenogen.ucdenver.edu.

GeneWeaver: finding consilience in heterogeneous cross-species functional genomics data

A persistent challenge lies in the interpretation of consensus and discord from functional genomics experimentation. Harmonizing and analyzing this data will enable investigators to discover relations of many genes to many diseases, and from many phenotypes and experimental paradigms to many diseases through their genomic substrates. The GeneWeaver.org system provides a platform for cross-species integration and interrogation of heterogeneous curated and experimentally derived functional genomics data. GeneWeaver enables researchers to store, share, analyze, and compare results of their own genome-wide functional genomics experiments in an environment containing rich companion data obtained from major curated repositories, including the Mouse Genome Database and other model organism databases, along with derived data from highly specialized resources, publications, and user submissions. The data, largely consisting of gene sets and putative biological networks, are mapped onto one another through gene identifiers and homology across species. A versatile suite of interactive tools enables investigators to perform a variety of set analysis operations to find consilience among these often noisy experimental results. Fast algorithms enable real-time analysis of large queries. Specific applications include prioritizing candidate genes for quantitative trait loci, identifying biologically valid mouse models and phenotypic assays for human disease, finding the common biological substrates of related diseases, classifying experiments and the biological concepts they represent from empirical data, and applying patterns of genomic evidence to implicate novel genes in disease. These results illustrate an alternative to strict emphasis on replicability, whereby researchers classify experimental results to identify the conditions that lead to their similarity.

Gene network of a phosphoglycerate mutase in muscle wasting in mice

We previously identified the insertion of an intracisternal A-particle retrotransposons (IAPs) sequence in a gene, 9630033F20Rik, that contains domains involved in glycolysis from a mouse model called lethal wasting (lew). However, because both IAP insertion and the muation of vesicle-associated membrane protein 1 (VAMP1) were discovered from lew, the impact of the IAP insertion and Vamp1 on the lew mouse phenotype needs further investigation. In this study, the effect of the 9630033F20Rik and Vamp1 on glycolysis and muscle-wasting genes in heart, muscle, and brain tissues was further investigated using data of gene expression profiles in these tissues. Our data indicated that the expression levels of 9630033F20Rik and Vamp1 are not associated with each other. While 9630033F20Rik affects the expression of several key genes in pathways of glycolysis and muscle wasting, Vamp1 affects a different set of genes, with fewer numbers. In situ hybridization indicated that the expression of 9630033F20Rik is different in musculoskeletal tissues between the muscle-wasting mouse model and the wild-type model. Our data indicated that 9630033F20Rik may play an important role in muscle wasting and that it has a distinguished characterization of gene network. Our data also suggest that both 9630033F20Rik and Vamp1 play functional roles in muscle development and lead to the muscle-wasting phenotype when they are mutated.

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.

Genetics of gene expression characterizes response to selective breeding for alcohol preference

Numerous selective breeding experiments have been performed with rodents, in an attempt to understand the genetic basis for innate differences in preference for alcohol consumption. Quantitative trait locus (QTL) analysis has been used to determine regions of the genome that are associated with the behavioral difference in alcohol preference/consumption. Recent work suggests that differences in gene expression represent a major genetic basis for complex traits. Therefore, the QTLs are likely to harbor regulatory regions (eQTLs) for the differentially expressed genes that are associated with the trait. In this study, we examined brain gene expression differences over generations of selection of the third replicate lines of high and low alcohol-preferring (HAP3 and LAP3) mice, and determined regions of the genome that control the expression of these differentially expressed genes (de eQTLs). We also determined eQTL regions (rv eQTLs) for genes that showed a decrease in variance of expression levels over the course of selection. We postulated that de eQTLs that overlap with rv eQTLs, and also with phenotypic QTLs, represent genomic regions that are affected by the process of selection. These overlapping regions controlled the expression of candidate genes (that displayed differential expression and reduced variance of expression) for the predisposition to differences in alcohol consumption by the HAP3/LAP3 mice.

Mass spectrometric characterization of transglutaminase based site-specific antibody-drug conjugates

Antibody drug conjugates (ADCs) are becoming an important new class of therapeutic agents for the treatment of cancer. ADCs are produced through the linkage of a cytotoxic small molecule (drug) to monoclonal antibodies that target tumor cells. Traditionally, most ADCs rely on chemical conjugation methods that yield heterogeneous mixtures of varying number of drugs attached at different positions. The potential benefits of site-specific drug conjugation in terms of stability, manufacturing, and improved therapeutic index has recently led to the development of several new site-specific conjugation technologies. However, detailed characterization of the degree of site specificity is currently lacking. In this study we utilize mass spectrometry to characterize the extent of site-specificity of an enzyme-based site-specific antibody-drug conjugation technology that we recently developed. We found that, in addition to conjugation of the engineered site, a small amount of aglycosylated antibody present in starting material led to conjugation at position Q295, resulting in approximately 1.3% of off-target conjugation. Based on our detection limits, we show that Q295N mutant eliminates the off-target conjugation yielding highly homogeneous conjugates that are better than 99.8% site-specific. Our study demonstrates the importance of detailed characterization of ADCs and describes methods that can be utilized to characterize not only our enzyme based conjugates, but also ADCs generated by other conjugation technologies.

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.

Coexpression and cosplicing network approaches for the study of mammalian brain transcriptomes

Next-generation sequencing experiments have demonstrated great potential for transcriptome profiling. While transcriptome sequencing greatly increases the level of biological detail, system-level analysis of these high-dimensional datasets is becoming essential. We illustrate gene network approaches to the analysis of transcriptional data, with particular focus on the advantage of RNA-Seq technology compared to microarray platforms. We introduce a novel methodology for constructing cosplicing networks, based on distance measures combined with matrix correlations. We find that the cosplicing network is distinct and complementary to the coexpression network, although it shares the scale-free properties. In the cosplicing network, we find a set of novel hubs that have unique characteristics distinguishing them from coexpression hubs: they are heavily represented in neurobiological functional pathways and have strong overlap with markers of neurons and neuroglia, long-coding lengths, and high number of both exons and annotated transcripts. We also find that gene networks are plastic in the face of genetic and environmental pressures.

Molecular and neurologic responses to chronic alcohol use

This chapter provides an overview of current knowledge on the molecular and clinical aspects of chronic alcohol effects on the central nervous system. This drug is almost ubiquitous, widely enjoyed socially, but produces a diverse spectrum of neurologic disease when abused. Acutely, alcohol interacts predominantly with γ-aminobutyric acid-A (GABA-A) and N-methyl-d-aspartate (NMDA) receptors, but triggers diverse signaling events within well-defined neural pathways. These events result in adaptive changes in gene expression that ultimately produce two major states: addiction and toxicity. Epigenetic modifications of chromatin could lead to long-lived or even transgenerational changes in gene expression, thus producing aspects of the heritability of alcohol use disorders (AUD) and long-term behaviors such as recidivism. The diverse clinical syndromes produced by chronic alcohol actions in the central nervous system reflect the molecular pathology and predominantly involve aspects of tolerance/withdrawal, selective vulnerability (manifest as central pontine myelinolysis, Marchiafava-Bignami disease), and additional environmental factors (e.g., thiamine deficiency in Wernicke-Korsakoff's syndrome). Additionally, deleterious aspects of chronic alcohol on signaling, synaptic transmission, and cell toxicity lead to primary alcoholic dementia. Genetically determined aspects of myelin structure and alcohol actions on myelin gene expression may be a prominent molecular mechanism resulting in a predisposition to, or causation of, AUD and multiple other neurologic complications of chronic alcohol. The dramatic progress made in understanding molecular actions of alcohol holds great promise for our eventual treatment or prevention of AUD and neurologic complications resulting from chronic alcohol abuse.

Out of the bottleneck: the Diversity Outcross and Collaborative Cross mouse populations in behavioral genetics research

The historical origins of classical laboratory mouse strains have led to a relatively limited range of genetic and phenotypic variation, particularly for the study of behavior. Many recent efforts have resulted in improved diversity and precision of mouse genetic resources for behavioral research, including the Collaborative Cross and Diversity Outcross population. These two populations, derived from an eight way cross of common and wild-derived strains, have high precision and allelic diversity. Behavioral variation in the population is expanded, both qualitatively and quantitatively. Variation that had once been canalized among the various inbred lines has been made amenable to genetic dissection. The genetic attributes of these complementary populations, along with advances in genetic and genomic technologies, makes a systems genetic analyses of behavior more readily tractable, enabling discovery of a greater range of neurobiological phenomena underlying behavioral variation.