Dexamethasone targets actin cytoskeleton signaling and inflammatory mediators to reverse sulfur mustard-induced toxicity in rabbit corneas.

PURPOSE: Sulfur mustard (SM), a bi-functional alkylating agent, was used during World War I and the Iran-Iraq war. SM toxicity is ten times higher in eyes than in other tissues. Cornea is exceptionally susceptible to SM-injuries due to its anterior positioning and mucous-aqueous interphase. Ocular SM exposure induces blepharitis, photosensitivity, dry eye, epithelial defects, limbal ischemia and stem cell deficiency, and mustard gas keratopathy leading to temporary or permanent vision impairments. We demonstrated that dexamethasone (Dex) is a potent therapeutic intervention against SM-induced corneal injuries; however, its mechanism of action is not well known. Investigations employing proteomic profiling (LC-MS/MS) to understand molecular mechanisms behind SM-induced corneal injury and Dex efficacy were performed in the rabbit cornea exposed to SM and then received Dex treatment. PEAKS studio was used to extract, search, and summarize peptide identity. Ingenuity Pathway Analysis was used for pathway identification. Validation was performed using immunofluorescence. One-Way ANOVA (FDR < 0.05; p < 0.005) and Student's t-test (p < 0.05) were utilized for analyzing proteomics and IF data, respectively. Proteomic analysis revealed that SM-exposure upregulated tissue repair pathways, particularly actin cytoskeleton signaling and inflammation. Prominently dysregulated proteins included lipocalin2, coronin1A, actin-related protein2, actin-related protein2/3 complex subunit2, actin-related protein2/3 complex subunit4, cell division cycle42, ezrin, bradykinin/kininogen1, moesin, and profilin. Upregulated actin cytoskeleton signaling increases F-actin formation, dysregulating cell shape and motility. Dex reversed SM-induced increases in the aforementioned proteins levels to near control expression profiles. Dex aids corneal wound healing and improves corneal integrity via actin cytoskeletal signaling and anti-inflammatory effects following SM-induced injuries.

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.

Albumin/Mitotane Interaction Affects Drug Activity in Adrenocortical Carcinoma Cells: Smoke and Mirrors on Mitotane Effect with Possible Implications for Patients' Management.

BACKGROUND: Mitotane is the only drug approved for the treatment of adrenocortical carcinoma (ACC). Although it has been used for many years, its mechanism of action remains elusive. H295R cells are, in ACC, an essential tool to evaluate drug mechanisms, although they often lead to conflicting results. METHODS: Using different in vitro biomolecular technologies and biochemical/biophysical experiments, we evaluated how the presence of "confounding factors" in culture media and patient sera could reduce the pharmacological effect of mitotane and its metabolites. RESULTS: We discovered that albumin, the most abundant protein in the blood, was able to bind mitotane. This interaction altered the effect of the drug by blocking its biological activity. This blocking effect was independent of the albumin source or methodology used and altered the assessment of drug sensitivity of the cell lines. CONCLUSIONS: In conclusion, we have for the first time demonstrated that albumin does not only act as an inert drug carrier when mitotane or its metabolites are present. Indeed, our experiments clearly indicated that both albumin and human serum were able to suppress the pharmacological effect of mitotane in vitro. These experiments could represent a first step towards the individualization of mitotane treatment in this rare tumor.

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.

MCMSeq: Bayesian hierarchical modeling of clustered and repeated measures RNA sequencing experiments.

BACKGROUND: As the barriers to incorporating RNA sequencing (RNA-Seq) into biomedical studies continue to decrease, the complexity and size of RNA-Seq experiments are rapidly growing. Paired, longitudinal, and other correlated designs are becoming commonplace, and these studies offer immense potential for understanding how transcriptional changes within an individual over time differ depending on treatment or environmental conditions. While several methods have been proposed for dealing with repeated measures within RNA-Seq analyses, they are either restricted to handling only paired measurements, can only test for differences between two groups, and/or have issues with maintaining nominal false positive and false discovery rates. In this work, we propose a Bayesian hierarchical negative binomial generalized linear mixed model framework that can flexibly model RNA-Seq counts from studies with arbitrarily many repeated observations, can include covariates, and also maintains nominal false positive and false discovery rates in its posterior inference. RESULTS: In simulation studies, we showed that our proposed method (MCMSeq) best combines high statistical power (i.e. sensitivity or recall) with maintenance of nominal false positive and false discovery rates compared the other available strategies, especially at the smaller sample sizes investigated. This behavior was then replicated in an application to real RNA-Seq data where MCMSeq was able to find previously reported genes associated with tuberculosis infection in a cohort with longitudinal measurements. CONCLUSIONS: Failing to account for repeated measurements when analyzing RNA-Seq experiments can result in significantly inflated false positive and false discovery rates. Of the methods we investigated, whether they model RNA-Seq counts directly or worked on transformed values, the Bayesian hierarchical model implemented in the mcmseq R package (available at https://github.com/stop-pre16/mcmseq ) best combined sensitivity and nominal error rate control.

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.

Complement Inhibitors Block Complement C3 Opsonization and Improve Targeting Selectivity of Nanoparticles in Blood.

Complement is one of the critical branches of innate immunity that determines the recognition of engineered nanoparticles by immune cells. Antibody-targeted iron oxide nanoparticles are a popular platform for magnetic separations, in vitro diagnostics, and molecular imaging. We used 60 nm cross-linked iron oxide nanoworms (CLIO NWs) modified with antibodies against Her2/neu and EpCAM, which are common markers of blood-borne cancer cells, to understand the role of complement in the selectivity of targeting of tumor cells in whole blood. CLIO NWs showed highly efficient targeting and magnetic isolation of tumor cells spiked in lepirudin-anticoagulated blood, but specificity was low due to high uptake by neutrophils, monocytes, and lymphocytes. Complement C3 opsonization in plasma was predominantly via the alternative pathway regardless of the presence of antibody, PEG, or fluorescent tag, but was higher for antibody-conjugated CLIO NWs. Addition of various soluble inhibitors of complement convertase (compstatin, soluble CD35, and soluble CD55) to whole human blood blocked up to 99% of the uptake of targeted CLIO NWs by leukocytes, which resulted in a more selective magnetic isolation of tumor cells. Using well-characterized nanomaterials, we demonstrate here that complement therapeutics can be used to improve targeting selectivity.

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.

Insight into genetic regulation of miRNA in mouse brain.

BACKGROUND: micro RNA (miRNA) are important regulators of gene expression and may influence phenotypes and disease traits. The connection between genetics and miRNA expression can be determined through expression quantitative loci (eQTL) analysis, which has been extensively used in a variety of tissues, and in both human and model organisms. miRNA play an important role in brain-related diseases, but eQTL studies of miRNA in brain tissue are limited. We aim to catalog miRNA eQTL in brain tissue using miRNA expression measured on a recombinant inbred mouse panel. Because samples were collected without any intervention or treatment (naïve), the panel allows characterization of genetic influences on miRNAs' expression levels. We used brain RNA expression levels of 881 miRNA and 1416 genomic locations to identify miRNA eQTL. To address multiple testing, we employed permutation p-values and subsequent zero permutation p-value correction. We also investigated the underlying biology of miRNA regulation using additional analyses, including hotspot analysis to search for regions controlling multiple miRNAs, and Bayesian network analysis to identify scenarios where a miRNA mediates the association between genotype and mRNA expression. We used addiction related phenotypes to illustrate the utility of our results. RESULTS: Thirty-eight miRNA eQTL were identified after appropriate multiple testing corrections. Ten of these miRNAs had target genes enriched for brain-related pathways and mapped to four miRNA eQTL hotspots. Bayesian network analysis revealed four biological networks relating genetic variation, miRNA expression and gene expression. CONCLUSIONS: Our extensive evaluation of miRNA eQTL provides valuable insight into the role of miRNA regulation in brain tissue. Our miRNA eQTL analysis and extended statistical exploration identifies miRNA candidates in brain for future study.

Condition-adaptive fused graphical lasso (CFGL): An adaptive procedure for inferring condition-specific gene co-expression network.

Co-expression network analysis provides useful information for studying gene regulation in biological processes. Examining condition-specific patterns of co-expression can provide insights into the underlying cellular processes activated in a particular condition. One challenge in this type of analysis is that the sample sizes in each condition are usually small, making the statistical inference of co-expression patterns highly underpowered. A joint network construction that borrows information from related structures across conditions has the potential to improve the power of the analysis. One possible approach to constructing the co-expression network is to use the Gaussian graphical model. Though several methods are available for joint estimation of multiple graphical models, they do not fully account for the heterogeneity between samples and between co-expression patterns introduced by condition specificity. Here we develop the condition-adaptive fused graphical lasso (CFGL), a data-driven approach to incorporate condition specificity in the estimation of co-expression networks. We show that this method improves the accuracy with which networks are learned. The application of this method on a rat multi-tissue dataset and The Cancer Genome Atlas (TCGA) breast cancer dataset provides interesting biological insights. In both analyses, we identify numerous modules enriched for Gene Ontology functions and observe that the modules that are upregulated in a particular condition are often involved in condition-specific activities. Interestingly, we observe that the genes strongly associated with survival time in the TCGA dataset are less likely to be network hubs, suggesting that genes associated with cancer progression are likely to govern specific functions or execute final biological functions in pathways, rather than regulating a large number of biological processes. Additionally, we observed that the tumor-specific hub genes tend to have few shared edges with normal tissue, revealing tumor-specific regulatory mechanism.

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.

Predictive modeling of miRNA-mediated predisposition to alcohol-related phenotypes in mouse.

BACKGROUND: MicroRNAs (miRNAs) are small non-coding RNAs that bind messenger RNAs and promote their degradation or repress their translation. There is increasing evidence of miRNAs playing an important role in alcohol related disorders. However, the role of miRNAs as mediators of the genetic effect on alcohol phenotypes is not fully understood. We conducted a high-throughput sequencing study to measure miRNA expression levels in alcohol naïve animals in the LXS panel of recombinant inbred (RI) mouse strains. We then combined the sequencing data with genotype data, microarry gene expression data, and data on alcohol-related behavioral phenotypes such as 'Drinking in the dark', 'Sleep time', and 'Low dose activation' from the same RI panel. SNP-miRNA-gene triplets with strong association within the triplet that were also associated with one of the 4 alcohol phenotypes were selected and a Bayesian network analysis was used to aggregate results into a directed network model. RESULTS: We found several triplets with strong association within the triplet that were also associated with one of the alcohol phenotypes. The Bayesian network analysis found two networks where a miRNA mediates the genetic effect on the alcohol phenotype. The miRNAs were found to influence the expression of protein-coding genes, which in turn influences the quantitative phenotypes. The pathways in which these genes are enriched have been previously associated with alcohol-related traits. CONCLUSION: This work enhances association studies by identifying miRNAs that may be mediating the association between genetic markers (SNPs) and the alcohol phenotypes. It suggests a mechanism of how genetic variants are affecting traits of interest through the modification of miRNA expression.

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.