Effect of chronic ethanol consumption in rhesus macaques on the nucleus accumbens core transcriptome.

The nucleus accumbens core (NAcc) has been repeatedly demonstrated to be a key component of the circuitry associated with excessive ethanol consumption. Previous studies have illustrated that in a nonhuman primate (NHP) model of chronic ethanol consumption, there is significant epigenetic remodeling of the NAcc. In the current study, RNA-Seq was used to examine genome-wide gene expression in eight each of control, low/binge (LD*), and high/very high (HD*) rhesus macaque drinkers. Using an FDR < 0.05, zero genes were significantly differentially expressed (DE) between LD* and controls, six genes between HD* and LD*, and 734 genes between HD* and controls. Focusing on HD* versus control DE genes, the upregulated genes (N = 366) were enriched in genes with annotations associated with signal recognition particle (SRP)-dependent co-translational protein targeting to membrane (FDR < 3 × 10-59 ), structural constituent of ribosome (FDR < 3 × 10-47 ), and ribosomal subunit (FDR < 5 × 10-48 ). Downregulated genes (N = 363) were enriched in annotations associated with behavior (FDR < 2 × 10-4 ), membrane organization (FDR < 1 × 10-4 ), inorganic cation transmembrane transporter activity (FDR < 2 × 10-3 ), synapse part (FDR < 4 × 10-10 ), glutamatergic synapse (FDR < 1 × 10-6 ), and GABAergic synapse (FDR < 6 × 10-4 ). Ingenuity Pathway Analysis (IPA) revealed that EIF2 signaling and mTOR pathways were significantly upregulated in HD* animals (FDR < 3 × 10-33 and <2 × 10-16 , respectively). Overall, the data supported our working hypothesis; excessive consumption would be associated with transcriptional differences in GABA/glutamate-related genes.

Chronic Voluntary Ethanol Drinking in Cynomolgus Macaques Elicits Gene Expression Changes in Prefrontal Cortical Area 46.

BACKGROUND: Genome-wide profiling to examine brain transcriptional features associated with excessive ethanol (EtOH) consumption has been applied to a variety of species including rodents, nonhuman primates (NHPs), and humans. However, these data were obtained from cross-sectional samples which are particularly vulnerable to individual variation when obtained from small outbred populations typical of human and NHP studies. In the current study, a novel within-subject design was used to examine the effects of voluntary EtOH consumption on prefrontal cortex (PFC) gene expression in a NHP model. METHODS: Two cohorts of cynomolgus macaques (n = 23) underwent a schedule-induced polydipsia procedure to establish EtOH self-administration followed by 6 months of daily open access to EtOH (4% w/v) and water. Individual daily EtOH intakes ranged from an average of 0.7 to 3.7 g/kg/d. Dorsal lateral PFC area 46 (A46) brain biopsies were collected in EtOH-naïve and control monkeys; contralateral A46 biopsies were collected from the same monkeys following the 6 months of fluid consumption. Gene expression changes were assessed using RNA-Seq paired analysis, which allowed for correction of individual baseline differences in gene expression. RESULTS: A total of 675 genes were significantly down-regulated following EtOH consumption; these were functionally enriched for immune response, cell adhesion, plasma membrane, and extracellular matrix. A total of 567 genes that were up-regulated following EtOH consumption were enriched in microRNA target sites and included target sites associated with Toll-like receptor pathways. The differentially expressed genes were also significantly enriched in transcription factor binding sites. CONCLUSIONS: The data presented here are the first to use a longitudinal biopsy strategy to examine how chronic EtOH consumption affects gene expression in the primate PFC. Prominent effects were seen in both cell adhesion and neuroimmune pathways; the latter contained both pro- and antiinflammatory genes. The data also indicate that changes in miRNAs and transcription factors may be important epigenetic regulators of EtOH consumption.

Gender-Specific Effects of Selection for Drinking in the Dark on the Network Roles of Coding and Noncoding RNAs.

BACKGROUND: Transcriptional differences between heterogeneous stock mice and high drinking-in-the-dark selected mouse lines have previously been described based on microarray technology coupled with network-based analysis. The network changes were reproducible in 2 independent selections and largely confined to 2 distinct network modules; in contrast, differential expression appeared more specific to each selected line. This study extends these results by utilizing RNA-Seq technology, allowing evaluation of the relationship between genetic risk and transcription of noncoding RNA (ncRNA); we additionally evaluate sex-specific transcriptional effects of selection. METHODS: Naïve mice (N = 24/group and sex) were utilized for gene expression analysis in the ventral striatum; the transcriptome was sequenced with the Illumina HiSeq platform. Differential gene expression and the weighted gene co-expression network analysis were implemented largely as described elsewhere, resulting in the identification of genes that change expression level or (co)variance structure. RESULTS: Across both sexes, we detect selection effects on the extracellular matrix and synaptic signaling, although the identity of individual genes varies. A majority of nc RNAs cluster in a single module of relatively low density in both the male and female network. The most strongly differentially expressed transcript in both sexes was Gm22513, a small nuclear RNA with unknown function. Associated with selection, we also found a number of network hubs that change edge strength and connectivity. At the individual gene level, there are many sex-specific effects; however, at the annotation level, results are more concordant. CONCLUSIONS: In addition to demonstrating sex-specific effects of selection on the transcriptome, the data point to the involvement of extracellular matrix genes as being associated with the binge drinking phenotype.

On the relationships in rhesus macaques between chronic ethanol consumption and the brain transcriptome.

This is the first description of the relationship between chronic ethanol self-administration and the brain transcriptome in a non-human primate (rhesus macaque). Thirty-one male animals self-administered ethanol on a daily basis for over 12 months. Gene transcription was quantified with RNA-Seq in the central nucleus of the amygdala (CeA) and cortical Area 32. We constructed coexpression and cosplicing networks, and we identified areas of preservation and areas of differentiation between regions and network types. Correlations between intake and transcription included largely distinct gene sets and annotation categories across brain regions and between expression and splicing; positive and negative correlations were also associated with distinct annotation groups. Membrane, synaptic and splicing annotation categories were over-represented in the modules (gene clusters) enriched in positive correlations (CeA); our cosplicing analysis further identified the genes affected only at the exon inclusion level. In the CeA coexpression network, we identified Rab6b, Cdk18 and Igsf21 among the intake-correlated hubs, while in the Area 32, we identified a distinct hub set that included Ppp3r1 and Myeov2. Overall, the data illustrate that excessive ethanol self-administration is associated with broad expression and splicing mechanisms that involve membrane and synapse genes.

Splicing landscape of the eight collaborative cross founder strains.

BACKGROUND: The Collaborative Cross (CC) is a large panel of genetically diverse recombinant inbred mouse strains specifically designed to provide a systems genetics resource for the study of complex traits. In part, the utility of the CC stems from the extensive genome-wide annotations of founder strain sequence and structural variation. Still missing, however, are transcriptome-specific annotations of the CC founder strains that could further enhance the utility of this resource. RESULTS: We provide a comprehensive survey of the splicing landscape of the 8 CC founder strains by leveraging the high level of alternative splicing within the brain. Using deep transcriptome sequencing, we found that a majority of the splicing landscape is conserved among the 8 strains, with ~65% of junctions being shared by at least 2 strains. We, however, found a large number of potential strain-specific splicing events as well, with an average of ~3000 and ~500 with ≥3 and ≥10 sequence read coverage, respectively, within each strain. To better understand strain-specific splicing within the CC founder strains, we defined criteria for and identified high-confidence strain-specific splicing events. These splicing events were defined as exon-exon junctions 1) found within only one strain, 2) with a read coverage ≥10, and 3) defined by a canonical splice site. With these criteria, a total of 1509 high-confidence strain-specific splicing events were identified, with the majority found within two of the wild-derived strains, CAST and PWK. Strikingly, the overwhelming majority, 94%, of these strain-specific splicing events are not yet annotated. Strain-specific splicing was also located within genomic regions recently reported to be over- and under-represented within CC populations. CONCLUSIONS: Phenotypic characterization of CC populations is increasing; thus these results will not only aid in further elucidating the transcriptomic architecture of the individual CC founder strains, but they will also help in guiding the utilization of the CC populations in the study of complex traits. This report is also the first to establish guidelines in defining and identifying strain-specific splicing across different mouse strains.

The genetics of gene expression in complex mouse crosses as a tool to study the molecular underpinnings of behavior traits.

Complex Mus musculus crosses provide increased resolution to examine the relationships between gene expression and behavior. While the advantages are clear, there are numerous analytical and technological concerns that arise from the increased genetic complexity that must be considered. Each of these issues is discussed, providing an initial framework for complex cross study design and planning.

Human lung-resident mucosal-associated invariant T cells are abundant, express antimicrobial proteins, and are cytokine responsive.

Mucosal-associated Invariant T (MAIT) cells are an innate-like T cell subset that recognize a broad array of microbial pathogens, including respiratory pathogens. Here we investigate the transcriptional profile of MAIT cells localized to the human lung, and postulate that MAIT cells may play a role in maintaining homeostasis at this mucosal barrier. Using the MR1/5-OP-RU tetramer, we identified MAIT cells and non-MAIT CD8+ T cells in lung tissue not suitable for transplant from human donors. We used RNA-sequencing of MAIT cells compared to non-MAIT CD8+ T cells to define the transcriptome of MAIT cells in the human lung. We show that, as a population, lung MAIT cells are polycytotoxic, secrete the directly antimicrobial molecule IL-26, express genes associated with persistence, and selectively express cytokine and chemokine- related molecules distinct from other lung-resident CD8+ T cells, such as interferon-γ- and IL-12- receptors. These data highlight MAIT cells' predisposition to rapid pro-inflammatory cytokine responsiveness and antimicrobial mechanisms in human lung tissue, concordant with findings of blood-derived counterparts, and support a function for MAIT cells as early sensors in the defense of respiratory barrier function.

An omic and multidimensional spatial atlas from serial biopsies of an evolving metastatic breast cancer.

Mechanisms of therapeutic resistance and vulnerability evolve in metastatic cancers as tumor cells and extrinsic microenvironmental influences change during treatment. To support the development of methods for identifying these mechanisms in individual people, here we present an omic and multidimensional spatial (OMS) atlas generated from four serial biopsies of an individual with metastatic breast cancer during 3.5 years of therapy. This resource links detailed, longitudinal clinical metadata that includes treatment times and doses, anatomic imaging, and blood-based response measurements to clinical and exploratory analyses, which includes comprehensive DNA, RNA, and protein profiles; images of multiplexed immunostaining; and 2- and 3-dimensional scanning electron micrographs. These data report aspects of heterogeneity and evolution of the cancer genome, signaling pathways, immune microenvironment, cellular composition and organization, and ultrastructure. We present illustrative examples of how integrative analyses of these data reveal potential mechanisms of response and resistance and suggest novel therapeutic vulnerabilities.

Functional genomic analysis identifies drug targetable pathways in invasive and metastatic cutaneous squamous cell carcinoma.

Although cutaneous squamous cell carcinoma (cSCC) is treatable in the majority of cases, deadly invasive and metastatic cases do occur. To date there are neither reliable predictive biomarkers of disease progression nor FDA-approved targeted therapies as standard of care. To address these issues, we screened patient-derived primary cultured cells from invasive/metastatic cSCC with 107 small-molecule inhibitors. In-house bioinformatics tools were used to cross-analyze drug responses and DNA mutations in tumors detected by whole-exome sequencing (WES). Aberrations in molecular pathways with evidence of potential drug targets were identified, including the Eph-ephrin and neutrophil degranulation signaling pathways. Using a screening panel of siRNAs, we identified EPHA6 and EPHA7 as targets within the Eph-ephrin pathway responsible for mitigating decreased cell viability. These studies form a plausible foundation for detecting biomarkers of high-risk progressive disease applicable in dermatopathology and for patient-specific therapeutic options for invasive/metastatic cSCC.

Illuminating biological pathways for drug targeting in head and neck squamous cell carcinoma.

Head and neck squamous cell carcinoma (HNSCC) remains a morbid disease with poor prognosis and treatment that typically leaves patients with permanent damage to critical functions such as eating and talking. Currently only three targeted therapies are FDA approved for use in HNSCC, two of which are recently approved immunotherapies. In this work, we identify biological pathways involved with this disease that could potentially be targeted by current FDA approved cancer drugs and thereby expand the pool of potential therapies for use in HNSCC treatment. We analyzed 508 HNSCC patients with sequencing information from the Genomic Data Commons (GDC) database and assessed which biological pathways were significantly enriched for somatic mutations or copy number alterations. We then further classified pathways as either "light" or "dark" to the current reach of FDA-approved cancer drugs using the Cancer Targetome, a compendium of drug-target information. Light pathways are statistically enriched with somatic mutations (or copy number alterations) and contain one or more targets of current FDA-approved cancer drugs, while dark pathways are enriched with somatic mutations (or copy number alterations) but not currently targeted by FDA-approved cancer drugs. Our analyses indicated that approximately 35-38% of disease-specific pathways are in scope for repurposing of current cancer drugs. We further assess light and dark pathways for subgroups of patient tumor samples according to HPV status. The framework of light and dark pathways for HNSCC-enriched biological pathways allows us to better prioritize targeted therapies for further research in HNSCC based on the HNSCC genetic landscape and FDA-approved cancer drug information. We also highlight the importance in the identification of sub-pathways where targeting and cross targeting of other pathways may be most beneficial to predict positive or negative synergy with potential clinical significance. This framework is ideal for precision drug panel development, as well as identification of highly aberrant, untargeted candidates for future drug development.

Two-dimensional transcriptome profiling: identification of messenger RNA isoform signatures in prostate cancer from archived paraffin-embedded cancer specimens.

The expression of specific mRNA isoforms may uniquely reflect the biological state of a cell because it reflects the integrated outcome of both transcriptional and posttranscriptional regulation. In this study, we constructed a splicing array to examine approximately 1,500 mRNA isoforms from a panel of genes previously implicated in prostate cancer and identified a large number of cell type-specific mRNA isoforms. We also developed a novel "two-dimensional" profiling strategy to simultaneously quantify changes in splicing and transcript abundance; the results revealed extensive covariation between transcription and splicing in prostate cancer cells. Taking advantage of the ability of our technology to analyze RNA from formalin-fixed, paraffin-embedded tissues, we derived a specific set of mRNA isoform biomarkers for prostate cancer using independent panels of tissue samples for feature selection and cross-analysis. A number of cancer-specific splicing switch events were further validated by laser capture microdissection. Quantitative changes in transcription/RNA stability and qualitative differences in splicing ratio may thus be combined to characterize tumorigenic programs and signature mRNA isoforms may serve as unique biomarkers for tumor diagnosis and prognosis.

Regional Differences and Similarities in the Brain Transcriptome for Mice Selected for Ethanol Preference From HS-CC Founders.

The high genetic complexity found in heterogeneous stock (HS-CC) mice, together with selective breeding, can be used to detect new pathways and mechanisms associated with ethanol preference and excessive ethanol consumption. We predicted that these pathways would provide new targets for therapeutic manipulation. Previously (Colville et al., 2017), we observed that preference selection strongly affected the accumbens shell (SH) genes associated with synaptic function and in particular genes associated with synaptic tethering. Here we expand our analyses to include substantially larger sample sizes and samples from two additional components of the "addiction circuit," the central nucleus of the amygdala (CeA) and the prelimbic cortex (PL). At the level of differential expression (DE), the majority of affected genes are region-specific; only in the CeA did the DE genes show a significant enrichment in GO annotation categories, e.g., neuron part. In all three brain regions the differentially variable genes were significantly enriched in a single network module characterized by genes associated with cell-to-cell signaling. The data point to glutamate plasticity as being a key feature of selection for ethanol preference. In this context the expression of Dlg2 which encodes for PSD-93 appears to have a key role. It was also observed that the expression of the clustered protocadherins was strongly associated with preference selection.

Induction of anaplastic lymphoma kinase (ALK) as a novel mechanism of EGFR inhibitor resistance in head and neck squamous cell carcinoma patient-derived models.

Head and neck squamous cell carcinoma (HNSCC) currently only has one FDA-approved cancer intrinsic targeted therapy, the epidermal growth factor receptor (EGFR) inhibitor cetuximab, to which only approximately 10% of tumors are sensitive. In order to extend therapy options, we subjected patient-derived HNSCC cells to small-molecule inhibitor and siRNA screens, first, to find effective combination therapies with an EGFR inhibitor, and second, to determine a potential mechanistic basis for repurposing the FDA approved agents for HNSCC. The combinations of EGFR inhibitor with anaplastic lymphoma kinase (ALK) inhibitors demonstrated synergy at the highest ratio in our cohort, 4/8 HNSCC patients' derived tumor cells, and this corresponded with an effectiveness of siRNA targeting ALK combined with the EGFR inhibitor gefitinib. Co-targeting EGFR and ALK decreased HNSCC cell number and colony formation ability and increased annexin V staining. Because ALK expression is low and ALK fusions are infrequent in HNSCC, we hypothesized that gefitinib treatment could induce ALK expression. We show that ALK expression was induced in HNSCC patient-derived cells both in 2D and 3D patient-derived cell culture models, and in patient-derived xenografts in mice. Four different ALK inhibitors, including two (ceritinib and brigatinib) FDA approved for lung cancer, were effective in combination with gefitinib. Together, we identified induction of ALK by EGFR inhibitor as a novel mechanism potentially relevant to resistance to EGFR inhibitor, a high ratio of response of HNSCC patient-derived tumor cells to a combination of ALK and EGFR inhibitors, and applicability of repurposing ALK inhibitors to HNSCC that lack ALK aberrations.

Cross-species molecular dissection across alcohol behavioral domains.

This review summarizes the proceedings of a symposium presented at the "Alcoholism and Stress: A Framework for Future Treatment Strategies" conference held in Volterra, Italy on May 9-12, 2017. Psychiatric diseases, including alcohol-use disorders (AUDs), are influenced through complex interactions of genes, neurobiological pathways, and environmental influences. A better understanding of the common neurobiological mechanisms underlying an AUD necessitates an integrative approach, involving a systematic assessment of diverse species and phenotype measures. As part of the World Congress on Stress and Alcoholism, this symposium provided a detailed account of current strategies to identify mechanisms underlying the development and progression of AUDs. Dr. Sean Farris discussed the integration and organization of transcriptome and postmortem human brain data to identify brain regional- and cell type-specific differences related to excessive alcohol consumption that are conserved across species. Dr. Brien Riley presented the results of a genome-wide association study of DSM-IV alcohol dependence; although replication of genetic associations with alcohol phenotypes in humans remains challenging, model organism studies show that COL6A3, KLF12, and RYR3 affect behavioral responses to ethanol, and provide substantial evidence for their role in human alcohol-related traits. Dr. Rob Williams expanded upon the systematic characterization of extensive genetic-genomic resources for quantifying and clarifying phenotypes across species that are relevant to precision medicine in human disease. The symposium concluded with Dr. Robert Hitzemann's description of transcriptome studies in a mouse model selectively bred for high alcohol ("binge-like") consumption and a non-human primate model of long-term alcohol consumption. Together, the different components of this session provided an overview of systems-based approaches that are pioneering the experimental prioritization and validation of novel genes and gene networks linked with a range of behavioral phenotypes associated with stress and AUDs.

Use of semantic workflows to enhance transparency and reproducibility in clinical omics.

BACKGROUND: Recent highly publicized cases of premature patient assignment into clinical trials, resulting from non-reproducible omics analyses, have prompted many to call for a more thorough examination of translational omics and highlighted the critical need for transparency and reproducibility to ensure patient safety. The use of workflow platforms such as Galaxy and Taverna have greatly enhanced the use, transparency and reproducibility of omics analysis pipelines in the research domain and would be an invaluable tool in a clinical setting. However, the use of these workflow platforms requires deep domain expertise that, particularly within the multi-disciplinary fields of translational and clinical omics, may not always be present in a clinical setting. This lack of domain expertise may put patient safety at risk and make these workflow platforms difficult to operationalize in a clinical setting. In contrast, semantic workflows are a different class of workflow platform where resultant workflow runs are transparent, reproducible, and semantically validated. Through semantic enforcement of all datasets, analyses and user-defined rules/constraints, users are guided through each workflow run, enhancing analytical validity and patient safety. METHODS: To evaluate the effectiveness of semantic workflows within translational and clinical omics, we have implemented a clinical omics pipeline for annotating DNA sequence variants identified through next generation sequencing using the Workflow Instance Generation and Specialization (WINGS) semantic workflow platform. RESULTS: We found that the implementation and execution of our clinical omics pipeline in a semantic workflow helped us to meet the requirements for enhanced transparency, reproducibility and analytical validity recommended for clinical omics. We further found that many features of the WINGS platform were particularly primed to help support the critical needs of clinical omics analyses. CONCLUSIONS: This is the first implementation and execution of a clinical omics pipeline using semantic workflows. Evaluation of this implementation provides guidance for their use in both translational and clinical settings.

MODULEWRITER: a program for automatic generation of database interfaces.

MODULEWRITER is a PERL object relational mapping (ORM) tool that automatically generates database specific application programming interfaces (APIs) for SQL databases. The APIs consist of a package of modules providing access to each table row and column. Methods for retrieving, updating and saving entries are provided, as well as other generally useful methods (such as retrieval of the highest numbered entry in a table). MODULEWRITER provides for the inclusion of user-written code, which can be preserved across multiple runs of the MODULEWRITER program.

Rival penalized competitive learning (RPCL): a topology-determining algorithm for analyzing gene expression data.

DNA arrays have become the immediate choice in the analysis of large-scale expression measurements. Understanding the expression pattern of genes provide functional information on newly identified genes by computational approaches. Gene expression pattern is an indicator of the state of the cell, and abnormal cellular states can be inferred by comparing expression profiles. Since co-regulated genes, and genes involved in a particular pathway, tend to show similar expression patterns, clustering expression patterns has become the natural method of choice to differentiate groups. However, most methods based on cluster analysis suffer from the usual problems (i) dead units, and (ii) the problem of determining the correct number of clusters (k) needed to classify the data. Selecting the k has been an open problem of pattern recognition and statistics for decades. Since clustering reveals similar patterns present in the data, fixing this number strongly influences the quality of the result. While there is no theoretical solution to this problem, the number of clusters can be decided by a heuristic clustering algorithm called rival penalized competitive learning (RPCL). We present a novel implementation of RPCL that transforms the correct number of clusters problem to the tractable problem of clustering based on the degree of similarity. This is biologically significant since our implementation clusters functionally co-regulated genes and genes that present similar patterns of expression. This new approach reveals potential genes that are co-involved in a biological process. This implementation of the RPCL algorithm is useful in differentiating groups involved in concerted functional regulation and helps to progressively home into patterns, which are closely similar.

On selecting features from splice junctions: an analysis using information theoretic and machine learning approaches.

The computational recognition of precise splice junctions is a challenge faced in the analysis of newly sequenced genomes. This is challenging due to the fact that the distribution of sequence patterns in these regions is not always distinct. Our objective is to understand the sequence signatures at the splice junctions, not simply to create an artificial recognition system. We use a combination of a neural network based calliper randomization approach and an information theoretic based feature selection approach for this purpose. This has been done in an effort to understand regions that harbor information content and to extract features relevant for the prediction of splice junctions. The analysis using the neural network based calliper randomization approach revealed regions important in the internal representation of the network model. The calliper approach captured both correlated as well as independently important features. The feature selection approach captures features that are independently informative. The two different methods can capture features with different properties. Comparative analysis of the results using both the methods help to infer about the kind of information present in the region.

Analysis considerations for utilizing RNA-Seq to characterize the brain transcriptome.

RNA-Seq allows one to examine only gene expression as well as expression of noncoding RNAs, alternative splicing, and allele-specific expression. With this increased sensitivity and dynamic range, there are computational and statistical considerations that need to be contemplated, which are highly dependent on the biological question being asked. We highlight these to provide an overview of their importance and the impact they can have on downstream interpretation of the brain transcriptome.

Transcription restores DNA repair to heterochromatin, determining regional mutation rates in cancer genomes.

Somatic mutations in cancer are more frequent in heterochromatic and late-replicating regions of the genome. We report that regional disparities in mutation density are virtually abolished within transcriptionally silent genomic regions of cutaneous squamous cell carcinomas (cSCCs) arising in an XPC(-/-) background. XPC(-/-) cells lack global genome nucleotide excision repair (GG-NER), thus establishing differential access of DNA repair machinery within chromatin-rich regions of the genome as the primary cause for the regional disparity. Strikingly, we find that increasing levels of transcription reduce mutation prevalence on both strands of gene bodies embedded within H3K9me3-dense regions, and only to those levels observed in H3K9me3-sparse regions, also in an XPC-dependent manner. Therefore, transcription appears to reduce mutation prevalence specifically by relieving the constraints imposed by chromatin structure on DNA repair. We model this relationship among transcription, chromatin state, and DNA repair, revealing a new, personalized determinant of cancer risk.