Androgen inhibits key atherosclerotic processes by directly activating ADTRP transcription.

Low androgen levels are associated with an increased risk of coronary artery disease (CAD), thrombosis and myocardial infarction (MI), suggesting that androgen has a protective role. However, little is known about the underlying molecular mechanism. Our genome-wide association study identified the ADTRP gene encoding the androgen-dependent TFPI regulating protein as a susceptibility gene for CAD and MI. The expression level of ADTRP was regulated by androgen, but the molecular mechanism is unknown. In this study, we identified the molecular mechanism by which androgen regulates ADTRP expression and tested the hypothesis that androgen plays a protective role in cardiovascular disease by activating ADTRP expression. Luciferase assays with an ADTRP promoter luciferase reporter revealed that androgen regulated ADTRP transcription in a dose- and time-dependent manner, and the effect was abolished by three different androgen inhibitors, including pyrvinium pamoate, bicalutamide, and cyproterone acetate. Chromatin-immunoprecipitation showed that the androgen receptor bound to a half androgen response element (ARE, TGTTCT) located at +324bp from the ADTRP transcription start site. The ARE is required for concentration-dependent transcriptional activation of ADTRP. HL-60 monocyte adhesion to EAhy926 endothelial cells (ECs) and transmigration across the EC layer, the two processes critical to development of CAD and MI, were inhibited by androgen, but the effect was rescued by ADTRP siRNA and exacerbated by overexpression of ADTRP and its downstream genes PIK3R3 and MIA3. These data suggest that one molecular mechanism by which androgen confers protection against CAD is stimulation of ADTRP expression.

Nova regulates brain-specific splicing to shape the synapse.

Alternative RNA splicing greatly increases proteome diversity and may thereby contribute to tissue-specific functions. We carried out genome-wide quantitative analysis of alternative splicing using a custom Affymetrix microarray to assess the role of the neuronal splicing factor Nova in the brain. We used a stringent algorithm to identify 591 exons that were differentially spliced in the brain relative to immune tissues, and 6.6% of these showed major splicing defects in the neocortex of Nova2-/- mice. We tested 49 exons with the largest predicted Nova-dependent splicing changes and validated all 49 by RT-PCR. We analyzed the encoded proteins and found that all those with defined brain functions acted in the synapse (34 of 40, including neurotransmitter receptors, cation channels, adhesion and scaffold proteins) or in axon guidance (8 of 40). Moreover, of the 35 proteins with known interaction partners, 74% (26) interact with each other. Validating a large set of Nova RNA targets has led us to identify a multi-tiered network in which Nova regulates the exon content of RNAs encoding proteins that interact in the synapse.

An integrative genomic analysis identifies Bhmt2 as a diet-dependent genetic factor protecting against acetaminophen-induced liver toxicity.

Acetaminophen-induced liver toxicity is the most frequent precipitating cause of acute liver failure and liver transplant, but contemporary medical practice has mainly focused on patient management after a liver injury has been induced. An integrative genetic, transcriptional, and two-dimensional NMR-based metabolomic analysis performed using multiple inbred mouse strains, along with knowledge-based filtering of these data, identified betaine-homocysteine methyltransferase 2 (Bhmt2) as a diet-dependent genetic factor that affected susceptibility to acetaminophen-induced liver toxicity in mice. Through an effect on methionine and glutathione biosynthesis, Bhmt2 could utilize its substrate (S-methylmethionine [SMM]) to confer protection against acetaminophen-induced injury in vivo. Since SMM is only synthesized in plants, Bhmt2 exerts its beneficial effect in a diet-dependent manner. Identification of Bhmt2 and the affected biosynthetic pathway demonstrates how a novel method of integrative genomic analysis in mice can provide a unique and clinically applicable approach to a major public health problem.

ADTRP regulates TFPI expression via transcription factor POU1F1 involved in coronary artery disease.

Genomic variants in both ADTRP and TFPI genes are associated with risk of coronary artery disease (CAD). ADTRP regulates TFPI expression and endothelial cell functions involved in the initiation of atherosclerotic CAD. ADTRP also specifies primitive myelopoiesis and definitive hematopoiesis by upregulating TFPI expression. However, the underlying molecular mechanism is unknown. Here we show that transcription factor POU1F1 is the key by which ADTRP regulates TFPI expression. Luciferase reporter assays, chromatin-immunoprecipitation (ChIP) and electrophoretic mobility shift assay (EMSA) in combination with analysis of large and small deletions of the TFPI promoter/regulatory region were used to identify the molecular mechanism by which ADTRP regulates TFPI expression. Genetic association was assessed using case-control association analysis and phenome-wide association analysis (PhenGWA). ADTRP regulates TFPI expression at the transcription level in a dose-dependent manner. The ADTRP-response element was localized to a 50 bp region between -806 bp and -756 bp upstream of TFPI transcription start site, which contains a binding site for POU1F1. Deletion of POU1F1-binding site or knockdown of POU1F1 expression abolished ADTRP-mediated transcription of TFPI. ChIP and EMSA demonstrated that POU1F1 binds to the ADTRP response element. Genetic analysis identified significant association between POU1F1 variants and risk of CAD. PhenGWA identified other phenotypic traits associated with the ADTRP-POU1F1-TFPI axis such as lymphocyte count (ADTRP), waist circumference (TFPI), and standing height (POU1F1). These data identify POU1F1 as a transcription factor that regulates TFPI transcription in response to ADTRP, and link POU1F1 variants to risk of CAD for the first time.

ANOSVA: a statistical method for detecting splice variation from expression data.

MOTIVATION: Many or most mammalian genes undergo alternative splicing, generating a variety of transcripts from a single gene. New information on splice variation is becoming available through technology for measuring expression levels of several exons or splice junctions per gene. We have developed a statistical method, ANalysis Of Splice VAriation (ANOSVA) to detect alternative splicing from expression data. Since ANOSVA requires no transcript information, it can be applied when the level of annotation is poor. When validated against spiked clone data, it generated no false positives and few false negatives. We demonstrated ANOSVA with data from a prototype mouse alternative splicing array, run against normal adult tissues, yielding a set of genes with evidence of tissue-specific splice variation. AVAILABILITY: The results are available at the supplementary information site. SUPPLEMENTARY INFORMATION: The results are available at the supplementary information site https://bioinfo.affymetrix.com/Papers/ANOSVA/

Gene structure-based splice variant deconvolution using a microarray platform.

MOTIVATION: Alternative splicing allows a single gene to generate multiple mRNAs, which can be translated into functionally and structurally diverse proteins. One gene can have multiple variants coexisting at different concentrations. Estimating the relative abundance of each variant is important for the study of underlying biological function. Microarrays are standard tools that measure gene expression. But most design and analysis has not accounted for splice variants. Thus splice variant-specific chip designs and analysis algorithms are needed for accurate gene expression profiling. RESULTS: Inspired by Li and Wong (2001), we developed a gene structure-based algorithm to determine the relative abundance of known splice variants. Probe intensities are modeled across multiple experiments using gene structures as constraints. Model parameters are obtained through a maximum likelihood estimation (MLE) process/framework. The algorithm produces the relative concentration of each variant, as well as an affinity term associated with each probe. Validation of the algorithm is performed by a set of controlled spike experiments as well as endogenous tissue samples using a human splice variant array.