RNA alternative splicing impacts the risk for alcohol use disorder.

Alcohol use disorder (AUD) is a complex genetic disorder characterized by problems arising from excessive alcohol consumption. Identifying functional genetic variations that contribute to risk for AUD is a major goal. Alternative splicing of RNA mediates the flow of genetic information from DNA to gene expression and expands proteome diversity. We asked whether alternative splicing could be a risk factor for AUD. Herein, we used a Mendelian randomization (MR)-based approach to identify skipped exons (the predominant splicing event in brain) that contribute to AUD risk. Genotypes and RNA-seq data from the CommonMind Consortium were used as the training dataset to develop predictive models linking individual genotypes to exon skipping in the prefrontal cortex. We applied these models to data from the Collaborative Studies on Genetics of Alcoholism to examine the association between the imputed cis-regulated splicing outcome and the AUD-related traits. We identified 27 exon skipping events that were predicted to affect AUD risk; six of these were replicated in the Australian Twin-family Study of Alcohol Use Disorder. Their host genes are DRC1, ELOVL7, LINC00665, NSUN4, SRRM2 and TBC1D5. The genes downstream of these splicing events are enriched in neuroimmune pathways. The MR-inferred impacts of the ELOVL7 skipped exon on AUD risk was further supported in four additional large-scale genome-wide association studies. Additionally, this exon contributed to changes of gray matter volumes in multiple brain regions, including the visual cortex known to be involved in AUD. In conclusion, this study provides strong evidence that RNA alternative splicing impacts the susceptibility to AUD and adds new information on AUD-relevant genes and pathways. Our framework is also applicable to other types of splicing events and to other complex genetic disorders.

Bioinformatics detection of modulators controlling splicing factor-dependent intron retention in the human brain.

Alternative RNA splicing is an important means of genetic control and transcriptome diversity. However, when alternative splicing events are studied independently, coordinated splicing modulated by common factors is often not recognized. As a result, the molecular mechanisms of how splicing regulators promote or repress splice site recognition in a context-dependent manner are not well understood. The functional coupling between multiple gene regulatory layers suggests that splicing is modulated by additional genetic or epigenetic components. Here, we developed a bioinformatics approach to identify causal modulators of splicing activity based on the variation of gene expression in large RNA sequencing datasets. We applied this approach in a neurological context with hundreds of dorsolateral prefrontal cortex samples. Our model is strengthened with the incorporation of genetic variants to impute gene expression in a Mendelian randomization-based approach. We identified novel modulators of the splicing factor SRSF1, including UIMC1 and the long noncoding RNA CBR3-AS1, that function over dozens of SRSF1 intron retention splicing targets. This strategy can be widely used to identify modulators of RNA-binding proteins involved in tissue-specific alternative splicing.

Endothelial disruptive proinflammatory effects of nicotine and e-cigarette vapor exposures.

The increased use of inhaled nicotine via e-cigarettes has unknown risks to lung health. Having previously shown that cigarette smoke (CS) extract disrupts the lung microvasculature barrier function by endothelial cell activation and cytoskeletal rearrangement, we investigated the contribution of nicotine in CS or e-cigarettes (e-Cig) to lung endothelial injury. Primary lung microvascular endothelial cells were exposed to nicotine, e-Cig solution, or condensed e-Cig vapor (1-20 mM nicotine) or to nicotine-free CS extract or e-Cig solutions. Compared with nicotine-containing extract, nicotine free-CS extract (10-20%) caused significantly less endothelial permeability as measured with electric cell-substrate impedance sensing. Nicotine exposures triggered dose-dependent loss of endothelial barrier in cultured cell monolayers and rapidly increased lung inflammation and oxidative stress in mice. The endothelial barrier disruptive effects were associated with increased intracellular ceramides, p38 MAPK activation, and myosin light chain (MLC) phosphorylation, and was critically mediated by Rho-activated kinase via inhibition of MLC-phosphatase unit MYPT1. Although nicotine at sufficient concentrations to cause endothelial barrier loss did not trigger cell necrosis, it markedly inhibited cell proliferation. Augmentation of sphingosine-1-phosphate (S1P) signaling via S1P1 improved both endothelial cell proliferation and barrier function during nicotine exposures. Nicotine-independent effects of e-Cig solutions were noted, which may be attributable to acrolein, detected along with propylene glycol, glycerol, and nicotine by NMR, mass spectrometry, and gas chromatography, in both e-Cig solutions and vapor. These results suggest that soluble components of e-Cig, including nicotine, cause dose-dependent loss of lung endothelial barrier function, which is associated with oxidative stress and brisk inflammation.

Integration of Alzheimer's disease genetics and myeloid genomics identifies disease risk regulatory elements and genes.

Genome-wide association studies (GWAS) have identified more than 40 loci associated with Alzheimer's disease (AD), but the causal variants, regulatory elements, genes and pathways remain largely unknown, impeding a mechanistic understanding of AD pathogenesis. Previously, we showed that AD risk alleles are enriched in myeloid-specific epigenomic annotations. Here, we show that they are specifically enriched in active enhancers of monocytes, macrophages and microglia. We integrated AD GWAS with myeloid epigenomic and transcriptomic datasets using analytical approaches to link myeloid enhancer activity to target gene expression regulation and AD risk modification. We identify AD risk enhancers and nominate candidate causal genes among their likely targets (including AP4E1, AP4M1, APBB3, BIN1, MS4A4A, MS4A6A, PILRA, RABEP1, SPI1, TP53INP1, and ZYX) in twenty loci. Fine-mapping of these enhancers nominates candidate functional variants that likely modify AD risk by regulating gene expression in myeloid cells. In the MS4A locus we identified a single candidate functional variant and validated it in human induced pluripotent stem cell (hiPSC)-derived microglia and brain. Taken together, this study integrates AD GWAS with multiple myeloid genomic datasets to investigate the mechanisms of AD risk alleles and nominates candidate functional variants, regulatory elements and genes that likely modulate disease susceptibility.

Modulator-Dependent RBPs Changes Alternative Splicing Outcomes in Kidney Cancer.

Alternative splicing alterations can contribute to human disease. The ability of an RNA-binding protein to regulate alternative splicing outcomes can be modulated by a variety of genetic and epigenetic mechanisms. In this study, we use a computational framework to investigate the roles of certain genes, termed modulators, on changing RBPs' effect on splicing regulation. A total of 1,040,254 modulator-mediated RBP-splicing interactions were identified, including 137 RBPs, 4,309 splicing events and 2,905 modulator candidates from TCGA-KIRC RNA sequencing data. Modulators function categories were defined according to the correlation changes between RBPs expression and their targets splicing outcomes. QKI, as one of the RBPs influencing the most splicing events, attracted our attention in this study: 2,014 changing triplets were identified, including 1,101 modulators and 187 splicing events. Pathway enrichment analysis showed that QKI splicing targets were enriched in tight junction pathway, endocytosis and MAPK signaling pathways, all of which are highly associated with cancer development and progression. This is the first instance of a comprehensive study on how alternative splicing outcomes changes are associated with different expression level of certain proteins, even though they were regulated by the same RBP. Our work may provide a novel view on understanding alternative splicing mechanisms in kidney cancer.

Neural mechanisms underlying the effects of physical fatigue on effort-based choice.

Physical fatigue crucially influences our decisions to partake in effortful action. However, there is a limited understanding of how fatigue impacts effort-based decision-making at the level of brain and behavior. We use functional magnetic resonance imaging to record markers of brain activity while human participants engage in uncertain choices for prospective physical effort, before and after bouts of exertion. Using computational modeling of choice behavior we find that fatiguing exertions cause participants to increase their subjective cost of effort, compared to a baseline/rested state. We describe a mechanism by which signals related to motor cortical state in premotor cortex influence effort value computations, instantiated by insula, thereby increasing an individual's subjective valuation of prospective physical effort while fatigued. Our findings provide a neurobiological account of how information about bodily state modulates decisions to engage in physical activity.

Highly robust model of transcription regulator activity predicts breast cancer overall survival.

BACKGROUND: While several multigene signatures are available for predicting breast cancer prognosis, particularly in early stage disease, effective molecular indicators are needed, especially for triple-negative carcinomas, to improve treatments and predict diagnostic outcomes. The objective of this study was to identify transcriptional regulatory networks to better understand mechanisms giving rise to breast cancer development and to incorporate this information into a model for predicting clinical outcomes. METHODS: Gene expression profiles from 1097 breast cancer patients were retrieved from The Cancer Genome Atlas (TCGA). Breast cancer-specific transcription regulatory information was identified by considering the binding site information from ENCODE and the top co-expressed targets in TCGA using a nonlinear approach. We then used this information to predict breast cancer patient survival outcome. RESULT: We built a multiple regulator-based prediction model for breast cancer. This model was validated in more than 5000 breast cancer patients from the Gene Expression Omnibus (GEO) databases. We demonstrated our regulator model was significantly associated with clinical stage and that cell cycle and DNA replication related pathways were significantly enriched in high regulator risk patients. CONCLUSION: Our findings demonstrate that transcriptional regulator activities can predict patient survival. This finding provides additional biological insights into the mechanisms of breast cancer progression.

Comprehensive Cis-Regulation Analysis of Genetic Variants in Human Lymphoblastoid Cell Lines.

Genetic variants can influence the expression of mRNA and protein. Genetic regulatory loci such as expression quantitative trait loci (eQTLs) and protein quantitative trait loci (pQTLs) exist in several species. However, it remains unclear how human genetic variants regulate mRNA and protein expression. Here, we characterized six mechanistic models for the genetic regulatory patterns of single-nucleotide polymorphisms (SNPs) and their actions on post-transcriptional expression. Data from Yoruba HapMap lymphoblastoid cell lines were analyzed to identify human cis-eQTLs and pQTLs, as well as protein-specific QTLs (psQTLs). Our results indicated that genetic regulatory loci primarily affected mRNA and protein abundance in patterns where the two were well-correlated. While this finding was observed in both humans and mice (57.5% and 70.3%, respectively), the genetic regulatory patterns differed between species, implying evolutionary differences. Mouse SNPs generally targeted changes in transcript expression (51%), whereas in humans, they largely regulated protein abundance, independent of transcription levels (55.9%). The latter independent function can be explained by psQTLs. Our analysis suggests that local functional genetic variants in the human genome mainly modulate protein abundance independent of mRNA levels through post-transcriptional mechanisms. These findings clarify the impact of genetic variation on phenotype, which is of particular relevance to disease risk and treatment response.