HAPPI: an online database of comprehensive human annotated and predicted protein interactions

<p>Abstract</p> <p>Background</p> <p>Human protein-protein interaction (PPIs) data are the foundation for understanding molecular signalling networks and the functional roles of biomolecules. Several human PPI databases have become available; however, comparisons of these datasets have suggested limited data coverage and poor data quality. Ongoing collection and integration of human PPIs from different sources, both experimentally and computationally, can enable disease-specific network biology modelling in translational bioinformatics studies.</p> <p>Results</p> <p>We developed a new web-based resource, the Human Annotated and Predicted Protein Interaction (HAPPI) database, located at <url>http://bio.informatics.iupui.edu/HAPPI/</url>. The HAPPI database was created by extracting and integrating publicly available protein interaction databases, including HPRD, BIND, MINT, STRING, and OPHID, using database integration techniques. We designed a unified entity-relationship data model to resolve semantic level differences of diverse concepts involved in PPI data integration. We applied a unified scoring model to give each PPI a measure of its reliability that can place each PPI at one of the five star rank levels from 1 to 5. We assessed the quality of PPIs contained in the new HAPPI database, using evolutionary conserved co-expression pairs called "MetaGene" pairs to measure the extent of MetaGene pair and PPI pair overlaps. While the overall quality of the HAPPI database across all star ranks is comparable to the overall qualities of HPRD or IntNetDB, the subset of the HAPPI database with star ranks between 3 and 5 has a much higher average quality than all other human PPI databases. As of summer 2008, the database contains 142,956 non-redundant, medium to high-confidence level human protein interaction pairs among 10,592 human proteins. The HAPPI database web application also provides …” should be “The HAPPI database web application also provides hyperlinked information of genes, pathways, protein domains, protein structure displays, and sequence feature maps for interactive exploration of PPI data in the database.</p> <p>Conclusion</p> <p>HAPPI is by far the most comprehensive public compilation of human protein interaction information. It enables its users to fully explore PPI data with quality measures and annotated information necessary for emerging network biology studies.</p

Computational Biomarker Discovery: From Systems Biology to Predictive and Personalized Medicine Applications

poster abstractWith the advent of Genome-based Medicine, there is an escalating need for discovering how the modifications of biological molecules, either individually or as an ensemble, can be uniquely associated with human physiological states. This knowledge could lead to breakthroughs in the development of clinical tests known as "biomarker tests" to assess disease risks, early onset, prognosis, and treatment outcome predictions. Therefore, development of molecular biomarkers is a key agenda in the next 5-10 years to take full advantage of the human genome to improve human well-beings. However, the complexity of human biological systems and imperfect instrumentations of high-throughput biological instruments/results have created significant hurdles in biomarker development. Only recently did computational methods become an important player of the research topic, which has seen conventional molecular biomarkers development both extremely long and cost-ineffective. At Indiana Center for Systems Biology and Personalized Medicine, we are developing several computational systems biology strategies to address these challenges. We will show examples of how we approach the problem using a variety of computational techniques, including data mining, algorithm development to take into account of biological contexts, biological knowledge integration, and information visualization. Finally, we outline how research in this direction to derive more robust molecular biomarkers may lead to predictive and personalized medicine. Indiana Center for Systems Biology and Personalized Medicine (CSBPM) was founded in 2007 as an IUPUI signature center by Dr. Jake Chen and his colleagues in the Indiana University School of Informatics, School of Medicine, and School of Science. CSBPM is the only research center in the State of Indiana with the primary goal of pursuing predictive and personalized medicine. CSBPM currently consists of eleven faculty members from the School of Medicine, School of Science, School of Engineering, School of Informatics, and Indiana University Simon Cancer Center. The primary mission of the center is to foster the development and use of systems biology and computational modeling techniques to address challenges in future genome-based medicine. The ultimate goal of the center is to shorten the discovery-to-practice gap between integrative ―Omics‖ biology studies—including genomics, transcriptomics, proteomics, and metabolomics—and predictive and personalized medicine applications

Integrative Genomics Analysis Unravels Tissue-Specific Pathways, Networks, and Key Regulators of Blood Pressure Regulation

Blood pressure (BP) is a highly heritable trait and a major cardiovascular disease risk factor. Genome wide association studies (GWAS) have implicated a number of susceptibility loci for systolic (SBP) and diastolic (DBP) blood pressure. However, a large portion of the heritability cannot be explained by the top GWAS loci and a comprehensive understanding of the underlying molecular mechanisms is still lacking. Here, we utilized an integrative genomics approach that leveraged multiple genetic and genomic datasets including (a) GWAS for SBP and DBP from the International Consortium for Blood Pressure (ICBP), (b) expression quantitative trait loci (eQTLs) from genetics of gene expression studies of human tissues related to BP, (c) knowledge-driven biological pathways, and (d) data-driven tissue-specific regulatory gene networks. Integration of these multidimensional datasets revealed tens of pathways and gene subnetworks in vascular tissues, liver, adipose, blood, and brain functionally associated with DBP and SBP. Diverse processes such as platelet production, insulin secretion/signaling, protein catabolism, cell adhesion and junction, immune and inflammation, and cardiac/smooth muscle contraction, were shared between DBP and SBP. Furthermore, “Wnt signaling” and “mammalian target of rapamycin (mTOR) signaling” pathways were found to be unique to SBP, while “cytokine network”, and “tryptophan catabolism” to DBP. Incorporation of gene regulatory networks in our analysis informed on key regulator genes that orchestrate tissue-specific subnetworks of genes whose variants together explain ~20% of BP heritability. Our results shed light on the complex mechanisms underlying BP regulation and highlight potential novel targets and pathways for hypertension and cardiovascular diseases

Cross-sectional relations of whole-blood miRNA expression levels and hand grip strength in a community sample

MicroRNAs (miRNAs) regulate gene expression with emerging data suggesting miRNAs play a role in skeletal muscle biology. We sought to examine the association of miRNAs with grip strength in a community-based sample. Framingham Heart Study Offspring and Generation 3 participants (n = 5668 54% women, mean age 55 years, range 24, 90 years) underwent grip strength measurement and miRNA profiling using whole blood from fasting morning samples. Linear mixed-effects regression modeling of grip strength (kg) versus continuous miRNA \u27Cq\u27 values and versus binary miRNA expression was performed. We conducted an integrative miRNA-mRNA coexpression analysis and examined the enrichment of biologic pathways for the top miRNAs associated with grip strength. Grip strength was lower in women than in men and declined with age with a mean 44.7 (10.0) kg in men and 26.5 (6.3) kg in women. Among 299 miRNAs interrogated for association with grip strength, 93 (31%) had FDR q value \u3c 0.05, 54 (18%) had an FDR q value \u3c 0.01, and 15 (5%) had FDR q value \u3c 0.001. For almost all miRNA-grip strength associations, increasing miRNA concentration is associated with increasing grip strength. miR-20a-5p (FDR q 1.8 x 10-6 ) had the most significant association and several among the top 15 miRNAs had links to skeletal muscle including miR-126-3p, miR-30a-5p, and miR-30d-5p. The top associated biologic pathways included metabolism, chemokine signaling, and ubiquitin-mediated proteolysis. Our comprehensive assessment in a community-based sample of miRNAs in blood associated with grip strength provides a framework to further our understanding of the biology of muscle strength

Genetic and Environmental Effects on Gene Expression Signatures of Blood Pressure A Transcriptome-Wide Twin Study

Recently, 2 transcriptome-wide studies identified 40 genes that were differentially expressed in relation to blood pressure. However, to what extent these BP-related gene expression signatures and their associations with BP are driven by genetic or environmental factors has not been investigated. In this study of 391 twins (193 twin pairs and 5 singletons; age 55-69 years; 40% male; 57% monozygous) recruited from the Finnish Twin Cohort, transcriptome-wide data on peripheral leukocytes were obtained using the Illumina HT12 V4 array. Our transcriptome-wide analysis identified 1 gene (MOK [MAPK/MAK/MRK overlapping kinase], P=7.16x10(-8)) with its expression levels associated with systolic BP at the cutoff of false-discovery rate</p

Age-associated microRNA expression in human peripheral blood is associated with all-cause mortality and age-related traits

Recent studies provide evidence of correlations of DNA methylation and expression of protein-coding genes with human aging. The relations of microRNA expression with age and age-related clinical outcomes have not been characterized thoroughly. We explored associations of age with whole-blood microRNA expression in 5221 adults and identified 127 microRNAs that were differentially expressed by age at P \u3c 3.3 x 10(-4) (Bonferroni-corrected). Most microRNAs were underexpressed in older individuals. Integrative analysis of microRNA and mRNA expression revealed changes in age-associated mRNA expression possibly driven by age-associated microRNAs in pathways that involve RNA processing, translation, and immune function. We fitted a linear model to predict \u27microRNA age\u27 that incorporated expression levels of 80 microRNAs. MicroRNA age correlated modestly with predicted age from DNA methylation (r = 0.3) and mRNA expression (r = 0.2), suggesting that microRNA age may complement mRNA and epigenetic age prediction models. We used the difference between microRNA age and chronological age as a biomarker of accelerated aging (Deltaage) and found that Deltaage was associated with all-cause mortality (hazards ratio 1.1 per year difference, P = 4.2 x 10(-5) adjusted for sex and chronological age). Additionally, Deltaage was associated with coronary heart disease, hypertension, blood pressure, and glucose levels. In conclusion, we constructed a microRNA age prediction model based on whole-blood microRNA expression profiling. Age-associated microRNAs and their targets have potential utility to detect accelerated aging and to predict risks for age-related diseases. Wiley and Sons Ltd

Micro RNAs from DNA Viruses are Found Widely in Plasma in a Large Observational Human Population

Viral infections associate with disease risk and select families of viruses encode miRNAs that control an efficient viral cycle. The association of viral miRNA expression with disease in a large human population has not been previously explored. We sequenced plasma RNA from 40 participants of the Framingham Heart Study (FHS, Offspring Cohort, Visit 8) and identified 3 viral miRNAs from 3 different human Herpesviridae. These miRNAs were mostly related to viral latency and have not been previously detected in human plasma. Viral miRNA expression was then screened in the plasma of 2763 participants of the remaining cohort utilizing high-throughput RT-qPCR. All 3 viral miRNAs associated with combinations of inflammatory or prothrombotic circulating biomarkers (sTNFRII, IL-6, sICAM1, OPG, P-selectin) but did not associate with hypertension, coronary heart disease or cancer. Using a large observational population, we demonstrate that the presence of select viral miRNAs in the human circulation associate with inflammatory biomarkers and possibly immune response, but fail to associate with overt disease. This study greatly extends smaller singular observations of viral miRNAs in the human circulation and suggests that select viral miRNAs, such as those for latency, may not impact disease manifestation

Whole blood microRNA expression associated with stroke: Results from the Framingham Heart Study

Emerging evidence suggests microRNAs (miRNAs) may play an important role in explaining variation in stroke risk and recovery in humans, yet there are still few longitudinal studies examining the association between whole blood miRNAs and stroke. Accounting for multiple testing and adjusting for potentially confounding technical and clinical variables, here we show that whole blood miR-574-3p expression was significantly lower in participants with chronic stroke compared to non-cases. To explore the functional relevance of our findings, we analyzed miRNA-mRNA whole blood co-expression, pathway enrichment, and brain tissue gene expression. Results suggest miR-574-3p is involved in neurometabolic and chronic neuronal injury response pathways, including brain gene expression of DBNDD2 and ELOVL1. These results suggest miR-574-3p plays a role in regulating chronic brain and systemic cellular response to stroke and thus may implicate miR-574-3p as a partial mediator of long-term stroke outcomes

Messenger RNA and MicroRNA transcriptomic signatures of cardiometabolic risk factors

Background: Cardiometabolic (CM) risk factors are heritable and cluster in individuals. We hypothesized that CM risk factors are associated with multiple shared and unique mRNA and microRNA (miRNA) signatures. We examined associations of mRNA and miRNA levels with 6 CM traits: body mass index, HDL-cholesterol and triglycerides, fasting glucose, and systolic and diastolic blood pressures through cross-sectional analysis of 2812 Framingham Heart Study who had whole blood collection for RNA isolation for mRNA and miRNA expression studies and who consented to genetic research. We excluded participants taking medication for hypertension, dyslipidemia, or diabetes. We measured mRNA (n = 17,318; using the Affymetrix GeneChip Human Exon 1.0 ST Array) and miRNA (n = 315; using qRT-PCR) expression in whole blood. We used linear regression for mRNA analyses and a combination of linear and logistic regression for miRNA analyses. We conducted miRNA-mRNA coexpression and gene ontology enrichment analyses to explore relations between pleiotropic miRNAs, mRNA expression, and CM trait clustering. Results: We identified hundreds of significant associations between mRNAs, miRNAs, and individual CM traits. Four mRNAs (FAM13A, CSF2RB, HIST1H2AC, WNK1) were associated with all 6 CM traits (FDR < 0.001) and four miRNAs (miR-197-3p, miR-328, miR-505-5p, miR-145-5p) were associated with four CM traits (FDR < 0.05). Twelve mRNAs, including WNK1, that were coexpressed with the four most pleiotropic miRNAs, were also miRNA targets. mRNAs coexpressed with pleiotropic miRNAs were enriched for RNA metabolism (miR-505-5p), ubiquitin-dependent protein catabolism (miR-197-3p, miR-328) and chromatin assembly (miR-328). Conclusions: We identified mRNA and miRNA signatures of individual CM traits and their clustering. Implicated transcripts may play causal roles in CM risk or be downstream consequences of CM risk factors on the transcriptome. Studies are needed to establish whether or not pleiotropic circulating transcripts illuminate causal pathways for CM risk. Electronic supplementary material The online version of this article (doi:10.1186/s12864-017-3533-9) contains supplementary material, which is available to authorized users