A Genomewide Functional Network for the Laboratory Mouse

Establishing a functional network is invaluable to our understanding of gene function, pathways, and systems-level properties of an organism and can be a powerful resource in directing targeted experiments. In this study, we present a functional network for the laboratory mouse based on a Bayesian integration of diverse genetic and functional genomic data. The resulting network includes probabilistic functional linkages among 20,581 protein-coding genes. We show that this network can accurately predict novel functional assignments and network components and present experimental evidence for predictions related to Nanog homeobox (Nanog), a critical gene in mouse embryonic stem cell pluripotency. An analysis of the global topology of the mouse functional network reveals multiple biologically relevant systems-level features of the mouse proteome. Specifically, we identify the clustering coefficient as a critical characteristic of central modulators that affect diverse pathways as well as genes associated with different phenotype traits and diseases. In addition, a cross-species comparison of functional interactomes on a genomic scale revealed distinct functional characteristics of conserved neighborhoods as compared to subnetworks specific to higher organisms. Thus, our global functional network for the laboratory mouse provides the community with a key resource for discovering protein functions and novel pathway components as well as a tool for exploring systems-level topological and evolutionary features of cellular interactomes. To facilitate exploration of this network by the biomedical research community, we illustrate its application in function and disease gene discovery through an interactive, Web-based, publicly available interface at http://mouseNET.princeton.edu

Genetical genomics: spotlight on QTL hotspots

No abstract available

Networks of intergenic long-range enhancers and snpRNAs drive castration-resistant phenotype of prostate cancer and contribute to pathogenesis of multiple common human disorders

Biological and mechanistic relevance of intergenic disease-associated genetic loci (IDAGL) containing highly statistically significant disease-linked SNPs remains unknown. Here we present the experimental and clinical evidence revealing important role of IDAGL in human diseases. Targeted RT-PCR screen coupled with sequencing of purified PCR products detects widespread transcription at multiple intergenic disease-associated genomic loci (IDAGL) and identifies 96 small non-coding trans-regulatory RNAs of ~ 100-300 nt in length containing SNPs associated with 21 common human disorders (snpRNAs). Functionality of snpRNAs is supported by multiple independent lines of experimental evidence demonstrating their cell-type-specific expression and evolutionary conservation of sequences, genomic coordinates, and biological effects. Analysis of chromatin state signatures, expression profiling experiments using microarray and Q-PCR technologies, and luciferase reporter assays indicate that many IDAGL are Polycomb-regulated long-range enhancers. Expression of snpRNAs in human and mouse cells markedly affects cellular behavior and induces allele-specific clinically-relevant phenotypic changes: NLRP1-locus snpRNAs exert regulatory effects on monocyte/macrophage trans-differentiation, induce prostate cancer (PC) susceptibility snpRNAs, and transform low-malignancy hormone-dependent human PC cells into highly malignant androgen-independent PC. Q-PCR analysis and luciferase reporter assays demonstrate that snpRNA sequences represent allele-specific “decoy” targets of microRNAs which function as SNP-allele-specific modifiers of microRNA expression and activity. We demonstrate that trans-acting RNA molecules facilitating androgen depletion-independent growth (ADIG) in vitro and castration-resistant (CR) phenotype in vivo of PC contain intergenic 8q24-locus SNP variants which were recently linked with increased risk of developing PC. Expression level of 8q24-locus PC susceptibility snpRNAs is regulated by NLRP1-locus snpRNAs, which are transcribed from the intergenic long-range enhancer sequence located in 17p13 region at ~ 30 kb distance from the NLRP1 gene. Q-PCR analysis of clinical PC samples reveals markedly increased snpRNA expression levels in tumor tissues compared to the adjacent normal prostate [122-fold and 45-fold in Gleason 7 tumors (p = 0.03); 370-fold and 127-fold in Gleason 8 tumors (p = 0.0001); for NLRP1-locus and 8q24-locus SnpRNAs, respectively]. Highly concordant expression profiles of the NLRP1-locus snpRNAs and 8q24 CR-locus snpRNAs (r = 0.896; p < 0.0001) in clinical PC samples and experimental evidence of trans-regulatory effects of NLRP1-locus snpRNAs on expression of 8q24-locus SnpRNAs indicate that ADIG and CR phenotype of human PC cells can be triggered by RNA molecules transcribed from the NLRP1-locus intergenic enhancer and down-stream activation of the 8q24-locus snpRNAs. Our results define the intergenic NLRP1 and 8q24 regions as regulatory loci of ADIG and CR phenotype of human PC, reveal previously unknown molecular links between the innate immunity/inflammasome system and development of hormone-independent PC, and identify novel diagnostic and therapeutic targets exploration of which should be highly beneficial for clinical management of PC

Automated Annotation-Based Bio-Ontology Alignment with Structural Validation

We outline the structure of an automated process to both align multiple bio-ontologies in terms of their genomic co-annotations, and then to measure the structural quality of that alignment. We illustrate the method with a genomic analysis of 70 genes implicated in lung disease against the Gene Ontology

Assessing individual differences in genome-wide gene expression in human whole blood: reliability over four hours and stability over 10 months.

Studying the causes and correlates of natural variation in gene expression in healthy populations assumes that individual differences in gene expression can be reliably and stably assessed across time. However, this is yet to be established. We examined 4-hour test-retest reliability and 10 month test-retest stability of individual differences in gene expression in ten 12-year-old children. Blood was collected on four occasions: 10 a.m. and 2 p.m. on Day 1 and 10 months later at 10 a.m. and 2 p.m. Total RNA was hybridized to Affymetrix-U133 plus 2.0 arrays. For each probeset, the correlation across individuals between 10 a.m. and 2 p.m. on Day 1 estimates test-retest reliability. We identified 3,414 variable and abundantly expressed probesets whose 4-hour test-retest reliability exceeded .70, a conventionally accepted level of reliability, which we had 80% power to detect. Of the 3,414 reliable probesets, 1,752 were also significantly reliable 10 months later. We assessed the long-term stability of individual differences in gene expression by correlating the average expression level for each probe-set across the two 4-hour assessments on Day 1 with the average level of each probe-set across the two 4-hour assessments 10 months later. 1,291 (73.7%) of the 1,752 probe-sets that reliably detected individual differences across 4 hours on two occasions, 10 months apart, also stably detected individual differences across 10 months. Heritability, as estimated from the MZ twin intraclass correlations, is twice as high for the 1,752 reliable probesets versus all present probesets on the array (0.68 vs 0.34), and is even higher (0.76) for the 1,291 reliable probesets that are also stable across 10 months. The 1,291 probesets that reliably detect individual differences from a single peripheral blood collection and stably detect individual differences over 10 months are promising targets for research on the causes (e.g., eQTLs) and correlates (e.g., psychopathology) of individual differences in gene expression

Genetic Determinants of Circulating Sphingolipid Concentrations in European Populations

Sphingolipids have essential roles as structural components of cell membranes and in cell signalling, and disruption of their metabolism causes several diseases, with diverse neurological, psychiatric, and metabolic consequences. Increasingly, variants within a few of the genes that encode enzymes involved in sphingolipid metabolism are being associated with complex disease phenotypes. Direct experimental evidence supports a role of specific sphingolipid species in several common complex chronic disease processes including atherosclerotic plaque formation, myocardial infarction (MI), cardiomyopathy, pancreatic beta-cell failure, insulin resistance, and type 2 diabetes mellitus. Therefore, sphingolipids represent novel and important intermediate phenotypes for genetic analysis, yet little is known about the major genetic variants that influence their circulating levels in the general population. We performed a genome-wide association study (GWAS) between 318,237 single-nucleotide polymorphisms (SNPs) and levels of circulating sphingomyelin (SM), dihydrosphingomyelin (Dih-SM), ceramide (Cer), and glucosylceramide (GluCer) single lipid species (33 traits); and 43 matched metabolite ratios measured in 4,400 subjects from five diverse European populations. Associated variants (32) in five genomic regions were identified with genome-wide significant corrected p-values ranging down to 9.08 x 10(-66). The strongest associations were observed in or near 7 genes functionally involved in ceramide biosynthesis and trafficking: SPTLC3, LASS4, SGPP1, ATP10D, and FADS1-3. Variants in 3 loci (ATP10D, FADS3, and SPTLC3) associate with MI in a series of three German MI studies. An additional 70 variants across 23 candidate genes involved in sphingolipid-metabolizing pathways also demonstrate association (p = 10(-4) or less). Circulating concentrations of several key components in sphingolipid metabolism are thus under strong genetic control, and variants in these loci can be tested for a role in the development of common cardiovascular, metabolic, neurological, and psychiatric diseases