98 results found
Beltrami C, Besnier M, Shantikumar S, et al., 2017, Human Pericardial Fluid Contains Exosomes Enriched with Cardiovascular-Expressed MicroRNAs and Promotes Therapeutic Angiogenesis, MOLECULAR THERAPY, Vol: 25, Pages: 679-693, ISSN: 1525-0016
Chen T-D, Rotival M, Chiu L-Y, et al., 2017, Identification of Ceruloplasmin as a Gene that Affects Susceptibility to Glomerulonephritis Through Macrophage Function, GENETICS, Vol: 206, Pages: 1139-1151, ISSN: 0016-6731
Coan PM, Hummel O, Diaz AG, et al., 2017, Genetic, physiological and comparative genomic studies of hypertension and insulin resistance in the spontaneously hypertensive rat, DISEASE MODELS & MECHANISMS, Vol: 10, Pages: 297-306, ISSN: 1754-8403
Heinig M, Adriaens ME, Schafer S, et al., 2017, Natural genetic variation of the cardiac transcriptome in non-diseased donors and patients with dilated cardiomyopathy, GENOME BIOLOGY, Vol: 18, ISSN: 1474-760X
Imprialou M, Petretto E, Bottolo L, 2017, Expression QTLs Mapping and Analysis: A Bayesian Perspective., Pages: 189-215
The aim of expression Quantitative Trait Locus (eQTL) mapping is the identification of DNA sequence variants that explain variation in gene expression. Given the recent yield of trait-associated genetic variants identified by large-scale genome-wide association analyses (GWAS), eQTL mapping has become a useful tool to understand the functional context where these variants operate and eventually narrow down functional gene targets for disease. Despite its extensive application to complex (polygenic) traits and disease, the majority of eQTL studies still rely on univariate data modeling strategies, i.e., testing for association of all transcript-marker pairs. However these "one at-a-time" strategies are (1) unable to control the number of false-positives when an intricate Linkage Disequilibrium structure is present and (2) are often underpowered to detect the full spectrum of trans-acting regulatory effects. Here we present our viewpoint on the most recent advances on eQTL mapping approaches, with a focus on Bayesian methodology. We review the advantages of the Bayesian approach over frequentist methods and provide an empirical example of polygenic eQTL mapping to illustrate the different properties of frequentist and Bayesian methods. Finally, we discuss how multivariate eQTL mapping approaches have distinctive features with respect to detection of polygenic effects, accuracy, and interpretability of the results.
Krishnan ML, Van Steenwinckel J, Schang A-L, et al., 2017, Integrative genomics of microglia implicates DLG4 (PSD95) in the white matter development of preterm infants, NATURE COMMUNICATIONS, Vol: 8, ISSN: 2041-1723
McDermott-Roe C, Leleu M, Rowe GC, et al., 2017, Transcriptome-wide co-expression analysis identifies LRRC2 as a novel mediator of mitochondrial and cardiac function, PLOS ONE, Vol: 12, ISSN: 1932-6203
Moreno-Moral A, Pesce F, Behmoaras J, et al., 2017, Systems Genetics as a Tool to Identify Master Genetic Regulators in Complex Disease., Pages: 337-362
Systems genetics stems from systems biology and similarly employs integrative modeling approaches to describe the perturbations and phenotypic effects observed in a complex system. However, in the case of systems genetics the main source of perturbation is naturally occurring genetic variation, which can be analyzed at the systems-level to explain the observed variation in phenotypic traits. In contrast with conventional single-variant association approaches, the success of systems genetics has been in the identification of gene networks and molecular pathways that underlie complex disease. In addition, systems genetics has proven useful in the discovery of master trans-acting genetic regulators of functional networks and pathways, which in many cases revealed unexpected gene targets for disease. Here we detail the central components of a fully integrated systems genetics approach to complex disease, starting from assessment of genetic and gene expression variation, linking DNA sequence variation to mRNA (expression QTL mapping), gene regulatory network analysis and mapping the genetic control of regulatory networks. By summarizing a few illustrative (and successful) examples, we highlight how different data-modeling strategies can be effectively integrated in a systems genetics study.
Petretto E, 2017, Genetics of Neurodevelopmental Disorders: Connecting The Dots in The Brain, 18th International Congress of Developmental Biology, Publisher: ELSEVIER SCIENCE BV, Pages: S4-S4, ISSN: 0925-4773
Rackham OJL, Langley SR, Oates T, et al., 2017, A Bayesian Approach for Analysis of Whole-Genome Bisulfite Sequencing Data Identifies Disease-Associated Changes in DNA Methylation, GENETICS, Vol: 205, Pages: 1443-1458, ISSN: 0016-6731
Rodriguez-Martinez A, Posma JM, Ayala R, et al., 2017, MWASTools: an R/Bioconductor package for metabolome-wide association studies., Bioinformatics
Summary: MWASTools is an R package designed to provide an integrated pipeline to analyze metabonomic data in large-scale epidemiological studies. Key functionalities of our package include: quality control analysis; metabolome-wide association analysis using various models (partial correlations, generalized linear models); visualization of statistical outcomes; metabolite assignment using statistical total correlation spectroscopy (STOCSY); and biological interpretation of MWAS results. Availability: The MWASTools R package is implemented in R (version > =3.4) and is available from Bioconductor: https://bioconductor.org/packages/MWASTools/. Supplementary information: Supplementary data are available at Bioinformatics online.
Schafer S, Viswanathan S, Widjaja AA, et al., 2017, IL11 is a crucial determinant of cardiovascular fibrosis., Nature
Fibrosis is a final common pathology in cardiovascular disease1. In the heart, fibrosis causes mechanical and electrical dysfunction1,2 and in the kidney, it predicts the onset of renal failure3. Transforming growth factor β1 (TGFB1) is the principal pro-fibrotic factor4,5 but its inhibition is associated with side effects due to its pleiotropic roles6,7. We hypothesised that downstream effectors of TGFB1 in fibroblasts could be attractive therapeutic targets and lack upstream toxicities. Using integrated imaging-genomics analyses of primary human fibroblasts, we found that Interleukin 11 (IL11) upregulation is the dominant transcriptional response to TGFB1 exposure and required for its profibrotic effect. IL11 and its receptor (IL11RA) are expressed specifically in fibroblasts where they drive non-canonical, ERK-dependent autocrine signalling that is required for fibrogenic protein synthesis. In mice, fibroblast-specific Il11 transgene expression or Il11 injection causes heart and kidney fibrosis and organ failure whereas genetic deletion of Il11ra1 is protective against disease. Thus, inhibition of IL11 prevents fibroblast activation across organs and species in response to a range of important pro-fibrotic stimuli. These data reveal a central role of IL11 in fibrosis and we propose inhibition of IL11 as a new therapeutic strategy to treat fibrotic diseases.
Solimena M, Schulte AM, Marselli L, et al., 2017, Systems biology of the IMIDIA biobank from organ donors and pancreatectomised patients defines a novel transcriptomic signature of islets from individuals with type 2 diabetes., Diabetologia
AIMS/HYPOTHESIS: Pancreatic islet beta cell failure causes type 2 diabetes in humans. To identify transcriptomic changes in type 2 diabetic islets, the Innovative Medicines Initiative for Diabetes: Improving beta-cell function and identification of diagnostic biomarkers for treatment monitoring in Diabetes (IMIDIA) consortium ( www.imidia.org ) established a comprehensive, unique multicentre biobank of human islets and pancreas tissues from organ donors and metabolically phenotyped pancreatectomised patients (PPP). METHODS: Affymetrix microarrays were used to assess the islet transcriptome of islets isolated either by enzymatic digestion from 103 organ donors (OD), including 84 non-diabetic and 19 type 2 diabetic individuals, or by laser capture microdissection (LCM) from surgical specimens of 103 PPP, including 32 non-diabetic, 36 with type 2 diabetes, 15 with impaired glucose tolerance (IGT) and 20 with recent-onset diabetes (<1 year), conceivably secondary to the pancreatic disorder leading to surgery (type 3c diabetes). Bioinformatics tools were used to (1) compare the islet transcriptome of type 2 diabetic vs non-diabetic OD and PPP as well as vs IGT and type 3c diabetes within the PPP group; and (2) identify transcription factors driving gene co-expression modules correlated with insulin secretion ex vivo and glucose tolerance in vivo. Selected genes of interest were validated for their expression and function in beta cells. RESULTS: Comparative transcriptomic analysis identified 19 genes differentially expressed (false discovery rate ≤0.05, fold change ≥1.5) in type 2 diabetic vs non-diabetic islets from OD and PPP. Nine out of these 19 dysregulated genes were not previously reported to be dysregulated in type 2 diabetic islets. Signature genes included TMEM37, which inhibited Ca2+-influx and insulin secretion in beta cells, and ARG2 and PPP1R1A, which promoted insulin secretion. Systems biology approaches identified HNF1A, PDX1 and REST as drivers o
Srivastava PK, Bagnati M, Delahaye-Duriez A, et al., 2017, Genome-wide analysis of differential RNA editing in epilepsy, GENOME RESEARCH, Vol: 27, Pages: 440-450, ISSN: 1088-9051
Suresh J, Harmston N, Lim KK, et al., 2017, An embryonic system to assess direct and indirect Wnt transcriptional targets, SCIENTIFIC REPORTS, Vol: 7, ISSN: 2045-2322
Delahaye-Duriez A, Srivastava P, Shkura K, et al., 2016, Rare and common epilepsies converge on a shared gene regulatory network providing opportunities for novel antiepileptic drug discovery, GENOME BIOLOGY, Vol: 17, ISSN: 1474-760X
Diaz-Montana JJ, Rackham OJL, Diaz-Diaz N, et al., 2016, Web-based Gene Pathogenicity Analysis (WGPA): a web platform to interpret gene pathogenicity from personal genome data, BIOINFORMATICS, Vol: 32, Pages: 635-637, ISSN: 1367-4803
Johnson MR, Shkura K, Langley SR, et al., 2016, Systems genetics identifies a convergent gene network for cognition and neurodevelopmental disease, NATURE NEUROSCIENCE, Vol: 19, Pages: 223-+, ISSN: 1097-6256
Lewin A, Saadi H, Peters JE, et al., 2016, MT-HESS: an efficient Bayesian approach for simultaneous association detection in OMICS datasets, with application to eQTL mapping in multiple tissues, BIOINFORMATICS, Vol: 32, Pages: 523-532, ISSN: 1367-4803
Madan B, Ke Z, Harmston N, et al., 2016, Wnt addiction of genetically defined cancers reversed by PORCN inhibition, ONCOGENE, Vol: 35, Pages: 2197-2207, ISSN: 0950-9232
Martinez-Micaelo N, Gonzalez-Abuin N, Ardevol A, et al., 2016, Leptin signal transduction underlies the differential metabolic response of LEW and WKY rats to cafeteria diet, JOURNAL OF MOLECULAR ENDOCRINOLOGY, Vol: 56, Pages: 1-10, ISSN: 0952-5041
Martinez-Micaelo N, Gonzalez-Abuin N, Terra X, et al., 2016, Identification of a nutrient-sensing transcriptional network in monocytes by using inbred rat models on a cafeteria diet, DISEASE MODELS & MECHANISMS, Vol: 9, Pages: 1231-1239, ISSN: 1754-8403
Moreno-Moral A, Petretto E, 2016, From integrative genomics to systems genetics in the rat to link genotypes to phenotypes, DISEASE MODELS & MECHANISMS, Vol: 9, Pages: 1097-1110, ISSN: 1754-8403
Pedrigi RM, Mehta VV, Bovens SM, et al., 2016, Influence of shear stress magnitude and direction on atherosclerotic plaque composition, Royal Society Open Science, Vol: 3, ISSN: 2054-5703
Rackham OJL, Firas J, Fang H, et al., 2016, A predictive computational framework for direct reprogramming between human cell types, NATURE GENETICS, Vol: 48, Pages: 331-335, ISSN: 1061-4036
Behmoaras J, Diaz AG, Venda L, et al., 2015, Macrophage Epoxygenase Determines a Profibrotic Transcriptome Signature, JOURNAL OF IMMUNOLOGY, Vol: 194, Pages: 4705-4716, ISSN: 0022-1767
Buyandelger B, Mansfield C, Kostin S, et al., 2015, MIp Interacting Protein 1 (MIP1) Plays a Role for Cardiomyopathy, Scientific Sessions and Resuscitation Science Symposium of the American-Heart-Association (AHA), Publisher: LIPPINCOTT WILLIAMS & WILKINS, ISSN: 0009-7322
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