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Multi-tissue transcriptome analyses identify genetic mechanisms underlying neuropsychiatric traits.
Gamazon ER, Zwinderman AH, Cox NJ, Denys D, Derks EM
(2019) Nat Genet 51: 933-940
MeSH Terms: Algorithms, Computational Biology, Gene Expression Profiling, Gene Expression Regulation, Gene Regulatory Networks, Genetic Association Studies, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Mental Disorders, Organ Specificity, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Quantitative Trait, Heritable, Transcriptome
Show Abstract · Added July 17, 2019
The genetic architecture of psychiatric disorders is characterized by a large number of small-effect variants located primarily in non-coding regions, suggesting that the underlying causal effects may influence disease risk by modulating gene expression. We provide comprehensive analyses using transcriptome data from an unprecedented collection of tissues to gain pathophysiological insights into the role of the brain, neuroendocrine factors (adrenal gland) and gastrointestinal systems (colon) in psychiatric disorders. In each tissue, we perform PrediXcan analysis and identify trait-associated genes for schizophrenia (n associations = 499; n unique genes = 275), bipolar disorder (n associations = 17; n unique genes = 13), attention deficit hyperactivity disorder (n associations = 19; n unique genes = 12) and broad depression (n associations = 41; n unique genes = 31). Importantly, both PrediXcan and summary-data-based Mendelian randomization/heterogeneity in dependent instruments analyses suggest potentially causal genes in non-brain tissues, showing the utility of these tissues for mapping psychiatric disease genetic predisposition. Our analyses further highlight the importance of joint tissue approaches as 76% of the genes were detected only in difficult-to-acquire tissues.
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Genome-wide association and transcriptome studies identify target genes and risk loci for breast cancer.
Ferreira MA, Gamazon ER, Al-Ejeh F, Aittomäki K, Andrulis IL, Anton-Culver H, Arason A, Arndt V, Aronson KJ, Arun BK, Asseryanis E, Azzollini J, Balmaña J, Barnes DR, Barrowdale D, Beckmann MW, Behrens S, Benitez J, Bermisheva M, Białkowska K, Blomqvist C, Bogdanova NV, Bojesen SE, Bolla MK, Borg A, Brauch H, Brenner H, Broeks A, Burwinkel B, Caldés T, Caligo MA, Campa D, Campbell I, Canzian F, Carter J, Carter BD, Castelao JE, Chang-Claude J, Chanock SJ, Christiansen H, Chung WK, Claes KBM, Clarke CL, EMBRACE Collaborators, GC-HBOC Study Collaborators, GEMO Study Collaborators, Couch FJ, Cox A, Cross SS, Czene K, Daly MB, de la Hoya M, Dennis J, Devilee P, Diez O, Dörk T, Dunning AM, Dwek M, Eccles DM, Ejlertsen B, Ellberg C, Engel C, Eriksson M, Fasching PA, Fletcher O, Flyger H, Friedman E, Frost D, Gabrielson M, Gago-Dominguez M, Ganz PA, Gapstur SM, Garber J, García-Closas M, García-Sáenz JA, Gaudet MM, Giles GG, Glendon G, Godwin AK, Goldberg MS, Goldgar DE, González-Neira A, Greene MH, Gronwald J, Guénel P, Haiman CA, Hall P, Hamann U, He W, Heyworth J, Hogervorst FBL, Hollestelle A, Hoover RN, Hopper JL, Hulick PJ, Humphreys K, Imyanitov EN, ABCTB Investigators, HEBON Investigators, BCFR Investigators, Isaacs C, Jakimovska M, Jakubowska A, James PA, Janavicius R, Jankowitz RC, John EM, Johnson N, Joseph V, Karlan BY, Khusnutdinova E, Kiiski JI, Ko YD, Jones ME, Konstantopoulou I, Kristensen VN, Laitman Y, Lambrechts D, Lazaro C, Leslie G, Lester J, Lesueur F, Lindström S, Long J, Loud JT, Lubiński J, Makalic E, Mannermaa A, Manoochehri M, Margolin S, Maurer T, Mavroudis D, McGuffog L, Meindl A, Menon U, Michailidou K, Miller A, Montagna M, Moreno F, Moserle L, Mulligan AM, Nathanson KL, Neuhausen SL, Nevanlinna H, Nevelsteen I, Nielsen FC, Nikitina-Zake L, Nussbaum RL, Offit K, Olah E, Olopade OI, Olsson H, Osorio A, Papp J, Park-Simon TW, Parsons MT, Pedersen IS, Peixoto A, Peterlongo P, Pharoah PDP, Plaseska-Karanfilska D, Poppe B, Presneau N, Radice P, Rantala J, Rennert G, Risch HA, Saloustros E, Sanden K, Sawyer EJ, Schmidt MK, Schmutzler RK, Sharma P, Shu XO, Simard J, Singer CF, Soucy P, Southey MC, Spinelli JJ, Spurdle AB, Stone J, Swerdlow AJ, Tapper WJ, Taylor JA, Teixeira MR, Terry MB, Teulé A, Thomassen M, Thöne K, Thull DL, Tischkowitz M, Toland AE, Torres D, Truong T, Tung N, Vachon CM, van Asperen CJ, van den Ouweland AMW, van Rensburg EJ, Vega A, Viel A, Wang Q, Wappenschmidt B, Weitzel JN, Wendt C, Winqvist R, Yang XR, Yannoukakos D, Ziogas A, Kraft P, Antoniou AC, Zheng W, Easton DF, Milne RL, Beesley J, Chenevix-Trench G
(2019) Nat Commun 10: 1741
MeSH Terms: Breast Neoplasms, Female, Gene Expression Profiling, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Quantitative Trait Loci
Show Abstract · Added July 17, 2019
Genome-wide association studies (GWAS) have identified more than 170 breast cancer susceptibility loci. Here we hypothesize that some risk-associated variants might act in non-breast tissues, specifically adipose tissue and immune cells from blood and spleen. Using expression quantitative trait loci (eQTL) reported in these tissues, we identify 26 previously unreported, likely target genes of overall breast cancer risk variants, and 17 for estrogen receptor (ER)-negative breast cancer, several with a known immune function. We determine the directional effect of gene expression on disease risk measured based on single and multiple eQTL. In addition, using a gene-based test of association that considers eQTL from multiple tissues, we identify seven (and four) regions with variants associated with overall (and ER-negative) breast cancer risk, which were not reported in previous GWAS. Further investigation of the function of the implicated genes in breast and immune cells may provide insights into the etiology of breast cancer.
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An Integrated, High-Throughput Strategy for Multiomic Systems Level Analysis.
Gutierrez DB, Gant-Branum RL, Romer CE, Farrow MA, Allen JL, Dahal N, Nei YW, Codreanu SG, Jordan AT, Palmer LD, Sherrod SD, McLean JA, Skaar EP, Norris JL, Caprioli RM
(2018) J Proteome Res 17: 3396-3408
MeSH Terms: Gene Expression Profiling, Genomics, HL-60 Cells, Humans, Metabolomics, NF-E2-Related Factor 2, NF-kappa B, Proteomics, Signal Transduction, Systems Biology, Zinc
Show Abstract · Added August 27, 2018
Proteomics, metabolomics, and transcriptomics generate comprehensive data sets, and current biocomputational capabilities allow their efficient integration for systems biology analysis. Published multiomics studies cover methodological advances as well as applications to biological questions. However, few studies have focused on the development of a high-throughput, unified sample preparation approach to complement high-throughput omic analytics. This report details the automation, benchmarking, and application of a strategy for transcriptomic, proteomic, and metabolomic analyses from a common sample. The approach, sample preparation for multi-omics technologies (SPOT), provides equivalent performance to typical individual omic preparation methods but greatly enhances throughput and minimizes the resources required for multiomic experiments. SPOT was applied to a multiomics time course experiment for zinc-treated HL-60 cells. The data reveal Zn effects on NRF2 antioxidant and NFkappaB signaling. High-throughput approaches such as these are critical for the acquisition of temporally resolved, multicondition, large multiomic data sets such as those necessary to assess complex clinical and biological concerns. Ultimately, this type of approach will provide an expanded understanding of challenging scientific questions across many fields.
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11 MeSH Terms
The CeNGEN Project: The Complete Gene Expression Map of an Entire Nervous System.
Hammarlund M, Hobert O, Miller DM, Sestan N
(2018) Neuron 99: 430-433
MeSH Terms: Animals, Caenorhabditis elegans, Caenorhabditis elegans Proteins, Chromosome Mapping, Gene Expression Profiling, National Institute of Neurological Disorders and Stroke (U.S.), Nervous System, Nervous System Physiological Phenomena, United States
Show Abstract · Added March 26, 2019
Differential gene expression defines individual neuron types and determines how each contributes to circuit physiology and responds to injury and disease. The C. elegans Neuronal Gene Expression Map & Network (CeNGEN) will establish a comprehensive gene expression atlas of an entire nervous system at single-neuron resolution.
Copyright © 2018. Published by Elsevier Inc.
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9 MeSH Terms
Using an atlas of gene regulation across 44 human tissues to inform complex disease- and trait-associated variation.
Gamazon ER, Segrè AV, van de Bunt M, Wen X, Xi HS, Hormozdiari F, Ongen H, Konkashbaev A, Derks EM, Aguet F, Quan J, GTEx Consortium, Nicolae DL, Eskin E, Kellis M, Getz G, McCarthy MI, Dermitzakis ET, Cox NJ, Ardlie KG
(2018) Nat Genet 50: 956-967
MeSH Terms: Disease, Gene Expression, Gene Expression Profiling, Gene Expression Regulation, Genome-Wide Association Study, Genotype, Humans, Phenotype, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Quantitative Trait, Heritable
Show Abstract · Added July 1, 2018
We apply integrative approaches to expression quantitative loci (eQTLs) from 44 tissues from the Genotype-Tissue Expression project and genome-wide association study data. About 60% of known trait-associated loci are in linkage disequilibrium with a cis-eQTL, over half of which were not found in previous large-scale whole blood studies. Applying polygenic analyses to metabolic, cardiovascular, anthropometric, autoimmune, and neurodegenerative traits, we find that eQTLs are significantly enriched for trait associations in relevant pathogenic tissues and explain a substantial proportion of the heritability (40-80%). For most traits, tissue-shared eQTLs underlie a greater proportion of trait associations, although tissue-specific eQTLs have a greater contribution to some traits, such as blood pressure. By integrating information from biological pathways with eQTL target genes and applying a gene-based approach, we validate previously implicated causal genes and pathways, and propose new variant and gene associations for several complex traits, which we replicate in the UK BioBank and BioVU.
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11 MeSH Terms
Transcriptional profiling of the ductus arteriosus: Comparison of rodent microarrays and human RNA sequencing.
Yarboro MT, Durbin MD, Herington JL, Shelton EL, Zhang T, Ebby CG, Stoller JZ, Clyman RI, Reese J
(2018) Semin Perinatol 42: 212-220
MeSH Terms: Animals, Animals, Newborn, Ductus Arteriosus, Embryo, Mammalian, Gene Expression Profiling, Gene Expression Regulation, Developmental, Genetic Association Studies, Humans, Microarray Analysis, Models, Animal, Rodentia, Sequence Analysis, RNA, Species Specificity, Vascular Patency
Show Abstract · Added November 26, 2018
DA closure is crucial for the transition from fetal to neonatal life. This closure is supported by changes to the DA's signaling and structural properties that distinguish it from neighboring vessels. Examining transcriptional differences between these vessels is key to identifying genes or pathways responsible for DA closure. Several microarray studies have explored the DA transcriptome in animal models but varied experimental designs have led to conflicting results. Thorough transcriptomic analysis of the human DA has yet to be performed. A clear picture of the DA transcriptome is key to guiding future research endeavors, both to allow more targeted treatments in the clinical setting, and to understand the basic biology of DA function. In this review, we use a cross-species cross-platform analysis to consider all available published rodent microarray data and novel human RNAseq data in order to provide high priority candidate genes for consideration in future DA studies.
Copyright © 2018 Elsevier Inc. All rights reserved.
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14 MeSH Terms
Quantitative in vivo whole genome motility screen reveals novel therapeutic targets to block cancer metastasis.
Stoletov K, Willetts L, Paproski RJ, Bond DJ, Raha S, Jovel J, Adam B, Robertson AE, Wong F, Woolner E, Sosnowski DL, Bismar TA, Wong GK, Zijlstra A, Lewis JD
(2018) Nat Commun 9: 2343
MeSH Terms: Animals, Cell Line, Tumor, Cell Movement, Chick Embryo, Collagen, Female, Gene Expression Profiling, Gene Expression Regulation, Neoplastic, Humans, Male, Mice, Mice, Nude, Mice, SCID, Neoplasm Invasiveness, Neoplasm Metastasis, Neoplasm Transplantation, Phenotype, Prostatic Neoplasms, RNA Interference, RNA, Small Interfering
Show Abstract · Added April 10, 2019
Metastasis is the most lethal aspect of cancer, yet current therapeutic strategies do not target its key rate-limiting steps. We have previously shown that the entry of cancer cells into the blood stream, or intravasation, is highly dependent upon in vivo cancer cell motility, making it an attractive therapeutic target. To systemically identify genes required for tumor cell motility in an in vivo tumor microenvironment, we established a novel quantitative in vivo screening platform based on intravital imaging of human cancer metastasis in ex ovo avian embryos. Utilizing this platform to screen a genome-wide shRNA library, we identified a panel of novel genes whose function is required for productive cancer cell motility in vivo, and whose expression is closely associated with metastatic risk in human cancers. The RNAi-mediated inhibition of these gene targets resulted in a nearly total (>99.5%) block of spontaneous cancer metastasis in vivo.
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Gene expression in triple-negative breast cancer in relation to survival.
Wang S, Beeghly-Fadiel A, Cai Q, Cai H, Guo X, Shi L, Wu J, Ye F, Qiu Q, Zheng Y, Zheng W, Bao PP, Shu XO
(2018) Breast Cancer Res Treat 171: 199-207
MeSH Terms: Adult, Aged, Biomarkers, Tumor, Female, Gene Expression, Gene Expression Profiling, Gene Expression Regulation, Neoplastic, Humans, Middle Aged, Neoplasm Grading, Neoplasm Staging, Population Surveillance, Prognosis, Registries, Survival Analysis, Triple Negative Breast Neoplasms
Show Abstract · Added December 6, 2018
PURPOSE - The identification of biomarkers related to the prognosis of triple-negative breast cancer (TNBC) is critically important for improved understanding of the biology that drives TNBC progression.
METHODS - We evaluated gene expression in total RNA isolated from formalin-fixed paraffin-embedded tumor samples using the NanoString nCounter assay for 469 TNBC cases from the Shanghai Breast Cancer Survival Study. We used Cox regression to quantify Hazard Ratios (HR) and corresponding confidence intervals (CI) for overall survival (OS) and disease-free survival (DFS) in models that included adjustment for breast cancer intrinsic subtype. Of 302 genes in our discovery analysis, 22 were further evaluated in relation to OS among 134 TNBC cases from the Nashville Breast Health Study and the Southern Community Cohort Study; 16 genes were further evaluated in relation to DFS in 335 TNBC cases from four gene expression omnibus datasets. Fixed-effect meta-analysis was used to combine results across data sources.
RESULTS - Twofold higher expression of EOMES (HR 0.90, 95% CI 0.83-0.97), RASGRP1 (HR 0.89, 95% CI 0.82-0.97), and SOD2 (HR 0.80, 95% CI 0.66-0.96) was associated with better OS. Twofold higher expression of EOMES (HR 0.89, 95% CI 0.81-0.97) and RASGRP1 (HR 0.87, 95% CI 0.81-0.95) was also associated with better DFS. On the contrary, a doubling of FA2H (HR 1.14, 95% CI 1.06-1.22) and GSPT1 (HR 1.33, 95% CI 1.14-1.55) expression was associated with shorter DFS.
CONCLUSIONS - We identified five genes (EOMES, FA2H, GSPT1, RASGRP1, and SOD2) that may serve as potential prognostic biomarkers and/or therapeutic targets for TNBC.
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16 MeSH Terms
Single-Cell Transcriptomic Profiling of Pluripotent Stem Cell-Derived SCGB3A2+ Airway Epithelium.
McCauley KB, Alysandratos KD, Jacob A, Hawkins F, Caballero IS, Vedaie M, Yang W, Slovik KJ, Morley M, Carraro G, Kook S, Guttentag SH, Stripp BR, Morrisey EE, Kotton DN
(2018) Stem Cell Reports 10: 1579-1595
MeSH Terms: Animals, Cell Differentiation, Cell Line, Cell Lineage, Cell Plasticity, Epithelium, Gene Expression Profiling, Genes, Reporter, Humans, Induced Pluripotent Stem Cells, Kinetics, Lung, Mice, Secretoglobins, Sequence Analysis, RNA, Single-Cell Analysis, Solubility, Spheroids, Cellular, Time Factors, Transcriptome, Wnt Signaling Pathway
Show Abstract · Added April 1, 2019
Lung epithelial lineages have been difficult to maintain in pure form in vitro, and lineage-specific reporters have proven invaluable for monitoring their emergence from cultured pluripotent stem cells (PSCs). However, reporter constructs for tracking proximal airway lineages generated from PSCs have not been previously available, limiting the characterization of these cells. Here, we engineer mouse and human PSC lines carrying airway secretory lineage reporters that facilitate the tracking, purification, and profiling of this lung subtype. Through bulk and single-cell-based global transcriptomic profiling, we find PSC-derived airway secretory cells are susceptible to phenotypic plasticity exemplified by the tendency to co-express both a proximal airway secretory program as well as an alveolar type 2 cell program, which can be minimized by inhibiting endogenous Wnt signaling. Our results provide global profiles of engineered lung cell fates, a guide for improving their directed differentiation, and a human model of the developing airway.
Copyright © 2018 The Author(s). Published by Elsevier Inc. All rights reserved.
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21 MeSH Terms
Hepatic Gi signaling regulates whole-body glucose homeostasis.
Rossi M, Zhu L, McMillin SM, Pydi SP, Jain S, Wang L, Cui Y, Lee RJ, Cohen AH, Kaneto H, Birnbaum MJ, Ma Y, Rotman Y, Liu J, Cyphert TJ, Finkel T, McGuinness OP, Wess J
(2018) J Clin Invest 128: 746-759
MeSH Terms: Animals, Blood Glucose, Extracellular Signal-Regulated MAP Kinases, Female, GTP-Binding Protein alpha Subunits, Gi-Go, Gene Expression Profiling, Glucagon, Gluconeogenesis, Glucose, Hepatocytes, Homeostasis, Humans, Liver, Male, Mice, Mice, Inbred C57BL, Mice, Knockout, Oxygen, Phosphatidylinositol 3-Kinases, Phosphorylation, Reactive Oxygen Species, Receptors, Glucagon, Signal Transduction
Show Abstract · Added March 14, 2018
An increase in hepatic glucose production (HGP) is a key feature of type 2 diabetes. Excessive signaling through hepatic Gs-linked glucagon receptors critically contributes to pathologically elevated HGP. Here, we tested the hypothesis that this metabolic impairment can be counteracted by enhancing hepatic Gi signaling. Specifically, we used a chemogenetic approach to selectively activate Gi-type G proteins in mouse hepatocytes in vivo. Unexpectedly, activation of hepatic Gi signaling triggered a pronounced increase in HGP and severely impaired glucose homeostasis. Moreover, increased Gi signaling stimulated glucose release in human hepatocytes. A lack of functional Gi-type G proteins in hepatocytes reduced blood glucose levels and protected mice against the metabolic deficits caused by the consumption of a high-fat diet. Additionally, we delineated a signaling cascade that links hepatic Gi signaling to ROS production, JNK activation, and a subsequent increase in HGP. Taken together, our data support the concept that drugs able to block hepatic Gi-coupled GPCRs may prove beneficial as antidiabetic drugs.
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23 MeSH Terms