Other search tools

About this data

The publication data currently available has been vetted by Vanderbilt faculty, staff, administrators and trainees. The data itself is retrieved directly from NCBI's PubMed and is automatically updated on a weekly basis to ensure accuracy and completeness.

If you have any questions or comments, please contact us.

Results: 1 to 10 of 100

Publication Record

Connections

Gene network transitions in embryos depend upon interactions between a pioneer transcription factor and core histones.
Iwafuchi M, Cuesta I, Donahue G, Takenaka N, Osipovich AB, Magnuson MA, Roder H, Seeholzer SH, Santisteban P, Zaret KS
(2020) Nat Genet 52: 418-427
MeSH Terms: Amino Acid Sequence, Animals, Cell Line, Chromatin, DNA, Female, Gene Expression Regulation, Developmental, Gene Regulatory Networks, Histones, Humans, Mice, Mice, Inbred C57BL, Nucleosomes, Transcription Factors, Transcription, Genetic
Show Abstract · Added April 7, 2020
Gene network transitions in embryos and other fate-changing contexts involve combinations of transcription factors. A subset of fate-changing transcription factors act as pioneers; they scan and target nucleosomal DNA and initiate cooperative events that can open the local chromatin. However, a gap has remained in understanding how molecular interactions with the nucleosome contribute to the chromatin-opening phenomenon. Here we identified a short α-helical region, conserved among FOXA pioneer factors, that interacts with core histones and contributes to chromatin opening in vitro. The same domain is involved in chromatin opening in early mouse embryos for normal development. Thus, local opening of chromatin by interactions between pioneer factors and core histones promotes genetic programming.
1 Communities
3 Members
0 Resources
15 MeSH Terms
Systems-level network modeling of Small Cell Lung Cancer subtypes identifies master regulators and destabilizers.
Wooten DJ, Groves SM, Tyson DR, Liu Q, Lim JS, Albert R, Lopez CF, Sage J, Quaranta V
(2019) PLoS Comput Biol 15: e1007343
MeSH Terms: Algorithms, Animals, Basic Helix-Loop-Helix Transcription Factors, Bayes Theorem, Cell Line, Tumor, Cluster Analysis, Databases, Genetic, Drug Resistance, Neoplasm, Gene Expression, Gene Expression Regulation, Neoplastic, Gene Ontology, Gene Regulatory Networks, Humans, Mice, Models, Theoretical, Small Cell Lung Carcinoma, Systems Analysis, Transcription Factors
Show Abstract · Added March 30, 2020
Adopting a systems approach, we devise a general workflow to define actionable subtypes in human cancers. Applied to small cell lung cancer (SCLC), the workflow identifies four subtypes based on global gene expression patterns and ontologies. Three correspond to known subtypes (SCLC-A, SCLC-N, and SCLC-Y), while the fourth is a previously undescribed ASCL1+ neuroendocrine variant (NEv2, or SCLC-A2). Tumor deconvolution with subtype gene signatures shows that all of the subtypes are detectable in varying proportions in human and mouse tumors. To understand how multiple stable subtypes can arise within a tumor, we infer a network of transcription factors and develop BooleaBayes, a minimally-constrained Boolean rule-fitting approach. In silico perturbations of the network identify master regulators and destabilizers of its attractors. Specific to NEv2, BooleaBayes predicts ELF3 and NR0B1 as master regulators of the subtype, and TCF3 as a master destabilizer. Since the four subtypes exhibit differential drug sensitivity, with NEv2 consistently least sensitive, these findings may lead to actionable therapeutic strategies that consider SCLC intratumoral heterogeneity. Our systems-level approach should generalize to other cancer types.
0 Communities
1 Members
0 Resources
MeSH Terms
Histone deacetylase 3 controls a transcriptional network required for B cell maturation.
Stengel KR, Bhaskara S, Wang J, Liu Q, Ellis JD, Sampathi S, Hiebert SW
(2019) Nucleic Acids Res 47: 10612-10627
MeSH Terms: Animals, Antigens, CD19, B-Lymphocytes, Base Sequence, Cell Differentiation, Gene Expression Regulation, Gene Regulatory Networks, Histone Deacetylase Inhibitors, Histone Deacetylases, Lipopolysaccharides, Lymphocyte Activation, Mice, Inbred C57BL, Plasma Cells, Positive Regulatory Domain I-Binding Factor 1, Proto-Oncogene Proteins c-bcl-6, Repressor Proteins, Transcription, Genetic, Up-Regulation
Show Abstract · Added October 25, 2019
Histone deacetylase 3 (Hdac3) is a target of the FDA approved HDAC inhibitors, which are used for the treatment of lymphoid malignancies. Here, we used Cd19-Cre to conditionally delete Hdac3 to define its role in germinal center B cells, which represent the cell of origin for many B cell malignancies. Cd19-Cre-Hdac3-/- mice showed impaired germinal center formation along with a defect in plasmablast production. Analysis of Hdac3-/- germinal centers revealed a reduction in dark zone centroblasts and accumulation of light zone centrocytes. RNA-seq revealed a significant correlation between genes up-regulated upon Hdac3 loss and those up-regulated in Foxo1-deleted germinal center B cells, even though Foxo1 typically activates transcription. Therefore, to determine whether gene expression changes observed in Hdac3-/- germinal centers were a result of direct effects of Hdac3 deacetylase activity, we used an HDAC3 selective inhibitor and examined nascent transcription in germinal center-derived cell lines. Transcriptional changes upon HDAC3 inhibition were enriched for light zone gene signatures as observed in germinal centers. Further comparison of PRO-seq data with ChIP-seq/exo data for BCL6, SMRT, FOXO1 and H3K27ac identified direct targets of HDAC3 function including CD86, CD83 and CXCR5 that are likely responsible for driving the light zone phenotype observed in vivo.
© The Author(s) 2019. Published by Oxford University Press on behalf of Nucleic Acids Research.
1 Communities
0 Members
0 Resources
18 MeSH Terms
A gene co-expression network-based analysis of multiple brain tissues reveals novel genes and molecular pathways underlying major depression.
Gerring ZF, Gamazon ER, Derks EM, Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium
(2019) PLoS Genet 15: e1008245
MeSH Terms: Brain Chemistry, Complement C4a, Depressive Disorder, Major, Gene Expression Profiling, Gene Expression Regulation, Gene Regulatory Networks, Genome-Wide Association Study, Humans, Organ Specificity, Quantitative Trait Loci, Sequence Analysis, RNA
Show Abstract · Added July 17, 2019
Major depression is a common and severe psychiatric disorder with a highly polygenic genetic architecture. Genome-wide association studies have successfully identified multiple independent genetic loci that harbour variants associated with major depression, but the exact causal genes and biological mechanisms are largely unknown. Tissue-specific network approaches may identify molecular mechanisms underlying major depression and provide a biological substrate for integrative analyses. We provide a framework for the identification of individual risk genes and gene co-expression networks using genome-wide association summary statistics and gene expression information across multiple human brain tissues and whole blood. We developed a novel gene-based method called eMAGMA that leverages tissue-specific eQTL information to identify 99 biologically plausible risk genes associated with major depression, of which 58 are novel. Among these novel associations is Complement Factor 4A (C4A), recently implicated in schizophrenia through its role in synaptic pruning during postnatal development. Major depression risk genes were enriched in gene co-expression modules in multiple brain tissues and the implicated gene modules contained genes involved in synaptic signalling, neuronal development, and cell transport pathways. Modules enriched with major depression signals were strongly preserved across brain tissues, but were weakly preserved in whole blood, highlighting the importance of using disease-relevant tissues in genetic studies of psychiatric traits. We identified tissue-specific genes and gene co-expression networks associated with major depression. Our novel analytical framework can be used to gain fundamental insights into the functioning of the nervous system in major depression and other brain-related traits.
0 Communities
1 Members
0 Resources
11 MeSH Terms
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.
0 Communities
1 Members
0 Resources
MeSH Terms
Comparative Molecular Analysis of Gastrointestinal Adenocarcinomas.
Liu Y, Sethi NS, Hinoue T, Schneider BG, Cherniack AD, Sanchez-Vega F, Seoane JA, Farshidfar F, Bowlby R, Islam M, Kim J, Chatila W, Akbani R, Kanchi RS, Rabkin CS, Willis JE, Wang KK, McCall SJ, Mishra L, Ojesina AI, Bullman S, Pedamallu CS, Lazar AJ, Sakai R, Cancer Genome Atlas Research Network, Thorsson V, Bass AJ, Laird PW
(2018) Cancer Cell 33: 721-735.e8
MeSH Terms: Adenocarcinoma, Aneuploidy, Chromosomal Instability, DNA Methylation, DNA Polymerase II, DNA-Binding Proteins, Epigenesis, Genetic, Female, Gastrointestinal Neoplasms, Gene Regulatory Networks, Heterogeneous-Nuclear Ribonucleoproteins, Humans, Male, Microsatellite Instability, MutL Protein Homolog 1, Mutation, Poly-ADP-Ribose Binding Proteins, Polymorphism, Single Nucleotide, Proto-Oncogene Proteins p21(ras), RNA-Binding Proteins, SOX9 Transcription Factor
Show Abstract · Added October 30, 2019
We analyzed 921 adenocarcinomas of the esophagus, stomach, colon, and rectum to examine shared and distinguishing molecular characteristics of gastrointestinal tract adenocarcinomas (GIACs). Hypermutated tumors were distinct regardless of cancer type and comprised those enriched for insertions/deletions, representing microsatellite instability cases with epigenetic silencing of MLH1 in the context of CpG island methylator phenotype, plus tumors with elevated single-nucleotide variants associated with mutations in POLE. Tumors with chromosomal instability were diverse, with gastroesophageal adenocarcinomas harboring fragmented genomes associated with genomic doubling and distinct mutational signatures. We identified a group of tumors in the colon and rectum lacking hypermutation and aneuploidy termed genome stable and enriched in DNA hypermethylation and mutations in KRAS, SOX9, and PCBP1.
Copyright © 2018 Elsevier Inc. All rights reserved.
0 Communities
1 Members
0 Resources
21 MeSH Terms
A Comprehensive Pan-Cancer Molecular Study of Gynecologic and Breast Cancers.
Berger AC, Korkut A, Kanchi RS, Hegde AM, Lenoir W, Liu W, Liu Y, Fan H, Shen H, Ravikumar V, Rao A, Schultz A, Li X, Sumazin P, Williams C, Mestdagh P, Gunaratne PH, Yau C, Bowlby R, Robertson AG, Tiezzi DG, Wang C, Cherniack AD, Godwin AK, Kuderer NM, Rader JS, Zuna RE, Sood AK, Lazar AJ, Ojesina AI, Adebamowo C, Adebamowo SN, Baggerly KA, Chen TW, Chiu HS, Lefever S, Liu L, MacKenzie K, Orsulic S, Roszik J, Shelley CS, Song Q, Vellano CP, Wentzensen N, Cancer Genome Atlas Research Network, Weinstein JN, Mills GB, Levine DA, Akbani R
(2018) Cancer Cell 33: 690-705.e9
MeSH Terms: Breast Neoplasms, DNA Copy Number Variations, Databases, Genetic, Female, Gene Expression Profiling, Gene Expression Regulation, Neoplastic, Gene Regulatory Networks, Genetic Predisposition to Disease, Genital Neoplasms, Female, Humans, Mutation, Organ Specificity, Prognosis, RNA, Long Noncoding, Receptors, Estrogen
Show Abstract · Added October 30, 2019
We analyzed molecular data on 2,579 tumors from The Cancer Genome Atlas (TCGA) of four gynecological types plus breast. Our aims were to identify shared and unique molecular features, clinically significant subtypes, and potential therapeutic targets. We found 61 somatic copy-number alterations (SCNAs) and 46 significantly mutated genes (SMGs). Eleven SCNAs and 11 SMGs had not been identified in previous TCGA studies of the individual tumor types. We found functionally significant estrogen receptor-regulated long non-coding RNAs (lncRNAs) and gene/lncRNA interaction networks. Pathway analysis identified subtypes with high leukocyte infiltration, raising potential implications for immunotherapy. Using 16 key molecular features, we identified five prognostic subtypes and developed a decision tree that classified patients into the subtypes based on just six features that are assessable in clinical laboratories.
Copyright © 2018 Elsevier Inc. All rights reserved.
0 Communities
1 Members
0 Resources
MeSH Terms
Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context.
Chiu HS, Somvanshi S, Patel E, Chen TW, Singh VP, Zorman B, Patil SL, Pan Y, Chatterjee SS, Cancer Genome Atlas Research Network, Sood AK, Gunaratne PH, Sumazin P
(2018) Cell Rep 23: 297-312.e12
MeSH Terms: Cell Line, Cell Line, Tumor, Gene Expression Regulation, Neoplastic, Gene Regulatory Networks, Genes, Tumor Suppressor, Humans, Neoplasms, Oncogenes, RNA, Long Noncoding
Show Abstract · Added October 30, 2019
Long noncoding RNAs (lncRNAs) are commonly dysregulated in tumors, but only a handful are known to play pathophysiological roles in cancer. We inferred lncRNAs that dysregulate cancer pathways, oncogenes, and tumor suppressors (cancer genes) by modeling their effects on the activity of transcription factors, RNA-binding proteins, and microRNAs in 5,185 TCGA tumors and 1,019 ENCODE assays. Our predictions included hundreds of candidate onco- and tumor-suppressor lncRNAs (cancer lncRNAs) whose somatic alterations account for the dysregulation of dozens of cancer genes and pathways in each of 14 tumor contexts. To demonstrate proof of concept, we showed that perturbations targeting OIP5-AS1 (an inferred tumor suppressor) and TUG1 and WT1-AS (inferred onco-lncRNAs) dysregulated cancer genes and altered proliferation of breast and gynecologic cancer cells. Our analysis indicates that, although most lncRNAs are dysregulated in a tumor-specific manner, some, including OIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergistically dysregulate cancer pathways in multiple tumor contexts.
Copyright © 2018 The Author(s). Published by Elsevier Inc. All rights reserved.
0 Communities
1 Members
0 Resources
MeSH Terms
An integrative functional genomics framework for effective identification of novel regulatory variants in genome-phenome studies.
Zhao J, Cheng F, Jia P, Cox N, Denny JC, Zhao Z
(2018) Genome Med 10: 7
MeSH Terms: Gene Regulatory Networks, Genetic Predisposition to Disease, Genome, Human, Genome-Wide Association Study, Genomics, Humans, Molecular Sequence Annotation, Organ Specificity, Phenotype, Polymorphism, Single Nucleotide, Promoter Regions, Genetic, Transcription Factors
Show Abstract · Added March 14, 2018
BACKGROUND - Genome-phenome studies have identified thousands of variants that are statistically associated with disease or traits; however, their functional roles are largely unclear. A comprehensive investigation of regulatory mechanisms and the gene regulatory networks between phenome-wide association study (PheWAS) and genome-wide association study (GWAS) is needed to identify novel regulatory variants contributing to risk for human diseases.
METHODS - In this study, we developed an integrative functional genomics framework that maps 215,107 significant single nucleotide polymorphism (SNP) traits generated from the PheWAS Catalog and 28,870 genome-wide significant SNP traits collected from the GWAS Catalog into a global human genome regulatory map via incorporating various functional annotation data, including transcription factor (TF)-based motifs, promoters, enhancers, and expression quantitative trait loci (eQTLs) generated from four major functional genomics databases: FANTOM5, ENCODE, NIH Roadmap, and Genotype-Tissue Expression (GTEx). In addition, we performed a tissue-specific regulatory circuit analysis through the integration of the identified regulatory variants and tissue-specific gene expression profiles in 7051 samples across 32 tissues from GTEx.
RESULTS - We found that the disease-associated loci in both the PheWAS and GWAS Catalogs were significantly enriched with functional SNPs. The integration of functional annotations significantly improved the power of detecting novel associations in PheWAS, through which we found a number of functional associations with strong regulatory evidence in the PheWAS Catalog. Finally, we constructed tissue-specific regulatory circuits for several complex traits: mental diseases, autoimmune diseases, and cancer, via exploring tissue-specific TF-promoter/enhancer-target gene interaction networks. We uncovered several promising tissue-specific regulatory TFs or genes for Alzheimer's disease (e.g. ZIC1 and STX1B) and asthma (e.g. CSF3 and IL1RL1).
CONCLUSIONS - This study offers powerful tools for exploring the functional consequences of variants generated from genome-phenome association studies in terms of their mechanisms on affecting multiple complex diseases and traits.
0 Communities
2 Members
0 Resources
12 MeSH Terms
Identifying -mediators for -eQTLs across many human tissues using genomic mediation analysis.
Yang F, Wang J, GTEx Consortium, Pierce BL, Chen LS
(2017) Genome Res 27: 1859-1871
MeSH Terms: Databases, Genetic, Gene Expression Profiling, Gene Expression Regulation, Gene Regulatory Networks, Genetic Predisposition to Disease, Genome-Wide Association Study, Genomics, Humans, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Selection, Genetic, Tissue Distribution
Show Abstract · Added November 29, 2017
The impact of inherited genetic variation on gene expression in humans is well-established. The majority of known expression quantitative trait loci (eQTLs) impact expression of local genes (-eQTLs). More research is needed to identify effects of genetic variation on distant genes (-eQTLs) and understand their biological mechanisms. One common -eQTLs mechanism is "mediation" by a local () transcript. Thus, mediation analysis can be applied to genome-wide SNP and expression data in order to identify transcripts that are "-mediators" of -eQTLs, including those "-hubs" involved in regulation of many -genes. Identifying such mediators helps us understand regulatory networks and suggests biological mechanisms underlying -eQTLs, both of which are relevant for understanding susceptibility to complex diseases. The multitissue expression data from the Genotype-Tissue Expression (GTEx) program provides a unique opportunity to study -mediation across human tissue types. However, the presence of complex hidden confounding effects in biological systems can make mediation analyses challenging and prone to confounding bias, particularly when conducted among diverse samples. To address this problem, we propose a new method: Genomic Mediation analysis with Adaptive Confounding adjustment (GMAC). It enables the search of a very large pool of variables, and adaptively selects potential confounding variables for each mediation test. Analyses of simulated data and GTEx data demonstrate that the adaptive selection of confounders by GMAC improves the power and precision of mediation analysis. Application of GMAC to GTEx data provides new insights into the observed patterns of -hubs and -eQTL regulation across tissue types.
© 2017 Yang et al.; Published by Cold Spring Harbor Laboratory Press.
0 Communities
1 Members
0 Resources
12 MeSH Terms