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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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An alternative N-terminal fold of the intestine-specific annexin A13a induces dimerization and regulates membrane-binding.
McCulloch KM, Yamakawa I, Shifrin DA, McConnell RE, Foegeding NJ, Singh PK, Mao S, Tyska MJ, Iverson TM
(2019) J Biol Chem 294: 3454-3463
MeSH Terms: Animals, Annexins, Cell Membrane, Epithelial Cells, Humans, Hydrogen-Ion Concentration, Intestinal Mucosa, Intestines, Liposomes, Mice, Models, Molecular, Organ Specificity, Protein Binding, Protein Conformation, alpha-Helical, Protein Multimerization, Protein Structure, Quaternary, Protein Transport
Show Abstract · Added April 1, 2019
Annexin proteins function as Ca-dependent regulators of membrane trafficking and repair that may also modulate membrane curvature. Here, using high-resolution confocal imaging, we report that the intestine-specific annexin A13 (ANX A13) localizes to the tips of intestinal microvilli and determined the crystal structure of the ANX A13a isoform to 2.6 Å resolution. The structure revealed that the N terminus exhibits an alternative fold that converts the first two helices and the associated helix-loop-helix motif into a continuous α-helix, as stabilized by a domain-swapped dimer. We also found that the dimer is present in solution and partially occludes the membrane-binding surfaces of annexin, suggesting that dimerization may function as a means for regulating membrane binding. Accordingly, as revealed by binding and cellular localization assays, ANX A13a variants that favor a monomeric state exhibited increased membrane association relative to variants that favor the dimeric form. Together, our findings support a mechanism for how the association of the ANX A13a isoform with the membrane is regulated.
© 2019 McCulloch et al.
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17 MeSH Terms
Regional differences in brain glucose metabolism determined by imaging mass spectrometry.
Kleinridders A, Ferris HA, Reyzer ML, Rath M, Soto M, Manier ML, Spraggins J, Yang Z, Stanton RC, Caprioli RM, Kahn CR
(2018) Mol Metab 12: 113-121
MeSH Terms: Adenosine Triphosphate, Animals, Basal Metabolism, Brain, Fasting, Glucose, Glycolysis, Male, Mass Spectrometry, Mice, Mice, Inbred C57BL, Organ Specificity, Pentose Phosphate Pathway
Show Abstract · Added March 26, 2019
OBJECTIVE - Glucose is the major energy substrate of the brain and crucial for normal brain function. In diabetes, the brain is subject to episodes of hypo- and hyperglycemia resulting in acute outcomes ranging from confusion to seizures, while chronic metabolic dysregulation puts patients at increased risk for depression and Alzheimer's disease. In the present study, we aimed to determine how glucose is metabolized in different regions of the brain using imaging mass spectrometry (IMS).
METHODS - To examine the relative abundance of glucose and other metabolites in the brain, mouse brain sections were subjected to imaging mass spectrometry at a resolution of 100 μm. This was correlated with immunohistochemistry, qPCR, western blotting and enzyme assays of dissected brain regions to determine the relative contributions of the glycolytic and pentose phosphate pathways to regional glucose metabolism.
RESULTS - In brain, there are significant regional differences in glucose metabolism, with low levels of hexose bisphosphate (a glycolytic intermediate) and high levels of the pentose phosphate pathway (PPP) enzyme glucose-6-phosphate dehydrogenase (G6PD) and PPP metabolite hexose phosphate in thalamus compared to cortex. The ratio of ATP to ADP is significantly higher in white matter tracts, such as corpus callosum, compared to less myelinated areas. While the brain is able to maintain normal ratios of hexose phosphate, hexose bisphosphate, ATP, and ADP during fasting, fasting causes a large increase in cortical and hippocampal lactate.
CONCLUSION - These data demonstrate the importance of direct measurement of metabolic intermediates to determine regional differences in brain glucose metabolism and illustrate the strength of imaging mass spectrometry for investigating the impact of changing metabolic states on brain function at a regional level with high resolution.
Copyright © 2018 The Authors. Published by Elsevier GmbH.. All rights reserved.
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Spin-lock imaging of early tissue pH changes in ischemic rat brain.
Zu Z, Afzal A, Li H, Xie J, Gore JC
(2018) NMR Biomed 31: e3893
MeSH Terms: Animals, Brain Ischemia, Computer Simulation, Hydrogen-Ion Concentration, Magnetic Resonance Imaging, Numerical Analysis, Computer-Assisted, Organ Specificity, Rats, Spin Labels
Show Abstract · Added March 26, 2019
We have previously reported that the dispersion of spin-lattice relaxation rates in the rotating frame (R ) of tissue water protons at high field can be dominated by chemical exchange contributions. Ischemia in brain causes changes in tissue pH, which in turn may affect proton exchange rates. Amide proton transfer (APT, a form of chemical exchange saturation transfer) has been shown to be sensitive to chemical exchange rates and able to detect pH changes non-invasively following ischemic stroke. However, the specificity of APT to pH changes is decreased because of the influence of several other factors that affect magnetization transfer. R is less influenced by such confounding factors and thus may be more specific for detecting variations in pH. Here, we applied a spin-locking sequence to detect ischemic stroke in animal models. Although R images acquired with a single spin-locking amplitude (ω ) have previously been used to assess stroke, here we use ΔR , which is the difference in R values acquired with two different locking fields to emphasize selectively the contribution of chemical exchange effects. Numerical simulations with different exchange rates and measurements of tissue homogenates with different pH were performed to evaluate the specificity of ΔR to detect tissue acidosis. Spin-lock and APT data were acquired on five rat brains after ischemic strokes induced via middle cerebral artery occlusions. Correlations between these data were analyzed at different time points after the onset of stroke. The results show that ΔR (but not R acquired with a single ω ) was significantly correlated with APT metrics consistent with ΔR varying with pH.
Copyright © 2018 John Wiley & Sons, Ltd.
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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.
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Selective killing of with pH-responsive helix-coil conformation transitionable antimicrobial polypeptides.
Xiong M, Bao Y, Xu X, Wang H, Han Z, Wang Z, Liu Y, Huang S, Song Z, Chen J, Peek RM, Yin L, Chen LF, Cheng J
(2017) Proc Natl Acad Sci U S A 114: 12675-12680
MeSH Terms: Amines, Animals, Anti-Bacterial Agents, Antimicrobial Cationic Peptides, Disease Models, Animal, Female, Glutamic Acid, Helicobacter Infections, Helicobacter pylori, Hydrogen-Ion Concentration, Mice, Mice, Inbred C57BL, Mice, Inbred ICR, Organ Specificity, Protein Conformation, alpha-Helical, Static Electricity, Stomach
Show Abstract · Added March 14, 2018
Current clinical treatment of infection, the main etiological factor in the development of gastritis, gastric ulcers, and gastric carcinoma, requires a combination of at least two antibiotics and one proton pump inhibitor. However, such triple therapy suffers from progressively decreased therapeutic efficacy due to the drug resistance and undesired killing of the commensal bacteria due to poor selectivity. Here, we report the development of antimicrobial polypeptide-based monotherapy, which can specifically kill under acidic pH in the stomach while inducing minimal toxicity to commensal bacteria under physiological pH. Specifically, we designed a class of pH-sensitive, helix-coil conformation transitionable antimicrobial polypeptides (HCT-AMPs) (PGA)--(PHLG-MHH), bearing randomly distributed negatively charged glutamic acid and positively charged poly(γ-6--(methyldihexylammonium)hexyl-l-glutamate) (PHLG-MHH) residues. The HCT-AMPs showed unappreciable toxicity at physiological pH when they adopted random coiled conformation. Under acidic condition in the stomach, they transformed to the helical structure and exhibited potent antibacterial activity against , including clinically isolated drug-resistant strains. After oral gavage, the HCT-AMPs afforded comparable killing efficacy to the triple-therapy approach while inducing minimal toxicity against normal tissues and commensal bacteria, in comparison with the remarkable killing of commensal bacteria by 65% and 86% in the ileal contents and feces, respectively, following triple therapy. This strategy renders an effective approach to specifically target and kill in the stomach while not harming the commensal bacteria/normal tissues.
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Landscape of X chromosome inactivation across human tissues.
Tukiainen T, Villani AC, Yen A, Rivas MA, Marshall JL, Satija R, Aguirre M, Gauthier L, Fleharty M, Kirby A, Cummings BB, Castel SE, Karczewski KJ, Aguet F, Byrnes A, GTEx Consortium, Laboratory, Data Analysis &Coordinating Center (LDACC)—Analysis Working Group, Statistical Methods groups—Analysis Working Group, Enhancing GTEx (eGTEx) groups, NIH Common Fund, NIH/NCI, NIH/NHGRI, NIH/NIMH, NIH/NIDA, Biospecimen Collection Source Site—NDRI, Biospecimen Collection Source Site—RPCI, Biospecimen Core Resource—VARI, Brain Bank Repository—University of Miami Brain Endowment Bank, Leidos Biomedical—Project Management, ELSI Study, Genome Browser Data Integration &Visualization—EBI, Genome Browser Data Integration &Visualization—UCSC Genomics Institute, University of California Santa Cruz, Lappalainen T, Regev A, Ardlie KG, Hacohen N, MacArthur DG
(2017) Nature 550: 244-248
MeSH Terms: Chromosomes, Human, X, Female, Genes, X-Linked, Genome, Human, Genomics, Humans, Male, Organ Specificity, Phenotype, Sequence Analysis, RNA, Single-Cell Analysis, Transcriptome, X Chromosome Inactivation
Show Abstract · Added October 27, 2017
X chromosome inactivation (XCI) silences transcription from one of the two X chromosomes in female mammalian cells to balance expression dosage between XX females and XY males. XCI is, however, incomplete in humans: up to one-third of X-chromosomal genes are expressed from both the active and inactive X chromosomes (Xa and Xi, respectively) in female cells, with the degree of 'escape' from inactivation varying between genes and individuals. The extent to which XCI is shared between cells and tissues remains poorly characterized, as does the degree to which incomplete XCI manifests as detectable sex differences in gene expression and phenotypic traits. Here we describe a systematic survey of XCI, integrating over 5,500 transcriptomes from 449 individuals spanning 29 tissues from GTEx (v6p release) and 940 single-cell transcriptomes, combined with genomic sequence data. We show that XCI at 683 X-chromosomal genes is generally uniform across human tissues, but identify examples of heterogeneity between tissues, individuals and cells. We show that incomplete XCI affects at least 23% of X-chromosomal genes, identify seven genes that escape XCI with support from multiple lines of evidence and demonstrate that escape from XCI results in sex biases in gene expression, establishing incomplete XCI as a mechanism that is likely to introduce phenotypic diversity. Overall, this updated catalogue of XCI across human tissues helps to increase our understanding of the extent and impact of the incompleteness in the maintenance of XCI.
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Genetic effects on gene expression across human tissues.
GTEx Consortium, Laboratory, Data Analysis &Coordinating Center (LDACC)—Analysis Working Group, Statistical Methods groups—Analysis Working Group, Enhancing GTEx (eGTEx) groups, NIH Common Fund, NIH/NCI, NIH/NHGRI, NIH/NIMH, NIH/NIDA, Biospecimen Collection Source Site—NDRI, Biospecimen Collection Source Site—RPCI, Biospecimen Core Resource—VARI, Brain Bank Repository—University of Miami Brain Endowment Bank, Leidos Biomedical—Project Management, ELSI Study, Genome Browser Data Integration &Visualization—EBI, Genome Browser Data Integration &Visualization—UCSC Genomics Institute, University of California Santa Cruz, Lead analysts:, Laboratory, Data Analysis &Coordinating Center (LDACC):, NIH program management:, Biospecimen collection:, Pathology:, eQTL manuscript working group:, Battle A, Brown CD, Engelhardt BE, Montgomery SB
(2017) Nature 550: 204-213
MeSH Terms: Alleles, Chromosomes, Human, Disease, Female, Gene Expression Profiling, Gene Expression Regulation, Genetic Variation, Genome, Human, Genotype, Humans, Male, Organ Specificity, Quantitative Trait Loci
Show Abstract · Added October 27, 2017
Characterization of the molecular function of the human genome and its variation across individuals is essential for identifying the cellular mechanisms that underlie human genetic traits and diseases. The Genotype-Tissue Expression (GTEx) project aims to characterize variation in gene expression levels across individuals and diverse tissues of the human body, many of which are not easily accessible. Here we describe genetic effects on gene expression levels across 44 human tissues. We find that local genetic variation affects gene expression levels for the majority of genes, and we further identify inter-chromosomal genetic effects for 93 genes and 112 loci. On the basis of the identified genetic effects, we characterize patterns of tissue specificity, compare local and distal effects, and evaluate the functional properties of the genetic effects. We also demonstrate that multi-tissue, multi-individual data can be used to identify genes and pathways affected by human disease-associated variation, enabling a mechanistic interpretation of gene regulation and the genetic basis of disease.
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Dynamic landscape and regulation of RNA editing in mammals.
Tan MH, Li Q, Shanmugam R, Piskol R, Kohler J, Young AN, Liu KI, Zhang R, Ramaswami G, Ariyoshi K, Gupte A, Keegan LP, George CX, Ramu A, Huang N, Pollina EA, Leeman DS, Rustighi A, Goh YPS, GTEx Consortium, Laboratory, Data Analysis &Coordinating Center (LDACC)—Analysis Working Group, Statistical Methods groups—Analysis Working Group, Enhancing GTEx (eGTEx) groups, NIH Common Fund, NIH/NCI, NIH/NHGRI, NIH/NIMH, NIH/NIDA, Biospecimen Collection Source Site—NDRI, Biospecimen Collection Source Site—RPCI, Biospecimen Core Resource—VARI, Brain Bank Repository—University of Miami Brain Endowment Bank, Leidos Biomedical—Project Management, ELSI Study, Genome Browser Data Integration &Visualization—EBI, Genome Browser Data Integration &Visualization—UCSC Genomics Institute, University of California Santa Cruz, Chawla A, Del Sal G, Peltz G, Brunet A, Conrad DF, Samuel CE, O'Connell MA, Walkley CR, Nishikura K, Li JB
(2017) Nature 550: 249-254
MeSH Terms: Adenosine Deaminase, Animals, Female, Genotype, HEK293 Cells, Humans, Male, Mice, Muscles, Nuclear Proteins, Organ Specificity, Primates, Proteolysis, RNA Editing, RNA-Binding Proteins, Spatio-Temporal Analysis, Species Specificity, Transcriptome
Show Abstract · Added October 27, 2017
Adenosine-to-inosine (A-to-I) RNA editing is a conserved post-transcriptional mechanism mediated by ADAR enzymes that diversifies the transcriptome by altering selected nucleotides in RNA molecules. Although many editing sites have recently been discovered, the extent to which most sites are edited and how the editing is regulated in different biological contexts are not fully understood. Here we report dynamic spatiotemporal patterns and new regulators of RNA editing, discovered through an extensive profiling of A-to-I RNA editing in 8,551 human samples (representing 53 body sites from 552 individuals) from the Genotype-Tissue Expression (GTEx) project and in hundreds of other primate and mouse samples. We show that editing levels in non-repetitive coding regions vary more between tissues than editing levels in repetitive regions. Globally, ADAR1 is the primary editor of repetitive sites and ADAR2 is the primary editor of non-repetitive coding sites, whereas the catalytically inactive ADAR3 predominantly acts as an inhibitor of editing. Cross-species analysis of RNA editing in several tissues revealed that species, rather than tissue type, is the primary determinant of editing levels, suggesting stronger cis-directed regulation of RNA editing for most sites, although the small set of conserved coding sites is under stronger trans-regulation. In addition, we curated an extensive set of ADAR1 and ADAR2 targets and showed that many editing sites display distinct tissue-specific regulation by the ADAR enzymes in vivo. Further analysis of the GTEx data revealed several potential regulators of editing, such as AIMP2, which reduces editing in muscles by enhancing the degradation of the ADAR proteins. Collectively, our work provides insights into the complex cis- and trans-regulation of A-to-I editing.
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The impact of rare variation on gene expression across tissues.
Li X, Kim Y, Tsang EK, Davis JR, Damani FN, Chiang C, Hess GT, Zappala Z, Strober BJ, Scott AJ, Li A, Ganna A, Bassik MC, Merker JD, GTEx Consortium, Laboratory, Data Analysis &Coordinating Center (LDACC)—Analysis Working Group, Statistical Methods groups—Analysis Working Group, Enhancing GTEx (eGTEx) groups, NIH Common Fund, NIH/NCI, NIH/NHGRI, NIH/NIMH, NIH/NIDA, Biospecimen Collection Source Site—NDRI, Biospecimen Collection Source Site—RPCI, Biospecimen Core Resource—VARI, Brain Bank Repository—University of Miami Brain Endowment Bank, Leidos Biomedical—Project Management, ELSI Study, Genome Browser Data Integration &Visualization—EBI, Genome Browser Data Integration &Visualization—UCSC Genomics Institute, University of California Santa Cruz, Hall IM, Battle A, Montgomery SB
(2017) Nature 550: 239-243
MeSH Terms: Bayes Theorem, Female, Gene Expression Profiling, Genetic Variation, Genome, Human, Genomics, Genotype, Humans, Male, Models, Genetic, Organ Specificity, Sequence Analysis, RNA
Show Abstract · Added October 27, 2017
Rare genetic variants are abundant in humans and are expected to contribute to individual disease risk. While genetic association studies have successfully identified common genetic variants associated with susceptibility, these studies are not practical for identifying rare variants. Efforts to distinguish pathogenic variants from benign rare variants have leveraged the genetic code to identify deleterious protein-coding alleles, but no analogous code exists for non-coding variants. Therefore, ascertaining which rare variants have phenotypic effects remains a major challenge. Rare non-coding variants have been associated with extreme gene expression in studies using single tissues, but their effects across tissues are unknown. Here we identify gene expression outliers, or individuals showing extreme expression levels for a particular gene, across 44 human tissues by using combined analyses of whole genomes and multi-tissue RNA-sequencing data from the Genotype-Tissue Expression (GTEx) project v6p release. We find that 58% of underexpression and 28% of overexpression outliers have nearby conserved rare variants compared to 8% of non-outliers. Additionally, we developed RIVER (RNA-informed variant effect on regulation), a Bayesian statistical model that incorporates expression data to predict a regulatory effect for rare variants with higher accuracy than models using genomic annotations alone. Overall, we demonstrate that rare variants contribute to large gene expression changes across tissues and provide an integrative method for interpretation of rare variants in individual genomes.
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12 MeSH Terms