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Hoskins I, Rao S, Tante C, Cenik C. Integrated multiplexed assays of variant effect reveal determinants of catechol-O-methyltransferase gene expression. Mol Syst Biol 2024; 20:481-505. [PMID: 38355921 PMCID: PMC11066095 DOI: 10.1038/s44320-024-00018-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 01/16/2024] [Accepted: 01/18/2024] [Indexed: 02/16/2024] Open
Abstract
Multiplexed assays of variant effect are powerful methods to profile the consequences of rare variants on gene expression and organismal fitness. Yet, few studies have integrated several multiplexed assays to map variant effects on gene expression in coding sequences. Here, we pioneered a multiplexed assay based on polysome profiling to measure variant effects on translation at scale, uncovering single-nucleotide variants that increase or decrease ribosome load. By combining high-throughput ribosome load data with multiplexed mRNA and protein abundance readouts, we mapped the cis-regulatory landscape of thousands of catechol-O-methyltransferase (COMT) variants from RNA to protein and found numerous coding variants that alter COMT expression. Finally, we trained machine learning models to map signatures of variant effects on COMT gene expression and uncovered both directional and divergent impacts across expression layers. Our analyses reveal expression phenotypes for thousands of variants in COMT and highlight variant effects on both single and multiple layers of expression. Our findings prompt future studies that integrate several multiplexed assays for the readout of gene expression.
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Affiliation(s)
- Ian Hoskins
- Department of Molecular Biosciences, University of Texas at Austin, Austin, TX, 78712, USA
| | - Shilpa Rao
- Department of Molecular Biosciences, University of Texas at Austin, Austin, TX, 78712, USA
| | - Charisma Tante
- Department of Molecular Biosciences, University of Texas at Austin, Austin, TX, 78712, USA
| | - Can Cenik
- Department of Molecular Biosciences, University of Texas at Austin, Austin, TX, 78712, USA.
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2
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Song R, Xie L, Ding J, Chen Y, Zou H, Pang H, Peng Y, Xia Y, Xie Z, Li X, Xiao Y, Zhou Z, Hu J. Association of RPS26 gene polymorphism with different types of diabetes in Chinese individuals. J Diabetes Investig 2024; 15:34-43. [PMID: 38041572 PMCID: PMC10759724 DOI: 10.1111/jdi.14117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 11/07/2023] [Accepted: 11/16/2023] [Indexed: 12/03/2023] Open
Abstract
AIMS/INTRODUCTION Different types of diabetes show distinct genetic characteristics, but the specific genetic susceptibility factors remain unclear. Our study aimed to explore the associations between the ribosomal protein S26 (RPS26) gene rs1131017 polymorphisms and susceptibility to type 1 diabetes mellitus, latent autoimmune diabetes in adults (LADA) and type 2 diabetes mellitus in the Chinese Han population, and their correlations with clinical features. MATERIALS AND METHODS Genotyping of the rs1131017 variant was carried out for 1,006 type 1 diabetes mellitus patients, 210 LADA patients, 642 type 2 diabetes mellitus patients and 2,099 control individuals. RESULTS We found that the rs1131017 C allele was a risk locus for both type 1 diabetes mellitus and LADA (odds ratio [OR] 1.50, 95% confidence interval [CI] 1.33-1.69, P < 0.001; OR 1.31, 95% CI 1.04-1.64, P = 0.021, respectively). Nevertheless, this association was not found for type 2 diabetes mellitus. Carrying the C allele genotype was associated with a lower postprandial C-peptide for type 1 diabetes mellitus (OR 1.41, 95% CI 1.11-1.80, P = 0.006) and lower fasting C-peptide for LADA (OR 1.55, 95% CI 1.01-2.38, P = 0.047). Interestingly, a lower GC frequency was noted for LADA than for type 1 diabetes mellitus, regardless of classification based on age at diagnosis, C-peptide or glutamic acid decarboxylase antibody positivity. CONCLUSIONS The RPS26 polymorphism was associated with susceptibility and clinical characteristics of type 1 diabetes mellitus and LADA in the Chinese population, but was not related to type 2 diabetes mellitus. Thus, it might serve as a novel biomarker for particular types of diabetes.
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Affiliation(s)
- Rong Song
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and EndocrinologyThe Second Xiangya Hospital of Central South UniversityChangshaHunanChina
| | - Lingxiang Xie
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and EndocrinologyThe Second Xiangya Hospital of Central South UniversityChangshaHunanChina
| | - Jin Ding
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and EndocrinologyThe Second Xiangya Hospital of Central South UniversityChangshaHunanChina
| | - Yan Chen
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and EndocrinologyThe Second Xiangya Hospital of Central South UniversityChangshaHunanChina
| | - Hailan Zou
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and EndocrinologyThe Second Xiangya Hospital of Central South UniversityChangshaHunanChina
| | - Haipeng Pang
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and EndocrinologyThe Second Xiangya Hospital of Central South UniversityChangshaHunanChina
| | - Yiman Peng
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and EndocrinologyThe Second Xiangya Hospital of Central South UniversityChangshaHunanChina
| | - Ying Xia
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and EndocrinologyThe Second Xiangya Hospital of Central South UniversityChangshaHunanChina
| | - Zhiguo Xie
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and EndocrinologyThe Second Xiangya Hospital of Central South UniversityChangshaHunanChina
| | - Xia Li
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and EndocrinologyThe Second Xiangya Hospital of Central South UniversityChangshaHunanChina
| | - Yang Xiao
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and EndocrinologyThe Second Xiangya Hospital of Central South UniversityChangshaHunanChina
| | - Zhiguang Zhou
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and EndocrinologyThe Second Xiangya Hospital of Central South UniversityChangshaHunanChina
| | - Jingyi Hu
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and EndocrinologyThe Second Xiangya Hospital of Central South UniversityChangshaHunanChina
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3
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Hoskins I, Rao S, Tante C, Cenik C. Integrated multiplexed assays of variant effect reveal cis-regulatory determinants of catechol- O-methyltransferase gene expression. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.02.551517. [PMID: 38014045 PMCID: PMC10680568 DOI: 10.1101/2023.08.02.551517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Multiplexed assays of variant effect are powerful methods to profile the consequences of rare variants on gene expression and organismal fitness. Yet, few studies have integrated several multiplexed assays to map variant effects on gene expression in coding sequences. Here, we pioneered a multiplexed assay based on polysome profiling to measure variant effects on translation at scale, uncovering single-nucleotide variants that increase and decrease ribosome load. By combining high-throughput ribosome load data with multiplexed mRNA and protein abundance readouts, we mapped the cis-regulatory landscape of thousands of catechol-O-methyltransferase (COMT) variants from RNA to protein and found numerous coding variants that alter COMT expression. Finally, we trained machine learning models to map signatures of variant effects on COMT gene expression and uncovered both directional and divergent impacts across expression layers. Our analyses reveal expression phenotypes for thousands of variants in COMT and highlight variant effects on both single and multiple layers of expression. Our findings prompt future studies that integrate several multiplexed assays for the readout of gene expression.
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Affiliation(s)
- Ian Hoskins
- Department of Molecular Biosciences, University of Texas at Austin, Austin, TX 78712, USA
| | - Shilpa Rao
- Department of Molecular Biosciences, University of Texas at Austin, Austin, TX 78712, USA
| | - Charisma Tante
- Department of Molecular Biosciences, University of Texas at Austin, Austin, TX 78712, USA
| | - Can Cenik
- Department of Molecular Biosciences, University of Texas at Austin, Austin, TX 78712, USA
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4
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Zhu Z, Chen X, Zhang S, Yu R, Qi C, Cheng L, Zhang X. Leveraging molecular quantitative trait loci to comprehend complex diseases/traits from the omics perspective. Hum Genet 2023; 142:1543-1560. [PMID: 37755483 DOI: 10.1007/s00439-023-02602-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 09/14/2023] [Indexed: 09/28/2023]
Abstract
Comprehending the molecular basis of quantitative genetic variation is a principal goal for complex diseases or traits. Molecular quantitative trait loci (molQTLs) have made it possible to investigate the effects of genetic variants hiding behind large-scale omics data. A deeper understanding of molQTL is urgently required in light of the multi-dimensionalization of omics data to more fully elucidate the pertinent biological mechanisms. Herein, we reviewed molQTLs with the corresponding resource from the omics perspective and further discussed the integrative strategy of GWAS-molQTL to infer their causal effects. Subsequently, we described the opportunities and challenges encountered by molQTL. The case studies showed that molQTL is essential for complex diseases and traits, whether single- or multi-omics QTLs. Overall, we highlighted the functional significance of genetic variants to employ the discovery of molQTL in complex diseases and traits.
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Affiliation(s)
- Zijun Zhu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China
| | - Xinyu Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China
| | - Sainan Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China
| | - Rui Yu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China
| | - Changlu Qi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China
| | - Liang Cheng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China.
- NHC Key Laboratory of Molecular Probe and Targeted Diagnosis and Therapy, Harbin Medical University, Harbin, 150028, Heilongjiang, China.
| | - Xue Zhang
- NHC Key Laboratory of Molecular Probe and Targeted Diagnosis and Therapy, Harbin Medical University, Harbin, 150028, Heilongjiang, China
- McKusick-Zhang Center for Genetic Medicine, State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100005, China
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5
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Hoskins I, Sun S, Cote A, Roth FP, Cenik C. satmut_utils: a simulation and variant calling package for multiplexed assays of variant effect. Genome Biol 2023; 24:82. [PMID: 37081510 PMCID: PMC10116734 DOI: 10.1186/s13059-023-02922-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 04/04/2023] [Indexed: 04/22/2023] Open
Abstract
The impact of millions of individual genetic variants on molecular phenotypes in coding sequences remains unknown. Multiplexed assays of variant effect (MAVEs) are scalable methods to annotate relevant variants, but existing software lacks standardization, requires cumbersome configuration, and does not scale to large targets. We present satmut_utils as a flexible solution for simulation and variant quantification. We then benchmark MAVE software using simulated and real MAVE data. We finally determine mRNA abundance for thousands of cystathionine beta-synthase variants using two experimental methods. The satmut_utils package enables high-performance analysis of MAVEs and reveals the capability of variants to alter mRNA abundance.
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Affiliation(s)
- Ian Hoskins
- Department of Molecular Biosciences, University of Texas at Austin, Austin, TX, 78712, USA
| | - Song Sun
- The Donnelly Centre and Departments of Molecular Genetics and Computer Science, University of Toronto, Toronto, ON, Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON, Canada
| | - Atina Cote
- The Donnelly Centre and Departments of Molecular Genetics and Computer Science, University of Toronto, Toronto, ON, Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON, Canada
| | - Frederick P Roth
- The Donnelly Centre and Departments of Molecular Genetics and Computer Science, University of Toronto, Toronto, ON, Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON, Canada
| | - Can Cenik
- Department of Molecular Biosciences, University of Texas at Austin, Austin, TX, 78712, USA.
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6
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Abstract
Diploidy has profound implications for population genetics and susceptibility to genetic diseases. Although two copies are present for most genes in the human genome, they are not necessarily both active or active at the same level in a given individual. Genomic imprinting, resulting in exclusive or biased expression in favor of the allele of paternal or maternal origin, is now believed to affect hundreds of human genes. A far greater number of genes display unequal expression of gene copies due to cis-acting genetic variants that perturb gene expression. The availability of data generated by RNA sequencing applied to large numbers of individuals and tissue types has generated unprecedented opportunities to assess the contribution of genetic variation to allelic imbalance in gene expression. Here we review the insights gained through the analysis of these data about the extent of the genetic contribution to allelic expression imbalance, the tools and statistical models for gene expression imbalance, and what the results obtained reveal about the contribution of genetic variants that alter gene expression to complex human diseases and phenotypes.
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Affiliation(s)
- Siobhan Cleary
- School of Mathematics, Statistics and Applied Mathematics, National University of Ireland, Galway H91 H3CY, Ireland;
| | - Cathal Seoighe
- School of Mathematics, Statistics and Applied Mathematics, National University of Ireland, Galway H91 H3CY, Ireland;
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7
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Robert F, Pelletier J. Exploring the Impact of Single-Nucleotide Polymorphisms on Translation. Front Genet 2018; 9:507. [PMID: 30425729 PMCID: PMC6218417 DOI: 10.3389/fgene.2018.00507] [Citation(s) in RCA: 105] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2018] [Accepted: 10/10/2018] [Indexed: 01/08/2023] Open
Abstract
Over the past 15 years, sequencing of the human genome and The Cancer Genome Atlas (TCGA) project have led to comprehensive lists of single-nucleotide polymorphisms (SNPs) and gene mutations across a large number of human samples. However, our ability to predict the functional impact of SNPs and mutations on gene expression is still in its infancy. Here, we provide key examples to help understand how mutations present in genes can affect translational output.
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Affiliation(s)
- Francis Robert
- Department of Biochemistry, McGill University, Montreal, QC, Canada
| | - Jerry Pelletier
- Department of Biochemistry, McGill University, Montreal, QC, Canada.,Department of Oncology, McGill University, Montreal, QC, Canada.,Rosalind and Morris Goodman Cancer Research Centre, McGill University, Montreal, QC, Canada
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8
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Pierce BL, Tong L, Argos M, Demanelis K, Jasmine F, Rakibuz-Zaman M, Sarwar G, Islam MT, Shahriar H, Islam T, Rahman M, Yunus M, Kibriya MG, Chen LS, Ahsan H. Co-occurring expression and methylation QTLs allow detection of common causal variants and shared biological mechanisms. Nat Commun 2018; 9:804. [PMID: 29476079 PMCID: PMC5824840 DOI: 10.1038/s41467-018-03209-9] [Citation(s) in RCA: 49] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2016] [Accepted: 01/26/2018] [Indexed: 12/21/2022] Open
Abstract
Inherited genetic variation affects local gene expression and DNA methylation in humans. Most expression quantitative trait loci (cis-eQTLs) occur at the same genomic location as a methylation QTL (cis-meQTL), suggesting a common causal variant and shared mechanism. Using DNA and RNA from peripheral blood of Bangladeshi individuals, here we use co-localization methods to identify eQTL-meQTL pairs likely to share a causal variant. We use partial correlation and mediation analyses to identify >400 of these pairs showing evidence of a causal relationship between expression and methylation (i.e., shared mechanism) with many additional pairs we are underpowered to detect. These co-localized pairs are enriched for SNPs showing opposite associations with expression and methylation, although many SNPs affect multiple CpGs in opposite directions. This work demonstrates the pervasiveness of co-regulated expression and methylation in the human genome. Applying this approach to other types of molecular QTLs can enhance our understanding of regulatory mechanisms.
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Affiliation(s)
- Brandon L Pierce
- Department of Public Health Sciences, The University of Chicago, Chicago, IL, 60637, USA.
- Department of Human Genetics, The University of Chicago, Chicago, IL, 60637, USA.
- Comprehensive Cancer Center, The University of Chicago, Chicago, IL, 60637, USA.
| | - Lin Tong
- Department of Public Health Sciences, The University of Chicago, Chicago, IL, 60637, USA
| | - Maria Argos
- Division of Epidemiology and Biostatistics, University of Illinois at Chicago, Chicago, IL, 60612, USA
| | - Kathryn Demanelis
- Department of Public Health Sciences, The University of Chicago, Chicago, IL, 60637, USA
| | - Farzana Jasmine
- Department of Public Health Sciences, The University of Chicago, Chicago, IL, 60637, USA
| | | | - Golam Sarwar
- UChicago Research Bangladesh, Mohakhali, Dhaka, 1230, Bangladesh
| | - Md Tariqul Islam
- UChicago Research Bangladesh, Mohakhali, Dhaka, 1230, Bangladesh
| | - Hasan Shahriar
- UChicago Research Bangladesh, Mohakhali, Dhaka, 1230, Bangladesh
| | - Tariqul Islam
- UChicago Research Bangladesh, Mohakhali, Dhaka, 1230, Bangladesh
| | - Mahfuzar Rahman
- UChicago Research Bangladesh, Mohakhali, Dhaka, 1230, Bangladesh
- Research and Evaluation Division, BRAC, Dhaka, 1212, Bangladesh
| | - Md Yunus
- International Centre for Diarrhoeal Disease Research Bangladesh, Dhaka, 1000, Bangladesh
| | - Muhammad G Kibriya
- Department of Public Health Sciences, The University of Chicago, Chicago, IL, 60637, USA
| | - Lin S Chen
- Department of Public Health Sciences, The University of Chicago, Chicago, IL, 60637, USA
| | - Habibul Ahsan
- Department of Public Health Sciences, The University of Chicago, Chicago, IL, 60637, USA.
- Department of Human Genetics, The University of Chicago, Chicago, IL, 60637, USA.
- Comprehensive Cancer Center, The University of Chicago, Chicago, IL, 60637, USA.
- Department of Medicine, The University of Chicago, Chicago, IL, 60637, USA.
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9
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Kasela S, Kisand K, Tserel L, Kaleviste E, Remm A, Fischer K, Esko T, Westra HJ, Fairfax BP, Makino S, Knight JC, Franke L, Metspalu A, Peterson P, Milani L. Pathogenic implications for autoimmune mechanisms derived by comparative eQTL analysis of CD4+ versus CD8+ T cells. PLoS Genet 2017; 13:e1006643. [PMID: 28248954 PMCID: PMC5352142 DOI: 10.1371/journal.pgen.1006643] [Citation(s) in RCA: 76] [Impact Index Per Article: 10.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2016] [Revised: 03/15/2017] [Accepted: 02/18/2017] [Indexed: 12/28/2022] Open
Abstract
Inappropriate activation or inadequate regulation of CD4+ and CD8+ T cells may contribute to the initiation and progression of multiple autoimmune and inflammatory diseases. Studies on disease-associated genetic polymorphisms have highlighted the importance of biological context for many regulatory variants, which is particularly relevant in understanding the genetic regulation of the immune system and its cellular phenotypes. Here we show cell type-specific regulation of transcript levels of genes associated with several autoimmune diseases in CD4+ and CD8+ T cells including a trans-acting regulatory locus at chr12q13.2 containing the rs1131017 SNP in the RPS26 gene. Most remarkably, we identify a common missense variant in IL27, associated with type 1 diabetes that results in decreased functional activity of the protein and reduced expression levels of downstream IRF1 and STAT1 in CD4+ T cells only. Altogether, our results indicate that eQTL mapping in purified T cells provides novel functional insights into polymorphisms and pathways associated with autoimmune diseases.
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Affiliation(s)
- Silva Kasela
- Estonian Genome Center, University of Tartu, Tartu, Estonia
- Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
| | - Kai Kisand
- Institute of Biomedicine and Translational Medicine, University of Tartu, Tartu, Estonia
| | - Liina Tserel
- Institute of Biomedicine and Translational Medicine, University of Tartu, Tartu, Estonia
| | - Epp Kaleviste
- Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
- Institute of Biomedicine and Translational Medicine, University of Tartu, Tartu, Estonia
| | - Anu Remm
- Institute of Biomedicine and Translational Medicine, University of Tartu, Tartu, Estonia
| | - Krista Fischer
- Estonian Genome Center, University of Tartu, Tartu, Estonia
| | - Tõnu Esko
- Estonian Genome Center, University of Tartu, Tartu, Estonia
| | - Harm-Jan Westra
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, United States of America
- Partners Center for Personalized Genetic Medicine, Boston, MA, United States of America
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, United States of America
| | - Benjamin P. Fairfax
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
| | - Seiko Makino
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
| | - Julian C. Knight
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
| | - Lude Franke
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Andres Metspalu
- Estonian Genome Center, University of Tartu, Tartu, Estonia
- Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
| | - Pärt Peterson
- Institute of Biomedicine and Translational Medicine, University of Tartu, Tartu, Estonia
| | - Lili Milani
- Estonian Genome Center, University of Tartu, Tartu, Estonia
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10
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Mascarenhas R, Pietrzak M, Smith RM, Webb A, Wang D, Papp AC, Pinsonneault JK, Seweryn M, Rempala G, Sadee W. Allele-Selective Transcriptome Recruitment to Polysomes Primed for Translation: Protein-Coding and Noncoding RNAs, and RNA Isoforms. PLoS One 2015; 10:e0136798. [PMID: 26331722 PMCID: PMC4558023 DOI: 10.1371/journal.pone.0136798] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2015] [Accepted: 08/07/2015] [Indexed: 11/19/2022] Open
Abstract
mRNA translation into proteins is highly regulated, but the role of mRNA isoforms, noncoding RNAs (ncRNAs), and genetic variants remains poorly understood. mRNA levels on polysomes have been shown to correlate well with expressed protein levels, pointing to polysomal loading as a critical factor. To study regulation and genetic factors of protein translation we measured levels and allelic ratios of mRNAs and ncRNAs (including microRNAs) in lymphoblast cell lines (LCL) and in polysomal fractions. We first used targeted assays to measure polysomal loading of mRNA alleles, confirming reported genetic effects on translation of OPRM1 and NAT1, and detecting no effect of rs1045642 (3435C>T) in ABCB1 (MDR1) on polysomal loading while supporting previous results showing increased mRNA turnover of the 3435T allele. Use of high-throughput sequencing of complete transcript profiles (RNA-Seq) in three LCLs revealed significant differences in polysomal loading of individual RNA classes and isoforms. Correlated polysomal distribution between protein-coding and non-coding RNAs suggests interactions between them. Allele-selective polysome recruitment revealed strong genetic influence for multiple RNAs, attributable either to differential expression of RNA isoforms or to differential loading onto polysomes, the latter defining a direct genetic effect on translation. Genes identified by different allelic RNA ratios between cytosol and polysomes were enriched with published expression quantitative trait loci (eQTLs) affecting RNA functions, and associations with clinical phenotypes. Polysomal RNA-Seq combined with allelic ratio analysis provides a powerful approach to study polysomal RNA recruitment and regulatory variants affecting protein translation.
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Affiliation(s)
- Roshan Mascarenhas
- Center for Pharmacogenomics, College of Medicine, The Ohio State University Wexner Medical Center, Columbus, Ohio, United States of America
| | - Maciej Pietrzak
- Center for Pharmacogenomics, College of Medicine, The Ohio State University Wexner Medical Center, Columbus, Ohio, United States of America
- Division of Biostatistics, College of Public Health, The Ohio State University, Columbus, Ohio, United States of America
| | - Ryan M. Smith
- Center for Pharmacogenomics, College of Medicine, The Ohio State University Wexner Medical Center, Columbus, Ohio, United States of America
| | - Amy Webb
- Department of Biomedical Informatics, College of Medicine, The Ohio State University Wexner Medical Center, Columbus, Ohio, United States of America
| | - Danxin Wang
- Center for Pharmacogenomics, College of Medicine, The Ohio State University Wexner Medical Center, Columbus, Ohio, United States of America
| | - Audrey C. Papp
- Center for Pharmacogenomics, College of Medicine, The Ohio State University Wexner Medical Center, Columbus, Ohio, United States of America
| | - Julia K. Pinsonneault
- Center for Pharmacogenomics, College of Medicine, The Ohio State University Wexner Medical Center, Columbus, Ohio, United States of America
| | - Michal Seweryn
- Division of Biostatistics, College of Public Health, The Ohio State University, Columbus, Ohio, United States of America
- Department of Mathematics and Computer Science, University of Lodz, Lodz, Poland
- Mathematical Biosciences Institute, The Ohio State University, Columbus, Ohio, United States of America
| | - Grzegorz Rempala
- Center for Pharmacogenomics, College of Medicine, The Ohio State University Wexner Medical Center, Columbus, Ohio, United States of America
- Division of Biostatistics, College of Public Health, The Ohio State University, Columbus, Ohio, United States of America
- Mathematical Biosciences Institute, The Ohio State University, Columbus, Ohio, United States of America
| | - Wolfgang Sadee
- Center for Pharmacogenomics, College of Medicine, The Ohio State University Wexner Medical Center, Columbus, Ohio, United States of America
- Department of Medical Genetics, College of Medicine, The Ohio State University Wexner Medical Center, Columbus, Ohio, United States of America
- * E-mail:
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11
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Frankish A, Uszczynska B, Ritchie GRS, Gonzalez JM, Pervouchine D, Petryszak R, Mudge JM, Fonseca N, Brazma A, Guigo R, Harrow J. Comparison of GENCODE and RefSeq gene annotation and the impact of reference geneset on variant effect prediction. BMC Genomics 2015; 16 Suppl 8:S2. [PMID: 26110515 PMCID: PMC4502323 DOI: 10.1186/1471-2164-16-s8-s2] [Citation(s) in RCA: 59] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
Background A vast amount of DNA variation is being identified by increasingly large-scale exome and genome sequencing projects. To be useful, variants require accurate functional annotation and a wide range of tools are available to this end. McCarthy et al recently demonstrated the large differences in prediction of loss-of-function (LoF) variation when RefSeq and Ensembl transcripts are used for annotation, highlighting the importance of the reference transcripts on which variant functional annotation is based. Results We describe a detailed analysis of the similarities and differences between the gene and transcript annotation in the GENCODE and RefSeq genesets. We demonstrate that the GENCODE Comprehensive set is richer in alternative splicing, novel CDSs, novel exons and has higher genomic coverage than RefSeq, while the GENCODE Basic set is very similar to RefSeq. Using RNAseq data we show that exons and introns unique to one geneset are expressed at a similar level to those common to both. We present evidence that the differences in gene annotation lead to large differences in variant annotation where GENCODE and RefSeq are used as reference transcripts, although this is predominantly confined to non-coding transcripts and UTR sequence, with at most ~30% of LoF variants annotated discordantly. We also describe an investigation of dominant transcript expression, showing that it both supports the utility of the GENCODE Basic set in providing a smaller set of more highly expressed transcripts and provides a useful, biologically-relevant filter for further reducing the complexity of the transcriptome. Conclusions The reference transcripts selected for variant functional annotation do have a large effect on the outcome. The GENCODE Comprehensive transcripts contain more exons, have greater genomic coverage and capture many more variants than RefSeq in both genome and exome datasets, while the GENCODE Basic set shows a higher degree of concordance with RefSeq and has fewer unique features. We propose that the GENCODE Comprehensive set has great utility for the discovery of new variants with functional potential, while the GENCODE Basic set is more suitable for applications demanding less complex interpretation of functional variants.
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Sadee W, Hartmann K, Seweryn M, Pietrzak M, Handelman SK, Rempala GA. Missing heritability of common diseases and treatments outside the protein-coding exome. Hum Genet 2014; 133:1199-215. [PMID: 25107510 PMCID: PMC4169001 DOI: 10.1007/s00439-014-1476-7] [Citation(s) in RCA: 52] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2014] [Accepted: 07/23/2014] [Indexed: 02/07/2023]
Abstract
Genetic factors strongly influence risk of common human diseases and treatment outcomes but the causative variants remain largely unknown; this gap has been called the 'missing heritability'. We propose several hypotheses that in combination have the potential to narrow the gap. First, given a multi-stage path from wellness to disease, we propose that common variants under positive evolutionary selection represent normal variation and gate the transition between wellness and an 'off-well' state, revealing adaptations to changing environmental conditions. In contrast, genome-wide association studies (GWAS) focus on deleterious variants conveying disease risk, accelerating the path from off-well to illness and finally specific diseases, while common 'normal' variants remain hidden in the noise. Second, epistasis (dynamic gene-gene interactions) likely assumes a central role in adaptations and evolution; yet, GWAS analyses currently are poorly designed to reveal epistasis. As gene regulation is germane to adaptation, we propose that epistasis among common normal regulatory variants, or between common variants and less frequent deleterious variants, can have strong protective or deleterious phenotypic effects. These gene-gene interactions can be highly sensitive to environmental stimuli and could account for large differences in drug response between individuals. Residing largely outside the protein-coding exome, common regulatory variants affect either transcription of coding and non-coding RNAs (regulatory SNPs, or rSNPs) or RNA functions and processing (structural RNA SNPs, or srSNPs). Third, with the vast majority of causative variants yet to be discovered, GWAS rely on surrogate markers, a confounding factor aggravated by the presence of more than one causative variant per gene and by epistasis. We propose that the confluence of these factors may be responsible to large extent for the observed heritability gap.
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Affiliation(s)
- Wolfgang Sadee
- Department of Pharmacology, Center for Pharmacogenomics, College of Medicine, The Ohio State University Wexner Medical Center, 5184A Graves Hall, 333 West 10th Avenue, Columbus, OH, 43210, USA,
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Dassi E, Quattrone A. Fingerprints of a message: integrating positional information on the transcriptome. Front Cell Dev Biol 2014; 2:39. [PMID: 25364746 PMCID: PMC4207014 DOI: 10.3389/fcell.2014.00039] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2014] [Accepted: 07/30/2014] [Indexed: 12/24/2022] Open
Abstract
The recent explosion of high-throughput sequencing methods applied to RNA molecules is allowing us to go beyond the description of sequence variants and their relative abundances, as measured by RNA-seq. We can now probe for RNA engagement in polysomes, for ribosomes, RNA binding proteins and microRNAs binding sites, for RNA secondary structure and for RNA methylation. These descriptors produce a steadily growing multidimensional array of positional information on RNA sequences, whose effective integration only would bring to decipher the regulatory interplay occurring between proteins, RNAs and their modifications on the transcriptome. This interplay ultimately dictates the degree of mRNA availability to translation, and thus the occurrence of cell phenotypes. However, several issues in data presentation are slowing down effective integration. A standardization effort for new dataset types produced should be urgently undertaken to solve these issues. Providing uniformed experimental details along with datasets processed to be directly usable and employing shared formats would greatly simplify integration efforts, strengthening hypotheses stemming from correlative observations and eventually bringing to mechanistic understanding.
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Affiliation(s)
- Erik Dassi
- Laboratory of Translational Genomics, Centre for Integrative Biology, University of Trento Trento, Italy
| | - Alessandro Quattrone
- Laboratory of Translational Genomics, Centre for Integrative Biology, University of Trento Trento, Italy
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Li MJ, Yan B, Sham PC, Wang J. Exploring the function of genetic variants in the non-coding genomic regions: approaches for identifying human regulatory variants affecting gene expression. Brief Bioinform 2014; 16:393-412. [PMID: 24916300 DOI: 10.1093/bib/bbu018] [Citation(s) in RCA: 45] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2014] [Accepted: 04/23/2014] [Indexed: 12/13/2022] Open
Abstract
Understanding the genetic basis of human traits/diseases and the underlying mechanisms of how these traits/diseases are affected by genetic variations is critical for public health. Current genome-wide functional genomics data uncovered a large number of functional elements in the noncoding regions of human genome, providing new opportunities to study regulatory variants (RVs). RVs play important roles in transcription factor bindings, chromatin states and epigenetic modifications. Here, we systematically review an array of methods currently used to map RVs as well as the computational approaches in annotating and interpreting their regulatory effects, with emphasis on regulatory single-nucleotide polymorphism. We also briefly introduce experimental methods to validate these functional RVs.
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