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Shao M, Chen K, Zhang S, Tian M, Shen Y, Cao C, Gu N. Multiome-wide Association Studies: Novel Approaches for Understanding Diseases. GENOMICS, PROTEOMICS & BIOINFORMATICS 2024; 22:qzae077. [PMID: 39471467 PMCID: PMC11630051 DOI: 10.1093/gpbjnl/qzae077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 10/06/2024] [Accepted: 10/23/2024] [Indexed: 11/01/2024]
Abstract
The rapid development of multiome (transcriptome, proteome, cistrome, imaging, and regulome)-wide association study methods have opened new avenues for biologists to understand the susceptibility genes underlying complex diseases. Thorough comparisons of these methods are essential for selecting the most appropriate tool for a given research objective. This review provides a detailed categorization and summary of the statistical models, use cases, and advantages of recent multiome-wide association studies. In addition, to illustrate gene-disease association studies based on transcriptome-wide association study (TWAS), we collected 478 disease entries across 22 categories from 235 manually reviewed publications. Our analysis reveals that mental disorders are the most frequently studied diseases by TWAS, indicating its potential to deepen our understanding of the genetic architecture of complex diseases. In summary, this review underscores the importance of multiome-wide association studies in elucidating complex diseases and highlights the significance of selecting the appropriate method for each study.
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Affiliation(s)
- Mengting Shao
- Key Laboratory for Bio-Electromagnetic Environment and Advanced Medical Theranostics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing 211166, China
| | - Kaiyang Chen
- Key Laboratory for Bio-Electromagnetic Environment and Advanced Medical Theranostics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing 211166, China
| | - Shuting Zhang
- Key Laboratory for Bio-Electromagnetic Environment and Advanced Medical Theranostics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing 211166, China
| | - Min Tian
- Key Laboratory for Bio-Electromagnetic Environment and Advanced Medical Theranostics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing 211166, China
| | - Yan Shen
- Key Laboratory for Bio-Electromagnetic Environment and Advanced Medical Theranostics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing 211166, China
| | - Chen Cao
- Key Laboratory for Bio-Electromagnetic Environment and Advanced Medical Theranostics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing 211166, China
| | - Ning Gu
- Key Laboratory for Bio-Electromagnetic Environment and Advanced Medical Theranostics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing 211166, China
- Nanjing Key Laboratory for Cardiovascular Information and Health Engineering Medicine, Institute of Clinical Medicine, Nanjing Drum Tower Hospital, Medical School, Nanjing University, Nanjing 210093, China
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Quiccione MS, Tirozzi A, Cassioli G, Morelli M, Costanzo S, Pepe A, Bracone F, Magnacca S, Cerletti C, Licastro D, Di Castelnuovo A, Donati MB, de Gaetano G, Iacoviello L, Gialluisi A. Are Methylation Patterns in the KALRN Gene Associated with Cognitive and Depressive Symptoms? Findings from the Moli-sani Cohort. Int J Mol Sci 2024; 25:10317. [PMID: 39408648 PMCID: PMC11476580 DOI: 10.3390/ijms251910317] [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: 08/01/2024] [Revised: 09/17/2024] [Accepted: 09/20/2024] [Indexed: 10/19/2024] Open
Abstract
The KALRN gene (encoding kalirin) has been implicated in several neuropsychiatric and neurodegenerative disorders. However, genetic evidence supporting this implication is limited and targeted epigenetic analyses are lacking. Here, we tested associations between epigenetic variation in KALRN and interindividual variation in depressive symptoms (PHQ9) and cognitive (MoCA) performance, in an Italian population cohort (N = 2409; mean (SD) age: 67 (9) years; 55% women). First, we analyzed the candidate region chr3:124584826-124584886 (hg38), within the KALRN promoter, through pyrosequencing of 1385 samples. Then, we widened the investigated region by analyzing 137 CpGs annotated to the whole gene, rescued from epigenome-wide (Illumina EPIC) data from 1024 independent samples from the same cohort. These were tested through stepwise regression models adjusted for age, sex, circulating leukocytes fractions, education, prevalent health conditions and lifestyles. We observed no statistically significant associations with methylation levels in the three CpGs tested through pyrosequencing, or in the gene-wide association analysis with MoCA score. However, we observed a statistically significant association between PHQ9 and cg13549966 (chr3:124106738; β (Standard Error) = 0.28 (0.08), Bonferroni-corrected p = 0.025), located close to the transcription start site of the gene. This association was driven by a polychoric factor tagging somatic depressive symptoms (β (SE) = 0.127 (0.064), p = 0.048). This evidence underscores the importance of studying epigenetic variation within the KALRN gene and the role that it may play in brain diseases, particularly in atypical depression, which is often characterized by somatic symptoms.
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Affiliation(s)
- Miriam Shasa Quiccione
- Department of Epidemiology and Prevention, IRCCS Neuromed, Via dell’Elettronica, 86077 Pozzilli, Italy; (M.S.Q.); (A.T.); (M.M.); (S.C.); (A.P.); (F.B.); (S.M.); (C.C.); (A.D.C.); (M.B.D.); (G.d.G.); (A.G.)
| | - Alfonsina Tirozzi
- Department of Epidemiology and Prevention, IRCCS Neuromed, Via dell’Elettronica, 86077 Pozzilli, Italy; (M.S.Q.); (A.T.); (M.M.); (S.C.); (A.P.); (F.B.); (S.M.); (C.C.); (A.D.C.); (M.B.D.); (G.d.G.); (A.G.)
| | - Giulia Cassioli
- Department of Experimental and Clinical Medicine, University of Florence, 50134 Florence, Italy;
| | - Martina Morelli
- Department of Epidemiology and Prevention, IRCCS Neuromed, Via dell’Elettronica, 86077 Pozzilli, Italy; (M.S.Q.); (A.T.); (M.M.); (S.C.); (A.P.); (F.B.); (S.M.); (C.C.); (A.D.C.); (M.B.D.); (G.d.G.); (A.G.)
| | - Simona Costanzo
- Department of Epidemiology and Prevention, IRCCS Neuromed, Via dell’Elettronica, 86077 Pozzilli, Italy; (M.S.Q.); (A.T.); (M.M.); (S.C.); (A.P.); (F.B.); (S.M.); (C.C.); (A.D.C.); (M.B.D.); (G.d.G.); (A.G.)
| | - Antonietta Pepe
- Department of Epidemiology and Prevention, IRCCS Neuromed, Via dell’Elettronica, 86077 Pozzilli, Italy; (M.S.Q.); (A.T.); (M.M.); (S.C.); (A.P.); (F.B.); (S.M.); (C.C.); (A.D.C.); (M.B.D.); (G.d.G.); (A.G.)
| | - Francesca Bracone
- Department of Epidemiology and Prevention, IRCCS Neuromed, Via dell’Elettronica, 86077 Pozzilli, Italy; (M.S.Q.); (A.T.); (M.M.); (S.C.); (A.P.); (F.B.); (S.M.); (C.C.); (A.D.C.); (M.B.D.); (G.d.G.); (A.G.)
| | - Sara Magnacca
- Department of Epidemiology and Prevention, IRCCS Neuromed, Via dell’Elettronica, 86077 Pozzilli, Italy; (M.S.Q.); (A.T.); (M.M.); (S.C.); (A.P.); (F.B.); (S.M.); (C.C.); (A.D.C.); (M.B.D.); (G.d.G.); (A.G.)
| | - Chiara Cerletti
- Department of Epidemiology and Prevention, IRCCS Neuromed, Via dell’Elettronica, 86077 Pozzilli, Italy; (M.S.Q.); (A.T.); (M.M.); (S.C.); (A.P.); (F.B.); (S.M.); (C.C.); (A.D.C.); (M.B.D.); (G.d.G.); (A.G.)
| | | | - Augusto Di Castelnuovo
- Department of Epidemiology and Prevention, IRCCS Neuromed, Via dell’Elettronica, 86077 Pozzilli, Italy; (M.S.Q.); (A.T.); (M.M.); (S.C.); (A.P.); (F.B.); (S.M.); (C.C.); (A.D.C.); (M.B.D.); (G.d.G.); (A.G.)
| | - Maria Benedetta Donati
- Department of Epidemiology and Prevention, IRCCS Neuromed, Via dell’Elettronica, 86077 Pozzilli, Italy; (M.S.Q.); (A.T.); (M.M.); (S.C.); (A.P.); (F.B.); (S.M.); (C.C.); (A.D.C.); (M.B.D.); (G.d.G.); (A.G.)
| | - Giovanni de Gaetano
- Department of Epidemiology and Prevention, IRCCS Neuromed, Via dell’Elettronica, 86077 Pozzilli, Italy; (M.S.Q.); (A.T.); (M.M.); (S.C.); (A.P.); (F.B.); (S.M.); (C.C.); (A.D.C.); (M.B.D.); (G.d.G.); (A.G.)
| | - Licia Iacoviello
- Department of Epidemiology and Prevention, IRCCS Neuromed, Via dell’Elettronica, 86077 Pozzilli, Italy; (M.S.Q.); (A.T.); (M.M.); (S.C.); (A.P.); (F.B.); (S.M.); (C.C.); (A.D.C.); (M.B.D.); (G.d.G.); (A.G.)
- Department of Medicine and Surgery, LUM University, 70010 Casamassima, Italy
| | - Alessandro Gialluisi
- Department of Epidemiology and Prevention, IRCCS Neuromed, Via dell’Elettronica, 86077 Pozzilli, Italy; (M.S.Q.); (A.T.); (M.M.); (S.C.); (A.P.); (F.B.); (S.M.); (C.C.); (A.D.C.); (M.B.D.); (G.d.G.); (A.G.)
- Department of Medicine and Surgery, LUM University, 70010 Casamassima, Italy
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Yuan C, Yu XT, Wang J, Shu B, Wang XY, Huang C, Lv X, Peng QQ, Qi WH, Zhang J, Zheng Y, Wang SJ, Liang QQ, Shi Q, Li T, Huang H, Mei ZD, Zhang HT, Xu HB, Cui J, Wang H, Zhang H, Shi BH, Sun P, Zhang H, Ma ZL, Feng Y, Chen L, Zeng T, Tang DZ, Wang YJ. Multi-modal molecular determinants of clinically relevant osteoporosis subtypes. Cell Discov 2024; 10:28. [PMID: 38472169 PMCID: PMC10933295 DOI: 10.1038/s41421-024-00652-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 01/24/2024] [Indexed: 03/14/2024] Open
Abstract
Due to a rapidly aging global population, osteoporosis and the associated risk of bone fractures have become a wide-spread public health problem. However, osteoporosis is very heterogeneous, and the existing standard diagnostic measure is not sufficient to accurately identify all patients at risk of osteoporotic fractures and to guide therapy. Here, we constructed the first prospective multi-omics atlas of the largest osteoporosis cohort to date (longitudinal data from 366 participants at three time points), and also implemented an explainable data-intensive analysis framework (DLSF: Deep Latent Space Fusion) for an omnigenic model based on a multi-modal approach that can capture the multi-modal molecular signatures (M3S) as explicit functional representations of hidden genotypes. Accordingly, through DLSF, we identified two subtypes of the osteoporosis population in Chinese individuals with corresponding molecular phenotypes, i.e., clinical intervention relevant subtypes (CISs), in which bone mineral density benefits response to calcium supplements in 2-year follow-up samples. Many snpGenes associated with these molecular phenotypes reveal diverse candidate biological mechanisms underlying osteoporosis, with xQTL preferences of osteoporosis and its subtypes indicating an omnigenic effect on different biological domains. Finally, these two subtypes were found to have different relevance to prior fracture and different fracture risk according to 4-year follow-up data. Thus, in clinical application, M3S could help us further develop improved diagnostic and treatment strategies for osteoporosis and identify a new composite index for fracture prediction, which were remarkably validated in an independent cohort (166 participants).
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Affiliation(s)
- Chunchun Yuan
- Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Key Laboratory of Theory and Therapy of Muscles and Bones, Ministry of Education, Shanghai, China
- Spine Institute, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Xiang-Tian Yu
- Clinical Research Center, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jing Wang
- Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Key Laboratory of Theory and Therapy of Muscles and Bones, Ministry of Education, Shanghai, China
- Shanghai Geriatric Institute of Chinese Medicine, Shanghai, China
| | - Bing Shu
- Key Laboratory of Theory and Therapy of Muscles and Bones, Ministry of Education, Shanghai, China
- Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xiao-Yun Wang
- Shanghai Research Institute of Acupuncture and Meridian, Shanghai, China
| | - Chen Huang
- Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Key Laboratory of Theory and Therapy of Muscles and Bones, Ministry of Education, Shanghai, China
- Spine Institute, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Xia Lv
- Hudong Hospital of Shanghai, Shanghai, China
| | - Qian-Qian Peng
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Wen-Hao Qi
- Ministry of Education Key Laboratory of Contemporary Anthropology, Department of Anthropology and Human Genetics, School of Life Science, Fudan University, Shanghai, China
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Fudan University, Shanghai, China
| | - Jing Zhang
- Green Valley (Shanghai) Pharmaceuticals Co., Ltd., Shanghai, China
| | - Yan Zheng
- Ministry of Education Key Laboratory of Contemporary Anthropology, Department of Anthropology and Human Genetics, School of Life Science, Fudan University, Shanghai, China
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Fudan University, Shanghai, China
| | - Si-Jia Wang
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Qian-Qian Liang
- Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Key Laboratory of Theory and Therapy of Muscles and Bones, Ministry of Education, Shanghai, China
- Spine Institute, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Qi Shi
- Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Key Laboratory of Theory and Therapy of Muscles and Bones, Ministry of Education, Shanghai, China
- Spine Institute, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
- Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Ting Li
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - He Huang
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
- Ministry of Education Key Laboratory of Contemporary Anthropology, Department of Anthropology and Human Genetics, School of Life Science, Fudan University, Shanghai, China
| | - Zhen-Dong Mei
- Ministry of Education Key Laboratory of Contemporary Anthropology, Department of Anthropology and Human Genetics, School of Life Science, Fudan University, Shanghai, China
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Fudan University, Shanghai, China
| | - Hai-Tao Zhang
- Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Key Laboratory of Theory and Therapy of Muscles and Bones, Ministry of Education, Shanghai, China
- Spine Institute, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Hong-Bin Xu
- Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Key Laboratory of Theory and Therapy of Muscles and Bones, Ministry of Education, Shanghai, China
- Spine Institute, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Jiarui Cui
- Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Key Laboratory of Theory and Therapy of Muscles and Bones, Ministry of Education, Shanghai, China
- Spine Institute, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Hongyu Wang
- Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Key Laboratory of Theory and Therapy of Muscles and Bones, Ministry of Education, Shanghai, China
- Spine Institute, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Hong Zhang
- Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Key Laboratory of Theory and Therapy of Muscles and Bones, Ministry of Education, Shanghai, China
- Spine Institute, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Bin-Hao Shi
- Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Key Laboratory of Theory and Therapy of Muscles and Bones, Ministry of Education, Shanghai, China
- Spine Institute, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Pan Sun
- Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Key Laboratory of Theory and Therapy of Muscles and Bones, Ministry of Education, Shanghai, China
- Spine Institute, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Hui Zhang
- Hudong Hospital of Shanghai, Shanghai, China
| | | | - Yuan Feng
- Green Valley (Shanghai) Pharmaceuticals Co., Ltd., Shanghai, China
| | - Luonan Chen
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, China.
| | - Tao Zeng
- Guangzhou National Laboratory, Guangzhou, China.
| | - De-Zhi Tang
- Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
- Key Laboratory of Theory and Therapy of Muscles and Bones, Ministry of Education, Shanghai, China.
- Spine Institute, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China.
| | - Yong-Jun Wang
- Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
- Key Laboratory of Theory and Therapy of Muscles and Bones, Ministry of Education, Shanghai, China.
- Spine Institute, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China.
- Shanghai University of Traditional Chinese Medicine, Shanghai, China.
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Yadav G, Kulshreshtha R. Pan-cancer analyses identify MIR210HG overexpression, epigenetic regulation and oncogenic role in human tumors and its interaction with the tumor microenvironment. Life Sci 2024; 339:122438. [PMID: 38242493 DOI: 10.1016/j.lfs.2024.122438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Revised: 01/09/2024] [Accepted: 01/13/2024] [Indexed: 01/21/2024]
Abstract
BACKGROUND Molecular entities showing dysregulation in multiple cancers may hold great biomarker or therapeutic potential. There is accumulating evidence that highlights the dysregulation of a long non-coding RNA, MIR210HG, in various cancers and its oncogenic role. However, a comprehensive analysis of MIR210HG expression pattern, molecular mechanisms, diagnostic or prognostic significance or evaluation of its interaction with tumor microenvironment across various cancers remains unstudied. METHODS A systematic pan-cancer analysis was done using multiple public databases and bioinformatic tools to study the molecular role and clinical significance of MIR210HG. We have analyzed expression patterns, genome alteration, transcriptional and epigenetic regulation, correlation with patient survival, immune infiltrates, co-expressed genes, interacting proteins, and pathways associated with MIR210HG. RESULTS The Pan cancer expression analysis of MIR210HG through various tumor datasets demonstrated that MIR210HG is significantly upregulated in most cancers and increased with the tumor stage in a subset of them. Furthermore, prognostic analysis revealed high MIR210HG expression is associated with poor overall and disease-free survival in specific cancer types. Genetic alteration analysis showed minimal alterations in the MIR210HG locus, indicating that overexpression in cancers is not due to gene amplification. The exploration of SNPs on MIR210HG suggested possible structural changes that may affect its interactions with the miRNAs. The correlation of MIR210HG with promoter methylation was found to be significantly negative in nature in majority of cancers depicting the possible epigenetic regulation of expression of MIR210HG. Additionally, MIR210HG showed negative correlations with immune cells and thus may have strong impact on the tumor microenvironment. Functional analysis indicates its association with hypoxia, angiogenesis, metastasis, and DNA damage repair processes. MIR210HG was found to interact with several proteins and potentially regulate chromatin modifications and transcriptional regulation. CONCLUSIONS A first pan-can cancer analysis of MIR210HG highlights its transcriptional and epigenetic deregulation and oncogenic role in the majority of cancers, its correlation with tumor microenvironment factors such as hypoxia and immune infiltration, and its potential as a prognostic biomarker and therapeutic target in several cancers.
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Affiliation(s)
- Garima Yadav
- Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, New Delhi 110016, India
| | - Ritu Kulshreshtha
- Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, New Delhi 110016, India.
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Smith DA, Sadler MC, Altman RB. Promises and challenges in pharmacoepigenetics. CAMBRIDGE PRISMS. PRECISION MEDICINE 2023; 1:e18. [PMID: 37560024 PMCID: PMC10406571 DOI: 10.1017/pcm.2023.6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 01/27/2023] [Accepted: 01/31/2023] [Indexed: 08/11/2023]
Abstract
Pharmacogenetics, the study of how interindividual genetic differences affect drug response, does not explain all observed heritable variance in drug response. Epigenetic mechanisms, such as DNA methylation, and histone acetylation may account for some of the unexplained variances. Epigenetic mechanisms modulate gene expression and can be suitable drug targets and can impact the action of nonepigenetic drugs. Pharmacoepigenetics is the field that studies the relationship between epigenetic variability and drug response. Much of this research focuses on compounds targeting epigenetic mechanisms, called epigenetic drugs, which are used to treat cancers, immune disorders, and other diseases. Several studies also suggest an epigenetic role in classical drug response; however, we know little about this area. The amount of information correlating epigenetic biomarkers to molecular datasets has recently expanded due to technological advances, and novel computational approaches have emerged to better identify and predict epigenetic interactions. We propose that the relationship between epigenetics and classical drug response may be examined using data already available by (1) finding regions of epigenetic variance, (2) pinpointing key epigenetic biomarkers within these regions, and (3) mapping these biomarkers to a drug-response phenotype. This approach expands on existing knowledge to generate putative pharmacoepigenetic relationships, which can be tested experimentally. Epigenetic modifications are involved in disease and drug response. Therefore, understanding how epigenetic drivers impact the response to classical drugs is important for improving drug design and administration to better treat disease.
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Affiliation(s)
- Delaney A Smith
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Marie C Sadler
- Department of Bioengineering, Stanford University, Stanford, CA, USA
- University Center for Primary Care and Public Health, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Russ B Altman
- Department of Bioengineering, Stanford University, Stanford, CA, USA
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EpiVisR: exploratory data analysis and visualization in epigenome-wide association analyses. BMC Bioinformatics 2022; 23:292. [PMID: 35870905 PMCID: PMC9308245 DOI: 10.1186/s12859-022-04836-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 07/13/2022] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
With the widespread availability of microarray technology for epigenetic research, methods for calling differentially methylated probes or differentially methylated regions have become effective tools to analyze this type of data. Furthermore, visualization is usually employed for quality check of results and for further insights. Expert knowledge is required to leverage capabilities of these methods. To overcome this limitation and make visualization in epigenetic research available to the public, we designed EpiVisR.
Results
The EpiVisR tool allows to select and visualize combinations of traits (i.e., concentrations of chemical compounds) and differentially methylated probes/regions. It supports various modes of enriched presentation to get the most knowledge out of existing data: (1) enriched Manhattan plot and enriched volcano plot for selection of probes, (2) trait-methylation plot for visualization of selected trait values against methylation values, (3) methylation profile plot for visualization of a selected range of probes against selected trait values as well as, (4) correlation profile plot for selection and visualization of further probes that are correlated to the selected probe. EpiVisR additionally allows exporting selected data to external tools for tasks such as network analysis.
Conclusion
The key advantage of EpiVisR is the annotation of data in the enriched plots (and tied tables) as well as linking to external data sources for further integrated data analysis. Using the EpiVisR approach will allow users to integrate data from traits with epigenetic analyses that are connected by belonging to the same individuals. Merging data from various data sources among the same cohort and visualizing them will enable users to gain more insights from existing data.
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7
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Kalyakulina A, Yusipov I, Bacalini MG, Franceschi C, Vedunova M, Ivanchenko M. Disease classification for whole-blood DNA methylation: Meta-analysis, missing values imputation, and XAI. Gigascience 2022; 11:giac097. [PMID: 36259657 PMCID: PMC9718659 DOI: 10.1093/gigascience/giac097] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 08/01/2022] [Accepted: 09/15/2022] [Indexed: 07/25/2023] Open
Abstract
BACKGROUND DNA methylation has a significant effect on gene expression and can be associated with various diseases. Meta-analysis of available DNA methylation datasets requires development of a specific workflow for joint data processing. RESULTS We propose a comprehensive approach of combined DNA methylation datasets to classify controls and patients. The solution includes data harmonization, construction of machine learning classification models, dimensionality reduction of models, imputation of missing values, and explanation of model predictions by explainable artificial intelligence (XAI) algorithms. We show that harmonization can improve classification accuracy by up to 20% when preprocessing methods of the training and test datasets are different. The best accuracy results were obtained with tree ensembles, reaching above 95% for Parkinson's disease. Dimensionality reduction can substantially decrease the number of features, without detriment to the classification accuracy. The best imputation methods achieve almost the same classification accuracy for data with missing values as for the original data. XAI approaches have allowed us to explain model predictions from both populational and individual perspectives. CONCLUSIONS We propose a methodologically valid and comprehensive approach to the classification of healthy individuals and patients with various diseases based on whole-blood DNA methylation data using Parkinson's disease and schizophrenia as examples. The proposed algorithm works better for the former pathology, characterized by a complex set of symptoms. It allows to solve data harmonization problems for meta-analysis of many different datasets, impute missing values, and build classification models of small dimensionality.
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Affiliation(s)
- Alena Kalyakulina
- Institute of Information Technologies, Mathematics and Mechanics, Lobachevsky State University, 603022 Nizhny Novgorod, Russia
| | - Igor Yusipov
- Institute of Information Technologies, Mathematics and Mechanics, Lobachevsky State University, 603022 Nizhny Novgorod, Russia
| | | | - Claudio Franceschi
- Institute of Information Technologies, Mathematics and Mechanics, Lobachevsky State University, 603022 Nizhny Novgorod, Russia
| | - Maria Vedunova
- Institute of Biology and Biomedicine, Lobachevsky State University, 603022 Nizhny Novgorod, Russia
| | - Mikhail Ivanchenko
- Institute of Information Technologies, Mathematics and Mechanics, Lobachevsky State University, 603022 Nizhny Novgorod, Russia
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Chen H, Xu J, Wei S, Jia Z, Sun C, Kang J, Guo X, Zhang N, Tao J, Dong Y, Zhang C, Ma Y, Lv W, Tian H, Bi S, Lv H, Huang C, Kong F, Tang G, Jiang Y, Zhang M. RABC: Rheumatoid Arthritis Bioinformatics Center. Nucleic Acids Res 2022; 51:D1381-D1387. [PMID: 36243962 PMCID: PMC9825551 DOI: 10.1093/nar/gkac850] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Revised: 09/13/2022] [Accepted: 09/21/2022] [Indexed: 01/30/2023] Open
Abstract
Advances in sequencing technologies have led to the rapid growth of multi-omics data on rheumatoid arthritis (RA). However, a comprehensive database that systematically collects and classifies the scattered data is still lacking. Here, we developed the Rheumatoid Arthritis Bioinformatics Center (RABC, http://www.onethird-lab.com/RABC/), the first multi-omics data resource platform (data hub) for RA. There are four categories of data in RABC: (i) 175 multi-omics sample sets covering transcriptome, epigenome, genome, and proteome; (ii) 175 209 differentially expressed genes (DEGs), 105 differentially expressed microRNAs (DEMs), 18 464 differentially DNA methylated (DNAm) genes, 1 764 KEGG pathways, 30 488 GO terms, 74 334 SNPs, 242 779 eQTLs, 105 m6A-SNPs and 18 491 669 meta-mQTLs; (iii) prior knowledge on seven types of RA molecular markers from nine public and credible databases; (iv) 127 073 literature information from PubMed (from 1972 to March 2022). RABC provides a user-friendly interface for browsing, searching and downloading these data. In addition, a visualization module also supports users to generate graphs of analysis results by inputting personalized parameters. We believe that RABC will become a valuable resource and make a significant contribution to the study of RA.
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Affiliation(s)
| | | | | | | | | | - Jingxuan Kang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China,The ABC Project, Harbin, China
| | - Xuying Guo
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Nan Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Junxian Tao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China,The ABC Project, Harbin, China
| | - Yu Dong
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Chen Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China,The ABC Project, Harbin, China
| | - Yingnan Ma
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Wenhua Lv
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Hongsheng Tian
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Shuo Bi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Hongchao Lv
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Chen Huang
- Dr. Neher's Biophysics Laboratory for Innovative Drug Discovery, Macau University of Science and Technology, Macau, SAR, China,Stat Key laboratory of Quality Research in Chinese Medicine, Macau Institute For Applied Research in Medicine and Health, Macau University of Science and Technology, Macau, SAR, China
| | - Fanwu Kong
- Department of Nephrology, The Second Affiliated Hospital, Harbin Medical University, Harbin, China
| | - Guoping Tang
- Correspondence may also be addressed to Guoping Tang.
| | - Yongshuai Jiang
- Correspondence may also be addressed to Yongshuai Jiang. Tel: +86 451 86620941; Fax: +86 451 86620941;
| | - Mingming Zhang
- To whom correspondence should be addressed. Tel: +86 451 86620941; Fax: +86 451 86620941;
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9
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Barbu MC, Harris M, Shen X, Aleks S, Green C, Amador C, Walker R, Morris S, Adams M, Sandu A, McNeil C, Waiter G, Evans K, Campbell A, Wardlaw J, Steele D, Murray A, Porteous D, McIntosh A, Whalley H. Epigenome-wide association study of global cortical volumes in generation Scotland: Scottish family health study. Epigenetics 2022; 17:1143-1158. [PMID: 34738878 PMCID: PMC9542280 DOI: 10.1080/15592294.2021.1997404] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 10/18/2021] [Accepted: 10/21/2021] [Indexed: 01/22/2023] Open
Abstract
A complex interplay of genetic and environmental risk factors influence global brain structural alterations associated with brain health and disease. Epigenome-wide association studies (EWAS) of global brain imaging phenotypes have the potential to reveal the mechanisms of brain health and disease and can lead to better predictive analytics through the development of risk scores.We perform an EWAS of global brain volumes in Generation Scotland using peripherally measured whole blood DNA methylation (DNAm) from two assessments, (i) at baseline recruitment, ~6 years prior to MRI assessment (N = 672) and (ii) concurrent with MRI assessment (N=565). Four CpGs at baseline were associated with global cerebral white matter, total grey matter, and whole-brain volume (Bonferroni p≤7.41×10-8, βrange = -1.46x10-6 to 9.59 × 10-7). These CpGs were annotated to genes implicated in brain-related traits, including psychiatric disorders, development, and ageing. We did not find significant associations in the meta-analysis of the EWAS of the two sets concurrent with imaging at the corrected level.These findings reveal global brain structural changes associated with DNAm measured ~6 years previously, indicating a potential role of early DNAm modifications in brain structure. Although concurrent DNAm was not associated with global brain structure, the nominally significant findings identified here present a rationale for future investigation of associations between DNA methylation and structural brain phenotypes in larger population-based samples.
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Affiliation(s)
- Miruna Carmen Barbu
- Division of Psychiatry, The University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
| | - Mat Harris
- Division of Psychiatry, The University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
| | - Xueyi Shen
- Division of Psychiatry, The University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
| | - Stolicyn Aleks
- Division of Psychiatry, The University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
| | - Claire Green
- Division of Psychiatry, The University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
| | - Carmen Amador
- Mrc Human Genetics Unit, Institute of Genetics and Cancer, the University of Edinburgh, UK
| | - Rosie Walker
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, the University of Edinburgh, UK
- Centre for Clinical Brain Sciences, The University of Edinburgh, UK
| | - Stewart Morris
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, the University of Edinburgh, UK
- Centre for Clinical Brain Sciences, The University of Edinburgh, UK
| | - Mark Adams
- Division of Psychiatry, The University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
| | - Anca Sandu
- Aberdeen Biomedical Imaging Centre, The Institute of Medical Sciences, University of Aberdeen, UK
| | - Christopher McNeil
- Aberdeen Biomedical Imaging Centre, The Institute of Medical Sciences, University of Aberdeen, UK
| | - Gordon Waiter
- Aberdeen Biomedical Imaging Centre, The Institute of Medical Sciences, University of Aberdeen, UK
| | - Kathryn Evans
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, the University of Edinburgh, UK
- Centre for Clinical Brain Sciences, The University of Edinburgh, UK
| | - Archie Campbell
- Mrc Human Genetics Unit, Institute of Genetics and Cancer, the University of Edinburgh, UK
| | - Joanna Wardlaw
- Centre for Clinical Brain Sciences, The University of Edinburgh, UK
| | - Douglas Steele
- Imaging Science and Technology, School of Medicine, University of Dundee, DundeeUK
| | - Alison Murray
- Aberdeen Biomedical Imaging Centre, The Institute of Medical Sciences, University of Aberdeen, UK
| | - David Porteous
- Mrc Human Genetics Unit, Institute of Genetics and Cancer, the University of Edinburgh, UK
- Centre for Cognitive Ageing and Cognitive Epidemiology, School of Philosophy, Psychology and Language Sciences, The University of Edinburgh, UK
| | - Andrew McIntosh
- Division of Psychiatry, The University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
- Centre for Cognitive Ageing and Cognitive Epidemiology, School of Philosophy, Psychology and Language Sciences, The University of Edinburgh, UK
| | - Heather Whalley
- Division of Psychiatry, The University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
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10
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Wang L, Zhang W, Wu X, Liang X, Cao L, Zhai J, Yang Y, Chen Q, Liu H, Zhang J, Ding Y, Zhu F, Tang J. MIAOME: Human Microbiome Affect The Host Epigenome. Comput Struct Biotechnol J 2022; 20:2455-2463. [PMID: 35664224 PMCID: PMC9136154 DOI: 10.1016/j.csbj.2022.05.024] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 05/11/2022] [Accepted: 05/12/2022] [Indexed: 01/10/2023] Open
Abstract
Besides the genetic factors having tremendous influences on the regulations of the epigenome, the microenvironmental factors have recently gained extensive attention for their roles in affecting the host epigenome. There are three major types of microenvironmental factors: microbiota-derived metabolites (MDM), microbiota-derived components (MDC) and microbiota-secreted proteins (MSP). These factors can regulate host physiology by modifying host gene expression through the three highly interconnected epigenetic mechanisms (e.g. histone modifications, DNA modifications, and non-coding RNAs). However, no database was available to provide the comprehensive factors of these types. Herein, a database entitled 'Human Microbiome Affect The Host Epigenome (MIAOME)' was constructed. Based on the types of epigenetic modifications confirmed in the literature review, the MIAOME database captures 1068 (63 genus, 281 species, 707 strains, etc.) human microbes, 91 unique microbiota-derived metabolites & components (16 fatty acids, 10 bile acids, 10 phenolic compounds, 10 vitamins, 9 tryptophan metabolites, etc.) derived from 967 microbes; 50 microbes that secreted 40 proteins; 98 microbes that directly influence the host epigenetic modification, and provides 3 classifications of the epigenome, including (1) 4 types of DNA modifications, (2) 20 histone modifications and (3) 490 ncRNAs regulations, involved in 160 human diseases. All in all, MIAOME has compiled the information on the microenvironmental factors influence host epigenome through the scientific literature and biochemical databases, and allows the collective considerations among the different types of factors. It can be freely assessed without login requirement by all users at: http://miaome.idrblab.net/ttd/
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Affiliation(s)
- Lidan Wang
- School of Basic Medicine, Chongqing Medical University, Chongqing 400016, China
| | - Wei Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Xianglu Wu
- Joint International Research Laboratory of Reproductive and Development, Department of Reproductive Biology, School of Public Health, Chongqing Medical University, Chongqing 400016, China
| | - Xiao Liang
- School of Basic Medicine, Chongqing Medical University, Chongqing 400016, China
| | - Lijie Cao
- School of Basic Medicine, Chongqing Medical University, Chongqing 400016, China
| | - Jincheng Zhai
- School of Basic Medicine, Chongqing Medical University, Chongqing 400016, China
| | - Yiyang Yang
- School of Basic Medicine, Chongqing Medical University, Chongqing 400016, China
| | - Qiuxiao Chen
- School of Basic Medicine, Chongqing Medical University, Chongqing 400016, China
| | - Hongqing Liu
- School of Basic Medicine, Chongqing Medical University, Chongqing 400016, China
| | - Jun Zhang
- School of Basic Medicine, Chongqing Medical University, Chongqing 400016, China
| | - Yubin Ding
- Joint International Research Laboratory of Reproductive and Development, Department of Reproductive Biology, School of Public Health, Chongqing Medical University, Chongqing 400016, China
- Corresponding authors at: School of Basic Medicine, Chongqing Medical University, Chongqing 400016, China (J. Tang).
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
- Corresponding authors at: School of Basic Medicine, Chongqing Medical University, Chongqing 400016, China (J. Tang).
| | - Jing Tang
- School of Basic Medicine, Chongqing Medical University, Chongqing 400016, China
- Joint International Research Laboratory of Reproductive and Development, Department of Reproductive Biology, School of Public Health, Chongqing Medical University, Chongqing 400016, China
- Corresponding authors at: School of Basic Medicine, Chongqing Medical University, Chongqing 400016, China (J. Tang).
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11
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Yang Z, He Y, Wang Y, Huang L, Tang Y, He Y, Chen Y, Han Z. Genome-Wide Analysis for the Regulation of Gene Alternative Splicing by DNA Methylation Level in Glioma and its Prognostic Implications. Front Genet 2022; 13:799913. [PMID: 35309147 PMCID: PMC8931337 DOI: 10.3389/fgene.2022.799913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Accepted: 01/20/2022] [Indexed: 11/13/2022] Open
Abstract
Glioma is a primary high malignant intracranial tumor with poorly understood molecular mechanisms. Previous studies found that both DNA methylation modification and gene alternative splicing (AS) play a key role in tumorigenesis of glioma, and there is an obvious regulatory relationship between them. However, to date, no comprehensive study has been performed to analyze the influence of DNA methylation level on gene AS in glioma on a genome-wide scale. Here, we performed this study by integrating DNA methylation, gene expression, AS, disease risk methylation at position, and clinical data from 537 low-grade glioma (LGG) and glioblastoma (GBM) individuals. We first conducted a differential analysis of AS events and DNA methylation positions between LGG and GBM subjects, respectively. Then, we evaluated the influence of differential methylation positions on differential AS events. Further, Fisher’s exact test was used to verify our findings and identify potential key genes in glioma. Finally, we performed a series of analyses to investigate influence of these genes on the clinical prognosis of glioma. In total, we identified 130 glioma-related genes whose AS significantly affected by DNA methylation level. Eleven of them play an important role in glioma prognosis. In short, these results will help to better understand the pathogenesis of glioma.
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Affiliation(s)
- Zeyuan Yang
- Department of Bioinformatics, School of Basic Medicine, Chongqing Medical University, Chongqing, China
| | - Yijie He
- Department of Bioinformatics, School of Basic Medicine, Chongqing Medical University, Chongqing, China
| | - Yongheng Wang
- Department of Bioinformatics, School of Basic Medicine, Chongqing Medical University, Chongqing, China
- International Research Laboratory of Reproduction and Development, Chongqing Medical University, Chongqing, China
| | - Lin Huang
- Department of Bioinformatics, School of Basic Medicine, Chongqing Medical University, Chongqing, China
| | - Yaqin Tang
- Department of Bioinformatics, School of Basic Medicine, Chongqing Medical University, Chongqing, China
| | - Yue He
- Group of Mathematics Education Teaching and Research, Chongqing Fudan Secondary School, Chongqing, China
| | - Yihan Chen
- Department of Bioinformatics, School of Basic Medicine, Chongqing Medical University, Chongqing, China
| | - Zhijie Han
- Department of Bioinformatics, School of Basic Medicine, Chongqing Medical University, Chongqing, China
- *Correspondence: Zhijie Han,
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12
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Barbu MC, Huider F, Campbell A, Amador C, Adams MJ, Lynall ME, Howard DM, Walker RM, Morris SW, Van Dongen J, Porteous DJ, Evans KL, Bullmore E, Willemsen G, Boomsma DI, Whalley HC, McIntosh AM. Methylome-wide association study of antidepressant use in Generation Scotland and the Netherlands Twin Register implicates the innate immune system. Mol Psychiatry 2022; 27:1647-1657. [PMID: 34880450 PMCID: PMC9095457 DOI: 10.1038/s41380-021-01412-7] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 10/11/2021] [Accepted: 11/26/2021] [Indexed: 12/28/2022]
Abstract
Antidepressants are an effective treatment for major depressive disorder (MDD), although individual response is unpredictable and highly variable. Whilst the mode of action of antidepressants is incompletely understood, many medications are associated with changes in DNA methylation in genes that are plausibly linked to their mechanisms. Studies of DNA methylation may therefore reveal the biological processes underpinning the efficacy and side effects of antidepressants. We performed a methylome-wide association study (MWAS) of self-reported antidepressant use accounting for lifestyle factors and MDD in Generation Scotland (GS:SFHS, N = 6428, EPIC array) and the Netherlands Twin Register (NTR, N = 2449, 450 K array) and ran a meta-analysis of antidepressant use across these two cohorts. We found ten CpG sites significantly associated with self-reported antidepressant use in GS:SFHS, with the top CpG located within a gene previously associated with mental health disorders, ATP6V1B2 (β = -0.055, pcorrected = 0.005). Other top loci were annotated to genes including CASP10, TMBIM1, MAPKAPK3, and HEBP2, which have previously been implicated in the innate immune response. Next, using penalised regression, we trained a methylation-based score of self-reported antidepressant use in a subset of 3799 GS:SFHS individuals that predicted antidepressant use in a second subset of GS:SFHS (N = 3360, β = 0.377, p = 3.12 × 10-11, R2 = 2.12%). In an MWAS analysis of prescribed selective serotonin reuptake inhibitors, we showed convergent findings with those based on self-report. In NTR, we did not find any CpGs significantly associated with antidepressant use. The meta-analysis identified the two CpGs of the ten above that were common to the two arrays used as being significantly associated with antidepressant use, although the effect was in the opposite direction for one of them. Antidepressants were associated with epigenetic alterations in loci previously associated with mental health disorders and the innate immune system. These changes predicted self-reported antidepressant use in a subset of GS:SFHS and identified processes that may be relevant to our mechanistic understanding of clinically relevant antidepressant drug actions and side effects.
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Affiliation(s)
- Miruna C Barbu
- Division of Psychiatry, The University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK.
| | - Floris Huider
- Faculty of Behavioural and Movement Sciences, Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Archie Campbell
- Centre for Genomic and Experimental Medicine, The Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
| | - Carmen Amador
- MRC Human Genetics Unit, The Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
| | - Mark J Adams
- Division of Psychiatry, The University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
| | | | - David M Howard
- Division of Psychiatry, The University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Rosie M Walker
- Centre for Genomic and Experimental Medicine, The Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
| | - Stewart W Morris
- Centre for Genomic and Experimental Medicine, The Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
| | - Jenny Van Dongen
- Faculty of Behavioural and Movement Sciences, Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - David J Porteous
- Centre for Genomic and Experimental Medicine, The Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
| | - Kathryn L Evans
- Centre for Genomic and Experimental Medicine, The Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
| | - Edward Bullmore
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Gonneke Willemsen
- Faculty of Behavioural and Movement Sciences, Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Dorret I Boomsma
- Faculty of Behavioural and Movement Sciences, Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Heather C Whalley
- Division of Psychiatry, The University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
| | - Andrew M McIntosh
- Division of Psychiatry, The University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
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13
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Hop PJ, Zwamborn RA, Hannon E, Shireby GL, Nabais MF, Walker EM, van Rheenen W, van Vugt JJ, Dekker AM, Westeneng HJ, Tazelaar GH, van Eijk KR, Moisse M, Baird D, Khleifat AA, Iacoangeli A, Ticozzi N, Ratti A, Cooper-Knock J, Morrison KE, Shaw PJ, Basak AN, Chiò A, Calvo A, Moglia C, Canosa A, Brunetti M, Grassano M, Gotkine M, Lerner Y, Zabari M, Vourc’h P, Corcia P, Couratier P, Pardina JSM, Salas T, Dion P, Ross JP, Henderson RD, Mathers S, McCombe PA, Needham M, Nicholson G, Rowe DB, Pamphlett R, Mather KA, Sachdev PS, Furlong S, Garton FC, Henders AK, Lin T, Ngo ST, Steyn FJ, Wallace L, Williams KL, Neto MM, Cauchi RJ, Blair IP, Kiernan MC, Drory V, Povedano M, de Carvalho M, Pinto S, Weber M, Rouleau GA, Silani V, Landers JE, Shaw CE, Andersen PM, McRae AF, van Es MA, Pasterkamp RJ, Wray NR, McLaughlin RL, Hardiman O, Kenna KP, Tsai E, Runz H, Al-Chalabi A, van den Berg LH, Van Damme P, Mill J, Veldink JH. Genome-wide study of DNA methylation shows alterations in metabolic, inflammatory, and cholesterol pathways in ALS. Sci Transl Med 2022; 14:eabj0264. [PMID: 35196023 PMCID: PMC10040186 DOI: 10.1126/scitranslmed.abj0264] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with an estimated heritability between 40 and 50%. DNA methylation patterns can serve as proxies of (past) exposures and disease progression, as well as providing a potential mechanism that mediates genetic or environmental risk. Here, we present a blood-based epigenome-wide association study meta-analysis in 9706 samples passing stringent quality control (6763 patients, 2943 controls). We identified a total of 45 differentially methylated positions (DMPs) annotated to 42 genes, which are enriched for pathways and traits related to metabolism, cholesterol biosynthesis, and immunity. We then tested 39 DNA methylation-based proxies of putative ALS risk factors and found that high-density lipoprotein cholesterol, body mass index, white blood cell proportions, and alcohol intake were independently associated with ALS. Integration of these results with our latest genome-wide association study showed that cholesterol biosynthesis was potentially causally related to ALS. Last, DNA methylation at several DMPs and blood cell proportion estimates derived from DNA methylation data were associated with survival rate in patients, suggesting that they might represent indicators of underlying disease processes potentially amenable to therapeutic interventions.
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Affiliation(s)
- Paul J. Hop
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht 3584 CX, Netherlands
| | - Ramona A.J. Zwamborn
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht 3584 CX, Netherlands
| | - Eilis Hannon
- University of Exeter Medical School, College of Medicine and Health, University of Exeter, Exeter EX1 2LU, UK
| | - Gemma L. Shireby
- University of Exeter Medical School, College of Medicine and Health, University of Exeter, Exeter EX1 2LU, UK
| | - Marta F. Nabais
- University of Exeter Medical School, College of Medicine and Health, University of Exeter, Exeter EX1 2LU, UK
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD4072, Australia
| | - Emma M. Walker
- University of Exeter Medical School, College of Medicine and Health, University of Exeter, Exeter EX1 2LU, UK
| | - Wouter van Rheenen
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht 3584 CX, Netherlands
| | - Joke J.F.A. van Vugt
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht 3584 CX, Netherlands
| | - Annelot M. Dekker
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht 3584 CX, Netherlands
| | - Henk-Jan Westeneng
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht 3584 CX, Netherlands
| | - Gijs H.P. Tazelaar
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht 3584 CX, Netherlands
| | - Kristel R. van Eijk
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht 3584 CX, Netherlands
| | - Matthieu Moisse
- KU Leuven–University of Leuven, Department of Neurosciences, Experimental Neurology and Leuven Brain Institute (LBI), Leuven 3000, Belgium
- VIB, Center for Brain and Disease Research, Leuven 3000, Belgium
- University Hospitals Leuven, Department of Neurology, Leuven 3000, Belgium
| | - Denis Baird
- Translational Biology, Biogen, Boston, MA 02142, USA
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, University of Bristol, Bristol BS8 2BN, UK
| | - Ahmad Al Khleifat
- Maurice Wohl Clinical Neuroscience Institute, Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London SE5 8AF, UK
| | - Alfredo Iacoangeli
- Maurice Wohl Clinical Neuroscience Institute, Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London SE5 8AF, UK
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London SE5 8AF, UK
- National Institute for Health Research Biomedical Research Centre and Dementia Unit, South London and Maudsley NHS Foundation Trust and King’s College London, London SE5 8AZ, UK
| | - Nicola Ticozzi
- Department of Neurology-Stroke Unit and Laboratory of Neuroscience, Istituto Auxologico Italiano IRCCS, Milan 20149, Italy
- Department of Pathophysiology and Transplantation, “Dino Ferrari” Center, Università degli Studi di Milano, Milan 20122, Italy
| | - Antonia Ratti
- Department of Neurology-Stroke Unit and Laboratory of Neuroscience, Istituto Auxologico Italiano IRCCS, Milan 20149, Italy
- Department of Medical Biotechnology and Translational Medicine, Università degli Studi di Milano, Milano 20145, Italy
| | - Jonathan Cooper-Knock
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield S10 2HQ, UK
| | - Karen E. Morrison
- School of Medicine, Dentistry, and Biomedical Sciences, Queen’s University Belfast, Belfast BT9 7BL, UK
| | - Pamela J. Shaw
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield S10 2HQ, UK
| | - A. Nazli Basak
- Koc University, School of Medicine, Translational Medicine Research Center, NDAL, Istanbul, 34450, Turkey
| | - Adriano Chiò
- “Rita Levi Montalcini” Department of Neuroscience, ALS Centre, University of Torino, Turin 10126, Italy
- Azienda Ospedaliero-Universitaria Città della Salute e della Scienza, SC Neurologia 1U, Turin 10126, Italy
| | - Andrea Calvo
- “Rita Levi Montalcini” Department of Neuroscience, ALS Centre, University of Torino, Turin 10126, Italy
- Azienda Ospedaliero-Universitaria Città della Salute e della Scienza, SC Neurologia 1U, Turin 10126, Italy
| | - Cristina Moglia
- “Rita Levi Montalcini” Department of Neuroscience, ALS Centre, University of Torino, Turin 10126, Italy
- Azienda Ospedaliero-Universitaria Città della Salute e della Scienza, SC Neurologia 1U, Turin 10126, Italy
| | - Antonio Canosa
- “Rita Levi Montalcini” Department of Neuroscience, ALS Centre, University of Torino, Turin 10126, Italy
- Azienda Ospedaliero-Universitaria Città della Salute e della Scienza, SC Neurologia 1U, Turin 10126, Italy
| | - Maura Brunetti
- “Rita Levi Montalcini” Department of Neuroscience, ALS Centre, University of Torino, Turin 10126, Italy
| | - Maurizio Grassano
- “Rita Levi Montalcini” Department of Neuroscience, ALS Centre, University of Torino, Turin 10126, Italy
| | - Marc Gotkine
- Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem 91904, Israel
- Agnes Ginges Center for Human Neurogenetics, Department of Neurology, Hadassah Medical Center, Jerusalem 91120, Israel
| | - Yossef Lerner
- Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem 91904, Israel
- Agnes Ginges Center for Human Neurogenetics, Department of Neurology, Hadassah Medical Center, Jerusalem 91120, Israel
| | - Michal Zabari
- Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem 91904, Israel
- Agnes Ginges Center for Human Neurogenetics, Department of Neurology, Hadassah Medical Center, Jerusalem 91120, Israel
| | - Patrick Vourc’h
- Service de Biochimie et Biologie moléculaire, CHU de Tours, Tours 37044, France
- UMR 1253, Université de Tours, Inserm, Tours 37044, France
| | - Philippe Corcia
- UMR 1253, Université de Tours, Inserm, Tours 37044, France
- Centre de référence sur la SLA, CHU de Tours, Tours 37044, France
| | - Philippe Couratier
- Centre de référence sur la SLA, CHRU de Limoges, Limoges 87042, France
- UMR 1094, Université de Limoges, Inserm, Limoges 87025, France
| | | | - Teresa Salas
- Department of Neurology, Hospital La Paz-Carlos III, Madrid 28046, Spain
| | - Patrick Dion
- Montréal Neurological Institute and Hospital, McGill University, Montréal, QC H3A 2B4, Canada
| | - Jay P. Ross
- Montréal Neurological Institute and Hospital, McGill University, Montréal, QC H3A 2B4, Canada
- Department of Human Genetics, McGill University, Montréal, QC H3A 0C7, Canada
| | - Robert D. Henderson
- Department of Neurology, Royal Brisbane and Women’s Hospital, Brisbane, QLD 4029, Australia
| | - Susan Mathers
- Calvary Health Care Bethlehem, Parkdale, VIC 3195, Australia
| | - Pamela A. McCombe
- Centre for Clinical Research, University of Queensland, Brisbane, QLD 4019, Australia
| | - Merrilee Needham
- Fiona Stanley Hospital, Perth, WA 6150, Australia
- Notre Dame University, Fremantle, WA 6160, Australia
- Institute for Immunology and Infectious Diseases, Murdoch University, Perth, WA 6150, Australia
| | - Garth Nicholson
- ANZAC Research Institute, Concord Repatriation General Hospital, Sydney, NSW 2139, Australia
| | - Dominic B. Rowe
- Centre for Motor Neuron Disease Research, Macquarie University, NSW 2109, Australia
| | - Roger Pamphlett
- Discipline of Pathology and Department of Neuropathology, Brain and Mind Centre, University of Sydney, Sydney, NSW 2050, Australia
| | - Karen A. Mather
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, NSW 2031, Australia
- Neuroscience Research Australia Institute, Randwick, NSW 2031, Australia
| | - Perminder S. Sachdev
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, NSW 2031, Australia
- Neuropsychiatric Institute, Prince of Wales Hospital, UNSW, Randwick, NSW 2031, Australia
| | - Sarah Furlong
- Centre for Motor Neuron Disease Research, Macquarie University, NSW 2109, Australia
| | - Fleur C. Garton
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD4072, Australia
| | - Anjali K. Henders
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD4072, Australia
| | - Tian Lin
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD4072, Australia
| | - Shyuan T. Ngo
- Centre for Clinical Research, University of Queensland, Brisbane, QLD 4019, Australia
- Australian Institute for Bioengineering and Nanotechnology, University of Queensland, Brisbane, QLD 4072, Australia
- Queensland Brain Institute, University of Queensland, Brisbane, QLD 4072, Australia
| | - Frederik J. Steyn
- Centre for Clinical Research, University of Queensland, Brisbane, QLD 4019, Australia
- School of Biomedical Sciences, University of Queensland, Brisbane, QLD 4072, Australia
| | - Leanne Wallace
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD4072, Australia
| | - Kelly L. Williams
- Centre for Motor Neuron Disease Research, Macquarie University, NSW 2109, Australia
| | | | | | | | - Ruben J. Cauchi
- Center for Molecular Medicine and Biobanking and Department of Physiology and Biochemistry, Faculty of Medicine and Surgery, University of Malta, 2023 Msida, Malta
| | - Ian P. Blair
- Centre for Motor Neuron Disease Research, Macquarie University, NSW 2109, Australia
| | - Matthew C. Kiernan
- Brain and Mind Centre, University of Sydney, Sydney, NSW, 2050, Australia
- Department of Neurology, Royal Prince Alfred Hospital, Sydney, NSW 2050, Australia
| | - Vivian Drory
- Department of Neurology, Tel-Aviv Sourasky Medical Centre, Tel-Aviv 64239, Israel
- Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv 6997801, Israel
| | - Monica Povedano
- Functional Unit of Amyotrophic Lateral Sclerosis (UFELA), Service of Neurology, Bellvitge University Hospital, L’Hospitalet de Llobregat, Barcelona 08907, Spain
| | - Mamede de Carvalho
- Instituto de Fisiologia, Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Lisbon 1649-028, Portugal
| | - Susana Pinto
- Instituto de Fisiologia, Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Lisbon 1649-028, Portugal
| | - Markus Weber
- Neuromuscular Diseases Unit/ALS Clinic, Kantonsspital St. Gallen, 9007 St. Gallen, Switzerland
| | - Guy A. Rouleau
- Montréal Neurological Institute and Hospital, McGill University, Montréal, QC H3A 2B4, Canada
| | - Vincenzo Silani
- Department of Neurology-Stroke Unit and Laboratory of Neuroscience, Istituto Auxologico Italiano IRCCS, Milan 20149, Italy
- Department of Pathophysiology and Transplantation, “Dino Ferrari” Center, Università degli Studi di Milano, Milan 20122, Italy
| | - John E. Landers
- Department of Neurology, University of Massachusetts Medical School, Worcester, MA 01655, USA
| | - Christopher E. Shaw
- Maurice Wohl Clinical Neuroscience Institute, Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London SE5 8AF, UK
| | - Peter M. Andersen
- Department of Clinical Science, Umeå University, Umeå SE-901 85, Sweden
| | - Allan F. McRae
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD4072, Australia
| | - Michael A. van Es
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht 3584 CX, Netherlands
| | - R. Jeroen Pasterkamp
- Department of Translational Neuroscience, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, 3584 CX, Netherlands
| | - Naomi R. Wray
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD4072, Australia
- Queensland Brain Institute, University of Queensland, Brisbane, QLD 4072, Australia
| | - Russell L. McLaughlin
- Complex Trait Genomics Laboratory, Smurfit Institute of Genetics, Trinity College Dublin, Dublin D02 PN40, Ireland
| | - Orla Hardiman
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin D02 PN40, Ireland
| | - Kevin P. Kenna
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht 3584 CX, Netherlands
- Department of Translational Neuroscience, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, 3584 CX, Netherlands
| | - Ellen Tsai
- Translational Biology, Biogen, Boston, MA 02142, USA
| | - Heiko Runz
- Translational Biology, Biogen, Boston, MA 02142, USA
| | - Ammar Al-Chalabi
- Maurice Wohl Clinical Neuroscience Institute, Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London SE5 8AF, UK
- King’s College Hospital, Denmark Hill, London SE5 9RS, UK
| | - Leonard H. van den Berg
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht 3584 CX, Netherlands
| | - Philip Van Damme
- KU Leuven–University of Leuven, Department of Neurosciences, Experimental Neurology and Leuven Brain Institute (LBI), Leuven 3000, Belgium
- VIB, Center for Brain and Disease Research, Leuven 3000, Belgium
- University Hospitals Leuven, Department of Neurology, Leuven 3000, Belgium
| | - Jonathan Mill
- University of Exeter Medical School, College of Medicine and Health, University of Exeter, Exeter EX1 2LU, UK
| | - Jan H. Veldink
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht 3584 CX, Netherlands
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14
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Zhu H, Tan J, Zhao Y, Wang Z, Wu Z, Li M. Potential Role of the Chemotaxis System in Formation and Progression of Intracranial Aneurysms Through Weighted Gene Co-Expression Network Analysis. Int J Gen Med 2022; 15:2217-2231. [PMID: 35250300 PMCID: PMC8893157 DOI: 10.2147/ijgm.s347420] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 02/22/2022] [Indexed: 12/21/2022] Open
Abstract
Background Intracranial aneurysm (IA) is the most common and is the main cause of spontaneous subarachnoid hemorrhage (SAH). The underlying molecular mechanisms for preventing IA progression have not been fully identified. Our research aimed to identify the key genes and critical pathways of IA through gene co-expression networks. Methods Gene Expression Omnibus (GEO) datasets GSE13353, GSE54083 and GSE75436 were used in the study. The genetic data were analyzed by weighted gene co-expression network analysis (WGCNA). Then the clinically significant modules were identified and the differentially expressed genes (DEGs) with the genes were intersected in these modules. GO (gene ontology) and KEGG (Kyoto Gene and Genomic Encyclopedia) were used for gene enrichment analysis to determine the function or pathway. In addition, the composition of immune cells was analyzed by CIBERSORT algorithm. Finally, the hub genes and key genes were identified by GSE122897. Results A total of 266 DEGs and two modules with clinical significance were identified. The inflammatory response and immune response were identified by GO and KEGG. CCR5, CCL4, CCL20, and FPR3 were the key genes in the module correlated with IA. The proportions of infiltrating immune cells in IA and normal tissues were different, especially in terms of macrophages and mast cells. Conclusion The chemotactic system has been identified as a key pathway of IA, and interacting macrophages may regulate this pathological process.
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Affiliation(s)
- Huaxin Zhu
- Department of Neurosurgery, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi, People’s Republic of China
| | - Jiacong Tan
- Department of Neurosurgery, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi, People’s Republic of China
| | - Yeyu Zhao
- Department of Neurosurgery, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi, People’s Republic of China
| | - Zhihua Wang
- Department of Neurosurgery, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi, People’s Republic of China
| | - Zhiwu Wu
- Department of Neurosurgery, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi, People’s Republic of China
| | - Meihua Li
- Department of Neurosurgery, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi, People’s Republic of China
- Correspondence: Meihua Li, Email
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15
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Xiong Z, Yang F, Li M, Ma Y, Zhao W, Wang G, Li Z, Zheng X, Zou D, Zong W, Kang H, Jia Y, Li R, Zhang Z, Bao Y. EWAS Open Platform: integrated data, knowledge and toolkit for epigenome-wide association study. Nucleic Acids Res 2022; 50:D1004-D1009. [PMID: 34718752 PMCID: PMC8728289 DOI: 10.1093/nar/gkab972] [Citation(s) in RCA: 64] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 10/03/2021] [Accepted: 10/20/2021] [Indexed: 12/17/2022] Open
Abstract
Epigenome-Wide Association Study (EWAS) has become a standard strategy to discover DNA methylation variation of different phenotypes. Since 2018, we have developed EWAS Atlas and EWAS Data Hub to integrate a growing volume of EWAS knowledge and data, respectively. Here, we present EWAS Open Platform (https://ngdc.cncb.ac.cn/ewas) that includes EWAS Atlas, EWAS Data Hub and the newly developed EWAS Toolkit. In the current implementation, EWAS Open Platform integrates 617 018 high-quality EWAS associations from 910 publications, covering 51 phenotypes, 275 diseases and 104 environmental factors. It also provides well-normalized DNA methylation array data and the corresponding metadata from 115 852 samples, which involve 707 tissues, 218 cell lines and 528 diseases. Taking advantage of integrated knowledge and data in EWAS Atlas and EWAS Data Hub, EWAS Open Platform equips with EWAS Toolkit, a powerful one-stop site for EWAS enrichment, annotation, and knowledge network construction and visualization. Collectively, EWAS Open Platform provides open access to EWAS knowledge, data and toolkit and thus bears great utility for a broader range of relevant research.
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Affiliation(s)
- Zhuang Xiong
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences / China National Center for Bioinformation, Beijing 100101, China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Fei Yang
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences / China National Center for Bioinformation, Beijing 100101, China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Mengwei Li
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences / China National Center for Bioinformation, Beijing 100101, China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
| | - Yingke Ma
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences / China National Center for Bioinformation, Beijing 100101, China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
| | - Wei Zhao
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences / China National Center for Bioinformation, Beijing 100101, China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Guoliang Wang
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences / China National Center for Bioinformation, Beijing 100101, China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhaohua Li
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences / China National Center for Bioinformation, Beijing 100101, China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xinchang Zheng
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences / China National Center for Bioinformation, Beijing 100101, China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
| | - Dong Zou
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences / China National Center for Bioinformation, Beijing 100101, China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
| | - Wenting Zong
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences / China National Center for Bioinformation, Beijing 100101, China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Hongen Kang
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences / China National Center for Bioinformation, Beijing 100101, China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yaokai Jia
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences / China National Center for Bioinformation, Beijing 100101, China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
| | - Rujiao Li
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences / China National Center for Bioinformation, Beijing 100101, China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
| | - Zhang Zhang
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences / China National Center for Bioinformation, Beijing 100101, China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yiming Bao
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences / China National Center for Bioinformation, Beijing 100101, China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
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16
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Wei S, Tao J, Xu J, Chen X, Wang Z, Zhang N, Zuo L, Jia Z, Chen H, Sun H, Yan Y, Zhang M, Lv H, Kong F, Duan L, Ma Y, Liao M, Xu L, Feng R, Liu G, Project TEWAS, Jiang Y. Ten Years of EWAS. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2021; 8:e2100727. [PMID: 34382344 PMCID: PMC8529436 DOI: 10.1002/advs.202100727] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 05/11/2021] [Indexed: 06/13/2023]
Abstract
Epigenome-wide association study (EWAS) has been applied to analyze DNA methylation variation in complex diseases for a decade, and epigenome as a research target has gradually become a hot topic of current studies. The DNA methylation microarrays, next-generation, and third-generation sequencing technologies have prepared a high-quality platform for EWAS. Here, the progress of EWAS research is reviewed, its contributions to clinical applications, and mainly describe the achievements of four typical diseases. Finally, the challenges encountered by EWAS and make bold predictions for its future development are presented.
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Affiliation(s)
- Siyu Wei
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityHarbin150081China
- The EWAS ProjectHarbinChina
| | - Junxian Tao
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityHarbin150081China
- The EWAS ProjectHarbinChina
| | - Jing Xu
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityHarbin150081China
- The EWAS ProjectHarbinChina
| | - Xingyu Chen
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityHarbin150081China
| | - Zhaoyang Wang
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityHarbin150081China
| | - Nan Zhang
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityHarbin150081China
| | - Lijiao Zuo
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityHarbin150081China
| | - Zhe Jia
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityHarbin150081China
| | - Haiyan Chen
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityHarbin150081China
| | - Hongmei Sun
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityHarbin150081China
| | - Yubo Yan
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityHarbin150081China
| | - Mingming Zhang
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityHarbin150081China
| | - Hongchao Lv
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityHarbin150081China
| | - Fanwu Kong
- The EWAS ProjectHarbinChina
- Department of NephrologyThe Second Affiliated HospitalHarbin Medical UniversityHarbin150001China
| | - Lian Duan
- The EWAS ProjectHarbinChina
- The First Affiliated Hospital of Wenzhou Medical UniversityWenzhou325000China
| | - Ye Ma
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityHarbin150081China
- The EWAS ProjectHarbinChina
| | - Mingzhi Liao
- The EWAS ProjectHarbinChina
- College of Life SciencesNorthwest A&F UniversityYanglingShanxi712100China
| | - Liangde Xu
- The EWAS ProjectHarbinChina
- School of Biomedical EngineeringWenzhou Medical UniversityWenzhou325035China
| | - Rennan Feng
- The EWAS ProjectHarbinChina
- Department of Nutrition and Food HygienePublic Health CollegeHarbin Medical UniversityHarbin150081China
| | - Guiyou Liu
- The EWAS ProjectHarbinChina
- Beijing Institute for Brain DisordersCapital Medical UniversityBeijing100069China
| | | | - Yongshuai Jiang
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityHarbin150081China
- The EWAS ProjectHarbinChina
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17
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Genome-wise engineering of ruminant nutrition- nutrigenomics: applications, challenges, and future perspectives – a review. ANNALS OF ANIMAL SCIENCE 2021. [DOI: 10.2478/aoas-2021-0057] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Abstract
Use of genomic information in ruminant production systems can help relieve concerns related to food security and sustainability of production. Nutritional genomics (i.e., Nutrigenomics) is a field of research that is interested in all types of reciprocal interactions between nutrients and genomes of organisms, i.e., variable patterns of gene expression and effect of genetic variations on the nutritional environment. Devising a revolutionizing analytical approach to traditional ruminant nutrition research, the relatively novel area of ruminant nutrigenomics has several studies concerning different aspects of animal production systems. This paper aims to review the current nutrigenomics research in the frame of how nutrition of ruminants can be modified accounting for individual genetic backgrounds and gene/diet relationships behind productivity, quality, efficiency, disease resistance, fertility, and GHG emissions. Furthermore, current challenges facing ruminant nutrigenomics are evaluated and future directions for the novel area are strongly argued by this review.
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18
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Suhre K, Zaghlool S. Connecting the epigenome, metabolome and proteome for a deeper understanding of disease. J Intern Med 2021; 290:527-548. [PMID: 33904619 DOI: 10.1111/joim.13306] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 03/18/2021] [Accepted: 03/22/2021] [Indexed: 12/26/2022]
Abstract
Epigenome-wide association studies (EWAS) identify genes that are dysregulated by the studied clinical endpoints, thereby indicating potential new diagnostic biomarkers, drug targets and therapy options. Combining EWAS with deep molecular phenotyping, such as approaches enabled by metabolomics and proteomics, allows further probing of the underlying disease-associated pathways. For instance, methylation of the TXNIP gene is associated robustly with prevalent type 2 diabetes and further with metabolites that are short-term markers of glycaemic control. These associations reflect TXNIP's function as a glucose uptake regulator by interaction with the major glucose transporter GLUT1 and suggest that TXNIP methylation can be used as a read-out for the organism's exposure to glucose stress. Another case is the association between DNA methylation of the AHRR and F2RL3 genes with smoking and a protein that is involved in the reprogramming of the bronchial epithelium. These examples show that associations between DNA methylation and intermediate molecular traits can open new windows into how the body copes with physiological challenges. This knowledge, if carefully interpreted, may indicate novel therapy options and, together with monitoring of the methylation state of specific methylation sites, may in the future allow the early diagnosis of impending disease. It is essential for medical practitioners to recognize the potential that this field holds in translating basic research findings to clinical practice. In this review, we present recent advances in the field of EWAS with metabolomics and proteomics and discuss both the potential and the challenges of translating epigenetic associations, with deep molecular phenotypes, to biomedical applications.
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Affiliation(s)
- K Suhre
- From the, Bioinformatics Core, Weill Cornell Medicine-Qatar, Education City, Doha, Qatar.,Department of Biophysics and Physiology, Weill Cornell Medicine, New York, USA
| | - S Zaghlool
- From the, Bioinformatics Core, Weill Cornell Medicine-Qatar, Education City, Doha, Qatar.,Department of Biophysics and Physiology, Weill Cornell Medicine, New York, USA
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19
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Maden SK, Thompson RF, Hansen KD, Nellore A. Human methylome variation across Infinium 450K data on the Gene Expression Omnibus. NAR Genom Bioinform 2021; 3:lqab025. [PMID: 33937763 PMCID: PMC8061458 DOI: 10.1093/nargab/lqab025] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 02/11/2021] [Accepted: 04/19/2021] [Indexed: 12/16/2022] Open
Abstract
While DNA methylation (DNAm) is the most-studied epigenetic mark, few recent studies probe the breadth of publicly available DNAm array samples. We collectively analyzed 35 360 Illumina Infinium HumanMethylation450K DNAm array samples published on the Gene Expression Omnibus. We learned a controlled vocabulary of sample labels by applying regular expressions to metadata and used existing models to predict various sample properties including epigenetic age. We found approximately two-thirds of samples were from blood, one-quarter were from brain and one-third were from cancer patients. About 19% of samples failed at least one of Illumina's 17 prescribed quality assessments; signal distributions across samples suggest modifying manufacturer-recommended thresholds for failure would make these assessments more informative. We further analyzed DNAm variances in seven tissues (adipose, nasal, blood, brain, buccal, sperm and liver) and characterized specific probes distinguishing them. Finally, we compiled DNAm array data and metadata, including our learned and predicted sample labels, into database files accessible via the recountmethylation R/Bioconductor companion package. Its vignettes walk the user through some analyses contained in this paper.
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Affiliation(s)
- Sean K Maden
- Computational Biology Program, Oregon Health & Science University, Portland, OR 97239, USA
| | - Reid F Thompson
- Computational Biology Program, Oregon Health & Science University, Portland, OR 97239, USA
| | - Kasper D Hansen
- Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Abhinav Nellore
- Computational Biology Program, Oregon Health & Science University, Portland, OR 97239, USA
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20
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Quan Y, Liang F, Deng SM, Zhu Y, Chen Y, Xiong J. Mining the Selective Remodeling of DNA Methylation in Promoter Regions to Identify Robust Gene-Level Associations With Phenotype. Front Mol Biosci 2021; 8:597513. [PMID: 33842534 PMCID: PMC8034267 DOI: 10.3389/fmolb.2021.597513] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Accepted: 02/01/2021] [Indexed: 11/13/2022] Open
Abstract
Epigenetics is an essential biological frontier linking genetics to the environment, where DNA methylation is one of the most studied epigenetic events. In recent years, through the epigenome-wide association study (EWAS), researchers have identified thousands of phenotype-related methylation sites. However, the overlaps of identified phenotype-related DNA methylation sites between various studies are often quite small, and it might be due to the fact that methylation remodeling has a certain degree of randomness within the genome. Thus, the identification of robust gene-phenotype associations is crucial to interpreting pathogenesis. How to integrate the methylation values of different sites on the same gene and to mine the DNA methylation at the gene level remains a challenge. A recent study found that the DNA methylation difference of the gene body and promoter region has a strong correlation with gene expression. In this study, we proposed a Statistical difference of DNA Methylation between Promoter and Other Body Region (SIMPO) algorithm to extract DNA methylation values at the gene level. First, by choosing to smoke as an environmental exposure factor, our method led to significant improvements in gene overlaps (from 5 to 17%) between different datasets. In addition, the biological significance of phenotype-related genes identified by SIMPO algorithm is comparable to that of the traditional probe-based methods. Then, we selected two disease contents (e.g., insulin resistance and Parkinson's disease) to show that the biological efficiency of disease-related gene identification increased from 15.43 to 44.44% (p-value = 1.20e-28). In summary, our results declare that mining the selective remodeling of DNA methylation in promoter regions can identify robust gene-level associations with phenotype, and the characteristic remodeling of a given gene's promoter region can reflect the essence of disease.
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Affiliation(s)
- Yuan Quan
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
- Lab of Epigenetics and Advanced Health Technology, Space Science and Technology Institute, Shenzhen, China
| | - Fengji Liang
- State Key Laboratory of Space Medicine Fundamentals and Application, China Astronaut Research and Training Center, Beijing, China
| | - Si-Min Deng
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Yuexing Zhu
- State Key Laboratory of Space Medicine Fundamentals and Application, China Astronaut Research and Training Center, Beijing, China
- Aromability Inc., Beijing, China
| | - Ying Chen
- Lab of Epigenetics and Advanced Health Technology, Space Science and Technology Institute, Shenzhen, China
| | - Jianghui Xiong
- Lab of Epigenetics and Advanced Health Technology, Space Science and Technology Institute, Shenzhen, China
- State Key Laboratory of Space Medicine Fundamentals and Application, China Astronaut Research and Training Center, Beijing, China
- Aromability Inc., Beijing, China
- Jiangsu Industrial Technology Research Institute (JITRI), Applied Adaptome Immunology Institute, Nanjing, Jiangsu, China
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21
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Lan Y, Sun R, Ouyang J, Ding W, Kim MJ, Wu J, Li Y, Shi T. AtMAD: Arabidopsis thaliana multi-omics association database. Nucleic Acids Res 2021; 49:D1445-D1451. [PMID: 33219693 PMCID: PMC7778929 DOI: 10.1093/nar/gkaa1042] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 10/08/2020] [Accepted: 10/21/2020] [Indexed: 12/22/2022] Open
Abstract
Integration analysis of multi-omics data provides a comprehensive landscape for understanding biological systems and mechanisms. The abundance of high-quality multi-omics data (genomics, transcriptomics, methylomics and phenomics) for the model organism Arabidopsis thaliana enables scientists to study the genetic mechanism of many biological processes. However, no resource is available to provide comprehensive and systematic multi-omics associations for Arabidopsis. Here, we developed an Arabidopsis thaliana Multi-omics Association Database (AtMAD, http://www.megabionet.org/atmad), a public repository for large-scale measurements of associations between genome, transcriptome, methylome, pathway and phenotype in Arabidopsis, designed for facilitating identification of eQTL, emQTL, Pathway-mQTL, Phenotype-pathway, GWAS, TWAS and EWAS. Candidate variants/methylations/genes were identified in AtMAD for specific phenotypes or biological processes, many of them are supported by experimental evidence. Based on the multi-omics association strategy, we have identified 11 796 cis-eQTLs and 10 119 trans-eQTLs. Among them, 68 837 environment-eQTL associations and 149 622 GWAS-eQTL associations were identified and stored in AtMAD. For expression–methylation quantitative trait loci (emQTL), we identified 265 776 emQTLs and 122 344 pathway-mQTLs. For TWAS and EWAS, we obtained 62 754 significant phenotype-gene associations and 3 993 379 significant phenotype-methylation associations, respectively. Overall, the multi-omics associated network in AtMAD will provide new insights into exploring biological mechanisms of plants at multi-omics levels.
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Affiliation(s)
- Yiheng Lan
- Key Laboratory of Saline-alkali Vegetation Ecology Restoration, Ministry of Education, Northeast Forestry University, Harbin, Heilongjiang 150040, China.,The Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai 200241, China
| | - Ruikun Sun
- The Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai 200241, China
| | - Jian Ouyang
- The Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai 200241, China
| | - Wubing Ding
- The Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai 200241, China
| | - Min-Jun Kim
- Key Laboratory of Saline-alkali Vegetation Ecology Restoration, Ministry of Education, Northeast Forestry University, Harbin, Heilongjiang 150040, China
| | - Jun Wu
- The Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai 200241, China
| | - Yuhua Li
- Key Laboratory of Saline-alkali Vegetation Ecology Restoration, Ministry of Education, Northeast Forestry University, Harbin, Heilongjiang 150040, China
| | - Tieliu Shi
- The Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai 200241, China.,Big Data and Engineering Research Center, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China
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22
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Cell lineage-specific methylome and genome alterations in gout. Aging (Albany NY) 2021; 13:3843-3865. [PMID: 33493135 PMCID: PMC7906142 DOI: 10.18632/aging.202353] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Accepted: 09/05/2020] [Indexed: 12/14/2022]
Abstract
In this study, we examined data from 69 gout patients and 1,455 non-gout controls using a MethylationEPIC BeadChip assay and Illumina HiSeq platform to identify lineage-specific epigenetic alterations and associated genetic factors that contributed to gouty inflammation. Cell lineage-specific differentially methylated sites were identified using CellDMC after adjusting for sex, age, alcohol drinking, smoking status, and smoking history (total pack-years). Different cell lineages displayed distinct differential methylation. Ingenuity Pathway Analysis and NetworkAnalyst indicated that many differential methylated sites were associated with interleukin-1β expression in monocytes. On the UCSC Genome Browser and WashU Epigenome Browser, metabolic trait, cis-methylation quantitative trait loci, genetic, and functional annotation analyses identified nine methylation loci located in interleukin-1β-regulating genes (PRKCZ, CIDEC, VDAC1, CPT1A, BIRC2, BRCA1, STK11, and NLRP12) that were associated specifically with gouty inflammation. All nine sites mapped to active regulatory elements in monocytes. MoLoTool and ReMap analyses indicated that the nine methylation loci overlapped with binding sites of several transcription factors that regulated interleukin-1β production and gouty inflammation. Decreases in PRKCZ and STK11 methylation were also associated with higher numbers of first-degree relatives who also had gout. The gouty-inflammation specific methylome and genome alterations could potentially aid in the identification of novel therapeutic targets.
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23
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Hop PJ, Luijk R, Daxinger L, van Iterson M, Dekkers KF, Jansen R, van Meurs JBJ, 't Hoen PAC, Ikram MA, van Greevenbroek MMJ, Boomsma DI, Slagboom PE, Veldink JH, van Zwet EW, Heijmans BT. Genome-wide identification of genes regulating DNA methylation using genetic anchors for causal inference. Genome Biol 2020; 21:220. [PMID: 32859263 PMCID: PMC7453518 DOI: 10.1186/s13059-020-02114-z] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Accepted: 07/21/2020] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND DNA methylation is a key epigenetic modification in human development and disease, yet there is limited understanding of its highly coordinated regulation. Here, we identify 818 genes that affect DNA methylation patterns in blood using large-scale population genomics data. RESULTS By employing genetic instruments as causal anchors, we establish directed associations between gene expression and distant DNA methylation levels, while ensuring specificity of the associations by correcting for linkage disequilibrium and pleiotropy among neighboring genes. The identified genes are enriched for transcription factors, of which many consistently increased or decreased DNA methylation levels at multiple CpG sites. In addition, we show that a substantial number of transcription factors affected DNA methylation at their experimentally determined binding sites. We also observe genes encoding proteins with heterogenous functions that have widespread effects on DNA methylation, e.g., NFKBIE, CDCA7(L), and NLRC5, and for several examples, we suggest plausible mechanisms underlying their effect on DNA methylation. CONCLUSION We report hundreds of genes that affect DNA methylation and provide key insights in the principles underlying epigenetic regulation.
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Affiliation(s)
- Paul J Hop
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, 2333 ZC, Leiden, The Netherlands
- Department of Neurology, UMC Utrecht Brain Center, University Medical Centre Utrecht, Utrecht University, 3584 CG, Utrecht, The Netherlands
| | - René Luijk
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, 2333 ZC, Leiden, The Netherlands
| | - Lucia Daxinger
- Department of Human Genetics, Leiden University Medical Center, 2333 ZC, Leiden, The Netherlands
| | - Maarten van Iterson
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, 2333 ZC, Leiden, The Netherlands
| | - Koen F Dekkers
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, 2333 ZC, Leiden, The Netherlands
| | - Rick Jansen
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, The Netherlands
| | - Joyce B J van Meurs
- Department of Internal Medicine, Erasmus Medical Centre, Rotterdam, The Netherlands
| | - Peter A C 't Hoen
- Centre for Molecular and Biomolecular Informatics, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center Nijmegen, Nijmegen, The Netherlands
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus University Medical Center, 3015 CE, Rotterdam, The Netherlands
| | - Marleen M J van Greevenbroek
- Department of Internal Medicine, Maastricht University Medical Center, 6211 LK, Maastricht, The Netherlands
- School for Cardiovascular Diseases (CARIM), Maastricht University Medical Center, 6229 ER, Maastricht, The Netherlands
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Neuroscience Campus Amsterdam, 1081 BT, Amsterdam, The Netherlands
| | - P Eline Slagboom
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, 2333 ZC, Leiden, The Netherlands
| | - Jan H Veldink
- Department of Neurology, UMC Utrecht Brain Center, University Medical Centre Utrecht, Utrecht University, 3584 CG, Utrecht, The Netherlands
| | - Erik W van Zwet
- Medical Statistics, Department of Biomedical Data Sciences, Leiden University Medical Center, 2333 ZC, Leiden, Zuid-Holland, The Netherlands
| | - Bastiaan T Heijmans
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, 2333 ZC, Leiden, The Netherlands.
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24
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Ivanov S, Lagunin A, Filimonov D, Tarasova O. Network-Based Analysis of OMICs Data to Understand the HIV-Host Interaction. Front Microbiol 2020; 11:1314. [PMID: 32625189 PMCID: PMC7311653 DOI: 10.3389/fmicb.2020.01314] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Accepted: 05/25/2020] [Indexed: 12/22/2022] Open
Abstract
The interaction of human immunodeficiency virus with human cells is responsible for all stages of the viral life cycle, from the infection of CD4+ cells to reverse transcription, integration, and the assembly of new viral particles. To date, a large amount of OMICs data as well as information from functional genomics screenings regarding the HIV–host interaction has been accumulated in the literature and in public databases. We processed databases containing HIV–host interactions and found 2910 HIV-1-human protein-protein interactions, mostly related to viral group M subtype B, 137 interactions between human and HIV-1 coding and non-coding RNAs, essential for viral lifecycle and cell defense mechanisms, 232 transcriptomics, 27 proteomics, and 34 epigenomics HIV-related experiments. Numerous studies regarding network-based analysis of corresponding OMICs data have been published in recent years. We overview various types of molecular networks, which can be created using OMICs data, including HIV–human protein–protein interaction networks, co-expression networks, gene regulatory and signaling networks, and approaches for the analysis of their topology and dynamics. The network-based analysis can be used to determine the critical pathways and key proteins involved in the HIV life cycle, cellular and immune responses to infection, viral escape from host defense mechanisms, and mechanisms mediating different susceptibility of humans to infection. The proteins and pathways identified in these studies represent a basis for developing new anti-HIV therapeutic strategies such as new drugs preventing infection of CD4+ cells and viral replication, effective vaccines, “shock and kill” and “block and lock” approaches to cure latent infection.
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Affiliation(s)
- Sergey Ivanov
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow, Russia.,Department of Bioinformatics, Pirogov Russian National Research Medical University, Moscow, Russia
| | - Alexey Lagunin
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow, Russia.,Department of Bioinformatics, Pirogov Russian National Research Medical University, Moscow, Russia
| | - Dmitry Filimonov
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow, Russia
| | - Olga Tarasova
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow, Russia
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25
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Xiong Z, Li M, Yang F, Ma Y, Sang J, Li R, Li Z, Zhang Z, Bao Y. EWAS Data Hub: a resource of DNA methylation array data and metadata. Nucleic Acids Res 2020; 48:D890-D895. [PMID: 31584095 PMCID: PMC6943079 DOI: 10.1093/nar/gkz840] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2019] [Revised: 09/09/2019] [Accepted: 10/01/2019] [Indexed: 01/12/2023] Open
Abstract
Epigenome-Wide Association Study (EWAS) has become an effective strategy to explore epigenetic basis of complex traits. Over the past decade, a large amount of epigenetic data, especially those sourced from DNA methylation array, has been accumulated as the result of numerous EWAS projects. We present EWAS Data Hub (https://bigd.big.ac.cn/ewas/datahub), a resource for collecting and normalizing DNA methylation array data as well as archiving associated metadata. The current release of EWAS Data Hub integrates a comprehensive collection of DNA methylation array data from 75 344 samples and employs an effective normalization method to remove batch effects among different datasets. Accordingly, taking advantages of both massive high-quality DNA methylation data and standardized metadata, EWAS Data Hub provides reference DNA methylation profiles under different contexts, involving 81 tissues/cell types (that contain 25 brain parts and 25 blood cell types), six ancestry categories, and 67 diseases (including 39 cancers). In summary, EWAS Data Hub bears great promise to aid the retrieval and discovery of methylation-based biomarkers for phenotype characterization, clinical treatment and health care.
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Affiliation(s)
- Zhuang Xiong
- National Genomics Data Center, Beijing 100101, China.,BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.,CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.,College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Mengwei Li
- National Genomics Data Center, Beijing 100101, China.,BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.,CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.,College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Fei Yang
- National Genomics Data Center, Beijing 100101, China.,BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.,CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.,College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yingke Ma
- National Genomics Data Center, Beijing 100101, China.,BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.,CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
| | - Jian Sang
- National Genomics Data Center, Beijing 100101, China.,BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.,CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.,College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Rujiao Li
- National Genomics Data Center, Beijing 100101, China.,BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.,CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
| | - Zhaohua Li
- National Genomics Data Center, Beijing 100101, China.,BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.,CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.,School of Future Technology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhang Zhang
- National Genomics Data Center, Beijing 100101, China.,BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.,CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.,College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China.,School of Future Technology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yiming Bao
- National Genomics Data Center, Beijing 100101, China.,BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.,CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.,College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China.,School of Future Technology, University of Chinese Academy of Sciences, Beijing 100049, China
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26
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Huang J, Bai L, Cui B, Wu L, Wang L, An Z, Ruan S, Yu Y, Zhang X, Chen J. Leveraging biological and statistical covariates improves the detection power in epigenome-wide association testing. Genome Biol 2020; 21:88. [PMID: 32252795 PMCID: PMC7132874 DOI: 10.1186/s13059-020-02001-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Accepted: 03/17/2020] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Epigenome-wide association studies (EWAS), which seek the association between epigenetic marks and an outcome or exposure, involve multiple hypothesis testing. False discovery rate (FDR) control has been widely used for multiple testing correction. However, traditional FDR control methods do not use auxiliary covariates, and they could be less powerful if the covariates could inform the likelihood of the null hypothesis. Recently, many covariate-adaptive FDR control methods have been developed, but application of these methods to EWAS data has not yet been explored. It is not clear whether these methods can significantly improve detection power, and if so, which covariates are more relevant for EWAS data. RESULTS In this study, we evaluate the performance of five covariate-adaptive FDR control methods with EWAS-related covariates using simulated as well as real EWAS datasets. We develop an omnibus test to assess the informativeness of the covariates. We find that statistical covariates are generally more informative than biological covariates, and the covariates of methylation mean and variance are almost universally informative. In contrast, the informativeness of biological covariates depends on specific datasets. We show that the independent hypothesis weighting (IHW) and covariate adaptive multiple testing (CAMT) method are overall more powerful, especially for sparse signals, and could improve the detection power by a median of 25% and 68% on real datasets, compared to the ST procedure. We further validate the findings in various biological contexts. CONCLUSIONS Covariate-adaptive FDR control methods with informative covariates can significantly increase the detection power for EWAS. For sparse signals, IHW and CAMT are recommended.
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Affiliation(s)
- Jinyan Huang
- State Key Laboratory of Medical Genomics, Shanghai Institute of Hematology, National Research Center for Translational Medicine, Rui-Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, 197 Ruijin Er Road, Shanghai, 200025, China.
| | - Ling Bai
- State Key Laboratory of Medical Genomics, Shanghai Institute of Hematology, National Research Center for Translational Medicine, Rui-Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, 197 Ruijin Er Road, Shanghai, 200025, China
| | - Bowen Cui
- State Key Laboratory of Medical Genomics, Shanghai Institute of Hematology, National Research Center for Translational Medicine, Rui-Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, 197 Ruijin Er Road, Shanghai, 200025, China
| | - Liang Wu
- State Key Laboratory of Medical Genomics, Shanghai Institute of Hematology, National Research Center for Translational Medicine, Rui-Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, 197 Ruijin Er Road, Shanghai, 200025, China
| | - Liwen Wang
- Department of General Surgery, Rui-Jin Hospital, Shanghai Jiao Tong University, 197 Ruijin Er Road, Shanghai, 200025, China
| | - Zhiyin An
- State Key Laboratory of Medical Genomics, Shanghai Institute of Hematology, National Research Center for Translational Medicine, Rui-Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, 197 Ruijin Er Road, Shanghai, 200025, China
| | - Shulin Ruan
- State Key Laboratory of Medical Genomics, Shanghai Institute of Hematology, National Research Center for Translational Medicine, Rui-Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, 197 Ruijin Er Road, Shanghai, 200025, China
| | - Yue Yu
- Division of Digital Health Sciences, Mayo Clinic, 200 1st St SW, Rochester, MN, 55905, USA
| | - Xianyang Zhang
- Department of Statistics, Texas A&M University, Blocker 449D, College Station, TX, 77843, USA.
| | - Jun Chen
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research and Center for Individualized Medicine, Mayo Clinic, 200 1st St SW, Rochester, MN, 55905, USA.
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27
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Gatev E, Gladish N, Mostafavi S, Kobor MS. CoMeBack: DNA methylation array data analysis for co-methylated regions. Bioinformatics 2020; 36:2675-2683. [DOI: 10.1093/bioinformatics/btaa049] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2019] [Revised: 12/23/2019] [Accepted: 01/20/2020] [Indexed: 01/06/2023] Open
Abstract
Abstract
Motivation
High-dimensional DNA methylation (DNAm) array coverage, while sparse in the context of the entire DNA methylome, still constitutes a very large number of CpG probes. The ensuing multiple-test corrections affect the statistical power to detect associations, likely contributing to prevalent limited reproducibility. Array probes measuring proximal CpG sites often have correlated levels of DNAm that may not only be biologically meaningful but also imply statistical dependence and redundancy. New methods that account for such correlations between adjacent probes may enable improved specificity, discovery and interpretation of statistical associations in DNAm array data.
Results
We developed a method named Co-Methylation with genomic CpG Background (CoMeBack) that estimates DNA co-methylation, defined as proximal CpG probes with correlated DNAm across individuals. CoMeBack outputs co-methylated regions (CMRs), spanning sets of array probes constructed based on all genomic CpG sites, including those not measured on the array, and without any phenotypic variable inputs. This approach can reduce the multiple-test correction burden, while enhancing the discovery and specificity of statistical associations. We constructed and validated CMRs in whole blood, using publicly available Illumina Infinium 450 K array data from over 5000 individuals. These CMRs were enriched for enhancer chromatin states, and binding site motifs for several transcription factors involved in blood physiology. We illustrated how CMR-based epigenome-wide association studies can improve discovery and reduce false positives for associations with chronological age.
Availability and implementation
https://bitbucket.org/flopflip/comeback.
Supplementary information
Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Evan Gatev
- Graduate Program in Bioinformatics, University of British Columbia, Vancouver, BC V5T 4S6, Canada
- Department of Finance, Beedie School of Business, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
- BC Children’s Hospital Research Institute, Vancouver, BC V5Z 4H4, Canada
- Centre for Molecular Medicine and Therapeutics, Vancouver, BC V5Z 4H4, Canada
| | - Nicole Gladish
- BC Children’s Hospital Research Institute, Vancouver, BC V5Z 4H4, Canada
- Centre for Molecular Medicine and Therapeutics, Vancouver, BC V5Z 4H4, Canada
- Department of Medical Genetics
| | - Sara Mostafavi
- BC Children’s Hospital Research Institute, Vancouver, BC V5Z 4H4, Canada
- Centre for Molecular Medicine and Therapeutics, Vancouver, BC V5Z 4H4, Canada
- Department of Medical Genetics
- Department of Statistics, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | - Michael S Kobor
- BC Children’s Hospital Research Institute, Vancouver, BC V5Z 4H4, Canada
- Centre for Molecular Medicine and Therapeutics, Vancouver, BC V5Z 4H4, Canada
- Department of Medical Genetics
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28
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Affiliation(s)
- Chathura J Gunasekara
- USDA/ARS Children's Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Robert A Waterland
- USDA/ARS Children's Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX 77030, USA.,Department of Molecular & Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
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29
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Abstract
Twin registries have developed as a valuable resource for the study of many aspects of disease and society over the years in many different countries. A number of these registries include large numbers of twins with data collected at varying information levels for twin cohorts over the past several decades. More recent expansion of twin datasets has allowed for the collection of genetic data, together with many other levels of 'omic' information along with multiple demographic, physiological, health outcomes and other measures typically used in epidemiologic research. Other twin data sources outside these registries reflect research interests in particular aspects of disease or specific phenotypic assessment. Twin registries have the potential to play a key role in many aspects of the artificial intelligence/machine learning-driven projects of the future and will continue to keep adapting to the changing research landscape.
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30
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Gunasekara CJ, Scott CA, Laritsky E, Baker MS, MacKay H, Duryea JD, Kessler NJ, Hellenthal G, Wood AC, Hodges KR, Gandhi M, Hair AB, Silver MJ, Moore SE, Prentice AM, Li Y, Chen R, Coarfa C, Waterland RA. A genomic atlas of systemic interindividual epigenetic variation in humans. Genome Biol 2019; 20:105. [PMID: 31155008 PMCID: PMC6545702 DOI: 10.1186/s13059-019-1708-1] [Citation(s) in RCA: 61] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2019] [Accepted: 05/06/2019] [Indexed: 01/05/2023] Open
Abstract
BACKGROUND DNA methylation is thought to be an important determinant of human phenotypic variation, but its inherent cell type specificity has impeded progress on this question. At exceptional genomic regions, interindividual variation in DNA methylation occurs systemically. Like genetic variants, systemic interindividual epigenetic variants are stable, can influence phenotype, and can be assessed in any easily biopsiable DNA sample. We describe an unbiased screen for human genomic regions at which interindividual variation in DNA methylation is not tissue-specific. RESULTS For each of 10 donors from the NIH Genotype-Tissue Expression (GTEx) program, CpG methylation is measured by deep whole-genome bisulfite sequencing of genomic DNA from tissues representing the three germ layer lineages: thyroid (endoderm), heart (mesoderm), and brain (ectoderm). We develop a computational algorithm to identify genomic regions at which interindividual variation in DNA methylation is consistent across all three lineages. This approach identifies 9926 correlated regions of systemic interindividual variation (CoRSIVs). These regions, comprising just 0.1% of the human genome, are inter-correlated over long genomic distances, associated with transposable elements and subtelomeric regions, conserved across diverse human ethnic groups, sensitive to periconceptional environment, and associated with genes implicated in a broad range of human disorders and phenotypes. CoRSIV methylation in one tissue can predict expression of associated genes in other tissues. CONCLUSIONS In addition to charting a previously unexplored molecular level of human individuality, this atlas of human CoRSIVs provides a resource for future population-based investigations into how interindividual epigenetic variation modulates risk of disease.
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Affiliation(s)
- Chathura J Gunasekara
- USDA/ARS Children's Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA
| | - C Anthony Scott
- USDA/ARS Children's Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA
| | - Eleonora Laritsky
- USDA/ARS Children's Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA
| | - Maria S Baker
- USDA/ARS Children's Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA
| | - Harry MacKay
- USDA/ARS Children's Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA
| | - Jack D Duryea
- USDA/ARS Children's Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA
| | - Noah J Kessler
- MRC Unit The Gambia at London School of Hygiene and Tropical Medicine, Keneba, The Gambia
- Department of Women and Children's Health, King's College London, London, UK
| | - Garrett Hellenthal
- Department of Genetics, Evolution and Environment, UCL Genetics Institute, University College London, London, WC1E 6BT, UK
| | - Alexis C Wood
- USDA/ARS Children's Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA
| | - Kelly R Hodges
- Department of Obstetrics and Gynecology, Baylor College of Medicine, Houston, TX, USA
| | - Manisha Gandhi
- Department of Obstetrics and Gynecology, Baylor College of Medicine, Houston, TX, USA
| | - Amy B Hair
- Department of Pediatrics - Neonatology, Baylor College of Medicine, Houston, TX, USA
| | - Matt J Silver
- MRC Unit The Gambia at London School of Hygiene and Tropical Medicine, Keneba, The Gambia
| | - Sophie E Moore
- MRC Unit The Gambia at London School of Hygiene and Tropical Medicine, Keneba, The Gambia
- Department of Women and Children's Health, King's College London, London, UK
| | - Andrew M Prentice
- MRC Unit The Gambia at London School of Hygiene and Tropical Medicine, Keneba, The Gambia
| | - Yumei Li
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Rui Chen
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Cristian Coarfa
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA.
| | - Robert A Waterland
- USDA/ARS Children's Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA.
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA.
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Rigden DJ, Fernández X. The 26th annual Nucleic Acids Research database issue and Molecular Biology Database Collection. Nucleic Acids Res 2019; 47:D1-D7. [PMID: 30626175 PMCID: PMC6323895 DOI: 10.1093/nar/gky1267] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
The 2019 Nucleic Acids Research (NAR) Database Issue contains 168 papers spanning molecular biology. Among them, 64 are new and another 92 are updates describing resources that appeared in the Issue previously. The remaining 12 are updates on databases most recently published elsewhere. This Issue contains two Breakthrough articles, on the Virtual Metabolic Human (VMH) database which links human and gut microbiota metabolism with diet and disease, and Vibrism DB, a database of mouse brain anatomy and gene (co-)expression with sophisticated visualization and session sharing. Major returning nucleic acid databases include RNAcentral, miRBase and LncRNA2Target. Protein sequence databases include UniProtKB, InterPro and Pfam, while wwPDB and RCSB cover protein structure. STRING and KEGG update in the section on metabolism and pathways. Microbial genomes are covered by IMG/M and resources for human and model organism genomics include Ensembl, UCSC Genome Browser, GENCODE and Flybase. Genomic variation and disease are well-covered by GWAS Catalog, PopHumanScan, OMIM and COSMIC, CADD being another major newcomer. Major new proteomics resources reporting here include iProX and jPOSTdb. The entire database issue is freely available online on the NAR website (https://academic.oup.com/nar). The NAR online Molecular Biology Database Collection has been updated, reviewing 506 entries, adding 66 new resources and eliminating 147 discontinued URLs, bringing the current total to 1613 databases. It is available at http://www.oxfordjournals.org/nar/database/c.
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Affiliation(s)
- Daniel J Rigden
- Institute of Integrative Biology, University of Liverpool, Crown Street, Liverpool L69 7ZB, UK
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