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Chen J, Hui Q, Titanji BK, So-Armah K, Freiberg M, Justice AC, Xu K, Zhu X, Gwinn M, Marconi VC, Sun YV. A multi-trait epigenome-wide association study identified DNA methylation signature of inflammation among people with HIV. RESEARCH SQUARE 2024:rs.3.rs-4419840. [PMID: 38854093 PMCID: PMC11160930 DOI: 10.21203/rs.3.rs-4419840/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
Inflammation underlies many conditions causing excess morbidity and mortality among people with HIV (PWH). A handful of single-trait epigenome-wide association studies (EWAS) have suggested that inflammation is associated with DNA methylation (DNAm) among PWH. Multi-trait EWAS may further improve statistical power and reveal pathways in common between different inflammatory markers. We conducted single-trait EWAS of three inflammatory markers (soluble CD14, D-dimers, and interleukin 6) in the Veteran Aging Cohort Study (n = 920). The study population was all male PWH with an average age of 51 years, and 82.3% self-reported as Black. We then applied two multi-trait EWAS methods-CPASSOC and OmniTest-to combine single-trait EWAS results. CPASSOC and OmniTest identified 189 and 157 inflammation-associated DNAm sites respectively, of which 112 overlapped. Among the identified sites, 56% were not significant in any single-trait EWAS. Top sites were mapped to inflammation-related genes including IFITM1 , PARP9 and STAT1 . These genes were significantly enriched in pathways such as "type I interferon signaling" and "immune response to virus". We demonstrate that multi-trait EWAS can improve the discovery of inflammation-associated DNAm sites, genes, and pathways. These DNAm sites suggest molecular mechanisms in response to inflammation associated with HIV and might hold the key to addressing persistent inflammation in PWH.
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Bell CG. Epigenomic insights into common human disease pathology. Cell Mol Life Sci 2024; 81:178. [PMID: 38602535 PMCID: PMC11008083 DOI: 10.1007/s00018-024-05206-2] [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: 01/19/2024] [Revised: 03/11/2024] [Accepted: 03/13/2024] [Indexed: 04/12/2024]
Abstract
The epigenome-the chemical modifications and chromatin-related packaging of the genome-enables the same genetic template to be activated or repressed in different cellular settings. This multi-layered mechanism facilitates cell-type specific function by setting the local sequence and 3D interactive activity level. Gene transcription is further modulated through the interplay with transcription factors and co-regulators. The human body requires this epigenomic apparatus to be precisely installed throughout development and then adequately maintained during the lifespan. The causal role of the epigenome in human pathology, beyond imprinting disorders and specific tumour suppressor genes, was further brought into the spotlight by large-scale sequencing projects identifying that mutations in epigenomic machinery genes could be critical drivers in both cancer and developmental disorders. Abrogation of this cellular mechanism is providing new molecular insights into pathogenesis. However, deciphering the full breadth and implications of these epigenomic changes remains challenging. Knowledge is accruing regarding disease mechanisms and clinical biomarkers, through pathogenically relevant and surrogate tissue analyses, respectively. Advances include consortia generated cell-type specific reference epigenomes, high-throughput DNA methylome association studies, as well as insights into ageing-related diseases from biological 'clocks' constructed by machine learning algorithms. Also, 3rd-generation sequencing is beginning to disentangle the complexity of genetic and DNA modification haplotypes. Cell-free DNA methylation as a cancer biomarker has clear clinical utility and further potential to assess organ damage across many disorders. Finally, molecular understanding of disease aetiology brings with it the opportunity for exact therapeutic alteration of the epigenome through CRISPR-activation or inhibition.
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Affiliation(s)
- Christopher G Bell
- William Harvey Research Institute, Barts & The London Faculty of Medicine, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK.
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3
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Wang L, Xiao J, Zhang B, Hou A. Epigenetic modifications in the development of bronchopulmonary dysplasia: a review. Pediatr Res 2024:10.1038/s41390-024-03167-7. [PMID: 38570557 DOI: 10.1038/s41390-024-03167-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 02/25/2024] [Accepted: 03/07/2024] [Indexed: 04/05/2024]
Abstract
While perinatal medicine advancements have bolstered survival outcomes for premature infants, bronchopulmonary dysplasia (BPD) continues to threaten their long-term health. Gene-environment interactions, mediated by epigenetic modifications such as DNA methylation, histone modification, and non-coding RNA regulation, take center stage in BPD pathogenesis. Recent discoveries link methylation variations across biological pathways with BPD. Also, the potential reversibility of histone modifications fuels new treatment avenues. The review also highlights the promise of utilizing mesenchymal stem cells and their exosomes as BPD therapies, given their ability to modulate non-coding RNA, opening novel research and intervention possibilities. IMPACT: The complexity and universality of epigenetic modifications in the occurrence and development of bronchopulmonary dysplasia were thoroughly discussed. Both molecular and cellular mechanisms contribute to the diverse nature of epigenetic changes, suggesting the need for deeper biochemical techniques to explore these molecular alterations. The utilization of innovative cell-specific drug delivery methods like exosomes and extracellular vesicles holds promise in achieving precise epigenetic regulation.
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Affiliation(s)
- Lichuan Wang
- Department of Pediatrics, Sheng Jing Hospital of China Medical University, Shenyang, China
| | - Jun Xiao
- Department of Pediatrics, Sheng Jing Hospital of China Medical University, Shenyang, China
| | - Bohan Zhang
- Department of Pediatrics, Sheng Jing Hospital of China Medical University, Shenyang, China
| | - Ana Hou
- Department of Pediatrics, Sheng Jing Hospital of China Medical University, Shenyang, China.
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Polakkattil BK, Vellichirammal NN, Nair IV, Nair CM, Banerjee M. Methylome-wide and meQTL analysis helps to distinguish treatment response from non-response and pathogenesis markers in schizophrenia. Front Psychiatry 2024; 15:1297760. [PMID: 38516266 PMCID: PMC10954811 DOI: 10.3389/fpsyt.2024.1297760] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 02/06/2024] [Indexed: 03/23/2024] Open
Abstract
Schizophrenia is a complex condition with entwined genetic and epigenetic risk factors, posing a challenge to disentangle the intermixed pathological and therapeutic epigenetic signatures. To resolve this, we performed 850K methylome-wide and 700K genome-wide studies on the same set of schizophrenia patients by stratifying them into responders, non-responders, and drug-naïve patients. The key genes that signified the response were followed up using real-time gene expression studies to understand the effect of antipsychotics at the gene transcription level. The study primarily implicates hypermethylation in therapeutic response and hypomethylation in the drug-non-responsive state. Several differentially methylated sites and regions colocalized with the schizophrenia genome-wide association study (GWAS) risk genes and variants, supporting the convoluted gene-environment association. Gene ontology and protein-protein interaction (PPI) network analyses revealed distinct patterns that differentiated the treatment response from drug resistance. The study highlights the strong involvement of several processes related to nervous system development, cell adhesion, and signaling in the antipsychotic response. The ability of antipsychotic medications to alter the pathology by modulating gene expression or methylation patterns is evident from the general increase in the gene expression of response markers and histone modifiers and the decrease in class II human leukocyte antigen (HLA) genes following treatment with varying concentrations of medications like clozapine, olanzapine, risperidone, and haloperidol. The study indicates a directional overlap of methylation markers between pathogenesis and therapeutic response, thereby suggesting a careful distinction of methylation markers of pathogenesis from treatment response. In addition, there is a need to understand the trade-off between genetic and epigenetic observations. It is suggested that methylomic changes brought about by drugs need careful evaluation for their positive effects on pathogenesis, course of disease progression, symptom severity, side effects, and refractoriness.
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Affiliation(s)
- Binithamol K. Polakkattil
- Human Molecular Genetics Laboratory, Rajiv Gandhi Centre for Biotechnology, Thiruvananthapuram, Kerala, India
- Research Center, University of Kerala, Thiruvananthapuram, Kerala, India
| | - Neetha N. Vellichirammal
- Human Molecular Genetics Laboratory, Rajiv Gandhi Centre for Biotechnology, Thiruvananthapuram, Kerala, India
| | - Indu V. Nair
- Mental Health Centre, Thiruvananthapuram, Kerala, India
| | | | - Moinak Banerjee
- Human Molecular Genetics Laboratory, Rajiv Gandhi Centre for Biotechnology, Thiruvananthapuram, Kerala, India
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Wu C, Mou X, Zhang H. Gbdmr: identifying differentially methylated CpG regions in the human genome via generalized beta regressions. BMC Bioinformatics 2024; 25:97. [PMID: 38443825 PMCID: PMC10916021 DOI: 10.1186/s12859-024-05711-y] [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: 10/03/2023] [Accepted: 02/19/2024] [Indexed: 03/07/2024] Open
Abstract
BACKGROUND DNA methylation is a biochemical process in which a methyl group is added to the cytosine-phosphate-guanine (CpG) site on DNA molecules without altering the DNA sequence. Multiple CpG sites in a certain genome region can be differentially methylated across phenotypes. Identifying these differentially methylated CpG regions (DMRs) associated with the phenotypes contributes to disease prediction and precision medicine development. RESULTS We propose a novel DMR detection algorithm, gbdmr. In contrast to existing methods under a linear regression framework, gbdmr assumes that DNA methylation levels follow a generalized beta distribution. We compare gbdmr to alternative approaches via simulations and real data analyses, including dmrff, a new DMR detection approach that shows promising performance among competitors, and the traditional EWAS that focuses on single CpG sites. Our simulations demonstrate that gbdmr is superior to the other two when the correlation between neighboring CpG sites is strong, while dmrff shows a higher power when the correlation is weak. We provide an explanation of these phenomena from a theoretical perspective. We further applied the three methods to multiple real DNA methylation datasets. One is from a birth cohort study undertaken on the Isle of Wight, United Kingdom, and the other two are from the Gene Expression Omnibus database repository. Overall, gbdmr identifies more DMR CpGs linked to phenotypes than dmrff, and the simulated results support the findings. CONCLUSIONS Gbdmr is an innovative method for detecting DMRs based on generalized beta regression. It demonstrated notable advantages over dmrff and traditional EWAS, particularly when adjacent CpGs exhibited moderate to strong correlations. Our real data analyses and simulated findings highlight the reliability of gbdmr as a robust DMR detection tool. The gbdmr approach is accessible and implemented by R on GitHub: https://github.com/chengzhouwu/gbdmr .
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Affiliation(s)
- Chengzhou Wu
- School of Public Health, University of Memphis, 3720 Alumni Ave, Memphis, TN, 38152, USA
| | - Xichen Mou
- School of Public Health, University of Memphis, 3720 Alumni Ave, Memphis, TN, 38152, USA.
| | - Hongmei Zhang
- School of Public Health, University of Memphis, 3720 Alumni Ave, Memphis, TN, 38152, USA
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Wang Z, Fu G, Ma G, Wang C, Wang Q, Lu C, Fu L, Zhang X, Cong B, Li S. The association between DNA methylation and human height and a prospective model of DNA methylation-based height prediction. Hum Genet 2024; 143:401-421. [PMID: 38507014 DOI: 10.1007/s00439-024-02659-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 02/13/2024] [Indexed: 03/22/2024]
Abstract
As a vital anthropometric characteristic, human height information not only helps to understand overall developmental status and genetic risk factors, but is also important for forensic DNA phenotyping. We utilized linear regression analysis to test the association between each CpG probe and the height phenotype. Next, we designed a methylation sequencing panel targeting 959 CpGs and subsequent height inference models were constructed for the Chinese population. A total of 11,730 height-associated sites were identified. By employing KPCA and deep neural networks, a prediction model was developed, of which the cross-validation RMSE, MAE and R2 were 5.62 cm, 4.45 cm and 0.64, respectively. Genetic factors could explain 39.4% of the methylation level variance of sites used in the height inference models. Collectively, we demonstrated an association between height and DNA methylation status through an EWAS analysis. Targeted methylation sequencing of only 959 CpGs combined with deep learning techniques could provide a model to estimate human height with higher accuracy than SNP-based prediction models.
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Affiliation(s)
- Zhonghua Wang
- College of Forensic Medicine, Hebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, Research Unit of Digestive Tract Microecosystem Pharmacology and Toxicology, Hebei Medical University, Chinese Academy of Medical Sciences, Shijiazhuang, 050017, Hebei, China
| | - Guangping Fu
- College of Forensic Medicine, Hebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, Research Unit of Digestive Tract Microecosystem Pharmacology and Toxicology, Hebei Medical University, Chinese Academy of Medical Sciences, Shijiazhuang, 050017, Hebei, China
| | - Guanju Ma
- College of Forensic Medicine, Hebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, Research Unit of Digestive Tract Microecosystem Pharmacology and Toxicology, Hebei Medical University, Chinese Academy of Medical Sciences, Shijiazhuang, 050017, Hebei, China
| | - Chunyan Wang
- Physical Examination Center of Shijiazhuang People's Hospital, Shijiazhuang, 050011, Hebei, China
| | - Qian Wang
- College of Forensic Medicine, Hebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, Research Unit of Digestive Tract Microecosystem Pharmacology and Toxicology, Hebei Medical University, Chinese Academy of Medical Sciences, Shijiazhuang, 050017, Hebei, China
| | - Chaolong Lu
- College of Forensic Medicine, Hebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, Research Unit of Digestive Tract Microecosystem Pharmacology and Toxicology, Hebei Medical University, Chinese Academy of Medical Sciences, Shijiazhuang, 050017, Hebei, China
| | - Lihong Fu
- College of Forensic Medicine, Hebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, Research Unit of Digestive Tract Microecosystem Pharmacology and Toxicology, Hebei Medical University, Chinese Academy of Medical Sciences, Shijiazhuang, 050017, Hebei, China
| | - Xiaojing Zhang
- College of Forensic Medicine, Hebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, Research Unit of Digestive Tract Microecosystem Pharmacology and Toxicology, Hebei Medical University, Chinese Academy of Medical Sciences, Shijiazhuang, 050017, Hebei, China
| | - Bin Cong
- College of Forensic Medicine, Hebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, Research Unit of Digestive Tract Microecosystem Pharmacology and Toxicology, Hebei Medical University, Chinese Academy of Medical Sciences, Shijiazhuang, 050017, Hebei, China
| | - Shujin Li
- College of Forensic Medicine, Hebei Key Laboratory of Forensic Medicine, Collaborative Innovation Center of Forensic Medical Molecular Identification, Research Unit of Digestive Tract Microecosystem Pharmacology and Toxicology, Hebei Medical University, Chinese Academy of Medical Sciences, Shijiazhuang, 050017, Hebei, China.
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Hubers N, Hagenbeek FA, Pool R, Déjean S, Harms AC, Roetman PJ, van Beijsterveldt CEM, Fanos V, Ehli EA, Vermeiren RRJM, Bartels M, Hottenga JJ, Hankemeier T, van Dongen J, Boomsma DI. Integrative multi-omics analysis of genomic, epigenomic, and metabolomics data leads to new insights for Attention-Deficit/Hyperactivity Disorder. Am J Med Genet B Neuropsychiatr Genet 2024; 195:e32955. [PMID: 37534875 DOI: 10.1002/ajmg.b.32955] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 06/13/2023] [Accepted: 07/11/2023] [Indexed: 08/04/2023]
Abstract
The evolving field of multi-omics combines data and provides methods for simultaneous analysis across several omics levels. Here, we integrated genomics (transmitted and non-transmitted polygenic scores [PGSs]), epigenomics, and metabolomics data in a multi-omics framework to identify biomarkers for Attention-Deficit/Hyperactivity Disorder (ADHD) and investigated the connections among the three omics levels. We first trained single- and next multi-omics models to differentiate between cases and controls in 596 twins (cases = 14.8%) from the Netherlands Twin Register (NTR) demonstrating reasonable in-sample prediction through cross-validation. The multi-omics model selected 30 PGSs, 143 CpGs, and 90 metabolites. We confirmed previous associations of ADHD with glucocorticoid exposure and the transmembrane protein family TMEM, show that the DNA methylation of the MAD1L1 gene associated with ADHD has a relation with parental smoking behavior, and present novel findings including associations between indirect genetic effects and CpGs of the STAP2 gene. However, out-of-sample prediction in NTR participants (N = 258, cases = 14.3%) and in a clinical sample (N = 145, cases = 51%) did not perform well (range misclassification was [0.40, 0.57]). The results highlighted connections between omics levels, with the strongest connections between non-transmitted PGSs, CpGs, and amino acid levels and show that multi-omics designs considering interrelated omics levels can help unravel the complex biology underlying ADHD.
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Affiliation(s)
- Nikki Hubers
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Reproduction & Development (AR&D) Research Institute, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, the Netherlands
| | - Fiona A Hagenbeek
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, the Netherlands
| | - René Pool
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, the Netherlands
| | - Sébastien Déjean
- Toulouse Mathematics Institute, UMR 5219, University of Toulouse, CNRS, Toulouse, France
| | - Amy C Harms
- Division of Analytical Biosciences, Leiden Academic Center for Drug Research, Leiden University, Leiden, the Netherlands
- The Netherlands Metabolomics Centre, Leiden, The Netherlands
| | - Peter J Roetman
- LUMC-Curium, Department of Child and Adolescent Psychiatry, Leiden University Medical Center, Leiden, the Netherlands
| | | | - Vassilios Fanos
- Department of Surgical Sciences, University of Cagliari and Neonatal Intensive Care Unit, Cagliari, Italy
| | - Erik A Ehli
- Avera Institute for Human Genetics, Sioux Falls, South Dakota, USA
| | - Robert R J M Vermeiren
- LUMC-Curium, Department of Child and Adolescent Psychiatry, Leiden University Medical Center, Leiden, the Netherlands
- Youz, Parnassia Group, the Netherlands
| | - Meike Bartels
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, the Netherlands
| | - Jouke Jan Hottenga
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Thomas Hankemeier
- Division of Analytical Biosciences, Leiden Academic Center for Drug Research, Leiden University, Leiden, the Netherlands
- The Netherlands Metabolomics Centre, Leiden, The Netherlands
| | - Jenny van Dongen
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Reproduction & Development (AR&D) Research Institute, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, the Netherlands
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Reproduction & Development (AR&D) Research Institute, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, the Netherlands
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Shorey-Kendrick LE, McEvoy CT, Milner K, Harris J, Brownsberger J, Tepper RS, Park B, Gao L, Vu A, Morris CD, Spindel ER. Improvements in lung function following vitamin C supplementation to pregnant smokers are associated with buccal DNA methylation at 5 years of age. Clin Epigenetics 2024; 16:35. [PMID: 38413986 PMCID: PMC10900729 DOI: 10.1186/s13148-024-01644-8] [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: 10/17/2023] [Accepted: 02/12/2024] [Indexed: 02/29/2024] Open
Abstract
BACKGROUND We previously reported in the "Vitamin C to Decrease the Effects of Smoking in Pregnancy on Infant Lung Function" randomized clinical trial (RCT) that vitamin C (500 mg/day) supplementation to pregnant smokers is associated with improved respiratory outcomes that persist through 5 years of age. The objective of this study was to assess whether buccal cell DNA methylation (DNAm), as a surrogate for airway epithelium, is associated with vitamin C supplementation, improved lung function, and decreased occurrence of wheeze. METHODS We conducted epigenome-wide association studies (EWAS) using Infinium MethylationEPIC arrays and buccal DNAm from 158 subjects (80 placebo; 78 vitamin C) with pulmonary function testing (PFT) performed at the 5-year visit. EWAS were performed on (1) vitamin C treatment, (2) forced expiratory flow between 25 and 75% of expired volume (FEF25-75), and (3) offspring wheeze. Models were adjusted for sex, race, study site, gestational age at randomization (≤ OR > 18 weeks), proportion of epithelial cells, and latent covariates in addition to child length at PFT in EWAS for FEF25-75. We considered FDR p < 0.05 as genome-wide significant and nominal p < 0.001 as candidates for downstream analyses. Buccal DNAm measured in a subset of subjects at birth and near 1 year of age was used to determine whether DNAm signatures originated in utero, or emerged with age. RESULTS Vitamin C treatment was associated with 457 FDR significant (q < 0.05) differentially methylated CpGs (DMCs; 236 hypermethylated; 221 hypomethylated) and 53 differentially methylated regions (DMRs; 26 hyper; 27 hypo) at 5 years of age. FEF25-75 was associated with one FDR significant DMC (cg05814800), 1,468 candidate DMCs (p < 0.001), and 44 DMRs. Current wheeze was associated with 0 FDR-DMCs, 782 candidate DMCs, and 19 DMRs (p < 0.001). In 365/457 vitamin C FDR significant DMCs at 5 years of age, there was no significant interaction between time and treatment. CONCLUSIONS Vitamin C supplementation to pregnant smokers is associated with buccal DNA methylation in offspring at 5 years of age, and most methylation signatures appear to be persistent from the prenatal period. Buccal methylation at 5 years was also associated with current lung function and occurrence of wheeze, and these functionally associated loci are enriched for vitamin C associated loci. Clinical trial registration ClinicalTrials.gov, NCT01723696 and NCT03203603.
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Affiliation(s)
- Lyndsey E Shorey-Kendrick
- Division of Neuroscience, Oregon National Primate Research Center, Oregon Health and Science University, Beaverton, OR, 97006, USA.
| | - Cindy T McEvoy
- Department of Pediatrics, Pape Pediatric Research Institute, Oregon Health and Science University, Portland, OR, USA
| | - Kristin Milner
- Department of Pediatrics, Oregon Health and Science University, Portland, OR, USA
| | - Julia Harris
- Department of Pediatrics, Oregon Health and Science University, Portland, OR, USA
| | - Julie Brownsberger
- Department of Pediatrics, Oregon Health and Science University, Portland, OR, USA
| | - Robert S Tepper
- Department of Pediatrics, Herman B Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Byung Park
- Biostatistics Shared Resources, Knight Cancer Institute, Bioinformatics and Biostatistics Core, Oregon National Primate Research Center, Oregon Health and Science University, Portland State University School of Public Health, Portland, OR, USA
| | - Lina Gao
- Biostatistics Shared Resources, Knight Cancer Institute, Bioinformatics and Biostatistics Core, Oregon National Primate Research Center, Oregon Health and Science University, Portland State University School of Public Health, Portland, OR, USA
| | - Annette Vu
- Oregon Clinical & Translational Research Institute, Oregon Health and Science; Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, OR, USA
| | - Cynthia D Morris
- Oregon Clinical & Translational Research Institute, Oregon Health and Science; Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, OR, USA
| | - Eliot R Spindel
- Division of Neuroscience, Oregon National Primate Research Center, Oregon Health and Science University, Beaverton, OR, 97006, USA
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Li R, Wen X, Lv RX, Ren XY, Cheng BL, Wang YK, Chen RZ, Hu W, Tang XR. DNA-methylome-derived epigenetic fingerprint as an immunophenotype indicator of durable clinical immunotherapeutic benefits in head and neck squamous cell carcinoma. Cell Oncol (Dordr) 2024:10.1007/s13402-024-00917-x. [PMID: 38315286 DOI: 10.1007/s13402-024-00917-x] [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] [Accepted: 01/09/2024] [Indexed: 02/07/2024] Open
Abstract
BACKGROUND Cancer immunotherapy provides durable response and improves survival in a subset of head and neck squamous cell carcinoma (HNSC) patients, which may due to discriminative tumor microenvironment (TME). Epigenetic regulations play critical roles in HNSC tumorigenesis, progression, and activation of functional immune cells. This study aims to identify an epigenetic signature as an immunophenotype indicator of durable clinical immunotherapeutic benefits in HNSC patients. METHODS Unsupervised consensus clustering approach was applied to distinguish immunophenotypes based on five immune signatures in The Cancer Genome Atlas (TCGA) HNSC cohort. Two immunophenotypes (immune 'Hot' and immune 'Cold') that had different TME features, diverse prognosis, and distinct DNA methylation patterns were recognized. Immunophenotype-related methylated signatures (IPMS) were identified by the least absolute shrinkage and selector operation algorithm. Additionally, the IPMS score by deconvolution algorithm was constructed as an immunophenotype classifier to predict clinical outcomes and immunotherapeutic response. RESULTS The 'Hot' HNSC immunophenotype had higher immunoactivity and better overall survival (p = 0.00055) compared to the 'Cold' tumors. The immunophenotypes had distinct DNA methylation patterns, which was closely associated with HNSC tumorigenesis and functional immune cell infiltration. 311 immunophenotype-related methylated CpG sites (IRMCs) was identified from TCGA-HNSC dataset. IPMS score model achieved a strong clinical predictive performance for classifying immunophenotypes. The area under the curve value (AUC) of the IPMS score model reached 85.9% and 89.8% in TCGA train and test datasets, respectively, and robustness was verified in five HNSC validation datasets. It was also validated as an immunophenotype classifier for predicting durable clinical benefits (DCB) in lung cancer patients who received anti-PD-1/PD-L1 immunotherapy (p = 0.017) and TCGA-SKCM patients who received distinct immunotherapy (p = 0.033). CONCLUSIONS This study systematically analyzed DNA methylation patterns in distinct immunophenotypes to identify IPMS with clinical prognostic potential for personalized epigenetic anticancer approaches in HNSC patients. The IPMS score model may serve as a reliable epigenome prognostic tool for clinical immunophenotyping to guide immunotherapeutic strategies in HNSC.
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Affiliation(s)
- Rui Li
- Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong Province, China
| | - Xin Wen
- Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong Province, China
| | - Ru-Xue Lv
- Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong Province, China
| | - Xian-Yue Ren
- Guangdong Provincial Key Laboratory of Stomatology, Guanghua School of Stomatology, Hospital of Stomatology, Sun Yat-sen University, Guangzhou, China
| | - Bing-Lin Cheng
- Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong Province, China
| | - Yi-Kai Wang
- Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong Province, China
| | - Ru-Zhen Chen
- Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong Province, China
| | - Wen Hu
- Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong Province, China
| | - Xin-Ran Tang
- Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong Province, China.
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Ouni M, Kovac L, Gancheva S, Jähnert M, Zuljan E, Gottmann P, Kahl S, de Angelis MH, Roden M, Schürmann A. Novel markers and networks related to restored skeletal muscle transcriptome after bariatric surgery. Obesity (Silver Spring) 2024; 32:363-375. [PMID: 38086776 DOI: 10.1002/oby.23954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 10/16/2023] [Accepted: 10/19/2023] [Indexed: 01/26/2024]
Abstract
OBJECTIVE The aim of this study was to discover novel markers underlying the improvement of skeletal muscle metabolism after bariatric surgery. METHODS Skeletal muscle transcriptome data of lean people and people with obesity, before and 1 year after bariatric surgery, were subjected to weighted gene co-expression network analysis (WGCNA) and least absolute shrinkage and selection operator (LASSO) regression. Results of LASSO were confirmed in a replication cohort. RESULTS The expression levels of 440 genes differing between individuals with and without obesity were no longer different 1 year after surgery, indicating restoration. WGCNA clustered 116 genes with normalized expression in one major module, particularly correlating to weight loss and decreased plasma free fatty acids (FFA), 44 of which showed an obesity-related phenotype upon deletion in mice. Among the genes of the major module, 105 represented prominent markers for reduced FFA concentration, including 55 marker genes for decreased BMI in both the discovery and replication cohorts. CONCLUSIONS Previously unknown gene networks and marker genes underlined the important role of FFA in restoring muscle gene expression after bariatric surgery and further suggest novel therapeutic targets for obesity.
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Affiliation(s)
- Meriem Ouni
- German Institute of Human Nutrition, Department of Experimental Diabetology, Potsdam, Germany
- German Center for Diabetes Research (DZD), Munich, Germany
| | - Leona Kovac
- German Institute of Human Nutrition, Department of Experimental Diabetology, Potsdam, Germany
- German Center for Diabetes Research (DZD), Munich, Germany
| | - Sofiya Gancheva
- German Center for Diabetes Research (DZD), Munich, Germany
- Department of Endocrinology and Diabetology, Medical Faculty and University Hospital, Heinrich Heine University, Düsseldorf, Germany
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research, Heinrich Heine University, Düsseldorf, Germany
| | - Markus Jähnert
- German Institute of Human Nutrition, Department of Experimental Diabetology, Potsdam, Germany
- German Center for Diabetes Research (DZD), Munich, Germany
| | - Erika Zuljan
- German Institute of Human Nutrition, Department of Experimental Diabetology, Potsdam, Germany
- German Center for Diabetes Research (DZD), Munich, Germany
| | - Pascal Gottmann
- German Institute of Human Nutrition, Department of Experimental Diabetology, Potsdam, Germany
- German Center for Diabetes Research (DZD), Munich, Germany
| | - Sabine Kahl
- German Center for Diabetes Research (DZD), Munich, Germany
- Department of Endocrinology and Diabetology, Medical Faculty and University Hospital, Heinrich Heine University, Düsseldorf, Germany
| | - Martin Hrabĕ de Angelis
- German Center for Diabetes Research (DZD), Munich, Germany
- Institute of Experimental Genetics, German Mouse Clinic, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- School of Life Sciences, Technical University Munich, Freising, Germany
| | - Michael Roden
- German Center for Diabetes Research (DZD), Munich, Germany
- Department of Endocrinology and Diabetology, Medical Faculty and University Hospital, Heinrich Heine University, Düsseldorf, Germany
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research, Heinrich Heine University, Düsseldorf, Germany
| | - Annette Schürmann
- German Institute of Human Nutrition, Department of Experimental Diabetology, Potsdam, Germany
- German Center for Diabetes Research (DZD), Munich, Germany
- Institute of Nutritional Sciences, University of Potsdam, Nuthetal, Germany
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11
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Hoang TT, Lee Y, McCartney DL, Kersten ETG, Page CM, Hulls PM, Lee M, Walker RM, Breeze CE, Bennett BD, Burkholder AB, Ward J, Brantsæter AL, Caspersen IH, Motsinger-Reif AA, Richards M, White JD, Zhao S, Richmond RC, Magnus MC, Koppelman GH, Evans KL, Marioni RE, Håberg SE, London SJ. Comprehensive evaluation of smoking exposures and their interactions on DNA methylation. EBioMedicine 2024; 100:104956. [PMID: 38199042 PMCID: PMC10825325 DOI: 10.1016/j.ebiom.2023.104956] [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: 07/07/2023] [Revised: 12/19/2023] [Accepted: 12/20/2023] [Indexed: 01/12/2024] Open
Abstract
BACKGROUND Smoking impacts DNA methylation, but data are lacking on smoking-related differential methylation by sex or dietary intake, recent smoking cessation (<1 year), persistence of differential methylation from in utero smoking exposure, and effects of environmental tobacco smoke (ETS). METHODS We meta-analysed data from up to 15,014 adults across 5 cohorts with DNA methylation measured in blood using Illumina's EPIC array for current smoking (2560 exposed), quit < 1 year (500 exposed), in utero (286 exposed), and ETS exposure (676 exposed). We also evaluated the interaction of current smoking with sex or diet (fibre, folate, and vitamin C). FINDINGS Using false discovery rate (FDR < 0.05), 65,857 CpGs were differentially methylated in relation to current smoking, 4025 with recent quitting, 594 with in utero exposure, and 6 with ETS. Most current smoking CpGs attenuated within a year of quitting. CpGs related to in utero exposure in adults were enriched for those previously observed in newborns. Differential methylation by current smoking at 4-71 CpGs may be modified by sex or dietary intake. Nearly half (35-50%) of differentially methylated CpGs on the 450 K array were associated with blood gene expression. Current smoking and in utero smoking CpGs implicated 3049 and 1067 druggable targets, including chemotherapy drugs. INTERPRETATION Many smoking-related methylation sites were identified with Illumina's EPIC array. Most signals revert to levels observed in never smokers within a year of cessation. Many in utero smoking CpGs persist into adulthood. Smoking-related druggable targets may provide insights into cancer treatment response and shared mechanisms across smoking-related diseases. FUNDING Intramural Research Program of the National Institutes of Health, Norwegian Ministry of Health and Care Services and the Ministry of Education and Research, Chief Scientist Office of the Scottish Government Health Directorates and the Scottish Funding Council, Medical Research Council UK and the Wellcome Trust.
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Affiliation(s)
- Thanh T Hoang
- Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA; Department of Pediatrics, Division of Hematology-Oncology, Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA; Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, USA; Cancer and Hematology Center, Texas Children's Hospital, Houston, TX, USA
| | - Yunsung Lee
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Daniel L McCartney
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Crewe Road South, Edinburgh EH4 2XU, UK
| | - Elin T G Kersten
- University of Groningen, University Medical Center Groningen, Beatrix Children's Hospital, Dept. of Pediatric Pulmonology and Pediatric Allergy, Groningen, the Netherlands; University of Groningen, University Medical Center Groningen, GRIAC Research Institute, Groningen, the Netherlands
| | - Christian M Page
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway; Department of Physical Health and Ageing, Division for Physical and Mental Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Paige M Hulls
- Population Health Sciences, Bristol Medical School, University of Bristol, BS8 2BN, UK; MRC Integrative Epidemiology Unit at University of Bristol, BS8 2BN, UK
| | - Mikyeong Lee
- Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA
| | - Rosie M Walker
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Crewe Road South, Edinburgh EH4 2XU, UK; Centre for Clinical Brain Sciences, University of Edinburgh, Chancellor's Building, 49 Little France Crescent, Edinburgh EH16 4SB, UK; School of Psychology, University of Exeter, Perry Road, Exeter, UK
| | - Charles E Breeze
- UCL Cancer Institute, University College London, Paul O'Gorman Building, London, UK; Altius Institute for Biomedical Sciences, Seattle, WA, USA
| | - Brian D Bennett
- Department of Health and Human Services, Integrative Bioinformatics Support Group, National Institutes of Health, Research Triangle Park, NC, USA
| | - Adam B Burkholder
- Department of Health and Human Services, Office of Environmental Science Cyberinfrastructure, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA
| | - James Ward
- Department of Health and Human Services, Integrative Bioinformatics Support Group, National Institutes of Health, Research Triangle Park, NC, USA
| | - Anne Lise Brantsæter
- Department of Food Safety, Division of Climate and Environmental Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Ida H Caspersen
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Alison A Motsinger-Reif
- Department of Health and Human Services, Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA
| | | | - Julie D White
- Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA; GenOmics and Translational Research Center, Analytics Practice Area, RTI International, Research Triangle Park, NC, USA
| | - Shanshan Zhao
- Department of Health and Human Services, Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA
| | - Rebecca C Richmond
- Population Health Sciences, Bristol Medical School, University of Bristol, BS8 2BN, UK; MRC Integrative Epidemiology Unit at University of Bristol, BS8 2BN, UK
| | - Maria C Magnus
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Gerard H Koppelman
- University of Groningen, University Medical Center Groningen, Beatrix Children's Hospital, Dept. of Pediatric Pulmonology and Pediatric Allergy, Groningen, the Netherlands; University of Groningen, University Medical Center Groningen, GRIAC Research Institute, Groningen, the Netherlands
| | - Kathryn L Evans
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Crewe Road South, Edinburgh EH4 2XU, UK
| | - Riccardo E Marioni
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Crewe Road South, Edinburgh EH4 2XU, UK
| | - Siri E Håberg
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Stephanie J London
- Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA.
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12
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Mirza S, Lima CNC, Del Favero-Campbell A, Rubinstein A, Topolski N, Cabrera-Mendoza B, Kovács EHC, Blumberg HP, Richards JG, Williams AJ, Wemmie JA, Magnotta VA, Fiedorowicz JG, Gaine ME, Walss-Bass C, Quevedo J, Soares JC, Fries GR. Blood epigenome-wide association studies of suicide attempt in adults with bipolar disorder. Transl Psychiatry 2024; 14:70. [PMID: 38296944 PMCID: PMC10831084 DOI: 10.1038/s41398-024-02760-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 01/05/2024] [Accepted: 01/10/2024] [Indexed: 02/02/2024] Open
Abstract
Suicide attempt (SA) risk is elevated in individuals with bipolar disorder (BD), and DNA methylation patterns may serve as possible biomarkers of SA. We conducted epigenome-wide association studies (EWAS) of blood DNA methylation associated with BD and SA. DNA methylation was measured at >700,000 positions in a discovery cohort of n = 84 adults with BD with a history of SA (BD/SA), n = 79 adults with BD without history of SA (BD/non-SA), and n = 76 non-psychiatric controls (CON). EWAS revealed six differentially methylated positions (DMPs) and seven differentially methylated regions (DMRs) between BD/SA and BD/non-SA, with multiple immune-related genes implicated. There were no epigenome-wide significant differences when BD/SA and BD/non-SA were each compared to CON, and patterns suggested that epigenetics differentiating BD/SA from BD/non-SA do not differentiate BD/non-SA from CON. Weighted gene co-methylation network analysis and trait enrichment analysis of the BD/SA vs. BD/non-SA contrast further corroborated immune system involvement, while gene ontology analysis implicated calcium signalling. In an independent replication cohort of n = 48 BD/SA and n = 47 BD/non-SA, fold changes at the discovery cohort's significant sites showed moderate correlation across cohorts and agreement on direction. In both cohorts, classification accuracy for SA history among individuals with BD was highest when methylation at the significant CpG sites as well as information from clinical interviews were combined, with an AUC of 88.8% (CI = 83.8-93.8%) and 82.1% (CI = 73.6-90.5%) for the combined epigenetic-clinical classifier in the discovery and replication cohorts, respectively. Our results provide novel insight to the role of immune system functioning in SA and BD and also suggest that integrating information from multiple levels of analysis holds promise to improve risk assessment for SA in adults with BD.
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Affiliation(s)
- Salahudeen Mirza
- Translational Psychiatry Program, Faillace Department of Psychiatry and Behavioral Sciences, McGovern Medical School, University of Texas Health Science Center at Houston, (UTHealth), 77054, Houston, TX, USA
- Institute of Child Development, University of Minnesota, 55455, Minneapolis, MN, USA
- Department of Psychiatry, Yale School of Medicine, 06510, New Haven, CT, USA
| | - Camila N C Lima
- Translational Psychiatry Program, Faillace Department of Psychiatry and Behavioral Sciences, McGovern Medical School, University of Texas Health Science Center at Houston, (UTHealth), 77054, Houston, TX, USA
| | - Alexandra Del Favero-Campbell
- Translational Psychiatry Program, Faillace Department of Psychiatry and Behavioral Sciences, McGovern Medical School, University of Texas Health Science Center at Houston, (UTHealth), 77054, Houston, TX, USA
| | - Alexandre Rubinstein
- Translational Psychiatry Program, Faillace Department of Psychiatry and Behavioral Sciences, McGovern Medical School, University of Texas Health Science Center at Houston, (UTHealth), 77054, Houston, TX, USA
| | - Natasha Topolski
- Translational Psychiatry Program, Faillace Department of Psychiatry and Behavioral Sciences, McGovern Medical School, University of Texas Health Science Center at Houston, (UTHealth), 77054, Houston, TX, USA
- Neuroscience Graduate Program, The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, 77054, Houston, TX, USA
| | | | - Emese H C Kovács
- Department of Neuroscience and Pharmacology, The University of Iowa, 51 Newton Rd, 52242, Iowa City, IA, USA
| | - Hilary P Blumberg
- Department of Psychiatry, Yale School of Medicine, 06510, New Haven, CT, USA
| | - Jenny Gringer Richards
- Department of Radiology, The University of Iowa, 200 Hawkins Dr, 52242, Iowa City, IA, USA
| | - Aislinn J Williams
- Department of Psychiatry, The University of Iowa, 200 Hawkins Dr, 52242, Iowa City, IA, USA
- Iowa Neuroscience Institute, The University of Iowa, 169 Newton Rd, 52242, Iowa City, IA, USA
| | - John A Wemmie
- Department of Psychiatry, The University of Iowa, 200 Hawkins Dr, 52242, Iowa City, IA, USA
- Iowa Neuroscience Institute, The University of Iowa, 169 Newton Rd, 52242, Iowa City, IA, USA
- Department of Veterans Affairs Medical Center, Iowa City, IA, USA
| | - Vincent A Magnotta
- Department of Radiology, The University of Iowa, 200 Hawkins Dr, 52242, Iowa City, IA, USA
- Department of Psychiatry, The University of Iowa, 200 Hawkins Dr, 52242, Iowa City, IA, USA
| | - Jess G Fiedorowicz
- University of Ottawa Brain and Mind Research Institute, Ottawa Hospital Research Institute, 501 Smyth, K1H 8L6, Ottawa, ON, Canada
| | - Marie E Gaine
- Iowa Neuroscience Institute, The University of Iowa, 169 Newton Rd, 52242, Iowa City, IA, USA
- Pharmaceutical Sciences and Experimental Therapeutics, The University of Iowa, 180 South Grand Ave, 52242, Iowa City, IA, USA
| | - Consuelo Walss-Bass
- Translational Psychiatry Program, Faillace Department of Psychiatry and Behavioral Sciences, McGovern Medical School, University of Texas Health Science Center at Houston, (UTHealth), 77054, Houston, TX, USA
- Neuroscience Graduate Program, The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, 77054, Houston, TX, USA
| | - Joao Quevedo
- Translational Psychiatry Program, Faillace Department of Psychiatry and Behavioral Sciences, McGovern Medical School, University of Texas Health Science Center at Houston, (UTHealth), 77054, Houston, TX, USA
- Neuroscience Graduate Program, The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, 77054, Houston, TX, USA
- Center of Excellence in Mood Disorders, Faillace Department of Psychiatry and Behavioral Sciences, McGovern Medical School, The University of Texas Health Science Center at Houston, 1941 East Rd, 77054, Houston, TX, USA
- Center for Interventional Psychiatry, Faillace Department of Psychiatry and Behavioral Sciences, McGovern Medical School, The University of Texas Health Science Center at Houston (UTHealth), Houston, 1941 East Rd, 77054, Houston, TX, USA
| | - Jair C Soares
- Translational Psychiatry Program, Faillace Department of Psychiatry and Behavioral Sciences, McGovern Medical School, University of Texas Health Science Center at Houston, (UTHealth), 77054, Houston, TX, USA
- Neuroscience Graduate Program, The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, 77054, Houston, TX, USA
- Center of Excellence in Mood Disorders, Faillace Department of Psychiatry and Behavioral Sciences, McGovern Medical School, The University of Texas Health Science Center at Houston, 1941 East Rd, 77054, Houston, TX, USA
| | - Gabriel R Fries
- Translational Psychiatry Program, Faillace Department of Psychiatry and Behavioral Sciences, McGovern Medical School, University of Texas Health Science Center at Houston, (UTHealth), 77054, Houston, TX, USA.
- Neuroscience Graduate Program, The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, 77054, Houston, TX, USA.
- Center of Excellence in Mood Disorders, Faillace Department of Psychiatry and Behavioral Sciences, McGovern Medical School, The University of Texas Health Science Center at Houston, 1941 East Rd, 77054, Houston, TX, USA.
- Center for Interventional Psychiatry, Faillace Department of Psychiatry and Behavioral Sciences, McGovern Medical School, The University of Texas Health Science Center at Houston (UTHealth), Houston, 1941 East Rd, 77054, Houston, TX, USA.
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13
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Li S, Spitz N, Ghantous A, Abrishamcar S, Reimann B, Marques I, Silver MJ, Aguilar-Lacasaña S, Kitaba N, Rezwan FI, Röder S, Sirignano L, Tuhkanen J, Mancano G, Sharp GC, Metayer C, Morimoto L, Stein DJ, Zar HJ, Alfano R, Nawrot T, Wang C, Kajantie E, Keikkala E, Mustaniemi S, Ronkainen J, Sebert S, Silva W, Vääräsmäki M, Jaddoe VWV, Bernstein RM, Prentice AM, Cosin-Tomas M, Dwyer T, Håberg SE, Herceg Z, Magnus MC, Munthe-Kaas MC, Page CM, Völker M, Gilles M, Send T, Witt S, Zillich L, Gagliardi L, Richiardi L, Czamara D, Räikkönen K, Chatzi L, Vafeiadi M, Arshad SH, Ewart S, Plusquin M, Felix JF, Moore SE, Vrijheid M, Holloway JW, Karmaus W, Herberth G, Zenclussen A, Streit F, Lahti J, Hüls A, Hoang TT, London SJ, Wiemels JL. A Pregnancy and Childhood Epigenetics Consortium (PACE) meta-analysis highlights potential relationships between birth order and neonatal blood DNA methylation. Commun Biol 2024; 7:66. [PMID: 38195839 PMCID: PMC10776586 DOI: 10.1038/s42003-023-05698-x] [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: 05/19/2023] [Accepted: 12/12/2023] [Indexed: 01/11/2024] Open
Abstract
Higher birth order is associated with altered risk of many disease states. Changes in placentation and exposures to in utero growth factors with successive pregnancies may impact later life disease risk via persistent DNA methylation alterations. We investigated birth order with Illumina DNA methylation array data in each of 16 birth cohorts (8164 newborns) with European, African, and Latino ancestries from the Pregnancy and Childhood Epigenetics Consortium. Meta-analyzed data demonstrated systematic DNA methylation variation in 341 CpGs (FDR adjusted P < 0.05) and 1107 regions. Forty CpGs were located within known quantitative trait loci for gene expression traits in blood, and trait enrichment analysis suggested a strong association with immune-related, transcriptional control, and blood pressure regulation phenotypes. Decreasing fertility rates worldwide with the concomitant increased proportion of first-born children highlights a potential reflection of birth order-related epigenomic states on changing disease incidence trends.
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Affiliation(s)
- Shaobo Li
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, California, USA
| | - Natalia Spitz
- Epigenomics and Mechanisms Branch, International Agency for Research on Cancer, Lyon, France
| | - Akram Ghantous
- Epigenomics and Mechanisms Branch, International Agency for Research on Cancer, Lyon, France
| | - Sarina Abrishamcar
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Brigitte Reimann
- Centre for Environmental Sciences, UHasselt, Agoralaan, Building D, 3590, Diepenbeek, Belgium
| | - Irene Marques
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Matt J Silver
- Medical Research Council Unit The Gambia at the London School of Hygiene and Tropical Medicine, London, UK
| | - Sofía Aguilar-Lacasaña
- ISGlobal, Institute for Global Health, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Madrid, Spain
| | - Negusse Kitaba
- Human Development and Health, Faculty of Medicine, Southampton General Hospital, University of Southampton, Southampton, UK
| | - Faisal I Rezwan
- Human Development and Health, Faculty of Medicine, Southampton General Hospital, University of Southampton, Southampton, UK
- Department of Computer Science, Aberystwyth University, Aberystwyth, Ceredigion, SY23 3DB, UK
| | - Stefan Röder
- Department of Environmental Immunology, Helmholtz Centre for Environmental Research -UFZ, Leipzig, Germany
| | - Lea Sirignano
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Johanna Tuhkanen
- Department of Psychology and Logopedics, University of Helsinki, Helsinki, Finland
| | - Giulia Mancano
- MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Gemma C Sharp
- MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- School of Psychology, Faculty of Health and Life Sciences, University of Exeter, Exeter, UK
| | - Catherine Metayer
- School of Public Health, University of California Berkeley, Berkeley, California, USA
| | - Libby Morimoto
- School of Public Health, University of California Berkeley, Berkeley, California, USA
| | - Dan J Stein
- SAMRC Unit on Risk & Resilience in Mental Disorders, Dept of Psychiatry & Neuroscience Institute, University of Cape Town, Rondebosch, South Africa
| | - Heather J Zar
- SAMRC Unit on Risk & Resilience in Mental Disorders, Dept of Psychiatry & Neuroscience Institute, University of Cape Town, Rondebosch, South Africa
- Department of Paediatrics and Child Health, Red Cross War Memorial Children's Hospital, University of Cape Town, Rondebosch, South Africa
| | - Rossella Alfano
- Centre for Environmental Sciences, UHasselt, Agoralaan, Building D, 3590, Diepenbeek, Belgium
| | - Tim Nawrot
- Centre for Environmental Sciences, UHasselt, Agoralaan, Building D, 3590, Diepenbeek, Belgium
| | - Congrong Wang
- Centre for Environmental Sciences, UHasselt, Agoralaan, Building D, 3590, Diepenbeek, Belgium
| | - Eero Kajantie
- Clinical Medicine Research Unit, Medical Research Center Oulu, Oulu University, Hospital and University of Oulu, Oulu, Finland
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
- Pediatric Research Centre, Children's Hospital, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Elina Keikkala
- Clinical Medicine Research Unit, Medical Research Center Oulu, Oulu University, Hospital and University of Oulu, Oulu, Finland
- Population Health Unit, Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Oulu, Finland
| | - Sanna Mustaniemi
- Clinical Medicine Research Unit, Medical Research Center Oulu, Oulu University, Hospital and University of Oulu, Oulu, Finland
- Population Health Unit, Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Oulu, Finland
| | - Justiina Ronkainen
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Sylvain Sebert
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Wnurinham Silva
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Marja Vääräsmäki
- Clinical Medicine Research Unit, Medical Research Center Oulu, Oulu University, Hospital and University of Oulu, Oulu, Finland
- Population Health Unit, Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Oulu, Finland
| | - Vincent W V Jaddoe
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Robin M Bernstein
- Department of Anthropology and Institute of Behavioral Science, University of Colorado Boulder, Boulder, CO, USA
| | - Andrew M Prentice
- MRC Unit The Gambia at the London School of Hygiene & Tropical Medicine, Fajara, The Gambia
| | - Marta Cosin-Tomas
- ISGlobal, Institute for Global Health, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Madrid, Spain
| | - Terence Dwyer
- Nuffield Department of Women's & Reproductive Health, University of Oxford, John Radcliffe Hospital, Oxford, OX3 9DU, UK
| | - Siri Eldevik Håberg
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Zdenko Herceg
- Epigenomics and Mechanisms Branch, International Agency for Research on Cancer, Lyon, France
| | - Maria C Magnus
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Monica Cheng Munthe-Kaas
- Department of Pediatric Oncology and Hematology, Oslo University Hospital, Norwegian Institute of Public Health, Oslo, Norway
| | - Christian M Page
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
- Department of Physical Health and Aging, Division for Mental and Physical Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Maja Völker
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Maria Gilles
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Tabea Send
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Stephanie Witt
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Lea Zillich
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Luigi Gagliardi
- Woman and Child Health Department, Ospedale Versilia, AUSL Toscana Nord Ovest, Pisa, Italy
| | - Lorenzo Richiardi
- Department of Medical Sciences, University of Turin, CPO Piemonte, Turin, Italy
| | - Darina Czamara
- Department of Psychology and Logopedics, University of Helsinki, Helsinki, Finland
| | - Katri Räikkönen
- Department of Psychology and Logopedics, University of Helsinki, Helsinki, Finland
| | - Lida Chatzi
- Department of Population and Public Health Sciences, Keck School of Medicine of USC. University of Southern California, Los Angeles, CA, USA
| | - Marina Vafeiadi
- Department of Social Medicine, Faculty of Medicine, University of Crete, Heraklion, Greece
| | - S Hasan Arshad
- Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, UK
- David Hide Asthma and Allergy Research Centre, Isle of Wight, UK
| | - Susan Ewart
- College of Veterinary Medicine, Michigan State University, East Lansing, MI, USA
| | - Michelle Plusquin
- Centre for Environmental Sciences, UHasselt, Agoralaan, Building D, 3590, Diepenbeek, Belgium
| | - Janine F Felix
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Sophie E Moore
- Department of Women & Children's Health, King's College London, London, UK
| | - Martine Vrijheid
- ISGlobal, Institute for Global Health, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Madrid, Spain
| | - John W Holloway
- Human Development and Health, Faculty of Medicine, Southampton General Hospital, University of Southampton, Southampton, UK
- Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, UK
| | - Wilfried Karmaus
- Division of Epidemiology, Biostatistics, and Environmental Health, University of Memphis, Memphis, TN, USA
| | - Gunda Herberth
- Department of Environmental Immunology, Helmholtz Centre for Environmental Research -UFZ, Leipzig, Germany
| | - Ana Zenclussen
- Department of Environmental Immunology, Helmholtz Centre for Environmental Research -UFZ, Leipzig, Germany
- Perinatal Immunology, Medical Faculty, Saxonian Incubator for Clinical Translation (SIKT), University of Leipzig, Leipzig, Germany
| | - Fabian Streit
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Jari Lahti
- Department of Psychology and Logopedics, University of Helsinki, Helsinki, Finland
| | - Anke Hüls
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Thanh T Hoang
- Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA
| | - Stephanie J London
- Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA
| | - Joseph L Wiemels
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, California, USA.
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Bai X, Bao Y, Bei S, Bu C, Cao R, Cao Y, Cen H, Chao J, Chen F, Chen H, Chen K, Chen M, Chen M, Chen M, Chen Q, Chen R, Chen S, Chen T, Chen X, Chen X, Cheng Y, Chu Y, Cui Q, Dong L, Du Z, Duan G, Fan S, Fan Z, Fang X, Fang Z, Feng Z, Fu S, Gao F, Gao G, Gao H, Gao W, Gao X, Gao X, Gao X, Gong J, Gong J, Gou Y, Gu S, Guo AY, Guo G, Guo X, Han C, Hao D, Hao L, He Q, He S, He S, Hu W, Huang K, Huang T, Huang X, Huang Y, Jia P, Jia Y, Jiang C, Jiang M, Jiang S, Jiang T, Jiang X, Jin E, Jin W, Kang H, Kang H, Kong D, Lan L, Lei W, Li CY, Li C, Li C, Li H, Li J, Li J, Li L, Li P, Li R, Li X, Li Y, Li Y, Li Z, Liao X, Lin S, Lin Y, Ling Y, Liu B, Liu CJ, Liu D, Liu GH, Liu L, Liu S, Liu W, Liu X, Liu X, Liu Y, Liu Y, Lu M, Lu T, Luo H, Luo H, Luo M, Luo S, Luo X, Ma L, Ma Y, Mai J, Meng J, Meng X, Meng Y, Meng Y, Miao W, Miao YR, Ni L, Nie Z, Niu G, Niu X, Niu Y, Pan R, Pan S, Peng D, Peng J, Qi J, Qi Y, Qian Q, Qin Y, Qu H, Ren J, Ren J, Sang Z, Shang K, Shen WK, Shen Y, Shi Y, Song S, Song T, Su T, Sun J, Sun Y, Sun Y, Sun Y, Tang B, Tang D, Tang Q, Tang Z, Tian D, Tian F, Tian W, Tian Z, Wang A, Wang G, Wang G, Wang J, Wang J, Wang P, Wang P, Wang W, Wang Y, Wang Y, Wang Y, Wang Y, Wang Z, Wei H, Wei Y, Wei Z, Wu D, Wu G, Wu S, Wu S, Wu W, Wu W, Wu Z, Xia Z, Xiao J, Xiao L, Xiao Y, Xie G, Xie GY, Xie J, Xie Y, Xiong J, Xiong Z, Xu D, Xu S, Xu T, Xu T, Xue Y, Xue Y, Yan C, Yang D, Yang F, Yang F, Yang H, Yang J, Yang K, Yang N, Yang QY, Yang S, Yang X, Yang X, Yang X, Yang YG, Ye W, Yu C, Yu F, Yu S, Yuan C, Yuan H, Zeng J, Zhai S, Zhang C, Zhang F, Zhang G, Zhang M, Zhang P, Zhang Q, Zhang R, Zhang S, Zhang W, Zhang W, Zhang W, Zhang X, Zhang X, Zhang Y, Zhang Y, Zhang Y, Zhang YE, Zhang Y, Zhang Z, Zhang Z, Zhao D, Zhao F, Zhao G, Zhao M, Zhao W, Zhao W, Zhao X, Zhao Y, Zhao Y, Zhao Z, Zheng X, Zheng Y, Zhou C, Zhou H, Zhou X, Zhou X, Zhou Y, Zhou Y, Zhu J, Zhu L, Zhu R, Zhu T, Zong W, Zou D, Zuo Z. Database Resources of the National Genomics Data Center, China National Center for Bioinformation in 2024. Nucleic Acids Res 2024; 52:D18-D32. [PMID: 38018256 PMCID: PMC10767964 DOI: 10.1093/nar/gkad1078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 10/12/2023] [Accepted: 10/27/2023] [Indexed: 11/30/2023] Open
Abstract
The National Genomics Data Center (NGDC), which is a part of the China National Center for Bioinformation (CNCB), provides a family of database resources to support the global academic and industrial communities. With the rapid accumulation of multi-omics data at an unprecedented pace, CNCB-NGDC continuously expands and updates core database resources through big data archiving, integrative analysis and value-added curation. Importantly, NGDC collaborates closely with major international databases and initiatives to ensure seamless data exchange and interoperability. Over the past year, significant efforts have been dedicated to integrating diverse omics data, synthesizing expanding knowledge, developing new resources, and upgrading major existing resources. Particularly, several database resources are newly developed for the biodiversity of protists (P10K), bacteria (NTM-DB, MPA) as well as plant (PPGR, SoyOmics, PlantPan) and disease/trait association (CROST, HervD Atlas, HALL, MACdb, BioKA, BioKA, RePoS, PGG.SV, NAFLDkb). All the resources and services are publicly accessible at https://ngdc.cncb.ac.cn.
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15
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Li H, Wu S, Li J, Xiong Z, Yang K, Ye W, Ren J, Wang Q, Xiong M, Zheng Z, Zhang S, Han Z, Yang P, Jiang B, Ping J, Zuo Y, Lu X, Zhai Q, Yan H, Wang S, Ma S, Zhang B, Ye J, Qu J, Yang YG, Zhang F, Liu GH, Bao Y, Zhang W. HALL: a comprehensive database for human aging and longevity studies. Nucleic Acids Res 2024; 52:D909-D918. [PMID: 37870433 PMCID: PMC10767887 DOI: 10.1093/nar/gkad880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 09/15/2023] [Accepted: 10/06/2023] [Indexed: 10/24/2023] Open
Abstract
Diverse individuals age at different rates and display variable susceptibilities to tissue aging, functional decline and aging-related diseases. Centenarians, exemplifying extreme longevity, serve as models for healthy aging. The field of human aging and longevity research is rapidly advancing, garnering significant attention and accumulating substantial data in recent years. Omics technologies, encompassing phenomics, genomics, transcriptomics, proteomics, metabolomics and microbiomics, have provided multidimensional insights and revolutionized cohort-based investigations into human aging and longevity. Accumulated data, covering diverse cells, tissues and cohorts across the lifespan necessitates the establishment of an open and integrated database. Addressing this, we established the Human Aging and Longevity Landscape (HALL), a comprehensive multi-omics repository encompassing a diverse spectrum of human cohorts, spanning from young adults to centenarians. The core objective of HALL is to foster healthy aging by offering an extensive repository of information on biomarkers that gauge the trajectory of human aging. Moreover, the database facilitates the development of diagnostic tools for aging-related conditions and empowers targeted interventions to enhance longevity. HALL is publicly available at https://ngdc.cncb.ac.cn/hall/index.
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Affiliation(s)
- Hao Li
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Song Wu
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jiaming Li
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhuang Xiong
- Interdisciplinary Institute for Medical Engineering, Fuzhou University, Fuzhou 350002, China
| | - Kuan Yang
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Sino-Danish College, University of Chinese Academy of Sciences, Beijing 101408, China
| | - Weidong Ye
- Department of Vascular Surgery, Quzhou Affiliated Hospital of Wenzhou Medical University, Beijing 100101, China
- The Joint Innovation Center for Engineering in Medicine, Quzhou Affiliated Hospital of Wenzhou Medical University, 324000, China
| | - Jie Ren
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Sino-Danish College, University of Chinese Academy of Sciences, Beijing 101408, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing 100101, China
| | - Qiaoran Wang
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Muzhao Xiong
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zikai Zheng
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Shuo Zhang
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zichu Han
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Peng Yang
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Beier Jiang
- Department of Vascular Surgery, Quzhou Affiliated Hospital of Wenzhou Medical University, Beijing 100101, China
| | - Jiale Ping
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yuesheng Zuo
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiaoyong Lu
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Qiaocheng Zhai
- Department of Vascular Surgery, Quzhou Affiliated Hospital of Wenzhou Medical University, Beijing 100101, China
| | - Haoteng Yan
- Advanced Innovation Center for Human Brain Protection, and National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing 100053, China
- Aging Translational Medicine Center, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
| | - Si Wang
- Advanced Innovation Center for Human Brain Protection, and National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing 100053, China
- Aging Translational Medicine Center, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
- Aging Biomarker Consortium, Beijing 100101, China
| | - Shuai Ma
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing 100101, China
- Aging Biomarker Consortium, Beijing 100101, China
| | - Bing Zhang
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Jinlin Ye
- Department of Vascular Surgery, Quzhou Affiliated Hospital of Wenzhou Medical University, Beijing 100101, China
| | - Jing Qu
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing 100101, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing 100101, China
- Aging Biomarker Consortium, Beijing 100101, China
| | - Yun-Gui Yang
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Sino-Danish College, University of Chinese Academy of Sciences, Beijing 101408, China
| | - Feng Zhang
- The Joint Innovation Center for Engineering in Medicine, Quzhou Affiliated Hospital of Wenzhou Medical University, 324000, China
| | - Guang-Hui Liu
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing 100101, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing 100101, China
- Advanced Innovation Center for Human Brain Protection, and National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing 100053, China
- Aging Translational Medicine Center, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
- Aging Biomarker Consortium, Beijing 100101, China
| | - Yiming Bao
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Weiqi Zhang
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Sino-Danish College, University of Chinese Academy of Sciences, Beijing 101408, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing 100101, China
- Aging Biomarker Consortium, Beijing 100101, China
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16
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Niehues A, de Visser C, Hagenbeek FA, Kulkarni P, Pool R, Karu N, Kindt ASD, Singh G, Vermeiren RRJM, Boomsma DI, van Dongen J, 't Hoen PAC, van Gool AJ. A multi-omics data analysis workflow packaged as a FAIR Digital Object. Gigascience 2024; 13:giad115. [PMID: 38217405 PMCID: PMC10787363 DOI: 10.1093/gigascience/giad115] [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: 06/13/2023] [Revised: 11/14/2023] [Accepted: 12/10/2023] [Indexed: 01/15/2024] Open
Abstract
BACKGROUND Applying good data management and FAIR (Findable, Accessible, Interoperable, and Reusable) data principles in research projects can help disentangle knowledge discovery, study result reproducibility, and data reuse in future studies. Based on the concepts of the original FAIR principles for research data, FAIR principles for research software were recently proposed. FAIR Digital Objects enable discovery and reuse of Research Objects, including computational workflows for both humans and machines. Practical examples can help promote the adoption of FAIR practices for computational workflows in the research community. We developed a multi-omics data analysis workflow implementing FAIR practices to share it as a FAIR Digital Object. FINDINGS We conducted a case study investigating shared patterns between multi-omics data and childhood externalizing behavior. The analysis workflow was implemented as a modular pipeline in the workflow manager Nextflow, including containers with software dependencies. We adhered to software development practices like version control, documentation, and licensing. Finally, the workflow was described with rich semantic metadata, packaged as a Research Object Crate, and shared via WorkflowHub. CONCLUSIONS Along with the packaged multi-omics data analysis workflow, we share our experiences adopting various FAIR practices and creating a FAIR Digital Object. We hope our experiences can help other researchers who develop omics data analysis workflows to turn FAIR principles into practice.
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Affiliation(s)
- Anna Niehues
- Department of Medical BioSciences, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
- Translational Metabolic Laboratory, Department of Laboratory Medicine, Radboud University Medical Center, 6525 GA Nijmegen, the Netherlands
| | - Casper de Visser
- Department of Medical BioSciences, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
| | - Fiona A Hagenbeek
- Department of Biological Psychology, Vrije Universiteit Amsterdam, 1081 BT Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, 1081 BT Amsterdam, The Netherlands
| | - Purva Kulkarni
- Department of Medical BioSciences, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
- Translational Metabolic Laboratory, Department of Laboratory Medicine, Radboud University Medical Center, 6525 GA Nijmegen, the Netherlands
- Department of Human Genetics, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
| | - René Pool
- Department of Biological Psychology, Vrije Universiteit Amsterdam, 1081 BT Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, 1081 BT Amsterdam, The Netherlands
| | - Naama Karu
- Metabolomics and Analytics Centre, Leiden Academic Centre for Drug Research, Leiden University, 2333 AL Leiden, The Netherlands
| | - Alida S D Kindt
- Metabolomics and Analytics Centre, Leiden Academic Centre for Drug Research, Leiden University, 2333 AL Leiden, The Netherlands
| | - Gurnoor Singh
- Department of Medical BioSciences, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
| | - Robert R J M Vermeiren
- Department of Child and Adolescent Psychiatry, LUMC-Curium, Leiden University Medical Center, 2342 AK Oegstgeest, The Netherlands
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, 1081 BT Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, 1081 BT Amsterdam, The Netherlands
- Amsterdam Reproduction & Development (AR&D) Research Institute, 1081 BT Amsterdam, The Netherlands
| | - Jenny van Dongen
- Department of Biological Psychology, Vrije Universiteit Amsterdam, 1081 BT Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, 1081 BT Amsterdam, The Netherlands
- Amsterdam Reproduction & Development (AR&D) Research Institute, 1081 BT Amsterdam, The Netherlands
| | - Peter A C 't Hoen
- Department of Medical BioSciences, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
| | - Alain J van Gool
- Translational Metabolic Laboratory, Department of Laboratory Medicine, Radboud University Medical Center, 6525 GA Nijmegen, the Netherlands
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17
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Powell RM, Moravec JC, Jones GT, Bhat B, Lin SM, Planer JD, Krymskaya VP, Cantu E, Pattison S, Morison IM, Gray B, Eccles MR, Macaulay EC. DNA Methylation Profiling of Heterogeneous Sporadic LAM and Matched Lung Tissue. Am J Respir Cell Mol Biol 2024; 70:81-84. [PMID: 38156802 DOI: 10.1165/rcmb.2023-0300le] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2024] Open
Affiliation(s)
- Ryan M Powell
- University of Otago Dunedin, New Zealand
- New Zealand LAM Charitable Trust Auckland, New Zealand
| | | | | | | | - Susan M Lin
- University of Pennsylvania Philadelphia, Pennsylvania
| | | | | | - Edward Cantu
- University of Pennsylvania Philadelphia, Pennsylvania
| | | | | | - Bronwyn Gray
- New Zealand LAM Charitable Trust Auckland, New Zealand
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18
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Tian A, Wu T, Zhang Y, Chen J, Sha J, Xia W. Triggering pyroptosis enhances the antitumor efficacy of PARP inhibitors in prostate cancer. Cell Oncol (Dordr) 2023; 46:1855-1870. [PMID: 37610690 DOI: 10.1007/s13402-023-00860-3] [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] [Accepted: 08/05/2023] [Indexed: 08/24/2023] Open
Abstract
PURPOSE PARP inhibitors have revolutionized the treatment landscape for advanced prostate cancer (PCa) patients who harboring mutations in homologous recombination repair (HRR) genes. However, the molecular mechanisms underlying PARP inhibitors function beyond DNA damage repair pathways remain elusive, and identifying novel predictive targets that favorably respond to PARP inhibitors in PCa is an active area of research. METHODS The expression of GSDME in PCa cell lines and human PCa samples was determined by western blotting. Targeted bisulfite sequencing, gene enrichment analysis (GSEA), clone formation, construction of the stably transfected cell lines, lactate dehydrogenase (LDH) assay, western blotting as well as a mouse model of subcutaneous xenografts were used to investigate the role of GSDME in PCa. The combinational therapeutic effect of olaparib and decitabine was determined using both in vitro and in vivo experiments. RESULTS We have found low expression of GSDME in PCa. Interestingly, we demonstrated that GSDME activity is robustly induced in olaparib-treated cells undergoing pyroptosis, and that high methylation of the GSDME promoter dampens its activity in PCa cells. Intriguingly, genetically overexpressing GSDME does not inhibit tumor cell proliferation but instead confers sensitivity to olaparib. Furthermore, pharmacological treatment with the combination of olaparib and decitabine synergistically induces GSDME expression and cleavage through caspase-3 activation, thus promoting pyroptosis and enhancing anti-tumor response, ultimately resulting in tumor remission. CONCLUSION Our findings highlight a novel therapeutic strategy for enhancing the long-term response to olaparib beyond HRR-deficient tumors in PCa, underscoring the critical role of GSDME in regulating tumorigenesis.
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Affiliation(s)
- Ao Tian
- State Key Laboratory of Systems Medicine for Cancer, Renji Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai, 200030, China
| | - Tingyu Wu
- State Key Laboratory of Systems Medicine for Cancer, Renji Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai, 200030, China
| | - Yanshuang Zhang
- State Key Laboratory of Systems Medicine for Cancer, Renji Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai, 200030, China
| | - Jiachen Chen
- State Key Laboratory of Systems Medicine for Cancer, Renji Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai, 200030, China
| | - Jianjun Sha
- Department of Urology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, 145 Shandong Middle road, Shanghai, 200001, China
| | - Weiliang Xia
- State Key Laboratory of Systems Medicine for Cancer, Renji Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai, 200030, China.
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19
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Liu Y, Zhao S, Chen Y, Ma W, Lu S, He L, Chen J, Chen X, Zhang X, Shi Y, Jiang X, Zhao K. Vimentin promotes glioma progression and maintains glioma cell resistance to oxidative phosphorylation inhibition. Cell Oncol (Dordr) 2023; 46:1791-1806. [PMID: 37646965 DOI: 10.1007/s13402-023-00844-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/12/2023] [Indexed: 09/01/2023] Open
Abstract
PURPOSE Glioma has been demonstrated as one of the most malignant intracranial tumors and currently there is no effective treatment. Based on our previous RNA-sequencing data for oxidative phosphorylation (OXPHOS)-inhibition resistant and OXPHOS-inhibition sensitive cancer cells, we found that vimentin (VIM) is highly expressed in the OXPHOS-inhibition resistant cancer cells, especially in glioma cancer cells. Further study of VIM in the literature indicates that it plays important roles in cancer progression, immunotherapy suppression, cancer stemness and drug resistance. However, its role in glioma remains elusive. This study aims to decipher the role of VIM in glioma, especially its role in OXPHOS-inhibition sensitivity, which may provide a promising therapeutic target for glioma treatment. METHODS The expression of VIM in glioma and the normal tissue has been obtained from The Cancer Genome Atlas (TCGA) database, and further validated in Human Protein Atlas (HPA) and Chinese Glioma Genome Atlas (CGGA). And the single-cell sequencing data was obtained from TISCH2. The immune infiltration was calculated via Tumor Immune Estimation Resource (TIMER), Estimation of Stromal and Immune Cells in Malignant Tumors using Expression Data (ESTIMATE) and ssGSEA, and the Immunophenoscore (IPS) was calculated via R package. The differentiated expressed genes were analyzed including GO/KEGG and Gene Set Enrichment Analysis (GSEA) between the VIM-high and -low groups. The methylation of VIM was checked at the EWAS and Methsurv. The correlation between VIM expression and cancer stemness was obtained from SangerBox. We also employed DepMap data and verified the role of VIM by knocking down it in VIM-high glioma cell and over-expressing it in VIM-low glioma cells to check the cell viability. RESULTS Vim is highly expressed in the glioma patients compared to normal samples and its high expression negatively correlates with patients' survival. The DNA methylation in VIM promoters in glioma patients is lower than that in the normal samples. High VIM expression positively correlates with the immune infiltration and tumor progression. Furthermore, Vim is expressed high in the OXPHOS-inhibition glioma cancer cells and low in the OXPHOS-inhibition sensitive ones and its expression maintains the OXPHOS-inhibition resistance. CONCLUSIONS In conclusion, we comprehensively deciphered the role of VIM in the progression of glioma and its clinical outcomes. Thus provide new insights into targeting VIM in glioma cancer immunotherapy in combination with the current treatment.
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Affiliation(s)
- Yu'e Liu
- Department of Neurosurgery, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200120, China
| | - Shu Zhao
- Department of Neurosurgery, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200120, China
| | - Yi Chen
- The China-US (Henan) Hormel Cancer Institute, Zhengzhou, 450000, China
| | - Wencong Ma
- Department of Hepatobiliary and Pancreatic Surgery, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200120, China
| | - Shiping Lu
- Center for Translational Research in Infection and Inflammation, School of Medicine, Tulane University, New Orleans, LA, 70112, USA
| | - Le He
- Department of Neurosurgery, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200120, China
| | - Jie Chen
- Department of Neurosurgery, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200120, China
| | - Xi Chen
- Children's Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Xiaoling Zhang
- National Joint Engineering Laboratory for Human Disease Animal Models, Key Laboratory of Organ Regeneration and Transplantation, First Hospital of Jilin University, Changchun, China
| | - Yufeng Shi
- Tongji University Cancer Center, Shanghai Tenth People's Hospital of Tongji University, Clinical Center for Brain and Spinal Cord Research, School of Medicine, Tongji University, Shanghai, 200092, China
| | - Xuan Jiang
- Department of Oncology, Huai'an Second People's Hospital, Affiliated to Xuzhou Medical University, Huai'an, Jiangsu, China.
| | - Kaijun Zhao
- Department of Neurosurgery, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200120, China.
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Hecker J, Lee S, Kachroo P, Prokopenko D, Maaser-Hecker A, Lutz SM, Hahn G, Irizarry R, Weiss ST, DeMeo DL, Lange C. A consistent pattern of slide effects in Illumina DNA methylation BeadChip array data. Epigenetics 2023; 18:2257437. [PMID: 37731367 PMCID: PMC11062373 DOI: 10.1080/15592294.2023.2257437] [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: 04/18/2023] [Accepted: 09/01/2023] [Indexed: 09/22/2023] Open
Abstract
Background: Recent studies have identified thousands of associations between DNA methylation CpGs and complex diseases/traits, emphasizing the critical role of epigenetics in understanding disease aetiology and identifying biomarkers. However, association analyses based on methylation array data are susceptible to batch/slide effects, which can lead to inflated false positive rates or reduced statistical powerResults: We use multiple DNA methylation datasets based on the popular Illumina Infinium MethylationEPIC BeadChip array to describe consistent patterns and the joint distribution of slide effects across CpGs, confirming and extending previous results. The susceptible CpGs overlap with the Illumina Infinium HumanMethylation450 BeadChip array content.Conclusions: Our findings reveal systematic patterns in slide effects. The observations provide further insights into the characteristics of these effects and can improve existing adjustment approaches.
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Affiliation(s)
- Julian Hecker
- Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Sanghun Lee
- Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Medical Consilience, Division of Medicine, Graduate School, Dankook University, Yongin-si, South Korea
| | - Priyadarshini Kachroo
- Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Dmitry Prokopenko
- Genetics and Aging Unit and McCance Center for Brain Health, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Anna Maaser-Hecker
- Genetics and Aging Unit and McCance Center for Brain Health, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Sharon M. Lutz
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Population Medicine, PRecisiOn Medicine Translational Research (PROMoTeR) Center, Harvard Pilgrim Health Care and Harvard Medical School, Boston, MA, USA
| | - Georg Hahn
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Rafael Irizarry
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Scott T. Weiss
- Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Dawn L. DeMeo
- Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Christoph Lange
- Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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21
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Wang Y, Gao X, Yang Z, Yan X, He X, Guo T, Zhao S, Zhao H, Chen ZJ. Deciphering the DNA methylome in women with PCOS diagnosed using the new international evidence-based guidelines. Hum Reprod 2023; 38:ii69-ii79. [PMID: 37982419 DOI: 10.1093/humrep/dead191] [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: 12/26/2022] [Revised: 07/16/2023] [Indexed: 11/21/2023] Open
Abstract
STUDY QUESTION Is there any methylome alteration in women with PCOS who were diagnosed using the new international evidence-based guidelines? SUMMARY ANSWER A total of 264 differentially methylated probes (DMPs) and 53 differentially methylated regions (DMRs) were identified in patients with PCOS and healthy controls. WHAT IS KNOWN ALREADY PCOS is a common endocrine disorder among women of reproductive age and polycystic ovarian morphology (PCOM) is one of the main features of the disease. Owing to the availability of more sensitive ultrasound machines, the traditional diagnosis of PCOM according to the Rotterdam criteria (≥12 antral follicles per ovary) is currently debated as there is a risk of overdiagnosis. The new international evidence-based guidelines set the threshold for PCOM as ≥20 antral follicles per ovary when using endovaginal ultrasound transducers with a frequency bandwidth that includes 8 MHz. However, current DNA methylation studies in PCOS are still based on the Rotterdam criteria. This study aimed to explore aberrant DNA methylation in patients diagnosed with PCOS according to the new evidence-based guidelines. STUDY DESIGN, SIZE, DURATION This cross-sectional case-control study included 34 PCOS cases diagnosed using new international evidence-based guidelines and 36 controls. PARTICIPANTS/MATERIALS, SETTING, METHODS A total of 70 women, including 34 PCOS cases and 36 controls, were recruited. DNA extracted from whole blood samples of participants were profiled using array technology. Data quality control, preprocessing, annotation, and statistical analyses were performed. Least absolute shrinkage and selection operator (LASSO) regression were used to build a PCOS diagnosis model with DNA methylation sites. MAIN RESULTS AND THE ROLE OF CHANCE We identified 264 DMPs between PCOS cases and controls, which were mainly located in intergenic regions or gene bodies of the genome, CpG open sea sites, and heterochromatin of functional elements. Pathway enrichment analysis showed that DMPs were significantly enriched in biological processes involved in triglyceride regulation. Three of these DMPs overlapped with the PCOS susceptibility genes thyroid adenoma-associated protein (THADA), aminopeptidase O (AOPEP), and tripartite motif family-like protein 2 (TRIML2). Fifty-three DMRs were identified and their annotated genes were largely enriched in allograft rejection, thyroid hormone production, and peripheral downstream signaling effects. Two DMRs were closely related to the PCOS susceptibility genes, potassium voltage-gated channel subfamily A member 4 (KCNA4) and farnesyl-diphosphate farnesyltransferase 1 (FDFT1). Finally, based on LASSO regression, we built a methylation marker model with high accuracy for PCOS diagnosis (AUC=0.952). LIMITATIONS, REASONS FOR CAUTION The study cohort was single-center and the sample size was relatively limited. Further analyses with a larger number of participants are required. WIDER IMPLICATIONS OF THE FINDINGS This is the first study to identify DNA methylation alterations in women with PCOS diagnosed using the new international evidence-based guideline, and it provided new molecular insight into the application of the new guidelines. STUDY FUNDING/COMPETING INTEREST(S) This study was supported by the National Key Research and Development Program of China (2021YFC2700400), Basic Science Center Program of NSFC (31988101), CAMS Innovation Fund for Medical Sciences (2021-I2M-5-001), National Natural Science Foundation of China (32370916, 82071606, 82101707, 82192874, and 31871509), Shandong Provincial Key Research and Development Program (2020ZLYS02), Taishan Scholars Program of Shandong Province (ts20190988), and Fundamental Research Funds of Shandong University. The authors declare no conflicts of interest. TRIAL REGISTRATION NUMBER N/A.
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Affiliation(s)
- Yuteng Wang
- Center for Reproductive Medicine, Shandong University, Jinan, Shandong, China
- State Key Laboratory of Reproductive Medicine and Offspring Health, Shandong University, Jinan, Shandong, China
- Key Laboratory of Reproductive Endocrinology of Ministry of Education, Shandong University, Jinan, Shandong, China
- Shandong Provincial Clinical Research Center for Reproductive Health, Jinan, Shandong, China
- Shandong Technology Innovation Center for Reproductive Health, Jinan, Shandong, China
- National Research Center for Assisted Reproductive Technology and Reproductive Genetics, Shandong University, Jinan, Shandong, China
| | - Xueying Gao
- Center for Reproductive Medicine, Shandong University, Jinan, Shandong, China
- State Key Laboratory of Reproductive Medicine and Offspring Health, Shandong University, Jinan, Shandong, China
- Key Laboratory of Reproductive Endocrinology of Ministry of Education, Shandong University, Jinan, Shandong, China
- Shandong Provincial Clinical Research Center for Reproductive Health, Jinan, Shandong, China
- Shandong Technology Innovation Center for Reproductive Health, Jinan, Shandong, China
- National Research Center for Assisted Reproductive Technology and Reproductive Genetics, Shandong University, Jinan, Shandong, China
- Shanghai Key Laboratory for Assisted Reproduction and Reproductive Genetics, Shanghai, China
- Center for Reproductive Medicine, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Ziyi Yang
- Center for Reproductive Medicine, Shandong University, Jinan, Shandong, China
- State Key Laboratory of Reproductive Medicine and Offspring Health, Shandong University, Jinan, Shandong, China
- Key Laboratory of Reproductive Endocrinology of Ministry of Education, Shandong University, Jinan, Shandong, China
- Shandong Provincial Clinical Research Center for Reproductive Health, Jinan, Shandong, China
- Shandong Technology Innovation Center for Reproductive Health, Jinan, Shandong, China
- National Research Center for Assisted Reproductive Technology and Reproductive Genetics, Shandong University, Jinan, Shandong, China
| | - Xueqi Yan
- Center for Reproductive Medicine, Shandong University, Jinan, Shandong, China
- State Key Laboratory of Reproductive Medicine and Offspring Health, Shandong University, Jinan, Shandong, China
- Key Laboratory of Reproductive Endocrinology of Ministry of Education, Shandong University, Jinan, Shandong, China
- Shandong Provincial Clinical Research Center for Reproductive Health, Jinan, Shandong, China
- Shandong Technology Innovation Center for Reproductive Health, Jinan, Shandong, China
- National Research Center for Assisted Reproductive Technology and Reproductive Genetics, Shandong University, Jinan, Shandong, China
| | - Xinmiao He
- Center for Reproductive Medicine, Shandong University, Jinan, Shandong, China
- State Key Laboratory of Reproductive Medicine and Offspring Health, Shandong University, Jinan, Shandong, China
- Key Laboratory of Reproductive Endocrinology of Ministry of Education, Shandong University, Jinan, Shandong, China
- Shandong Provincial Clinical Research Center for Reproductive Health, Jinan, Shandong, China
- Shandong Technology Innovation Center for Reproductive Health, Jinan, Shandong, China
- National Research Center for Assisted Reproductive Technology and Reproductive Genetics, Shandong University, Jinan, Shandong, China
| | - Ting Guo
- Center for Reproductive Medicine, Shandong University, Jinan, Shandong, China
- State Key Laboratory of Reproductive Medicine and Offspring Health, Shandong University, Jinan, Shandong, China
- Key Laboratory of Reproductive Endocrinology of Ministry of Education, Shandong University, Jinan, Shandong, China
- Shandong Provincial Clinical Research Center for Reproductive Health, Jinan, Shandong, China
- Shandong Technology Innovation Center for Reproductive Health, Jinan, Shandong, China
- National Research Center for Assisted Reproductive Technology and Reproductive Genetics, Shandong University, Jinan, Shandong, China
| | - Shigang Zhao
- Center for Reproductive Medicine, Shandong University, Jinan, Shandong, China
- State Key Laboratory of Reproductive Medicine and Offspring Health, Shandong University, Jinan, Shandong, China
- Key Laboratory of Reproductive Endocrinology of Ministry of Education, Shandong University, Jinan, Shandong, China
- Shandong Provincial Clinical Research Center for Reproductive Health, Jinan, Shandong, China
- Shandong Technology Innovation Center for Reproductive Health, Jinan, Shandong, China
- National Research Center for Assisted Reproductive Technology and Reproductive Genetics, Shandong University, Jinan, Shandong, China
| | - Han Zhao
- Center for Reproductive Medicine, Shandong University, Jinan, Shandong, China
- State Key Laboratory of Reproductive Medicine and Offspring Health, Shandong University, Jinan, Shandong, China
- Key Laboratory of Reproductive Endocrinology of Ministry of Education, Shandong University, Jinan, Shandong, China
- Shandong Provincial Clinical Research Center for Reproductive Health, Jinan, Shandong, China
- Shandong Technology Innovation Center for Reproductive Health, Jinan, Shandong, China
- National Research Center for Assisted Reproductive Technology and Reproductive Genetics, Shandong University, Jinan, Shandong, China
| | - Zi-Jiang Chen
- Center for Reproductive Medicine, Shandong University, Jinan, Shandong, China
- State Key Laboratory of Reproductive Medicine and Offspring Health, Shandong University, Jinan, Shandong, China
- Key Laboratory of Reproductive Endocrinology of Ministry of Education, Shandong University, Jinan, Shandong, China
- Shandong Provincial Clinical Research Center for Reproductive Health, Jinan, Shandong, China
- Shandong Technology Innovation Center for Reproductive Health, Jinan, Shandong, China
- National Research Center for Assisted Reproductive Technology and Reproductive Genetics, Shandong University, Jinan, Shandong, China
- Shanghai Key Laboratory for Assisted Reproduction and Reproductive Genetics, Shanghai, China
- Center for Reproductive Medicine, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
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22
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Bukowski A, Hoyo C, Vielot NA, Graff M, Kosorok MR, Brewster WR, Maguire RL, Murphy SK, Nedjai B, Ladoukakis E, North KE, Smith JS. Epigenome-wide methylation and progression to high-grade cervical intraepithelial neoplasia (CIN2+): a prospective cohort study in the United States. BMC Cancer 2023; 23:1072. [PMID: 37932662 PMCID: PMC10629205 DOI: 10.1186/s12885-023-11518-6] [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: 05/04/2023] [Accepted: 10/11/2023] [Indexed: 11/08/2023] Open
Abstract
BACKGROUND Methylation levels may be associated with and serve as markers to predict risk of progression of precancerous cervical lesions. We conducted an epigenome-wide association study (EWAS) of CpG methylation and progression to high-grade cervical intraepithelial neoplasia (CIN2 +) following an abnormal screening test. METHODS A prospective US cohort of 289 colposcopy patients with normal or CIN1 enrollment histology was assessed. Baseline cervical sample DNA was analyzed using Illumina HumanMethylation 450K (n = 76) or EPIC 850K (n = 213) arrays. Participants returned at provider-recommended intervals and were followed up to 5 years via medical records. We assessed continuous CpG M values for 9 cervical cancer-associated genes and time-to-progression to CIN2+. We estimated CpG-specific time-to-event ratios (TTER) and hazard ratios using adjusted, interval-censored Weibull accelerated failure time models. We also conducted an exploratory EWAS to identify novel CpGs with false discovery rate (FDR) < 0.05. RESULTS At enrollment, median age was 29.2 years; 64.0% were high-risk HPV-positive, and 54.3% were non-white. During follow-up (median 24.4 months), 15 participants progressed to CIN2+. Greater methylation levels were associated with a shorter time-to-CIN2+ for CADM1 cg03505501 (TTER = 0.28; 95%CI 0.12, 0.63; FDR = 0.03) and RARB Cluster 1 (TTER = 0.46; 95% CI 0.29, 0.71; FDR = 0.01). There was evidence of similar trends for DAPK1 cg14286732, PAX1 cg07213060, and PAX1 Cluster 1. The EWAS detected 336 novel progression-associated CpGs, including those located in CpG islands associated with genes FGF22, TOX, COL18A1, GPM6A, XAB2, TIMP2, GSPT1, NR4A2, and APBB1IP. CONCLUSIONS Using prospective time-to-event data, we detected associations between CADM1-, DAPK1-, PAX1-, and RARB-related CpGs and cervical disease progression, and we identified novel progression-associated CpGs. IMPACT Methylation levels at novel CpG sites may help identify individuals with ≤CIN1 histology at higher risk of progression to CIN2+ and inform risk-based cervical cancer screening guidelines.
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Affiliation(s)
- Alexandra Bukowski
- Department of Epidemiology, University of North Carolina at Chapel Hill, 60 Bondurant Hall, Chapel Hill, NC, 27599, USA.
| | - Cathrine Hoyo
- Department of Biological Sciences, Center for Human Health and the Environment, North Carolina State University, Raleigh, NC, 27695, USA
| | - Nadja A Vielot
- Department of Family Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Misa Graff
- Department of Epidemiology, University of North Carolina at Chapel Hill, 60 Bondurant Hall, Chapel Hill, NC, 27599, USA
| | - Michael R Kosorok
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Wendy R Brewster
- Department of Epidemiology, University of North Carolina at Chapel Hill, 60 Bondurant Hall, Chapel Hill, NC, 27599, USA
- Department of Obstetrics and Gynecology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Rachel L Maguire
- Department of Biological Sciences, Center for Human Health and the Environment, North Carolina State University, Raleigh, NC, 27695, USA
- Department of Obstetrics and Gynecology, Duke University Medical Center, Durham, NC, 27701, USA
| | - Susan K Murphy
- Department of Obstetrics and Gynecology, Duke University Medical Center, Durham, NC, 27701, USA
| | - Belinda Nedjai
- Centre for Prevention, Detection and Diagnosis, Wolfson Institute of Population Health, Queen Mary University London, London, UK
| | - Efthymios Ladoukakis
- Centre for Prevention, Detection and Diagnosis, Wolfson Institute of Population Health, Queen Mary University London, London, UK
| | - Kari E North
- Department of Epidemiology, University of North Carolina at Chapel Hill, 60 Bondurant Hall, Chapel Hill, NC, 27599, USA
| | - Jennifer S Smith
- Department of Epidemiology, University of North Carolina at Chapel Hill, 60 Bondurant Hall, Chapel Hill, NC, 27599, USA
- University of North Carolina Lineberger Comprehensive Cancer Center, Chapel Hill, NC, 27599, USA
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23
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Zhang X, Cong P, Tian L, Zheng Y, Zhang H, Liu Q, Wu T, Zhang Q, Wu H, Huang X, Xiong L. Genomic gain/methylation modification/hsa-miR-132-3p increases RRS1 overexpression in liver hepatocellular carcinoma. Cancer Sci 2023; 114:4329-4342. [PMID: 37705317 PMCID: PMC10637089 DOI: 10.1111/cas.15933] [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: 05/30/2023] [Revised: 08/01/2023] [Accepted: 08/07/2023] [Indexed: 09/15/2023] Open
Abstract
This study aimed to determine the upstream regulatory factors affecting ribosome biogenesis regulator 1 homolog (RRS1) expression and the development and prognosis of liver hepatocellular carcinoma (LIHC). The expression profiles of RRS1 were evaluated in pan-cancer tissues and liver tumor cell lines. The associations of RRS1 with pan-cancer survival, immune infiltrations, immune checkpoints, and drug sensitivity were identified. We explored the potential upstream regulatory mechanisms of RRS1 expression. Hsa-miR-132-3p knockdown, CCK-8 assays, transwell, and wound healing assays were performed to validate the regulatory effect of hsa-miR-132-3p on RRS1 expression and the development of LIHC. Our findings demonstrated that RRS1 was significantly elevated in 27 types of cancers. RRS1 predicts a poor outcome of LIHC, lung adenocarcinoma, head and neck cancer, and kidney papillary cell carcinoma. RRS1 expression showed a significant association with immune cell infiltrates and the expression of immune checkpoints-related genes in LIHC tissues. Increased RRS1 expression may have a negative effect on these anticancer drugs of LIHC. Low methylation of the RRS1 promoter and its genomic gain may elevate RRS1 expression and predict poor prognosis for LIHC. Increased hsa-miR-132-3p expression may elevate RRS1 expression and result in poor prognosis for LIHC. Hsa-miR-132-3p inhibition can decrease RRS1 expression and the development of liver tumor cell lines. Low methylation of the RRS1 promoter, RRS1 genomic gain, and hsa-miR-132-3p upregulation in LIHC may promote RRS1 upregulation and thus lead to the development and poor prognosis for LIHC. RRS1 is a promising therapeutic target for LIHC.
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Affiliation(s)
- Xiaoxia Zhang
- Department of Hospital Infection Management, Shanghai Fourth People's Hospital, School of MedicineTongji UniversityShanghaiChina
| | - Peilin Cong
- Clinical Research Center for Anesthesiology and Perioperative MedicineTongji UniversityShanghaiChina
- Shanghai Key Laboratory of Anesthesiology and Brain Functional ModulationShanghaiChina
- Translational Research Institute of Brain and Brain‐Like Intelligence, Shanghai Fourth People's Hospital, School of MedicineTongji UniversityShanghaiChina
- Department of Anesthesiology and Perioperative Medicine, Shanghai Fourth People's Hospital, School of MedicineTongji UniversityShanghaiChina
| | - Li Tian
- Clinical Research Center for Anesthesiology and Perioperative MedicineTongji UniversityShanghaiChina
- Shanghai Key Laboratory of Anesthesiology and Brain Functional ModulationShanghaiChina
- Translational Research Institute of Brain and Brain‐Like Intelligence, Shanghai Fourth People's Hospital, School of MedicineTongji UniversityShanghaiChina
- Department of Anesthesiology and Perioperative Medicine, Shanghai Fourth People's Hospital, School of MedicineTongji UniversityShanghaiChina
| | - Yinggang Zheng
- Clinical Research Center for Anesthesiology and Perioperative MedicineTongji UniversityShanghaiChina
- Shanghai Key Laboratory of Anesthesiology and Brain Functional ModulationShanghaiChina
- Translational Research Institute of Brain and Brain‐Like Intelligence, Shanghai Fourth People's Hospital, School of MedicineTongji UniversityShanghaiChina
- Department of Anesthesiology and Perioperative Medicine, Shanghai Fourth People's Hospital, School of MedicineTongji UniversityShanghaiChina
| | - Hong Zhang
- Department of Rehabilitation Medicine, Shanghai Fourth People's Hospital, School of MedicineTongji UniversityShanghaiChina
| | - Qiong Liu
- Clinical Research Center for Anesthesiology and Perioperative MedicineTongji UniversityShanghaiChina
- Shanghai Key Laboratory of Anesthesiology and Brain Functional ModulationShanghaiChina
- Translational Research Institute of Brain and Brain‐Like Intelligence, Shanghai Fourth People's Hospital, School of MedicineTongji UniversityShanghaiChina
- Department of Anesthesiology and Perioperative Medicine, Shanghai Fourth People's Hospital, School of MedicineTongji UniversityShanghaiChina
| | - Tingmei Wu
- Clinical Research Center for Anesthesiology and Perioperative MedicineTongji UniversityShanghaiChina
- Shanghai Key Laboratory of Anesthesiology and Brain Functional ModulationShanghaiChina
- Translational Research Institute of Brain and Brain‐Like Intelligence, Shanghai Fourth People's Hospital, School of MedicineTongji UniversityShanghaiChina
- Department of Anesthesiology and Perioperative Medicine, Shanghai Fourth People's Hospital, School of MedicineTongji UniversityShanghaiChina
| | - Qian Zhang
- Clinical Research Center for Anesthesiology and Perioperative MedicineTongji UniversityShanghaiChina
- Shanghai Key Laboratory of Anesthesiology and Brain Functional ModulationShanghaiChina
- Translational Research Institute of Brain and Brain‐Like Intelligence, Shanghai Fourth People's Hospital, School of MedicineTongji UniversityShanghaiChina
- Department of Anesthesiology and Perioperative Medicine, Shanghai Fourth People's Hospital, School of MedicineTongji UniversityShanghaiChina
| | - Huanghui Wu
- Clinical Research Center for Anesthesiology and Perioperative MedicineTongji UniversityShanghaiChina
- Shanghai Key Laboratory of Anesthesiology and Brain Functional ModulationShanghaiChina
- Translational Research Institute of Brain and Brain‐Like Intelligence, Shanghai Fourth People's Hospital, School of MedicineTongji UniversityShanghaiChina
- Department of Anesthesiology and Perioperative Medicine, Shanghai Fourth People's Hospital, School of MedicineTongji UniversityShanghaiChina
| | - Xinwei Huang
- Clinical Research Center for Anesthesiology and Perioperative MedicineTongji UniversityShanghaiChina
- Shanghai Key Laboratory of Anesthesiology and Brain Functional ModulationShanghaiChina
- Translational Research Institute of Brain and Brain‐Like Intelligence, Shanghai Fourth People's Hospital, School of MedicineTongji UniversityShanghaiChina
- Department of Anesthesiology and Perioperative Medicine, Shanghai Fourth People's Hospital, School of MedicineTongji UniversityShanghaiChina
| | - Lize Xiong
- Clinical Research Center for Anesthesiology and Perioperative MedicineTongji UniversityShanghaiChina
- Shanghai Key Laboratory of Anesthesiology and Brain Functional ModulationShanghaiChina
- Translational Research Institute of Brain and Brain‐Like Intelligence, Shanghai Fourth People's Hospital, School of MedicineTongji UniversityShanghaiChina
- Department of Anesthesiology and Perioperative Medicine, Shanghai Fourth People's Hospital, School of MedicineTongji UniversityShanghaiChina
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24
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Cao TV, Sutherland HG, Benton MC, Haupt LM, Lea RA, Griffiths LR. Exploring the Functional Basis of Epigenetic Aging in Relation to Body Fat Phenotypes in the Norfolk Island Cohort. Curr Issues Mol Biol 2023; 45:7862-7877. [PMID: 37886940 PMCID: PMC10605526 DOI: 10.3390/cimb45100497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 09/18/2023] [Accepted: 09/25/2023] [Indexed: 10/28/2023] Open
Abstract
DNA methylation is an epigenetic factor that is modifiable and can change over a lifespan. While many studies have identified methylation sites (CpGs) related to aging, the relationship of these to gene function and age-related disease phenotypes remains unclear. This research explores this question by testing for the conjoint association of age-related CpGs with gene expression and the relation of these to body fat phenotypes. The study included blood-based gene transcripts and intragenic CpG methylation data from Illumina 450 K arrays in 74 healthy adults from the Norfolk Island population. First, a series of regression analyses were performed to detect associations between gene transcript level and intragenic CpGs and their conjoint relationship with age. Second, we explored how these age-related expression CpGs (eCpGs) correlated with obesity-related phenotypes, including body fat percentage, body mass index, and waist-to-hip ratio. We identified 35 age-related eCpGs associated with age. Of these, ten eCpGs were associated with at least one body fat phenotype. Collagen Type XI Alpha 2 Chain (COL11A2), Complement C1s (C1s), and four and a half LIM domains 2 (FHL2) genes were among the most significant genes with multiple eCpGs associated with both age and multiple body fat phenotypes. The COL11A2 gene contributes to the correct assembly of the extracellular matrix in maintaining the healthy structural arrangement of various components, with the C1s gene part of complement systems functioning in inflammation. Moreover, FHL2 expression was upregulated under hypermethylation in both blood and adipose tissue with aging. These results suggest new targets for future studies and require further validation to confirm the specific function of these genes on body fat regulation.
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Affiliation(s)
- Thao Van Cao
- Centre for Genomics and Personalised Health, School of Biomedical Sciences, Queensland University of Technology (QUT), Kelvin Grove, QLD 4059, Australia; (T.V.C.); (H.G.S.); (M.C.B.); (L.M.H.); (L.R.G.)
| | - Heidi G. Sutherland
- Centre for Genomics and Personalised Health, School of Biomedical Sciences, Queensland University of Technology (QUT), Kelvin Grove, QLD 4059, Australia; (T.V.C.); (H.G.S.); (M.C.B.); (L.M.H.); (L.R.G.)
| | - Miles C. Benton
- Centre for Genomics and Personalised Health, School of Biomedical Sciences, Queensland University of Technology (QUT), Kelvin Grove, QLD 4059, Australia; (T.V.C.); (H.G.S.); (M.C.B.); (L.M.H.); (L.R.G.)
| | - Larisa M. Haupt
- Centre for Genomics and Personalised Health, School of Biomedical Sciences, Queensland University of Technology (QUT), Kelvin Grove, QLD 4059, Australia; (T.V.C.); (H.G.S.); (M.C.B.); (L.M.H.); (L.R.G.)
- ARC Training Centre for Cell and Tissue Engineering Technologies, Queensland University of Technology (QUT), Kelvin Grove, QLD 4059, Australia
- Max Planck Queensland Centre for the Materials Sciences of Extracellular Matrices, Queensland University of Technology (QUT), Kelvin Grove, QLD 4059, Australia
| | - Rodney A. Lea
- Centre for Genomics and Personalised Health, School of Biomedical Sciences, Queensland University of Technology (QUT), Kelvin Grove, QLD 4059, Australia; (T.V.C.); (H.G.S.); (M.C.B.); (L.M.H.); (L.R.G.)
| | - Lyn R. Griffiths
- Centre for Genomics and Personalised Health, School of Biomedical Sciences, Queensland University of Technology (QUT), Kelvin Grove, QLD 4059, Australia; (T.V.C.); (H.G.S.); (M.C.B.); (L.M.H.); (L.R.G.)
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25
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Liu Y, Chen Y, Wang F, Lin J, Tan X, Chen C, Wu LL, Zhang X, Wang Y, Shi Y, Yan X, Zhao K. Caveolin-1 promotes glioma progression and maintains its mitochondrial inhibition resistance. Discov Oncol 2023; 14:161. [PMID: 37642765 PMCID: PMC10465474 DOI: 10.1007/s12672-023-00765-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/28/2023] [Accepted: 08/07/2023] [Indexed: 08/31/2023] Open
Abstract
BACKGROUND Glioma is a lethal brain cancer and lacking effective therapies. Challenges include no effective therapeutic target, intra- and intertumoral heterogeneity, inadequate effective drugs, and an immunosuppressive microenvironment, etc. Deciphering the pathogenesis of gliomas and finding out the working mechanisms are urgent and necessary for glioma treatment. Identification of prognostic biomarkers and targeting the biomarker genes will be a promising therapy. METHODS From our RNA-sequencing data of the oxidative phosphorylation (OXPHOS)-inhibition sensitive and OXPHOS-resistant cell lines, we found that the scaffolding protein caveolin 1 (CAV1) is highly expressed in the resistant group but not in the sensitive group. By comprehensive analysis of our RNA sequencing data, Whole Genome Bisulfite Sequencing (WGBS) data and public databases, we found that CAV1 is highly expressed in gliomas and its expression is positively related with pathological processes, higher CAV1 predicts shorter overall survival. RESULTS Further analysis indicated that (1) the differentiated genes in CAV1-high groups are enriched in immune infiltration and immune response; (2) CAV1 is positively correlated with tumor metastasis markers; (3) the methylation level of CAV1 promoters in glioma group is lower in higher stage than that in lower stage; (4) CAV1 is positively correlated with glioma stemness; (5) higher expression of CAV1 renders the glioma cells' resistant to oxidative phosphorylation inhibitors. CONCLUSION Therefore, we identified a key gene CAV1 and deciphered its function in glioma progression and prognosis, proposing that CAV1 may be a therapeutic target for gliomas.
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Affiliation(s)
- Yu'e Liu
- Department of Neurosurgery, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200120, China
| | - Yi Chen
- The China-US (Henan) Hormel Cancer Institute, Zhengzhou, 450000, China
| | - Fei Wang
- Shanghai Pudong Hospital, Pudong Medical Center, Fudan University, Shanghai, 201399, China
| | - Jianghua Lin
- Department of Neurosurgery, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200120, China
| | - Xiao Tan
- Tongji University Cancer Center, Shanghai Tenth People's Hospital of Tongji University, School of Medicine, Tongji University, Shanghai, 200092, China
| | - Chao Chen
- Department of Neurosurgery, Changhai Hospital, No. 168 Changhai Road, Shanghai, 200433, China
| | - Lei-Lei Wu
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, 200433, China
| | - Xiaoling Zhang
- National Joint Engineering Laboratory for Human Disease Animal Models, First Affiliated Hospital of Jilin University, Changchun, China
- Key Laboratory of Organ Regeneration and Transplantation, First Hospital of Jilin University, Changchun, China
| | - Yi Wang
- Department of Critical Care Medicine, Sichuan Academy of Medical Science and Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Yufeng Shi
- Tongji University Cancer Center, Shanghai Tenth People's Hospital of Tongji University, School of Medicine, Tongji University, Shanghai, 200092, China
- Clinical Center for Brain and Spinal Cord Research, Tongji University, Shanghai, 200092, China
| | - Xiaoli Yan
- Laboratory of Immunology and Pathogen Biology, School of Medicine, Tongji University, Shanghai, 200092, China.
| | - Kaijun Zhao
- Department of Neurosurgery, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200120, China.
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O'Connor LM, O'Connor BA, Lim SB, Zeng J, Lo CH. Integrative multi-omics and systems bioinformatics in translational neuroscience: A data mining perspective. J Pharm Anal 2023; 13:836-850. [PMID: 37719197 PMCID: PMC10499660 DOI: 10.1016/j.jpha.2023.06.011] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 06/20/2023] [Accepted: 06/25/2023] [Indexed: 09/19/2023] Open
Abstract
Bioinformatic analysis of large and complex omics datasets has become increasingly useful in modern day biology by providing a great depth of information, with its application to neuroscience termed neuroinformatics. Data mining of omics datasets has enabled the generation of new hypotheses based on differentially regulated biological molecules associated with disease mechanisms, which can be tested experimentally for improved diagnostic and therapeutic targeting of neurodegenerative diseases. Importantly, integrating multi-omics data using a systems bioinformatics approach will advance the understanding of the layered and interactive network of biological regulation that exchanges systemic knowledge to facilitate the development of a comprehensive human brain profile. In this review, we first summarize data mining studies utilizing datasets from the individual type of omics analysis, including epigenetics/epigenomics, transcriptomics, proteomics, metabolomics, lipidomics, and spatial omics, pertaining to Alzheimer's disease, Parkinson's disease, and multiple sclerosis. We then discuss multi-omics integration approaches, including independent biological integration and unsupervised integration methods, for more intuitive and informative interpretation of the biological data obtained across different omics layers. We further assess studies that integrate multi-omics in data mining which provide convoluted biological insights and offer proof-of-concept proposition towards systems bioinformatics in the reconstruction of brain networks. Finally, we recommend a combination of high dimensional bioinformatics analysis with experimental validation to achieve translational neuroscience applications including biomarker discovery, therapeutic development, and elucidation of disease mechanisms. We conclude by providing future perspectives and opportunities in applying integrative multi-omics and systems bioinformatics to achieve precision phenotyping of neurodegenerative diseases and towards personalized medicine.
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Affiliation(s)
- Lance M. O'Connor
- College of Biological Sciences, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Blake A. O'Connor
- School of Pharmacy, University of Wisconsin, Madison, WI, 53705, USA
| | - Su Bin Lim
- Department of Biochemistry and Molecular Biology, Ajou University School of Medicine, Suwon, 16499, South Korea
| | - Jialiu Zeng
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 308232, Singapore
| | - Chih Hung Lo
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 308232, Singapore
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Mirza S, de Carvalho Lima CN, Del Favero-Campbell A, Rubinstein A, Topolski N, Cabrera-Mendoza B, Kovács EH, Blumberg HP, Richards JG, Williams AJ, Wemmie JA, Magnotta VA, Fiedorowicz JG, Gaine ME, Walss-Bass C, Quevedo J, Soares JC, Fries GR. Blood epigenome-wide association studies of suicide attempt in adults with bipolar disorder. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.07.20.23292968. [PMID: 37546994 PMCID: PMC10402220 DOI: 10.1101/2023.07.20.23292968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
Suicide attempt (SA) risk is elevated in individuals with bipolar disorder (BD), and DNA methylation patterns may serve as possible biomarkers of SA. We conducted epigenome-wide association studies (EWAS) of blood DNA methylation associated with BD and SA. DNA methylation was measured at > 700,000 positions in a discovery cohort of n = 84 adults with BD with a history of SA (BD/SA), n = 79 adults with BD without history of SA (BD/non-SA), and n = 76 non-psychiatric controls (CON). EWAS revealed six differentially methylated positions (DMPs) and seven differentially methylated regions (DMRs) between BD/SA and BD/non-SA, with multiple immune-related genes implicated. There were no epigenome-wide significant differences when BD/SA and BD/non-SA were each compared to CON, and patterns suggested that epigenetics differentiating BD/SA from BD/non-SA do not differentiate BD/non-SA from CON. Weighted gene co-methylation network analysis and trait enrichment analysis of the BD/SA vs. BD/non-SA contrast further corroborated immune system involvement, while gene ontology analysis implicated calcium signalling. In an independent replication cohort of n = 48 BD/SA and n = 47 BD/non-SA, fold-changes at the discovery cohort's significant sites showed moderate correlation across cohorts and agreement on direction. In both cohorts, classification accuracy for SA history among individuals with BD was highest when methylation at the significant CpG sites as well as information from clinical interviews were combined, with an AUC of 88.8% (CI = 83.8-93.8%) and 82.1% (CI = 73.6-90.5%) for the combined epigenetic-clinical predictor in the discovery and replication cohorts, respectively. Our results provide novel insight to the role of immune system functioning in SA and BD and also suggest that integrating information from multiple levels of analysis holds promise to improve risk assessment for SA in adults with BD.
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Affiliation(s)
- Salahudeen Mirza
- Translational Psychiatry Program, Faillace Department of Psychiatry and Behavioral Sciences, McGovern Medical School, University of Texas Health Science Center at Houston, (UTHealth), 77054 Houston, Texas, USA
- Institute of Child Development, University of Minnesota, 55455 Minneapolis, Minnesota, USA
| | - Camila N. de Carvalho Lima
- Translational Psychiatry Program, Faillace Department of Psychiatry and Behavioral Sciences, McGovern Medical School, University of Texas Health Science Center at Houston, (UTHealth), 77054 Houston, Texas, USA
| | - Alexandra Del Favero-Campbell
- Translational Psychiatry Program, Faillace Department of Psychiatry and Behavioral Sciences, McGovern Medical School, University of Texas Health Science Center at Houston, (UTHealth), 77054 Houston, Texas, USA
| | - Alexandre Rubinstein
- Translational Psychiatry Program, Faillace Department of Psychiatry and Behavioral Sciences, McGovern Medical School, University of Texas Health Science Center at Houston, (UTHealth), 77054 Houston, Texas, USA
| | - Natasha Topolski
- Translational Psychiatry Program, Faillace Department of Psychiatry and Behavioral Sciences, McGovern Medical School, University of Texas Health Science Center at Houston, (UTHealth), 77054 Houston, Texas, USA
- Neuroscience Graduate Program, The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, 77054 Houston, Texas, USA
| | | | - Emese H.C. Kovács
- Department of Neuroscience and Pharmacology, The University of Iowa, 51 Newton Rd, 52242 Iowa City, Iowa, USA
| | - Hilary P. Blumberg
- Department of Psychiatry, Yale School of Medicine, 06510 New Haven, Connecticut, USA
| | - Jenny Gringer Richards
- Department of Radiology, The University of Iowa. 200 Hawkins Dr, 52242 Iowa City, Iowa, USA
| | - Aislinn J. Williams
- Department of Psychiatry, The University of Iowa. 200 Hawkins Dr, 52242 Iowa City, Iowa, USA
- Iowa Neuroscience Institute, The University of Iowa. 169 Newton Rd, 52242 Iowa City, Iowa USA
| | - John A. Wemmie
- Department of Psychiatry, The University of Iowa. 200 Hawkins Dr, 52242 Iowa City, Iowa, USA
- Iowa Neuroscience Institute, The University of Iowa. 169 Newton Rd, 52242 Iowa City, Iowa USA
- Department of Veterans Affairs Medical Center, Iowa City, Iowa, USA
| | - Vincent A. Magnotta
- Department of Radiology, The University of Iowa. 200 Hawkins Dr, 52242 Iowa City, Iowa, USA
- Department of Psychiatry, The University of Iowa. 200 Hawkins Dr, 52242 Iowa City, Iowa, USA
| | - Jess G. Fiedorowicz
- University of Ottawa Brain and Mind Research Institute, Ottawa Hospital Research Institute. 501 Smyth, K1H 8L6, Ottawa, Ontario, Canada
| | - Marie E. Gaine
- Iowa Neuroscience Institute, The University of Iowa. 169 Newton Rd, 52242 Iowa City, Iowa USA
- Pharmaceutical Sciences and Experimental Therapeutics, The University of Iowa, 180 South Grand Ave, 52242, Iowa City, Iowa, USA
| | - Consuelo Walss-Bass
- Translational Psychiatry Program, Faillace Department of Psychiatry and Behavioral Sciences, McGovern Medical School, University of Texas Health Science Center at Houston, (UTHealth), 77054 Houston, Texas, USA
- Neuroscience Graduate Program, The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, 77054 Houston, Texas, USA
| | - Joao Quevedo
- Translational Psychiatry Program, Faillace Department of Psychiatry and Behavioral Sciences, McGovern Medical School, University of Texas Health Science Center at Houston, (UTHealth), 77054 Houston, Texas, USA
- Neuroscience Graduate Program, The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, 77054 Houston, Texas, USA
- Center of Excellence in Mood Disorders, Faillace Department of Psychiatry and Behavioral Sciences, The University of Texas Health Science Center at Houston, 1941 East Rd, 77054, Houston, Texas, USA
| | - Jair C. Soares
- Translational Psychiatry Program, Faillace Department of Psychiatry and Behavioral Sciences, McGovern Medical School, University of Texas Health Science Center at Houston, (UTHealth), 77054 Houston, Texas, USA
- Neuroscience Graduate Program, The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, 77054 Houston, Texas, USA
- Center of Excellence in Mood Disorders, Faillace Department of Psychiatry and Behavioral Sciences, The University of Texas Health Science Center at Houston, 1941 East Rd, 77054, Houston, Texas, USA
| | - Gabriel R. Fries
- Translational Psychiatry Program, Faillace Department of Psychiatry and Behavioral Sciences, McGovern Medical School, University of Texas Health Science Center at Houston, (UTHealth), 77054 Houston, Texas, USA
- Neuroscience Graduate Program, The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, 77054 Houston, Texas, USA
- Center of Excellence in Mood Disorders, Faillace Department of Psychiatry and Behavioral Sciences, The University of Texas Health Science Center at Houston, 1941 East Rd, 77054, Houston, Texas, USA
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Wang X, Campbell MR, Cho HY, Pittman GS, Martos SN, Bell DA. Epigenomic profiling of isolated blood cell types reveals highly specific B cell smoking signatures and links to disease risk. Clin Epigenetics 2023; 15:90. [PMID: 37231515 PMCID: PMC10211291 DOI: 10.1186/s13148-023-01507-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 05/17/2023] [Indexed: 05/27/2023] Open
Abstract
BACKGROUND Tobacco smoking alters the DNA methylation profiles of immune cells which may underpin some of the pathogenesis of smoking-associated diseases. To link smoking-driven epigenetic effects in specific immune cell types with disease risk, we isolated six leukocyte subtypes, CD14+ monocytes, CD15+ granulocytes, CD19+ B cells, CD4+ T cells, CD8+ T cells, and CD56+ natural killer cells, from whole blood of 67 healthy adult smokers and 74 nonsmokers for epigenome-wide association study (EWAS) using Illumina 450k and EPIC methylation arrays. RESULTS Numbers of smoking-associated differentially methylated sites (smCpGs) at genome-wide significance (p < 1.2 × 10-7) varied widely across cell types, from 5 smCpGs in CD8+ T cells to 111 smCpGs in CD19+ B cells. We found unique smoking effects in each cell type, some of which were not apparent in whole blood. Methylation-based deconvolution to estimate B cell subtypes revealed that smokers had 7.2% (p = 0.033) less naïve B cells. Adjusting for naïve and memory B cell proportions in EWAS and RNA-seq allowed the identification of genes enriched for B cell activation-related cytokine signaling pathways, Th1/Th2 responses, and hematopoietic cancers. Integrating with large-scale public datasets, 62 smCpGs were among CpGs associated with health-relevant EWASs. Furthermore, 74 smCpGs had reproducible methylation quantitative trait loci single nucleotide polymorphisms (SNPs) that were in complete linkage disequilibrium with genome-wide association study SNPs, associating with lung function, disease risks, and other traits. CONCLUSIONS We observed blood cell-type-specific smCpGs, a naïve-to-memory shift among B cells, and by integrating genome-wide datasets, we identified their potential links to disease risks and health traits.
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Affiliation(s)
- Xuting Wang
- Environmental Epigenomics and Disease Group, Immunity, Inflammation and Disease Laboratory, Intramural Research Program, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, 27709, USA.
| | - Michelle R Campbell
- Environmental Epigenomics and Disease Group, Immunity, Inflammation and Disease Laboratory, Intramural Research Program, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, 27709, USA
| | - Hye-Youn Cho
- Environmental Epigenomics and Disease Group, Immunity, Inflammation and Disease Laboratory, Intramural Research Program, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, 27709, USA
| | - Gary S Pittman
- Environmental Epigenomics and Disease Group, Immunity, Inflammation and Disease Laboratory, Intramural Research Program, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, 27709, USA
| | - Suzanne N Martos
- Environmental Epigenomics and Disease Group, Immunity, Inflammation and Disease Laboratory, Intramural Research Program, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, 27709, USA
| | - Douglas A Bell
- Environmental Epigenomics and Disease Group, Immunity, Inflammation and Disease Laboratory, Intramural Research Program, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, 27709, USA.
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Xu H, Lin S, Zhou Z, Li D, Zhang X, Yu M, Zhao R, Wang Y, Qian J, Li X, Li B, Wei C, Chen K, Yoshimura T, Wang JM, Huang J. New genetic and epigenetic insights into the chemokine system: the latest discoveries aiding progression toward precision medicine. Cell Mol Immunol 2023:10.1038/s41423-023-01032-x. [PMID: 37198402 DOI: 10.1038/s41423-023-01032-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 04/14/2023] [Indexed: 05/19/2023] Open
Abstract
Over the past thirty years, the importance of chemokines and their seven-transmembrane G protein-coupled receptors (GPCRs) has been increasingly recognized. Chemokine interactions with receptors trigger signaling pathway activity to form a network fundamental to diverse immune processes, including host homeostasis and responses to disease. Genetic and nongenetic regulation of both the expression and structure of chemokines and receptors conveys chemokine functional heterogeneity. Imbalances and defects in the system contribute to the pathogenesis of a variety of diseases, including cancer, immune and inflammatory diseases, and metabolic and neurological disorders, which render the system a focus of studies aiming to discover therapies and important biomarkers. The integrated view of chemokine biology underpinning divergence and plasticity has provided insights into immune dysfunction in disease states, including, among others, coronavirus disease 2019 (COVID-19). In this review, by reporting the latest advances in chemokine biology and results from analyses of a plethora of sequencing-based datasets, we outline recent advances in the understanding of the genetic variations and nongenetic heterogeneity of chemokines and receptors and provide an updated view of their contribution to the pathophysiological network, focusing on chemokine-mediated inflammation and cancer. Clarification of the molecular basis of dynamic chemokine-receptor interactions will help advance the understanding of chemokine biology to achieve precision medicine application in the clinic.
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Affiliation(s)
- Hanli Xu
- College of Life Sciences and Bioengineering, School of Physical Science and Engineering, Beijing Jiaotong University, 3 ShangyuanCun, Haidian District, 100044, Beijing, P.R. China
| | - Shuye Lin
- Cancer Research Center, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Institute, 101149, Beijing, China
| | - Ziyun Zhou
- College of Life Sciences and Bioengineering, School of Physical Science and Engineering, Beijing Jiaotong University, 3 ShangyuanCun, Haidian District, 100044, Beijing, P.R. China
| | - Duoduo Li
- College of Life Sciences and Bioengineering, School of Physical Science and Engineering, Beijing Jiaotong University, 3 ShangyuanCun, Haidian District, 100044, Beijing, P.R. China
| | - Xiting Zhang
- College of Life Sciences and Bioengineering, School of Physical Science and Engineering, Beijing Jiaotong University, 3 ShangyuanCun, Haidian District, 100044, Beijing, P.R. China
| | - Muhan Yu
- College of Life Sciences and Bioengineering, School of Physical Science and Engineering, Beijing Jiaotong University, 3 ShangyuanCun, Haidian District, 100044, Beijing, P.R. China
| | - Ruoyi Zhao
- College of Life Sciences and Bioengineering, School of Physical Science and Engineering, Beijing Jiaotong University, 3 ShangyuanCun, Haidian District, 100044, Beijing, P.R. China
| | - Yiheng Wang
- College of Life Sciences and Bioengineering, School of Physical Science and Engineering, Beijing Jiaotong University, 3 ShangyuanCun, Haidian District, 100044, Beijing, P.R. China
| | - Junru Qian
- College of Life Sciences and Bioengineering, School of Physical Science and Engineering, Beijing Jiaotong University, 3 ShangyuanCun, Haidian District, 100044, Beijing, P.R. China
| | - Xinyi Li
- College of Life Sciences and Bioengineering, School of Physical Science and Engineering, Beijing Jiaotong University, 3 ShangyuanCun, Haidian District, 100044, Beijing, P.R. China
| | - Bohan Li
- College of Life Sciences and Bioengineering, School of Physical Science and Engineering, Beijing Jiaotong University, 3 ShangyuanCun, Haidian District, 100044, Beijing, P.R. China
| | - Chuhan Wei
- College of Life Sciences and Bioengineering, School of Physical Science and Engineering, Beijing Jiaotong University, 3 ShangyuanCun, Haidian District, 100044, Beijing, P.R. China
| | - Keqiang Chen
- Laboratory of Cancer Innovation, Center for Cancer Research, National Cancer Institute at Frederick, Frederick, MD, 21702, USA
| | - Teizo Yoshimura
- Laboratory of Cancer Innovation, Center for Cancer Research, National Cancer Institute at Frederick, Frederick, MD, 21702, USA
| | - Ji Ming Wang
- Laboratory of Cancer Innovation, Center for Cancer Research, National Cancer Institute at Frederick, Frederick, MD, 21702, USA
| | - Jiaqiang Huang
- College of Life Sciences and Bioengineering, School of Physical Science and Engineering, Beijing Jiaotong University, 3 ShangyuanCun, Haidian District, 100044, Beijing, P.R. China.
- Cancer Research Center, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Institute, 101149, Beijing, China.
- Laboratory of Cancer Innovation, Center for Cancer Research, National Cancer Institute at Frederick, Frederick, MD, 21702, USA.
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Romanowska J, Nustad HE, Page CM, Denault WRP, Lee Y, Magnus MC, Haftorn KL, Gjerdevik M, Novakovic B, Saffery R, Gjessing HK, Lyle R, Magnus P, Håberg SE, Jugessur A. The X-factor in ART: does the use of assisted reproductive technologies influence DNA methylation on the X chromosome? Hum Genomics 2023; 17:35. [PMID: 37085889 PMCID: PMC10122315 DOI: 10.1186/s40246-023-00484-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 04/10/2023] [Indexed: 04/23/2023] Open
Abstract
BACKGROUND Assisted reproductive technologies (ART) may perturb DNA methylation (DNAm) in early embryonic development. Although a handful of epigenome-wide association studies of ART have been published, none have investigated CpGs on the X chromosome. To bridge this knowledge gap, we leveraged one of the largest collections of mother-father-newborn trios of ART and non-ART (natural) conceptions to date to investigate sex-specific DNAm differences on the X chromosome. The discovery cohort consisted of 982 ART and 963 non-ART trios from the Norwegian Mother, Father, and Child Cohort Study (MoBa). To verify our results from the MoBa cohort, we used an external cohort of 149 ART and 58 non-ART neonates from the Australian 'Clinical review of the Health of adults conceived following Assisted Reproductive Technologies' (CHART) study. The Illumina EPIC array was used to measure DNAm in both datasets. In the MoBa cohort, we performed a set of X-chromosome-wide association studies ('XWASs' hereafter) to search for sex-specific DNAm differences between ART and non-ART newborns. We tested several models to investigate the influence of various confounders, including parental DNAm. We also searched for differentially methylated regions (DMRs) and regions of co-methylation flanking the most significant CpGs. Additionally, we ran an analogous model to our main model on the external CHART dataset. RESULTS In the MoBa cohort, we found more differentially methylated CpGs and DMRs in girls than boys. Most of the associations persisted after controlling for parental DNAm and other confounders. Many of the significant CpGs and DMRs were in gene-promoter regions, and several of the genes linked to these CpGs are expressed in tissues relevant for both ART and sex (testis, placenta, and fallopian tube). We found no support for parental DNAm-dependent features as an explanation for the observed associations in the newborns. The most significant CpG in the boys-only analysis was in UBE2DNL, which is expressed in testes but with unknown function. The most significant CpGs in the girls-only analysis were in EIF2S3 and AMOT. These three loci also displayed differential DNAm in the CHART cohort. CONCLUSIONS Genes that co-localized with the significant CpGs and DMRs associated with ART are implicated in several key biological processes (e.g., neurodevelopment) and disorders (e.g., intellectual disability and autism). These connections are particularly compelling in light of previous findings indicating that neurodevelopmental outcomes differ in ART-conceived children compared to those naturally conceived.
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Affiliation(s)
- Julia Romanowska
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway.
- Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway.
| | - Haakon E Nustad
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
- DeepInsight, 0154, Oslo, Norway
| | - Christian M Page
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
- Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo, Norway
| | - William R P Denault
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
| | - Yunsung Lee
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Maria C Magnus
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Kristine L Haftorn
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Miriam Gjerdevik
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
- Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, Bergen, Norway
| | - Boris Novakovic
- Murdoch Children's Research Institute, Melbourne, Australia
- Department of Paediatrics, University of Melbourne, Melbourne, Australia
| | - Richard Saffery
- Murdoch Children's Research Institute, Melbourne, Australia
- Department of Paediatrics, University of Melbourne, Melbourne, Australia
| | - Håkon K Gjessing
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
- Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway
| | - Robert Lyle
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
- Department of Medical Genetics, Oslo University Hospital and University of Oslo, Oslo, Norway
| | - Per Magnus
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Siri E Håberg
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Astanand Jugessur
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
- Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway
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van der Linden EL, Meeks KAC, Chilunga F, Hayfron-Benjamin C, Bahendeka S, Klipstein-Grobusch K, Venema A, van den Born BJ, Agyemang C, Henneman P, Adeyemo A. Epigenome-wide association study of plasma lipids in West Africans: the RODAM study. EBioMedicine 2023; 89:104469. [PMID: 36791658 PMCID: PMC10025759 DOI: 10.1016/j.ebiom.2023.104469] [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: 09/27/2022] [Revised: 01/23/2023] [Accepted: 01/24/2023] [Indexed: 02/16/2023] Open
Abstract
BACKGROUND DNA-methylation has been associated with plasma lipid concentration in populations of diverse ethnic backgrounds, but epigenome-wide association studies (EWAS) in West-Africans are lacking. The aim of this study was to identify DNA-methylation loci associated with plasma lipids in Ghanaians. METHODS We conducted an EWAS using Illumina 450k DNA-methylation array profiles of extracted DNA from 663 Ghanaian participants. Differentially methylated positions (DMPs) were examined for association with plasma total cholesterol (TC), LDL-cholesterol, HDL-cholesterol, and triglycerides concentrations using linear regression models adjusted for age, sex, body mass index, diabetes mellitus, and technical covariates. Findings were replicated in independent cohorts of different ethnicities. FINDINGS We identified one significantly associated DMP with triglycerides (cg19693031 annotated to TXNIP, regression coefficient beta -0.26, false discovery rate adjusted p-value 0.001), which replicated in-silico in South African Batswana, African American, and European populations. From the top five DMPs with the lowest nominal p-values, two additional DMPs for triglycerides (CPT1A, ABCG1), two DMPs for LDL-cholesterol (EPSTI1, cg13781819), and one for TC (TXNIP) replicated. With the exception of EPSTI1, these loci are involved in lipid transport/metabolism or are known GWAS-associated loci. The top 5 DMPs per lipid trait explained 9.5% in the variance of TC, 8.3% in LDL-cholesterol, 6.1% in HDL-cholesterol, and 11.0% in triglycerides. INTERPRETATION The top DMPs identified in this study are in loci that play a role in lipid metabolism across populations, including West-Africans. Future studies including larger sample size, longitudinal study design and translational research is needed to increase our understanding on the epigenetic regulation of lipid metabolism among West-African populations. FUNDING European Commission under the Framework Programme (grant number: 278901).
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Affiliation(s)
- Eva L van der Linden
- Department of Public and Occupational Health, Amsterdam UMC, University of Amsterdam, Amsterdam Public Health Research Institute, Amsterdam, the Netherlands; Department of Vascular Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam Cardiovascular Sciences, Amsterdam, the Netherlands.
| | - Karlijn A C Meeks
- Department of Public and Occupational Health, Amsterdam UMC, University of Amsterdam, Amsterdam Public Health Research Institute, Amsterdam, the Netherlands; Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Felix Chilunga
- Department of Public and Occupational Health, Amsterdam UMC, University of Amsterdam, Amsterdam Public Health Research Institute, Amsterdam, the Netherlands
| | - Charles Hayfron-Benjamin
- Department of Physiology, University of Ghana Medical School, Accra, Ghana; Department of Anesthesia and Critical Care, Korle Bu Teaching Hospital, Accra, Ghana
| | | | - Kerstin Klipstein-Grobusch
- Julius Global Health, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, the Netherlands; Division of Epidemiology and Biostatistics, School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Andrea Venema
- Department of Human Genetics, Genome Diagnostics Laboratory Amsterdam, Reproduction & Development, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Bert-Jan van den Born
- Department of Public and Occupational Health, Amsterdam UMC, University of Amsterdam, Amsterdam Public Health Research Institute, Amsterdam, the Netherlands; Department of Vascular Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam Cardiovascular Sciences, Amsterdam, the Netherlands
| | - Charles Agyemang
- Department of Public and Occupational Health, Amsterdam UMC, University of Amsterdam, Amsterdam Public Health Research Institute, Amsterdam, the Netherlands
| | - Peter Henneman
- Department of Human Genetics, Genome Diagnostics Laboratory Amsterdam, Reproduction & Development, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Adebowale Adeyemo
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
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Martin-Almeida M, Perez-Garcia J, Herrera-Luis E, Rosa-Baez C, Gorenjak M, Neerincx AH, Sardón-Prado O, Toncheva AA, Harner S, Wolff C, Brandstetter S, Valletta E, Abdel-Aziz MI, Hashimoto S, Berce V, Corcuera-Elosegui P, Korta-Murua J, Buntrock-Döpke H, Vijverberg SJH, Verster JC, Kerssemakers N, Hedman AM, Almqvist C, Villar J, Kraneveld AD, Potočnik U, Kabesch M, der Zee AHMV, Pino-Yanes M, Consortium OBOTS. Epigenome-Wide Association Studies of the Fractional Exhaled Nitric Oxide and Bronchodilator Drug Response in Moderate-to-Severe Pediatric Asthma. Biomedicines 2023; 11:biomedicines11030676. [PMID: 36979655 PMCID: PMC10044864 DOI: 10.3390/biomedicines11030676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 02/14/2023] [Accepted: 02/18/2023] [Indexed: 02/25/2023] Open
Abstract
Asthma is the most prevalent pediatric chronic disease. Bronchodilator drug response (BDR) and fractional exhaled nitric oxide (FeNO) are clinical biomarkers of asthma. Although DNA methylation (DNAm) contributes to asthma pathogenesis, the influence of DNAm on BDR and FeNO is scarcely investigated. This study aims to identify DNAm markers in whole blood associated either with BDR or FeNO in pediatric asthma. We analyzed 121 samples from children with moderate-to-severe asthma. The association of genome-wide DNAm with BDR and FeNO has been assessed using regression models, adjusting for age, sex, ancestry, and tissue heterogeneity. Cross-tissue validation was assessed in 50 nasal samples. Differentially methylated regions (DMRs) and enrichment in traits and biological pathways were assessed. A false discovery rate (FDR) < 0.1 and a genome-wide significance threshold of p < 9 × 10−8 were used to control for false-positive results. The CpG cg12835256 (PLA2G12A) was genome-wide associated with FeNO in blood samples (coefficient= −0.015, p = 2.53 × 10−9) and nominally associated in nasal samples (coefficient = −0.015, p = 0.045). Additionally, three CpGs were suggestively associated with BDR (FDR < 0.1). We identified 12 and four DMRs associated with FeNO and BDR (FDR < 0.05), respectively. An enrichment in allergic and inflammatory processes, smoking, and aging was observed. We reported novel associations of DNAm markers associated with BDR and FeNO enriched in asthma-related processes.
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Affiliation(s)
- Mario Martin-Almeida
- Genomics and Health Group, Department of Biochemistry, Microbiology, Cell Biology, and Genetics, Universidad de La Laguna (ULL), 38200 San Cristóbal de La Laguna, Spain
| | - Javier Perez-Garcia
- Genomics and Health Group, Department of Biochemistry, Microbiology, Cell Biology, and Genetics, Universidad de La Laguna (ULL), 38200 San Cristóbal de La Laguna, Spain
| | - Esther Herrera-Luis
- Genomics and Health Group, Department of Biochemistry, Microbiology, Cell Biology, and Genetics, Universidad de La Laguna (ULL), 38200 San Cristóbal de La Laguna, Spain
| | - Carlos Rosa-Baez
- Genomics and Health Group, Department of Biochemistry, Microbiology, Cell Biology, and Genetics, Universidad de La Laguna (ULL), 38200 San Cristóbal de La Laguna, Spain
| | - Mario Gorenjak
- Center for Human Molecular Genetics and Pharmacogenomics, Faculty of Medicine, University of Maribor, 2000 Maribor, Slovenia
| | - Anne H. Neerincx
- Department of Respiratory Medicine, Amsterdam University Medical Centres—Loc. AMC, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands
| | - Olaia Sardón-Prado
- Division of Pediatric Respiratory Medicine, Donostia University Hospital, 20014 San Sebastián, Spain
- Department of Pediatrics, University of the Basque Country (UPV/EHU), 48013 San Sebastián, Spain
| | - Antoaneta A. Toncheva
- Department of Pediatric Pneumology and Allergy, University Children’s Hospital Regensburg (KUNO) at the Hospital St. Hedwig of the Order of St. John, University of Regensburg, D-93049 Regensburg, Germany
| | - Susanne Harner
- Department of Pediatric Pneumology and Allergy, University Children’s Hospital Regensburg (KUNO) at the Hospital St. Hedwig of the Order of St. John, University of Regensburg, D-93049 Regensburg, Germany
| | - Christine Wolff
- Department of Pediatric Pneumology and Allergy, University Children’s Hospital Regensburg (KUNO) at the Hospital St. Hedwig of the Order of St. John, University of Regensburg, D-93049 Regensburg, Germany
| | - Susanne Brandstetter
- Department of Pediatric Pneumology and Allergy, University Children’s Hospital Regensburg (KUNO) at the Hospital St. Hedwig of the Order of St. John, University of Regensburg, D-93049 Regensburg, Germany
| | - Elisa Valletta
- Department of Pediatric Pneumology and Allergy, University Children’s Hospital Regensburg (KUNO) at the Hospital St. Hedwig of the Order of St. John, University of Regensburg, D-93049 Regensburg, Germany
| | - Mahmoud I. Abdel-Aziz
- Department of Respiratory Medicine, Amsterdam University Medical Centres—Loc. AMC, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands
- Department of Clinical Pharmacy, Faculty of Pharmacy, Assiut University, Assiut 71515, Egypt
| | - Simone Hashimoto
- Department of Respiratory Medicine, Amsterdam University Medical Centres—Loc. AMC, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands
- Department of Pediatric Respiratory Medicine, Emma Children’s Hospital, Amsterdam UMC, 1105 AZ Amsterdam, The Netherlands
| | - Vojko Berce
- Center for Human Molecular Genetics and Pharmacogenomics, Faculty of Medicine, University of Maribor, 2000 Maribor, Slovenia
- Clinic of Pediatrics, University Medical Centre Maribor, 2000 Maribor, Slovenia
| | - Paula Corcuera-Elosegui
- Division of Pediatric Respiratory Medicine, Donostia University Hospital, 20014 San Sebastián, Spain
| | - Javier Korta-Murua
- Division of Pediatric Respiratory Medicine, Donostia University Hospital, 20014 San Sebastián, Spain
- Department of Pediatrics, University of the Basque Country (UPV/EHU), 48013 San Sebastián, Spain
| | - Heike Buntrock-Döpke
- Department of Pediatric Pneumology and Allergy, University Children’s Hospital Regensburg (KUNO) at the Hospital St. Hedwig of the Order of St. John, University of Regensburg, D-93049 Regensburg, Germany
| | - Susanne J. H. Vijverberg
- Department of Respiratory Medicine, Amsterdam University Medical Centres—Loc. AMC, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands
| | - Joris C. Verster
- Division of Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Faculty of Science, Utrecht University, 3584 CG Utrecht, The Netherlands
- Centre for Human Psychopharmacology, Swinburne University, Melbourne, VIC 3122, Australia
| | - Nikki Kerssemakers
- Division of Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Faculty of Science, Utrecht University, 3584 CG Utrecht, The Netherlands
| | - Anna M Hedman
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Solna, 171 77 Stockholm, Sweden
| | - Catarina Almqvist
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Solna, 171 77 Stockholm, Sweden
| | - Jesús Villar
- CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, 28029 Madrid, Spain
- Multidisciplinary Organ Dysfunction Evaluation Research Network, Research Unit, Hospital Universitario Dr. Negrín, 35010 Las Palmas de Gran Canaria, Spain
| | - Aletta D. Kraneveld
- Division of Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Faculty of Science, Utrecht University, 3584 CG Utrecht, The Netherlands
| | - Uroš Potočnik
- Center for Human Molecular Genetics and Pharmacogenomics, Faculty of Medicine, University of Maribor, 2000 Maribor, Slovenia
- Clinic of Pediatrics, University Medical Centre Maribor, 2000 Maribor, Slovenia
- Laboratory for Biochemistry, Molecular Biology, and Genomics, Faculty of Chemistry and Chemical Engineering, University of Maribor, 2000 Maribor, Slovenia
| | - Michael Kabesch
- Department of Pediatric Pneumology and Allergy, University Children’s Hospital Regensburg (KUNO) at the Hospital St. Hedwig of the Order of St. John, University of Regensburg, D-93049 Regensburg, Germany
| | - Anke H. Maitland-van der Zee
- Department of Respiratory Medicine, Amsterdam University Medical Centres—Loc. AMC, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands
- Department of Pediatric Respiratory Medicine, Emma Children’s Hospital, Amsterdam UMC, 1105 AZ Amsterdam, The Netherlands
| | - Maria Pino-Yanes
- Genomics and Health Group, Department of Biochemistry, Microbiology, Cell Biology, and Genetics, Universidad de La Laguna (ULL), 38200 San Cristóbal de La Laguna, Spain
- CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, 28029 Madrid, Spain
- Instituto de Tecnologías Biomédicas (ITB), Universidad de La Laguna (ULL), 38200 San Cristóbal de La Laguna, Spain
- Correspondence: ; Tel.: +34-9223-16502-6343
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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: 3.0] [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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Database Resources of the National Genomics Data Center, China National Center for Bioinformation in 2023. Nucleic Acids Res 2023. [PMID: 36420893 DOI: 10.1093/nar/gkac1073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
Abstract
The National Genomics Data Center (NGDC), part of the China National Center for Bioinformation (CNCB), provides a family of database resources to support global academic and industrial communities. With the explosive accumulation of multi-omics data generated at an unprecedented rate, CNCB-NGDC constantly expands and updates core database resources by big data archive, integrative analysis and value-added curation. In the past year, efforts have been devoted to integrating multiple omics data, synthesizing the growing knowledge, developing new resources and upgrading a set of major resources. Particularly, several database resources are newly developed for infectious diseases and microbiology (MPoxVR, KGCoV, ProPan), cancer-trait association (ASCancer Atlas, TWAS Atlas, Brain Catalog, CCAS) as well as tropical plants (TCOD). Importantly, given the global health threat caused by monkeypox virus and SARS-CoV-2, CNCB-NGDC has newly constructed the monkeypox virus resource, along with frequent updates of SARS-CoV-2 genome sequences, variants as well as haplotypes. All the resources and services are publicly accessible at https://ngdc.cncb.ac.cn.
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Hagenbeek FA, van Dongen J, Pool R, Roetman PJ, Harms AC, Hottenga JJ, Kluft C, Colins OF, van Beijsterveldt CEM, Fanos V, Ehli EA, Hankemeier T, Vermeiren RRJM, Bartels M, Déjean S, Boomsma DI. Integrative Multi-omics Analysis of Childhood Aggressive Behavior. Behav Genet 2023; 53:101-117. [PMID: 36344863 PMCID: PMC9922241 DOI: 10.1007/s10519-022-10126-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Accepted: 10/25/2022] [Indexed: 11/09/2022]
Abstract
This study introduces and illustrates the potential of an integrated multi-omics approach in investigating the underlying biology of complex traits such as childhood aggressive behavior. In 645 twins (cases = 42%), we trained single- and integrative multi-omics models to identify biomarkers for subclinical aggression and investigated the connections among these biomarkers. Our data comprised transmitted and two non-transmitted polygenic scores (PGSs) for 15 traits, 78,772 CpGs, and 90 metabolites. The single-omics models selected 31 PGSs, 1614 CpGs, and 90 metabolites, and the multi-omics model comprised 44 PGSs, 746 CpGs, and 90 metabolites. The predictive accuracy for these models in the test (N = 277, cases = 42%) and independent clinical data (N = 142, cases = 45%) ranged from 43 to 57%. We observed strong connections between DNA methylation, amino acids, and parental non-transmitted PGSs for ADHD, Autism Spectrum Disorder, intelligence, smoking initiation, and self-reported health. Aggression-related omics traits link to known and novel risk factors, including inflammation, carcinogens, and smoking.
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Affiliation(s)
- Fiona A. Hagenbeek
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Van der Boechorststraat 7-10, 1081 BT Amsterdam, The Netherlands ,Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Jenny van Dongen
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Van der Boechorststraat 7-10, 1081 BT Amsterdam, The Netherlands ,Amsterdam Public Health Research Institute, Amsterdam, The Netherlands ,Amsterdam Reproduction & Development (AR&D) Research Institute, Amsterdam, The Netherlands
| | - René Pool
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Van der Boechorststraat 7-10, 1081 BT Amsterdam, The Netherlands ,Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Peter J. Roetman
- Department of Child and Adolescent Psychiatry, LUMC-Curium, Leiden University Medical Center, Leiden, The Netherlands
| | - Amy C. Harms
- Division of Analytical Biosciences, Leiden Academic Center for Drug Research, Leiden University, Leiden, The Netherlands ,The Netherlands Metabolomics Centre, Leiden, The Netherlands
| | - Jouke Jan Hottenga
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Van der Boechorststraat 7-10, 1081 BT Amsterdam, The Netherlands
| | | | - Olivier F. Colins
- Department of Child and Adolescent Psychiatry, LUMC-Curium, Leiden University Medical Center, Leiden, The Netherlands ,Department Special Needs Education, Ghent University, Ghent, Belgium
| | | | - Vassilios Fanos
- Department of Surgical Sciences, University of Cagliari and Neonatal Intensive Care Unit, Cagliari, Italy
| | - Erik A. Ehli
- Avera Institute for Human Genetics, Sioux Falls, South Dakota USA
| | - Thomas Hankemeier
- Division of Analytical Biosciences, Leiden Academic Center for Drug Research, Leiden University, Leiden, The Netherlands ,The Netherlands Metabolomics Centre, Leiden, The Netherlands
| | - Robert R. J. M. Vermeiren
- Department of Child and Adolescent Psychiatry, LUMC-Curium, Leiden University Medical Center, Leiden, The Netherlands ,Youz, Parnassia Psychiatric Institute, The Hague, The Netherlands
| | - Meike Bartels
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Van der Boechorststraat 7-10, 1081 BT Amsterdam, The Netherlands ,Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Sébastien Déjean
- Toulouse Mathematics Institute, University of Toulouse, CNRS, Toulouse, France
| | - Dorret I. Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Van der Boechorststraat 7-10, 1081 BT Amsterdam, The Netherlands ,Amsterdam Public Health Research Institute, Amsterdam, The Netherlands ,Amsterdam Reproduction & Development (AR&D) Research Institute, Amsterdam, The Netherlands
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36
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van der Linden EL, Halley A, Meeks KAC, Chilunga F, Hayfron-Benjamin C, Venema A, Garrelds IM, Danser AHJ, van den Born BJ, Henneman P, Agyemang C. An explorative epigenome-wide association study of plasma renin and aldosterone concentration in a Ghanaian population: the RODAM study. Clin Epigenetics 2022; 14:159. [PMID: 36457109 PMCID: PMC9714193 DOI: 10.1186/s13148-022-01378-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Accepted: 11/16/2022] [Indexed: 12/05/2022] Open
Abstract
BACKGROUND The epigenetic regulation of the renin-angiotensin-aldosterone system (RAAS) potentially plays a role in the pathophysiology underlying the high burden of hypertension in sub-Saharan Africans (SSA). Here we report the first epigenome-wide association study (EWAS) of plasma renin and aldosterone concentrations and the aldosterone-to-renin ratio (ARR). METHODS Epigenome-wide DNA methylation was measured using the Illumina 450K array on whole blood samples of 68 Ghanaians. Differentially methylated positions (DMPs) were assessed for plasma renin concentration, aldosterone, and ARR using linear regression models adjusted for age, sex, body mass index, diabetes mellitus, hypertension, and technical covariates. Additionally, we extracted methylation loci previously associated with hypertension, kidney function, or that were annotated to RAAS-related genes and associated these with renin and aldosterone concentration. RESULTS We identified one DMP for renin, ten DMPs for aldosterone, and one DMP associated with ARR. Top DMPs were annotated to the PTPRN2, SKIL, and KCNT1 genes, which have been reported in relation to cardiometabolic risk factors, atherosclerosis, and sodium-potassium handling. Moreover, EWAS loci previously associated with hypertension, kidney function, or RAAS-related genes were also associated with renin, aldosterone, and ARR. CONCLUSION In this first EWAS on RAAS hormones, we identified DMPs associated with renin, aldosterone, and ARR in a SSA population. These findings are a first step in understanding the role of DNA methylation in regulation of the RAAS in general and in a SSA population specifically. Replication and translational studies are needed to establish the role of these DMPs in the hypertension burden in SSA populations.
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Affiliation(s)
- Eva L. van der Linden
- Department of Public and Occupational Health, Amsterdam UMC, University of Amsterdam, Amsterdam Public Health Research Institute, Location AMC, Meibergdreef 9, 1105AZ Amsterdam, The Netherlands ,grid.7177.60000000084992262Department of Vascular Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam Cardiovascular Sciences, Amsterdam, The Netherlands
| | - Adrienne Halley
- Department of Public and Occupational Health, Amsterdam UMC, University of Amsterdam, Amsterdam Public Health Research Institute, Location AMC, Meibergdreef 9, 1105AZ Amsterdam, The Netherlands
| | - Karlijn A. C. Meeks
- Department of Public and Occupational Health, Amsterdam UMC, University of Amsterdam, Amsterdam Public Health Research Institute, Location AMC, Meibergdreef 9, 1105AZ Amsterdam, The Netherlands ,grid.280128.10000 0001 2233 9230Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD USA
| | - Felix Chilunga
- Department of Public and Occupational Health, Amsterdam UMC, University of Amsterdam, Amsterdam Public Health Research Institute, Location AMC, Meibergdreef 9, 1105AZ Amsterdam, The Netherlands
| | - Charles Hayfron-Benjamin
- grid.8652.90000 0004 1937 1485Department of Physiology, University of Ghana Medical School, Accra, Ghana ,grid.415489.50000 0004 0546 3805Department of Anesthesia and Critical Care, Korle Bu Teaching Hospital, Accra, Ghana
| | - Andrea Venema
- grid.7177.60000000084992262Department of Human Genetics, Genome Diagnostics Laboratory Amsterdam, Amsterdam UMC, University of Amsterdam, Amsterdam Reproduction and Development, Amsterdam, The Netherlands
| | - Ingrid M. Garrelds
- grid.5645.2000000040459992XDivision of Pharmacology and Vascular Medicine, Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Amsterdam, The Netherlands
| | - A. H. Jan Danser
- grid.5645.2000000040459992XDivision of Pharmacology and Vascular Medicine, Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Amsterdam, The Netherlands
| | - Bert-Jan van den Born
- Department of Public and Occupational Health, Amsterdam UMC, University of Amsterdam, Amsterdam Public Health Research Institute, Location AMC, Meibergdreef 9, 1105AZ Amsterdam, The Netherlands ,grid.7177.60000000084992262Department of Vascular Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam Cardiovascular Sciences, Amsterdam, The Netherlands
| | - Peter Henneman
- grid.7177.60000000084992262Department of Human Genetics, Genome Diagnostics Laboratory Amsterdam, Amsterdam UMC, University of Amsterdam, Amsterdam Reproduction and Development, Amsterdam, The Netherlands
| | - Charles Agyemang
- Department of Public and Occupational Health, Amsterdam UMC, University of Amsterdam, Amsterdam Public Health Research Institute, Location AMC, Meibergdreef 9, 1105AZ Amsterdam, The Netherlands
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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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Zhang M, Zong W, Zou D, Wang G, Zhao W, Yang F, Wu S, Zhang X, Guo X, Ma Y, Xiong Z, Zhang Z, Bao Y, Li R. MethBank 4.0: an updated database of DNA methylation across a variety of species. Nucleic Acids Res 2022; 51:D208-D216. [PMID: 36318250 PMCID: PMC9825483 DOI: 10.1093/nar/gkac969] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 10/05/2022] [Accepted: 10/13/2022] [Indexed: 11/05/2022] Open
Abstract
DNA methylation, as the most intensively studied epigenetic mark, regulates gene expression in numerous biological processes including development, aging, and disease. With the rapid accumulation of whole-genome bisulfite sequencing data, integrating, archiving, analyzing, and visualizing those data becomes critical. Since its first publication in 2015, MethBank has been continuously updated to include more DNA methylomes across more diverse species. Here, we present MethBank 4.0 (https://ngdc.cncb.ac.cn/methbank/), which reports an increase of 309% in data volume, with 1449 single-base resolution methylomes of 23 species, covering 236 tissues/cell lines and 15 biological contexts. Value-added information, such as more rigorous quality evaluation, more standardized metadata, and comprehensive downstream annotations have been integrated in the new version. Moreover, expert-curated knowledge modules of featured differentially methylated genes associated with biological contexts and methylation analysis tools have been incorporated as new components of MethBank. In addition, MethBank 4.0 is equipped with a series of new web interfaces to browse, search, and visualize DNA methylation profiles and related information. With all these improvements, we believe the updated MethBank 4.0 will serve as a fundamental resource to provide a wide range of data services for the global research community.
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Affiliation(s)
| | | | | | | | - Wei Zhao
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China,China National Center for Bioinformation, Beijing 100101, China,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Fei Yang
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China,China National Center for Bioinformation, Beijing 100101, China
| | - Song Wu
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China,China National Center for Bioinformation, Beijing 100101, China,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xinran Zhang
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China,China National Center for Bioinformation, Beijing 100101, China,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xutong Guo
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China,China National Center for Bioinformation, Beijing 100101, China,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yingke Ma
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China,China National Center for Bioinformation, Beijing 100101, China
| | - Zhuang Xiong
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China,China National Center for Bioinformation, Beijing 100101, China,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhang Zhang
- Correspondence may also be addressed to Zhang Zhang. Tel: +86 10 84097261;
| | - Yiming Bao
- Correspondence may also be addressed to Yiming Bao. Tel: +86 10 84097858;
| | - Rujiao Li
- To whom correspondence should be addressed. Tel: +86 10 84097638;
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Wu S, Huang Y, Zhang M, Gong Z, Wang G, Zheng X, Zong W, Zhao W, Xing P, Li R, Liu Z, Bao Y. ASCancer Atlas: a comprehensive knowledgebase of alternative splicing in human cancers. Nucleic Acids Res 2022; 51:D1196-D1204. [PMID: 36318242 PMCID: PMC9825479 DOI: 10.1093/nar/gkac955] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 09/29/2022] [Accepted: 10/13/2022] [Indexed: 11/05/2022] Open
Abstract
Alternative splicing (AS) is a fundamental process that governs almost all aspects of cellular functions, and dysregulation in this process has been implicated in tumor initiation, progression and treatment resistance. With accumulating studies of carcinogenic mis-splicing in cancers, there is an urgent demand to integrate cancer-associated splicing changes to better understand their internal cross-talks and functional consequences from a global view. However, a resource of key functional AS events in human cancers is still lacking. To fill the gap, we developed ASCancer Atlas (https://ngdc.cncb.ac.cn/ascancer), a comprehensive knowledgebase of aberrant splicing in human cancers. Compared to extant databases, ASCancer Atlas features a high-confidence collection of 2006 cancer-associated splicing events experimentally proved to promote tumorigenesis, a systematic splicing regulatory network, and a suit of multi-scale online analysis tools. For each event, we manually curated the functional axis including upstream splicing regulators, splicing event annotations, downstream oncogenic effects, and possible therapeutic strategies. ASCancer Atlas also houses about 2 million computationally putative splicing events. Additionally, a user-friendly web interface was built to enable users to easily browse, search, visualize, analyze, and download all splicing events. Overall, ASCancer Atlas provides a unique resource to study the functional roles of splicing dysregulation in human cancers.
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Affiliation(s)
| | | | - Mochen Zhang
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zheng Gong
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Guoliang Wang
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xinchang Zheng
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Wenting Zong
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Wei Zhao
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Peiqi Xing
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Rujiao Li
- Correspondence may also be addressed to Rujiao Li. Tel: +86 10 84097638;
| | - Zhaoqi Liu
- Correspondence may also be addressed to Zhaoqi Liu. Tel: +86 10 84097398;
| | - Yiming Bao
- To whom correspondence should be addressed. Tel: +86 10 84097858; Fax: +86 10 84097720;
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40
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Zou J, Zhang H, Huang Y, Xu W, Huang Y, Zuo S, Li Z, Zhou H. Multi-Omics Analysis of the Tumor Microenvironment in Liver Metastasis of Colorectal Cancer Identified FJX1 as a Novel Biomarker. Front Genet 2022; 13:960954. [PMID: 35928453 PMCID: PMC9343787 DOI: 10.3389/fgene.2022.960954] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Accepted: 06/24/2022] [Indexed: 12/24/2022] Open
Abstract
Colorectal cancer incidence and mortality have increased in recent years, with more than half of patients who died of colorectal cancer developing liver metastases. Consequently, colorectal cancer liver metastasis is the focus of clinical treatment, as well as being the most difficult. The primary target genes related to colorectal cancer liver metastasis were via bioinformatics analysis. First, five prognosis-related genes, CTAG1A, CSTL1, FJX1, IER5L, and KLHL35, were identified through screening, and the prognosis of the CSTL1, FJX1, IER5L, and KLHL35 high expression group was considerably poorer than that of the low expression group. Furthermore, the clinical correlation analysis revealed that in distinct pathological stages T, N, and M, the mRNA expression levels of CSTL1, IER5L, and KLHL35 were higher than in normal tissues. Finally, a correlation study of the above genes and clinical manifestations revealed that FJX1 was strongly linked to colorectal cancer liver metastasis. FJX1 is thought to affect chromogenic modification enzymes, the Notch signaling system, cell senescence, and other signaling pathways, according to KEGG enrichment analysis. FJX1 may be a critical target in colorectal cancer metastasis, and thus has the potential as a new biomarker to predict and treat colorectal cancer liver metastases.
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Affiliation(s)
- Junwei Zou
- Department of Gastrointestinal Surgery, The Second Affiliated Hospital of Wannan Medical College, Wuhu, China
| | - Hesong Zhang
- Department of Hepatobiliary Surgery, The Second People’s Hospital of Wuhu, Wuhu, China
| | - Yong Huang
- Department of Gastrointestinal Surgery, The Second Affiliated Hospital of Wannan Medical College, Wuhu, China
| | - Wenjing Xu
- School of Clinical Medicine, Wannan Medical College, Wuhu, China
| | - Yujin Huang
- School of Pharmacy, Wannan Medical College, Wuhu, China
| | - Siyuan Zuo
- School of Clinical Medicine, Wannan Medical College, Wuhu, China
| | - Zhenhan Li
- School of Clinical Medicine, Wannan Medical College, Wuhu, China
- *Correspondence: Hailang Zhou, ; Zhenhan Li,
| | - Hailang Zhou
- Department of Gastroenterology, Lianshui People’s Hospital Affiliated to Kangda College of Nanjing Medical University, Huai’an, China
- *Correspondence: Hailang Zhou, ; Zhenhan Li,
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41
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Chilunga FP, Meeks KAC, Henneman P, Agyemang C, Doumatey AP, Rotimi CN, Adeyemo AA. An epigenome-wide association study of insulin resistance in African Americans. Clin Epigenetics 2022; 14:88. [PMID: 35836279 PMCID: PMC9281172 DOI: 10.1186/s13148-022-01309-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 07/04/2022] [Indexed: 11/18/2022] Open
Abstract
Background African Americans have a high risk for type 2 diabetes (T2D) and insulin resistance. Studies among other population groups have identified DNA methylation loci associated with insulin resistance, but data in African Americans are lacking. Using DNA methylation profiles of blood samples obtained from the Illumina Infinium® HumanMethylation450 BeadChip, we performed an epigenome-wide association study to identify DNA methylation loci associated with insulin resistance among 136 non-diabetic, unrelated African American men (mean age 41.6 years) from the Howard University Family Study. Results We identified three differentially methylated positions (DMPs) for homeostatic model assessment of insulin resistance (HOMA-IR) at 5% FDR. One DMP (cg14013695, HOXA5) is a known locus among Mexican Americans, while the other two DMPs are novel—cg00456326 (OSR1; beta = 0.027) and cg20259981 (ST18; beta = 0.010). Although the cg00456326 DMP is novel, the OSR1 gene has previously been found associated with both insulin resistance and T2D in Europeans. The genes HOXA5 and ST18 have been implicated in biological processes relevant to insulin resistance. Differential methylation at the significant HOXA5 and OSR1 DMPs is associated with differences in gene expression in the iMETHYL database. Analysis of differentially methylated regions (DMRs) did not identify any epigenome-wide DMRs for HOMA-IR. We tested transferability of HOMA-IR associated DMPs from five previous EWAS in Mexican Americans, Indian Asians, Europeans, and European ancestry Americans. Out of the 730 previously reported HOMA-IR DMPs, 47 (6.4%) were associated with HOMA-IR in this cohort of African Americans. Conclusions The findings from our study suggest substantial differences in DNA methylation patterns associated with insulin resistance across populations. Two of the DMPs we identified in African Americans have not been reported in other populations, and we found low transferability of HOMA-IR DMPs reported in other populations in African Americans. More work in African-ancestry populations is needed to confirm our findings as well as functional analyses to understand how such DNA methylation alterations contribute to T2D pathology. Supplementary Information The online version contains supplementary material available at 10.1186/s13148-022-01309-4.
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Affiliation(s)
- Felix P Chilunga
- Department of Public & Occupational Health, Amsterdam Public Health Research Institute, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands
| | - Karlijn A C Meeks
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Peter Henneman
- Department of Human Genetics, Amsterdam Reproduction & Development Research Institute, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands
| | - Charles Agyemang
- Department of Public & Occupational Health, Amsterdam Public Health Research Institute, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands
| | - Ayo P Doumatey
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Charles N Rotimi
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Adebowale A Adeyemo
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA.
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Identification of COVID-19-Associated DNA Methylation Variations by Integrating Methylation Array and scRNA-Seq Data at Cell-Type Resolution. Genes (Basel) 2022; 13:genes13071109. [DOI: 10.3390/genes13071109] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 06/16/2022] [Accepted: 06/17/2022] [Indexed: 01/27/2023] Open
Abstract
Single-cell transcriptome studies have revealed immune dysfunction in COVID-19 patients, including lymphopenia, T cell exhaustion, and increased levels of pro-inflammatory cytokines, while DNA methylation plays an important role in the regulation of immune response and inflammatory response. The specific cell types of immune responses regulated by DNA methylation in COVID-19 patients will be better understood by exploring the COVID-19 DNA methylation variation at the cell-type level. Here, we developed an analytical pipeline to explore single-cell DNA methylation variations in COVID-19 patients by transferring bulk-tissue-level knowledge to the single-cell level. We discovered that the methylation variations in the whole blood of COVID-19 patients showed significant cell-type specificity with remarkable enrichment in gamma-delta T cells and presented a phenomenon of hypermethylation and low expression. Furthermore, we identified five genes whose methylation variations were associated with several cell types. Among them, S100A9, AHNAK, and CX3CR1 have been reported as potential COVID-19 biomarkers previously, and the others (TRAF3IP3 and LFNG) are closely associated with the immune and virus-related signaling pathways. We propose that they might serve as potential epigenetic biomarkers for COVID-19 and could play roles in important biological processes such as the immune response and antiviral activity.
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Identifying the Potential Roles of PBX4 in Human Cancers Based on Integrative Analysis. Biomolecules 2022; 12:biom12060822. [PMID: 35740947 PMCID: PMC9221482 DOI: 10.3390/biom12060822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 06/09/2022] [Accepted: 06/11/2022] [Indexed: 02/05/2023] Open
Abstract
PBX4 belongs to the pre-B-cell leukemia homeobox (PBX) transcription factors family and acts as a transcriptional cofactor of HOX proteins participating in several pathophysiological processes. Recent studies have revealed that the dysregulation of PBX4 is closely related to multiple diseases, especially cancers. However, the research on PBX4’s potential roles in 33 cancers from the Cancer Genome Atlas (TCGA) is still insufficient. Therefore, we performed a comprehensive pan-cancer analysis to explore the roles of PBX4with multiple public databases. Our results showed that PBX4 was differentially expressed in 17 types of human cancer and significantly correlated to the pathological stage, tumor grade, and immune and molecular subtypes. We used the Kaplan–Meier plotter and PrognoScan databases to find the significant associations between PBX4 expression and prognostic values of multiple cancers. It was also found that PBX4 expression was statistically related to mutation status, DNA methylation, immune infiltration, drug sensitivity, and immune checkpoint blockade (ICB) therapy. Additionally, we found that PBX4 was involved in different functional states of multiple cancers from the single-cell resolution perspective. Enrichment analysis results showed that PBX4-related genes were enriched in the cell cycle process, MAPK cascade, ncRNA metabolic process, positive regulation of GTPase activity, and regulation of lipase activity and mainly participated in the pathways of cholesterol metabolism, base excision repair, herpes simplex virus 1 infection, transcriptional misregulation in cancer, and Epstein–Barr virus infection. Altogether, our integrative analysis could help in better understanding the potential roles of PBX4 in different human cancers.
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Domingo-Relloso A, Riffo-Campos AL, Powers M, Tellez-Plaza M, Haack K, Brown RH, Umans JG, Fallin MD, Cole SA, Navas-Acien A, Sanchez TR. An epigenome-wide study of DNA methylation profiles and lung function among American Indians in the Strong Heart Study. Clin Epigenetics 2022; 14:75. [PMID: 35681244 PMCID: PMC9185990 DOI: 10.1186/s13148-022-01294-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Accepted: 05/25/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Epigenetic modifications, including DNA methylation (DNAm), are often related to environmental exposures, and are increasingly recognized as key processes in the pathogenesis of chronic lung disease. American Indian communities have a high burden of lung disease compared to the national average. The objective of this study was to investigate the association of DNAm and lung function in the Strong Heart Study (SHS). We conducted a cross-sectional study of American Indian adults, 45-74 years of age who participated in the SHS. DNAm was measured using the Illumina Infinium Human MethylationEPIC platform at baseline (1989-1991). Lung function was measured via spirometry, including forced expiratory volume in 1 s (FEV1) and forced vital capacity (FVC), at visit 2 (1993-1995). Airflow limitation was defined as FEV1 < 70% predicted and FEV1/FVC < 0.7, restriction was defined as FEV1/FVC > 0.7 and FVC < 80% predicted, and normal spirometry was defined as FEV1/FVC > 0.7, FEV1 > 70% predicted, FVC > 80% predicted. We used elastic-net models to select relevant CpGs for lung function and spirometry-defined lung disease. We also conducted bioinformatic analyses to evaluate the biological plausibility of the findings. RESULTS Among 1677 participants, 21.2% had spirometry-defined airflow limitation and 13.6% had spirometry-defined restrictive pattern lung function. Elastic-net models selected 1118 Differentially Methylated Positions (DMPs) as predictors of airflow limitation and 1385 for restrictive pattern lung function. A total of 12 DMPs overlapped between airflow limitation and restrictive pattern. EGFR, MAPK1 and PRPF8 genes were the most connected nodes in the protein-protein interaction network. Many of the DMPs targeted genes with biological roles related to lung function such as protein kinases. CONCLUSION We found multiple differentially methylated CpG sites associated with chronic lung disease. These signals could contribute to better understand molecular mechanisms involved in lung disease, as assessed systemically, as well as to identify patterns that could be useful for diagnostic purposes. Further experimental and longitudinal studies are needed to assess whether DNA methylation has a causal role in lung disease.
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Affiliation(s)
- Arce Domingo-Relloso
- Integrative Epidemiology Group, Department of Chronic Diseases Epidemiology, National Center for Epidemiology, Carlos III Health Institute, 28029, Madrid, Spain. .,Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, USA. .,Department of Statistics and Operations Research, University of Valencia, Valencia, Spain.
| | - Angela L Riffo-Campos
- Millennium Nucleus on Sociomedicine (SocioMed) and Vicerrectoría Académica, Universidad de La Frontera, Temuco, Chile.,Department of Computer Science, ETSE, University of Valencia, Valencia, Spain
| | - Martha Powers
- United States Environmental Protection Agency, Washington, DC, USA
| | - Maria Tellez-Plaza
- Integrative Epidemiology Group, Department of Chronic Diseases Epidemiology, National Center for Epidemiology, Carlos III Health Institute, 28029, Madrid, Spain
| | - Karin Haack
- Population Health Program, Texas Biomedical Research Institute, San Antonio, TX, USA
| | - Robert H Brown
- Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, USA
| | - Jason G Umans
- MedStar Health Research Institute, Hyattsville, MD, USA.,Georgetown-Howard Universities Center for Clinical and Translational Science, Washington, DC, USA
| | - M Daniele Fallin
- Departments of Mental Health and Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, USA
| | - Shelley A Cole
- Population Health Program, Texas Biomedical Research Institute, San Antonio, TX, USA
| | - Ana Navas-Acien
- Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, USA
| | - Tiffany R Sanchez
- Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, USA
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Li B, Aouizerat BE, Cheng Y, Anastos K, Justice AC, Zhao H, Xu K. Incorporating local ancestry improves identification of ancestry-associated methylation signatures and meQTLs in African Americans. Commun Biol 2022; 5:401. [PMID: 35488087 PMCID: PMC9054854 DOI: 10.1038/s42003-022-03353-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Accepted: 04/11/2022] [Indexed: 12/03/2022] Open
Abstract
Here we report three epigenome-wide association studies (EWAS) of DNA methylation on self-reported race, global genetic ancestry, and local genetic ancestry in admixed Americans from three sets of samples, including internal and external replications (Ntotal = 1224). Our EWAS on local ancestry (LA) identified the largest number of ancestry-associated DNA methylation sites and also featured the highest replication rate. Furthermore, by incorporating ancestry origins of genetic variations, we identified 36 methylation quantitative trait loci (meQTL) clumps for LA-associated CpGs that cannot be captured by a model that assumes identical genetic effects across ancestry origins. Lead SNPs at 152 meQTL clumps had significantly different genetic effects in the context of an African or European ancestry background. Local ancestry information enables superior capture of ancestry-associated methylation signatures and identification of ancestry-specific genetic effects on DNA methylation. These findings highlight the importance of incorporating local ancestry for EWAS in admixed samples from multi-ancestry cohorts.
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Affiliation(s)
- Boyang Li
- Department of Biostatistics, School of Public Health, Yale University, New Haven, CT, United States
- VA Connecticut Healthcare System, US Department of Veterans Affairs, West Haven, CT, United States
| | - Bradley E Aouizerat
- Bluestone Center for Clinical Research, New York University, New York, NY, United States
- Department of Oral and Maxillofacial Surgery, New York University, New York, NY, United States
| | - Youshu Cheng
- Department of Biostatistics, School of Public Health, Yale University, New Haven, CT, United States
| | - Kathryn Anastos
- Division of General Internal Medicine, Albert Einstein College of Medicine, Montefiore Health System, Bronx, NY, United States
| | - Amy C Justice
- VA Connecticut Healthcare System, US Department of Veterans Affairs, West Haven, CT, United States
- Department of Health Policy and Management, Yale University, New Haven, CT, United States
| | - Hongyu Zhao
- Department of Biostatistics, School of Public Health, Yale University, New Haven, CT, United States.
- VA Connecticut Healthcare System, US Department of Veterans Affairs, West Haven, CT, United States.
| | - Ke Xu
- VA Connecticut Healthcare System, US Department of Veterans Affairs, West Haven, CT, United States.
- Department of Psychiatry, School of Medicine, Yale University, New Haven, CT, United States.
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Xiong Z, Li M, Ma Y, Li R, Bao Y. GMQN: A Reference-Based Method for Correcting Batch Effects and Probe Bias in HumanMethylation BeadChip. Front Genet 2022; 12:810985. [PMID: 35069703 PMCID: PMC8777061 DOI: 10.3389/fgene.2021.810985] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 12/13/2021] [Indexed: 11/13/2022] Open
Abstract
The Illumina HumanMethylation BeadChip is one of the most cost-effective methods to quantify DNA methylation levels at single-base resolution across the human genome, which makes it a routine platform for epigenome-wide association studies. It has accumulated tens of thousands of DNA methylation array samples in public databases, providing great support for data integration and further analysis. However, the majority of public DNA methylation data are deposited as processed data without background probes which are widely used in data normalization. Here, we present Gaussian mixture quantile normalization (GMQN), a reference based method for correcting batch effects as well as probe bias in the HumanMethylation BeadChip. Availability and implementation: https://github.com/MengweiLi-project/gmqn.
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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, China.,CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Mengwei Li
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences/China National Center for Bioinformation, Beijing, China.,CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Yingke Ma
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences/China National Center for Bioinformation, Beijing, China.,CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
| | - Rujiao Li
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences/China National Center for Bioinformation, Beijing, China.,CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
| | - Yiming Bao
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences/China National Center for Bioinformation, Beijing, China.,CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
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Xue Y, Bao Y, Zhang Z, Zhao W, Xiao J, He S, Zhang G, Li Y, Zhao G, Chen R, Zeng J, Zhang Y, Shang Y, Mai J, Shi S, Lu M, Bu C, Zhang Z, Du Z, Xiao J, Wang Y, Kang H, Xu T, Hao L, Bao Y, Jia P, Jiang S, Qian Q, Zhu T, Shang Y, Zong W, Jin T, Zhang Y, Zou D, Bao Y, Xiao J, Zhang Z, Jiang S, Du Q, Feng C, Ma L, Zhang S, Wang A, Dong L, Wang Y, Zou D, Zhang Z, Liu W, Yan X, Ling Y, Zhao G, Zhou Z, Zhang G, Kang W, Jin T, Zhang T, Ma S, Yan H, Liu Z, Ji Z, Cai Y, Wang S, Song M, Ren J, Zhou Q, Qu J, Zhang W, Bao Y, Liu G, Chen X, Chen T, Zhang S, Sun Y, Yu C, Tang B, Zhu J, Dong L, Zhai S, Sun Y, Chen Q, Yang X, Zhang X, Sang Z, Wang Y, Zhao Y, Chen H, Lan L, Wang Y, Zhao W, Ma Y, Jia Y, Zheng X, Chen M, Zhang Y, Zou D, Zhu T, Xu T, Chen M, Niu G, Zong W, Pan R, Jing W, Sang J, Liu C, Xiong Y, Sun Y, Zhai S, Chen H, Zhao W, Xiao J, Bao Y, Hao L, Zhang M, Wang G, Zou D, Yi L, Zhao W, Zong W, Wu S, Xiong Z, Li R, Zong W, Kang H, Xiong Z, Ma Y, Jin T, Gong Z, Yi L, Zhang M, Wu S, Wang G, Li R, Liu L, Li Z, Liu C, Zou D, Li Q, Feng C, Jing W, Luo S, Ma L, Wang J, Shi Y, Zhou H, Zhang P, Song T, Li Y, He S, Xiong Z, Yang F, Li M, Zhao W, Wang G, Li Z, Ma Y, Zou D, Zong W, Kang H, Jia Y, Zheng X, Li R, Tian D, Liu X, Li C, Teng X, Song S, Liu L, Zhang Y, Niu G, Li Q, Li Z, Zhu T, Feng C, Liu X, Zhang Y, Xu T, Chen R, Teng X, Zhang R, Zou D, Ma L, Xu F, Wang Y, Ling Y, Zhou C, Wang H, Teschendorff AE, He Y, Zhang G, Yang Z, Song S, Ma L, Zou D, Tian D, Li C, Zhu J, Li L, Li N, Gong Z, Chen M, Wang A, Ma Y, Teng X, Cui Y, Duan G, Zhang M, Jin T, Wu G, Huang T, Jin E, Zhao W, Kang H, Wang Z, Du Z, Zhang Y, Li R, Zeng J, Hao L, Jiang S, Chen H, Li M, Xiao J, Zhang Z, Zhao W, Xue Y, Bao Y, Ning W, Xue Y, Tang B, Liu Y, Sun Y, Duan G, Cui Y, Zhou Q, Dong L, Jin E, Liu X, Zhang L, Mao B, Zhang S, Zhang Y, Wang G, Zhao W, Wang Z, Zhu Q, Li X, Zhu J, Tian D, Kang H, Li C, Zhang S, Song S, Li M, Zhao W, Liu Y, Wang Z, Luo H, Zhu J, Wu X, Tian D, Li C, Zhao W, Jing H, Zhu J, Tang B, Zou D, Liu L, Pan Y, Liu C, Chen M, Liu X, Zhang Y, Li Z, Feng C, Du Q, Chen R, Zhu T, Ma L, Zou D, Jiang S, Zhang Z, Gong Z, Zhu J, Li C, Jiang S, Ma L, Tang B, Zou D, Chen M, Sun Y, Shi L, Song S, Zhang Z, Li M, Xiao J, Xue Y, Bao Y, Du Z, Zhao W, Li Z, Du Q, Jiang S, Ma L, Zhang Z, Xiong Z, Li M, Zou D, Zong W, Li R, Chen M, Du Z, Zhao W, Bao Y, Ma Y, Zhang X, Lan L, Xue Y, Bao Y, Jiang S, Feng C, Zhao W, Xiao J, Bao Y, Zhang Z, Zuo Z, Ren J, Zhang X, Xiao Y, Li X, Zhang X, Xiao Y, Li X, Liu D, Zhang C, Xue Y, Zhao Z, Jiang T, Wu W, Zhao F, Meng X, Chen M, Peng D, Xue Y, Luo H, Gao F, Ning W, Xue Y, Lin S, Xue Y, Liu C, Guo A, Yuan H, Su T, Zhang YE, Zhou Y, Chen M, Guo G, Fu S, Tan X, Xue Y, Zhang W, Xue Y, Luo M, Guo A, Xie Y, Ren J, Zhou Y, Chen M, Guo G, Wang C, Xue Y, Liao X, Gao X, Wang J, Xie G, Guo A, Yuan C, Chen M, Tian F, Yang D, Gao G, Tang D, Xue Y, Wu W, Chen M, Gou Y, Han C, Xue Y, Cui Q, Li X, Li CY, Luo X, Ren J, Zhang X, Xiao Y, Li X. Database Resources of the National Genomics Data Center, China National Center for Bioinformation in 2022. Nucleic Acids Res 2022; 50:D27-D38. [PMID: 34718731 PMCID: PMC8728233 DOI: 10.1093/nar/gkab951] [Citation(s) in RCA: 305] [Impact Index Per Article: 152.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 09/29/2021] [Accepted: 10/08/2021] [Indexed: 12/21/2022] Open
Abstract
The National Genomics Data Center (NGDC), part of the China National Center for Bioinformation (CNCB), provides a family of database resources to support global research in both academia and industry. With the explosively accumulated multi-omics data at ever-faster rates, CNCB-NGDC is constantly scaling up and updating its core database resources through big data archive, curation, integration and analysis. In the past year, efforts have been made to synthesize the growing data and knowledge, particularly in single-cell omics and precision medicine research, and a series of resources have been newly developed, updated and enhanced. Moreover, CNCB-NGDC has continued to daily update SARS-CoV-2 genome sequences, variants, haplotypes and literature. Particularly, OpenLB, an open library of bioscience, has been established by providing easy and open access to a substantial number of abstract texts from PubMed, bioRxiv and medRxiv. In addition, Database Commons is significantly updated by cataloguing a full list of global databases, and BLAST tools are newly deployed to provide online sequence search services. All these resources along with their services are publicly accessible at https://ngdc.cncb.ac.cn.
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Tost J. Current and Emerging Technologies for the Analysis of the Genome-Wide and Locus-Specific DNA Methylation Patterns. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1389:395-469. [DOI: 10.1007/978-3-031-11454-0_16] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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