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Sahoo K, Sundararajan V. Methods in DNA methylation array dataset analysis: A review. Comput Struct Biotechnol J 2024; 23:2304-2325. [PMID: 38845821 PMCID: PMC11153885 DOI: 10.1016/j.csbj.2024.05.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 04/25/2024] [Accepted: 05/08/2024] [Indexed: 06/09/2024] Open
Abstract
Understanding the intricate relationships between gene expression levels and epigenetic modifications in a genome is crucial to comprehending the pathogenic mechanisms of many diseases. With the advancement of DNA Methylome Profiling techniques, the emphasis on identifying Differentially Methylated Regions (DMRs/DMGs) has become crucial for biomarker discovery, offering new insights into the etiology of illnesses. This review surveys the current state of computational tools/algorithms for the analysis of microarray-based DNA methylation profiling datasets, focusing on key concepts underlying the diagnostic/prognostic CpG site extraction. It addresses methodological frameworks, algorithms, and pipelines employed by various authors, serving as a roadmap to address challenges and understand changing trends in the methodologies for analyzing array-based DNA methylation profiling datasets derived from diseased genomes. Additionally, it highlights the importance of integrating gene expression and methylation datasets for accurate biomarker identification, explores prognostic prediction models, and discusses molecular subtyping for disease classification. The review also emphasizes the contributions of machine learning, neural networks, and data mining to enhance diagnostic workflow development, thereby improving accuracy, precision, and robustness.
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Affiliation(s)
| | - Vino Sundararajan
- Correspondence to: Department of Bio Sciences, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore 632 014, Tamil Nadu, India.
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2
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Yu S, Meng G, Tang W, Ma W, Wang R, Zhu X, Sun X, Feng H. cypress: an R/Bioconductor package for cell-type-specific differential expression analysis power assessment. Bioinformatics 2024; 40:btae511. [PMID: 39153205 PMCID: PMC11357793 DOI: 10.1093/bioinformatics/btae511] [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: 02/26/2024] [Revised: 07/24/2024] [Accepted: 08/13/2024] [Indexed: 08/19/2024] Open
Abstract
SUMMARY Recent methodology advances in computational signal deconvolution have enabled bulk transcriptome data analysis at a finer cell-type level. Through deconvolution, identifying cell-type-specific differentially expressed (csDE) genes is drawing increasing attention in clinical applications. However, researchers still face a number of difficulties in adopting csDE genes detection methods in practice, especially in their experimental design. Here we present cypress, the first experimental design and statistical power analysis tool in csDE genes identification. This tool can reliably model purified cell-type-specific (CTS) profiles, cell-type compositions, biological and technical variations, offering a high-fidelity simulator for bulk RNA-seq convolution and deconvolution. cypress conducts simulation and evaluates the impact of multiple influencing factors, by various statistical metrics, to help researchers optimize experimental design and conduct power analysis. AVAILABILITY AND IMPLEMENTATION cypress is an open-source R/Bioconductor package at https://bioconductor.org/packages/cypress/.
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Affiliation(s)
- Shilin Yu
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH 44106, United States
| | - Guanqun Meng
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH 44106, United States
| | - Wen Tang
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH 44106, United States
| | - Wenjing Ma
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, United States
| | - Rui Wang
- Department of Surgery, Case Western Reserve University, Cleveland, OH 44106, United States
- Division of Surgical Oncology, Department of Surgery, University Hospitals Cleveland Medical Center, Cleveland, OH 44106, United States
| | - Xiongwei Zhu
- Department of Pathology, Case Western Reserve University, Cleveland, OH 44106, United States
| | - Xiaobo Sun
- School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan, Hubei 430073, China
| | - Hao Feng
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH 44106, United States
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3
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Finkelman T, Furman-Haran E, Aberg KC, Paz R, Tal A. Inhibitory mechanisms in the prefrontal-cortex differentially mediate Putamen activity during valence-based learning. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.29.605168. [PMID: 39131397 PMCID: PMC11312490 DOI: 10.1101/2024.07.29.605168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
Learning from appetitive and aversive stimuli is important for survival. It involves interactions between the prefrontal cortex and subcortical structures, with inhibition playing a crucial role. However, direct evidence for this in humans is limited. Here, we overcome the difficulty of measuring inhibition in the human brain and find that GABA, the main inhibitory neurotransmitter, affects how the dACC interacts with subcortical structures during appetitive and aversive learning differently. We used 7T magnetic resonance spectroscopy (MRS) to track GABA levels in the dACC alongside whole-brain fMRI scans while participants engaged in appetitive and aversive learning tasks. During appetitive learning, dACC GABA levels were negatively correlated with learning performance and BOLD activity measured from the dACC and the Putamen. While under aversive learning, dACC GABA concentration negatively correlated with the functional connectivity between the dACC and the Putamen. Our results show that inhibition in the dACC mediates appetitive and aversive learning in humans through distinct mechanisms.
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Affiliation(s)
- Tal Finkelman
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot, Israel
- Department of Chemical and Biological Physics, Weizmann Institute of Science, Rehovot, Israel
| | - Edna Furman-Haran
- Life Sciences Core Facilities, Weizmann Institute of Science, Rehovot, Israel
| | - Kristoffer C Aberg
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Rony Paz
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Assaf Tal
- Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel
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4
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Penny L, Main SC, De Michino SD, Bratman SV. Chromatin- and nucleosome-associated features in liquid biopsy: implications for cancer biomarker discovery. Biochem Cell Biol 2024; 102:291-298. [PMID: 38478957 DOI: 10.1139/bcb-2024-0004] [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] [Indexed: 06/12/2024] Open
Abstract
Cell-free DNA (cfDNA) from the bloodstream has been studied for cancer biomarker discovery, and chromatin-derived epigenetic features have come into the spotlight for their potential to expand clinical applications. Methylation, fragmentation, and nucleosome positioning patterns of cfDNA have previously been shown to reveal epigenomic and inferred transcriptomic information. More recently, histone modifications have emerged as a tool to further identify tumor-specific chromatin variants in plasma. A number of sequencing methods have been developed to analyze these epigenetic markers, offering new insights into tumor biology. Features within cfDNA allow for cancer detection, subtype and tissue of origin classification, and inference of gene expression. These methods provide a window into the complexity of cancer and the dynamic nature of its progression. In this review, we highlight the array of epigenetic features in cfDNA that can be extracted from chromatin- and nucleosome-associated organization and outline potential use cases in cancer management.
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Affiliation(s)
- Lucas Penny
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2C1, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Sasha C Main
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2C1, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Steven D De Michino
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2C1, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Scott V Bratman
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2C1, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, ON M5T 1P5, Canada
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5
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Dai R, Chu T, Zhang M, Wang X, Jourdon A, Wu F, Mariani J, Vaccarino FM, Lee D, Fullard JF, Hoffman GE, Roussos P, Wang Y, Wang X, Pinto D, Wang SH, Zhang C, Chen C, Liu C. Evaluating performance and applications of sample-wise cell deconvolution methods on human brain transcriptomic data. SCIENCE ADVANCES 2024; 10:eadh2588. [PMID: 38781336 PMCID: PMC11114236 DOI: 10.1126/sciadv.adh2588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 01/05/2024] [Indexed: 05/25/2024]
Abstract
Sample-wise deconvolution methods estimate cell-type proportions and gene expressions in bulk tissue samples, yet their performance and biological applications remain unexplored, particularly in human brain transcriptomic data. Here, nine deconvolution methods were evaluated with sample-matched data from bulk tissue RNA sequencing (RNA-seq), single-cell/nuclei (sc/sn) RNA-seq, and immunohistochemistry. A total of 1,130,767 nuclei per cells from 149 adult postmortem brains and 72 organoid samples were used. The results showed the best performance of dtangle for estimating cell proportions and bMIND for estimating sample-wise cell-type gene expressions. For eight brain cell types, 25,273 cell-type eQTLs were identified with deconvoluted expressions (decon-eQTLs). The results showed that decon-eQTLs explained more schizophrenia GWAS heritability than bulk tissue or single-cell eQTLs did alone. Differential gene expressions associated with Alzheimer's disease, schizophrenia, and brain development were also examined using the deconvoluted data. Our findings, which were replicated in bulk tissue and single-cell data, provided insights into the biological applications of deconvoluted data in multiple brain disorders.
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Affiliation(s)
- Rujia Dai
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Tianyao Chu
- MOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Ming Zhang
- MOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Xuan Wang
- MOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
| | | | - Feinan Wu
- Child Study Center, Yale University, New Haven, CT, USA
| | | | - Flora M. Vaccarino
- Child Study Center, Yale University, New Haven, CT, USA
- Department of Neuroscience, Yale University, New Haven, CT, USA
| | - Donghoon Lee
- Center for Disease Neurogenomics, Departments of Psychiatry and Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - John F. Fullard
- Center for Disease Neurogenomics, Departments of Psychiatry and Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Gabriel E. Hoffman
- Center for Disease Neurogenomics, Departments of Psychiatry and Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Panos Roussos
- Center for Disease Neurogenomics, Departments of Psychiatry and Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Yue Wang
- Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA
| | - Xusheng Wang
- Department of Biology, University of North Dakota, Grand Forks, ND, USA
| | - Dalila Pinto
- Departments of Psychiatry and Genetics and Genomic Sciences, Mindich Child Health and Development Institute, and Icahn Genomics Institute for Data Science and Genomic Technology, Seaver Autism Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sidney H. Wang
- Center for Human Genetics, The Brown foundation Institute of Molecular Medicine, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Chunling Zhang
- Department of Neuroscience & Physiology, SUNY Upstate Medical University, Syracuse, NY, USA
| | | | - Chao Chen
- MOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Chunyu Liu
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY, USA
- MOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
- Department of Neuroscience & Physiology, SUNY Upstate Medical University, Syracuse, NY, USA
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Tang C, Sun Q, Zeng X, Yang X, Liu F, Zhao J, Shen Y, Liu B, Wen J, Li Y. Cell-type specific inference from bulk RNA-sequencing data by integrating single cell reference profiles via EPIC-unmix. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.23.595514. [PMID: 38826297 PMCID: PMC11142188 DOI: 10.1101/2024.05.23.595514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Cell type specific (CTS) analysis is essential to reveal biological insights obscured in bulk tissue data. However, single-cell (sc) or single-nuclei (sn) resolution data are still cost-prohibitive for large-scale samples. Thus, computational methods to perform deconvolution from bulk tissue data are highly valuable. We here present EPIC-unmix, a novel two-step empirical Bayesian method integrating reference sc/sn RNA-seq data and bulk RNA-seq data from target samples to enhance the accuracy of CTS inference. We demonstrate through comprehensive simulations across three tissues that EPIC-unmix achieved 4.6% - 109.8% higher accuracy compared to alternative methods. By applying EPIC-unmix to human bulk brain RNA-seq data from the ROSMAP and MSBB cohorts, we identified multiple genes differentially expressed between Alzheimer's disease (AD) cases versus controls in a CTS manner, including 57.4% novel genes not identified using similar sample size sc/snRNA-seq data, indicating the power of our in-silico approach. Among the 6-69% overlapping, 83%-100% are in consistent direction with those from sc/snRNA-seq data, supporting the reliability of our findings. EPIC-unmix inferred CTS expression profiles similarly empowers CTS eQTL analysis. Among the novel eQTLs, we highlight a microglia eQTL for AD risk gene AP3B2, obscured in bulk and missed by sc/snRNA-seq based eQTL analysis. The variant resides in a microglia-specific cCRE, forming chromatin loop with AP3B2 promoter region in microglia. Taken together, we believe EPIC-unmix will be a valuable tool to enable more powerful CTS analysis.
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Affiliation(s)
- Chenwei Tang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Quan Sun
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Xinyue Zeng
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Xiaoyu Yang
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA
| | - Fei Liu
- Department of Pharmacy and Pharmaceutical Sciences, Faculty of Science, National University of Singapore, Singapore
| | - Jinying Zhao
- Department of Epidemiology, College of Public Health & Health Professions and College of Medicine, University of Florida, Gainesville, FL, USA; Center for Genetic Epidemiology and Bioinformatics, University of Florida, Gainesville, FL, USA
| | - Yin Shen
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA
| | - Bixiang Liu
- Department of Pharmacy and Pharmaceutical Sciences, Faculty of Science, National University of Singapore, Singapore
- Department of Biomedical Informatics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Jia Wen
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC
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Cheng B, Wu C, Wei W, Niu H, Wen Y, Li C, Chen P, Chang H, Yang Z, Zhang F. Identification of cell-specific epigenetic patterns associated with chondroitin sulfate treatment response in an endemic arthritis, Kashin-Beck disease. Bone Joint Res 2024; 13:237-246. [PMID: 38754865 PMCID: PMC11098597 DOI: 10.1302/2046-3758.135.bjr-2023-0271.r1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/18/2024] Open
Abstract
Aims To assess the alterations in cell-specific DNA methylation associated with chondroitin sulphate response using peripheral blood collected from Kashin-Beck disease (KBD) patients before initiation of chondroitin sulphate treatment. Methods Peripheral blood samples were collected from KBD patients at baseline of chondroitin sulphate treatment. Methylation profiles were generated using reduced representation bisulphite sequencing (RRBS) from peripheral blood. Differentially methylated regions (DMRs) were identified using MethylKit, while DMR-related genes were defined as those annotated to the gene body or 2.2-kilobase upstream regions of DMRs. Selected DMR-related genes were further validated by quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) to assess expression levels. Tensor composition analysis was performed to identify cell-specific differential DNA methylation from bulk tissue. Results This study revealed 21,060 hypermethylated and 44,472 hypomethylated DMRs, and 13,194 hypermethylated and 22,448 hypomethylated CpG islands for differential global methylation for chondroitin sulphate treatment response. A total of 12,666 DMR-related genes containing DMRs were identified in their promoter regions, such as CHL1 (false discovery rate (FDR) = 2.11 × 10-11), RIC8A (FDR = 7.05 × 10-4), and SOX12 (FDR = 1.43 × 10-3). Additionally, RIC8A and CHL1 were hypermethylated in responders, while SOX12 was hypomethylated in responders, all showing decreased gene expression. The patterns of cell-specific differential global methylation associated with chondroitin sulphate response were observed. Specifically, we found that DMRs located in TESPA1 and ATP11A exhibited differential DNA methylation between responders and non-responders in granulocytes, monocytes, and B cells. Conclusion Our study identified cell-specific changes in DNA methylation associated with chondroitin sulphate response in KBD patients.
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Affiliation(s)
- Bolun Cheng
- Key Laboratory of Trace Elements and Endemic Diseases (Xi'an Jiaotong University), National Health and Family Planning Commission, Xi'an, China
- Key Laboratory of Environment and Genes Related to Diseases (Xi'an Jiaotong University), Ministry of Education, Xi'an, China
- Collaborative Innovation Center of Endemic Disease and Health Promotion for Silk Road Region, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Cuiyan Wu
- Key Laboratory of Trace Elements and Endemic Diseases (Xi'an Jiaotong University), National Health and Family Planning Commission, Xi'an, China
- Key Laboratory of Environment and Genes Related to Diseases (Xi'an Jiaotong University), Ministry of Education, Xi'an, China
- Collaborative Innovation Center of Endemic Disease and Health Promotion for Silk Road Region, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Wenming Wei
- Key Laboratory of Trace Elements and Endemic Diseases (Xi'an Jiaotong University), National Health and Family Planning Commission, Xi'an, China
- Key Laboratory of Environment and Genes Related to Diseases (Xi'an Jiaotong University), Ministry of Education, Xi'an, China
- Collaborative Innovation Center of Endemic Disease and Health Promotion for Silk Road Region, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Hui Niu
- Key Laboratory of Trace Elements and Endemic Diseases (Xi'an Jiaotong University), National Health and Family Planning Commission, Xi'an, China
- Key Laboratory of Environment and Genes Related to Diseases (Xi'an Jiaotong University), Ministry of Education, Xi'an, China
- Collaborative Innovation Center of Endemic Disease and Health Promotion for Silk Road Region, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Yan Wen
- Key Laboratory of Trace Elements and Endemic Diseases (Xi'an Jiaotong University), National Health and Family Planning Commission, Xi'an, China
- Key Laboratory of Environment and Genes Related to Diseases (Xi'an Jiaotong University), Ministry of Education, Xi'an, China
- Collaborative Innovation Center of Endemic Disease and Health Promotion for Silk Road Region, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Cheng Li
- Research Laboratory of Kashin-Beck Disease and Keshan Disease, Shaanxi Institute for Endemic Disease Prevention and Control, Xi'an, China
| | - Ping Chen
- Research Laboratory of Kashin-Beck Disease and Keshan Disease, Shaanxi Institute for Endemic Disease Prevention and Control, Xi'an, China
| | - Hong Chang
- Research Laboratory of Kashin-Beck Disease and Keshan Disease, Shaanxi Institute for Endemic Disease Prevention and Control, Xi'an, China
| | - Zhengjun Yang
- Research Laboratory of Kashin-Beck Disease and Keshan Disease, Shaanxi Institute for Endemic Disease Prevention and Control, Xi'an, China
| | - Feng Zhang
- Key Laboratory of Trace Elements and Endemic Diseases (Xi'an Jiaotong University), National Health and Family Planning Commission, Xi'an, China
- Key Laboratory of Environment and Genes Related to Diseases (Xi'an Jiaotong University), Ministry of Education, Xi'an, China
- Collaborative Innovation Center of Endemic Disease and Health Promotion for Silk Road Region, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
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Eulalio T, Sun MW, Gevaert O, Greicius MD, Montine TJ, Nachun D, Montgomery SB. regionalpcs: improved discovery of DNA methylation associations with complex traits. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.01.590171. [PMID: 38746367 PMCID: PMC11092597 DOI: 10.1101/2024.05.01.590171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
We have developed the regional principal components (rPCs) method, a novel approach for summarizing gene-level methylation. rPCs address the challenge of deciphering complex epigenetic mechanisms in diseases like Alzheimer's disease (AD). In contrast to traditional averaging, rPCs leverage principal components analysis to capture complex methylation patterns across gene regions. Our method demonstrated a 54% improvement in sensitivity over averaging in simulations, offering a robust framework for identifying subtle epigenetic variations. Applying rPCs to the AD brain methylation data in ROSMAP, combined with cell type deconvolution, we uncovered 838 differentially methylated genes associated with neuritic plaque burden-significantly outperforming conventional methods. Integrating methylation quantitative trait loci (meQTL) with genome-wide association studies (GWAS) identified 17 genes with potential causal roles in AD, including MS4A4A and PICALM. Our approach is available in the Bioconductor package regionalpcs, opening avenues for research and facilitating a deeper understanding of the epigenetic landscape in complex diseases.
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Affiliation(s)
- Tiffany Eulalio
- Department of Biomedical Data Science, Stanford University, Stanford, CA, 94305, USA
| | - Min Woo Sun
- Department of Biomedical Data Science, Stanford University, Stanford, CA, 94305, USA
| | - Olivier Gevaert
- Department of Biomedical Data Science, Stanford University, Stanford, CA, 94305, USA
| | - Michael D Greicius
- Department of Neurology & Neurological Sciences, Stanford University, Stanford, CA, 94305, USA
| | - Thomas J Montine
- Department of Pathology, Stanford University, Stanford, CA, 94305, USA
| | - Daniel Nachun
- Department of Pathology, Stanford University, Stanford, CA, 94305, USA
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9
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Lee MK, Azizgolshani N, Zhang Z, Perreard L, Kolling FW, Nguyen LN, Zanazzi GJ, Salas LA, Christensen BC. Associations in cell type-specific hydroxymethylation and transcriptional alterations of pediatric central nervous system tumors. Nat Commun 2024; 15:3635. [PMID: 38688903 PMCID: PMC11061294 DOI: 10.1038/s41467-024-47943-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2023] [Accepted: 04/16/2024] [Indexed: 05/02/2024] Open
Abstract
Although intratumoral heterogeneity has been established in pediatric central nervous system tumors, epigenomic alterations at the cell type level have largely remained unresolved. To identify cell type-specific alterations to cytosine modifications in pediatric central nervous system tumors, we utilize a multi-omic approach that integrated bulk DNA cytosine modification data (methylation and hydroxymethylation) with both bulk and single-cell RNA-sequencing data. We demonstrate a large reduction in the scope of significantly differentially modified cytosines in tumors when accounting for tumor cell type composition. In the progenitor-like cell types of tumors, we identify a preponderance differential Cytosine-phosphate-Guanine site hydroxymethylation rather than methylation. Genes with differential hydroxymethylation, like histone deacetylase 4 and insulin-like growth factor 1 receptor, are associated with cell type-specific changes in gene expression in tumors. Our results highlight the importance of epigenomic alterations in the progenitor-like cell types and its role in cell type-specific transcriptional regulation in pediatric central nervous system tumors.
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Affiliation(s)
- Min Kyung Lee
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA.
| | - Nasim Azizgolshani
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
- Department of Surgery, Columbia University Medical Center, New York, NY, USA
| | - Ze Zhang
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
| | - Laurent Perreard
- Dartmouth Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
| | - Fred W Kolling
- Dartmouth Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
| | - Lananh N Nguyen
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
| | - George J Zanazzi
- Dartmouth Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
- Department of Pathology and Laboratory Medicine, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
| | - Lucas A Salas
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
| | - Brock C Christensen
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA.
- Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA.
- Department of Community and Family Medicine, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA.
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10
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Meng G, Pan Y, Tang W, Zhang L, Cui Y, Schumacher FR, Wang M, Wang R, He S, Krischer J, Li Q, Feng H. imply: improving cell-type deconvolution accuracy using personalized reference profiles. Genome Med 2024; 16:65. [PMID: 38685057 PMCID: PMC11057104 DOI: 10.1186/s13073-024-01338-z] [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: 09/26/2023] [Accepted: 04/18/2024] [Indexed: 05/02/2024] Open
Abstract
Using computational tools, bulk transcriptomics can be deconvoluted to estimate the abundance of constituent cell types. However, existing deconvolution methods are conditioned on the assumption that the whole study population is served by a single reference panel, ignoring person-to-person heterogeneity. Here, we present imply, a novel algorithm to deconvolute cell type proportions using personalized reference panels. Simulation studies demonstrate reduced bias compared with existing methods. Real data analyses on longitudinal consortia show disparities in cell type proportions are associated with several disease phenotypes in Type 1 diabetes and Parkinson's disease. imply is available through the R/Bioconductor package ISLET at https://bioconductor.org/packages/ISLET/ .
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Affiliation(s)
- Guanqun Meng
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA
| | - Yue Pan
- Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, 38105, TN, USA
| | - Wen Tang
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA
| | - Lijun Zhang
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA
| | - Ying Cui
- Department of Biomedical Data Science, Stanford University, Stanford, 94305, CA, USA
| | - Fredrick R Schumacher
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA
| | - Ming Wang
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA
| | - Rui Wang
- Department of Surgery, Division of Surgical Oncology, University Hospitals Cleveland Medical Center, Cleveland, 44106, OH, USA
| | - Sijia He
- Department of Biostatistics, University of Michigan, Ann Arbor, 48109, MI, USA
| | - Jeffrey Krischer
- Health Informatics Institute, University of South Florida, Tampa, 38105, FL, USA
| | - Qian Li
- Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, 38105, TN, USA.
| | - Hao Feng
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA.
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Huuki-Myers LA, Montgomery KD, Kwon SH, Cinquemani S, Eagles NJ, Gonzalez-Padilla D, Maden SK, Kleinman JE, Hyde TM, Hicks SC, Maynard KR, Collado-Torres L. Benchmark of cellular deconvolution methods using a multi-assay reference dataset from postmortem human prefrontal cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.09.579665. [PMID: 38405805 PMCID: PMC10888823 DOI: 10.1101/2024.02.09.579665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
Background Cellular deconvolution of bulk RNA-sequencing (RNA-seq) data using single cell or nuclei RNA-seq (sc/snRNA-seq) reference data is an important strategy for estimating cell type composition in heterogeneous tissues, such as human brain. Computational methods for deconvolution have been developed and benchmarked against simulated data, pseudobulked sc/snRNA-seq data, or immunohistochemistry reference data. A major limitation in developing improved deconvolution algorithms has been the lack of integrated datasets with orthogonal measurements of gene expression and estimates of cell type proportions on the same tissue sample. Deconvolution algorithm performance has not yet been evaluated across different RNA extraction methods (cytosolic, nuclear, or whole cell RNA), different library preparation types (mRNA enrichment vs. ribosomal RNA depletion), or with matched single cell reference datasets. Results A rich multi-assay dataset was generated in postmortem human dorsolateral prefrontal cortex (DLPFC) from 22 tissue blocks. Assays included spatially-resolved transcriptomics, snRNA-seq, bulk RNA-seq (across six library/extraction RNA-seq combinations), and RNAScope/Immunofluorescence (RNAScope/IF) for six broad cell types. The Mean Ratio method, implemented in the DeconvoBuddies R package, was developed for selecting cell type marker genes. Six computational deconvolution algorithms were evaluated in DLPFC and predicted cell type proportions were compared to orthogonal RNAScope/IF measurements. Conclusions Bisque and hspe were the most accurate methods, were robust to differences in RNA library types and extractions. This multi-assay dataset showed that cell size differences, marker genes differentially quantified across RNA libraries, and cell composition variability in reference snRNA-seq impact the accuracy of current deconvolution methods.
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Affiliation(s)
- Louise A. Huuki-Myers
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
| | - Kelsey D. Montgomery
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
| | - Sang Ho Kwon
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
| | - Sophia Cinquemani
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
| | - Nicholas J. Eagles
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
| | | | - Sean K. Maden
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA
| | - Joel E. Kleinman
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
| | - Thomas M. Hyde
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
| | - Stephanie C. Hicks
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, 21205, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21205, USA
- Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Kristen R. Maynard
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
| | - Leonardo Collado-Torres
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, 21205, USA
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12
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Ferro dos Santos MR, Giuili E, De Koker A, Everaert C, De Preter K. Computational deconvolution of DNA methylation data from mixed DNA samples. Brief Bioinform 2024; 25:bbae234. [PMID: 38762790 PMCID: PMC11102637 DOI: 10.1093/bib/bbae234] [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: 02/07/2024] [Revised: 03/30/2024] [Accepted: 04/30/2024] [Indexed: 05/20/2024] Open
Abstract
In this review, we provide a comprehensive overview of the different computational tools that have been published for the deconvolution of bulk DNA methylation (DNAm) data. Here, deconvolution refers to the estimation of cell-type proportions that constitute a mixed sample. The paper reviews and compares 25 deconvolution methods (supervised, unsupervised or hybrid) developed between 2012 and 2023 and compares the strengths and limitations of each approach. Moreover, in this study, we describe the impact of the platform used for the generation of methylation data (including microarrays and sequencing), the applied data pre-processing steps and the used reference dataset on the deconvolution performance. Next to reference-based methods, we also examine methods that require only partial reference datasets or require no reference set at all. In this review, we provide guidelines for the use of specific methods dependent on the DNA methylation data type and data availability.
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Affiliation(s)
- Maísa R Ferro dos Santos
- VIB-UGent Center for Medical Biotechnology (CMB), Technologiepark-Zwijnaarde 75, 9052 Zwijnaarde, Belgium
- Cancer Research Institute Ghent (CRIG), 9000 Ghent, Belgium
| | - Edoardo Giuili
- VIB-UGent Center for Medical Biotechnology (CMB), Technologiepark-Zwijnaarde 75, 9052 Zwijnaarde, Belgium
- Cancer Research Institute Ghent (CRIG), 9000 Ghent, Belgium
| | - Andries De Koker
- VIB-UGent Center for Medical Biotechnology (CMB), Technologiepark-Zwijnaarde 75, 9052 Zwijnaarde, Belgium
- Cancer Research Institute Ghent (CRIG), 9000 Ghent, Belgium
| | - Celine Everaert
- VIB-UGent Center for Medical Biotechnology (CMB), Technologiepark-Zwijnaarde 75, 9052 Zwijnaarde, Belgium
- Cancer Research Institute Ghent (CRIG), 9000 Ghent, Belgium
| | - Katleen De Preter
- VIB-UGent Center for Medical Biotechnology (CMB), Technologiepark-Zwijnaarde 75, 9052 Zwijnaarde, Belgium
- Cancer Research Institute Ghent (CRIG), 9000 Ghent, Belgium
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13
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Youssef A, Paul I, Crovella M, Emili A. DESP demixes cell-state profiles from dynamic bulk molecular measurements. CELL REPORTS METHODS 2024; 4:100729. [PMID: 38490205 PMCID: PMC10985230 DOI: 10.1016/j.crmeth.2024.100729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 12/22/2023] [Accepted: 02/16/2024] [Indexed: 03/17/2024]
Abstract
Understanding the dynamic expression of proteins and other key molecules driving phenotypic remodeling in development and pathobiology has garnered widespread interest, yet the exploration of these systems at the foundational resolution of the underlying cell states has been significantly limited by technical constraints. Here, we present DESP, an algorithm designed to leverage independent estimates of cell-state proportions, such as from single-cell RNA sequencing, to resolve the relative contributions of cell states to bulk molecular measurements, most notably quantitative proteomics, recorded in parallel. We applied DESP to an in vitro model of the epithelial-to-mesenchymal transition and demonstrated its ability to accurately reconstruct cell-state signatures from bulk-level measurements of both the proteome and transcriptome, providing insights into transient regulatory mechanisms. DESP provides a generalizable computational framework for modeling the relationship between bulk and single-cell molecular measurements, enabling the study of proteomes and other molecular profiles at the cell-state level using established bulk-level workflows.
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Affiliation(s)
- Ahmed Youssef
- Graduate Program in Bioinformatics, Boston University, Boston, MA, USA; Center for Network Systems Biology, Boston University, Boston, MA, USA
| | - Indranil Paul
- Center for Network Systems Biology, Boston University, Boston, MA, USA
| | - Mark Crovella
- Graduate Program in Bioinformatics, Boston University, Boston, MA, USA; Computer Science Department, Boston University, Boston, MA, USA; Faculty of Computing and Data Sciences, Boston University, Boston, MA, USA.
| | - Andrew Emili
- Graduate Program in Bioinformatics, Boston University, Boston, MA, USA; Center for Network Systems Biology, Boston University, Boston, MA, USA; Faculty of Computing and Data Sciences, Boston University, Boston, MA, USA; Knight Cancer Institute, Oregon Health and Science University, Portland, OR, USA.
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14
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Wu CT, Du D, Chen L, Dai R, Liu C, Yu G, Bhardwaj S, Parker SJ, Zhang Z, Clarke R, Herrington DM, Wang Y. CAM3.0: determining cell type composition and expression from bulk tissues with fully unsupervised deconvolution. Bioinformatics 2024; 40:btae107. [PMID: 38407991 PMCID: PMC10924278 DOI: 10.1093/bioinformatics/btae107] [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: 04/03/2023] [Revised: 01/13/2024] [Accepted: 02/25/2024] [Indexed: 02/28/2024] Open
Abstract
MOTIVATION Complex tissues are dynamic ecosystems consisting of molecularly distinct yet interacting cell types. Computational deconvolution aims to dissect bulk tissue data into cell type compositions and cell-specific expressions. With few exceptions, most existing deconvolution tools exploit supervised approaches requiring various types of references that may be unreliable or even unavailable for specific tissue microenvironments. RESULTS We previously developed a fully unsupervised deconvolution method-Convex Analysis of Mixtures (CAM), that enables estimation of cell type composition and expression from bulk tissues. We now introduce CAM3.0 tool that improves this framework with three new and highly efficient algorithms, namely, radius-fixed clustering to identify reliable markers, linear programming to detect an initial scatter simplex, and a smart floating search for the optimum latent variable model. The comparative experimental results obtained from both realistic simulations and case studies show that the CAM3.0 tool can help biologists more accurately identify known or novel cell markers, determine cell proportions, and estimate cell-specific expressions, complementing the existing tools particularly when study- or datatype-specific references are unreliable or unavailable. AVAILABILITY AND IMPLEMENTATION The open-source R Scripts of CAM3.0 is freely available at https://github.com/ChiungTingWu/CAM3/(https://github.com/Bioconductor/Contributions/issues/3205). A user's guide and a vignette are provided.
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Affiliation(s)
- Chiung-Ting Wu
- Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, United States
| | - Dongping Du
- Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, United States
| | - Lulu Chen
- Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, United States
| | - Rujia Dai
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY 13210, United States
| | - Chunyu Liu
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY 13210, United States
| | - Guoqiang Yu
- Department of Automation, Tsinghua University, Beijing 100084, P. R. China
| | - Saurabh Bhardwaj
- Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, United States
- Department of Electrical and Instrumentation Engineering, Thapar Institute of Engineering & Technology, Punjab 147004, India
| | - Sarah J Parker
- Advanced Clinical Biosystems Research Institute, Cedars Sinai Medical Center, Los Angeles, CA 90048, United States
| | - Zhen Zhang
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21231, United States
| | - Robert Clarke
- The Hormel Institute, University of Minnesota, Austin, MN 55912, United States
| | - David M Herrington
- Department of Internal Medicine, Wake Forest University, Winston-Salem, NC 27157, United States
| | - Yue Wang
- Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, United States
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15
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Zhang X, Hu Y, Vandenhoudt RE, Yan C, Marconi VC, Cohen MH, Wang Z, Justice AC, Aouizerat BE, Xu K. Computationally inferred cell-type specific epigenome-wide DNA methylation analysis unveils distinct methylation patterns among immune cells for HIV infection in three cohorts. PLoS Pathog 2024; 20:e1012063. [PMID: 38466776 PMCID: PMC10957090 DOI: 10.1371/journal.ppat.1012063] [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: 10/11/2023] [Revised: 03/21/2024] [Accepted: 02/20/2024] [Indexed: 03/13/2024] Open
Abstract
BACKGROUND Epigenome-wide association studies (EWAS) have identified CpG sites associated with HIV infection in blood cells in bulk, which offer limited knowledge of cell-type specific methylation patterns associated with HIV infection. In this study, we aim to identify differentially methylated CpG sites for HIV infection in immune cell types: CD4+ T-cells, CD8+ T-cells, B cells, Natural Killer (NK) cells, and monocytes. METHODS Applying a computational deconvolution method, we performed a cell-type based EWAS for HIV infection in three independent cohorts (Ntotal = 1,382). DNA methylation in blood or in peripheral blood mononuclear cells (PBMCs) was profiled by an array-based method and then deconvoluted by Tensor Composition Analysis (TCA). The TCA-computed CpG methylation in each cell type was first benchmarked by bisulfite DNA methylation capture sequencing in a subset of the samples. Cell-type EWAS of HIV infection was performed in each cohort separately and a meta-EWAS was conducted followed by gene set enrichment analysis. RESULTS The meta-analysis unveiled a total of 2,021 cell-type unique significant CpG sites for five inferred cell types. Among these inferred cell-type unique CpG sites, the concordance rate in the three cohorts ranged from 96% to 100% in each cell type. Cell-type level meta-EWAS unveiled distinct patterns of HIV-associated differential CpG methylation, where 74% of CpG sites were unique to individual cell types (false discovery rate, FDR <0.05). CD4+ T-cells had the largest number of unique HIV-associated CpG sites (N = 1,624) compared to any other cell type. Genes harboring significant CpG sites are involved in immunity and HIV pathogenesis (e.g. CD4+ T-cells: NLRC5, CX3CR1, B cells: IFI44L, NK cells: IL12R, monocytes: IRF7), and in oncogenesis (e.g. CD4+ T-cells: BCL family, PRDM16, monocytes: PRDM16, PDCD1LG2). HIV-associated CpG sites were enriched among genes involved in HIV pathogenesis and oncogenesis that were enriched among interferon-α and -γ, TNF-α, inflammatory response, and apoptotic pathways. CONCLUSION Our findings uncovered computationally inferred cell-type specific modifications in the host epigenome for people with HIV that contribute to the growing body of evidence regarding HIV pathogenesis.
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Affiliation(s)
- Xinyu Zhang
- Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut, United States of America
- VA Connecticut Healthcare System, West Haven, Connecticut, United States of America
| | - Ying Hu
- Center for Biomedical Information and Information Technology, National Cancer Institute, Rockville, Maryland, United States of America
| | - Ral E. Vandenhoudt
- Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut, United States of America
- VA Connecticut Healthcare System, West Haven, Connecticut, United States of America
| | - Chunhua Yan
- Center for Biomedical Information and Information Technology, National Cancer Institute, Rockville, Maryland, United States of America
| | - Vincent C. Marconi
- Division of Infectious Diseases, Emory University School of Medicine and Department of Global Health, Rollins School of Public Health, Emory University, Georgia, United States of America
- Atlanta Veterans Affairs Healthcare System, Decatur, Georgia, United States of America
| | - Mardge H. Cohen
- Department of Medicine, Stroger Hospital of Cook County, Chicago, Illinois, United States of America
| | - Zuoheng Wang
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, United States of America
| | - Amy C. Justice
- VA Connecticut Healthcare System, West Haven, Connecticut, United States of America
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, United States of America
- Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, United States of America
| | - Bradley E. Aouizerat
- Translational Research Center, College of Dentistry, New York University, New York, New York, United States of America
- Department of Oral and Maxillofacial Surgery, College of Dentistry, New York University, New York, New York, United States of America
| | - Ke Xu
- Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut, United States of America
- VA Connecticut Healthcare System, West Haven, Connecticut, United States of America
- Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, Connecticut, United States of America
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16
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Lee JU, Chang HS, Shim JS, Kim MH, Cho YJ, Kim MK, Park SL, Lee SJ, Park JS, Park CS. Aspirin Challenge-Induced Genome-Wide DNA Methylation Profile of Peripheral Blood Lymphocytes in Aspirin-Exacerbated Respiratory Disease. DNA Cell Biol 2024; 43:132-140. [PMID: 38386995 DOI: 10.1089/dna.2023.0218] [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] [Indexed: 02/24/2024] Open
Abstract
Genetic variation and epigenetic factors are thought to contribute to the development of hypersensitivity to aspirin. DNA methylation fluctuates dynamically throughout the day. To discover new CpG methylation in lymphocytes associated with aspirin-exacerbated respiratory disease (AERD), we evaluated changes in global CpG methylation profiles from before to after an oral aspirin challenge in patients with AERD and aspirin-tolerant asthma (ATA). Whole-genome CpG methylation levels of peripheral blood mononuclear cells were quantified with an Illumina 860K Infinium Methylation EPIC BeadChip array and then adjusted for inferred lymphocyte fraction (ILF) with GLINT and Tensor Composition Analysis. Among the 866,091 CpGs in the array, differentially methylated CpGs (DMCs) were found in 6 CpGs in samples from all 12 patients with asthma included in the study (AERD, n = 6; ATA, n = 6). DMCs were found in 3 CpGs in the 6 ATA samples and in 615 CpGs in the 6 AERD samples. A total of 663 DMCs in 415 genes and 214 intergenic regions differed significantly in the AERD compared with the ATA. In promoters, 126 CpG loci were predicted to bind to 38 transcription factors (TFs), many of which were factors already known to be involved in the pathogenesis of asthma and immune responses. In conclusion, we identified 615 new CpGs methylated in peripheral blood lymphocytes by oral aspirin challenge in AERD but not in ATA. These findings indicate that oral aspirin challenge induces epigenetic changes in ILFs, specifically in AERD patients, possibly via changes in TF binding, which may have epigenetic effects on the development of AERD.
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Affiliation(s)
- Jong-Uk Lee
- Division of Allergy and Respiratory Medicine, Department of Internal Medicine, Soonchunhyang University Bucheon Hospital, Bucheon, Korea
| | - Hun Soo Chang
- Department of Microbiology and BK21 FOUR Project, College of Medicine, Soonchunhyang University, Cheonan, Korea
| | - Ji-Su Shim
- Department of Internal Medicine, Ewha Womans University College of Medicine, Seoul, Korea
| | - Min-Hye Kim
- Department of Internal Medicine, Ewha Womans University College of Medicine, Seoul, Korea
| | - Young-Joo Cho
- Department of Internal Medicine, Ewha Womans University College of Medicine, Seoul, Korea
| | - Min Kyung Kim
- Department of Interdisciplinary Program in Biomedical Science Major, Soonchuhyang University, Asan, Korea
| | - Seung-Lee Park
- Department of Interdisciplinary Program in Biomedical Science Major, Soonchuhyang University, Asan, Korea
| | - Sun Ju Lee
- Department of Interdisciplinary Program in Biomedical Science Major, Soonchuhyang University, Asan, Korea
| | - Jong-Sook Park
- Division of Allergy and Respiratory Medicine, Department of Internal Medicine, Soonchunhyang University Bucheon Hospital, Bucheon, Korea
| | - Choon-Sik Park
- Division of Allergy and Respiratory Medicine, Department of Internal Medicine, Soonchunhyang University Bucheon Hospital, Bucheon, Korea
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17
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Binder MD, Nwoke EC, Morwitch E, Dwyer C, Li V, Xavier A, Lea RA, Lechner-Scott J, Taylor BV, Ponsonby AL, Kilpatrick TJ. HLA-DRB1*15:01 and the MERTK Gene Interact to Selectively Influence the Profile of MERTK-Expressing Monocytes in Both Health and MS. NEUROLOGY(R) NEUROIMMUNOLOGY & NEUROINFLAMMATION 2024; 11:e200190. [PMID: 38150649 PMCID: PMC10752576 DOI: 10.1212/nxi.0000000000200190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 10/31/2023] [Indexed: 12/29/2023]
Abstract
BACKGROUND AND OBJECTIVES HLA-DRB1*15:01 (DR15) and MERTK are 2 risk genes for multiple sclerosis (MS). The variant rs7422195 is an expression quantitative trait locus for MERTK in CD14+ monocytes; cells with phagocytic and immunomodulatory potential. We aimed to understand how drivers of disease risk and pathogenesis vary with HLA and MERTK genotype and disease activity. METHODS We investigated how proportions of monocytes vary with HLA and MERTK genotype and disease activity in MS. CD14+ monocytes were isolated from patients with MS at relapse (n = 40) and 3 months later (n = 23). Healthy controls (HCs) underwent 2 blood collections 3 months apart. Immunophenotypic profiling of monocytes was performed by flow cytometry. Methylation of 35 CpG sites within and near the MERTK gene was assessed in whole blood samples of individuals experiencing their first episode of clinical CNS demyelination (n = 204) and matched HCs (n = 345) using an Illumina EPIC array. RESULTS DR15-positive patients had lower proportions of CD14+ MERTK+ monocytes than DR15-negative patients, independent of genotype at the MERTK SNP rs7422195. Proportions of CD14+ MERTK+ monocytes were further reduced during relapse in DR15-positive but not DR15-negative patients. Patients homozygous for the major G allele at rs7422195 exhibited higher proportions of CD14+ MERTK+ monocytes at both relapse and remission compared with controls. We observed that increased methylation of the MERTK gene was significantly associated with the presence of DR15. DISCUSSION DR15 and MERTK genotype independently influence proportions of CD14+ MERTK+ monocytes in MS. We confirmed previous observations that the MERTK risk SNP rs7422195 is associated with altered MERTK expression in monocytes. We identified that expression of MERTK is stratified by disease in people homozygous for the major G allele of rs7422195. The finding that the proportion of CD14+ MERTK+ monocytes is reduced in DR15-positive individuals supports prior data identifying genetic links between these 2 loci in influencing MS risk. DR15 genotype-dependent alterations in methylation of the MERTK gene provides a molecular link between these loci and identifies a potential mechanism by which MERTK expression is influenced by DR15. This links DR15 haplotype to MS susceptibility beyond direct influence on antigen presentation and suggests the need for HLA-based stratification of approaches to MERTK as a therapeutic target.
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Affiliation(s)
- Michele D Binder
- From the Florey Institute of Neuroscience and Mental Health (M.D.B., E.C.N., E.M., C.D., V.L., A.-L.P., T.J.K.); Department of Anatomy and Physiology (M.D.B.), University of Melbourne, Parkville; Crux Biolabs (E.C.N.), Bayswater; Department of Neurology (C.D.), Royal Melbourne Hospital, Parkville; Department of Neurology (A.X., J.L.-S.), John Hunter Hospital, Newcastle; Hunter Medical Research Institute (A.X., J.L.-S.), University of Newcastle, New South Wales Genomics Research Centre (R.A.L.), Centre of Genomics and Personalised Health, Queensland University of Technology; and Menzies Institute for Medical Research (B.V.T.), University of Tasmania, Hobart, Australia
| | - Eze C Nwoke
- From the Florey Institute of Neuroscience and Mental Health (M.D.B., E.C.N., E.M., C.D., V.L., A.-L.P., T.J.K.); Department of Anatomy and Physiology (M.D.B.), University of Melbourne, Parkville; Crux Biolabs (E.C.N.), Bayswater; Department of Neurology (C.D.), Royal Melbourne Hospital, Parkville; Department of Neurology (A.X., J.L.-S.), John Hunter Hospital, Newcastle; Hunter Medical Research Institute (A.X., J.L.-S.), University of Newcastle, New South Wales Genomics Research Centre (R.A.L.), Centre of Genomics and Personalised Health, Queensland University of Technology; and Menzies Institute for Medical Research (B.V.T.), University of Tasmania, Hobart, Australia
| | - Ellen Morwitch
- From the Florey Institute of Neuroscience and Mental Health (M.D.B., E.C.N., E.M., C.D., V.L., A.-L.P., T.J.K.); Department of Anatomy and Physiology (M.D.B.), University of Melbourne, Parkville; Crux Biolabs (E.C.N.), Bayswater; Department of Neurology (C.D.), Royal Melbourne Hospital, Parkville; Department of Neurology (A.X., J.L.-S.), John Hunter Hospital, Newcastle; Hunter Medical Research Institute (A.X., J.L.-S.), University of Newcastle, New South Wales Genomics Research Centre (R.A.L.), Centre of Genomics and Personalised Health, Queensland University of Technology; and Menzies Institute for Medical Research (B.V.T.), University of Tasmania, Hobart, Australia
| | - Chris Dwyer
- From the Florey Institute of Neuroscience and Mental Health (M.D.B., E.C.N., E.M., C.D., V.L., A.-L.P., T.J.K.); Department of Anatomy and Physiology (M.D.B.), University of Melbourne, Parkville; Crux Biolabs (E.C.N.), Bayswater; Department of Neurology (C.D.), Royal Melbourne Hospital, Parkville; Department of Neurology (A.X., J.L.-S.), John Hunter Hospital, Newcastle; Hunter Medical Research Institute (A.X., J.L.-S.), University of Newcastle, New South Wales Genomics Research Centre (R.A.L.), Centre of Genomics and Personalised Health, Queensland University of Technology; and Menzies Institute for Medical Research (B.V.T.), University of Tasmania, Hobart, Australia
| | - Vivien Li
- From the Florey Institute of Neuroscience and Mental Health (M.D.B., E.C.N., E.M., C.D., V.L., A.-L.P., T.J.K.); Department of Anatomy and Physiology (M.D.B.), University of Melbourne, Parkville; Crux Biolabs (E.C.N.), Bayswater; Department of Neurology (C.D.), Royal Melbourne Hospital, Parkville; Department of Neurology (A.X., J.L.-S.), John Hunter Hospital, Newcastle; Hunter Medical Research Institute (A.X., J.L.-S.), University of Newcastle, New South Wales Genomics Research Centre (R.A.L.), Centre of Genomics and Personalised Health, Queensland University of Technology; and Menzies Institute for Medical Research (B.V.T.), University of Tasmania, Hobart, Australia
| | - Alexandre Xavier
- From the Florey Institute of Neuroscience and Mental Health (M.D.B., E.C.N., E.M., C.D., V.L., A.-L.P., T.J.K.); Department of Anatomy and Physiology (M.D.B.), University of Melbourne, Parkville; Crux Biolabs (E.C.N.), Bayswater; Department of Neurology (C.D.), Royal Melbourne Hospital, Parkville; Department of Neurology (A.X., J.L.-S.), John Hunter Hospital, Newcastle; Hunter Medical Research Institute (A.X., J.L.-S.), University of Newcastle, New South Wales Genomics Research Centre (R.A.L.), Centre of Genomics and Personalised Health, Queensland University of Technology; and Menzies Institute for Medical Research (B.V.T.), University of Tasmania, Hobart, Australia
| | - Rodney A Lea
- From the Florey Institute of Neuroscience and Mental Health (M.D.B., E.C.N., E.M., C.D., V.L., A.-L.P., T.J.K.); Department of Anatomy and Physiology (M.D.B.), University of Melbourne, Parkville; Crux Biolabs (E.C.N.), Bayswater; Department of Neurology (C.D.), Royal Melbourne Hospital, Parkville; Department of Neurology (A.X., J.L.-S.), John Hunter Hospital, Newcastle; Hunter Medical Research Institute (A.X., J.L.-S.), University of Newcastle, New South Wales Genomics Research Centre (R.A.L.), Centre of Genomics and Personalised Health, Queensland University of Technology; and Menzies Institute for Medical Research (B.V.T.), University of Tasmania, Hobart, Australia
| | - Jeannette Lechner-Scott
- From the Florey Institute of Neuroscience and Mental Health (M.D.B., E.C.N., E.M., C.D., V.L., A.-L.P., T.J.K.); Department of Anatomy and Physiology (M.D.B.), University of Melbourne, Parkville; Crux Biolabs (E.C.N.), Bayswater; Department of Neurology (C.D.), Royal Melbourne Hospital, Parkville; Department of Neurology (A.X., J.L.-S.), John Hunter Hospital, Newcastle; Hunter Medical Research Institute (A.X., J.L.-S.), University of Newcastle, New South Wales Genomics Research Centre (R.A.L.), Centre of Genomics and Personalised Health, Queensland University of Technology; and Menzies Institute for Medical Research (B.V.T.), University of Tasmania, Hobart, Australia
| | - Bruce V Taylor
- From the Florey Institute of Neuroscience and Mental Health (M.D.B., E.C.N., E.M., C.D., V.L., A.-L.P., T.J.K.); Department of Anatomy and Physiology (M.D.B.), University of Melbourne, Parkville; Crux Biolabs (E.C.N.), Bayswater; Department of Neurology (C.D.), Royal Melbourne Hospital, Parkville; Department of Neurology (A.X., J.L.-S.), John Hunter Hospital, Newcastle; Hunter Medical Research Institute (A.X., J.L.-S.), University of Newcastle, New South Wales Genomics Research Centre (R.A.L.), Centre of Genomics and Personalised Health, Queensland University of Technology; and Menzies Institute for Medical Research (B.V.T.), University of Tasmania, Hobart, Australia
| | - Anne-Louise Ponsonby
- From the Florey Institute of Neuroscience and Mental Health (M.D.B., E.C.N., E.M., C.D., V.L., A.-L.P., T.J.K.); Department of Anatomy and Physiology (M.D.B.), University of Melbourne, Parkville; Crux Biolabs (E.C.N.), Bayswater; Department of Neurology (C.D.), Royal Melbourne Hospital, Parkville; Department of Neurology (A.X., J.L.-S.), John Hunter Hospital, Newcastle; Hunter Medical Research Institute (A.X., J.L.-S.), University of Newcastle, New South Wales Genomics Research Centre (R.A.L.), Centre of Genomics and Personalised Health, Queensland University of Technology; and Menzies Institute for Medical Research (B.V.T.), University of Tasmania, Hobart, Australia
| | - Trevor J Kilpatrick
- From the Florey Institute of Neuroscience and Mental Health (M.D.B., E.C.N., E.M., C.D., V.L., A.-L.P., T.J.K.); Department of Anatomy and Physiology (M.D.B.), University of Melbourne, Parkville; Crux Biolabs (E.C.N.), Bayswater; Department of Neurology (C.D.), Royal Melbourne Hospital, Parkville; Department of Neurology (A.X., J.L.-S.), John Hunter Hospital, Newcastle; Hunter Medical Research Institute (A.X., J.L.-S.), University of Newcastle, New South Wales Genomics Research Centre (R.A.L.), Centre of Genomics and Personalised Health, Queensland University of Technology; and Menzies Institute for Medical Research (B.V.T.), University of Tasmania, Hobart, Australia
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18
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Hannon E, Dempster EL, Davies JP, Chioza B, Blake GET, Burrage J, Policicchio S, Franklin A, Walker EM, Bamford RA, Schalkwyk LC, Mill J. Quantifying the proportion of different cell types in the human cortex using DNA methylation profiles. BMC Biol 2024; 22:17. [PMID: 38273288 PMCID: PMC10809680 DOI: 10.1186/s12915-024-01827-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: 07/05/2023] [Accepted: 01/11/2024] [Indexed: 01/27/2024] Open
Abstract
BACKGROUND Due to interindividual variation in the cellular composition of the human cortex, it is essential that covariates that capture these differences are included in epigenome-wide association studies using bulk tissue. As experimentally derived cell counts are often unavailable, computational solutions have been adopted to estimate the proportion of different cell types using DNA methylation data. Here, we validate and profile the use of an expanded reference DNA methylation dataset incorporating two neuronal and three glial cell subtypes for quantifying the cellular composition of the human cortex. RESULTS We tested eight reference panels containing different combinations of neuronal- and glial cell types and characterised their performance in deconvoluting cell proportions from computationally reconstructed or empirically derived human cortex DNA methylation data. Our analyses demonstrate that while these novel brain deconvolution models produce accurate estimates of cellular proportions from profiles generated on postnatal human cortex samples, they are not appropriate for the use in prenatal cortex or cerebellum tissue samples. Applying our models to an extensive collection of empirical datasets, we show that glial cells are twice as abundant as neuronal cells in the human cortex and identify significant associations between increased Alzheimer's disease neuropathology and the proportion of specific cell types including a decrease in NeuNNeg/SOX10Neg nuclei and an increase of NeuNNeg/SOX10Pos nuclei. CONCLUSIONS Our novel deconvolution models produce accurate estimates for cell proportions in the human cortex. These models are available as a resource to the community enabling the control of cellular heterogeneity in epigenetic studies of brain disorders performed on bulk cortex tissue.
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Affiliation(s)
- Eilis Hannon
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, University of Exeter, Barrack Road, RILD Building, Royal Devon & Exeter Hospital, Barrack Road, Exeter, Devon, EX2 5DW, UK.
| | - Emma L Dempster
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, University of Exeter, Barrack Road, RILD Building, Royal Devon & Exeter Hospital, Barrack Road, Exeter, Devon, EX2 5DW, UK
| | - Jonathan P Davies
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, University of Exeter, Barrack Road, RILD Building, Royal Devon & Exeter Hospital, Barrack Road, Exeter, Devon, EX2 5DW, UK
| | - Barry Chioza
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, University of Exeter, Barrack Road, RILD Building, Royal Devon & Exeter Hospital, Barrack Road, Exeter, Devon, EX2 5DW, UK
| | - Georgina E T Blake
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, University of Exeter, Barrack Road, RILD Building, Royal Devon & Exeter Hospital, Barrack Road, Exeter, Devon, EX2 5DW, UK
| | - Joe Burrage
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, University of Exeter, Barrack Road, RILD Building, Royal Devon & Exeter Hospital, Barrack Road, Exeter, Devon, EX2 5DW, UK
| | - Stefania Policicchio
- Italian Institute of Technology, Center for Human Technologies (CHT), Genova, Italy
| | - Alice Franklin
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, University of Exeter, Barrack Road, RILD Building, Royal Devon & Exeter Hospital, Barrack Road, Exeter, Devon, EX2 5DW, UK
| | - Emma M Walker
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, University of Exeter, Barrack Road, RILD Building, Royal Devon & Exeter Hospital, Barrack Road, Exeter, Devon, EX2 5DW, UK
| | - Rosemary A Bamford
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, University of Exeter, Barrack Road, RILD Building, Royal Devon & Exeter Hospital, Barrack Road, Exeter, Devon, EX2 5DW, UK
| | - Leonard C Schalkwyk
- School of Life Sciences, University of Essex, Wivenhoe Park, Colchester, Essex, CO4 3SQ, UK
| | - Jonathan Mill
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, University of Exeter, Barrack Road, RILD Building, Royal Devon & Exeter Hospital, Barrack Road, Exeter, Devon, EX2 5DW, UK
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19
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Abstract
Lymphoid neoplasms represent a heterogeneous group of disease entities and subtypes with markedly different molecular and clinical features. Beyond genetic alterations, lymphoid tumors also show widespread epigenomic changes. These severely affect the levels and distribution of DNA methylation, histone modifications, chromatin accessibility, and three-dimensional genome interactions. DNA methylation stands out as a tracer of cell identity and memory, as B cell neoplasms show epigenetic imprints of their cellular origin and proliferative history, which can be quantified by an epigenetic mitotic clock. Chromatin-associated marks are informative to uncover altered regulatory regions and transcription factor networks contributing to the development of distinct lymphoid tumors. Tumor-intrinsic epigenetic and genetic aberrations cooperate and interact with microenvironmental cells to shape the transcriptome at different phases of lymphoma evolution, and intraclonal heterogeneity can now be characterized by single-cell profiling. Finally, epigenetics offers multiple clinical applications, including powerful diagnostic and prognostic biomarkers as well as therapeutic targets.
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Affiliation(s)
- Martí Duran-Ferrer
- Institut d'Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), Barcelona, Spain;
| | - José Ignacio Martín-Subero
- Institut d'Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), Barcelona, Spain;
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
- Departamento de Fundamentos Clínicos, Universitat de Barcelona, Barcelona, Spain
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20
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Wu Z, Huang D, Wang J, Zhao Y, Sun W, Shen X. Engineering Heterogeneous Tumor Models for Biomedical Applications. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2304160. [PMID: 37946674 PMCID: PMC10767453 DOI: 10.1002/advs.202304160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 09/16/2023] [Indexed: 11/12/2023]
Abstract
Tumor tissue engineering holds great promise for replicating the physiological and behavioral characteristics of tumors in vitro. Advances in this field have led to new opportunities for studying the tumor microenvironment and exploring potential anti-cancer therapeutics. However, the main obstacle to the widespread adoption of tumor models is the poor understanding and insufficient reconstruction of tumor heterogeneity. In this review, the current progress of engineering heterogeneous tumor models is discussed. First, the major components of tumor heterogeneity are summarized, which encompasses various signaling pathways, cell proliferations, and spatial configurations. Then, contemporary approaches are elucidated in tumor engineering that are guided by fundamental principles of tumor biology, and the potential of a bottom-up approach in tumor engineering is highlighted. Additionally, the characterization approaches and biomedical applications of tumor models are discussed, emphasizing the significant role of engineered tumor models in scientific research and clinical trials. Lastly, the challenges of heterogeneous tumor models in promoting oncology research and tumor therapy are described and key directions for future research are provided.
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Affiliation(s)
- Zhuhao Wu
- Department of Rheumatology and ImmunologyNanjing Drum Tower HospitalSchool of Biological Science and Medical EngineeringSoutheast UniversityNanjing210096China
| | - Danqing Huang
- Department of Rheumatology and ImmunologyNanjing Drum Tower HospitalSchool of Biological Science and Medical EngineeringSoutheast UniversityNanjing210096China
| | - Jinglin Wang
- Department of Rheumatology and ImmunologyNanjing Drum Tower HospitalSchool of Biological Science and Medical EngineeringSoutheast UniversityNanjing210096China
| | - Yuanjin Zhao
- Department of Rheumatology and ImmunologyNanjing Drum Tower HospitalSchool of Biological Science and Medical EngineeringSoutheast UniversityNanjing210096China
- Department of Gastrointestinal SurgeryThe First Affiliated HospitalWenzhou Medical UniversityWenzhou325035China
| | - Weijian Sun
- Department of Gastrointestinal SurgeryThe Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical UniversityWenzhou325027China
| | - Xian Shen
- Department of Rheumatology and ImmunologyNanjing Drum Tower HospitalSchool of Biological Science and Medical EngineeringSoutheast UniversityNanjing210096China
- Department of Gastrointestinal SurgeryThe First Affiliated HospitalWenzhou Medical UniversityWenzhou325035China
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21
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Knodel F, Pinter S, Kroll C, Rathert P. Fluorescent Reporter Systems to Investigate Chromatin Effector Proteins in Living Cells. Methods Mol Biol 2024; 2842:225-252. [PMID: 39012599 DOI: 10.1007/978-1-0716-4051-7_12] [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] [Indexed: 07/17/2024]
Abstract
Epigenetic research faces the challenge of the high complexity and tight regulation in chromatin modification networks. Although many isolated mechanisms of chromatin-mediated gene regulation have been described, solid approaches for the comprehensive analysis of specific processes as parts of the bigger epigenome network are missing. In order to expand the toolbox of methods by a system that will help to capture and describe the complexity of transcriptional regulation, we describe here a robust protocol for the generation of stable reporter systems for transcriptional activity and summarize their applications. The system allows for the induced recruitment of a chromatin regulator to a fluorescent reporter gene, followed by the detection of transcriptional changes using flow cytometry. The reporter gene is integrated into an endogenous chromatin environment, thus enabling the detection of regulatory dependencies of the investigated chromatin regulator on endogenous cofactors. The system allows for an easy and dynamic readout at the single-cell level and the ability to compensate for cell-to-cell variances of transcription. The modular design of the system enables the simple adjustment of the method for the investigation of different chromatin regulators in a broad panel of cell lines. We also summarize applications of this technology to characterize the silencing velocity of different chromatin effectors, removal of activating histone modifications, analysis of stability and reversibility of epigenome modifications, the investigation of the effects of small molecule on chromatin effectors and of functional effector-coregulator relationships. The presented method allows to investigate the complexity of transcriptional regulation by epigenetic effector proteins in living cells.
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Affiliation(s)
- Franziska Knodel
- Department of Biochemistry, Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Stuttgart, Germany
| | - Sabine Pinter
- Department of Biochemistry, Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Stuttgart, Germany
| | - Carolin Kroll
- Department of Biochemistry, Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Stuttgart, Germany
| | - Philipp Rathert
- Department of Biochemistry, Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Stuttgart, Germany.
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22
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Vellame DS, Shireby G, MacCalman A, Dempster EL, Burrage J, Gorrie-Stone T, Schalkwyk LS, Mill J, Hannon E. Uncertainty quantification of reference-based cellular deconvolution algorithms. Epigenetics 2023; 18:2137659. [PMID: 36539387 PMCID: PMC9980651 DOI: 10.1080/15592294.2022.2137659] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 10/12/2022] [Indexed: 12/24/2022] Open
Abstract
The majority of epigenetic epidemiology studies to date have generated genome-wide profiles from bulk tissues (e.g., whole blood) however these are vulnerable to confounding from variation in cellular composition. Proxies for cellular composition can be mathematically derived from the bulk tissue profiles using a deconvolution algorithm; however, there is no method to assess the validity of these estimates for a dataset where the true cellular proportions are unknown. In this study, we describe, validate and characterize a sample level accuracy metric for derived cellular heterogeneity variables. The CETYGO score captures the deviation between a sample's DNA methylation profile and its expected profile given the estimated cellular proportions and cell type reference profiles. We demonstrate that the CETYGO score consistently distinguishes inaccurate and incomplete deconvolutions when applied to reconstructed whole blood profiles. By applying our novel metric to >6,300 empirical whole blood profiles, we find that estimating accurate cellular composition is influenced by both technical and biological variation. In particular, we show that when using a common reference panel for whole blood, less accurate estimates are generated for females, neonates, older individuals and smokers. Our results highlight the utility of a metric to assess the accuracy of cellular deconvolution, and describe how it can enhance studies of DNA methylation that are reliant on statistical proxies for cellular heterogeneity. To facilitate incorporating our methodology into existing pipelines, we have made it freely available as an R package (https://github.com/ds420/CETYGO).
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Affiliation(s)
| | - Gemma Shireby
- University of Exeter Medical School, University of Exeter, Exeter EX2 5DW, UK
| | - Ailsa MacCalman
- University of Exeter Medical School, University of Exeter, Exeter EX2 5DW, UK
| | - Emma L Dempster
- University of Exeter Medical School, University of Exeter, Exeter EX2 5DW, UK
| | - Joe Burrage
- University of Exeter Medical School, University of Exeter, Exeter EX2 5DW, UK
| | - Tyler Gorrie-Stone
- School of Biological Sciences, University of Essex, Colchester CO4 3SQ, UK
| | | | - Jonathan Mill
- University of Exeter Medical School, University of Exeter, Exeter EX2 5DW, UK
| | - Eilis Hannon
- University of Exeter Medical School, University of Exeter, Exeter EX2 5DW, UK
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23
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Bakulski KM, Blostein F, London SJ. Linking Prenatal Environmental Exposures to Lifetime Health with Epigenome-Wide Association Studies: State-of-the-Science Review and Future Recommendations. ENVIRONMENTAL HEALTH PERSPECTIVES 2023; 131:126001. [PMID: 38048101 PMCID: PMC10695268 DOI: 10.1289/ehp12956] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 10/06/2023] [Accepted: 10/16/2023] [Indexed: 12/05/2023]
Abstract
BACKGROUND The prenatal environment influences lifetime health; epigenetic mechanisms likely predominate. In 2016, the first international consortium paper on cigarette smoking during pregnancy and offspring DNA methylation identified extensive, reproducible exposure signals. This finding raised expectations for epigenome-wide association studies (EWAS) of other exposures. OBJECTIVE We review the current state-of-the-science for DNA methylation associations across prenatal exposures in humans and provide future recommendations. METHODS We reviewed 134 prenatal environmental EWAS of DNA methylation in newborns, focusing on 51 epidemiological studies with meta-analysis or replication testing. Exposures spanned cigarette smoking, alcohol consumption, air pollution, dietary factors, psychosocial stress, metals, other chemicals, and other exogenous factors. Of the reproducible DNA methylation signatures, we examined implementation as exposure biomarkers. RESULTS Only 19 (14%) of these prenatal EWAS were conducted in cohorts of 1,000 or more individuals, reflecting the still early stage of the field. To date, the largest perinatal EWAS sample size was 6,685 participants. For comparison, the most recent genome-wide association study for birth weight included more than 300,000 individuals. Replication, at some level, was successful with exposures to cigarette smoking, folate, dietary glycemic index, particulate matter with aerodynamic diameter < 10 μ m and < 2.5 μ m , nitrogen dioxide, mercury, cadmium, arsenic, electronic waste, PFAS, and DDT. Reproducible effects of a more limited set of prenatal exposures (smoking, folate) enabled robust methylation biomarker creation. DISCUSSION Current evidence demonstrates the scientific premise for reproducible DNA methylation exposure signatures. Better powered EWAS could identify signatures across many exposures and enable comprehensive biomarker development. Whether methylation biomarkers of exposures themselves cause health effects remains unclear. We expect that larger EWAS with enhanced coverage of epigenome and exposome, along with improved single-cell technologies and evolving methods for integrative multi-omics analyses and causal inference, will expand mechanistic understanding of causal links between environmental exposures, the epigenome, and health outcomes throughout the life course. https://doi.org/10.1289/EHP12956.
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Affiliation(s)
| | - Freida Blostein
- University of Michigan, Ann Arbor, Michigan, USA
- Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Stephanie J. London
- National Institute of Environmental Health Sciences, National Institutes of Health, U.S. Department of Health and Human Services, Research Triangle Park, North Carolina, USA
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24
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Meng G, Pan Y, Tang W, Zhang L, Cui Y, Schumacher FR, Wang M, Wang R, He S, Krischer J, Li Q, Feng H. imply: improving cell-type deconvolution accuracy using personalized reference profiles. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.27.559579. [PMID: 37808714 PMCID: PMC10557724 DOI: 10.1101/2023.09.27.559579] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
Real-world clinical samples are often admixtures of signal mosaics from multiple pure cell types. Using computational tools, bulk transcriptomics can be deconvoluted to solve for the abundance of constituent cell types. However, existing deconvolution methods are conditioned on the assumption that the whole study population is served by a single reference panel, which ignores person-to-person heterogeneity. Here we present imply, a novel algorithm to deconvolute cell type proportions using personalized reference panels. imply can borrow information across repeatedly measured samples for each subject, and obtain precise cell type proportion estimations. Simulation studies demonstrate reduced bias in cell type abundance estimation compared with existing methods. Real data analyses on large longitudinal consortia show more realistic deconvolution results that align with biological facts. Our results suggest that disparities in cell type proportions are associated with several disease phenotypes in type 1 diabetes and Parkinson's disease. Our proposed tool imply is available through the R/Bioconductor package ISLET at https://bioconductor.org/packages/ISLET/.
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Affiliation(s)
- Guanqun Meng
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA
| | - Yue Pan
- Department of Biostatistics, St. Jude Children’s Research Hospital, Memphis, 38105, TN, USA
| | - Wen Tang
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA
| | - Lijun Zhang
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA
| | - Ying Cui
- Department of Biomedical Data Science, Stanford University, Stanford, 94305, CA, USA
| | - Fredrick R. Schumacher
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA
| | - Ming Wang
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA
| | - Rui Wang
- Department of Surgery, Division of Surgical Oncology, University Hospitals Cleveland Medical Center, Cleveland, 44106, OH, USA
| | - Sijia He
- Department of Biostatistics, University of Michigan, Ann Arbor, 48109, MI, USA
| | - Jeffrey Krischer
- Health Informatics Institute, University of South Florida, Tampa, 38105, FL, USA
| | - Qian Li
- Department of Biostatistics, St. Jude Children’s Research Hospital, Memphis, 38105, TN, USA
| | - Hao Feng
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA
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25
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Wang W, Zhou X, Wang J, Yao J, Wen H, Wang Y, Sun M, Zhang C, Tao W, Zou J, Ni T. Approximate estimation of cell-type resolution transcriptome in bulk tissue through matrix completion. Brief Bioinform 2023; 24:bbad273. [PMID: 37529921 DOI: 10.1093/bib/bbad273] [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: 04/21/2023] [Revised: 06/20/2023] [Accepted: 07/10/2023] [Indexed: 08/03/2023] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for uncovering cellular heterogeneity. However, the high costs associated with this technique have rendered it impractical for studying large patient cohorts. We introduce ENIGMA (Deconvolution based on Regularized Matrix Completion), a method that addresses this limitation through accurately deconvoluting bulk tissue RNA-seq data into a readout with cell-type resolution by leveraging information from scRNA-seq data. By employing a matrix completion strategy, ENIGMA minimizes the distance between the mixture transcriptome obtained with bulk sequencing and a weighted combination of cell-type-specific expression. This allows the quantification of cell-type proportions and reconstruction of cell-type-specific transcriptomes. To validate its performance, ENIGMA was tested on both simulated and real datasets, including disease-related tissues, demonstrating its ability in uncovering novel biological insights.
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Affiliation(s)
- Weixu Wang
- State Key Laboratory of Genetic Engineering, National Clinical Research Center for Aging and Medicine, Huashan Hospital, Collaborative Innovation Center of Genetics and Development, Human Phenome Institute, Center for Evolutionary Biology, Shanghai Engineering Research Center of Industrial Microorganisms, School of Life Sciences, Fudan University, Shanghai 200438, P.R. China
| | - Xiaolan Zhou
- State Key Laboratory of Genetic Engineering, National Clinical Research Center for Aging and Medicine, Huashan Hospital, Collaborative Innovation Center of Genetics and Development, Human Phenome Institute, Center for Evolutionary Biology, Shanghai Engineering Research Center of Industrial Microorganisms, School of Life Sciences, Fudan University, Shanghai 200438, P.R. China
| | - Jing Wang
- State Key Laboratory of Genetic Engineering, National Clinical Research Center for Aging and Medicine, Huashan Hospital, Collaborative Innovation Center of Genetics and Development, Human Phenome Institute, Center for Evolutionary Biology, Shanghai Engineering Research Center of Industrial Microorganisms, School of Life Sciences, Fudan University, Shanghai 200438, P.R. China
| | - Jun Yao
- State Key Laboratory of Genetic Engineering, National Clinical Research Center for Aging and Medicine, Huashan Hospital, Collaborative Innovation Center of Genetics and Development, Human Phenome Institute, Center for Evolutionary Biology, Shanghai Engineering Research Center of Industrial Microorganisms, School of Life Sciences, Fudan University, Shanghai 200438, P.R. China
| | - Haimei Wen
- State Key Laboratory of Genetic Engineering, National Clinical Research Center for Aging and Medicine, Huashan Hospital, Collaborative Innovation Center of Genetics and Development, Human Phenome Institute, Center for Evolutionary Biology, Shanghai Engineering Research Center of Industrial Microorganisms, School of Life Sciences, Fudan University, Shanghai 200438, P.R. China
| | - Yi Wang
- Ministry of Education (MOE) Key Laboratory of Contemporary Anthropology, Human Phenome Institute, School of Life Sciences, Fudan University, Shanghai 200438, P.R. China
| | - Mingwan Sun
- Key Laboratory of Gene Engineering of the Ministry of Education and State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou 510006, P.R. China
| | - Chao Zhang
- MOE Key Laboratory of Cell Proliferation and Differentiation, School of Life Sciences, Peking University, Beijing 100871, P.R. China
| | - Wei Tao
- MOE Key Laboratory of Cell Proliferation and Differentiation, School of Life Sciences, Peking University, Beijing 100871, P.R. China
| | - Jiahua Zou
- Guangdong Provincial Key Laboratory of Bioengineering Medicine, National Engineering Research Center of Genetic Medicine, Institute of Biomedicine, College of Life Science and Technology, Jinan University, Guangzhou 510632, P.R. China
| | - Ting Ni
- State Key Laboratory of Genetic Engineering, National Clinical Research Center for Aging and Medicine, Huashan Hospital, Collaborative Innovation Center of Genetics and Development, Human Phenome Institute, Center for Evolutionary Biology, Shanghai Engineering Research Center of Industrial Microorganisms, School of Life Sciences, Fudan University, Shanghai 200438, P.R. China
- State key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Institutes of Biomedical Sciences, School of Life Sciences, Inner Mongolia University, Hohhot 010070, P.R. China
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26
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Feng H, Meng G, Lin T, Parikh H, Pan Y, Li Z, Krischer J, Li Q. ISLET: individual-specific reference panel recovery improves cell-type-specific inference. Genome Biol 2023; 24:174. [PMID: 37496087 PMCID: PMC10373385 DOI: 10.1186/s13059-023-03014-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 07/12/2023] [Indexed: 07/28/2023] Open
Abstract
We propose a statistical framework ISLET to infer individual-specific and cell-type-specific transcriptome reference panels. ISLET models the repeatedly measured bulk gene expression data, to optimize the usage of shared information within each subject. ISLET is the first available method to achieve individual-specific reference estimation in repeated samples. Using simulation studies, we show outstanding performance of ISLET in the reference estimation and downstream cell-type-specific differentially expressed genes testing. We apply ISLET to longitudinal transcriptomes profiled from blood samples in a large observational study of young children and confirm the cell-type-specific gene signatures for pancreatic islet autoantibody. ISLET is available at https://bioconductor.org/packages/ISLET .
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Affiliation(s)
- Hao Feng
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA.
| | - Guanqun Meng
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Tong Lin
- Department of Biostatistics, St. Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, TN, 38105, USA
| | - Hemang Parikh
- Health Informatics Institute, University of South Florida, Tampa, FL, 33620, USA
| | - Yue Pan
- Department of Biostatistics, St. Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, TN, 38105, USA
| | - Ziyi Li
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Jeffrey Krischer
- Health Informatics Institute, University of South Florida, Tampa, FL, 33620, USA
| | - Qian Li
- Department of Biostatistics, St. Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, TN, 38105, USA.
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Zheng Y, Jun J, Brennan K, Gevaert O. EpiMix is an integrative tool for epigenomic subtyping using DNA methylation. CELL REPORTS METHODS 2023; 3:100515. [PMID: 37533639 PMCID: PMC10391348 DOI: 10.1016/j.crmeth.2023.100515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 04/12/2023] [Accepted: 06/01/2023] [Indexed: 08/04/2023]
Abstract
DNA methylation (DNAme) is a major epigenetic factor influencing gene expression with alterations leading to cancer and immunological and cardiovascular diseases. Recent technological advances have enabled genome-wide profiling of DNAme in large human cohorts. There is a need for analytical methods that can more sensitively detect differential methylation profiles present in subsets of individuals from these heterogeneous, population-level datasets. We developed an end-to-end analytical framework named "EpiMix" for population-level analysis of DNAme and gene expression. Compared with existing methods, EpiMix showed higher sensitivity in detecting abnormal DNAme that was present in only small patient subsets. We extended the model-based analyses of EpiMix to cis-regulatory elements within protein-coding genes, distal enhancers, and genes encoding microRNAs and long non-coding RNAs (lncRNAs). Using cell-type-specific data from two separate studies, we discover epigenetic mechanisms underlying childhood food allergy and survival-associated, methylation-driven ncRNAs in non-small cell lung cancer.
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Affiliation(s)
- Yuanning Zheng
- Stanford Center for Biomedical Informatics Research (BMIR), Department of Medicine & Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
| | - John Jun
- Stanford Center for Biomedical Informatics Research (BMIR), Department of Medicine & Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
| | - Kevin Brennan
- Stanford Center for Biomedical Informatics Research (BMIR), Department of Medicine & Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
| | - Olivier Gevaert
- Stanford Center for Biomedical Informatics Research (BMIR), Department of Medicine & Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
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Adams C, Nair N, Plant D, Verstappen SMM, Quach HL, Quach DL, Carvidi A, Nititham J, Nakamura M, Graf J, Barton A, Criswell LA, Barcellos LF. Identification of Cell-Specific Differential DNA Methylation Associated With Methotrexate Treatment Response in Rheumatoid Arthritis. Arthritis Rheumatol 2023; 75:1088-1097. [PMID: 36716083 PMCID: PMC10313739 DOI: 10.1002/art.42464] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 01/13/2023] [Accepted: 01/26/2023] [Indexed: 01/31/2023]
Abstract
OBJECTIVE We undertook this study to estimate changes in cell-specific DNA methylation (DNAm) associated with methotrexate (MTX) response using whole blood samples collected from rheumatoid arthritis (RA) patients before and after initiation of MTX treatment. METHODS Patients included in this study were from the Rheumatoid Arthritis Medication Study (n = 66) and the University of California San Francisco Rheumatoid Arthritis study (n = 11). All patients met the American College of Rheumatology RA classification criteria. Blood samples were collected at baseline and following treatment. Disease Activity Scores in 28 joints using the C-reactive protein level were collected at baseline and after 3-6 months of treatment with MTX. Methylation profiles were generated using the Illumina Infinium HumanMethylation450 and MethylationEPIC v1.0 BeadChip arrays using DNA from whole blood. MTX response was defined using the EULAR response criteria (responders showed good/moderate response; nonresponders showed no response). Differentially methylated positions were identified using the Limma software package and Tensor Composition Analysis, which is a method for identifying cell-specific differential DNAm at the CpG level from tissue-level ("bulk") data. Differentially methylated regions were identified using Comb-p software. RESULTS We found evidence of differential global methylation between treatment response groups. Further, we found patterns of cell-specific differential global methylation associated with MTX response. After correction for multiple testing, 1 differentially methylated position was associated with differential DNAm between responders and nonresponders at baseline in CD4+ T cells, CD8+ T cells, and natural killer cells. Thirty-nine cell-specific differentially methylated regions associated with MTX treatment response were identified. There were no significant findings in analyses of whole blood samples. CONCLUSION We identified cell-specific changes in DNAm that were associated with MTX treatment response in RA patients. Future studies of DNAm and MTX treatment response should include measurements of DNAm from sorted cells.
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Affiliation(s)
- Cameron Adams
- School of Public Health, University of CaliforniaBerkeley
| | - Nisha Nair
- Centre of Genetics and Genomics Versus Arthritis, Manchester Academic Health Sciences Centre, The University of ManchesterManchesterUK
| | - Darren Plant
- Centre of Genetics and Genomics Versus Arthritis, Manchester Academic Health Sciences Centre, NIHR Manchester BRC, Manchester University Foundation Trust, The University of ManchesterManchesterUK
| | - Suzanne M. M. Verstappen
- NIHR Manchester BRC, Manchester University Foundation Trust, and Centre for Epidemiology Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, Manchester Academic Health Sciences Centre, The University of Manchester, Manchester, UK, Institute of Cellular Medicine, Newcastle University, and NIHR Newcastle BRC, Newcastle upon Tyne Hospitals NHS Foundation TrustNewcastle upon TyneUK
| | - Hong L. Quach
- School of Public Health, University of CaliforniaBerkeley
| | - Diana L. Quach
- School of Public Health, University of CaliforniaBerkeley
| | | | - Joanne Nititham
- National Human Genome Research Institute, NIHBethesdaMaryland
| | - Mary Nakamura
- University of California and San Francisco Veterans Administration Health SystemSan FranciscoCalifornia
| | | | - Anne Barton
- Centre of Genetics and Genomics Versus Arthritis, Manchester Academic Health Sciences Centre, NIHR Manchester BRC, Manchester University Foundation Trust, The University of ManchesterManchesterUK
| | | | - Lisa F. Barcellos
- School of Public Health, University of California, Berkeley, and National Human Genome Research Institute, NIHBethesdaMaryland
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Fenton CG, Ray MK, Meng W, Paulssen RH. Methylation-Regulated Long Non-Coding RNA Expression in Ulcerative Colitis. Int J Mol Sci 2023; 24:10500. [PMID: 37445676 DOI: 10.3390/ijms241310500] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 05/26/2023] [Accepted: 06/20/2023] [Indexed: 07/15/2023] Open
Abstract
Long non-coding RNAs (lncRNAs) have been shown to play a role in the pathogenesis of ulcerative colitis (UC). Although epigenetic processes such as DNA methylation and lncRNA expression are well studied in UC, the importance of the interplay between the two processes has not yet been fully explored. It is, therefore, believed that interactions between environmental factors and epigenetics contribute to disease development. Mucosal biopsies from 11 treatment-naïve UC patients and 13 normal controls were used in this study. From each individual sample, both whole-genome bisulfite sequencing data (WGBS) and lncRNA expression data were analyzed. Correlation analysis between lncRNA expression and upstream differentially methylated regions (DMRs) was used to identify lncRNAs that might be regulated by DMRs. Furthermore, proximal protein-coding genes associated with DMR-regulated lncRNAs were identified by correlating their expression. The study identified UC-associated lncRNAs such as MIR4435-2HG, ZFAS1, IL6-AS1, and Pvt1, which may be regulated by DMRs. Several genes that are involved in inflammatory immune responses were found downstream of DMR-regulated lncRNAs, including SERPINB1, CCL18, and SLC15A4. The interplay between lncRNA expression regulated by DNA methylation in UC might improve our understanding of UC pathogenesis.
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Affiliation(s)
- Christopher G Fenton
- Clinical Bioinformatics Research Group, Department of Clinical Medicine, UiT-The Arctic University of Norway, N-9037 Tromsø, Norway
- Genomic Support Centre Tromsø (GSCT), Department of Clinical Medicine, UiT-The Arctic University of Norway, N-9037 Tromsø, Norway
| | - Mithlesh Kumar Ray
- Clinical Bioinformatics Research Group, Department of Clinical Medicine, UiT-The Arctic University of Norway, N-9037 Tromsø, Norway
| | - Wei Meng
- Clinical Bioinformatics Research Group, Department of Clinical Medicine, UiT-The Arctic University of Norway, N-9037 Tromsø, Norway
| | - Ruth H Paulssen
- Clinical Bioinformatics Research Group, Department of Clinical Medicine, UiT-The Arctic University of Norway, N-9037 Tromsø, Norway
- Genomic Support Centre Tromsø (GSCT), Department of Clinical Medicine, UiT-The Arctic University of Norway, N-9037 Tromsø, Norway
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Mataix-Cols D, Fernández de la Cruz L, De Schipper E, Kuja-Halkola R, Bulik CM, Crowley JJ, Neufeld J, Rück C, Tammimies K, Lichtenstein P, Bölte S, Beucke JC. In search of environmental risk factors for obsessive-compulsive disorder: study protocol for the OCDTWIN project. BMC Psychiatry 2023; 23:442. [PMID: 37328750 PMCID: PMC10273515 DOI: 10.1186/s12888-023-04897-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Accepted: 05/22/2023] [Indexed: 06/18/2023] Open
Abstract
BACKGROUND The causes of obsessive-compulsive disorder (OCD) remain unknown. Gene-searching efforts are well underway, but the identification of environmental risk factors is at least as important and should be a priority because some of them may be amenable to prevention or early intervention strategies. Genetically informative studies, particularly those employing the discordant monozygotic (MZ) twin design, are ideally suited to study environmental risk factors. This protocol paper describes the study rationale, aims, and methods of OCDTWIN, an open cohort of MZ twin pairs who are discordant for the diagnosis of OCD. METHODS OCDTWIN has two broad aims. In Aim 1, we are recruiting MZ twin pairs from across Sweden, conducting thorough clinical assessments, and building a biobank of biological specimens, including blood, saliva, urine, stool, hair, nails, and multimodal brain imaging. A wealth of early life exposures (e.g., perinatal variables, health-related information, psychosocial stressors) are available through linkage with the nationwide registers and the Swedish Twin Registry. Blood spots stored in the Swedish phenylketonuria (PKU) biobank will be available to extract DNA, proteins, and metabolites, providing an invaluable source of biomaterial taken at birth. In Aim 2, we will perform within-pair comparisons of discordant MZ twins, which will allow us to isolate unique environmental risk factors that are in the causal pathway to OCD, while strictly controlling for genetic and early shared environmental influences. To date (May 2023), 43 pairs of twins (21 discordant for OCD) have been recruited. DISCUSSION OCDTWIN hopes to generate unique insights into environmental risk factors that are in the causal pathway to OCD, some of which have the potential of being actionable targets.
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Affiliation(s)
- David Mataix-Cols
- Department of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institutet, Stockholm, Sweden.
- Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden.
- Department of Clinical Sciences, Lund University, Lund, Sweden.
| | - Lorena Fernández de la Cruz
- Department of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institutet, Stockholm, Sweden
- Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden
| | - Elles De Schipper
- Department of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institutet, Stockholm, Sweden
- Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden
| | - Ralf Kuja-Halkola
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Cynthia M Bulik
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - James J Crowley
- Department of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institutet, Stockholm, Sweden
- Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, USA
| | - Janina Neufeld
- Center of Neurodevelopmental Disorders at Karolinska Institutet (KIND), Centre for Psychiatry Research, Department of Women's and Children's Health, Karolinska Institutet, Stockholm, Sweden
- Swedish Collegium for Advanced Study (SCAS), Uppsala, Sweden
| | - Christian Rück
- Department of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institutet, Stockholm, Sweden
- Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden
| | - Kristiina Tammimies
- Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden
- Center of Neurodevelopmental Disorders at Karolinska Institutet (KIND), Centre for Psychiatry Research, Department of Women's and Children's Health, Karolinska Institutet, Stockholm, Sweden
- Astrid Lindgren Children's Hospital, Karolinska University Hospital, Region Stockholm, Solna, Sweden
| | - Paul Lichtenstein
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Sven Bölte
- Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden
- Center of Neurodevelopmental Disorders at Karolinska Institutet (KIND), Centre for Psychiatry Research, Department of Women's and Children's Health, Karolinska Institutet, Stockholm, Sweden
- Curtin Autism Research Group, Curtin School of Allied Health, Curtin University, Perth, WA, Australia
| | - Jan C Beucke
- Department of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institutet, Stockholm, Sweden
- Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany
- Institute for Systems Medicine, Faculty of Human Medicine, MSH Medical School Hamburg, Hamburg, Germany
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Tamarelle J, Creze MM, Savathdy V, Phonekeo S, Wallenborn J, Siengsounthone L, Fink G, Odermatt P, Kounnavong S, Sayasone S, Vonaesch P. Dynamics and consequences of nutrition-related microbial dysbiosis in early life: study protocol of the VITERBI GUT project. Front Nutr 2023; 10:1111478. [PMID: 37275646 PMCID: PMC10232750 DOI: 10.3389/fnut.2023.1111478] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 05/02/2023] [Indexed: 06/07/2023] Open
Abstract
Introduction Early life under- and overnutrition (jointly termed malnutrition) is increasingly recognized as an important risk factor for adult obesity and metabolic syndrome, a diet-related cluster of conditions including high blood sugar, fat and cholesterol. Nevertheless, the exact factors linking early life malnutrition with metabolic syndrome remain poorly characterized. We hypothesize that the microbiota plays a crucial role in this trajectory and that the pathophysiological mechanisms underlying under- and overnutrition are, to some extent, shared. We further hypothesize that a "dysbiotic seed microbiota" is transmitted to children during the birth process, altering the children's microbiota composition and metabolic health. The overall objective of this project is to understand the precise causes and biological mechanisms linking prenatal or early life under- or overnutrition with the predisposition to develop overnutrition and/or metabolic disease in later life, as well as to investigate the possibility of a dysbiotic seed microbiota inheritance in the context of maternal malnutrition. Methods/design VITERBI GUT is a prospective birth cohort allowing to study the link between early life malnutrition, the microbiota and metabolic health. VITERBI GUT will include 100 undernourished, 100 normally nourished and 100 overnourished pregnant women living in Vientiane, Lao People's Democratic Republic (PDR). Women will be recruited during their third trimester of pregnancy and followed with their child until its second birthday. Anthropometric, clinical, metabolic and nutritional data are collected from both the mother and the child. The microbiota composition of maternal and child's fecal and oral samples as well as maternal vaginal and breast milk samples will be determined using amplicon and shotgun metagenomic sequencing. Epigenetic modifications and lipid profiles will be assessed in the child's blood at 2 years of age. We will investigate for possible associations between metabolic health, epigenetics, and microbial changes. Discussion We expect the VITERBI GUT project to contribute to the emerging literature linking the early life microbiota, epigenetic changes and growth/metabolic health. We also expect this project to give new (molecular) insights into the mechanisms linking malnutrition-induced early life dysbiosis and metabolic health in later life, opening new avenues for microbiota-engineering using microbiota-targeted interventions.
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Affiliation(s)
- Jeanne Tamarelle
- Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland
| | - Margaux M. Creze
- Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland
| | - Vanthanom Savathdy
- Lao Tropical and Public Health Institute, Ministry of Health, Vientiane, Lao People’s Democratic Republic (PDR)
| | - Sengrloun Phonekeo
- Lao Tropical and Public Health Institute, Ministry of Health, Vientiane, Lao People’s Democratic Republic (PDR)
| | - Jordyn Wallenborn
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
| | - Latsamy Siengsounthone
- Lao Tropical and Public Health Institute, Ministry of Health, Vientiane, Lao People’s Democratic Republic (PDR)
| | - Günther Fink
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
| | - Peter Odermatt
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
| | - Sengchanh Kounnavong
- Lao Tropical and Public Health Institute, Ministry of Health, Vientiane, Lao People’s Democratic Republic (PDR)
| | - Somphou Sayasone
- Lao Tropical and Public Health Institute, Ministry of Health, Vientiane, Lao People’s Democratic Republic (PDR)
| | - Pascale Vonaesch
- Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland
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32
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Mataix-Cols D, de la Cruz LF, de Schipper E, Kuja-Halkola R, Bulik CM, Crowley JJ, Neufeld J, Rück C, Tammimies K, Lichtenstein P, Bölte S, Beucke JC. In search of environmental risk factors for obsessive-compulsive disorder: Study protocol for the OCDTWIN project. RESEARCH SQUARE 2023:rs.3.rs-2897566. [PMID: 37215041 PMCID: PMC10197758 DOI: 10.21203/rs.3.rs-2897566/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Background The causes of obsessive-compulsive disorder (OCD) remain unknown. Gene-searching efforts are well underway, but the identification of environmental risk factors is at least as important and should be a priority because some of them may be amenable to prevention or early intervention strategies. Genetically informative studies, particularly those employing the discordant monozygotic (MZ) twin design, are ideally suited to study environmental risk factors. This protocol paper describes the study rationale, aims, and methods of OCDTWIN, an open cohort of MZ twin pairs who are discordant for the diagnosis of OCD. Methods OCDTWIN has two broad aims. In Aim 1, we are recruiting MZ twin pairs from across Sweden, conducting thorough clinical assessments, and building a biobank of biological specimens, including blood, saliva, urine, stool, hair, nails, and multimodal brain imaging. A wealth of early life exposures (e.g., perinatal variables, health-related information, psychosocial stressors) are available through linkage with the nationwide registers and the Swedish Twin Registry. Blood spots stored in the Swedish phenylketonuria (PKU) biobank will be available to extract DNA, proteins, and metabolites, providing an invaluable source of biomaterial taken at birth. In Aim 2, we will perform within-pair comparisons of discordant MZ twins, which will allow us to isolate unique environmental risk factors that are in the causal pathway to OCD, while strictly controlling for genetic and early shared environmental influences. To date (May 2023), 43 pairs of twins (21 discordant for OCD) have been recruited. Discussion OCDTWIN hopes to generate unique insights into environmental risk factors that are in the causal pathway to OCD, some of which have the potential of being actionable targets.
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Herrera-Luis E, Forno E, Celedón JC, Pino-Yanes M. Asthma Exacerbations: The Genes Behind the Scenes. J Investig Allergol Clin Immunol 2023; 33:76-94. [PMID: 36420738 PMCID: PMC10638677 DOI: 10.18176/jiaci.0878] [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] [Indexed: 11/25/2022] Open
Abstract
The clinical and socioeconomic burden of asthma exacerbations (AEs) constitutes a major public health problem. In the last 4 years, there has been an increase in ethnic diversity in candidate-gene and genome-wide association studies of AEs, which in the latter case led to the identification of novel genes and underlying pathobiological processes. Pharmacogenomics, admixture mapping analyses, and the combination of multiple "omics" layers have helped to prioritize genomic regions of interest and/or facilitated our understanding of the functional consequences of genetic variation. Nevertheless, the field still lags behind the genomics of asthma, where a vast compendium of genetic approaches has been used (eg, gene-environment nteractions, next-generation sequencing, and polygenic risk scores). Furthermore, the roles of the DNA methylome and histone modifications in AEs have received little attention, and microRNA findings remain to be validated in independent studies. Likewise, the most recent transcriptomic studies highlight the importance of the host-airway microbiome interaction in the modulation of risk of AEs. Leveraging -omics and deep-phenotyping data from subtypes or homogenous subgroups of patients will be crucial if we are to overcome the inherent heterogeneity of AEs, boost the identification of potential therapeutic targets, and implement precision medicine approaches to AEs in clinical practice.
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Affiliation(s)
- E Herrera-Luis
- Genomics and Health Group, Department of Biochemistry, Microbiology, Cell Biology and Genetics, Universidad de La Laguna (ULL), La Laguna, Tenerife, Spain
| | - E Forno
- Division of Pediatric Pulmonary Medicine, UPMC Children´s Hospital of Pittsburgh, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - J C Celedón
- Division of Pediatric Pulmonary Medicine, UPMC Children´s Hospital of Pittsburgh, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - M Pino-Yanes
- Genomics and Health Group, Department of Biochemistry, Microbiology, Cell Biology and Genetics, Universidad de La Laguna (ULL), La Laguna, Tenerife, Spain
- CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain 4 Instituto de Tecnologías Biomédicas (ITB), Universidad de La Laguna (ULL), La Laguna, Tenerife, Spain
- Instituto de Tecnologías Biomédicas (ITB), Universidad de La Laguna (ULL), La Laguna, Tenerife, Spain
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Zhang X, Hu Y, Vandenhoudt RE, Yan C, Marconi VC, Cohen MH, Justice AC, Aouizerat BE, Xu K. Cell-type specific EWAS identifies genes involved in HIV pathogenesis and oncogenesis among people with HIV infection. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.21.533691. [PMID: 36993343 PMCID: PMC10055405 DOI: 10.1101/2023.03.21.533691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Epigenome-wide association studies (EWAS) of heterogenous blood cells have identified CpG sites associated with chronic HIV infection, which offer limited knowledge of cell-type specific methylation patterns associated with HIV infection. Applying a computational deconvolution method validated by capture bisulfite DNA methylation sequencing, we conducted a cell type-based EWAS and identified differentially methylated CpG sites specific for chronic HIV infection among five immune cell types in blood: CD4+ T-cells, CD8+ T-cells, B cells, Natural Killer (NK) cells, and monocytes in two independent cohorts (N total =1,134). Differentially methylated CpG sites for HIV-infection were highly concordant between the two cohorts. Cell-type level meta-EWAS revealed distinct patterns of HIV-associated differential CpG methylation, where 67% of CpG sites were unique to individual cell types (false discovery rate, FDR <0.05). CD4+ T-cells had the largest number of HIV-associated CpG sites (N=1,472) compared to any other cell type. Genes harboring statistically significant CpG sites are involved in immunity and HIV pathogenesis (e.g. CX3CR1 in CD4+ T-cells, CCR7 in B cells, IL12R in NK cells, LCK in monocytes). More importantly, HIV-associated CpG sites were overrepresented for hallmark genes involved in cancer pathology ( FDR <0.05) (e.g. BCL family, PRDM16, PDCD1LGD, ESR1, DNMT3A, NOTCH2 ). HIV-associated CpG sites were enriched among genes involved in HIV pathogenesis and oncogenesis such as Kras-signaling, interferon-α and -γ, TNF-α, inflammatory, and apoptotic pathways. Our findings are novel, uncovering cell-type specific modifications in the host epigenome for people with HIV that contribute to the growing body of evidence regarding pathogen-induced epigenetic oncogenicity, specifically on HIV and its comorbidity with cancers.
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Dai R, Chu T, Zhang M, Wang X, Jourdon A, Wu F, Mariani J, Vaccarino FM, Lee D, Fullard JF, Hoffman GE, Roussos P, Wang Y, Wang X, Pinto D, Wang SH, Zhang C, Chen C, Liu C. Evaluating performance and applications of sample-wise cell deconvolution methods on human brain transcriptomic data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.13.532468. [PMID: 36993743 PMCID: PMC10054947 DOI: 10.1101/2023.03.13.532468] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Sample-wise deconvolution methods have been developed to estimate cell-type proportions and gene expressions in bulk-tissue samples. However, the performance of these methods and their biological applications has not been evaluated, particularly on human brain transcriptomic data. Here, nine deconvolution methods were evaluated with sample-matched data from bulk-tissue RNAseq, single-cell/nuclei (sc/sn) RNAseq, and immunohistochemistry. A total of 1,130,767 nuclei/cells from 149 adult postmortem brains and 72 organoid samples were used. The results showed the best performance of dtangle for estimating cell proportions and bMIND for estimating sample-wise cell-type gene expression. For eight brain cell types, 25,273 cell-type eQTLs were identified with deconvoluted expressions (decon-eQTLs). The results showed that decon-eQTLs explained more schizophrenia GWAS heritability than bulk-tissue or single-cell eQTLs alone. Differential gene expression associated with multiple phenotypes were also examined using the deconvoluted data. Our findings, which were replicated in bulk-tissue RNAseq and sc/snRNAseq data, provided new insights into the biological applications of deconvoluted data.
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Affiliation(s)
- Rujia Dai
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Tianyao Chu
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, China
| | - Ming Zhang
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, China
| | - Xuan Wang
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, China
| | | | - Feinan Wu
- Child Study Center, Yale University, New Haven, CT, USA
| | | | - Flora M Vaccarino
- Child Study Center, Yale University, New Haven, CT, USA
- Department of Neuroscience, Yale University, New Haven, CT, USA
| | - Donghoon Lee
- Center for Disease Neurogenomics, Departments of Psychiatry and Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - John F Fullard
- Center for Disease Neurogenomics, Departments of Psychiatry and Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Gabriel E Hoffman
- Center for Disease Neurogenomics, Departments of Psychiatry and Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Panos Roussos
- Center for Disease Neurogenomics, Departments of Psychiatry and Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Yue Wang
- Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, VA, USA
| | - Xusheng Wang
- Department of Biology, University of North Dakota, Grand Forks, ND, USA
| | - Dalila Pinto
- Department of Psychiatry, Department of Genetics and Genomic Sciences, Mindich Child Health and Development Institute, and Icahn Genomics Institute for Data Science and Genomic Technology, Seaver Autism Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sidney H Wang
- Center for Human Genetics, The Brown foundation Institute of Molecular Medicine, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Chunling Zhang
- Department of Neuroscience & Physiology, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Chao Chen
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, China
| | - Chunyu Liu
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY, USA
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, China
- Department of Neuroscience & Physiology, SUNY Upstate Medical University, Syracuse, NY, USA
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Lee MK, Azizgolshani N, Zhang Z, Perreard L, Kolling FW, Nguyen LN, Zanazzi GJ, Salas LA, Christensen BC. Hydroxymethylation alterations in progenitor-like cell types of pediatric central nervous system tumors are associated with cell type-specific transcriptional changes. RESEARCH SQUARE 2023:rs.3.rs-2517758. [PMID: 36909536 PMCID: PMC10002842 DOI: 10.21203/rs.3.rs-2517758/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
Abstract
Although intratumoral heterogeneity has been established in pediatric central nervous system tumors, epigenomic alterations at the cell type level have largely remained unresolved. To identify cell type-specific alterations to cytosine modifications in pediatric central nervous system tumors we utilized a multi-omic approach that integrated bulk DNA cytosine modification data (methylation and hydroxymethylation) with both bulk and single-cell RNA-sequencing data. We demonstrate a large reduction in the scope of significantly differentially modified cytosines in tumors when accounting for tumor cell type composition. In the progenitor-like cell types of tumors, we identified a preponderance differential CpG hydroxymethylation rather than methylation. Genes with differential hydroxymethylation, like HDAC4 and IGF1R, were associated with cell type-specific changes in gene expression in tumors. Our results highlight the importance of epigenomic alterations in the progenitor-like cell types and its role in cell type-specific transcriptional regulation in pediatric CNS tumors.
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Affiliation(s)
- Min Kyung Lee
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
| | - Nasim Azizgolshani
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
- Department of Cardiothoracic Surgery, Columbia University Medical Center, New York, NY, USA
| | - Ze Zhang
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
| | - Laurent Perreard
- Dartmouth Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
| | - Fred W Kolling
- Dartmouth Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
| | - Lananh N Nguyen
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
| | - George J Zanazzi
- Dartmouth Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
- Department of Pathology and Laboratory Medicine, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
| | - Lucas A Salas
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
| | - Brock C Christensen
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
- Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
- Department of Community and Family Medicine, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
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37
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Chen L, Li Z, Wu H. CeDAR: incorporating cell type hierarchy improves cell type-specific differential analyses in bulk omics data. Genome Biol 2023; 24:37. [PMID: 36855165 PMCID: PMC9972684 DOI: 10.1186/s13059-023-02857-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 01/17/2023] [Indexed: 03/02/2023] Open
Abstract
Bulk high-throughput omics data contain signals from a mixture of cell types. Recent developments of deconvolution methods facilitate cell type-specific inferences from bulk data. Our real data exploration suggests that differential expression or methylation status is often correlated among cell types. Based on this observation, we develop a novel statistical method named CeDAR to incorporate the cell type hierarchy in cell type-specific differential analyses of bulk data. Extensive simulation and real data analyses demonstrate that this approach significantly improves the accuracy and power in detecting cell type-specific differential signals compared with existing methods, especially in low-abundance cell types.
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Affiliation(s)
- Luxiao Chen
- Department of Biostatistics and Bioinformatics, Emory University, GA 30322 Atlanta, USA
| | - Ziyi Li
- Department of Biostatistics, The University of MD Anderson Cancer Center, 77030 Houston, TX, USA
| | - Hao Wu
- Faculty of Computer Science and Control Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen University Town, Shenzhen, 518055 P.R. China
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38
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Haftorn KL, Denault WRP, Lee Y, Page CM, Romanowska J, Lyle R, Næss ØE, Kristjansson D, Magnus PM, Håberg SE, Bohlin J, Jugessur A. Nucleated red blood cells explain most of the association between DNA methylation and gestational age. Commun Biol 2023; 6:224. [PMID: 36849614 PMCID: PMC9971030 DOI: 10.1038/s42003-023-04584-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Accepted: 02/13/2023] [Indexed: 03/01/2023] Open
Abstract
Determining if specific cell type(s) are responsible for an association between DNA methylation (DNAm) and a given phenotype is important for understanding the biological mechanisms underlying the association. Our EWAS of gestational age (GA) in 953 newborns from the Norwegian MoBa study identified 13,660 CpGs significantly associated with GA (pBonferroni<0.05) after adjustment for cell type composition. When the CellDMC algorithm was applied to explore cell-type specific effects, 2,330 CpGs were significantly associated with GA, mostly in nucleated red blood cells [nRBCs; n = 2,030 (87%)]. Similar patterns were found in another dataset based on a different array and when applying an alternative algorithm to CellDMC called Tensor Composition Analysis (TCA). Our findings point to nRBCs as the main cell type driving the DNAm-GA association, implicating an epigenetic signature of erythropoiesis as a likely mechanism. They also explain the poor correlation observed between epigenetic age clocks for newborns and those for adults.
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Affiliation(s)
- Kristine L Haftorn
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway.
- Institute of Health and Society, 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, 60637, USA
| | - Yunsung Lee
- Centre for Fertility and Health, 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 Ageing, Division of Mental and Physical Health, Norwegian Institute of Public Health, Oslo, Norway
| | - 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
| | - 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
| | - Øyvind E Næss
- Institute of Health and Society, University of Oslo, Oslo, Norway
- Division of Mental and Physical Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Dana Kristjansson
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
- Department of Genetics and Bioinformatics, Norwegian Institute of Public Health, Oslo, Norway
| | - Per M 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
| | - Jon Bohlin
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
- Division for Infection Control and Environmental Health, Department of Infectious Disease Epidemiology and Modelling, 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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39
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Nabais MF, Gadd DA, Hannon E, Mill J, McRae AF, Wray NR. An overview of DNA methylation-derived trait score methods and applications. Genome Biol 2023; 24:28. [PMID: 36797751 PMCID: PMC9936670 DOI: 10.1186/s13059-023-02855-7] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 01/17/2023] [Indexed: 02/18/2023] Open
Abstract
Microarray technology has been used to measure genome-wide DNA methylation in thousands of individuals. These studies typically test the associations between individual DNA methylation sites ("probes") and complex traits or diseases. The results can be used to generate methylation profile scores (MPS) to predict outcomes in independent data sets. Although there are many parallels between MPS and polygenic (risk) scores (PGS), there are key differences. Here, we review motivations, methods, and applications of DNA methylation-based trait prediction, with a focus on common diseases. We contrast MPS with PGS, highlighting where assumptions made in genetic modeling may not hold in epigenetic data.
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Affiliation(s)
- Marta F Nabais
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
- University of Exeter Medical School, RILD Building, RD&E Hospital Wonford, Barrack Road, Exeter, EX2 5DW, UK
| | - Danni A Gadd
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Eilis Hannon
- University of Exeter Medical School, RILD Building, RD&E Hospital Wonford, Barrack Road, Exeter, EX2 5DW, UK
| | - Jonathan Mill
- University of Exeter Medical School, RILD Building, RD&E Hospital Wonford, Barrack Road, Exeter, EX2 5DW, UK
| | - Allan F McRae
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Naomi R Wray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia.
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, 4072, Australia.
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40
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Fu MP, Merrill SM, Sharma M, Gibson WT, Turvey SE, Kobor MS. Rare diseases of epigenetic origin: Challenges and opportunities. Front Genet 2023; 14:1113086. [PMID: 36814905 PMCID: PMC9939656 DOI: 10.3389/fgene.2023.1113086] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 01/24/2023] [Indexed: 02/09/2023] Open
Abstract
Rare diseases (RDs), more than 80% of which have a genetic origin, collectively affect approximately 350 million people worldwide. Progress in next-generation sequencing technology has both greatly accelerated the pace of discovery of novel RDs and provided more accurate means for their diagnosis. RDs that are driven by altered epigenetic regulation with an underlying genetic basis are referred to as rare diseases of epigenetic origin (RDEOs). These diseases pose unique challenges in research, as they often show complex genetic and clinical heterogeneity arising from unknown gene-disease mechanisms. Furthermore, multiple other factors, including cell type and developmental time point, can confound attempts to deconvolute the pathophysiology of these disorders. These challenges are further exacerbated by factors that contribute to epigenetic variability and the difficulty of collecting sufficient participant numbers in human studies. However, new molecular and bioinformatics techniques will provide insight into how these disorders manifest over time. This review highlights recent studies addressing these challenges with innovative solutions. Further research will elucidate the mechanisms of action underlying unique RDEOs and facilitate the discovery of treatments and diagnostic biomarkers for screening, thereby improving health trajectories and clinical outcomes of affected patients.
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Affiliation(s)
- Maggie P. Fu
- Department of Medical Genetics, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada,Centre for Molecular Medicine and Therapeutics, University of British Columbia, Vancouver, BC, Canada,BC Children’s Hospital Research Institute, Vancouver, BC, Canada
| | - Sarah M. Merrill
- Department of Medical Genetics, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada,Centre for Molecular Medicine and Therapeutics, University of British Columbia, Vancouver, BC, Canada,BC Children’s Hospital Research Institute, Vancouver, BC, Canada
| | - Mehul Sharma
- BC Children’s Hospital Research Institute, Vancouver, BC, Canada,Department of Pediatrics, Faculty of Medicine, BC Children’s Hospital, University of British Columbia, Vancouver, BC, Canada
| | - William T. Gibson
- Department of Medical Genetics, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada,BC Children’s Hospital Research Institute, Vancouver, BC, Canada
| | - Stuart E. Turvey
- BC Children’s Hospital Research Institute, Vancouver, BC, Canada,Department of Pediatrics, Faculty of Medicine, BC Children’s Hospital, University of British Columbia, Vancouver, BC, Canada
| | - Michael S. Kobor
- Department of Medical Genetics, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada,Centre for Molecular Medicine and Therapeutics, University of British Columbia, Vancouver, BC, Canada,BC Children’s Hospital Research Institute, Vancouver, BC, Canada,*Correspondence: Michael S. Kobor,
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41
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Song X, Ji J, Rothstein JH, Alexeeff SE, Sakoda LC, Sistig A, Achacoso N, Jorgenson E, Whittemore AS, Klein RJ, Habel LA, Wang P, Sieh W. MiXcan: a framework for cell-type-aware transcriptome-wide association studies with an application to breast cancer. Nat Commun 2023; 14:377. [PMID: 36690614 PMCID: PMC9871010 DOI: 10.1038/s41467-023-35888-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Accepted: 01/05/2023] [Indexed: 01/25/2023] Open
Abstract
Human bulk tissue samples comprise multiple cell types with diverse roles in disease etiology. Conventional transcriptome-wide association study approaches predict genetically regulated gene expression at the tissue level, without considering cell-type heterogeneity, and test associations of predicted tissue-level expression with disease. Here we develop MiXcan, a cell-type-aware transcriptome-wide association study approach that predicts cell-type-level expression, identifies disease-associated genes via combination of cell-type-level association signals for multiple cell types, and provides insight into the disease-critical cell type. As a proof of concept, we conducted cell-type-aware analyses of breast cancer in 58,648 women and identified 12 transcriptome-wide significant genes using MiXcan compared with only eight genes using conventional approaches. Importantly, MiXcan identified genes with distinct associations in mammary epithelial versus stromal cells, including three new breast cancer susceptibility genes. These findings demonstrate that cell-type-aware transcriptome-wide analyses can reveal new insights into the genetic and cellular etiology of breast cancer and other diseases.
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Affiliation(s)
- Xiaoyu Song
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Jiayi Ji
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Joseph H Rothstein
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Stacey E Alexeeff
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Lori C Sakoda
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Adriana Sistig
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ninah Achacoso
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Eric Jorgenson
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
- Regeneron Genetics Center, Tarrytown, NY, USA
| | - Alice S Whittemore
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA
| | - Robert J Klein
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Laurel A Habel
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Pei Wang
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Weiva Sieh
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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42
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Meng G, Tang W, Huang E, Li Z, Feng H. A comprehensive assessment of cell type-specific differential expression methods in bulk data. Brief Bioinform 2023; 24:bbac516. [PMID: 36472568 PMCID: PMC9851321 DOI: 10.1093/bib/bbac516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 10/08/2022] [Accepted: 10/29/2022] [Indexed: 12/12/2022] Open
Abstract
Accounting for cell type compositions has been very successful at analyzing high-throughput data from heterogeneous tissues. Differential gene expression analysis at cell type level is becoming increasingly popular, yielding biomarker discovery in a finer granularity within a particular cell type. Although several computational methods have been developed to identify cell type-specific differentially expressed genes (csDEG) from RNA-seq data, a systematic evaluation is yet to be performed. Here, we thoroughly benchmark six recently published methods: CellDMC, CARseq, TOAST, LRCDE, CeDAR and TCA, together with two classical methods, csSAM and DESeq2, for a comprehensive comparison. We aim to systematically evaluate the performance of popular csDEG detection methods and provide guidance to researchers. In simulation studies, we benchmark available methods under various scenarios of baseline expression levels, sample sizes, cell type compositions, expression level alterations, technical noises and biological dispersions. Real data analyses of three large datasets on inflammatory bowel disease, lung cancer and autism provide evaluation in both the gene level and the pathway level. We find that csDEG calling is strongly affected by effect size, baseline expression level and cell type compositions. Results imply that csDEG discovery is a challenging task itself, with room to improvements on handling low signal-to-noise ratio and low expression genes.
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Affiliation(s)
- Guanqun Meng
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, Ohio, USA
| | - Wen Tang
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, Ohio, USA
| | - Emina Huang
- Department of Surgery, The University of Texas Southwestern Medical Center, Dallas, 75390, Texas, USA
| | - Ziyi Li
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, 77030, Texas, USA
| | - Hao Feng
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, Ohio, USA
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43
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Zheng Y, Jun J, Brennan K, Gevaert O. EpiMix: an integrative tool for epigenomic subtyping using DNA methylation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.03.522660. [PMID: 36711917 PMCID: PMC9881910 DOI: 10.1101/2023.01.03.522660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
DNA methylation (DNAme) is a major epigenetic factor influencing gene expression with alterations leading to cancer, immunological, and cardiovascular diseases. Recent technological advances enable genome-wide quantification of DNAme in large human cohorts. So far, existing methods have not been evaluated to identify differential DNAme present in large and heterogeneous patient cohorts. We developed an end-to-end analytical framework named "EpiMix" for population-level analysis of DNAme and gene expression. Compared to existing methods, EpiMix showed higher sensitivity in detecting abnormal DNAme that was present in only small patient subsets. We extended the model-based analyses of EpiMix to cis-regulatory elements within protein-coding genes, distal enhancers, and genes encoding microRNAs and lncRNAs. Using cell-type specific data from two separate studies, we discovered novel epigenetic mechanisms underlying childhood food allergy and survival-associated, methylation-driven non-coding RNAs in non-small cell lung cancer.
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Affiliation(s)
- Yuanning Zheng
- Stanford Center for Biomedical Informatics Research (BMIR), Department of Medicine & Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
| | - John Jun
- Stanford Center for Biomedical Informatics Research (BMIR), Department of Medicine & Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
| | - Kevin Brennan
- Stanford Center for Biomedical Informatics Research (BMIR), Department of Medicine & Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
| | - Olivier Gevaert
- Stanford Center for Biomedical Informatics Research (BMIR), Department of Medicine & Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
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44
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Yan L, Sun X. Benchmarking and integration of methods for deconvoluting spatial transcriptomic data. Bioinformatics 2022; 39:6900924. [PMID: 36515467 PMCID: PMC9825747 DOI: 10.1093/bioinformatics/btac805] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 11/11/2022] [Accepted: 12/13/2022] [Indexed: 12/15/2022] Open
Abstract
MOTIVATION The rapid development of spatial transcriptomics (ST) approaches has provided new insights into understanding tissue architecture and function. However, the gene expressions measured at a spot may contain contributions from multiple cells due to the low-resolution of current ST technologies. Although many computational methods have been developed to disentangle discrete cell types from spatial mixtures, the community lacks a thorough evaluation of the performance of those deconvolution methods. RESULTS Here, we present a comprehensive benchmarking of 14 deconvolution methods on four datasets. Furthermore, we investigate the robustness of different methods to sequencing depth, spot size and the choice of normalization. Moreover, we propose a new ensemble learning-based deconvolution method (EnDecon) by integrating multiple individual methods for more accurate deconvolution. The major new findings include: (i) cell2loction, RCTD and spatialDWLS are more accurate than other ST deconvolution methods, based on the evaluation of three metrics: RMSE, PCC and JSD; (ii) cell2location and spatialDWLS are more robust to the variation of sequencing depth than RCTD; (iii) the accuracy of the existing methods tends to decrease as the spot size becomes smaller; (iv) most deconvolution methods perform best when they normalize ST data using the method described in their original papers; and (v) the integrative method, EnDecon, could achieve more accurate ST deconvolution. Our study provides valuable information and guideline for practically applying ST deconvolution tools and developing new and more effective methods. AVAILABILITY AND IMPLEMENTATION The benchmarking pipeline is available at https://github.com/SunXQlab/ST-deconvoulution. An R package for EnDecon is available at https://github.com/SunXQlab/EnDecon. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Lulu Yan
- School of Mathematics, Sun Yat-sen University, Guangzhou 510275, China
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45
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Lang AL, Eulalio T, Fox E, Yakabi K, Bukhari SA, Kawas CH, Corrada MM, Montgomery SB, Heppner FL, Capper D, Nachun D, Montine TJ. Methylation differences in Alzheimer's disease neuropathologic change in the aged human brain. Acta Neuropathol Commun 2022; 10:174. [PMID: 36447297 PMCID: PMC9710143 DOI: 10.1186/s40478-022-01470-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Accepted: 10/24/2022] [Indexed: 12/05/2022] Open
Abstract
Alzheimer's disease (AD) is the most common cause of dementia with advancing age as its strongest risk factor. AD neuropathologic change (ADNC) is known to be associated with numerous DNA methylation changes in the human brain, but the oldest old (> 90 years) have so far been underrepresented in epigenetic studies of ADNC. Our study participants were individuals aged over 90 years (n = 47) from The 90+ Study. We analyzed DNA methylation from bulk samples in eight precisely dissected regions of the human brain: middle frontal gyrus, cingulate gyrus, entorhinal cortex, dentate gyrus, CA1, substantia nigra, locus coeruleus and cerebellar cortex. We deconvolved our bulk data into cell-type-specific (CTS) signals using computational methods. CTS methylation differences were analyzed across different levels of ADNC. The highest amount of ADNC related methylation differences was found in the dentate gyrus, a region that has so far been underrepresented in large scale multi-omic studies. In neurons of the dentate gyrus, DNA methylation significantly differed with increased burden of amyloid beta (Aβ) plaques at 5897 promoter regions of protein-coding genes. Amongst these, higher Aβ plaque burden was associated with promoter hypomethylation of the Presenilin enhancer 2 (PEN-2) gene, one of the rate limiting genes in the formation of gamma-secretase, a multicomponent complex that is responsible in part for the endoproteolytic cleavage of amyloid precursor protein into Aβ peptides. In addition to novel ADNC related DNA methylation changes, we present the most detailed array-based methylation survey of the old aged human brain to date. Our open-sourced dataset can serve as a brain region reference panel for future studies and help advance research in aging and neurodegenerative diseases.
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Affiliation(s)
- Anna-Lena Lang
- Department of Neuropathology, Charité–Universitätsmedizin Berlin, Corporate member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Tiffany Eulalio
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94305 USA
| | - Eddie Fox
- Department of Pathology, Stanford University, 300 Pasteur Drive, Stanford, CA 94305 USA
| | - Koya Yakabi
- Department of Pathology, Stanford University, 300 Pasteur Drive, Stanford, CA 94305 USA
| | - Syed A. Bukhari
- Department of Pathology, Stanford University, 300 Pasteur Drive, Stanford, CA 94305 USA
| | - Claudia H. Kawas
- Department of Neurology, University of California Irvine, Orange, CA 92868-4280 USA
- Department of Neurobiology and Behavior, University of California, Irvine, CA 92697 USA
| | - Maria M. Corrada
- Department of Neurology, University of California Irvine, Orange, CA 92868-4280 USA
- Department of Epidemiology, University of California, Irvine, CA 92617 USA
| | - Stephen B. Montgomery
- Department of Pathology, Stanford University, 300 Pasteur Drive, Stanford, CA 94305 USA
- Department of Genetics, Stanford University, Stanford, CA 94305 USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94305 USA
| | - Frank L. Heppner
- Department of Neuropathology, Charité–Universitätsmedizin Berlin, Corporate member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Charitéplatz 1, 10117 Berlin, Germany
- German Center for Neurodegenerative Diseases (DZNE), 10117 Berlin, Germany
- Cluster of Excellence, NeuroCure, 10117 Berlin, Germany
| | - David Capper
- Department of Neuropathology, Charité–Universitätsmedizin Berlin, Corporate member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Daniel Nachun
- Department of Genetics, Stanford University, Stanford, CA 94305 USA
| | - Thomas J. Montine
- Department of Pathology, Stanford University, 300 Pasteur Drive, Stanford, CA 94305 USA
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46
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Song J, Kuan PF. A systematic assessment of cell type deconvolution algorithms for DNA methylation data. Brief Bioinform 2022; 23:bbac449. [PMID: 36242584 PMCID: PMC9947552 DOI: 10.1093/bib/bbac449] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 08/11/2022] [Accepted: 09/20/2022] [Indexed: 12/14/2022] Open
Abstract
We performed systematic assessment of computational deconvolution methods that play an important role in the estimation of cell type proportions from bulk methylation data. The proposed framework methylDeConv (available as an R package) integrates several deconvolution methods for methylation profiles (Illumina HumanMethylation450 and MethylationEPIC arrays) and offers different cell-type-specific CpG selection to construct the extended reference library which incorporates the main immune cell subsets, epithelial cells and cell-free DNAs. We compared the performance of different deconvolution algorithms via simulations and benchmark datasets and further investigated the associations of the estimated cell type proportions to cancer therapy in breast cancer and subtypes in melanoma methylation case studies. Our results indicated that the deconvolution based on the extended reference library is critical to obtain accurate estimates of cell proportions in non-blood tissues.
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Affiliation(s)
- Junyan Song
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY
| | - Pei-Fen Kuan
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY
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Dias S, Willmer T, Adam S, Pheiffer C. The role of maternal DNA methylation in pregnancies complicated by gestational diabetes. FRONTIERS IN CLINICAL DIABETES AND HEALTHCARE 2022; 3:982665. [PMID: 36992770 PMCID: PMC10012132 DOI: 10.3389/fcdhc.2022.982665] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 08/22/2022] [Indexed: 11/13/2022]
Abstract
Diabetes in pregnancy is associated with adverse pregnancy outcomes and poses a serious threat to the health of mother and child. Although the pathophysiological mechanisms that underlie the association between maternal diabetes and pregnancy complications have not yet been elucidated, it has been suggested that the frequency and severity of pregnancy complications are linked to the degree of hyperglycemia. Epigenetic mechanisms reflect gene-environment interactions and have emerged as key players in metabolic adaptation to pregnancy and the development of complications. DNA methylation, the best characterized epigenetic mechanism, has been reported to be dysregulated during various pregnancy complications, including pre-eclampsia, hypertension, diabetes, early pregnancy loss and preterm birth. The identification of altered DNA methylation patterns may serve to elucidate the pathophysiological mechanisms that underlie the different types of maternal diabetes during pregnancy. This review aims to provide a summary of existing knowledge on DNA methylation patterns in pregnancies complicated by pregestational type 1 (T1DM) and type 2 diabetes mellitus (T2DM), and gestational diabetes mellitus (GDM). Four databases, CINAHL, Scopus, PubMed and Google Scholar, were searched for studies on DNA methylation profiling in pregnancies complicated with diabetes. A total of 1985 articles were identified, of which 32 met the inclusion criteria and are included in this review. All studies profiled DNA methylation during GDM or impaired glucose tolerance (IGT), while no studies investigated T1DM or T2DM. We highlight the increased methylation of two genes, Hypoxia‐inducible Factor‐3α (HIF3α) and Peroxisome Proliferator-activated Receptor Gamma-coactivator-Alpha (PGC1-α), and the decreased methylation of one gene, Peroxisome Proliferator Activated Receptor Alpha (PPARα), in women with GDM compared to pregnant women with normoglycemia that were consistently methylated across diverse populations with varying pregnancy durations, and using different diagnostic criteria, methodologies and biological sources. These findings support the candidacy of these three differentially methylated genes as biomarkers for GDM. Furthermore, these genes may provide insight into the pathways that are epigenetically influenced during maternal diabetes and which should be prioritized and replicated in longitudinal studies and in larger populations to ensure their clinical applicability. Finally, we discuss the challenges and limitations of DNA methylation analysis, and the need for DNA methylation profiling to be conducted in different types of maternal diabetes in pregnancy.
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Affiliation(s)
- Stephanie Dias
- Biomedical Research and Innovation Platform, South African Medical Research Council, Cape Town, South Africa
| | - Tarryn Willmer
- Biomedical Research and Innovation Platform, South African Medical Research Council, Cape Town, South Africa
- Centre for Cardio-Metabolic Research in Africa, Division of Medical Physiology, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Sumaiya Adam
- Department of Obstetrics and Gynecology, Faculty of Health Sciences, University of Pretoria, Pretoria, South Africa
- Diabetes Research Center, Faculty of Health Sciences, University of Pretoria, Pretoria, South Africa
| | - Carmen Pheiffer
- Biomedical Research and Innovation Platform, South African Medical Research Council, Cape Town, South Africa
- Centre for Cardio-Metabolic Research in Africa, Division of Medical Physiology, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
- Department of Obstetrics and Gynecology, Faculty of Health Sciences, University of Pretoria, Pretoria, South Africa
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Desale H, Buekens P, Alger J, Cafferata ML, Harville EW, Herrera C, Truyens C, Dumonteil E. Epigenetic signature of exposure to maternal Trypanosoma cruzi infection in cord blood cells from uninfected newborns. Epigenomics 2022; 14:913-927. [PMID: 36039408 PMCID: PMC9475499 DOI: 10.2217/epi-2022-0153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Aims: To assess the epigenetic effects of in utero exposure to maternal Trypanosoma cruzi infection. Methods: We performed an epigenome-wide association study to compare the DNA methylation patterns of umbilical cord blood cells from uninfected babies from chagasic and uninfected mothers. DNA methylation was measured using Infinium EPIC arrays. Results: We identified a differential DNA methylation signature of fetal exposure to maternal T. cruzi infection, in the absence of parasite transmission, with 12 differentially methylated sites in B cells and CD4+ T cells, including eight protein-coding genes. Conclusion: These genes participate in hematopoietic cell differentiation and the immune response and may be involved in immune disorders. They also have been associated with several developmental disorders and syndromes.
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Affiliation(s)
- Hans Desale
- Department of Tropical Medicine, Tulane University School of Public Health & Tropical Medicine & Tulane University Vector-Borne & Infectious Disease Research Center, New Orleans, LA 70112, USA
| | - Pierre Buekens
- Department of Epidemiology, Tulane University School of Public Health & Tropical Medicine, New Orleans, LA 70112, USA
| | - Jackeline Alger
- Instituto de Enfermedades Infecciosas y Parasitologia Antonio Vidal, Tegucigalpa, Honduras.,Ministry of Health, Hospital Escuela, Tegucigalpa, Honduras
| | - Maria Luisa Cafferata
- Unidad de Investigación Clínica y Epidemiológica Montevideo (UNICEM), Hospital de Clínicas, Montevideo, 11600, Uruguay
| | - Emily Wheeler Harville
- Department of Epidemiology, Tulane University School of Public Health & Tropical Medicine, New Orleans, LA 70112, USA
| | - Claudia Herrera
- Department of Tropical Medicine, Tulane University School of Public Health & Tropical Medicine & Tulane University Vector-Borne & Infectious Disease Research Center, New Orleans, LA 70112, USA
| | - Carine Truyens
- Laboratory of Parasitology, Faculty of Medicine, & ULB Center for Research in Immunology (UCRI), Université Libre de Bruxelles, Brussels, Belgium
| | - Eric Dumonteil
- Department of Tropical Medicine, Tulane University School of Public Health & Tropical Medicine & Tulane University Vector-Borne & Infectious Disease Research Center, New Orleans, LA 70112, USA
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McKinney BC, McClain LL, Hensler CM, Wei Y, Klei L, Lewis DA, Devlin B, Wang J, Ding Y, Sweet RA. Schizophrenia-associated differential DNA methylation in brain is distributed across the genome and annotated to MAD1L1, a locus at which DNA methylation and transcription phenotypes share genetic variation with schizophrenia risk. Transl Psychiatry 2022; 12:340. [PMID: 35987687 PMCID: PMC9392724 DOI: 10.1038/s41398-022-02071-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 06/21/2022] [Accepted: 07/15/2022] [Indexed: 11/09/2022] Open
Abstract
DNA methylation (DNAm), the addition of a methyl group to a cytosine in DNA, plays an important role in the regulation of gene expression. Single-nucleotide polymorphisms (SNPs) associated with schizophrenia (SZ) by genome-wide association studies (GWAS) often influence local DNAm levels. Thus, DNAm alterations, acting through effects on gene expression, represent one potential mechanism by which SZ-associated SNPs confer risk. In this study, we investigated genome-wide DNAm in postmortem superior temporal gyrus from 44 subjects with SZ and 44 non-psychiatric comparison subjects using Illumina Infinium MethylationEPIC BeadChip microarrays, and extracted cell-type-specific methylation signals by applying tensor composition analysis. We identified SZ-associated differential methylation at 242 sites, and 44 regions containing two or more sites (FDR cutoff of q = 0.1) and determined a subset of these were cell-type specific. We found mitotic arrest deficient 1-like 1 (MAD1L1), a gene within an established GWAS risk locus, harbored robust SZ-associated differential methylation. We investigated the potential role of MAD1L1 DNAm in conferring SZ risk by assessing for colocalization among quantitative trait loci for methylation and gene transcripts (mQTLs and tQTLs) in brain tissue and GWAS signal at the locus using multiple-trait-colocalization analysis. We found that mQTLs and tQTLs colocalized with the GWAS signal (posterior probability >0.8). Our findings suggest that alterations in MAD1L1 methylation and transcription may mediate risk for SZ at the MAD1L1-containing locus. Future studies to identify how SZ-associated differential methylation affects MAD1L1 biological function are indicated.
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Affiliation(s)
- Brandon C McKinney
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA.
- Translational Neuroscience Program, Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA.
| | - Lora L McClain
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Christopher M Hensler
- Translational Neuroscience Program, Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Yue Wei
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Lambertus Klei
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - David A Lewis
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
- Translational Neuroscience Program, Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Bernie Devlin
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jiebiao Wang
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ying Ding
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Robert A Sweet
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA.
- Translational Neuroscience Program, Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA.
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, USA.
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Lee JU, Soo Chang H, Kyung Kim M, Park SL, Kim JH, Park JS, Park CS. Genome-wide DNA methylation profile of peripheral blood lymphocytes from subjects with nonsteroidal anti-inflammatory drug-induced respiratory diseases. Pharmacogenet Genomics 2022; 32:226-234. [PMID: 35696287 DOI: 10.1097/fpc.0000000000000475] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
BACKGROUND Significant changes in CpG methylation have been identified in nasal polyps, which are the main targets of nonsteroidal anti-inflammatory drug-exacerbated respiratory disease (NERD); however, these polyps are composed of various cellular components. In the present study, whole-genome CpG methylation in peripheral blood lymphocytes (PBLs) was analyzed to define the epigenetic changes in lymphocytes, which are the primary immune cells involved in NERD. MATERIALS AND METHODS Genomic DNA from peripheral blood mononuclear cells from 27 NERD and 24 aspirin-tolerant asthma (ATA) was subjected to bisulfate conversion and a methylation array. Quantitative CpG methylation, the β-values as a quantitative measure of DNA methylation, in lymphocytes were calculated after adjustments for cellular composition. RESULTS Fifty-six hypermethylated and three hypomethylated differentially methylated CpGs (DMCs) in PBLs in the NERD compared with ATA. The top 10 CpG loci predicted the methylation risk score, with a positive predictive value of 91.3%, a negative predictive value of 81.5% and an accuracy of 84.3%. As demonstrated in the nasal polyps, 30 DMCs were predicted to bind to the following 10 transcription factors, ranked in descending order: AP-2alphaA, TFII-1, STAT4, FOXP3, GR, c-Est-1, E2F-1, XBP1, ENKTF-1 and NF-1. Gene ontology analysis identified 13 categories such as regulation of T-helper 17 cell differentiation, including SMAD7 and NFKBIZ. PBLs in NERD contained no DMCs in genes associated with the prostaglandin and leukotriene pathways, which were found in ATA. CONCLUSION PBLs in NERD form a unique pattern of DNA CpG methylation, and the combined analysis may provide predictive values for NERD.
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Affiliation(s)
- Jong-Uk Lee
- Division of Allergy and Respiratory Medicine, Department of Internal Medicine, Soonchunhyang University Bucheon Hospital
| | - Hun Soo Chang
- Department of Anatomy and BK21 FOUR Project, College of Medicine, Soonchunhyang University, Cheonan
| | - Min Kyung Kim
- Department of Interdisciplinary Program in Biomedical Science Major, Soonchuhyang University
| | - Seung-Lee Park
- Department of Interdisciplinary Program in Biomedical Science Major, Soonchuhyang University
| | - Jung Hyun Kim
- Department of Internal Medicine, Korean Armed Forces Capital Hospital, Seongnam
| | - Jong-Sook Park
- Division of Allergy and Respiratory Medicine, Department of Internal Medicine, Soonchunhyang University Bucheon Hospital
| | - Choon-Sik Park
- Division of Allergy and Respiratory Medicine, Department of Internal Medicine, Soonchunhyang University Bucheon Hospital
- PulmoBioPark Co., Ltd., Soonchunhyang University Bucheon Hospital, Bucheon, Korea
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