1
|
Xu H, Zhang G, Chen J. A novel method for cell deconvolution using DNA methylation in PCA space. BMC Genomics 2024; 25:798. [PMID: 39179972 PMCID: PMC11344294 DOI: 10.1186/s12864-024-10652-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Accepted: 07/22/2024] [Indexed: 08/26/2024] Open
Abstract
BACKGROUND In this study, we present a novel method for reference-based cell deconvolution using data from DNA methylation arrays. Different from existing methods like IDOL-Ext, which operate on probe-level data, our approach represents features in the principal component analysis (PCA) space for cell type deconvolution. RESULTS Our method's accuracy in estimating cell compositions is validated across various public datasets, including blood samples from glioma patients. It demonstrates precision comparable to IDOL-Ext, with R2 values ranging from 0.73 to 0.99 for most cell types, while offering improved discrimination between similar cell types, particularly T cell subtypes in glioma patient samples (R2 0.42-0.75 vs. 0.36-0.66 for IDOL-Ext). However, both methods showed lower accuracy for certain cell types, such as memory CD8 T cells in glioma patients (R2 0.42 vs. 0.36 for IDOL-Ext), highlighting the challenges in distinguishing closely related cell populations. We have made this method available as an R package "BloodCellDecon" on GitHub. CONCLUSIONS Our study confirms the efficacy of cell type deconvolution in PCA space. The results indicate wide-ranging applicability and potential for adaptation to other forms of genomic data.
Collapse
Affiliation(s)
- Huan Xu
- Division of Human Genetics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Ge Zhang
- Division of Human Genetics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- Center for Prevention of Preterm Birth, Perinatal Institute, Cincinnati Children's Hospital Medical Center and March of Dimes Prematurity Research Center Ohio Collaborative, Cincinnati, OH, USA
| | - Jing Chen
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA.
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
| |
Collapse
|
2
|
Teschendorff AE. Computational single-cell methods for predicting cancer risk. Biochem Soc Trans 2024; 52:1503-1514. [PMID: 38856037 DOI: 10.1042/bst20231488] [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: 01/29/2024] [Revised: 05/22/2024] [Accepted: 05/24/2024] [Indexed: 06/11/2024]
Abstract
Despite recent biotechnological breakthroughs, cancer risk prediction remains a formidable computational and experimental challenge. Addressing it is critical in order to improve prevention, early detection and survival rates. Here, I briefly summarize some key emerging theoretical and computational challenges as well as recent computational advances that promise to help realize the goals of cancer-risk prediction. The focus is on computational strategies based on single-cell data, in particular on bottom-up network modeling approaches that aim to estimate cancer stemness and dedifferentiation at single-cell resolution from a systems-biological perspective. I will describe two promising methods, a tissue and cell-lineage independent one based on the concept of diffusion network entropy, and a tissue and cell-lineage specific one that uses transcription factor regulons. Application of these tools to single-cell and single-nucleus RNA-seq data from stages prior to invasive cancer reveal that they can successfully delineate the heterogeneous inter-cellular cancer-risk landscape, identifying those cells that are more likely to turn cancerous. Bottom-up systems biological modeling of single-cell omic data is a novel computational analysis paradigm that promises to facilitate the development of preventive, early detection and cancer-risk prediction strategies.
Collapse
Affiliation(s)
- Andrew E Teschendorff
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| |
Collapse
|
3
|
Hood RB, Everson TM, Ford JB, Hauser R, Knight A, Smith AK, Gaskins AJ. Epigenetic age acceleration in follicular fluid and markers of ovarian response among women undergoing IVF. Hum Reprod 2024:deae136. [PMID: 38890131 DOI: 10.1093/humrep/deae136] [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: 12/04/2023] [Revised: 05/07/2024] [Indexed: 06/20/2024] Open
Abstract
STUDY QUESTION Are markers of epigenetic age acceleration in follicular fluid associated with outcomes of ovarian stimulation? SUMMARY ANSWER Increased epigenetic age acceleration of follicular fluid using the Horvath clock, but not other epigenetic clocks (GrimAge and Granulosa Cell), was associated with lower peak estradiol levels and decreased number of total and mature oocytes. WHAT IS KNOWN ALREADY In granulosa cells, there are inconsistent findings between epigenetic age acceleration and ovarian response outcomes. STUDY DESIGN, SIZE, DURATION Our study included 61 women undergoing IVF at an academic fertility clinic in the New England area who were part of the Environment and Reproductive Health Study (2006-2016). PARTICIPANTS/MATERIALS, SETTING, METHODS Participants provided a follicular fluid sample during oocyte retrieval. DNA methylation of follicular fluid was assessed using a genome-wide methylation screening tool. Three established epigenetic clocks (Horvath, GrimAge, and Granulosa Cell) were used to predict DNA-methylation-based epigenetic age. To calculate the age acceleration, we regressed epigenetic age on chronological age and extracted the residuals. The association between epigenetic age acceleration and ovarian response outcomes (peak estradiol levels, follicle stimulation hormone, number of total, and mature oocytes) was assessed using linear and Poisson regression adjusted for chronological age, three surrogate variables (to account for cellular heterogeneity), race, smoking status, initial infertility diagnosis, and stimulation protocol. MAIN RESULTS AND ROLE OF CHANCE Compared to the median chronological age of our participants (34 years), the Horvath clock predicted, on an average, a younger epigenetic age (median: 24.2 years) while the GrimAge (median: 38.6 years) and Granulosa Cell (median: 39.0 years) clocks predicted, on an average, an older epigenetic age. Age acceleration based on the Horvath clock was associated with lower peak estradiol levels (-819.4 unit decrease in peak estradiol levels per standard deviation increase; 95% CI: -1265.7, -373.1) and fewer total (% change in total oocytes retrieved per standard deviation increase: -21.8%; 95% CI: -37.1%, -2.8%) and mature oocytes retrieved (% change in mature oocytes retrieved per standard deviation increase: -23.8%; 95% CI: -39.9%, -3.4%). The age acceleration based on the two other epigenetic clocks was not associated with markers of ovarian response. LIMITATIONS, REASONS FOR CAUTION Our sample size was small and we did not specifically isolate granulosa cells from follicular fluid samples so our samples could have included mixed cell types. WIDER IMPLICATIONS OF THE FINDINGS Our results highlight that certain epigenetic clocks may be predictive of ovarian stimulation outcomes when applied to follicular fluid; however, the inconsistent findings for specific clocks across studies indicate a need for further research to better understand the clinical utility of epigenetic clocks to improve IVF treatment. STUDY FUNDING/COMPETING INTEREST(S) The study was supported by grants ES009718, ES022955, ES000002, and ES026648 from the National Institute of Environmental Health Sciences (NIEHS) and a pilot grant from the NIEHS-funded HERCULES Center at Emory University (P30 ES019776). RBH was supported by the Emory University NIH Training Grant (T32-ES012870). TRIAL REGISTRATION NUMBER N/A.
Collapse
Affiliation(s)
- Robert B Hood
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Todd M Everson
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Jennifer B Ford
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Russ Hauser
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Anna Knight
- Department of Gynecology and Obstetrics, School of Medicine, Emory University, Atlanta, GA, USA
| | - Alicia K Smith
- Department of Gynecology and Obstetrics, School of Medicine, Emory University, Atlanta, GA, USA
| | - Audrey J Gaskins
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| |
Collapse
|
4
|
Unterman I, Avrahami D, Katsman E, Triche TJ, Glaser B, Berman BP. CelFiE-ISH: a probabilistic model for multi-cell type deconvolution from single-molecule DNA methylation haplotypes. Genome Biol 2024; 25:151. [PMID: 38858759 PMCID: PMC11163775 DOI: 10.1186/s13059-024-03275-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Accepted: 05/13/2024] [Indexed: 06/12/2024] Open
Abstract
Deconvolution methods infer quantitative cell type estimates from bulk measurement of mixed samples including blood and tissue. DNA methylation sequencing measures multiple CpGs per read, but few existing deconvolution methods leverage this within-read information. We develop CelFiE-ISH, which extends an existing method (CelFiE) to use within-read haplotype information. CelFiE-ISH outperforms CelFiE and other existing methods, achieving 30% better accuracy and more sensitive detection of rare cell types. We also demonstrate the importance of marker selection and of tailoring markers for haplotype-aware methods. While here we use gold-standard short-read sequencing data, haplotype-aware methods will be well-suited for long-read sequencing.
Collapse
Affiliation(s)
- Irene Unterman
- Department of Developmental Biology and Cancer Research, Institute for Medical Research Israel-Canada, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Dana Avrahami
- Department of Developmental Biology and Cancer Research, Institute for Medical Research Israel-Canada, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
- Department of Endocrinology and Metabolism, Hadassah Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Efrat Katsman
- Department of Developmental Biology and Cancer Research, Institute for Medical Research Israel-Canada, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Timothy J Triche
- Center for Epigenetics, Van Andel Research Institute, Grand Rapids, MI, USA
| | - Benjamin Glaser
- Department of Endocrinology and Metabolism, Hadassah Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel.
| | - Benjamin P Berman
- Department of Developmental Biology and Cancer Research, Institute for Medical Research Israel-Canada, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel.
| |
Collapse
|
5
|
Yap CX, Vo DD, Heffel MG, Bhattacharya A, Wen C, Yang Y, Kemper KE, Zeng J, Zheng Z, Zhu Z, Hannon E, Vellame DS, Franklin A, Caggiano C, Wamsley B, Geschwind DH, Zaitlen N, Gusev A, Pasaniuc B, Mill J, Luo C, Gandal MJ. Brain cell-type shifts in Alzheimer's disease, autism, and schizophrenia interrogated using methylomics and genetics. SCIENCE ADVANCES 2024; 10:eadn7655. [PMID: 38781333 PMCID: PMC11114225 DOI: 10.1126/sciadv.adn7655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Accepted: 03/14/2024] [Indexed: 05/25/2024]
Abstract
Few neuropsychiatric disorders have replicable biomarkers, prompting high-resolution and large-scale molecular studies. However, we still lack consensus on a more foundational question: whether quantitative shifts in cell types-the functional unit of life-contribute to neuropsychiatric disorders. Leveraging advances in human brain single-cell methylomics, we deconvolve seven major cell types using bulk DNA methylation profiling across 1270 postmortem brains, including from individuals diagnosed with Alzheimer's disease, schizophrenia, and autism. We observe and replicate cell-type compositional shifts for Alzheimer's disease (endothelial cell loss), autism (increased microglia), and schizophrenia (decreased oligodendrocytes), and find age- and sex-related changes. Multiple layers of evidence indicate that endothelial cell loss contributes to Alzheimer's disease, with comparable effect size to APOE genotype among older people. Genome-wide association identified five genetic loci related to cell-type composition, involving plausible genes for the neurovascular unit (P2RX5 and TRPV3) and excitatory neurons (DPY30 and MEMO1). These results implicate specific cell-type shifts in the pathophysiology of neuropsychiatric disorders.
Collapse
Affiliation(s)
- Chloe X. Yap
- Mater Research Institute, University of Queensland, Brisbane, Queensland, Australia
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Daniel D. Vo
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Lifespan Brain Institute at Penn Medicine and The Children’s Hospital of Philadelphia, Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Matthew G. Heffel
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA
| | - Arjun Bhattacharya
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Institute for Quantitative and Computational Biosciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Department of Epidemiology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Institute for Data Science in Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Cindy Wen
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Yuanhao Yang
- Mater Research Institute, University of Queensland, Brisbane, Queensland, Australia
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
| | - Kathryn E. Kemper
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
| | - Jian Zeng
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
| | - Zhili Zheng
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
| | - Zhihong Zhu
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
- The National Centre for Register-based Research, Aarhus University, Denmark
| | - Eilis Hannon
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Dorothea Seiler Vellame
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Alice Franklin
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Christa Caggiano
- Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA
- Department of Neurology, University of California Los Angeles, Los Angeles, CA, USA
| | - Brie Wamsley
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Neurology, University of California Los Angeles, Los Angeles, CA, USA
- Center for Autism Research and Treatment, Semel Institute, University of California, Los Angeles, Los Angeles, CA, USA
| | - Daniel H. Geschwind
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Neurology, University of California Los Angeles, Los Angeles, CA, USA
- Center for Autism Research and Treatment, Semel Institute, University of California, Los Angeles, Los Angeles, CA, USA
| | - Noah Zaitlen
- Department of Neurology, University of California Los Angeles, Los Angeles, CA, USA
- Department of Computational Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Alexander Gusev
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA
- Division of Genetics, Brigham & Women’s Hospital, Boston, MA, USA
- Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
| | - Bogdan Pasaniuc
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Department of Computational Medicine, University of California Los Angeles, Los Angeles, CA, USA
- Institute for Precision Health, University of California, Los Angeles, Los Angeles, CA, USA
| | - Jonathan Mill
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Chongyuan Luo
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Michael J. Gandal
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Lifespan Brain Institute at Penn Medicine and The Children’s Hospital of Philadelphia, Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| |
Collapse
|
6
|
De Ridder K, Che H, Leroy K, Thienpont B. Benchmarking of methods for DNA methylome deconvolution. Nat Commun 2024; 15:4134. [PMID: 38755121 PMCID: PMC11099101 DOI: 10.1038/s41467-024-48466-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: 10/20/2023] [Accepted: 04/30/2024] [Indexed: 05/18/2024] Open
Abstract
Defining the number and abundance of different cell types in tissues is important for understanding disease mechanisms as well as for diagnostic and prognostic purposes. Typically, this is achieved by immunohistological analyses, cell sorting, or single-cell RNA-sequencing. Alternatively, cell-specific DNA methylome information can be leveraged to deconvolve cell fractions from a bulk DNA mixture. However, comprehensive benchmarking of deconvolution methods and modalities was not yet performed. Here we evaluate 16 deconvolution algorithms, developed either specifically for DNA methylome data or more generically. We assess the performance of these algorithms, and the effect of normalization methods, while modeling variables that impact deconvolution performance, including cell abundance, cell type similarity, reference panel size, method for methylome profiling (array or sequencing), and technical variation. We observe differences in algorithm performance depending on each these variables, emphasizing the need for tailoring deconvolution analyses. The complexity of the reference, marker selection method, number of marker loci and, for sequencing-based assays, sequencing depth have a marked influence on performance. By developing handles to select the optimal analysis configuration, we provide a valuable source of information for studies aiming to deconvolve array- or sequencing-based methylation data.
Collapse
Affiliation(s)
- Kobe De Ridder
- Laboratory for Functional Epigenetics, Department of Human Genetics, KU Leuven, 3000, Leuven, Belgium
| | - Huiwen Che
- Laboratory for Functional Epigenetics, Department of Human Genetics, KU Leuven, 3000, Leuven, Belgium
| | - Kaat Leroy
- Laboratory for Functional Epigenetics, Department of Human Genetics, KU Leuven, 3000, Leuven, Belgium
| | - Bernard Thienpont
- Laboratory for Functional Epigenetics, Department of Human Genetics, KU Leuven, 3000, Leuven, Belgium.
- KU Leuven Institute for Single Cell Omics (LISCO), KU Leuven, 3000, Leuven, Belgium.
- KU Leuven Cancer Institute (LKI), KU Leuven, 3000, Leuven, Belgium.
| |
Collapse
|
7
|
Biskup E, Lopacinska-Jørgensen J, Vestergaard LK, Høgdall E. Validating reference-based algorithms to determine cell-type heterogeneity in ovarian cancer DNA methylation studies. Sci Rep 2024; 14:11048. [PMID: 38745057 PMCID: PMC11094148 DOI: 10.1038/s41598-024-61857-y] [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: 02/16/2024] [Accepted: 05/10/2024] [Indexed: 05/16/2024] Open
Abstract
Information about cell composition in tissue samples is crucial for biomarker discovery and prognosis. Specifically, cancer tissue samples present challenges in deconvolution studies due to mutations and genetic rearrangements. Here, we optimized a robust, DNA methylation-based protocol, to be used for deconvolution of ovarian cancer samples. We compared several state-of-the-art methods (HEpiDISH, MethylCIBERSORT and ARIC) and validated the proposed protocol in an in-silico mixture and in an external dataset containing samples from ovarian cancer patients and controls. The deconvolution protocol we eventually implemented is based on MethylCIBERSORT. Comparing deconvolution methods, we paid close attention to the role of a reference panel. We postulate that a possibly high number of samples (in our case: 247) should be used when building a reference panel to ensure robustness and to compensate for biological and technical variation between samples. Subsequently, we tested the performance of the validated protocol in our own study cohort, consisting of 72 patients with malignant and benign ovarian disease as well as in five external cohorts. In conclusion, we refined and validated a reference-based algorithm to determine cell type composition of ovarian cancer tissue samples to be used in cancer biology studies in larger cohorts.
Collapse
Affiliation(s)
- Edyta Biskup
- Department of Pathology, Copenhagen University Hospital, Herlev, Denmark.
| | | | | | - Estrid Høgdall
- Department of Pathology, Copenhagen University Hospital, Herlev, Denmark
| |
Collapse
|
8
|
Campbell KA, Colacino JA, Dou J, Dolinoy DC, Park SK, Loch-Caruso R, Padmanabhan V, Bakulski KM. Placental and Immune Cell DNA Methylation Reference Panel for Bulk Tissue Cell Composition Estimation in Epidemiological Studies. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.06.588886. [PMID: 38766167 PMCID: PMC11100803 DOI: 10.1101/2024.05.06.588886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
To distinguish DNA methylation (DNAm) from cell proportion changes in whole placental tissue research, we developed a robust cell type-specific DNAm reference to estimate cell composition. We collated newly collected and existing cell type DNAm profiles quantified via Illumina EPIC or 450k microarrays. To estimate cell composition, we deconvoluted whole placental samples (n=36) with robust partial correlation based on the top 50 hyper- and hypomethylated sites per cell type. To test deconvolution performance, we evaluated RMSE in predicting principal component one of DNAm variation in 204 external placental samples. We analyzed DNAm profiles (n=368,435 sites) from 12 cell types: cytotrophoblasts (n=18), endothelial cells (n=19), Hofbauer cells (n=26), stromal cells (n=21), syncytiotrophoblasts (n=4), six lymphocyte types (n=36), and nucleated red blood cells (n=11). Median cell composition was consistent with placental biology: 60.4% syncytiotrophoblast, 17.1% stromal, 8.8% endothelial, 4.5% cytotrophoblast, 3.9% Hofbauer, 1.7% nucleated red blood cells, and 1.2% neutrophils. Our expanded reference outperformed an existing reference in predicting DNAm variation (15.4% variance explained, IQR=21.61) with cell composition estimates (RMSE:10.51 vs. 11.43, p-value<0.001). This cell type reference can robustly estimate cell composition from whole placental DNAm data to detect important cell types, reveal biological mechanisms, and improve casual inference.
Collapse
Affiliation(s)
- Kyle A. Campbell
- Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
| | - Justin A. Colacino
- Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
- Nutritional Sciences, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
| | - John Dou
- Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
| | - Dana C. Dolinoy
- Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
- Nutritional Sciences, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
| | - Sung Kyun Park
- Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
- Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
| | - Rita Loch-Caruso
- Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
| | - Vasantha Padmanabhan
- Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
- Nutritional Sciences, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
- Pediatrics, Michigan Medicine, University of Michigan, Ann Arbor, MI 48109, USA
- Obstetrics and Gynecology, Michigan Medicine, University of Michigan, Ann Arbor, MI 48109, USA
| | - Kelly M. Bakulski
- Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
| |
Collapse
|
9
|
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.
Collapse
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.
| |
Collapse
|
10
|
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.
Collapse
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
| |
Collapse
|
11
|
Viola TW, Danzer C, Mardini V, Szobot C, Chrusciel JH, Stertz L, Schmitz JM, Walss-Bass C, Fries GR, Grassi-Oliveira R. Prenatal cocaine exposure and its influence on pediatric epigenetic clocks and epigenetic scores in humans. Sci Rep 2024; 14:1946. [PMID: 38253635 PMCID: PMC10803757 DOI: 10.1038/s41598-024-52433-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 01/18/2024] [Indexed: 01/24/2024] Open
Abstract
The investigation of the effects of prenatal cocaine exposure (PCE) on offspring has been inconsistent, with few studies investigating biological outcomes in humans. We profiled genome-wide DNA methylation (DNAm) of umbilical cord blood (UCB) from newborns with (n = 35) and without (n = 47) PCE. We used DNAm data to (1) assess pediatric epigenetic clocks at birth and (2) to estimate epigenetic scores (ES) for lifetime disorders. We generated gestational epigenetic age estimates (DNAmGA) based on Knight and Bohlin epigenetic clocks. We also investigated the association between DNAmGA and UCB serum brain-derived neurotrophic factor (BDNF) levels. Considering the large-scale DNAm data availability and existing evidence regarding PCE as a risk for health problems later in life, we generated ES for tobacco smoking, psychosis, autism, diabetes, and obesity. A gene ontology (GO) analysis on the CpGs included in the ES with group differences was performed. PCE was associated with lower DNAmGA in newborns, and this effect remained significant when controlling for potential confounders, such as blood cell type composition predicted by DNAm and obstetric data. DNAmGA was negatively correlated with BDNF levels in the serum of UCB. Higher tobacco smoking, psychosis, and diabetes ES were found in the PCE group. The GO analysis revealed GABAergic synapses as a potential pathway altered by PCE. Our findings of decelerated DNAmGA and ES for adverse phenotypes associated with PCE, suggest that the effects of gestational cocaine exposure on the epigenetic landscape of human newborns are detectable at birth.
Collapse
Affiliation(s)
- Thiago Wendt Viola
- School of Medicine, Developmental Cognitive Neuroscience Lab, Pontifical Catholic University of Rio Grande Do Sul (PUCRS), Porto Alegre, RS, Brazil
| | - Christina Danzer
- Translational Neuropsychiatry Unit, Department of Clinical Medicine, Aarhus University, Palle Juul-Jensens Boulevard 11, A701-129, 8200, Aarhus, Denmark
| | - Victor Mardini
- Clinical Hospital of Porto Alegre, Porto Alegre, RS, Brazil
| | - Claudia Szobot
- Clinical Hospital of Porto Alegre, Porto Alegre, RS, Brazil
| | - João Henrique Chrusciel
- School of Medicine, Developmental Cognitive Neuroscience Lab, Pontifical Catholic University of Rio Grande Do Sul (PUCRS), Porto Alegre, RS, Brazil
| | - Laura Stertz
- Faillace Department of Psychiatry and Behavioral Sciences, Translational Psychiatry Program, The University of Texas Health Science Center at Houston, Houston, USA
| | - Joy M Schmitz
- Faillace Department of Psychiatry and Behavioral Sciences, Translational Psychiatry Program, The University of Texas Health Science Center at Houston, Houston, USA
| | - Consuelo Walss-Bass
- Faillace Department of Psychiatry and Behavioral Sciences, Translational Psychiatry Program, The University of Texas Health Science Center at Houston, Houston, USA
| | - Gabriel R Fries
- Faillace Department of Psychiatry and Behavioral Sciences, Translational Psychiatry Program, The University of Texas Health Science Center at Houston, Houston, USA
| | - Rodrigo Grassi-Oliveira
- School of Medicine, Developmental Cognitive Neuroscience Lab, Pontifical Catholic University of Rio Grande Do Sul (PUCRS), Porto Alegre, RS, Brazil.
- Translational Neuropsychiatry Unit, Department of Clinical Medicine, Aarhus University, Palle Juul-Jensens Boulevard 11, A701-129, 8200, Aarhus, Denmark.
| |
Collapse
|
12
|
Pike SC, Havrda M, Gilli F, Zhang Z, Salas LA. Immunological shifts during early-stage Parkinson's disease identified with DNA methylation data on longitudinally collected blood samples. NPJ Parkinsons Dis 2024; 10:21. [PMID: 38212355 PMCID: PMC10784484 DOI: 10.1038/s41531-023-00626-6] [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: 06/08/2023] [Accepted: 12/18/2023] [Indexed: 01/13/2024] Open
Abstract
Parkinson's disease (PD) is the second most common neurodegenerative disease in the United States. Decades before motor symptoms manifest, non-motor symptoms such as hyposmia and rapid eye movement (REM) sleep behavior disorder are highly predictive of PD. Previous immune profiling studies have identified alterations to the proportions of immune cells in the blood of clinically defined PD patients. However, it remains unclear if these phenotypes manifest before the clinical diagnosis of PD. We utilized longitudinal DNA methylation (DNAm) microarray data from the Parkinson's Progression Marker's Initiative (PPMI) to perform immune profiling in clinically defined PD and prodromal PD patients (Prod). We identified previously reported changes in neutrophil, monocyte, and T cell numbers in PD patients. Additionally, we noted previously unrecognized decreases in the naive B cell compartment in the defined PD and Prod patient group. Over time, we observed the proportion of innate immune cells in PD blood increased, but the proportion of adaptive immune cells decreased. We identified decreases in T and B cell subsets associated with REM sleep disturbances and early cognitive decline. Lastly, we identified increases in B memory cells associated with both genetic (LRRK2 genotype) and infectious (cytomegalovirus seropositivity) risk factors of PD. Our analysis shows that the peripheral immune system is dynamic as the disease progresses. The study provides a platform to understand how and when peripheral immune alterations occur in PD and whether intervention at particular stages may be therapeutically advantageous.
Collapse
Affiliation(s)
- Steven C Pike
- Integrative Neuroscience at Dartmouth, Guarini School of Graduate and Advanced Studies at Dartmouth College, Hanover, NH, USA.
- Department of Epidemiology, Geisel School of Medicine at Dartmouth College, Lebanon, NH, USA.
- Department of Neurology, Dartmouth Hitchcock Medical Center, Lebanon, NH, USA.
| | - Matthew Havrda
- Integrative Neuroscience at Dartmouth, Guarini School of Graduate and Advanced Studies at Dartmouth College, Hanover, NH, USA
- Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth College, Hanover, NH, USA
| | - Francesca Gilli
- Integrative Neuroscience at Dartmouth, Guarini School of Graduate and Advanced Studies at Dartmouth College, Hanover, NH, USA
- Department of Neurology, Dartmouth Hitchcock Medical Center, Lebanon, NH, USA
| | - Ze Zhang
- Department of Epidemiology, Geisel School of Medicine at Dartmouth College, Lebanon, NH, USA
| | - Lucas A Salas
- Integrative Neuroscience at Dartmouth, Guarini School of Graduate and Advanced Studies at Dartmouth College, Hanover, NH, USA.
- Department of Epidemiology, Geisel School of Medicine at Dartmouth College, Lebanon, NH, USA.
| |
Collapse
|
13
|
Chen JQ, Salas LA, Wiencke JK, Koestler DC, Molinaro AM, Andrew AS, Seigne JD, Karagas MR, Kelsey KT, Christensen BC. Matched analysis of detailed peripheral blood and tumor immune microenvironment profiles in bladder cancer. Epigenomics 2024; 16:41-56. [PMID: 38221889 PMCID: PMC10804212 DOI: 10.2217/epi-2023-0358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Accepted: 12/11/2023] [Indexed: 01/16/2024] Open
Abstract
Background: Bladder cancer and therapy responses hinge on immune profiles in the tumor microenvironment (TME) and blood, yet studies linking tumor-infiltrating immune cells to peripheral immune profiles are limited. Methods: DNA methylation cytometry quantified TME and matched peripheral blood immune cell proportions. With tumor immune profile data as the input, subjects were grouped by immune infiltration status and consensus clustering. Results: Immune hot and cold groups had different immune compositions in the TME but not in circulating blood. Two clusters of patients identified with consensus clustering had different immune compositions not only in the TME but also in blood. Conclusion: Detailed immune profiling via methylation cytometry reveals the significance of understanding tumor and systemic immune relationships in cancer patients.
Collapse
Affiliation(s)
- Ji-Qing Chen
- Department of Epidemiology, Geisel School of Medicine, Dartmouth College, Lebanon, NH 03766, USA
| | - Lucas A Salas
- Department of Epidemiology, Geisel School of Medicine, Dartmouth College, Lebanon, NH 03766, USA
| | - John K Wiencke
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA 94143, USA
| | - Devin C Koestler
- Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, KS 66160, USA
| | - Annette M Molinaro
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA 94143, USA
| | - Angeline S Andrew
- Department of Neurology, Geisel School of Medicine, Dartmouth College, Lebanon, NH 03766, USA
| | - John D Seigne
- Department of Surgery, Section of Urology, Geisel School of Medicine, Dartmouth College, Lebanon, NH 03766, USA
| | - Margaret R Karagas
- Department of Epidemiology, Geisel School of Medicine, Dartmouth College, Lebanon, NH 03766, USA
| | - Karl T Kelsey
- Departments of Epidemiology & Pathology & Laboratory Medicine, Brown University, Providence, RI 02912, USA
| | - Brock C Christensen
- Department of Epidemiology, Geisel School of Medicine, Dartmouth College, Lebanon, NH 03766, USA
- Departments of Molecular and Systems Biology, Geisel School of Medicine, Dartmouth College, Lebanon, NH 03766, USA
| |
Collapse
|
14
|
Derakhshan M, Kessler NJ, Hellenthal G, Silver MJ. Metastable epialleles in humans. Trends Genet 2024; 40:52-68. [PMID: 38000919 DOI: 10.1016/j.tig.2023.09.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 09/20/2023] [Accepted: 09/21/2023] [Indexed: 11/26/2023]
Abstract
First identified in isogenic mice, metastable epialleles (MEs) are loci where the extent of DNA methylation (DNAm) is variable between individuals but correlates across tissues derived from different germ layers within a given individual. This property, termed systemic interindividual variation (SIV), is attributed to stochastic methylation establishment before germ layer differentiation. Evidence suggests that some putative human MEs are sensitive to environmental exposures in early development. In this review we introduce key concepts pertaining to human MEs, describe methods used to identify MEs in humans, and review their genomic features. We also highlight studies linking DNAm at putative human MEs to early environmental exposures and postnatal (including disease) phenotypes.
Collapse
Affiliation(s)
- Maria Derakhshan
- London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
| | - Noah J Kessler
- Department of Genetics, University of Cambridge, Cambridge CB2 3EH, UK
| | | | - Matt J Silver
- London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK; Medical Research Council (MRC) Unit The Gambia at the London School of Hygiene and Tropical Medicine, Fajara, Banjul, The Gambia.
| |
Collapse
|
15
|
Zeng Y, Jain R, Lam M, Ahmed M, Guo H, Xu W, Zhong Y, Wei GH, Xu W, He HH. DNA methylation modulated genetic variant effect on gene transcriptional regulation. Genome Biol 2023; 24:285. [PMID: 38066556 PMCID: PMC10709945 DOI: 10.1186/s13059-023-03130-5] [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: 04/21/2023] [Accepted: 11/28/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND Expression quantitative trait locus (eQTL) analysis has emerged as an important tool in elucidating the link between genetic variants and gene expression, thereby bridging the gap between risk SNPs and associated diseases. We recently identified and validated a specific case where the methylation of a CpG site influences the relationship between the genetic variant and gene expression. RESULTS Here, to systematically evaluate this regulatory mechanism, we develop an extended eQTL mapping method, termed DNA methylation modulated eQTL (memo-eQTL). Applying this memo-eQTL mapping method to 128 normal prostate samples enables identification of 1063 memo-eQTLs, the majority of which are not recognized as conventional eQTLs in the same cohort. We observe that the methylation of the memo-eQTL CpG sites can either enhance or insulate the interaction between SNP and gene expression by altering CTCF-based chromatin 3D structure. CONCLUSIONS This study demonstrates the prevalence of memo-eQTLs paving the way to identify novel causal genes for traits or diseases associated with genetic variations.
Collapse
Affiliation(s)
- Yong Zeng
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada.
| | - Rahi Jain
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
| | - Magnus Lam
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - Musaddeque Ahmed
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
| | - Haiyang Guo
- Department of Clinical Laboratory, the Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, 250033, Shandong, China
| | - Wenjie Xu
- MOE Key Laboratory of Metabolism and Molecular Medicine and Department of Biochemistry and Molecular Biology of School of Basic Medical Sciences, Fudan University Shanghai Cancer Center, Shanghai Medical College of Fudan University, Shanghai, China
| | - Yuan Zhong
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
| | - Gong-Hong Wei
- MOE Key Laboratory of Metabolism and Molecular Medicine and Department of Biochemistry and Molecular Biology of School of Basic Medical Sciences, Fudan University Shanghai Cancer Center, Shanghai Medical College of Fudan University, Shanghai, China
- Biocenter Oulu & Faculty of Biochemistry and Molecular Medicine, University of Oulu, Oulu, Finland
| | - Wei Xu
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.
| | - Housheng Hansen He
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada.
- Department of Medical Biophysics, University of Toronto, Toronto, Canada.
| |
Collapse
|
16
|
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).
Collapse
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
| |
Collapse
|
17
|
Ho PJ, Khng AJ, Tan BKT, Lim GH, Tan SM, Tan VKM, Tan RSYC, Lim EH, Iau PTC, Chew YJ, Lim YY, Hartman M, Tan EY, Li J. Alterations to DNA methylation patterns induced by chemotherapy treatment are associated with negative impacts on the olfactory pathway. Breast Cancer Res 2023; 25:136. [PMID: 37932858 PMCID: PMC10626732 DOI: 10.1186/s13058-023-01730-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 10/15/2023] [Indexed: 11/08/2023] Open
Abstract
BACKGROUND Exposure to cytotoxic chemotherapy treatment may alter DNA methylation (DNAm) in breast cancer patients. METHODS We performed DNAm analysis in 125 breast cancer patients with blood drawn before and after chemotherapy, using the Illumina MethylationEPIC array. DNAm changes of 588,798 individual CpGs (including 41,207 promoter regions) were evaluated using linear regression models adjusted for monocyte proportion. Gene set enrichment analyses (GSEA) were conducted to identify key Gene Ontology (GO) biological processes or Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways associated with chemotherapy. Results were validated in a separate cohort of breast cancer patients who were treated (n = 1273) and not treated (n = 872) by chemotherapy (1808 blood, 337 saliva). RESULTS A total of 141 differentially methylated CpGs and 11 promoters were significantly associated with chemotherapy after multiple testing corrections in both the paired sample and single time point analyses. GSEA of promoter regions (pre-ranked by test statistics) identified six suppressed biological processes (p < 4.67e-8) related to sensory perception and detection of chemical stimuli, including smell perception (GO:0007606, GO:0007608, GO:0009593, GO:0050906, GO:0050907, and GO:0050911). The same six biological processes were significantly suppressed in the validation dataset (p < 9.02e-14). The KEGG pathway olfactory transduction (hsa04740) was also found to be significantly suppressed (ppaired-samples = 1.72e-9, psingle-timepoint-blood = 2.03e-15 and psingle-timepoint-saliva = 7.52e-56). CONCLUSION The enrichment of imprinted genes within biological processes and pathways suggests a biological mechanism by which chemotherapy could affect the perception of smell.
Collapse
Affiliation(s)
- Peh Joo Ho
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), 60 Biopolis Street, Genome, Singapore, 138672, Republic of Singapore
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Republic of Singapore
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Republic of Singapore
| | - Alexis Jiaying Khng
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), 60 Biopolis Street, Genome, Singapore, 138672, Republic of Singapore
| | - Benita Kiat-Tee Tan
- Department of Breast Surgery, Singapore General Hospital, Singapore, Republic of Singapore
- Division of Surgery and Surgical Oncology, National Cancer Centre Singapore, Singapore, Republic of Singapore
- Department of General Surgery, Sengkang General Hospital, Singapore, Republic of Singapore
| | - Geok Hoon Lim
- KK Breast Department, KK Women's and Children's Hospital, Singapore, 229899, Republic of Singapore
| | - Su-Ming Tan
- Division of Breast Surgery, Changi General Hospital, Singapore, Republic of Singapore
| | - Veronique Kiak Mien Tan
- Department of Breast Surgery, Singapore General Hospital, Singapore, Republic of Singapore
- Division of Surgery and Surgical Oncology, National Cancer Centre Singapore, Singapore, Republic of Singapore
| | - Ryan Shea Ying Cong Tan
- Division of Medical Oncology, National Cancer Centre Singapore, Singapore, Republic of Singapore
- Oncology Academic Programme, Duke-NUS Medical School, Singapore, Republic of Singapore
| | - Elaine Hsuen Lim
- Division of Medical Oncology, National Cancer Centre Singapore, Singapore, Republic of Singapore
| | - Philip Tsau-Choong Iau
- Department of Surgery, University Surgical Cluster, National University Health System, Singapore, 119228, Singapore
- Department of General Surgery, Ng Teng Fong General Hospital, 1 Jurong East St 21, Singapore, 609606, Republic of Singapore
| | - Ying Jia Chew
- Department of Surgery, University Surgical Cluster, National University Health System, Singapore, 119228, Singapore
- Department of General Surgery, Ng Teng Fong General Hospital, 1 Jurong East St 21, Singapore, 609606, Republic of Singapore
| | - Yi Ying Lim
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Republic of Singapore
| | - Mikael Hartman
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Republic of Singapore
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Republic of Singapore
- Department of Surgery, University Surgical Cluster, National University Health System, Singapore, 119228, Singapore
| | - Ern Yu Tan
- Department of General Surgery, Tan Tock Seng Hospital, Singapore, 308433, Republic of Singapore
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Republic of Singapore
| | - Jingmei Li
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), 60 Biopolis Street, Genome, Singapore, 138672, Republic of Singapore.
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Republic of Singapore.
| |
Collapse
|
18
|
Ramezani M, Zobeiry M, Abdolahi S, Hatami B, Zali MR, Baghaei K. A crosstalk between epigenetic modulations and non-alcoholic fatty liver disease progression. Pathol Res Pract 2023; 251:154809. [PMID: 37797383 DOI: 10.1016/j.prp.2023.154809] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 09/08/2023] [Accepted: 09/08/2023] [Indexed: 10/07/2023]
Abstract
Non-alcoholic fatty liver disease (NAFLD) has recently emerged as a major public health concern worldwide due to its rapidly rising prevalence and its potential to progress into end-stage liver disease. While the precise pathophysiology underlying NAFLD remains incompletely understood, it is strongly associated with various environmental triggers and other metabolic disorders. Epigenetics examines changes in gene expression that are not caused by alterations in the DNA sequence itself. There is accumulating evidence that epigenetics plays a key role in linking environmental cues to the onset and progression of NAFLD. Our understanding of how epigenetic mechanisms contribute to NAFLD pathophysiology has expanded considerably in recent years as research on the epigenetics of NAFLD has developed. This review summarizes recent insights into major epigenetic processes that have been implicated in NAFLD pathogenesis including DNA methylation, histone acetylation, and microRNAs that have emerged as promising targets for further investigation. Elucidating epigenetic mechanisms in NAFLD may uncover novel diagnostic biomarkers and therapeutic targets for this disease. However, many questions have remained unanswered regarding how epigenetics promotes NAFLD onset and progression. Additional studies are needed to further characterize the epigenetic landscape of NAFLD and validate the potential of epigenetic markers as clinical tools. Nevertheless, an enhanced understanding of the epigenetic underpinnings of NAFLD promises to provide key insights into disease mechanisms and pave the way for novel prognostic and therapeutic approaches.
Collapse
Affiliation(s)
- Meysam Ramezani
- Basic and Molecular Epidemiology of Gastrointestinal Disorders Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | | | - Shahrokh Abdolahi
- Basic and Molecular Epidemiology of Gastrointestinal Disorders Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Behzad Hatami
- Basic and Molecular Epidemiology of Gastrointestinal Disorders Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mohammad Reza Zali
- Basic and Molecular Epidemiology of Gastrointestinal Disorders Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Kaveh Baghaei
- Basic and Molecular Epidemiology of Gastrointestinal Disorders Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran; Gastroenterology and Liver Diseases Research center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| |
Collapse
|
19
|
Tost J. Do not be scared of the genome's 5th base-Explaining phenotypic variability and evolutionary dynamics through DNA methylation analysis. Mol Ecol Resour 2023; 23:1473-1476. [PMID: 37466235 DOI: 10.1111/1755-0998.13824] [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: 05/16/2023] [Accepted: 06/02/2023] [Indexed: 07/20/2023]
Abstract
Epigenetic processes have taken center stage for the investigation of many biological processes, and epigenetic modifications have shown to influence phenotype, morphology and behavioural traits such as stress resistance by affecting gene regulation and expression without altering the underlying genomic sequence. The multiple molecular layers of epigenetics synergistically construct the cell type-specific gene regulatory networks, characterized by a high degree of plasticity and redundancy to create cell-type-specific morphology and function. DNA methylation occurring on the 5' carbon of cytosines in different genomic sequence contexts is the most studied epigenetic modification. DNA methylation has been shown to provide a molecular record of the exposure to a large variety of environmental factors, which might be persistent through the entire lifetime of an organism and even be passed onto the offspring. Animals might display altered phenotypes mediated by epigenetic modifications depending on the developmental stage or the environmental conditions as well as during evolution. Therefore, the analysis of DNA methylation patterns might allow deciphering previous exposures, explaining ecologically relevant phenotypic diversity and predicting evolutionary trajectories enabling accelerated adaption to changing environmental conditions. Despite the explanatory potential of DNA methylation integrating genetic and environmental factors to shape phenotypic variation and contribute significantly to evolutionary dynamics, studies of DNA methylation are still scarce in the field of ecology. This might be at least partly due to the complexity of DNA methylation analysis and the interpretation of the acquired data. In the current issue of Molecular Ecology Resources, Laine and colleagues (Molecular Ecology Resources, 2022) provide a detailed summary of guidelines and valuable recommendations for researchers in the field of ecology to avoid common pitfalls and perform interpretable genome-wide DNA methylation analyses.
Collapse
Affiliation(s)
- Jorg Tost
- Laboratory for Epigenetics and Environment, Centre National de Recherche en Génomique Humaine, CEA-Institut de Biologie François Jacob, Université Paris-Saclay, Evry, France
| |
Collapse
|
20
|
Takeuchi F, Liang YQ, Shimizu-Furusawa H, Isono M, Ang MY, Mori K, Mori T, Kakazu E, Yoshio S, Kato N. Gene-regulation modules in nonalcoholic fatty liver disease revealed by single-nucleus ATAC-seq. Life Sci Alliance 2023; 6:e202301988. [PMID: 37491046 PMCID: PMC10368228 DOI: 10.26508/lsa.202301988] [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: 02/13/2023] [Revised: 07/14/2023] [Accepted: 07/14/2023] [Indexed: 07/27/2023] Open
Abstract
We investigated the progression of nonalcoholic fatty liver disease from fatty liver to steatohepatitis using single-nucleus and bulk ATAC-seq on the livers of rats fed a high-fat diet (HFD). Rats fed HFD for 4 wk developed fatty liver, and those fed HFD for 8 wk further progressed to steatohepatitis. We observed an increase in the proportion of inflammatory macrophages, consistent with the pathological progression. Utilizing machine learning, we divided global gene regulation into modules, wherein transcription factors within a module could regulate genes within the same module, reaffirming known regulatory relationships between transcription factors and biological processes. We identified core genes-central to co-expression and protein-protein interaction-for the biological processes discovered. Notably, a large part of the core genes overlapped with genes previously implicated in nonalcoholic fatty liver disease. Single-nucleus ATAC-seq, combined with data-driven statistical analysis, offers insight into in vivo global gene regulation as a combination of modules and assists in identifying core genes of relevant biological processes.
Collapse
Affiliation(s)
- Fumihiko Takeuchi
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
- Medical Genomics Center, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
- Systems Genomics Laboratory, Baker Heart and Diabetes Institute, Melbourne, Australia
| | - Yi-Qiang Liang
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
| | - Hana Shimizu-Furusawa
- Department of Hygiene and Public Health, School of Medicine, Teikyo University, Tokyo, Japan
| | - Masato Isono
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
| | - Mia Yang Ang
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
- Department of Clinical Genome Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Kotaro Mori
- Medical Genomics Center, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
| | - Taizo Mori
- Department of Liver Diseases, The Research Center for Hepatitis and Immunology, National Center for Global Health and Medicine, Chiba, Japan
| | - Eiji Kakazu
- Department of Liver Diseases, The Research Center for Hepatitis and Immunology, National Center for Global Health and Medicine, Chiba, Japan
| | - Sachiyo Yoshio
- Department of Liver Diseases, The Research Center for Hepatitis and Immunology, National Center for Global Health and Medicine, Chiba, Japan
| | - Norihiro Kato
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
- Medical Genomics Center, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
- Department of Clinical Genome Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| |
Collapse
|
21
|
Van Asselt AJ, Beck JJ, Finnicum CT, Johnson BN, Kallsen N, Hottenga JJ, de Geus EJC, Boomsma DI, Ehli EA, van Dongen J. Genome-Wide DNA Methylation Profiles in Whole-Blood and Buccal Samples-Cross-Sectional, Longitudinal, and across Platforms. Int J Mol Sci 2023; 24:14640. [PMID: 37834090 PMCID: PMC10572275 DOI: 10.3390/ijms241914640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 09/22/2023] [Accepted: 09/24/2023] [Indexed: 10/15/2023] Open
Abstract
The field of DNA methylation research is rapidly evolving, focusing on disease and phenotype changes over time using methylation measurements from diverse tissue sources and multiple array platforms. Consequently, identifying the extent of longitudinal, inter-tissue, and inter-platform variation in DNA methylation is crucial for future advancement. DNA methylation was measured in 375 individuals, with 197 of those having 2 blood sample measurements ~10 years apart. Whole-blood samples were measured on Illumina Infinium 450K and EPIC methylation arrays, and buccal samples from a subset of 58 participants were measured on EPIC array. The data were analyzed with the aims to examine the correlation between methylation levels in longitudinal blood samples in 197 individuals, examine the correlation between methylation levels in the blood and buccal samples in 58 individuals, and examine the correlation between blood methylation profiles assessed on the EPIC and 450K arrays in 83 individuals. We identified 136,833, 7674, and 96,891 CpGs significantly and strongly correlated (>0.50) longitudinally, across blood and buccal samples as well as array platforms, respectively. A total of 3674 of these CpGs were shared across all three sets. Analysis of these shared CpGs identified previously found associations with aging, ancestry, and 7016 mQTLs as well.
Collapse
Affiliation(s)
- Austin J. Van Asselt
- Avera McKennan Hospital, University Health Center, Sioux Falls, SD 57105, USA; (A.J.V.A.)
- Department of Biological Psychology, Vrije Universiteit, 1081 HV Amsterdam, The Netherlands
| | - Jeffrey J. Beck
- Avera McKennan Hospital, University Health Center, Sioux Falls, SD 57105, USA; (A.J.V.A.)
| | - Casey T. Finnicum
- Avera McKennan Hospital, University Health Center, Sioux Falls, SD 57105, USA; (A.J.V.A.)
| | - Brandon N. Johnson
- Avera McKennan Hospital, University Health Center, Sioux Falls, SD 57105, USA; (A.J.V.A.)
| | - Noah Kallsen
- Avera McKennan Hospital, University Health Center, Sioux Falls, SD 57105, USA; (A.J.V.A.)
| | - Jouke Jan Hottenga
- Department of Biological Psychology, Vrije Universiteit, 1081 HV Amsterdam, The Netherlands
| | - Eco J. C. de Geus
- Department of Biological Psychology, Vrije Universiteit, 1081 HV Amsterdam, The Netherlands
| | | | - Dorret I. Boomsma
- Department of Biological Psychology, Vrije Universiteit, 1081 HV Amsterdam, The Netherlands
| | - Erik A. Ehli
- Avera McKennan Hospital, University Health Center, Sioux Falls, SD 57105, USA; (A.J.V.A.)
| | - Jenny van Dongen
- Department of Biological Psychology, Vrije Universiteit, 1081 HV Amsterdam, The Netherlands
| |
Collapse
|
22
|
Miller RG, Mychaleckyj JC, Onengut-Gumuscu S, Feingold E, Orchard TJ, Costacou T. DNA methylation and 28-year cardiovascular disease risk in type 1 diabetes: the Epidemiology of Diabetes Complications (EDC) cohort study. Clin Epigenetics 2023; 15:122. [PMID: 37533055 PMCID: PMC10394855 DOI: 10.1186/s13148-023-01539-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 07/22/2023] [Indexed: 08/04/2023] Open
Abstract
BACKGROUND The potential for DNA methylation (DNAm) as an early marker for cardiovascular disease (CVD) and how such an association might differ by glycemic exposure has not been examined in type 1 diabetes, a population at increased CVD risk. We thus performed a prospective epigenome-wide association study of blood leukocyte DNAm (EPIC array) and time to CVD incidence over 28 years in a childhood-onset (< 17 years) type 1 diabetes cohort, the Pittsburgh Epidemiology of Diabetes Complications (EDC) study (n = 368 with DNA and no CVD at baseline), both overall and separately by glycemic exposure, as measured by HbA1c at baseline (split at the median: < 8.9% and ≥ 8.9%). We also assessed whether DNAm-CVD associations were independent of established cardiometabolic risk factors, including body mass index, estimated glucose disposal rate, cholesterol, triglycerides, blood pressure, pulse rate, albumin excretion rate, and estimated glomerular filtration rate. RESULTS CVD (first instance of CVD death, myocardial infarction, coronary revascularization, ischemic ECG, angina, or stroke) developed in 172 participants (46.7%) over 28 years. Overall, in Cox regression models for time to CVD, none of the 683,597 CpGs examined reached significance at a false discovery rate (FDR) ≤ 0.05. In participants with HbA1c < 8.9% (n = 180), again none reached FDR ≤ 0.05, but three were associated at the a priori nominal significance level FDR ≤ 0.10: cg07147033 in MIB2, cg12324048 (intergenic, chromosome 3), and cg15883830 (intergenic, chromosome 1). In participants with HbA1c ≥ 8.9% (n = 188), two CpGs in loci involved in calcium channel activity were significantly associated with CVD (FDR ≤ 0.05): cg21823999 in GPM6A and cg23621817 in CHRNA9; four additional CpGs were nominally associated (FDR ≤ 0.10). In participants with HbA1c ≥ 8.9%, DNAm-CVD associations were only modestly attenuated after cardiometabolic risk factor adjustment, while attenuation was greater in those with HbA1c < 8.9%. No pathways were enriched in those with HbA1c < 8.9%, while pathways for calcium channel activity and integral component of synaptic membrane were significantly enriched in those with HbA1c ≥ 8.9%. CONCLUSIONS These results provide novel evidence that DNAm at loci involved in calcium channel activity and development may contribute to long-term CVD risk beyond known risk factors in type 1 diabetes, particularly in individuals with greater glycemic exposure, warranting further study.
Collapse
Affiliation(s)
- Rachel G Miller
- Department of Epidemiology, University of Pittsburgh, 130 N. Bellefield Avenue, Suite 339, Pittsburgh, PA, 15213, USA.
| | - Josyf C Mychaleckyj
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Suna Onengut-Gumuscu
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Eleanor Feingold
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Trevor J Orchard
- Department of Epidemiology, University of Pittsburgh, 130 N. Bellefield Avenue, Suite 339, Pittsburgh, PA, 15213, USA
| | - Tina Costacou
- Department of Epidemiology, University of Pittsburgh, 130 N. Bellefield Avenue, Suite 339, Pittsburgh, PA, 15213, USA
| |
Collapse
|
23
|
Wen X, Palma-Gudiel H, Miao G, Chen M, Huo Z, Peng H, Anton S, Hu G, Brock R, Brantley PJ, Zhao J. DNA methylation is differentially associated with glycemic outcomes by different types of weight-loss interventions: an epigenome-wide association study. Clin Epigenetics 2023; 15:108. [PMID: 37393279 PMCID: PMC10314401 DOI: 10.1186/s13148-023-01522-9] [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: 04/23/2023] [Accepted: 06/19/2023] [Indexed: 07/03/2023] Open
Abstract
BACKGROUND Alterations in DNA methylation (DNAm) have been reported to be a mechanism by which bariatric surgeries resulted in considerable metabolic improvements. Previous studies have mostly focused on change in DNAm following weight-loss interventions, yet whether DNAm prior to intervention can explain the variability in glycemic outcomes has not been investigated. Here, we aim to examine whether baseline DNAm is differentially associated with glycemic outcomes induced by different types of weight-loss interventions. METHODS Participants were 75 adults with severe obesity who underwent non-surgical intensive medical intervention (IMI), adjustable gastric band (BAND) or Roux-en-Y gastric bypass (RYGB) (n = 25 each). Changes in fasting plasma glucose (FPG) and glycated hemoglobin (HbA1c) were measured at 1-year after intervention. DNAm was quantified by Illumina 450 K arrays in baseline peripheral blood DNA. Epigenome-wide association studies were performed to identify CpG probes that modify the effects of different weight-loss interventions on glycemic outcomes, i.e., changes in FPG and HbA1c, by including an interaction term between types of intervention and DNAm. Models were adjusted for weight loss and baseline clinical factors. RESULTS Baseline DNAm levels at 3216 and 117 CpGs were differentially associated with changes in FPG and HbA1c, respectively, when comparing RYGB versus IMI. Of these, 79 CpGs were significant for both FPG and HbA1c. The identified genes are enriched in adaptive thermogenesis, temperature homeostasis and regulation of cell population proliferation. Additionally, DNAm at 6 CpGs was differentially associated with changes in HbA1c when comparing RYGB versus BAND. CONCLUSIONS Baseline DNAm is differentially associated with glycemic outcomes in response to different types of weight-loss interventions, independent of weight loss and other clinical factors. Such findings provided initial evidence that baseline DNAm levels may serve as potential biomarkers predictive of differential glycemic outcomes in response to different types of weight-loss interventions.
Collapse
Affiliation(s)
- Xiaoxiao Wen
- Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, 2004 Mowry Road, CTRB 4230, Gainesville, FL, 32610, USA
| | - Helena Palma-Gudiel
- Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, 2004 Mowry Road, CTRB 4230, Gainesville, FL, 32610, USA
| | - Guanhong Miao
- Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, 2004 Mowry Road, CTRB 4230, Gainesville, FL, 32610, USA
| | - Mingjing Chen
- Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, 2004 Mowry Road, CTRB 4230, Gainesville, FL, 32610, USA
| | - Zhiguang Huo
- Department of Biostatistics, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, FL, USA
| | - Hao Peng
- Department of Epidemiology, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China
| | - Stephen Anton
- Department of Aging and Geriatric Research, University of Florida, Gainesville, FL, USA
| | - Gang Hu
- Chronic Disease Epidemiology Laboratory, Pennington Biomedical Research Center, Louisiana State University System, Baton Rouge, LA, USA
| | - Ricky Brock
- Behavioral Medicine Laboratory, Pennington Biomedical Research Center, Louisiana State University System, Baton Rouge, LA, USA
| | - Phillip J Brantley
- Behavioral Medicine Laboratory, Pennington Biomedical Research Center, Louisiana State University System, Baton Rouge, LA, USA
| | - Jinying Zhao
- Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, 2004 Mowry Road, CTRB 4230, Gainesville, FL, 32610, USA.
| |
Collapse
|
24
|
Song L, Sun X, Qi T, Yang J. Mixed model-based deconvolution of cell-state abundances (MeDuSA) along a one-dimensional trajectory. NATURE COMPUTATIONAL SCIENCE 2023; 3:630-643. [PMID: 38177744 PMCID: PMC10766563 DOI: 10.1038/s43588-023-00487-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 06/13/2023] [Indexed: 01/06/2024]
Abstract
Deconvoluting cell-state abundances from bulk RNA-sequencing data can add considerable value to existing data, but achieving fine-resolution and high-accuracy deconvolution remains a challenge. Here we introduce MeDuSA, a mixed model-based method that leverages single-cell RNA-sequencing data as a reference to estimate cell-state abundances along a one-dimensional trajectory in bulk RNA-sequencing data. The advantage of MeDuSA lies primarily in estimating cell abundance in each state while fitting the remaining cells of the same type individually as random effects. Extensive simulations and real-data benchmark analyses demonstrate that MeDuSA greatly improves the estimation accuracy over existing methods for one-dimensional trajectories. Applying MeDuSA to cohort-level RNA-sequencing datasets reveals associations of cell-state abundances with disease or treatment conditions and cell-state-dependent genetic control of transcription. Our study provides a high-accuracy and fine-resolution method for cell-state deconvolution along a one-dimensional trajectory and demonstrates its utility in characterizing the dynamics of cell states in various biological processes.
Collapse
Affiliation(s)
- Liyang Song
- College of Life Sciences, Zhejiang University, Hangzhou, Zhejiang, China
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China
| | - Xiwei Sun
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China
| | - Ting Qi
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China
| | - Jian Yang
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China.
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China.
| |
Collapse
|
25
|
Villar JD, Stavrum AK, Spindola LM, Torsvik A, Bjella T, Steen NE, Djurovic S, Andreassen OA, Steen VM, Le Hellard S. Differences in white blood cell proportions between schizophrenia cases and controls are influenced by medication and variations in time of day. Transl Psychiatry 2023; 13:211. [PMID: 37330513 DOI: 10.1038/s41398-023-02507-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 05/27/2023] [Accepted: 06/01/2023] [Indexed: 06/19/2023] Open
Abstract
Cases with schizophrenia (SCZ) and healthy controls show differences in white blood cell (WBC) counts and blood inflammation markers. Here, we investigate whether time of blood draw and treatment with psychiatric medications are related to differences in estimated WBC proportions between SCZ cases and controls. DNA methylation data from whole blood was used to estimate proportions of six subtypes of WBCs in SCZ patients (n = 333) and healthy controls (n = 396). We tested the association of case-control status with estimated cell-type proportions and the neutrophil-to-lymphocyte ratio (NLR) in 4 models: with/without adjusting for time of blood draw, and then compared results from blood samples drawn during a 12-h (07:00-19:00) or 7-h (07:00-14:00) period. We also investigated WBC proportions in a subgroup of medication-free patients (n = 51). Neutrophil proportions were significantly higher in SCZ cases (mean=54.1%) vs. controls (mean=51.1%; p = <0.001), and CD8+T lymphocyte proportions were lower in SCZ cases (mean=12.1%) vs. controls (mean=13.2%; p = 0.001). The effect sizes in the 12-h sample (07:00-19:00) showed a significant difference between SCZ vs. controls for neutrophils, CD4+T, CD8+T, and B-cells, which remained significant after adjusting for time of blood draw. In the samples matched for time of blood draw during 07.00-14.00, we also observed an association with neutrophils, CD4+T, CD8+T, and B-cells that was unaffected by further adjustment for time of blood draw. In the medication-free patients, we observed differences that remained significant in neutrophils (p = 0.01) and CD4+T (p = 0.01) after adjusting for time of day. The association of SCZ with NLR was significant in all models (range: p < 0.001 to p = 0.03) in both medicated and unmedicated patients. In conclusion, controlling for pharmacological treatment and circadian cycling of WBC is necessary for unbiased estimates in case-control studies. Nevertheless, the association of WBC with SCZ remains, even after adjusting for the time of day.
Collapse
Affiliation(s)
- Jonelle D Villar
- NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway.
- Dr. Einar Martens Research Group for Biological Psychiatry, Department of Medical Genetics, Haukeland University Hospital, Bergen, Norway.
| | - Anne-Kristin Stavrum
- NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway
- Dr. Einar Martens Research Group for Biological Psychiatry, Department of Medical Genetics, Haukeland University Hospital, Bergen, Norway
| | - Leticia M Spindola
- NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway
- Dr. Einar Martens Research Group for Biological Psychiatry, Department of Medical Genetics, Haukeland University Hospital, Bergen, Norway
| | - Anja Torsvik
- NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway
- Dr. Einar Martens Research Group for Biological Psychiatry, Department of Medical Genetics, Haukeland University Hospital, Bergen, Norway
| | - Thomas Bjella
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Niels Eiel Steen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Srdjan Djurovic
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Ole A Andreassen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Vidar M Steen
- NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway
- Dr. Einar Martens Research Group for Biological Psychiatry, Department of Medical Genetics, Haukeland University Hospital, Bergen, Norway
| | - Stephanie Le Hellard
- NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway.
- Dr. Einar Martens Research Group for Biological Psychiatry, Department of Medical Genetics, Haukeland University Hospital, Bergen, Norway.
| |
Collapse
|
26
|
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.
Collapse
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
| |
Collapse
|
27
|
Campbell KA, Colacino JA, Puttabyatappa M, Dou JF, Elkin ER, Hammoud SS, Domino SE, Dolinoy DC, Goodrich JM, Loch-Caruso R, Padmanabhan V, Bakulski KM. Placental cell type deconvolution reveals that cell proportions drive preeclampsia gene expression differences. Commun Biol 2023; 6:264. [PMID: 36914823 PMCID: PMC10011423 DOI: 10.1038/s42003-023-04623-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Accepted: 02/22/2023] [Indexed: 03/14/2023] Open
Abstract
The placenta mediates adverse pregnancy outcomes, including preeclampsia, which is characterized by gestational hypertension and proteinuria. Placental cell type heterogeneity in preeclampsia is not well-understood and limits mechanistic interpretation of bulk gene expression measures. We generated single-cell RNA-sequencing samples for integration with existing data to create the largest deconvolution reference of 19 fetal and 8 maternal cell types from placental villous tissue (n = 9 biological replicates) at term (n = 40,494 cells). We deconvoluted eight published microarray case-control studies of preeclampsia (n = 173 controls, 157 cases). Preeclampsia was associated with excess extravillous trophoblasts and fewer mesenchymal and Hofbauer cells. Adjustment for cellular composition reduced preeclampsia-associated differentially expressed genes (log2 fold-change cutoff = 0.1, FDR < 0.05) from 1154 to 0, whereas downregulation of mitochondrial biogenesis, aerobic respiration, and ribosome biogenesis were robust to cell type adjustment, suggesting direct changes to these pathways. Cellular composition mediated a substantial proportion of the association between preeclampsia and FLT1 (37.8%, 95% CI [27.5%, 48.8%]), LEP (34.5%, 95% CI [26.0%, 44.9%]), and ENG (34.5%, 95% CI [25.0%, 45.3%]) overexpression. Our findings indicate substantial placental cellular heterogeneity in preeclampsia contributes to previously observed bulk gene expression differences. This deconvolution reference lays the groundwork for cellular heterogeneity-aware investigation into placental dysfunction and adverse birth outcomes.
Collapse
Affiliation(s)
- Kyle A Campbell
- Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Justin A Colacino
- Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, MI, USA
- Nutritional Sciences, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | | | - John F Dou
- Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Elana R Elkin
- Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Saher S Hammoud
- Human Genetics, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA
- Obstetrics and Gynecology, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA
- Department of Urology, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Steven E Domino
- Obstetrics and Gynecology, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Dana C Dolinoy
- Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, MI, USA
- Nutritional Sciences, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Jaclyn M Goodrich
- Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Rita Loch-Caruso
- Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Vasantha Padmanabhan
- Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, MI, USA
- Nutritional Sciences, School of Public Health, University of Michigan, Ann Arbor, MI, USA
- Pediatrics, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA
- Obstetrics and Gynecology, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Kelly M Bakulski
- Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA.
| |
Collapse
|
28
|
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.
Collapse
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
| |
Collapse
|
29
|
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.
Collapse
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
| |
Collapse
|
30
|
A new blood based epigenetic age predictor for adolescents and young adults. Sci Rep 2023; 13:2303. [PMID: 36759656 PMCID: PMC9911637 DOI: 10.1038/s41598-023-29381-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 02/03/2023] [Indexed: 02/11/2023] Open
Abstract
Children have special rights for protection compared to adults in our society. However, more than 1/4 of children globally have no documentation of their date of birth. Hence, there is a pressing need to develop biological methods for chronological age prediction, robust to differences in genetics, psychosocial events and physical living conditions. At present, DNA methylation is the most promising biological biomarker applied for age assessment. The human genome contains around 28 million DNA methylation sites, many of which change with age. Several epigenetic clocks accurately predict chronological age using methylation levels at age associated GpG-sites. However, variation in DNA methylation increases with age, and there is no epigenetic clock specifically designed for adolescents and young adults. Here we present a novel age Predictor for Adolescents and Young Adults (PAYA), using 267 CpG methylation sites to assess the chronological age of adolescents and young adults. We compared different preprocessing approaches and investigated the effect on prediction performance of the epigenetic clock. We evaluated performance using an independent validation data set consisting of 18-year-old individuals, where we obtained a median absolute deviation of just below 0.7 years. This tool may be helpful in age assessment of adolescents and young adults. However, there is a need to investigate the robustness of the age predictor across geographical and disease populations as well as environmental effects.
Collapse
|
31
|
Louise J, Deussen AR, Dodd JM. Data processing choices can affect findings in differential methylation analyses: an investigation using data from the LIMIT RCT. PeerJ 2023; 11:e14786. [PMID: 36755865 PMCID: PMC9901304 DOI: 10.7717/peerj.14786] [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: 10/11/2022] [Accepted: 01/03/2023] [Indexed: 02/05/2023] Open
Abstract
Objective A wide array of methods exist for processing and analysing DNA methylation data. We aimed to perform a systematic comparison of the behaviour of these methods, using cord blood DNAm from the LIMIT RCT, in relation to detecting hypothesised effects of interest (intervention and pre-pregnancy maternal BMI) as well as effects known to be spurious, and known to be present. Methods DNAm data, from 645 cord blood samples analysed using Illumina 450K BeadChip arrays, were normalised using three different methods (with probe filtering undertaken pre- or post- normalisation). Batch effects were handled with a supervised algorithm, an unsupervised algorithm, or adjustment in the analysis model. Analysis was undertaken with and without adjustment for estimated cell type proportions. The effects estimated included intervention and BMI (effects of interest in the original study), infant sex and randomly assigned groups. Data processing and analysis methods were compared in relation to number and identity of differentially methylated probes, rankings of probes by p value and log-fold-change, and distributions of p values and log-fold-change estimates. Results There were differences corresponding to each of the processing and analysis choices. Importantly, some combinations of data processing choices resulted in a substantial number of spurious 'significant' findings. We recommend greater emphasis on replication and greater use of sensitivity analyses.
Collapse
Affiliation(s)
- Jennie Louise
- Discipline of Obstetrics & Gynaecology and The Robinson Research Institute, The University of Adelaide, Adelaide, Australia,Adelaide Health Technology Asseessment, The University of Adelaide, Adelaide, Australia
| | - Andrea R. Deussen
- Discipline of Obstetrics & Gynaecology and The Robinson Research Institute, The University of Adelaide, Adelaide, Australia
| | - Jodie M. Dodd
- Discipline of Obstetrics & Gynaecology and The Robinson Research Institute, The University of Adelaide, Adelaide, Australia,Department of Perinatal Medicine, Women’s and Babies Division, The Women’s and Children’s Hospital, Adelaide, South Australia, Australia
| |
Collapse
|
32
|
Miller RG, Mychaleckyj JC, Onengut-Gumuscu S, Orchard TJ, Costacou T. TXNIP DNA methylation is associated with glycemic control over 28 years in type 1 diabetes: findings from the Pittsburgh Epidemiology of Diabetes Complications (EDC) study. BMJ Open Diabetes Res Care 2023; 11:e003068. [PMID: 36604111 PMCID: PMC9827189 DOI: 10.1136/bmjdrc-2022-003068] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 12/21/2022] [Indexed: 01/07/2023] Open
Abstract
INTRODUCTION DNA methylation (DNAme) has been cross-sectionally associated with type 2 diabetes and hemoglobin A1c (HbA1c) in the general population. However, longitudinal data and data in type 1 diabetes are currently very limited. Thus, we performed an epigenome-wide association study (EWAS) in an observational type 1 diabetes cohort to identify loci with DNAme associated with concurrent and future HbA1cs, as well as other clinical risk factors, over 28 years. RESEARCH DESIGN AND METHODS Whole blood DNAme in 683 597 CpGs was analyzed in the Pittsburgh Epidemiology of Diabetes Complications study of childhood onset (<17 years) type 1 diabetes (n=411). An EWAS of DNAme beta values and concurrent HbA1c was performed using linear models adjusted for diabetes duration, sex, pack years of smoking, estimated cell type composition variables, and technical/batch covariates. A longitudinal EWAS of subsequent repeated HbA1c measures was performed using mixed models. We further identified methylation quantitative trait loci (meQTLs) for significant CpGs and conducted a Mendelian randomization. RESULTS DNAme at cg19693031 (Chr 1, Thioredoxin-Interacting Protein (TXNIP)) and cg21534330 (Chr 17, Casein Kinase 1 Isoform Delta) was significantly inversely associated with concurrent HbA1c. In longitudinal analyses, hypomethylation of cg19693031 was associated with consistently higher HbA1c over 28 years, and with higher triglycerides, pulse rate, and albumin:creatinine ratio (ACR) independently of HbA1c. We further identified 34 meQTLs in SLC2A1/SLC2A1-AS1 significantly associated with cg19693031 DNAme. CONCLUSIONS Our results extend prior findings that TXNIP hypomethylation relates to worse glycemic control in type 1 diabetes by demonstrating the association persists over the long term. Additionally, the associations with triglycerides, pulse rate, and ACR suggest TXNIP DNAme could play a role in vascular damage independent of HbA1c. These findings strengthen potential for interventions targeting TXNIP to improve glycemic control in type 1 diabetes through its role in SLC2A1/glucose transporter 1-mediated glucose regulation.
Collapse
Affiliation(s)
- Rachel G Miller
- Department of Epidemiology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Josyf C Mychaleckyj
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, USA
| | - Suna Onengut-Gumuscu
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, USA
| | - Trevor J Orchard
- Department of Epidemiology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Tina Costacou
- Department of Epidemiology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| |
Collapse
|
33
|
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.
Collapse
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
| |
Collapse
|
34
|
Designing Studies for Epigenetic Biomarker Development in Autoimmune Rheumatic Diseases. RHEUMATOLOGY AND IMMUNOLOGY RESEARCH 2022; 3:103-110. [PMID: 36788968 PMCID: PMC9895872 DOI: 10.2478/rir-2022-0018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 08/13/2022] [Indexed: 02/16/2023]
Abstract
In just a few years, the number of epigenetic studies in autoimmune rheumatic and inflammatory diseases has greatly increased. This is in part due to the need of identifying additional determinants to genetics to explain the pathogenesis and development of these disorders. In this regard, epigenetics provides potential mechanisms that determine gene function, are linked to environmental factors, and could explain a wide range of phenotypic variability among patients with these diseases. Despite the high interest and number of studies describing epigenetic alterations under these conditions and exploring their relationship to various clinical aspects, few of the proposed biomarkers have yet reached clinical practice. The potential of epigenetic markers is high, as these alterations link measurable features with a number of biological traits. In the present article, we present published studies in the field, discuss some frequent limitations in the existing research, and propose a number of considerations that should be taken into account by those starting new projects in the field, with an aim to generate biomarkers that could make it into the clinics.
Collapse
|
35
|
Ma Z, Wang Y, Quan Y, Wang Z, Liu Y, Ding Z. Maternal obesity alters methylation level of cytosine in CpG island for epigenetic inheritance in fetal umbilical cord blood. Hum Genomics 2022; 16:34. [PMID: 36045397 PMCID: PMC9429776 DOI: 10.1186/s40246-022-00410-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Accepted: 08/22/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Over the past few decades, global maternal obesity prevalence has rapidly increased. This condition may induce long-lasting pathophysiological effects on either fetal or infant health that could be attributable to unknown unique changes in the umbilical blood composition. METHODS A total of 34 overweight/obese and 32 normal-weight pregnant women were recruited. Fifteen umbilical blood samples including 8 overweight/obese subjects and 7 normal weight women were sequenced using Targeted Bisulfite Sequencing technology to detect the average methylation level of cytosine and identify the differentially methylated region (DMR). GO and KEGG analyses were then employed to perform pathway enrichment analysis of DMR-related genes and promoters. Moreover, the mRNA levels of methylation-related genes histone deacetylases (HDACs) and DNA methyltransferases (DNMTs) were characterized in the samples obtained from these two groups. RESULTS Average methylated cytosine levels in both the CpG islands (CGI) and promoter significantly decreased in overweight/obese groups. A total of 1669 DMRs exhibited differences in their DNA methylation status between the overweight/obese and control groups. GO and KEGG analyses revealed that DMR-related genes and promoters were enriched in the metabolism, cancer and cardiomyopathy signaling pathways. Furthermore, the HDACs and DNMTs mRNA levels trended to decline in overweight/obese groups. CONCLUSIONS Decreased methylated cytosine levels in overweight/obese women induce the gene expression activity at a higher level than in the control group. DMRs between these two groups in the fetal blood may contribute to the changes in gene transcription that underlie the increased risk of metabolic disorders, cancers and cardiomyopathy in their offspring.
Collapse
Affiliation(s)
- Zhuoyao Ma
- Department of Histology, Embryology, Genetics and Developmental Biology, Shanghai Key Laboratory for Reproductive Medicine, Shanghai Jiao Tong University School of Medicine, No.280, Chongqing Road (South), Shanghai, 200025, China
| | - Yingjin Wang
- Department of Obstetrics and Gynecology, Shanghai Eighth People's Hospital, Shanghai, 200235, China
| | - Yanmei Quan
- Department of Histology, Embryology, Genetics and Developmental Biology, Shanghai Key Laboratory for Reproductive Medicine, Shanghai Jiao Tong University School of Medicine, No.280, Chongqing Road (South), Shanghai, 200025, China
| | - Zhijie Wang
- Department of Obstetrics and Gynecology, Shanghai Eighth People's Hospital, Shanghai, 200235, China.
| | - Yue Liu
- Department of Histology, Embryology, Genetics and Developmental Biology, Shanghai Key Laboratory for Reproductive Medicine, Shanghai Jiao Tong University School of Medicine, No.280, Chongqing Road (South), Shanghai, 200025, China.
| | - Zhide Ding
- Department of Histology, Embryology, Genetics and Developmental Biology, Shanghai Key Laboratory for Reproductive Medicine, Shanghai Jiao Tong University School of Medicine, No.280, Chongqing Road (South), Shanghai, 200025, China.
| |
Collapse
|
36
|
Bracci PM, Rice T, Hansen HM, Francis SS, Lee S, McCoy LS, Shrestha PP, Warrier G, Clarke JL, Molinaro AM, Taylor JW, Wiencke JK, Wrensch MR. Pre-surgery immune profiles of adult glioma patients. J Neurooncol 2022; 159:103-115. [PMID: 35716311 PMCID: PMC9325836 DOI: 10.1007/s11060-022-04047-y] [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] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Accepted: 05/24/2022] [Indexed: 10/26/2022]
Abstract
INTRODUCTION Although immunosuppression is a known characteristic of glioma, no previous large studies have reported peripheral blood immune cell profiles prior to patient surgery and chemoradiation. This report describes blood immune cell characteristics and associated variables prior to surgery among typical glioma patients seen at a large University practice. METHODS We analyzed pre-surgery blood samples from 139 glioma patients diagnosed with a new or recurrent grade II/III glioma (LrGG, n = 64) or new glioblastoma (GBM, n = 75) and 454 control participants without glioma. Relative cell fractions of CD4, CD8, B-cells, Natural Killer cells, monocytes, and neutrophils, were estimated via a validated deconvolution algorithm from blood DNA methylation measures from Illumina EPIC arrays. RESULTS Dexamethasone use at time of blood draw varied by glioma type being highest among patients with IDH wild-type (wt) GBM (75%) and lowest for those with oligodendroglioma (14%). Compared to controls, glioma patients showed statistically significant lower cell fractions for all immune cell subsets except for neutrophils which were higher (all p-values < 0.001), in part because of the higher prevalence of dexamethasone use at time of blood draw for IDHwt GBM. Patients who were taking dexamethasone were more likely to have a low CD4 count (< 200, < 500), increased neutrophils, low absolute lymphocyte counts, higher total cell count and higher NLR. CONCLUSION We show that pre-surgery blood immune profiles vary by glioma subtype, age, and more critically, by use of dexamethasone. Our results highlight the importance of considering dexamethasone exposures in all studies of immune profiles and of obtaining immune measures prior to use of dexamethasone, if possible.
Collapse
Affiliation(s)
- Paige M Bracci
- Department of Epidemiology and Biostatistics, UCSF, 1450 3rd Street, San Francisco, CA, 94158, USA.
| | - Terri Rice
- Department of Neurological Surgery, UCSF, San Francisco, CA, USA
| | - Helen M Hansen
- Department of Neurological Surgery, UCSF, San Francisco, CA, USA
| | | | - Sean Lee
- Department of Neurological Surgery, UCSF, San Francisco, CA, USA
| | - Lucie S McCoy
- Department of Neurological Surgery, UCSF, San Francisco, CA, USA
| | - Pavan P Shrestha
- Department of Neurological Surgery, UCSF, San Francisco, CA, USA
| | - Gayathri Warrier
- Department of Neurological Surgery, UCSF, San Francisco, CA, USA
| | - Jennifer L Clarke
- Department of Neurological Surgery, UCSF, San Francisco, CA, USA
- Department of Neurology, UCSF, San Francisco, CA, USA
| | - Annette M Molinaro
- Department of Epidemiology and Biostatistics, UCSF, 1450 3rd Street, San Francisco, CA, 94158, USA
- Department of Neurological Surgery, UCSF, San Francisco, CA, USA
| | - Jennie W Taylor
- Department of Neurological Surgery, UCSF, San Francisco, CA, USA
- Department of Neurology, UCSF, San Francisco, CA, USA
| | - John K Wiencke
- Department of Neurological Surgery, UCSF, San Francisco, CA, USA
| | | |
Collapse
|
37
|
Niehues A, Bizzarri D, Reinders MJT, Slagboom PE, van Gool AJ, van den Akker EB, 't Hoen PAC. Metabolomic predictors of phenotypic traits can replace and complement measured clinical variables in population-scale expression profiling studies. BMC Genomics 2022; 23:546. [PMID: 35907790 PMCID: PMC9339202 DOI: 10.1186/s12864-022-08771-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 07/12/2022] [Indexed: 11/10/2022] Open
Abstract
Population-scale expression profiling studies can provide valuable insights into biological and disease-underlying mechanisms. The availability of phenotypic traits is essential for studying clinical effects. Therefore, missing, incomplete, or inaccurate phenotypic information can make analyses challenging and prevent RNA-seq or other omics data to be reused. A possible solution are predictors that infer clinical or behavioral phenotypic traits from molecular data. While such predictors have been developed based on different omics data types and are being applied in various studies, metabolomics-based surrogates are less commonly used than predictors based on DNA methylation profiles.In this study, we inferred 17 traits, including diabetes status and exposure to lipid medication, using previously trained metabolomic predictors. We evaluated whether these metabolomic surrogates can be used as an alternative to reported information for studying the respective phenotypes using expression profiling data of four population cohorts. For the majority of the 17 traits, the metabolomic surrogates performed similarly to the reported phenotypes in terms of effect sizes, number of significant associations, replication rates, and significantly enriched pathways.The application of metabolomics-derived surrogate outcomes opens new possibilities for reuse of multi-omics data sets. In studies where availability of clinical metadata is limited, missing or incomplete information can be complemented by these surrogates, thereby increasing the size of available data sets. Additionally, the availability of such surrogates could be used to correct for potential biological confounding. In the future, it would be interesting to further investigate the use of molecular predictors across different omics types and cohorts.
Collapse
Affiliation(s)
- Anna Niehues
- Center for Molecular and Biomolecular Informatics, Radboud Institute for Molecular Life Sciences, Radboud university medical center, Geert Grooteplein Zuid 26-28, Nijmegen, 6525 GA, Netherlands.,Translational Metabolic Laboratory, Department Laboratory Medicine, Radboud university medical center, Geert Grooteplein Zuid 10, Nijmegen, 6525 GA, Netherlands
| | - Daniele Bizzarri
- Molecular Epidemiology, LUMC, Einthovenweg 20, Leiden, 2333 ZC, Netherlands.,Leiden Computational Biology Center, LUMC, Einthovenweg 20, Leiden, 2333 ZC, Netherlands
| | - Marcel J T Reinders
- Leiden Computational Biology Center, LUMC, Einthovenweg 20, Leiden, 2333 ZC, Netherlands.,Delft Bioinformatics Lab, TU Delft, Van Mourik Broekmanweg 6, Delft, 2628 XE, Netherlands
| | - P Eline Slagboom
- Molecular Epidemiology, LUMC, Einthovenweg 20, Leiden, 2333 ZC, Netherlands.,Max Planck Institute for the Biology of Ageing, Cologne, Germany
| | - Alain J van Gool
- Translational Metabolic Laboratory, Department Laboratory Medicine, Radboud university medical center, Geert Grooteplein Zuid 10, Nijmegen, 6525 GA, Netherlands
| | - Erik B van den Akker
- Molecular Epidemiology, LUMC, Einthovenweg 20, Leiden, 2333 ZC, Netherlands.,Leiden Computational Biology Center, LUMC, Einthovenweg 20, Leiden, 2333 ZC, Netherlands.,Delft Bioinformatics Lab, TU Delft, Van Mourik Broekmanweg 6, Delft, 2628 XE, Netherlands
| | | | | | - Peter A C 't Hoen
- Center for Molecular and Biomolecular Informatics, Radboud Institute for Molecular Life Sciences, Radboud university medical center, Geert Grooteplein Zuid 26-28, Nijmegen, 6525 GA, Netherlands.
| |
Collapse
|
38
|
Oberhofer A, Bronkhorst AJ, Uhlig C, Ungerer V, Holdenrieder S. Tracing the Origin of Cell-Free DNA Molecules through Tissue-Specific Epigenetic Signatures. Diagnostics (Basel) 2022; 12:diagnostics12081834. [PMID: 36010184 PMCID: PMC9406971 DOI: 10.3390/diagnostics12081834] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 07/15/2022] [Accepted: 07/25/2022] [Indexed: 12/11/2022] Open
Abstract
All cell and tissue types constantly release DNA fragments into human body fluids by various mechanisms including programmed cell death, accidental cell degradation and active extrusion. Particularly, cell-free DNA (cfDNA) in plasma or serum has been utilized for minimally invasive molecular diagnostics. Disease onset or pathological conditions that lead to increased cell death alter the contribution of different tissues to the total pool of cfDNA. Because cfDNA molecules retain cell-type specific epigenetic features, it is possible to infer tissue-of-origin from epigenetic characteristics. Recent research efforts demonstrated that analysis of, e.g., methylation patterns, nucleosome occupancy, and fragmentomics determined the cell- or tissue-of-origin of individual cfDNA molecules. This novel tissue-of origin-analysis enables to estimate the contributions of different tissues to the total cfDNA pool in body fluids and find tissues with increased cell death (pathologic condition), expanding the portfolio of liquid biopsies towards a wide range of pathologies and early diagnosis. In this review, we summarize the currently available tissue-of-origin approaches and point out the next steps towards clinical implementation.
Collapse
|
39
|
Jackson WM, Santos HP, Hartwell HJ, Gower WA, Chhabra D, Hagood JS, Laughon MM, Payton A, Smeester L, Roell K, O’Shea TM, Fry RC. Differential placental CpG methylation is associated with chronic lung disease of prematurity. Pediatr Res 2022; 91:1428-1435. [PMID: 34857876 PMCID: PMC9160210 DOI: 10.1038/s41390-021-01868-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 10/26/2021] [Accepted: 11/13/2021] [Indexed: 02/03/2023]
Abstract
BACKGROUND Chronic lung disease (CLD) is the most common pulmonary morbidity in extremely preterm infants. It is unclear to what extent prenatal exposures influence the risk of CLD. Epigenetic variation in placenta DNA methylation may be associated with differential risk of CLD, and these associations may be dependent upon sex. METHODS Data were obtained from a multi-center cohort of infants born extremely preterm (<28 weeks' gestation) and an epigenome-wide approach was used to identify associations between placental DNA methylation and CLD (n = 423). Associations were evaluated using robust linear regression adjusting for covariates, with a false discovery rate of 0.05. Analyses stratified by sex were used to assess differences in methylation-CLD associations. RESULTS CLD was associated with differential methylation at 49 CpG sites representing 46 genes in the placenta. CLD was associated with differential methylation of probes within genes related to pathways involved in fetal lung development, such as p53 signaling and myo-inositol biosynthesis. Associations between CpG methylation and CLD differed by sex. CONCLUSIONS Differential placental methylation within genes with key roles in fetal lung development may reflect complex cell signaling between the placenta and fetus which mediate CLD risk. These pathways appear to be distinct based on fetal sex. IMPACT In extremely preterm infants, differential methylation of CpG sites within placental genes involved in pathways related to cell signaling, oxidative stress, and trophoblast invasion is associated with chronic lung disease of prematurity. DNA methylation patterns associated with chronic lung disease were distinctly based on fetal sex, suggesting a potential mechanism underlying dimorphic phenotypes. Mechanisms related to fetal hypoxia and placental myo-inositol signaling may play a role in fetal lung programming and the developmental origins of chronic lung disease. Continued research of the relationship between the placental epigenome and chronic lung disease could inform efforts to ameliorate or prevent this condition.
Collapse
Affiliation(s)
- Wesley M. Jackson
- Department of Pediatrics, School of Medicine, The University of North Carolina, Chapel Hill, NC
| | - Hudson P. Santos
- Department of Environmental Sciences and Engineering, Gilling School of Global Public Health, The University of North Carolina, Chapel Hill, NC.,Biobehavioral Laboratory, School of Nursing, The University of North Carolina, Chapel Hill, NC
| | - Hadley J. Hartwell
- Department of Environmental Sciences and Engineering, Gilling School of Global Public Health, The University of North Carolina, Chapel Hill, NC
| | - William Adam Gower
- Department of Pediatrics, School of Medicine, The University of North Carolina, Chapel Hill, NC
| | - Divya Chhabra
- Department of Pediatrics, University of California, San Diego, CA
| | - James S. Hagood
- Department of Pediatrics, School of Medicine, The University of North Carolina, Chapel Hill, NC
| | - Matthew M. Laughon
- Department of Pediatrics, School of Medicine, The University of North Carolina, Chapel Hill, NC
| | - Alexis Payton
- Department of Environmental Sciences and Engineering, Gilling School of Global Public Health, The University of North Carolina, Chapel Hill, NC.,Institute for Environmental Health Solutions, Gilling School of Global Public Health, The University of North Carolina, Chapel Hill, NC
| | - Lisa Smeester
- Department of Environmental Sciences and Engineering, Gilling School of Global Public Health, The University of North Carolina, Chapel Hill, NC.,Institute for Environmental Health Solutions, Gilling School of Global Public Health, The University of North Carolina, Chapel Hill, NC
| | - Kyle Roell
- Department of Environmental Sciences and Engineering, Gilling School of Global Public Health, The University of North Carolina, Chapel Hill, NC.,Institute for Environmental Health Solutions, Gilling School of Global Public Health, The University of North Carolina, Chapel Hill, NC
| | - T. Michael O’Shea
- Department of Pediatrics, School of Medicine, The University of North Carolina, Chapel Hill, NC
| | - Rebecca C. Fry
- Department of Environmental Sciences and Engineering, Gilling School of Global Public Health, The University of North Carolina, Chapel Hill, NC.,Institute for Environmental Health Solutions, Gilling School of Global Public Health, The University of North Carolina, Chapel Hill, NC
| |
Collapse
|
40
|
Zhu T, Liu J, Beck S, Pan S, Capper D, Lechner M, Thirlwell C, Breeze CE, Teschendorff AE. A pan-tissue DNA methylation atlas enables in silico decomposition of human tissue methylomes at cell-type resolution. Nat Methods 2022; 19:296-306. [PMID: 35277705 PMCID: PMC8916958 DOI: 10.1038/s41592-022-01412-7] [Citation(s) in RCA: 42] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2021] [Accepted: 01/28/2022] [Indexed: 02/07/2023]
Abstract
Bulk-tissue DNA methylomes represent an average over many different cell types, hampering our understanding of cell-type-specific contributions to disease development. As single-cell methylomics is not scalable to large cohorts of individuals, cost-effective computational solutions are needed, yet current methods are limited to tissues such as blood. Here we leverage the high-resolution nature of tissue-specific single-cell RNA-sequencing datasets to construct a DNA methylation atlas defined for 13 solid tissue types and 40 cell types. We comprehensively validate this atlas in independent bulk and single-nucleus DNA methylation datasets. We demonstrate that it correctly predicts the cell of origin of diverse cancer types and discovers new prognostic associations in olfactory neuroblastoma and stage 2 melanoma. In brain, the atlas predicts a neuronal origin for schizophrenia, with neuron-specific differential DNA methylation enriched for corresponding genome-wide association study risk loci. In summary, the DNA methylation atlas enables the decomposition of 13 different human tissue types at a high cellular resolution, paving the way for an improved interpretation of epigenetic data. This resource presents an in silico generated DNA methylation atlas that can be used for cell-type deconvolution of human tissues.
Collapse
|
41
|
Qi L, Teschendorff AE. Cell-type heterogeneity: Why we should adjust for it in epigenome and biomarker studies. Clin Epigenetics 2022; 14:31. [PMID: 35227298 PMCID: PMC8887190 DOI: 10.1186/s13148-022-01253-3] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 02/21/2022] [Indexed: 12/18/2022] Open
Abstract
Most studies aiming to identify epigenetic biomarkers do so from complex tissues that are composed of many different cell-types. By definition, these cell-types vary substantially in terms of their epigenetic profiles. This cell-type specific variation among healthy cells is completely independent of the variation associated with disease, yet it dominates the epigenetic variability landscape. While cell-type composition of tissues can change in disease and this may provide accurate and reproducible biomarkers, not adjusting for the underlying cell-type heterogeneity may seriously limit the sensitivity and precision to detect disease-relevant biomarkers or hamper our understanding of such biomarkers. Given that computational and experimental tools for tackling cell-type heterogeneity are available, we here stress that future epigenetic biomarker studies should aim to provide estimates of underlying cell-type fractions for all samples in the study, and to identify biomarkers before and after adjustment for cell-type heterogeneity, in order to obtain a more complete and unbiased picture of the biomarker-landscape. This is critical, not only to improve reproducibility and for the eventual clinical application of such biomarkers, but importantly, to also improve our molecular understanding of disease itself.
Collapse
Affiliation(s)
- Luo Qi
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Andrew E Teschendorff
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China. .,UCL Cancer Institute, University College London, London, WC1E 8BT, UK.
| |
Collapse
|
42
|
Salas LA, Zhang Z, Koestler DC, Butler RA, Hansen HM, Molinaro AM, Wiencke JK, Kelsey KT, Christensen BC. Enhanced cell deconvolution of peripheral blood using DNA methylation for high-resolution immune profiling. Nat Commun 2022; 13:761. [PMID: 35140201 PMCID: PMC8828780 DOI: 10.1038/s41467-021-27864-7] [Citation(s) in RCA: 94] [Impact Index Per Article: 47.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Accepted: 12/20/2021] [Indexed: 12/12/2022] Open
Abstract
DNA methylation microarrays can be employed to interrogate cell-type composition in complex tissues. Here, we expand reference-based deconvolution of blood DNA methylation to include 12 leukocyte subtypes (neutrophils, eosinophils, basophils, monocytes, naïve and memory B cells, naïve and memory CD4 + and CD8 + T cells, natural killer, and T regulatory cells). Including derived variables, our method provides 56 immune profile variables. The IDOL (IDentifying Optimal Libraries) algorithm was used to identify libraries for deconvolution of DNA methylation data for current and previous platforms. The accuracy of deconvolution estimates obtained using our enhanced libraries was validated using artificial mixtures and whole-blood DNA methylation with known cellular composition from flow cytometry. We applied our libraries to deconvolve cancer, aging, and autoimmune disease datasets. In conclusion, these libraries enable a detailed representation of immune-cell profiles in blood using only DNA and facilitate a standardized, thorough investigation of immune profiles in human health and disease.
Collapse
Affiliation(s)
- Lucas A Salas
- Department of Epidemiology, Geisel School of Medicine, Dartmouth College, Lebanon, NH, USA
| | - Ze Zhang
- Department of Epidemiology, Geisel School of Medicine, Dartmouth College, Lebanon, NH, USA
| | - Devin C Koestler
- Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, KS, USA
| | - Rondi A Butler
- Departments of Epidemiology and Pathology and Laboratory Medicine, Brown University, Providence, RI, USA
| | - Helen M Hansen
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA
| | - Annette M Molinaro
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA
| | - John K Wiencke
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA
- Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA
| | - Karl T Kelsey
- Departments of Epidemiology and Pathology and Laboratory Medicine, Brown University, Providence, RI, USA.
| | - Brock C Christensen
- Department of Epidemiology, Geisel School of Medicine, Dartmouth College, Lebanon, NH, USA.
- Department of Molecular and Systems Biology, Geisel School of Medicine, Dartmouth College, Lebanon, NH, USA.
- Department of Community and Family Medicine, Geisel School of Medicine, Dartmouth College, Lebanon, NH, USA.
| |
Collapse
|
43
|
Reliability of a novel approach for reference-based cell type estimation in human placental DNA methylation studies. Cell Mol Life Sci 2022; 79:115. [PMID: 35113241 PMCID: PMC8813756 DOI: 10.1007/s00018-021-04091-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 11/24/2021] [Accepted: 12/13/2021] [Indexed: 02/02/2023]
Abstract
The placenta is a central organ during early development, influencing trajectories of health and disease. DNA methylation (DNAm) studies of human placenta improve our understanding of how its function relates to disease risk. However, DNAm studies can be biased by cell type heterogeneity, so it is essential to control for this in order to reduce confounding and increase precision. Computational cell type deconvolution approaches have proven to be very useful for this purpose. For human placenta, however, an assessment of the performance of these estimation methods is still lacking. Here, we examine the performance of a newly available reference-based cell type estimation approach and compare it to an often-used reference-free cell type estimation approach, namely RefFreeEWAS, in placental genome-wide DNAm samples taken at birth and from chorionic villus biopsies early in pregnancy using three independent studies comprising over 1000 samples. We found both reference-free and reference-based estimated cell type proportions to have predictive value for DNAm, however, reference-based cell type estimation outperformed reference-free estimation for the majority of data sets. Reference-based cell type estimations mirror previous histological knowledge on changes in cell type proportions through gestation. Further, CpGs whose variation in DNAm was largely explained by reference-based estimated cell type proportions were in the proximity of genes that are highly tissue-specific for placenta. This was not the case for reference-free estimated cell type proportions. We provide a list of these CpGs as a resource to help researchers to interpret results of existing studies and improve future DNAm studies of human placenta.
Collapse
|
44
|
Chen JQ, Salas LA, Wiencke JK, Koestler DC, Molinaro AM, Andrew AS, Seigne JD, Karagas MR, Kelsey KT, Christensen BC. Immune profiles and DNA methylation alterations related with non-muscle-invasive bladder cancer outcomes. Clin Epigenetics 2022; 14:14. [PMID: 35063012 PMCID: PMC8783448 DOI: 10.1186/s13148-022-01234-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 01/12/2022] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Non-muscle-invasive bladder cancer (NMIBC) patients receive frequent monitoring because ≥ 70% will have recurrent disease. However, screening is invasive, expensive, and associated with significant morbidity making bladder cancer the most expensive cancer to treat per capita. There is an urgent need to expand the understanding of markers related to recurrence and survival outcomes of NMIBC. METHODS AND RESULTS We used the Illumina HumanMethylationEPIC array to measure peripheral blood DNA methylation profiles of NMIBC patients (N = 603) enrolled in a population-based cohort study in New Hampshire and applied cell type deconvolution to estimate immune cell-type proportions. Using Cox proportional hazard models, we identified that increasing CD4T and CD8T cell proportions were associated with a statistically significant decreased hazard of tumor recurrence or death (CD4T: HR = 0.98, 95% CI = 0.97-1.00; CD8T: HR = 0.97, 95% CI = 0.95-1.00), whereas increasing monocyte proportion and methylation-derived neutrophil-to-lymphocyte ratio (mdNLR) were associated with the increased hazard of tumor recurrence or death (monocyte: HR = 1.04, 95% CI = 1.00-1.07; mdNLR: HR = 1.12, 95% CI = 1.04-1.20). Then, using an epigenome-wide association study (EWAS) approach adjusting for age, sex, smoking status, BCG treatment status, and immune cell profiles, we identified 2528 CpGs associated with the hazard of tumor recurrence or death (P < 0.005). Among these CpGs, the 1572 were associated with an increased hazard and were significantly enriched in open sea regions; the 956 remaining CpGs were associated with a decreased hazard and were significantly enriched in enhancer regions and DNase hypersensitive sites. CONCLUSIONS Our results expand on the knowledge of immune profiles and methylation alteration associated with NMIBC outcomes and represent a first step toward the development of DNA methylation-based biomarkers of tumor recurrence.
Collapse
Affiliation(s)
- Ji-Qing Chen
- Department of Epidemiology, Geisel School of Medicine, Dartmouth College, Lebanon, NH, 03766, USA
| | - Lucas A Salas
- Department of Epidemiology, Geisel School of Medicine, Dartmouth College, Lebanon, NH, 03766, USA
| | - John K Wiencke
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, 94143, USA
| | - Devin C Koestler
- Department of Biostatistics and Data Science, University of Kansas Medical Center, Kansas City, KS, 66160, USA
| | - Annette M Molinaro
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, 94143, USA
| | - Angeline S Andrew
- Department of Neurology, Geisel School of Medicine, Dartmouth College, Lebanon, NH, 03766, USA
| | - John D Seigne
- Department of Surgery, Section of Urology, Geisel School of Medicine, Dartmouth College, Lebanon, NH, 03766, USA
| | - Margaret R Karagas
- Department of Epidemiology, Geisel School of Medicine, Dartmouth College, Lebanon, NH, 03766, USA
| | - Karl T Kelsey
- Departments of Epidemiology and Pathology and Laboratory Medicine, Brown University, Providence, RI, 02912, USA
| | - Brock C Christensen
- Department of Epidemiology, Geisel School of Medicine, Dartmouth College, Lebanon, NH, 03766, USA.
- Departments of Molecular and Systems Biology, and Community and Family Medicine, Geisel School of Medicine, Dartmouth College, Lebanon, NH, 03766, USA.
- Dartmouth Hitchcock Medical Center, 1 Medical Center Dr, 660 Williamson Translation Research Building, Lebanon, NH, 03756, USA.
| |
Collapse
|
45
|
Yang B, Eliot M, McClean MD, Waterboer T, Pawlita M, Butler R, Nelson HH, Langevin SM, Christensen BC, Kelsey KT. DNA methylation-derived systemic inflammation indices and their association with oropharyngeal cancer risk and survival. Head Neck 2022; 44:904-913. [PMID: 35048488 DOI: 10.1002/hed.26981] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 12/14/2021] [Accepted: 01/10/2022] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Head and neck squamous cell carcinomas are associated with systemic inflammation (SI). We evaluated whether DNA methylation-derived SI (mdSI) indices are associated with oropharyngeal cancer risk and survival. METHODS Ninety-four oropharyngeal squamous cell carcinoma (OPSCC) cases and 57 controls with DNA methylation data were included. Logistic regression analysis and survival analysis were performed to test the association of mdSI indices with OPSCC risk and survival. RESULTS Higher methylation-derived neutrophil-to-lymphocyte ratio (mdNLR) was associated with increased risk of OPSCC (OR = 1.21, 95%CI: 1.11-1.40) while no association was found with methylation-derived lymphocyte-to-monocyte ratio (mdLMR). For 5-year overall survival, higher mdLMR was significantly associated with decreased risk of death (HR = 0.25, 95%CI: 0.10-0.64) while the converse was observed for mdNLR (HR = 2.48, 95%CI: 1.04-5.92). CONCLUSION We observed an association between mdSI indices and OPSCC risk and 5-year overall survival. It is possible to use mdLMR as an independent prognostic factor for OPSCC.
Collapse
Affiliation(s)
- Bo Yang
- Department of Epidemiology, Brown University, Providence, Rhode Island, USA
| | - Melissa Eliot
- Department of Epidemiology, Brown University, Providence, Rhode Island, USA
| | - Michael D McClean
- Department of Environmental Health, Boston University School of Public Health, Boston, Massachusetts, USA
| | - Tim Waterboer
- Division of Infections and Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Michael Pawlita
- Division of Infections and Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Rondi Butler
- Department of Epidemiology, Brown University, Providence, Rhode Island, USA
| | - Heather H Nelson
- Masonic Cancer Center, University of Minnesota, Minneapolis, Minnesota, USA.,Department of Epidemiology and Community Health, University of Minnesota, Minneapolis, Minnesota, USA
| | - Scott M Langevin
- Department of Environmental & Public Health Sciences, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - Brock C Christensen
- Department of Epidemiology, Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire, USA.,Department of Community and Family Medicine, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire, USA
| | - Karl T Kelsey
- Department of Epidemiology, Brown University, Providence, Rhode Island, USA.,Department of Pathology and Laboratory Medicine, Brown University, Providence, Rhode Island, USA
| |
Collapse
|
46
|
Merkel A, Esteller M. Experimental and Bioinformatic Approaches to Studying DNA Methylation in Cancer. Cancers (Basel) 2022; 14:349. [PMID: 35053511 PMCID: PMC8773752 DOI: 10.3390/cancers14020349] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Revised: 12/26/2021] [Accepted: 01/06/2022] [Indexed: 02/04/2023] Open
Abstract
DNA methylation is an essential epigenetic mark. Alterations of normal DNA methylation are a defining feature of cancer. Here, we review experimental and bioinformatic approaches to showcase the breadth and depth of information that this epigenetic mark provides for cancer research. First, we describe classical approaches for interrogating bulk DNA from cell populations as well as more recently developed approaches for single cells and multi-Omics. Second, we focus on the computational analysis from primary data processing to the identification of unique methylation signatures. Additionally, we discuss challenges such as sparse data and cellular heterogeneity.
Collapse
Affiliation(s)
- Angelika Merkel
- Bioinformatics Unit, Josep Carreras Leukemia Research Institute (IJC), 08916 Barcelona, Spain
| | - Manel Esteller
- Cancer Epigenetics Group, Josep Carreras Leukemia Research Institute (IJC), 08916 Barcelona, Spain
- Centro de Investigación Biomédica en Red Cáncer (CIBERONC), 28029 Madrid, Spain
- Institucio Catalana de Recerca Avançats (ICREA), 08010 Barcelona, Spain
- Physiological Sciences Department, School of Medicine and Health Sciences, University of Catalonia, 08017 Barcelona, Spain
| |
Collapse
|
47
|
Wu C. Using R for Cell-Type Composition Imputation in Epigenome-Wide Association Studies. Methods Mol Biol 2022; 2432:49-56. [PMID: 35505206 DOI: 10.1007/978-1-0716-1994-0_4] [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: 06/14/2023]
Abstract
Adjusting cell type composition is challenging but critical in epigenome-wide association studies (EWAS). In this chapter, we describe how to apply reference-based and reference-free methods in R to impute cell type composition in whole blood samples.
Collapse
Affiliation(s)
- Chong Wu
- Department of Statistics, Florida State University, Tallahassee, FL, USA.
| |
Collapse
|
48
|
Shanthikumar S, Neeland MR, Saffery R, Ranganathan SC, Oshlack A, Maksimovic J. DNA Methylation Profiles of Purified Cell Types in Bronchoalveolar Lavage: Applications for Mixed Cell Paediatric Pulmonary Studies. Front Immunol 2021; 12:788705. [PMID: 35003108 PMCID: PMC8727592 DOI: 10.3389/fimmu.2021.788705] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Accepted: 12/03/2021] [Indexed: 01/15/2023] Open
Abstract
In epigenome-wide association studies analysing DNA methylation from samples containing multiple cell types, it is essential to adjust the analysis for cell type composition. One well established strategy for achieving this is reference-based cell type deconvolution, which relies on knowledge of the DNA methylation profiles of purified constituent cell types. These are then used to estimate the cell type proportions of each sample, which can then be incorporated to adjust the association analysis. Bronchoalveolar lavage is commonly used to sample the lung in clinical practice and contains a mixture of different cell types that can vary in proportion across samples, affecting the overall methylation profile. A current barrier to the use of bronchoalveolar lavage in DNA methylation-based research is the lack of reference DNA methylation profiles for each of the constituent cell types, thus making reference-based cell composition estimation difficult. Herein, we use bronchoalveolar lavage samples collected from children with cystic fibrosis to define DNA methylation profiles for the four most common and clinically relevant cell types: alveolar macrophages, granulocytes, lymphocytes and alveolar epithelial cells. We then demonstrate the use of these methylation profiles in conjunction with an established reference-based methylation deconvolution method to estimate the cell type composition of two different tissue types; a publicly available dataset derived from artificial blood-based cell mixtures and further bronchoalveolar lavage samples. The reference DNA methylation profiles developed in this work can be used for future reference-based cell type composition estimation of bronchoalveolar lavage. This will facilitate the use of this tissue in studies examining the role of DNA methylation in lung health and disease.
Collapse
Affiliation(s)
- Shivanthan Shanthikumar
- Respiratory and Sleep Medicine, Royal Children’s Hospital, Parkville, VIC, Australia
- Department of Paediatrics, University of Melbourne, Parkville, VIC, Australia
- Respiratory Diseases, Murdoch Children’s Research Institute, Parkville, VIC, Australia
- *Correspondence: Shivanthan Shanthikumar,
| | - Melanie R. Neeland
- Department of Paediatrics, University of Melbourne, Parkville, VIC, Australia
- Molecular Immunity, Murdoch Children’s Research Institute, Parkville, VIC, Australia
| | - Richard Saffery
- Department of Paediatrics, University of Melbourne, Parkville, VIC, Australia
- Molecular Immunity, Murdoch Children’s Research Institute, Parkville, VIC, Australia
| | - Sarath C. Ranganathan
- Respiratory and Sleep Medicine, Royal Children’s Hospital, Parkville, VIC, Australia
- Department of Paediatrics, University of Melbourne, Parkville, VIC, Australia
- Respiratory Diseases, Murdoch Children’s Research Institute, Parkville, VIC, Australia
| | - Alicia Oshlack
- Computational Biology Program, Peter MacCallum Cancer Centre, Parkville, VIC, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Parkville, VIC, Australia
- School of BioScience, University of Melbourne, Parkville, VIC, Australia
| | - Jovana Maksimovic
- Department of Paediatrics, University of Melbourne, Parkville, VIC, Australia
- Respiratory Diseases, Murdoch Children’s Research Institute, Parkville, VIC, Australia
- Computational Biology Program, Peter MacCallum Cancer Centre, Parkville, VIC, Australia
| |
Collapse
|
49
|
Sharma M, Verma RK, Kumar S, Kumar V. Computational challenges in detection of cancer using cell-free DNA methylation. Comput Struct Biotechnol J 2021; 20:26-39. [PMID: 34976309 PMCID: PMC8669313 DOI: 10.1016/j.csbj.2021.12.001] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 12/02/2021] [Accepted: 12/02/2021] [Indexed: 12/18/2022] Open
Abstract
Cell-free DNA(cfDNA) methylation profiling is considered promising and potentially reliable for liquid biopsy to study progress of diseases and develop reliable and consistent diagnostic and prognostic biomarkers. There are several different mechanisms responsible for the release of cfDNA in blood plasma, and henceforth it can provide information regarding dynamic changes in the human body. Due to the fragmented nature, low concentration of cfDNA, and high background noise, there are several challenges in its analysis for regular use in diagnosis of cancer. Such challenges in the analysis of the methylation profile of cfDNA are further aggravated due to heterogeneity, biomarker sensitivity, platform biases, and batch effects. This review delineates the origin of cfDNA methylation, its profiling, and associated computational problems in analysis for diagnosis. Here we also contemplate upon the multi-marker approach to handle the scenario of cancer heterogeneity and explore the utility of markers for 5hmC based cfDNA methylation pattern. Further, we provide a critical overview of deconvolution and machine learning methods for cfDNA methylation analysis. Our review of current methods reveals the potential for further improvement in analysis strategies for detecting early cancer using cfDNA methylation.
Collapse
Key Words
- Cancer heterogeneity
- Cell free DNA
- Computation
- DMP, Differentially methylated base position
- DMR, Differentially methylated regions
- Diagnosis
- HELP-seq, HpaII-tiny fragment Enrichment by Ligation-mediated PCR sequencing
- MBD-seq, Methyl-CpG Binding Domain Protein Capture Sequencing
- MCTA-seq, Methylated CpG tandems amplification and sequencing
- MSCC, Methylation Sensitive Cut Counting
- MSRE, methylation sensitive restriction enzymes
- MeDIP-seq, Methylated DNA Immunoprecipitation Sequencing
- RRBS, Reduced-Representation Bisulfite Sequencing
- WGBS, Whole Genome Bisulfite Sequencing
- cfDNA, cell free DNA
- ctDNA, circulating tumor DNA
- dPCR, digital polymerase chain reaction
- ddMCP, droplet digital methylation-specific PCR
- ddPCR, droplet digital polymerase chain reaction
- scCGI, methylated CGIs at single cell level
Collapse
Affiliation(s)
- Madhu Sharma
- Department for Computational Biology, Indraprastha Institute of Information Technology, Delhi 110020, India
| | - Rohit Kumar Verma
- Department for Computational Biology, Indraprastha Institute of Information Technology, Delhi 110020, India
| | - Sunil Kumar
- Department of Surgical oncology, All India Institute of Medical sciences, New Delhi 110029, India
| | - Vibhor Kumar
- Department for Computational Biology, Indraprastha Institute of Information Technology, Delhi 110020, India
| |
Collapse
|
50
|
Charras A, Garau J, Hofmann SR, Carlsson E, Cereda C, Russ S, Abraham S, Hedrich CM. DNA Methylation Patterns in CD8 + T Cells Discern Psoriasis From Psoriatic Arthritis and Correlate With Cutaneous Disease Activity. Front Cell Dev Biol 2021; 9:746145. [PMID: 34746142 PMCID: PMC8567019 DOI: 10.3389/fcell.2021.746145] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 09/16/2021] [Indexed: 12/12/2022] Open
Abstract
Background: Psoriasis is a T cell-mediated chronic autoimmune/inflammatory disease. While some patients experience disease limited to the skin (skin psoriasis), others develop joint involvement (psoriatic arthritis; PsA). In the absence of disease- and/or outcome-specific biomarkers, and as arthritis can precede skin manifestations, diagnostic and therapeutic delays are common and contribute to disease burden and damage accrual. Objective: Altered epigenetic marks, including DNA methylation, contribute to effector T cell phenotypes and altered cytokine expression in autoimmune/inflammatory diseases. This project aimed at the identification of disease-/outcome-specific DNA methylation signatures in CD8+ T cells from patients with psoriasis and PsA as compared to healthy controls. Method: Peripheral blood CD8+ T cells from nine healthy controls, 10 psoriasis, and seven PsA patients were collected to analyze DNA methylation marks using Illumina Human Methylation EPIC BeadChips (>850,000 CpGs per sample). Bioinformatic analysis was performed using R (minfi, limma, ChAMP, and DMRcate packages). Results: DNA methylation profiles in CD8+ T cells differentiate healthy controls from psoriasis patients [397 Differentially Methylated Positions (DMPs); 9 Differentially Methylated Regions (DMRs) when ≥CpGs per DMR were considered; 2 DMRs for ≥10 CpGs]. Furthermore, patients with skin psoriasis can be discriminated from PsA patients [1,861 DMPs, 20 DMRs (≥5 CpGs per region), 4 DMRs (≥10 CpGs per region)]. Gene ontology (GO) analyses considering genes with ≥1 DMP in their promoter delivered methylation defects in skin psoriasis and PsA primarily affecting the BMP signaling pathway and endopeptidase regulator activity, respectively. GO analysis of genes associated with DMRs between skin psoriasis and PsA demonstrated an enrichment of GABAergic neuron and cortex neuron development pathways. Treatment with cytokine blockers associated with DNA methylation changes [2,372 DMPs; 1,907 DMPs within promoters, 7 DMRs (≥5 CpG per regions)] affecting transforming growth factor beta receptor and transmembrane receptor protein serine/threonine kinase signaling pathways. Lastly, a methylation score including TNF and IL-17 pathway associated DMPs inverse correlates with skin disease activity scores (PASI). Conclusion: Patients with skin psoriasis exhibit DNA methylation patterns in CD8+ T cells that allow differentiation from PsA patients and healthy individuals, and reflect clinical activity of skin disease. Thus, DNA methylation profiling promises potential as diagnostic and prognostic tool to be used for molecular patient stratification toward individualized treatment.
Collapse
Affiliation(s)
- Amandine Charras
- Department of Women's and Children's Health, Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, United Kingdom
| | - Jessica Garau
- Genomic and Post-Genomic Unit, IRCCS Mondino Foundation, Pavia, Italy
| | - Sigrun R Hofmann
- Klinik und Poliklinik für Kinder- und Jugendmedizin, Universitätsklinikum Carl Gustav Carus, TU Dresden, Dresden, Germany
| | - Emil Carlsson
- Department of Women's and Children's Health, Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, United Kingdom
| | - Cristina Cereda
- Genomic and Post-Genomic Unit, IRCCS Mondino Foundation, Pavia, Italy
| | - Susanne Russ
- Klinik und Poliklinik für Kinder- und Jugendmedizin, Universitätsklinikum Carl Gustav Carus, TU Dresden, Dresden, Germany
| | - Susanne Abraham
- Klinik und Poliklinik für Dermatologie, Universitätsklinikum Carl Gustav Carus, TU Dresden, Dresden, Germany
| | - Christian M Hedrich
- Department of Women's and Children's Health, Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, United Kingdom.,Department of Paediatric Rheumatology, Alder Hey Children's NHS Foundation Trust Hospital, Liverpool, United Kingdom
| |
Collapse
|