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Ruprecht NA, Kennedy JD, Bansal B, Singhal S, Sens D, Maggio A, Doe V, Hawkins D, Campbel R, O’Connell K, Gill JS, Schaefer K, Singhal SK. Transcriptomics and epigenetic data integration learning module on Google Cloud. Brief Bioinform 2024; 25:bbae352. [PMID: 39101486 PMCID: PMC11299028 DOI: 10.1093/bib/bbae352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 05/12/2024] [Accepted: 07/06/2024] [Indexed: 08/06/2024] Open
Abstract
Multi-omics (genomics, transcriptomics, epigenomics, proteomics, metabolomics, etc.) research approaches are vital for understanding the hierarchical complexity of human biology and have proven to be extremely valuable in cancer research and precision medicine. Emerging scientific advances in recent years have made high-throughput genome-wide sequencing a central focus in molecular research by allowing for the collective analysis of various kinds of molecular biological data from different types of specimens in a single tissue or even at the level of a single cell. Additionally, with the help of improved computational resources and data mining, researchers are able to integrate data from different multi-omics regimes to identify new prognostic, diagnostic, or predictive biomarkers, uncover novel therapeutic targets, and develop more personalized treatment protocols for patients. For the research community to parse the scientifically and clinically meaningful information out of all the biological data being generated each day more efficiently with less wasted resources, being familiar with and comfortable using advanced analytical tools, such as Google Cloud Platform becomes imperative. This project is an interdisciplinary, cross-organizational effort to provide a guided learning module for integrating transcriptomics and epigenetics data analysis protocols into a comprehensive analysis pipeline for users to implement in their own work, utilizing the cloud computing infrastructure on Google Cloud. The learning module consists of three submodules that guide the user through tutorial examples that illustrate the analysis of RNA-sequence and Reduced-Representation Bisulfite Sequencing data. The examples are in the form of breast cancer case studies, and the data sets were procured from the public repository Gene Expression Omnibus. The first submodule is devoted to transcriptomics analysis with the RNA sequencing data, the second submodule focuses on epigenetics analysis using the DNA methylation data, and the third submodule integrates the two methods for a deeper biological understanding. The modules begin with data collection and preprocessing, with further downstream analysis performed in a Vertex AI Jupyter notebook instance with an R kernel. Analysis results are returned to Google Cloud buckets for storage and visualization, removing the computational strain from local resources. The final product is a start-to-finish tutorial for the researchers with limited experience in multi-omics to integrate transcriptomics and epigenetics data analysis into a comprehensive pipeline to perform their own biological research.This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [16] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses. HIGHLIGHTS
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Affiliation(s)
- Nathan A Ruprecht
- Department of Biomedical Engineering, University of North Dakota, 501 N. Columbia Road Stop 8380, Grand Forks, ND 58202, United States
| | - Joshua D Kennedy
- Department of Biomedical Engineering, University of North Dakota, 501 N. Columbia Road Stop 8380, Grand Forks, ND 58202, United States
- Department of Chemistry and Physics, Drury University, 900 N. Benton Avenue, Springfield, MO 65802, United States
| | - Benu Bansal
- Department of Biomedical Engineering, University of North Dakota, 501 N. Columbia Road Stop 8380, Grand Forks, ND 58202, United States
| | - Sonalika Singhal
- Department of Pathology, University of North Dakota, 1301 N. Columbia Road Stop 9037, Grand Forks, ND 58202, United States
| | - Donald Sens
- Department of Pathology, University of North Dakota, 1301 N. Columbia Road Stop 9037, Grand Forks, ND 58202, United States
| | - Angela Maggio
- Deloitte, Health Data and AI, Deloitte Consulting LLP, 1919 N. Lynn Street, Suite 1500, Arlington, VA 22209, United States
| | - Valena Doe
- Google, Google Cloud, 1900 Reston Metro Plaza, Reston, VA 20190, United States
| | - Dale Hawkins
- Google, Google Cloud, 1900 Reston Metro Plaza, Reston, VA 20190, United States
| | - Ross Campbel
- NIH Center for Information Technology (CIT), 6555 Rock Spring Drive, Bethesda, MD 20892, United States
| | - Kyle O’Connell
- NIH Center for Information Technology (CIT), 6555 Rock Spring Drive, Bethesda, MD 20892, United States
| | - Jappreet Singh Gill
- Department of Biomedical Engineering, University of North Dakota, 501 N. Columbia Road Stop 8380, Grand Forks, ND 58202, United States
| | - Kalli Schaefer
- Department of Biomedical Engineering, University of North Dakota, 501 N. Columbia Road Stop 8380, Grand Forks, ND 58202, United States
| | - Sandeep K Singhal
- Department of Biomedical Engineering, University of North Dakota, 501 N. Columbia Road Stop 8380, Grand Forks, ND 58202, United States
- Department of Pathology, University of North Dakota, 1301 N. Columbia Road Stop 9037, Grand Forks, ND 58202, United States
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Wu Y, Seufert I, Al-Shaheri FN, Kurilov R, Bauer AS, Manoochehri M, Moskalev EA, Brors B, Tjaden C, Giese NA, Hackert T, Büchler MW, Hoheisel JD. DNA-methylation signature accurately differentiates pancreatic cancer from chronic pancreatitis in tissue and plasma. Gut 2023; 72:2344-2353. [PMID: 37709492 PMCID: PMC10715533 DOI: 10.1136/gutjnl-2023-330155] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 08/31/2023] [Indexed: 09/16/2023]
Abstract
OBJECTIVE Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy. Differentiation from chronic pancreatitis (CP) is currently inaccurate in about one-third of cases. Misdiagnoses in both directions, however, have severe consequences for patients. We set out to identify molecular markers for a clear distinction between PDAC and CP. DESIGN Genome-wide variations of DNA-methylation, messenger RNA and microRNA level as well as combinations thereof were analysed in 345 tissue samples for marker identification. To improve diagnostic performance, we established a random-forest machine-learning approach. Results were validated on another 48 samples and further corroborated in 16 liquid biopsy samples. RESULTS Machine-learning succeeded in defining markers to differentiate between patients with PDAC and CP, while low-dimensional embedding and cluster analysis failed to do so. DNA-methylation yielded the best diagnostic accuracy by far, dwarfing the importance of transcript levels. Identified changes were confirmed with data taken from public repositories and validated in independent sample sets. A signature of six DNA-methylation sites in a CpG-island of the protein kinase C beta type gene achieved a validated diagnostic accuracy of 100% in tissue and in circulating free DNA isolated from patient plasma. CONCLUSION The success of machine-learning to identify an effective marker signature documents the power of this approach. The high diagnostic accuracy of discriminating PDAC from CP could have tremendous consequences for treatment success, once the result from still a limited number of liquid biopsy samples would be confirmed in a larger cohort of patients with suspected pancreatic cancer.
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Affiliation(s)
- Yenan Wu
- Division of Functional Genome Analysis, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Faculty of Biosciences, Heidelberg University, Heidelberg, Germany
| | - Isabelle Seufert
- Division of Functional Genome Analysis, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Faculty of Biosciences, Heidelberg University, Heidelberg, Germany
| | - Fawaz N Al-Shaheri
- Division of Functional Genome Analysis, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Medical Faculty Heidelberg, University of Heidelberg, Heidelberg, Germany
| | - Roman Kurilov
- Division of Applied Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Andrea S Bauer
- Division of Functional Genome Analysis, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Mehdi Manoochehri
- Division of Functional Genome Analysis, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Evgeny A Moskalev
- Institute of Pathology, Universitätsklinikum Erlangen, Friedrich Alexander Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Benedikt Brors
- Division of Applied Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Christin Tjaden
- Department of Surgery, Heidelberg University Hospital, Heidelberg, Germany
| | - Nathalia A Giese
- Department of Surgery, Heidelberg University Hospital, Heidelberg, Germany
| | - Thilo Hackert
- Department of Surgery, Heidelberg University Hospital, Heidelberg, Germany
| | - Markus W Büchler
- Department of Surgery, Heidelberg University Hospital, Heidelberg, Germany
| | - Jörg D Hoheisel
- Division of Functional Genome Analysis, German Cancer Research Center (DKFZ), Heidelberg, Germany
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3
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Philibert R, Dogan TK, Knight S, Ahmad F, Lau S, Miles G, Knowlton KU, Dogan MV. Validation of an Integrated Genetic-Epigenetic Test for the Assessment of Coronary Heart Disease. J Am Heart Assoc 2023; 12:e030934. [PMID: 37982274 PMCID: PMC10727271 DOI: 10.1161/jaha.123.030934] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 10/16/2023] [Indexed: 11/21/2023]
Abstract
BACKGROUND Coronary heart disease (CHD) is the leading cause of death in the world. Unfortunately, many of the key diagnostic tools for CHD are insensitive, invasive, and costly; require significant specialized infrastructure investments; and do not provide information to guide postdiagnosis therapy. In prior work using data from the Framingham Heart Study, we provided in silico evidence that integrated genetic-epigenetic tools may provide a new avenue for assessing CHD. METHODS AND RESULTS In this communication, we use an improved machine learning approach and data from 2 additional cohorts, totaling 449 cases and 2067 controls, to develop a better model for ascertaining symptomatic CHD. Using the DNA from the 2 new cohorts, we translate and validate the in silico findings into an artificial intelligence-guided, clinically implementable method that uses input from 6 methylation-sensitive digital polymerase chain reaction and 10 genotyping assays. Using this method, the overall average area under the curve, sensitivity, and specificity in the 3 test cohorts is 82%, 79%, and 76%, respectively. Analysis of targeted cytosine-phospho-guanine loci shows that they map to key risk pathways involved in atherosclerosis that suggest specific therapeutic approaches. CONCLUSIONS We conclude that this scalable integrated genetic-epigenetic approach is useful for the diagnosis of symptomatic CHD, performs favorably as compared with many existing methods, and may provide personalized insight to CHD therapy. Furthermore, given the dynamic nature of DNA methylation and the ease of methylation-sensitive digital polymerase chain reaction methodologies, these findings may pave a pathway for precision epigenetic approaches for monitoring CHD treatment response.
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Affiliation(s)
- Robert Philibert
- Cardio Diagnostics IncChicagoILUSA
- Department of PsychiatryUniversity of IowaIowa CityIAUSA
- Department of Biomedical EngineeringUniversity of IowaIowa CityIAUSA
| | | | - Stacey Knight
- Intermountain Heart Institute, Intermountain HealthcareSalt Lake CityUTUSA
- Department of Internal MedicineUniversity of UtahSalt Lake CityUTUSA
| | - Ferhaan Ahmad
- Division of Cardiovascular Medicine, Department of Internal MedicineUniversity of IowaIowa CityIAUSA
| | - Stanley Lau
- Southern California Heart CentersSan GabrielCAUSA
| | - George Miles
- Department of Molecular and Human GeneticsBaylor College of MedicineHoustonTXUSA
| | - Kirk U. Knowlton
- Intermountain Heart Institute, Intermountain HealthcareSalt Lake CityUTUSA
| | - Meeshanthini V. Dogan
- Cardio Diagnostics IncChicagoILUSA
- Department of Biomedical EngineeringUniversity of IowaIowa CityIAUSA
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Fang F, Andersen AM, Philibert R, Hancock DB. Epigenetic biomarkers for smoking cessation. ADDICTION NEUROSCIENCE 2023; 6:100079. [PMID: 37123087 PMCID: PMC10136056 DOI: 10.1016/j.addicn.2023.100079] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Cigarette smoking has been associated with epigenetic alterations that may be reversible upon cessation. As the most-studied epigenetic modification, DNA methylation is strongly associated with smoking exposure, providing a potential mechanism that links smoking to adverse health outcomes. Here, we reviewed the reversibility of DNA methylation in accessible peripheral tissues, mainly blood, in relation to cigarette smoking cessation and the utility of DNA methylation as a biomarker signature to differentiate current, former, and never smokers and to quantify time since cessation. We summarized thousands of differentially methylated Cytosine-Guanine (CpG) dinucleotides and regions associated with smoking cessation from candidate gene and epigenome-wide association studies, as well as the prediction accuracy of the multi-CpG predictors for smoking status. Overall, there is robust evidence for DNA methylation signature of cigarette smoking cessation. However, there are still gaps to fill, including (1) cell-type heterogeneity in measuring blood DNA methylation; (2) underrepresentation of non-European ancestry populations; (3) limited longitudinal data to quantitatively measure DNA methylation after smoking cessation over time; and (4) limited data to study the impact of smoking cessation on other epigenetic features, noncoding RNAs, and histone modifications. Epigenetic machinery provides promising biomarkers that can improve success in smoking cessation in the clinical setting. To achieve this goal, larger and more-diverse samples with longitudinal measures of a broader spectrum of epigenetic marks will be essential to developing a robust DNA methylation biomarker assay, followed by meeting validation requirements for the assay before being implemented as a clinically useful tool.
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Affiliation(s)
- Fang Fang
- GenOmics, Bioinformatics, and Translational Research Center, RTI International, 3040 East Cornwallis Road, P.O. Box 12194, Research Triangle Park, NC 27709, USA
| | - Allan M. Andersen
- Department of Psychiatry, University of Iowa, 200 Hawkins Drive, Iowa City, IA 52242, USA
| | - Robert Philibert
- Department of Psychiatry, University of Iowa, 200 Hawkins Drive, Iowa City, IA 52242, USA
- Behavioral Diagnostics LLC, 2500 Crosspark Rd, Coralville, IA 52241, USA
- Department of Biomedical Engineering, 5601 Seamans Center for the Engineering Arts and Sciences, University of Iowa, Iowa City, IA 52242, USA
| | - Dana B. Hancock
- GenOmics, Bioinformatics, and Translational Research Center, RTI International, 3040 East Cornwallis Road, P.O. Box 12194, Research Triangle Park, NC 27709, USA
- Corresponding author. (D.B. Hancock)
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5
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Yazar V, Ruf WP, Knehr A, Günther K, Ammerpohl O, Danzer KM, Ludolph AC. DNA Methylation Analysis in Monozygotic Twins Discordant for ALS in Blood Cells. Epigenet Insights 2023; 16:25168657231172159. [PMID: 37152709 PMCID: PMC10161312 DOI: 10.1177/25168657231172159] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 04/09/2023] [Indexed: 05/09/2023] Open
Abstract
ALS is a fatal motor neuron disease that displays a broad variety of phenotypes ranging from early fatal courses to slowly progressing and rather benign courses. Such divergence can also be seen in genetic ALS cases with varying phenotypes bearing specific mutations, suggesting epigenetic mechanisms like DNA methylation act as disease modifiers. However, the epigenotype dictated by, in addition to other mechanisms, DNA methylation is also strongly influenced by the individual's genotype. Hence, we performed a DNA methylation study using EPIC arrays on 7 monozygotic (MZ) twin pairs discordant for ALS in whole blood, which serves as an ideal model for eliminating the effects of the genetic-epigenetic interplay to a large extent. We found one CpG site showing intra-pair hypermethylation in the affected co-twins, which maps to the Glutamate Ionotropic Receptor Kainate Type Subunit 1 gene (GRIK1). Additionally, we found 4 DMPs which were subsequently confirmed using 2 different statistical approaches. Differentially methylated regions or blocks could not be detected within the scope of this work. In conclusion, we revealed that despite a low sample size, monozygotic twin studies discordant for the disease can bring new insights into epigenetic processes in ALS, pointing to new target loci for further investigations.
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Affiliation(s)
- Volkan Yazar
- Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), German Center for Neurodegenerative Diseases, Ulm, Germany
| | - Wolfgang P Ruf
- Department of Neurology, Ulm University, Ulm, Baden-Württemberg, Germany
| | - Antje Knehr
- Department of Neurology, Ulm University, Ulm, Baden-Württemberg, Germany
| | - Kornelia Günther
- Department of Neurology, Ulm University, Ulm, Baden-Württemberg, Germany
| | - Ole Ammerpohl
- Institute of Human Genetics, Ulm University & Ulm University Medical Center, Ulm, Germany
| | - Karin M Danzer
- Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), German Center for Neurodegenerative Diseases, Ulm, Germany
- Department of Neurology, Ulm University, Ulm, Baden-Württemberg, Germany
| | - Albert C Ludolph
- Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), German Center for Neurodegenerative Diseases, Ulm, Germany
- Department of Neurology, Ulm University, Ulm, Baden-Württemberg, Germany
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Broséus L, Vaiman D, Tost J, Martin CRS, Jacobi M, Schwartz JD, Béranger R, Slama R, Heude B, Lepeule J. Maternal blood pressure associates with placental DNA methylation both directly and through alterations in cell-type composition. BMC Med 2022; 20:397. [PMID: 36266660 PMCID: PMC9585724 DOI: 10.1186/s12916-022-02610-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 10/14/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Maternal blood pressure levels reflect cardiovascular adaptation to pregnancy and proper maternal-fetal exchanges through the placenta and are very sensitive to numerous environmental stressors. Maternal hypertension during pregnancy has been associated with impaired placental functions and with an increased risk for children to suffer from cardiovascular and respiratory diseases later on. Investigating changes in placental DNA methylation levels and cell-type composition in association with maternal blood pressure could help elucidate its relationships with placental and fetal development. METHODS Taking advantage of a large cohort of 666 participants, we investigated the association between epigenome-wide DNA methylation patterns in the placenta, measured using the Infinium HumanMethylation450 BeadChip, placental cell-type composition, estimated in silico, and repeated measurements of maternal steady and pulsatile blood pressure indicators during pregnancy. RESULTS At the site-specific level, no significant association was found between maternal blood pressure and DNA methylation levels after correction for multiple testing (false discovery rate < 0.05), but 5 out of 24 previously found CpG associations were replicated (p-value < 0.05). At the regional level, our analyses highlighted 64 differentially methylated regions significantly associated with at least one blood pressure component, including 35 regions associated with mean arterial pressure levels during late pregnancy. These regions were found enriched for genes implicated in lung development and diseases. Further mediation analyses show that a significant part of the association between steady blood pressure-but not pulsatile pressure-and placental methylation can be explained by alterations in placental cell-type composition. In particular, elevated blood pressure levels are associated with a decrease in the ratio between mesenchymal stromal cells and syncytiotrophoblasts, even in the absence of preeclampsia. CONCLUSIONS This study provides the first evidence that the association between maternal steady blood pressure during pregnancy and placental DNA methylation is both direct and partly explained by changes in cell-type composition. These results could hint at molecular mechanisms linking maternal hypertension to lung development and early origins of childhood respiratory problems and at the importance of controlling maternal blood pressure during pregnancy.
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Affiliation(s)
- Lucile Broséus
- University Grenoble Alpes, INSERM, Team of Environmental Epidemiology Applied to Development and Respiratory Health, Institute for Advanced Biosciences (IAB), Grenoble, France.
| | - Daniel Vaiman
- From Gametes to Birth, Institut Cochin, U1016 INSERM, UMR 8104 CNRS, Paris-Descartes University, Paris, France
| | - Jörg Tost
- Laboratory for Epigenetics and Environment, Centre National de Recherche en Génomique Humaine, CEA - Institut de Biologie François Jacob, University Paris Saclay, Evry, France
| | - Camino Ruano San Martin
- From Gametes to Birth, Institut Cochin, U1016 INSERM, UMR 8104 CNRS, Paris-Descartes University, Paris, France
| | - Milan Jacobi
- University Grenoble Alpes, INSERM, Team of Environmental Epidemiology Applied to Development and Respiratory Health, Institute for Advanced Biosciences (IAB), Grenoble, France
| | - Joel D Schwartz
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Rémi Béranger
- Univ. Rennes, CHU Rennes, INSERM, EHESP, IRSET (Institut de recherche en santé, environnement et travail), UMR 1085, Rennes, France
| | - Rémy Slama
- University Grenoble Alpes, INSERM, Team of Environmental Epidemiology Applied to Development and Respiratory Health, Institute for Advanced Biosciences (IAB), Grenoble, France
| | - Barbara Heude
- Univ. Paris, Centre for Research in Epidemiology and Statistics (CRESS), INSERM, INRAE, Paris, France
| | - Johanna Lepeule
- University Grenoble Alpes, INSERM, Team of Environmental Epidemiology Applied to Development and Respiratory Health, Institute for Advanced Biosciences (IAB), Grenoble, France.
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Lei MK, Gibbons FX, Gerrard M, Beach SRH, Dawes K, Philibert R. Digital methylation assessments of alcohol and cigarette consumption account for common variance in accelerated epigenetic ageing. Epigenetics 2022; 17:1991-2005. [PMID: 35866695 PMCID: PMC9665121 DOI: 10.1080/15592294.2022.2100684] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Smoking and Heavy Alcohol Consumption (HAC) are established risk factors for myriad complex disorders of ageing. Yet many prior studies of Epigenetic Ageing (EA) have shown only modest effects of smoking and drinking on accelerated ageing. One potential reason for this conundrum might be the reliance of some prior EA studies on self-reported substance use, which may be unreliable in many samples. To test whether novel, non-self-reported indices would show a stronger association of smoking and HAC to EA, we used methylation sensitive digital PCR (MSdPCR) and data from 437 African American subjects from Wave 7 of the Family and Community Health Study Offspring Cohort to examine the effects of subjective and objective measures of smoking and HAC on 7 indices of EA. Because of limited overall correlations between the various EA indices, we examined patterns of association separately for each index. Consistent with expectations, MSdPCR assessments of smoking and HAC, but not self-reported alcohol consumption, were strongly correlated with accelerated EA. MSdPCR assessments of smoking and HAC accounted for 57% of GrimAge acceleration and the shared variance in GrimAge and DunedinPOAM accelerated EA. We conclude that MSdPCR assessments of smoking and HAC are valuable tools for understanding EA, represent directly targetable conditions for the prevention of premature ageing, and substantially improve upon self-reported assessment of smoking and HAC.
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Affiliation(s)
- Man-Kit Lei
- Department of Sociology, University of Georgia, Athens, GA, USA.,Center for Family Research, University of Georgia, Athens, GA, USA
| | - Frederick X Gibbons
- Department of Psychological Sciences, University of Connecticut, Storrs, CT, USA
| | - Meg Gerrard
- Department of Psychological Sciences, University of Connecticut, Storrs, CT, USA
| | - Steven R H Beach
- Center for Family Research, University of Georgia, Athens, GA, USA.,Department of Psychology, University of Georgia, Athens, GA, USA
| | - Kelsey Dawes
- Department of Psychiatry, University of Iowa, Iowa City, IA, USA
| | - Robert Philibert
- Department of Psychiatry, University of Iowa, Iowa City, IA, USA.,Behavioral Diagnostics LLC, Coralville, IA, USA
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Dawes K, Andersen A, Reimer R, Mills JA, Hoffman E, Long JD, Miller S, Philibert R. The relationship of smoking to cg05575921 methylation in blood and saliva DNA samples from several studies. Sci Rep 2021; 11:21627. [PMID: 34732805 PMCID: PMC8566492 DOI: 10.1038/s41598-021-01088-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Accepted: 10/18/2021] [Indexed: 11/23/2022] Open
Abstract
Numerous studies have shown that cg05575921 methylation decreases in response to smoking. However, secondary to methodological issues, the magnitude and dose dependency of that response is as of yet unclear. This lack of certainty is a barrier to the use of DNA methylation clinically to assess and monitor smoking status. To better define this relationship, we conducted a joint analysis of methylation sensitive PCR digital (MSdPCR) assessments of cg05575921 methylation in whole blood and/or saliva DNA to smoking using samples from 421 smokers and 423 biochemically confirmed non-smokers from 4 previously published studies. We found that cg05575921 methylation manifested a curvilinear dose dependent decrease in response to increasing cigarette consumption. In whole blood DNA, the Receiver Operating Characteristic (ROC) Area Under the Curve (AUC) of cg05575921 methylation for predicting daily smoking status was 0.98. In saliva DNA, the gross AUC was 0.91 with correction for cellular heterogeneity improving the AUC to 0.94. Methylation status was significantly associated with the Fagerstrom Test for Nicotine Dependence score, but with significant sampling heterogeneity. We conclude that MSdPCR assessments of cg05575921 methylation are a potentially powerful, clinically implementable tool for the assessment and management of smoking.
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Affiliation(s)
- Kelsey Dawes
- Department of Psychiatry, University of Iowa, Iowa City, IA, 52242, USA
- Molecular Medicine Program, University of Iowa, Iowa City, IA, 52242, USA
| | - Allan Andersen
- Department of Psychiatry, University of Iowa, Iowa City, IA, 52242, USA
| | - Rachel Reimer
- Department of Public Health, Des Moines University, Des Moines, IA, 50312, USA
| | - James A Mills
- Department of Psychiatry, University of Iowa, Iowa City, IA, 52242, USA
| | - Eric Hoffman
- Department of Radiology, University of Iowa, Iowa City, IA, 52242, USA
- Department of Biomedical Engineering, University of Iowa, Iowa City, IA, 52242, USA
| | - Jeffrey D Long
- Department of Psychiatry, University of Iowa, Iowa City, IA, 52242, USA
- Department of Biostatistics, University of Iowa, Iowa City, IA, 52242, USA
| | - Shelly Miller
- Behavioral Diagnostics LLC, Coralville, IA, 52241, USA
| | - Robert Philibert
- Department of Psychiatry, University of Iowa, Iowa City, IA, 52242, USA.
- Department of Biomedical Engineering, University of Iowa, Iowa City, IA, 52242, USA.
- Behavioral Diagnostics LLC, Coralville, IA, 52241, USA.
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9
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Richter GM, Kruppa J, Keceli HG, Ataman-Duruel ET, Graetz C, Pischon N, Wagner G, Rendenbach C, Jockel-Schneider Y, Martins O, Bruckmann C, Staufenbiel I, Franke A, Nohutcu RM, Jepsen S, Dommisch H, Schaefer AS. Epigenetic adaptations of the masticatory mucosa to periodontal inflammation. Clin Epigenetics 2021; 13:203. [PMID: 34732256 PMCID: PMC8567676 DOI: 10.1186/s13148-021-01190-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Accepted: 10/25/2021] [Indexed: 12/13/2022] Open
Abstract
Background In mucosal barrier interfaces, flexible responses of gene expression to long-term environmental changes allow adaptation and fine-tuning for the balance of host defense and uncontrolled not-resolving inflammation. Epigenetic modifications of the chromatin confer plasticity to the genetic information and give insight into how tissues use the genetic information to adapt to environmental factors. The oral mucosa is particularly exposed to environmental stressors such as a variable microbiota. Likewise, persistent oral inflammation is the most important intrinsic risk factor for the oral inflammatory disease periodontitis and has strong potential to alter DNA-methylation patterns. The aim of the current study was to identify epigenetic changes of the oral masticatory mucosa in response to long-term inflammation that resulted in periodontitis. Methods and results Genome-wide CpG methylation of both inflamed and clinically uninflamed solid gingival tissue biopsies of 60 periodontitis cases was analyzed using the Infinium MethylationEPIC BeadChip. We validated and performed cell-type deconvolution for infiltrated immune cells using the EpiDish algorithm. Effect sizes of DMPs in gingival epithelial and fibroblast cells were estimated and adjusted for confounding factors using our recently developed “intercept-method”. In the current EWAS, we identified various genes that showed significantly different methylation between periodontitis-inflamed and uninflamed oral mucosa in periodontitis patients. The strongest differences were observed for genes with roles in wound healing (ROBO2, PTP4A3), cell adhesion (LPXN) and innate immune response (CCL26, DNAJC1, BPI). Enrichment analyses implied a role of epigenetic changes for vesicle trafficking gene sets. Conclusions Our results imply specific adaptations of the oral mucosa to a persistent inflammatory environment that involve wound repair, barrier integrity, and innate immune defense. Supplementary Information The online version contains supplementary material available at 10.1186/s13148-021-01190-7.
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Affiliation(s)
- Gesa M Richter
- Department of Periodontology and Synoptic Dentistry, Oral Medicine and Oral Surgery, Institute for Dental and Craniofacial Sciences, Charité - Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt-Universität Zu Berlin, and Berlin Institute of Health, Aßmannshauser Str. 4-6, 14197, Berlin, Germany.
| | - Jochen Kruppa
- Institute of Medical Informatics, Charité - Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt-Universität Zu Berlin, and Berlin Institute of Health, Charitéplatz 1, 10117, Berlin, Germany
| | - H Gencay Keceli
- Periodontology Department, Faculty of Dentistry, Hacettepe University, 06230, Sihhiye/Altindag/Ankara, Turkey
| | - Emel Tuğba Ataman-Duruel
- Periodontology Department, Faculty of Dentistry, Hacettepe University, 06230, Sihhiye/Altindag/Ankara, Turkey
| | - Christian Graetz
- Clinic of Conservative Dentistry and Periodontology, University Medical Center Schleswig-Holstein, Arnold-Heller-Straße 3, 24105, Kiel, Germany
| | - Nicole Pischon
- Private Practice, Karl-Marx-Straße 24, 12529, Schönefeld, Germany
| | - Gunar Wagner
- Department of Restorative Dentistry and Periodontology, University Medical Center Leipzig, 04103, Leipzig, Germany
| | - Carsten Rendenbach
- Department of Oral and Maxillofacial Surgery, Charité - Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt-Universität Zu Berlin, and Berlin Institute of Health, Augustenburger Platz 1, 13353, Berlin, Germany
| | - Yvonne Jockel-Schneider
- Department of Periodontology, Clinic of Preventive Dentistry and Periodontology, University Medical Center of the Julius-Maximilians-University, Pleicherwall, 97070, Würzburg, Germany
| | - Orlando Martins
- Institute of Periodontology, Institute of Medicine and Oral Surgery, Dentistry Department, Faculty of Medicine, University of Coimbra, Av. Bissaya Barreto, Bloco de Celas, 3000-075, Coimbra, Portugal
| | - Corinna Bruckmann
- Department of Conservative Dentistry and Periodontology, Medical University Vienna, School of Dentistry, Sensengasse 2a, 1090, Vienna, Austria
| | - Ingmar Staufenbiel
- Department of Conservative Dentistry, Periodontology & Preventive Dentistry, School of Dentistry, Hannover Medical School (MHH), Carl-Neuberg-Str. 1, 30625, Hannover, Germany
| | - Andre Franke
- Institute of Clinical Molecular Biology, Christian-Albrechts-University, Rosalind-Franklin-Straße 12, 24105, Kiel, Germany
| | - Rahime M Nohutcu
- Periodontology Department, Faculty of Dentistry, Hacettepe University, 06230, Sihhiye/Altindag/Ankara, Turkey
| | - Søren Jepsen
- Department of Periodontology, Operative and Preventive Dentistry, University of Bonn, Welschnonnenstraße 17, 53111, Bonn, Germany
| | - Henrik Dommisch
- Department of Periodontology and Synoptic Dentistry, Oral Medicine and Oral Surgery, Institute for Dental and Craniofacial Sciences, Charité - Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt-Universität Zu Berlin, and Berlin Institute of Health, Aßmannshauser Str. 4-6, 14197, Berlin, Germany
| | - Arne S Schaefer
- Department of Periodontology and Synoptic Dentistry, Oral Medicine and Oral Surgery, Institute for Dental and Craniofacial Sciences, Charité - Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt-Universität Zu Berlin, and Berlin Institute of Health, Aßmannshauser Str. 4-6, 14197, Berlin, Germany
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10
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Dogan MV, Knight S, Dogan TK, Knowlton KU, Philibert R. External validation of integrated genetic-epigenetic biomarkers for predicting incident coronary heart disease. Epigenomics 2021; 13:1095-1112. [PMID: 34148365 PMCID: PMC8356680 DOI: 10.2217/epi-2021-0123] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Accepted: 06/07/2021] [Indexed: 12/27/2022] Open
Abstract
Aim: The Framingham Risk Score (FRS) and atherosclerotic cardiovascular disease (ASCVD) Pooled Cohort Equation (PCE) for predicting risk for incident coronary heart disease (CHD) work poorly. To improve risk stratification for CHD, we developed a novel integrated genetic-epigenetic tool. Materials & methods: Using machine learning techniques and datasets from the Framingham Heart Study (FHS) and Intermountain Healthcare (IM), we developed and validated an integrated genetic-epigenetic model for predicting 3-year incident CHD. Results: Our approach was more sensitive than FRS and PCE and had high generalizability across cohorts. It performed with sensitivity/specificity of 79/75% in the FHS test set and 75/72% in the IM set. The sensitivity/specificity was 15/93% in FHS and 31/89% in IM for FRS, and sensitivity/specificity was 41/74% in FHS and 69/55% in IM for PCE. Conclusion: The use of our tool in a clinical setting could better identify patients at high risk for a heart attack.
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Affiliation(s)
- Meeshanthini V Dogan
- Cardio Diagnostics, Inc., Coralville, IA 52241, USA
- Department of Biomedical Engineering, University of Iowa, Iowa City, IA 52242, USA
| | - Stacey Knight
- Intermountain Heart Institute, Intermountain Healthcare, Salt Lake City, UT 84103, USA
- Department of Medicine, University of Utah, Salt Lake City, UT 84112, USA
| | | | - Kirk U Knowlton
- Intermountain Heart Institute, Intermountain Healthcare, Salt Lake City, UT 84103, USA
| | - Robert Philibert
- Cardio Diagnostics, Inc., Coralville, IA 52241, USA
- Department of Psychiatry, University of Iowa, Iowa City, IA 52242, USA
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