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Carry PM, Vanderlinden LA, Johnson RK, Buckner T, Steck AK, Kechris K, Yang IV, Fingerlin TE, Fiehn O, Rewers M, Norris JM. Longitudinal changes in DNA methylation during the onset of islet autoimmunity differentiate between reversion versus progression of islet autoimmunity. Front Immunol 2024; 15:1345494. [PMID: 38915393 PMCID: PMC11194352 DOI: 10.3389/fimmu.2024.1345494] [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: 11/27/2023] [Accepted: 05/21/2024] [Indexed: 06/26/2024] Open
Abstract
Background Type 1 diabetes (T1D) is preceded by a heterogenous pre-clinical phase, islet autoimmunity (IA). We aimed to identify pre vs. post-IA seroconversion (SV) changes in DNAm that differed across three IA progression phenotypes, those who lose autoantibodies (reverters), progress to clinical T1D (progressors), or maintain autoantibody levels (maintainers). Methods This epigenome-wide association study (EWAS) included longitudinal DNAm measurements in blood (Illumina 450K and EPIC) from participants in Diabetes Autoimmunity Study in the Young (DAISY) who developed IA, one or more islet autoantibodies on at least two consecutive visits. We compared reverters - individuals who sero-reverted, negative for all autoantibodies on at least two consecutive visits and did not develop T1D (n=41); maintainers - continued to test positive for autoantibodies but did not develop T1D (n=60); progressors - developed clinical T1D (n=42). DNAm data were measured before (pre-SV visit) and after IA (post-SV visit). Linear mixed models were used to test for differences in pre- vs post-SV changes in DNAm across the three groups. Linear mixed models were also used to test for group differences in average DNAm. Cell proportions, age, and sex were adjusted for in all models. Median follow-up across all participants was 15.5 yrs. (interquartile range (IQR): 10.8-18.7). Results The median age at the pre-SV visit was 2.2 yrs. (IQR: 0.8-5.3) in progressors, compared to 6.0 yrs. (IQR: 1.3-8.4) in reverters, and 5.7 yrs. (IQR: 1.4-9.7) in maintainers. Median time between the visits was similar in reverters 1.4 yrs. (IQR: 1-1.9), maintainers 1.3 yrs. (IQR: 1.0-2.0), and progressors 1.8 yrs. (IQR: 1.0-2.0). Changes in DNAm, pre- vs post-SV, differed across the groups at one site (cg16066195) and 11 regions. Average DNAm (mean of pre- and post-SV) differed across 22 regions. Conclusion Differentially changing DNAm regions were located in genomic areas related to beta cell function, immune cell differentiation, and immune cell function.
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Affiliation(s)
- Patrick M. Carry
- Colorado Program for Musculoskeletal Research, Department of Orthopedics, University of Colorado, Aurora, CO, United States
- Department of Epidemiology, Colorado School of Public Health, Aurora, CO, United States
- Department of Biomedical Informatics, School of Medicine, University of Colorado, Aurora, CO, United States
| | | | - Randi K. Johnson
- Department of Epidemiology, Colorado School of Public Health, Aurora, CO, United States
- Department of Biomedical Informatics, School of Medicine, University of Colorado, Aurora, CO, United States
| | - Teresa Buckner
- Department of Epidemiology, Colorado School of Public Health, Aurora, CO, United States
- Department of Kinesiology, Nutrition, and Dietetics, University of Northern Colorado, Greeley, CO, United States
| | - Andrea K. Steck
- Barbara Davis Center, Department of Pediatrics, University of Colorado, Aurora, CO, United States
| | - Katerina Kechris
- Department of Epidemiology, Colorado School of Public Health, Aurora, CO, United States
- Department of Biomedical Informatics, School of Medicine, University of Colorado, Aurora, CO, United States
- Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO, United States
| | - Ivana V. Yang
- Department of Biomedical Informatics, School of Medicine, University of Colorado, Aurora, CO, United States
- Department of Medicine, University of Colorado, Aurora, CO, United States
| | - Tasha E. Fingerlin
- Department of Epidemiology, Colorado School of Public Health, Aurora, CO, United States
- Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO, United States
- Department of Immunology and Genomic Medicine, National Jewish Health, Aurora, CO, United States
| | - Oliver Fiehn
- University of California Davis West Coast Metabolomics Center, Davis, CA, United States
| | - Marian Rewers
- Barbara Davis Center, Department of Pediatrics, University of Colorado, Aurora, CO, United States
| | - Jill M. Norris
- Department of Epidemiology, Colorado School of Public Health, Aurora, CO, United States
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2
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Nazer N, Sepehri MH, Mohammadzade H, Mehrmohamadi M. A novel approach toward optimal workflow selection for DNA methylation biomarker discovery. BMC Bioinformatics 2024; 25:37. [PMID: 38262949 PMCID: PMC10804576 DOI: 10.1186/s12859-024-05658-0] [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: 01/06/2023] [Accepted: 01/15/2024] [Indexed: 01/25/2024] Open
Abstract
DNA methylation is a major epigenetic modification involved in many physiological processes. Normal methylation patterns are disrupted in many diseases and methylation-based biomarkers have shown promise in several contexts. Marker discovery typically involves the analysis of publicly available DNA methylation data from high-throughput assays. Numerous methods for identification of differentially methylated biomarkers have been developed, making the need for best practices guidelines and context-specific analyses workflows exceedingly high. To this end, here we propose TASA, a novel method for simulating methylation array data in various scenarios. We then comprehensively assess different data analysis workflows using real and simulated data and suggest optimal start-to-finish analysis workflows. Our study demonstrates that the choice of analysis pipeline for DNA methylation-based marker discovery is crucial and different across different contexts.
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Affiliation(s)
- Naghme Nazer
- Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran
| | | | - Hoda Mohammadzade
- Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran
| | - Mahya Mehrmohamadi
- Department of Biotechnology, College of Science, University of Tehran, Tehran, Iran.
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3
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Ehlinger JV, Goodrich JM, Dolinoy DC, Watkins DJ, Cantoral A, Mercado-García A, Téllez-Rojo MM, Peterson KE. Associations between blood leukocyte DNA methylation and sustained attention in mid-to-late childhood. Epigenomics 2023; 15:965-981. [PMID: 37942546 PMCID: PMC10718163 DOI: 10.2217/epi-2023-0169] [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: 05/15/2023] [Accepted: 10/20/2023] [Indexed: 11/10/2023] Open
Abstract
Aims: To identify associations between DNA methylation (DNAm) across the epigenome and symptoms related to attention-deficit/hyperactivity disorder in a population of Hispanic children. Materials & methods: Among 517 participants in the ELEMENT study aged 9-18 years, we conducted an epigenome-wide association study examining associations between blood leukocyte DNAm and performance on the Conners' continuous performance test (CPT3). Results: DNAm at loci in or near ZNF814, ELF4 and OR6K6 and functional enrichment for gene pathways pertaining to ferroptosis, inflammation, immune response and neurotransmission were significantly related to CPT3 scores. Conclusion: DNAm was associated with CPT3 performance. Further analysis is warranted to understand how these genes and enriched pathways contribute to attention-deficit/hyperactivity disorder.
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Affiliation(s)
- Jessa V Ehlinger
- Environmental Health Sciences, University of Michigan, Ann Arbor, MI 48109, USA
| | - Jaclyn M Goodrich
- Environmental Health Sciences, University of Michigan, Ann Arbor, MI 48109, USA
| | - Dana C Dolinoy
- Environmental Health Sciences, University of Michigan, Ann Arbor, MI 48109, USA
- Nutritional Sciences, University of Michigan, Ann Arbor, MI 48109, USA
| | - Deborah J Watkins
- Environmental Health Sciences, University of Michigan, Ann Arbor, MI 48109, USA
| | | | | | | | - Karen E Peterson
- Environmental Health Sciences, University of Michigan, Ann Arbor, MI 48109, USA
- Nutritional Sciences, University of Michigan, Ann Arbor, MI 48109, USA
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4
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Johnson RK, Ireton AJ, Carry PM, Vanderlinden LA, Dong F, Romero A, Johnson DR, Ghosh D, Yang F, Frohnert B, Yang IV, Kechris K, Rewers M, Norris JM. DNA Methylation Near DLGAP2 May Mediate the Relationship between Family History of Type 1 Diabetes and Type 1 Diabetes Risk. Pediatr Diabetes 2023; 2023:5367637. [PMID: 38765731 PMCID: PMC11100224 DOI: 10.1155/2023/5367637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/22/2024] Open
Abstract
Given the differential risk of type 1 diabetes (T1D) in offspring of affected fathers versus affected mothers and our observation that T1D cases have differential DNA methylation near the imprinted DLGAP2 gene compared to controls, we examined whether methylation near DLGAP2 mediates the association between T1D family history and T1D risk. In a nested case-control study of 87 T1D cases and 87 controls from the Diabetes Autoimmunity Study in the Young, we conducted causal mediation analyses at 12 DLGAP2 region CpGs to decompose the effect of family history on T1D risk into indirect and direct effects. These effects were estimated from two regression models adjusted for the human leukocyte antigen DR3/4 genotype: a linear regression of family history on methylation (mediator model) and a logistic regression of family history and methylation on T1D (outcome model). For 8 of the 12 CpGs, we identified a significant interaction between T1D family history and methylation on T1D risk. Accounting for this interaction, we found that the increased risk of T1D for children with affected mothers compared to those with no family history was mediated through differences in methylation at two CpGs (cg27351978, cg00565786) in the DLGAP2 region, as demonstrated by a significant pure natural indirect effect (odds ratio (OR) = 1.98, 95% confidence interval (CI): 1.06-3.71) and nonsignificant total natural direct effect (OR = 1.65, 95% CI: 0.16-16.62) (for cg00565786). In contrast, the increased risk of T1D for children with an affected father or sibling was not explained by DNA methylation changes at these CpGs. Results were similar for cg27351978 and robust in sensitivity analyses. Lastly, we found that DNA methylation in the DLGAP2 region was associated (P<0:05) with gene expression of nearby protein-coding genes DLGAP2, ARHGEF10, ZNF596, and ERICH1. Results indicate that the maternal protective effect conferred through exposure to T1D in utero may operate through changes to DNA methylation that have functional downstream consequences.
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Affiliation(s)
- Randi K. Johnson
- Department of Biomedical Informatics, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Amanda J. Ireton
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Patrick M. Carry
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
- Colorado Program for Musculoskeletal Research, Department of Orthopedics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Lauren A. Vanderlinden
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Fran Dong
- Barbara Davis Center for Diabetes, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Alex Romero
- Department of Biomedical Informatics, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - David R. Johnson
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Debashis Ghosh
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Fan Yang
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Brigitte Frohnert
- Barbara Davis Center for Diabetes, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Ivana V. Yang
- Department of Biomedical Informatics, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Katerina Kechris
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Marian Rewers
- Barbara Davis Center for Diabetes, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Jill M. Norris
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
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5
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Welsh H, Batalha CMPF, Li W, Mpye KL, Souza-Pinto NC, Naslavsky MS, Parra EJ. A systematic evaluation of normalization methods and probe replicability using infinium EPIC methylation data. Clin Epigenetics 2023; 15:41. [PMID: 36906598 PMCID: PMC10008016 DOI: 10.1186/s13148-023-01459-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: 10/10/2022] [Accepted: 02/24/2023] [Indexed: 03/13/2023] Open
Abstract
BACKGROUND The Infinium EPIC array measures the methylation status of > 850,000 CpG sites. The EPIC BeadChip uses a two-array design: Infinium Type I and Type II probes. These probe types exhibit different technical characteristics which may confound analyses. Numerous normalization and pre-processing methods have been developed to reduce probe type bias as well as other issues such as background and dye bias. METHODS This study evaluates the performance of various normalization methods using 16 replicated samples and three metrics: absolute beta-value difference, overlap of non-replicated CpGs between replicate pairs, and effect on beta-value distributions. Additionally, we carried out Pearson's correlation and intraclass correlation coefficient (ICC) analyses using both raw and SeSAMe 2 normalized data. RESULTS The method we define as SeSAMe 2, which consists of the application of the regular SeSAMe pipeline with an additional round of QC, pOOBAH masking, was found to be the best performing normalization method, while quantile-based methods were found to be the worst performing methods. Whole-array Pearson's correlations were found to be high. However, in agreement with previous studies, a substantial proportion of the probes on the EPIC array showed poor reproducibility (ICC < 0.50). The majority of poor performing probes have beta values close to either 0 or 1, and relatively low standard deviations. These results suggest that probe reliability is largely the result of limited biological variation rather than technical measurement variation. Importantly, normalizing the data with SeSAMe 2 dramatically improved ICC estimates, with the proportion of probes with ICC values > 0.50 increasing from 45.18% (raw data) to 61.35% (SeSAMe 2).
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Affiliation(s)
- H Welsh
- Department of Anthropology, University of Toronto at Mississauga, Mississauga, Canada.
| | - C M P F Batalha
- Department of Biochemistry, University of São Paulo, São Paulo, Brazil
| | - W Li
- The Centre for Applied Genomics, Hospital for Sick Children, Toronto, Canada
| | - K L Mpye
- Department of Anthropology, University of Toronto at Mississauga, Mississauga, Canada
| | - N C Souza-Pinto
- Department of Biochemistry, University of São Paulo, São Paulo, Brazil
| | - M S Naslavsky
- Department of Genetics and Evolutionary Biology, University of São Paulo, São Paulo, Brazil
| | - E J Parra
- Department of Anthropology, University of Toronto at Mississauga, Mississauga, Canada
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6
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Faul JD, Kim JK, Levine ME, Thyagarajan B, Weir DR, Crimmins EM. Epigenetic-based age acceleration in a representative sample of older Americans: Associations with aging-related morbidity and mortality. Proc Natl Acad Sci U S A 2023; 120:e2215840120. [PMID: 36802439 PMCID: PMC9992763 DOI: 10.1073/pnas.2215840120] [Citation(s) in RCA: 50] [Impact Index Per Article: 50.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 01/12/2023] [Indexed: 02/23/2023] Open
Abstract
Biomarkers developed from DNA methylation (DNAm) data are of growing interest as predictors of health outcomes and mortality in older populations. However, it is unknown how epigenetic aging fits within the context of known socioeconomic and behavioral associations with aging-related health outcomes in a large, population-based, and diverse sample. This study uses data from a representative, panel study of US older adults to examine the relationship between DNAm-based age acceleration measures in the prediction of cross-sectional and longitudinal health outcomes and mortality. We examine whether recent improvements to these scores, using principal component (PC)-based measures designed to remove some of the technical noise and unreliability in measurement, improve the predictive capability of these measures. We also examine how well DNAm-based measures perform against well-known predictors of health outcomes such as demographics, SES, and health behaviors. In our sample, age acceleration calculated using "second and third generation clocks," PhenoAge, GrimAge, and DunedinPACE, is consistently a significant predictor of health outcomes including cross-sectional cognitive dysfunction, functional limitations and chronic conditions assessed 2 y after DNAm measurement, and 4-y mortality. PC-based epigenetic age acceleration measures do not significantly change the relationship of DNAm-based age acceleration measures to health outcomes or mortality compared to earlier versions of these measures. While the usefulness of DNAm-based age acceleration as a predictor of later life health outcomes is quite clear, other factors such as demographics, SES, mental health, and health behaviors remain equally, if not more robust, predictors of later life outcomes.
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Affiliation(s)
- Jessica D. Faul
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI48104
| | - Jung Ki Kim
- Davis School of Gerontology, University of Southern California, Los Angeles, CA90089
| | - Morgan E. Levine
- Department of Pathology, Yale School of Medicine, New Haven, CT06510
| | - Bharat Thyagarajan
- Department of Laboratory Medicine and Pathology, University of Minnesota Medical School, Minneapolis, MN55455
| | - David R. Weir
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI48104
| | - Eileen M. Crimmins
- Davis School of Gerontology, University of Southern California, Los Angeles, CA90089
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7
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Gim JA. Integrative Approaches of DNA Methylation Patterns According to Age, Sex and Longitudinal Changes. Curr Genomics 2023; 23:385-399. [PMID: 37920553 PMCID: PMC10173416 DOI: 10.2174/1389202924666221207100513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 10/04/2022] [Accepted: 11/04/2022] [Indexed: 12/12/2022] Open
Abstract
Background In humans, age-related DNA methylation has been studied in blood, tissues, buccal swabs, and fibroblasts, and changes in DNA methylation patterns according to age and sex have been detected. To date, approximately 137,000 samples have been analyzed from 14,000 studies, and the information has been uploaded to the NCBI GEO database. Methods A correlation between age and methylation level and longitudinal changes in methylation levels was revealed in both sexes. Here, 20 public datasets derived from whole blood were analyzed using the Illumina BeadChip. Batch effects with respect to the time differences were correlated. The overall change in the pattern was provided as the inverse of the coefficient of variation (COV). Results Of the 20 datasets, nine were from a longitudinal study. All data had age and sex as common variables. Comprehensive details of age-, sex-, and longitudinal change-based DNA methylation levels in the whole blood sample were elucidated in this study. ELOVL2 and FHL2 showed the maximum correlation between age and DNA methylation. The methylation patterns of genes related to mental health differed according to age. Age-correlated genes have been associated with malformations (anteverted nostril, craniofacial abnormalities, and depressed nasal bridge) and drug addiction (drug habituation and smoking). Conclusion Based on 20 public DNA methylation datasets, methylation levels according to age and longitudinal changes by sex were identified and visualized using an integrated approach. The results highlight the molecular mechanisms underlying the association of sex and biological age with changes in DNA methylation, and the importance of optimal genomic information management.
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Affiliation(s)
- Jeong-An Gim
- Medical Science Research Center, College of Medicine, Korea University Guro Hospital, Seoul 08308, Republic of Korea
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8
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Lussier AA, Zhu Y, Smith BJ, Simpkin AJ, Smith AD, Suderman MJ, Walton E, Ressler KJ, Dunn EC. Updates to data versions and analytic methods influence the reproducibility of results from epigenome-wide association studies. Epigenetics 2022; 17:1373-1388. [PMID: 35156895 PMCID: PMC9601563 DOI: 10.1080/15592294.2022.2028072] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Revised: 12/02/2021] [Accepted: 01/04/2022] [Indexed: 11/03/2022] Open
Abstract
Biomedical research has grown increasingly cooperative through the sharing of consortia-level epigenetic data. Since consortia preprocess data prior to distribution, new processing pipelines can lead to different versions of the same dataset. Similarly, analytic frameworks evolve to incorporate cutting-edge methods and best practices. However, it remains unknown how different data and analytic versions alter the results of epigenome-wide analyses, which could influence the replicability of epigenetic associations. Thus, we assessed the impact of these changes using data from the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort. We analysed DNA methylation from two data versions, processed using separate preprocessing and analytic pipelines, examining associations between seven childhood adversities or prenatal smoking exposure and DNA methylation at age 7. We performed two sets of analyses: (1) epigenome-wide association studies (EWAS); (2) Structured Life Course Modelling Approach (SLCMA), a two-stage method that models time-dependent effects. SLCMA results were also compared across two analytic versions. Data version changes impacted both EWAS and SLCMA analyses, yielding different associations at conventional p-value thresholds. However, the magnitude and direction of associations was generally consistent between data versions, regardless of p-values. Differences were especially apparent in analyses of childhood adversity, while smoking associations were more consistent using significance thresholds. SLCMA analytic versions similarly altered top associations, but time-dependent effects remained concordant. Alterations to data and analytic versions influenced the results of epigenome-wide analyses. Our findings highlight that magnitude and direction are better measures for replication and stability than p-value thresholds.
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Affiliation(s)
- Alexandre A. Lussier
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Stanley Center for Psychiatric Research, The Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Yiwen Zhu
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Brooke J. Smith
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Andrew J. Simpkin
- School of Mathematics,Statistics and Applied Mathematics, National University of Ireland, Galway, Ireland
| | - Andrew D.A.C. Smith
- Mathematics and Statistics Research Group, University of the West of England, Bristol, UK
| | - Matthew J. Suderman
- MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Esther Walton
- Department of Psychology, University of Bath, Bath, UK
| | - Kerry J. Ressler
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- McLean Hospital, Belmont, MA, USA
| | - Erin C. Dunn
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Stanley Center for Psychiatric Research, The Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Center on the Developing Child, Harvard University, Cambridge, MA, USA
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Carry PM, Waugh K, Vanderlinden LA, Johnson RK, Buckner T, Rewers M, Steck AK, Yang I, Fingerlin TE, Kechris K, Norris JM. Changes in the Coexpression of Innate Immunity Genes During Persistent Islet Autoimmunity Are Associated With Progression of Islet Autoimmunity: Diabetes Autoimmunity Study in the Young (DAISY). Diabetes 2022; 71:2048-2057. [PMID: 35724268 PMCID: PMC9450568 DOI: 10.2337/db21-1111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 06/08/2022] [Indexed: 11/13/2022]
Abstract
Longitudinal changes in gene expression during islet autoimmunity (IA) may provide insight into biological processes that explain progression to type 1 diabetes (T1D). We identified individuals from Diabetes Autoimmunity Study in the Young (DAISY) who developed IA, autoantibodies present on two or more visits. Illumina's NovaSeq 6000 was used to quantify gene expression in whole blood. With linear mixed models we tested for changes in expression after IA that differed across individuals who progressed to T1D (progressors) (n = 25), reverted to an autoantibody-negative stage (reverters) (n = 47), or maintained IA positivity but did not develop T1D (maintainers) (n = 66). Weighted gene coexpression network analysis was used to identify coexpression modules. Gene Ontology pathway analysis of the top 150 differentially expressed genes (nominal P < 0.01) identified significantly enriched pathways including leukocyte activation involved in immune response, innate immune response, and regulation of immune response. We identified a module of 14 coexpressed genes with roles in the innate immunity. The hub gene, LTF, is known to have immunomodulatory properties. Another gene within the module, CAMP, is potentially relevant based on its role in promoting β-cell survival in a murine model. Overall, results provide evidence of alterations in expression of innate immune genes prior to onset of T1D.
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Affiliation(s)
- Patrick M. Carry
- Department of Epidemiology, Colorado School of Public Health, Aurora, CO
| | - Kathleen Waugh
- Barbara Davis Center, Department of Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, CO
| | | | - Randi K. Johnson
- Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO
| | - Teresa Buckner
- Department of Epidemiology, Colorado School of Public Health, Aurora, CO
| | - Marian Rewers
- Barbara Davis Center, Department of Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, CO
| | - Andrea K. Steck
- Barbara Davis Center, Department of Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, CO
| | - Ivana Yang
- Department of Epidemiology, Colorado School of Public Health, Aurora, CO
- Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO
| | - Tasha E. Fingerlin
- Department of Epidemiology, Colorado School of Public Health, Aurora, CO
- Department of Immunology and Genomic Medicine, National Jewish Health, Denver, CO
| | | | - Jill M. Norris
- Department of Epidemiology, Colorado School of Public Health, Aurora, CO
- Barbara Davis Center, Department of Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, CO
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Corella D. Why is it important to know DNA methylation patterns in people with hypertriglyceridaemia? CLINICA E INVESTIGACION EN ARTERIOSCLEROSIS : PUBLICACION OFICIAL DE LA SOCIEDAD ESPANOLA DE ARTERIOSCLEROSIS 2022; 34:33-35. [PMID: 35151430 DOI: 10.1016/j.arteri.2022.01.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Affiliation(s)
- Dolores Corella
- Department of Preventive Medicine and Public Health, School of Medicine, University of Valencia, Valencia, Spain; CIBER Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain.
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11
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Walker-Short E, Buckner T, Vigers T, Carry P, Vanderlinden LA, Dong F, Johnson RK, Yang IV, Kechris K, Rewers M, Norris JM. Epigenome-Wide Association Study of Infant Feeding and DNA Methylation in Infancy and Childhood in a Population at Increased Risk for Type 1 Diabetes. Nutrients 2021; 13:4057. [PMID: 34836312 PMCID: PMC8618577 DOI: 10.3390/nu13114057] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 11/05/2021] [Accepted: 11/08/2021] [Indexed: 12/16/2022] Open
Abstract
We assessed associations between infant diet (e.g., breastfeeding and introduction to solid foods) and DNA methylation in infancy and childhood. We measured DNA methylation in peripheral blood collected in infancy (9-15 months of age) in 243 children; and in a subset of 50 children, we also measured methylation in childhood (6-9 years of age) to examine persistence, and at birth (in cord blood) to examine temporality. We performed multivariable linear regression of infant diet on the outcome of methylation using epigenome-wide and candidate site approaches. We identified six novel CpG sites associated with breastfeeding duration using an EWAS approach. One differentially methylated site presented directionally consistent associations with breastfeeding (cg00574958, CPT1A) in infancy and childhood but not at birth. Two differentially methylated sites in infancy (cg19693031, TXNIP; cg23307264, KHSRP) were associated with breastfeeding and were not present at birth; however, these associations did not persist into childhood. Associations between infant diet and methylation in infancy at three sites (cg22369607, AP001525.1; cg2409200, TBCD; cg27173510, PGBD5) were also present at birth, suggesting the influence of exposures other than infant diet. Infant diet exposures are associated with persistent methylation differences in CPT1A, which may be one mechanism behind infant diet's long-term health effects.
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Affiliation(s)
- Elizabeth Walker-Short
- Department of Epidemiology, Colorado School of Public Health, Aurora, CO 80045, USA; (E.W.-S.); (T.B.); (P.C.); (L.A.V.); (R.K.J.); (I.V.Y.)
| | - Teresa Buckner
- Department of Epidemiology, Colorado School of Public Health, Aurora, CO 80045, USA; (E.W.-S.); (T.B.); (P.C.); (L.A.V.); (R.K.J.); (I.V.Y.)
| | - Timothy Vigers
- Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO 80045, USA; (T.V.); (K.K.)
- Barbara Davis Center for Diabetes, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; (F.D.); (M.R.)
| | - Patrick Carry
- Department of Epidemiology, Colorado School of Public Health, Aurora, CO 80045, USA; (E.W.-S.); (T.B.); (P.C.); (L.A.V.); (R.K.J.); (I.V.Y.)
| | - Lauren A. Vanderlinden
- Department of Epidemiology, Colorado School of Public Health, Aurora, CO 80045, USA; (E.W.-S.); (T.B.); (P.C.); (L.A.V.); (R.K.J.); (I.V.Y.)
| | - Fran Dong
- Barbara Davis Center for Diabetes, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; (F.D.); (M.R.)
| | - Randi K. Johnson
- Department of Epidemiology, Colorado School of Public Health, Aurora, CO 80045, USA; (E.W.-S.); (T.B.); (P.C.); (L.A.V.); (R.K.J.); (I.V.Y.)
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Ivana V. Yang
- Department of Epidemiology, Colorado School of Public Health, Aurora, CO 80045, USA; (E.W.-S.); (T.B.); (P.C.); (L.A.V.); (R.K.J.); (I.V.Y.)
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Katerina Kechris
- Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO 80045, USA; (T.V.); (K.K.)
| | - Marian Rewers
- Barbara Davis Center for Diabetes, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; (F.D.); (M.R.)
| | - Jill M. Norris
- Department of Epidemiology, Colorado School of Public Health, Aurora, CO 80045, USA; (E.W.-S.); (T.B.); (P.C.); (L.A.V.); (R.K.J.); (I.V.Y.)
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