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Harvey J, Smith AR, Weymouth LS, Smith RG, Castanho I, Hubbard L, Creese B, Bresner C, Williams N, Pishva E, Lunnon K. Epigenetic insights into neuropsychiatric and cognitive symptoms in Parkinson's disease: A DNA co-methylation network analysis. NPJ Parkinsons Dis 2025; 11:39. [PMID: 40025048 PMCID: PMC11873129 DOI: 10.1038/s41531-025-00877-5] [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: 07/19/2023] [Accepted: 01/24/2025] [Indexed: 03/04/2025] Open
Abstract
Parkinson's disease is a highly heterogeneous disorder, encompassing a complex spectrum of clinical presentation including motor, sleep, cognitive and neuropsychiatric symptoms. We aimed to investigate genome-wide DNA methylation networks in post-mortem Parkinson's disease brain samples and test for region-specific association with common neuropsychiatric and cognitive symptoms. Of traits tested, we identify a co-methylation module in the substantia nigra with significant correlation to depressive symptoms. Notably, expression of the genes annotated to the methylation loci present within this module are found to be significantly enriched in neuronal subtypes within the substantia nigra. These findings highlight the potential involvement of neuronal-specific changes within the substantia nigra with regards to depressive symptoms in Parkinson's disease.
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Affiliation(s)
- Joshua Harvey
- University of Exeter Medical School, Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, UK
| | - Adam R Smith
- University of Exeter Medical School, Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, UK
| | - Luke S Weymouth
- University of Exeter Medical School, Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, UK
| | - Rebecca G Smith
- University of Exeter Medical School, Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, UK
| | - Isabel Castanho
- Department of Pathology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Leon Hubbard
- Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, Wales, UK
| | - Byron Creese
- University of Exeter Medical School, Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, UK
- Department of Life Sciences, College of Health, Medicine and Life Sciences, Brunel University of London, London, UK
| | - Catherine Bresner
- Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, Wales, UK
| | - Nigel Williams
- Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, Wales, UK
| | - Ehsan Pishva
- University of Exeter Medical School, Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, UK
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience (MHeNS), Maastricht University, Maastricht, The Netherlands
| | - Katie Lunnon
- University of Exeter Medical School, Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, UK.
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Linares-Pineda TM, Lendínez-Jurado A, Piserra-López A, Suárez-Arana M, Pozo M, Molina-Vega M, Picón-César MJ, Morcillo S. Longitudinal DNA methylation profiles in saliva of offspring from mothers with gestational diabetes: associations with early childhood growth patterns. Cardiovasc Diabetol 2025; 24:15. [PMID: 39806399 PMCID: PMC11730480 DOI: 10.1186/s12933-024-02568-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/08/2024] [Accepted: 12/29/2024] [Indexed: 01/16/2025] Open
Abstract
BACKGROUND The prevalence of obesity and type 2 diabetes mellitus (T2DM) is rising globally, particularly among children exposed to adverse intrauterine environments, such as those associated with gestational diabetes mellitus (GDM). Epigenetic modifications, specifically DNA methylation, have emerged as mechanisms by which early environmental exposures can predispose offspring to metabolic diseases. This study aimed to investigate DNA methylation differences in children born to mothers with GDM compared to non-GDM mothers, using saliva samples, and to assess the association of these epigenetic patterns with early growth measurements. METHODS This study analyzed saliva DNA methylation patterns in 30 children (15 born to GDM mothers and 15 to non-GDM mothers) from the EPIDG cohort. Samples were collected at two time points: 8-10 weeks postpartum and at one year of age. Epigenome-wide analysis of over 850,000 CpG sites was conducted using the Illumina Methylation EPIC Bead Chip. Differential methylation positions (DMPs) were identified with the limma package, using a significance threshold of p < 0.01 and delta β ≥ 5%. Correlation analysis examined associations between methylation and growth variables (weight, height, BMI and annual growth) using Spearman tests. RESULTS We identified 6,968 DMPs at the postpartum stage and 5,132 after one year, with 50 sites remaining differentially methylated over time, 16 of which maintained consistent methylation directionality. Functional analysis linked several of these DMPs to genes involved in inflammation and metabolic processes, including CYTH3 and FARP2, both implicated in growth and metabolic pathways. Significant correlations were found between specific CpG sites and growth-related variables such as weight, head circumference, height, and BMI. CONCLUSIONS This study's longitudinal design reveals stable DNA methylation patterns in saliva samples that differentiate GDM-exposed children from controls across the first year of life, highlighting the feasibility of saliva as a minimally invasive biomarker source. The persistence of these epigenetic signatures underscores their potential as early indicators of metabolic risk, offering valuable insights into the long-term impact of maternal GDM on child health. Although the use of saliva offers a practical and non-invasive tool for pediatric epigenetic research, further studies are necessary to validate these findings in larger populations.
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Affiliation(s)
- Teresa M Linares-Pineda
- Department of Endocrinology and Nutrition, Virgen de la Victoria University Hospital, 29010, Málaga, Spain
- CIBER Pathophysiology of Obesity and Nutrition-CIBERON, Instituto de Salud Carlos III, 28029, Madrid, Spain
- Biomedical Research Institute-IBIMA Plataforma BIONAND, 29010, Málaga, Spain
| | - Alfonso Lendínez-Jurado
- Biomedical Research Institute-IBIMA Plataforma BIONAND, 29010, Málaga, Spain
- Andalucía Tech, Universidad de Málaga, Campus de Teatinos s/n, 29071, Málaga, Spain
- Department of Pediatric Endocrinology, Regional University Hospital of Málaga, 29011, Málaga, Spain
- Distrito Sanitario Málaga-Guadalhorce, 29009, Málaga, Spain
| | - Alberto Piserra-López
- Department of Cardiology, Virgen de la Victoria University Hospital, Málaga, 29010, Spain
| | - María Suárez-Arana
- Department of Obstetrics and Gynecology, Regional University Hospital of Málaga, Málaga, 29011, Spain
| | - María Pozo
- Biomedical Research Institute-IBIMA Plataforma BIONAND, 29010, Málaga, Spain
| | - María Molina-Vega
- Department of Endocrinology and Nutrition, Virgen de la Victoria University Hospital, 29010, Málaga, Spain
| | - María José Picón-César
- Department of Endocrinology and Nutrition, Virgen de la Victoria University Hospital, 29010, Málaga, Spain
- CIBER Pathophysiology of Obesity and Nutrition-CIBERON, Instituto de Salud Carlos III, 28029, Madrid, Spain
- Biomedical Research Institute-IBIMA Plataforma BIONAND, 29010, Málaga, Spain
| | - Sonsoles Morcillo
- Department of Endocrinology and Nutrition, Virgen de la Victoria University Hospital, 29010, Málaga, Spain.
- CIBER Pathophysiology of Obesity and Nutrition-CIBERON, Instituto de Salud Carlos III, 28029, Madrid, Spain.
- Biomedical Research Institute-IBIMA Plataforma BIONAND, 29010, Málaga, Spain.
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3
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Majumdar K, Silva R, Perry AS, Watson RW, Rau A, Jaffrezic F, Murphy TB, Gormley IC. A novel family of beta mixture models for the differential analysis of DNA methylation data: An application to prostate cancer. PLoS One 2024; 19:e0314014. [PMID: 39661598 PMCID: PMC11633993 DOI: 10.1371/journal.pone.0314014] [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: 03/19/2024] [Accepted: 11/04/2024] [Indexed: 12/13/2024] Open
Abstract
Identifying differentially methylated cytosine-guanine dinucleotide (CpG) sites between benign and tumour samples can assist in understanding disease. However, differential analysis of bounded DNA methylation data often requires data transformation, reducing biological interpretability. To address this, a family of beta mixture models (BMMs) is proposed that (i) objectively infers methylation state thresholds and (ii) identifies differentially methylated CpG sites (DMCs) given untransformed, beta-valued methylation data. The BMMs achieve this through model-based clustering of CpG sites and by employing parameter constraints, facilitating application to different study settings. Inference proceeds via an expectation-maximisation algorithm, with an approximate maximization step providing tractability and computational feasibility. Performance of the BMMs is assessed through thorough simulation studies, and the BMMs are used for differential analyses of DNA methylation data from a prostate cancer study. Intuitive and biologically interpretable methylation state thresholds are inferred and DMCs are identified, including those related to genes such as GSTP1, RASSF1 and RARB, known for their role in prostate cancer development. Gene ontology analysis of the DMCs revealed significant enrichment in cancer-related pathways, demonstrating the utility of BMMs to reveal biologically relevant insights. An R package betaclust facilitates widespread use of BMMs.
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Affiliation(s)
- Koyel Majumdar
- School of Mathematics and Statistics, University College Dublin, Dublin, Ireland
| | - Romina Silva
- School of Medicine, University College Dublin, Dublin, Ireland
- Conway Institute of Biomedical and Biomolecular Research, University College Dublin, Dublin, Ireland
| | - Antoinette Sabrina Perry
- Conway Institute of Biomedical and Biomolecular Research, University College Dublin, Dublin, Ireland
- School of Biology and Environmental Science, University College Dublin, Dublin, Ireland
| | - Ronald William Watson
- School of Medicine, University College Dublin, Dublin, Ireland
- Conway Institute of Biomedical and Biomolecular Research, University College Dublin, Dublin, Ireland
| | - Andrea Rau
- INRAE, UMR1313 AgroParisTech, GABI, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Florence Jaffrezic
- INRAE, UMR1313 AgroParisTech, GABI, Université Paris-Saclay, Gif-sur-Yvette, France
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Alhassan D, Olbricht GR, Adekpedjou A. Differential methylation region detection via an array-adaptive normalized kernel-weighted model. PLoS One 2024; 19:e0306036. [PMID: 38941289 PMCID: PMC11213316 DOI: 10.1371/journal.pone.0306036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2023] [Accepted: 06/09/2024] [Indexed: 06/30/2024] Open
Abstract
A differentially methylated region (DMR) is a genomic region that has significantly different methylation patterns between biological conditions. Identifying DMRs between different biological conditions is critical for developing disease biomarkers. Although methods for detecting DMRs in microarray data have been introduced, developing methods with high precision, recall, and accuracy in determining the true length of DMRs remains a challenge. In this study, we propose a normalized kernel-weighted model to account for similar methylation profiles using the relative probe distance from "nearby" CpG sites. We also extend this model by proposing an array-adaptive version in attempt to account for the differences in probe spacing between Illumina's Infinium 450K and EPIC bead array respectively. We also study the asymptotic results of our proposed statistic. We compare our approach with a popular DMR detection method via simulation studies under large and small treatment effect settings. We also discuss the susceptibility of our method in detecting the true length of the DMRs under these two settings. Lastly, we demonstrate the biological usefulness of our method when combined with pathway analysis methods on oral cancer data. We have created an R package called idDMR, downloadable from GitHub repository with link: https://github.com/DanielAlhassan/idDMR, that allows for the convenient implementation of our array-adaptive DMR method.
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Affiliation(s)
- Daniel Alhassan
- Department of Mathematics and Statistics, Missouri University of Science and Technology, Rolla, MO, United States of America
| | - Gayla R. Olbricht
- Department of Mathematics and Statistics, Missouri University of Science and Technology, Rolla, MO, United States of America
| | - Akim Adekpedjou
- Department of Mathematics and Statistics, Missouri University of Science and Technology, Rolla, MO, United States of America
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5
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Hussein R, Abou-Shanab AM, Badr E. A multi-omics approach for biomarker discovery in neuroblastoma: a network-based framework. NPJ Syst Biol Appl 2024; 10:52. [PMID: 38760476 PMCID: PMC11101461 DOI: 10.1038/s41540-024-00371-3] [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: 11/09/2023] [Accepted: 04/16/2024] [Indexed: 05/19/2024] Open
Abstract
Neuroblastoma (NB) is one of the leading causes of cancer-associated death in children. MYCN amplification is a prominent genetic marker for NB, and its targeting to halt NB progression is difficult to achieve. Therefore, an in-depth understanding of the molecular interactome of NB is needed to improve treatment outcomes. Analysis of NB multi-omics unravels valuable insight into the interplay between MYCN transcriptional and miRNA post-transcriptional modulation. Moreover, it aids in the identification of various miRNAs that participate in NB development and progression. This study proposes an integrated computational framework with three levels of high-throughput NB data (mRNA-seq, miRNA-seq, and methylation array). Similarity Network Fusion (SNF) and ranked SNF methods were utilized to identify essential genes and miRNAs. The specified genes included both miRNA-target genes and transcription factors (TFs). The interactions between TFs and miRNAs and between miRNAs and their target genes were retrieved where a regulatory network was developed. Finally, an interaction network-based analysis was performed to identify candidate biomarkers. The candidate biomarkers were further analyzed for their potential use in prognosis and diagnosis. The candidate biomarkers included three TFs and seven miRNAs. Four biomarkers have been previously studied and tested in NB, while the remaining identified biomarkers have known roles in other types of cancer. Although the specific molecular role is yet to be addressed, most identified biomarkers possess evidence of involvement in NB tumorigenesis. Analyzing cellular interactome to identify potential biomarkers is a promising approach that can contribute to optimizing efficient therapeutic regimens to target NB vulnerabilities.
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Affiliation(s)
- Rahma Hussein
- Biomedical Sciences Program, University of Science and Technology, Zewail City of Science and Technology, Giza, 12578, Egypt
| | - Ahmed M Abou-Shanab
- Biomedical Sciences Program, University of Science and Technology, Zewail City of Science and Technology, Giza, 12578, Egypt
| | - Eman Badr
- Biomedical Sciences Program, University of Science and Technology, Zewail City of Science and Technology, Giza, 12578, Egypt.
- Faculty of Computers and Artificial Intelligence, Cairo University, Giza, 12613, Egypt.
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Ferro dos Santos MR, Giuili E, De Koker A, Everaert C, De Preter K. Computational deconvolution of DNA methylation data from mixed DNA samples. Brief Bioinform 2024; 25:bbae234. [PMID: 38762790 PMCID: PMC11102637 DOI: 10.1093/bib/bbae234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 03/30/2024] [Accepted: 04/30/2024] [Indexed: 05/20/2024] Open
Abstract
In this review, we provide a comprehensive overview of the different computational tools that have been published for the deconvolution of bulk DNA methylation (DNAm) data. Here, deconvolution refers to the estimation of cell-type proportions that constitute a mixed sample. The paper reviews and compares 25 deconvolution methods (supervised, unsupervised or hybrid) developed between 2012 and 2023 and compares the strengths and limitations of each approach. Moreover, in this study, we describe the impact of the platform used for the generation of methylation data (including microarrays and sequencing), the applied data pre-processing steps and the used reference dataset on the deconvolution performance. Next to reference-based methods, we also examine methods that require only partial reference datasets or require no reference set at all. In this review, we provide guidelines for the use of specific methods dependent on the DNA methylation data type and data availability.
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Affiliation(s)
- Maísa R Ferro dos Santos
- VIB-UGent Center for Medical Biotechnology (CMB), Technologiepark-Zwijnaarde 75, 9052 Zwijnaarde, Belgium
- Cancer Research Institute Ghent (CRIG), 9000 Ghent, Belgium
| | - Edoardo Giuili
- VIB-UGent Center for Medical Biotechnology (CMB), Technologiepark-Zwijnaarde 75, 9052 Zwijnaarde, Belgium
- Cancer Research Institute Ghent (CRIG), 9000 Ghent, Belgium
| | - Andries De Koker
- VIB-UGent Center for Medical Biotechnology (CMB), Technologiepark-Zwijnaarde 75, 9052 Zwijnaarde, Belgium
- Cancer Research Institute Ghent (CRIG), 9000 Ghent, Belgium
| | - Celine Everaert
- VIB-UGent Center for Medical Biotechnology (CMB), Technologiepark-Zwijnaarde 75, 9052 Zwijnaarde, Belgium
- Cancer Research Institute Ghent (CRIG), 9000 Ghent, Belgium
| | - Katleen De Preter
- VIB-UGent Center for Medical Biotechnology (CMB), Technologiepark-Zwijnaarde 75, 9052 Zwijnaarde, Belgium
- Cancer Research Institute Ghent (CRIG), 9000 Ghent, Belgium
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Walsh JR, Sun G, Balan J, Hardcastle J, Vollenweider J, Jerde C, Rumilla K, Koellner C, Koleilat A, Hasadsri L, Kipp B, Jenkinson G, Klee E. A supervised learning method for classifying methylation disorders. BMC Bioinformatics 2024; 25:66. [PMID: 38347515 PMCID: PMC10863277 DOI: 10.1186/s12859-024-05673-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 01/24/2024] [Indexed: 02/15/2024] Open
Abstract
BACKGROUND DNA methylation is one of the most stable and well-characterized epigenetic alterations in humans. Accordingly, it has already found clinical utility as a molecular biomarker in a variety of disease contexts. Existing methods for clinical diagnosis of methylation-related disorders focus on outlier detection in a small number of CpG sites using standardized cutoffs which differentiate healthy from abnormal methylation levels. The standardized cutoff values used in these methods do not take into account methylation patterns which are known to differ between the sexes and with age. RESULTS Here we profile genome-wide DNA methylation from blood samples drawn from within a cohort composed of healthy controls of different age and sex alongside patients with Prader-Willi syndrome (PWS), Beckwith-Wiedemann syndrome, Fragile-X syndrome, Angelman syndrome, and Silver-Russell syndrome. We propose a Generalized Additive Model to perform age and sex adjusted outlier analysis of around 700,000 CpG sites throughout the human genome. Utilizing z-scores among the cohort for each site, we deployed an ensemble based machine learning pipeline and achieved a combined prediction accuracy of 0.96 (Binomial 95% Confidence Interval 0.868[Formula: see text]0.995). CONCLUSION We demonstrate a method for age and sex adjusted outlier detection of differentially methylated loci based on a large cohort of healthy individuals. We present a custom machine learning pipeline utilizing this outlier analysis to classify samples for potential methylation associated congenital disorders. These methods are able to achieve high accuracy when used with machine learning methods to classify abnormal methylation patterns.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Alaa Koleilat
- Department of Molecular and Medical Genetics, Oregon Health and Science University, Portland, OR, USA
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Díaz-Campos MÁ, Vasquez-Arriaga J, Ochoa S, Hernández-Lemus E. Functional impact of multi-omic interactions in lung cancer. Front Genet 2024; 15:1282241. [PMID: 38389572 PMCID: PMC10881857 DOI: 10.3389/fgene.2024.1282241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 01/23/2024] [Indexed: 02/24/2024] Open
Abstract
Lung tumors are a leading cause of cancer-related death worldwide. Lung cancers are highly heterogeneous on their phenotypes, both at the cellular and molecular levels. Efforts to better understand the biological origins and outcomes of lung cancer in terms of this enormous variability often require of high-throughput experimental techniques paired with advanced data analytics. Anticipated advancements in multi-omic methodologies hold potential to reveal a broader molecular perspective of these tumors. This study introduces a theoretical and computational framework for generating network models depicting regulatory constraints on biological functions in a semi-automated way. The approach successfully identifies enriched functions in analyzed omics data, focusing on Adenocarcinoma (LUAD) and Squamous cell carcinoma (LUSC, a type of NSCLC) in the lung. Valuable information about novel regulatory characteristics, supported by robust biological reasoning, is illustrated, for instance by considering the role of genes, miRNAs and CpG sites associated with NSCLC, both novel and previously reported. Utilizing multi-omic regulatory networks, we constructed robust models elucidating omics data interconnectedness, enabling systematic generation of mechanistic hypotheses. These findings offer insights into complex regulatory mechanisms underlying these cancer types, paving the way for further exploring their molecular complexity.
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Affiliation(s)
| | - Jorge Vasquez-Arriaga
- Computational Genomics Division, National Institute of Genomic Medicine, Mexico City, Mexico
| | - Soledad Ochoa
- Computational Genomics Division, National Institute of Genomic Medicine, Mexico City, Mexico
- Department of Obstetrics and Gynecology, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Enrique Hernández-Lemus
- Computational Genomics Division, National Institute of Genomic Medicine, Mexico City, Mexico
- Center for Complexity Sciences, Universidad Nacional Autónoma de México, Mexico City, Mexico
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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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10
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Chatzikyriakou P, Brempou D, Quinn M, Fishbein L, Noberini R, Anastopoulos IN, Tufton N, Lim ES, Obholzer R, Hubbard JG, Moonim M, Bonaldi T, Nathanson KL, Izatt L, Oakey RJ. A comprehensive characterisation of phaeochromocytoma and paraganglioma tumours through histone protein profiling, DNA methylation and transcriptomic analysis genome wide. Clin Epigenetics 2023; 15:196. [PMID: 38124114 PMCID: PMC10734084 DOI: 10.1186/s13148-023-01598-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 11/08/2023] [Indexed: 12/23/2023] Open
Abstract
BACKGROUND Phaeochromocytomas and paragangliomas (PPGLs) are rare neuroendocrine tumours. Pathogenic variants have been identified in more than 15 susceptibility genes; associated tumours are grouped into three Clusters, reinforced by their transcriptional profiles. Cluster 1A PPGLs have pathogenic variants affecting enzymes of the tricarboxylic acid cycle, including succinate dehydrogenase. Within inherited PPGLs, these are the most common. PPGL tumours are known to undergo epigenetic reprograming, and here, we report on global histone post-translational modifications and DNA methylation levels, alongside clinical phenotypes. RESULTS Out of the 25 histone post-translational modifications examined, Cluster 1A PPGLs were distinguished from other tumours by a decrease in hyper-acetylated peptides and an increase in H3K4me2. DNA methylation was compared between tumours from individuals who developed metastatic disease versus those that did not. The majority of differentially methylated sites identified tended to be completely methylated or unmethylated in non-metastatic tumours, with low inter-sample variance. Metastatic tumours by contrast consistently had an intermediate DNA methylation state, including the ephrin receptor EPHA4 and its ligand EFNA3. Gene expression analyses performed to identify genes involved in metastatic tumour behaviour pin-pointed a number of genes previously described as mis-regulated in Cluster 1A tumours, as well as highlighting the tumour suppressor RGS22 and the pituitary tumour-transforming gene PTTG1. CONCLUSIONS Combined transcriptomic and DNA methylation analyses revealed aberrant pathways, including ones that could be implicated in metastatic phenotypes and, for the first time, we report a decrease in hyper-acetylated histone marks in Cluster 1 PPGLs.
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Affiliation(s)
- Prodromos Chatzikyriakou
- Department of Medical and Molecular Genetics, King's College London, London, SE1 9RT, UK
- Comprehensive Cancer Centre, King's College London, London, SE5 8AF, UK
| | - Dimitria Brempou
- Department of Medical and Molecular Genetics, King's College London, London, SE1 9RT, UK
| | - Mark Quinn
- Department of Medical and Molecular Genetics, King's College London, London, SE1 9RT, UK
| | - Lauren Fishbein
- Division of Translational Medicine and Human Genetics, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Abramson Cancer Center, Perelman School of Medicine, Philadelphia, PA, USA
- Division of Endocrinology, Diabetes and Metabolism in the Department of Medicine Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Division of Endocrinology, Metabolism and Diabetes, Department of Medicine, University of Colorado School of Medicine, Aurora, CO, USA
| | - Roberta Noberini
- Department of Experimental Oncology, IEO, European Institute of Oncology IRCCS, Via Adamello 16, 20139, Milan, Italy
| | - Ioannis N Anastopoulos
- Department of Biomolecular Engineering, UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, 95064, USA
| | - Nicola Tufton
- Department of Endocrinology, St. Bartholomew's Hospital, Barts Health NHS Trust, and William Harvey Research Institute, Queen Mary University of London, London, UK
| | - Eugenie S Lim
- Department of Endocrinology, St. Bartholomew's Hospital, Barts Health NHS Trust, and William Harvey Research Institute, Queen Mary University of London, London, UK
| | - Rupert Obholzer
- Department of ENT and Skull Base Surgery, Guy's and St Thomas' NHS Foundation Trust, London, SE1 9RT, UK
| | - Johnathan G Hubbard
- Department of Endocrine Surgery, Guy's and St Thomas' NHS Foundation Trust, London, SE1 9RT, UK
| | - Mufaddal Moonim
- Department of Cellular Pathology, Guy's and St Thomas' NHS Foundation Trust, London, SE1 9RT, UK
- Imperial College Healthcare NHS Trust, London, UK
| | - Tiziana Bonaldi
- Department of Experimental Oncology, IEO, European Institute of Oncology IRCCS, Via Adamello 16, 20139, Milan, Italy
- Department of Oncology and Hematology-Oncology, University of Milano, Via Festa del Perdono 7, 20122, Milan, Italy
| | - Katherine L Nathanson
- Division of Translational Medicine and Human Genetics, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Abramson Cancer Center, Perelman School of Medicine, Philadelphia, PA, USA
| | - Louise Izatt
- Department of Clinical Genetics, Guy's and St Thomas' NHS Foundation Trust, London, SE1 9RT, UK
| | - Rebecca J Oakey
- Department of Medical and Molecular Genetics, King's College London, London, SE1 9RT, UK.
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Krause BJ, Vega-Tapia FA, Soto-Carrasco G, Lefever I, Letelier C, Saez CG, Castro-Rodriguez JA. Maternal obesity and high leptin levels prime pro-inflammatory pathways in human cord blood leukocytes. Placenta 2023; 142:75-84. [PMID: 37651852 DOI: 10.1016/j.placenta.2023.08.069] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 08/18/2023] [Accepted: 08/20/2023] [Indexed: 09/02/2023]
Abstract
INTRODUCTION Maternal obesity alters the immune function in the offspring. We hypothesize that maternal obesity and pro-inflammatory pathways induce leptin-related genes in neonatal monocytes, whereby high leptin levels enhance their inflammatory response. METHODS Transcriptional profiles of cord blood leukocytes (CBL) in basal and pro-inflammatory conditions were studied to determine differentially expressed genes (DEG). The DNA methylation profile of CB monocytes (CBM) of neonates born to control BMI mothers and women with obesity was assayed to identify differentially methylated probes (DMP). CBM-derived macrophages were cultured with or without leptin (10-100 ng/ml) and then stimulated with lipopolysaccharide (LPS, 100 ng/ml) and interferon-gamma (20 ng/ml) to assess the induction of TNF-α and IL-10 transcripts. RESULTS CBL from pregnancies with obesity (CBL-Ob) showed 12,183 DEG, affecting 49 out of 78 from the leptin pathway. Control CBM exposed to LPS showed 45 leptin-related DEG, an effect prevented by the co-exposure to LPS and IL-10. Conversely, CBM-Ob showed 5279 DMP enriched in insulin- and leptin-related genes, and Lasso regression of leptin-related DMP showed high predictive value for plasma leptin levels (r2 = 0.9897) and maternal BMI categories (AUC = 1). Chronic exposure to leptin increased TNF-α and decreased IL-10 levels in control BMI samples but not in Ob-CBM. Enhanced TNF-α induction after proinflammatory stimulation was observed in leptin-treated control BMI samples. DISCUSSION Obesity in pregnancy is associated with a distinctive expression and DNA methylation profile of leptin-related genes in cord blood monocytes, meanwhile, leptin enhances the expression of pro-inflammatory cytokines upon stimulation with M1-skewing agents.
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Affiliation(s)
- Bernardo J Krause
- Institute of Health Sciences, Universidad de O'Higgins, Rancagua, Chile.
| | - Fabian A Vega-Tapia
- Laboratory of Ocular and Systemic Autoimmune Diseases, Faculty of Medicine, Universidad de Chile, Santiago, Chile
| | - Gustavo Soto-Carrasco
- Division of Pediatrics, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Isidora Lefever
- Division of Pediatrics, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Catalina Letelier
- Division of Pediatrics, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Claudia G Saez
- Hematology-Oncology Department, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Jose A Castro-Rodriguez
- Division of Pediatrics, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile.
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12
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Tran TO, Lam LHT, Le NQK. Hyper-methylation of ABCG1 as an epigenetics biomarker in non-small cell lung cancer. Funct Integr Genomics 2023; 23:256. [PMID: 37523012 DOI: 10.1007/s10142-023-01185-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Revised: 07/22/2023] [Accepted: 07/24/2023] [Indexed: 08/01/2023]
Abstract
Non-small cell lung cancer (NSCLC) is the most prevalent histological type of lung cancer and the leading cause of death globally. Patients with NSCLC have a poor prognosis for various factors, and a late diagnosis is one of them. The DNA methylation of CpG island sequences found in the promoter regions of tumor suppressor genes has recently received attention as a potential biomarker of human cancer. In this study, we report DNA methylation changes of the adenosine triphosphate (ATP)-binding cassette transporter G1 (ABCG1), which belongs to the ATP cassette transporter family in NSCLC patients. Our results demonstrate that ABCG1 is hyper-methylation in NSCLC samples, and these changes are negatively correlated to gene and protein expression. Furthermore, the expression of the ABCG1 gene is significantly associated with the survival time of lung adenocarcinoma (LUAD) patients; however, it did not show a correlation to overall survival (OS) of lung squamous cell carcinoma (LUSC) patients. Notably, we found ABCG1 methylation status at locus cg20214535 is strongly associated with the survival time and consistently observed hyper-methylation in LUAD samples. This novel finding suggests ABCG1 is a potential candidate for targeted therapy in lung cancer via this specific probe. In addition, we illustrate the protein-protein interaction (PPI) of ABCG1 with other proteins and the strong communication of ABCG1 with immune cells.
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Affiliation(s)
- Thi-Oanh Tran
- International Ph.D. Program in Cell Therapy and Regenerative Medicine, College of Medicine, Taipei Medical University, 110, Taipei, Taiwan
- AIBioMed Research Group, Taipei Medical University, Taipei, 110, Taiwan
- Hematology and Blood Transfusion Center, Bach Mai Hospital, No. 78, Giai Phong street, Hanoi, Vietnam
| | - Luu Ho Thanh Lam
- Department of Pediatrics, Pham Ngoc Thach University of Medicine, Ho Chi Minh City, Vietnam
- Children's Hospital 1, Ho Chi Minh City, Vietnam
| | - Nguyen Quoc Khanh Le
- AIBioMed Research Group, Taipei Medical University, Taipei, 110, Taiwan.
- Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei, 110, Taiwan.
- Research Center for Artificial Intelligence in Medicine, Taipei Medical University, Taipei, 110, Taiwan.
- Translational Imaging Research Center, Taipei Medical University Hospital, Taipei, 110, Taiwan.
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13
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Janssens K, Neefs I, Ibrahim J, Schepers A, Pauwels P, Peeters M, Van Camp G, Op de Beeck K. Epigenome-wide methylation analysis of colorectal carcinoma, adenoma and normal tissue reveals novel biomarkers addressing unmet clinical needs. Clin Epigenetics 2023; 15:111. [PMID: 37415235 PMCID: PMC10327366 DOI: 10.1186/s13148-023-01516-7] [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: 03/22/2023] [Accepted: 06/01/2023] [Indexed: 07/08/2023] Open
Abstract
BACKGROUND Biomarker discovery in colorectal cancer has mostly focused on methylation patterns in normal and colorectal tumor tissue, but adenomas remain understudied. Therefore, we performed the first epigenome-wide study to profile methylation of all three tissue types combined and to identify discriminatory biomarkers. RESULTS Public methylation array data (Illumina EPIC and 450K) were collected from a total of 1 892 colorectal samples. Pairwise differential methylation analyses between tissue types were performed for both array types to "double evidence" differentially methylated probes (DE DMPs). Subsequently, the identified DMPs were filtered on methylation level and used to build a binary logistic regression prediction model. Focusing on the clinically most interesting group (adenoma vs carcinoma), we identified 13 DE DMPs that could effectively discriminate between them (AUC = 0.996). We validated this model in an in-house experimental methylation dataset of 13 adenomas and 9 carcinomas. It reached a sensitivity and specificity of 96% and 95%, respectively, with an overall accuracy of 96%. Our findings raise the possibility that the 13 DE DMPs identified in this study can be used as molecular biomarkers in the clinic. CONCLUSIONS Our analyses show that methylation biomarkers have the potential to discriminate between normal, precursor and carcinoma tissues of the colorectum. More importantly, we highlight the power of the methylome as a source of markers for discriminating between colorectal adenomas and carcinomas, which currently remains an unmet clinical need.
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Affiliation(s)
- Katleen Janssens
- Centre of Medical Genetics, University of Antwerp and Antwerp University Hospital, Prins Boudewijnlaan 43, 2650, Edegem, Belgium
- Centre for Oncological Research Antwerp (CORE), University of Antwerp and Antwerp University Hospital, Universiteitsplein 1, 2610, Wilrijk, Belgium
| | - Isabelle Neefs
- Centre of Medical Genetics, University of Antwerp and Antwerp University Hospital, Prins Boudewijnlaan 43, 2650, Edegem, Belgium
- Centre for Oncological Research Antwerp (CORE), University of Antwerp and Antwerp University Hospital, Universiteitsplein 1, 2610, Wilrijk, Belgium
| | - Joe Ibrahim
- Centre of Medical Genetics, University of Antwerp and Antwerp University Hospital, Prins Boudewijnlaan 43, 2650, Edegem, Belgium
- Centre for Oncological Research Antwerp (CORE), University of Antwerp and Antwerp University Hospital, Universiteitsplein 1, 2610, Wilrijk, Belgium
| | - Anne Schepers
- Centre of Medical Genetics, University of Antwerp and Antwerp University Hospital, Prins Boudewijnlaan 43, 2650, Edegem, Belgium
| | - Patrick Pauwels
- Centre for Oncological Research Antwerp (CORE), University of Antwerp and Antwerp University Hospital, Universiteitsplein 1, 2610, Wilrijk, Belgium
| | - Marc Peeters
- Centre for Oncological Research Antwerp (CORE), University of Antwerp and Antwerp University Hospital, Universiteitsplein 1, 2610, Wilrijk, Belgium
| | - Guy Van Camp
- Centre of Medical Genetics, University of Antwerp and Antwerp University Hospital, Prins Boudewijnlaan 43, 2650, Edegem, Belgium
- Centre for Oncological Research Antwerp (CORE), University of Antwerp and Antwerp University Hospital, Universiteitsplein 1, 2610, Wilrijk, Belgium
| | - Ken Op de Beeck
- Centre of Medical Genetics, University of Antwerp and Antwerp University Hospital, Prins Boudewijnlaan 43, 2650, Edegem, Belgium.
- Centre for Oncological Research Antwerp (CORE), University of Antwerp and Antwerp University Hospital, Universiteitsplein 1, 2610, Wilrijk, Belgium.
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14
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Inkster AM, Wong MT, Matthews AM, Brown CJ, Robinson WP. Who's afraid of the X? Incorporating the X and Y chromosomes into the analysis of DNA methylation array data. Epigenetics Chromatin 2023; 16:1. [PMID: 36609459 PMCID: PMC9825011 DOI: 10.1186/s13072-022-00477-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 12/27/2022] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND Many human disease phenotypes manifest differently by sex, making the development of methods for incorporating X and Y-chromosome data into analyses vital. Unfortunately, X and Y chromosome data are frequently excluded from large-scale analyses of the human genome and epigenome due to analytical complexity associated with sex chromosome dosage differences between XX and XY individuals, and the impact of X-chromosome inactivation (XCI) on the epigenome. As such, little attention has been given to considering the methods by which sex chromosome data may be included in analyses of DNA methylation (DNAme) array data. RESULTS With Illumina Infinium HumanMethylation450 DNAme array data from 634 placental samples, we investigated the effects of probe filtering, normalization, and batch correction on DNAme data from the X and Y chromosomes. Processing steps were evaluated in both mixed-sex and sex-stratified subsets of the analysis cohort to identify whether including both sexes impacted processing results. We found that identification of probes that have a high detection p-value, or that are non-variable, should be performed in sex-stratified data subsets to avoid over- and under-estimation of the quantity of probes eligible for removal, respectively. All normalization techniques investigated returned X and Y DNAme data that were highly correlated with the raw data from the same samples. We found no difference in batch correction results after application to mixed-sex or sex-stratified cohorts. Additionally, we identify two analytical methods suitable for XY chromosome data, the choice between which should be guided by the research question of interest, and we performed a proof-of-concept analysis studying differential DNAme on the X and Y chromosome in the context of placental acute chorioamnionitis. Finally, we provide an annotation of probe types that may be desirable to filter in X and Y chromosome analyses, including probes in repetitive elements, the X-transposed region, and cancer-testis gene promoters. CONCLUSION While there may be no single "best" approach for analyzing DNAme array data from the X and Y chromosome, analysts must consider key factors during processing and analysis of sex chromosome data to accommodate the underlying biology of these chromosomes, and the technical limitations of DNA methylation arrays.
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Affiliation(s)
- Amy M Inkster
- BC Children's Hospital Research Institute, 950 W 28th Ave, Vancouver, BC, V6H 3N1, Canada.
- Department of Medical Genetics, University of British Columbia, 4500 Oak St, Vancouver, V6H 3N1, Canada.
| | - Martin T Wong
- Department of Medical Genetics, University of British Columbia, 4500 Oak St, Vancouver, V6H 3N1, Canada
| | - Allison M Matthews
- BC Children's Hospital Research Institute, 950 W 28th Ave, Vancouver, BC, V6H 3N1, Canada
- Department of Pathology & Laboratory Medicine, University of British Columbia, 2211 Wesbrook Mall, Vancouver, V6T 1Z7, Canada
| | - Carolyn J Brown
- Department of Medical Genetics, University of British Columbia, 4500 Oak St, Vancouver, V6H 3N1, Canada
| | - Wendy P Robinson
- BC Children's Hospital Research Institute, 950 W 28th Ave, Vancouver, BC, V6H 3N1, Canada
- Department of Medical Genetics, University of British Columbia, 4500 Oak St, Vancouver, V6H 3N1, Canada
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15
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Di Lena P, Sala C, Nardini C. Evaluation of different computational methods for DNA methylation-based biological age. Brief Bioinform 2022; 23:6632619. [PMID: 35794713 DOI: 10.1093/bib/bbac274] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 05/27/2022] [Accepted: 06/14/2022] [Indexed: 11/13/2022] Open
Abstract
In recent years there has been a widespread interest in researching biomarkers of aging that could predict physiological vulnerability better than chronological age. Aging, in fact, is one of the most relevant risk factors for a wide range of maladies, and molecular surrogates of this phenotype could enable better patients stratification. Among the most promising of such biomarkers is DNA methylation-based biological age. Given the potential and variety of computational implementations (epigenetic clocks), we here present a systematic review of such clocks. Furthermore, we provide a large-scale performance comparison across different tissues and diseases in terms of age prediction accuracy and age acceleration, a measure of deviance from physiology. Our analysis offers both a state-of-the-art overview of the computational techniques developed so far and a heterogeneous picture of performances, which can be helpful in orienting future research.
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Affiliation(s)
- Pietro Di Lena
- Department of Computer Science and Engineering, University of Bologna, Mura Anteo Zamboni 7, 40126 Bologna, Italy
| | - Claudia Sala
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Via Massarenti 9, 40138, Bologna, Italy
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16
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Jiang M, Wang X, Gao X, Cardenas A, Baccarelli AA, Guo X, Huang J, Wu S. Association of DNA methylation in circulating CD4 +T cells with short-term PM 2.5 pollution waves: A quasi-experimental study of healthy young adults. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2022; 239:113634. [PMID: 35617899 DOI: 10.1016/j.ecoenv.2022.113634] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Revised: 04/15/2022] [Accepted: 05/09/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND Fine particulate matter (PM2.5) is a modifiable environmental risk factor with established adverse effects on human health. However, associations between acute PM2.5 fluctuation and DNA methylation remain unknown. METHODS A quasi-experimental study utilizing naturally occurring PM2.5 pollution waves (PPWs) was conducted on 32 healthy young adults. Repeated follow-up measurements were performed and participants served as their own controls before, during, and after PPWs. Exposure measurements including indoor and ambient PM2.5 levels, and equivalent personal PM2.5 exposure were further estimated based on the time-location information. DNA methylation profiles of circulating CD4+T cells were obtained using Illumina HumanMethylationEPIC BeadChip. Linear mixed-effect models were applied to estimate the associations between two scenarios (during-PPWs vs. pre-PPWs periods and during-PPWs vs. post-PPWs periods) and methylation level of each CpG site. We further validated their associations with the personal PM2.5 exposure, and GO and KEGG analyses and mediation analysis were conducted accordingly. RESULTS Data from 26 participants were included in final analysis after quality control. Short-term high PM2.5 exposure was associated with DNA methylation changes of participants. Nine differently methylated CpG sites were not only significantly associated with PPWs periods but also with personal PM2.5 exposure in 24-h prior to the health examinations (p < 0.01). Gene ontology analysis found that five sites were associated with two pathways relating to membrane protein synthesis. PM2.5-related changes in CpG sites were mediated by sP-selectin, 8-isoPGF2α, EGF, GRO, IL-15, and IFN-α2, with mediated proportions ranging from 9.65% to 23.40%. CONCLUSIONS This is the first quasi-experimental study showing that short-term high PM2.5 exposure could alter the DNA methylation of CD4+T cells, which provided valuable information for further exploring underlying biological mechanisms and epigenetic biomarkers for PM2.5-related acute health effects.
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Affiliation(s)
- Meijie Jiang
- Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing, China
| | - Xinmei Wang
- Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing, China
| | - Xu Gao
- Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing, China
| | - Andres Cardenas
- Division of Environmental Health Sciences, School of Public Health and Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA
| | - Andrea A Baccarelli
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Xinbiao Guo
- Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing, China
| | - Jing Huang
- Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing, China.
| | - Shaowei Wu
- Department of Occupational and Environmental Health, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, China; Key Laboratory of Trace Elements and Endemic Diseases in Ministry of Health, Xi'an, Shaanxi, China; Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an, Shaanxi, China.
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17
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Lancaster EE, Vladimirov VI, Riley BP, Landry JW, Roberson-Nay R, York TP. LARGE-SCALE INTEGRATION OF DNA METHYLATION AND GENE EXPRESSION ARRAY PLATFORMS IDENTIFIES BOTH cis AND trans RELATIONSHIPS. Epigenetics 2022; 17:1753-1773. [PMID: 35608069 PMCID: PMC9621057 DOI: 10.1080/15592294.2022.2079293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Although epigenome-wide association studies (EWAS) have been successful in identifying DNA methylation (DNAm) patterns associated with disease states, any further characterization of etiologic mechanisms underlying disease remains elusive. This knowledge gap does not originate from a lack of DNAm–trait associations, but rather stems from study design issues that affect the interpretability of EWAS results. Despite known limitations in predicting the function of a particular CpG site, most EWAS maintain the broad assumption that altered DNAm results in a concomitant change of transcription at the most proximal gene. This study integrated DNAm and gene expression (GE) measurements in two cohorts, the Adolescent and Young Adult Twin Study (AYATS) and the Pregnancy, Race, Environment, Genes (PREG) study, to improve the understanding of epigenomic regulatory mechanisms. CpG sites associated with GE in cis were enriched in areas of transcription factor binding and areas of intermediate-to-low CpG density. CpG sites associated with trans GE were also enriched in areas of known regulatory significance, including enhancer regions. These results highlight issues with restricting DNAm-transcript annotations to small genomic intervals and question the validity of assuming a cis DNAm–GE pathway. Based on these findings, the interpretation of EWAS results is limited in studies without multi-omic support and further research should identify genomic regions in which GE-associated DNAm is overrepresented. An in-depth characterization of GE-associated CpG sites could improve predictions of the downstream functional impact of altered DNAm and inform best practices for interpreting DNAm–trait associations generated by EWAS.
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Affiliation(s)
- Eva E Lancaster
- Department of Psychiatry, Virginia Commonwealth University, Richmond, VA 23220
| | | | - Brien P Riley
- Department of Psychiatry, Virginia Commonwealth University, Richmond, VA 23220.,Department of Human and Molecular Genetics, Virginia Commonwealth University, Richmond, VA 23220
| | - Joseph W Landry
- Department of Human and Molecular Genetics, Virginia Commonwealth University, Richmond, VA 23220
| | - Roxann Roberson-Nay
- Department of Psychiatry, Virginia Commonwealth University, Richmond, VA 23220.,Department of Psychology, Virginia Commonwealth University, Richmond, VA 23220
| | - Timothy P York
- Department of Human and Molecular Genetics, Virginia Commonwealth University, Richmond, VA 23220.,Department of Obstetrics and Gynecology, Virginia Commonwealth University, Richmond, VA 23220
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18
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Winkler S, Winkler I, Figaschewski M, Tiede T, Nordheim A, Kohlbacher O. De novo identification of maximally deregulated subnetworks based on multi-omics data with DeRegNet. BMC Bioinformatics 2022; 23:139. [PMID: 35439941 PMCID: PMC9020058 DOI: 10.1186/s12859-022-04670-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2021] [Accepted: 03/29/2022] [Indexed: 12/14/2022] Open
Abstract
Background With a growing amount of (multi-)omics data being available, the extraction of knowledge from these datasets is still a difficult problem. Classical enrichment-style analyses require predefined pathways or gene sets that are tested for significant deregulation to assess whether the pathway is functionally involved in the biological process under study. De novo identification of these pathways can reduce the bias inherent in predefined pathways or gene sets. At the same time, the definition and efficient identification of these pathways de novo from large biological networks is a challenging problem. Results We present a novel algorithm, DeRegNet, for the identification of maximally deregulated subnetworks on directed graphs based on deregulation scores derived from (multi-)omics data. DeRegNet can be interpreted as maximum likelihood estimation given a certain probabilistic model for de-novo subgraph identification. We use fractional integer programming to solve the resulting combinatorial optimization problem. We can show that the approach outperforms related algorithms on simulated data with known ground truths. On a publicly available liver cancer dataset we can show that DeRegNet can identify biologically meaningful subgraphs suitable for patient stratification. DeRegNet can also be used to find explicitly multi-omics subgraphs which we demonstrate by presenting subgraphs with consistent methylation-transcription patterns. DeRegNet is freely available as open-source software. Conclusion The proposed algorithmic framework and its available implementation can serve as a valuable heuristic hypothesis generation tool contextualizing omics data within biomolecular networks.
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Affiliation(s)
- Sebastian Winkler
- Applied Bioinformatics, Department of Computer Science, University of Tuebingen, Tübingen, Germany. .,International Max Planck Research School (IMPRS) "From Molecules to Organism", Tübingen, Germany.
| | - Ivana Winkler
- International Max Planck Research School (IMPRS) "From Molecules to Organism", Tübingen, Germany.,Interfaculty Institute for Cell Biology (IFIZ), University of Tuebingen, Tübingen, Germany.,German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Mirjam Figaschewski
- Applied Bioinformatics, Department of Computer Science, University of Tuebingen, Tübingen, Germany
| | - Thorsten Tiede
- Applied Bioinformatics, Department of Computer Science, University of Tuebingen, Tübingen, Germany
| | - Alfred Nordheim
- Interfaculty Institute for Cell Biology (IFIZ), University of Tuebingen, Tübingen, Germany.,Leibniz Institute on Aging (FLI), Jena, Germany
| | - Oliver Kohlbacher
- Applied Bioinformatics, Department of Computer Science, University of Tuebingen, Tübingen, Germany.,Institute for Bioinformatics and Medical Informatics, University of Tuebingen, Tübingen, Germany.,Translational Bioinformatics, University Hospital Tuebingen, Tübingen, Germany
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19
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Cristoferi I, Giacon TA, Boer K, van Baardwijk M, Neri F, Campisi M, Kimenai HJAN, Clahsen-van Groningen MC, Pavanello S, Furian L, Minnee RC. The applications of DNA methylation as a biomarker in kidney transplantation: a systematic review. Clin Epigenetics 2022; 14:20. [PMID: 35130936 PMCID: PMC8822833 DOI: 10.1186/s13148-022-01241-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Accepted: 01/27/2022] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Although kidney transplantation improves patient survival and quality of life, long-term results are hampered by both immune- and non-immune-mediated complications. Current biomarkers of post-transplant complications, such as allograft rejection, chronic renal allograft dysfunction, and cutaneous squamous cell carcinoma, have a suboptimal predictive value. DNA methylation is an epigenetic modification that directly affects gene expression and plays an important role in processes such as ischemia/reperfusion injury, fibrosis, and alloreactive immune response. Novel techniques can quickly assess the DNA methylation status of multiple loci in different cell types, allowing a deep and interesting study of cells' activity and function. Therefore, DNA methylation has the potential to become an important biomarker for prediction and monitoring in kidney transplantation. PURPOSE OF THE STUDY The aim of this study was to evaluate the role of DNA methylation as a potential biomarker of graft survival and complications development in kidney transplantation. MATERIAL AND METHODS: A systematic review of several databases has been conducted. The Newcastle-Ottawa scale and the Jadad scale have been used to assess the risk of bias for observational and randomized studies, respectively. RESULTS Twenty articles reporting on DNA methylation as a biomarker for kidney transplantation were included, all using DNA methylation for prediction and monitoring. DNA methylation pattern alterations in cells isolated from different tissues, such as kidney biopsies, urine, and blood, have been associated with ischemia-reperfusion injury and chronic renal allograft dysfunction. These alterations occurred in different and specific loci. DNA methylation status has also proved to be important for immune response modulation, having a crucial role in regulatory T cell definition and activity. Research also focused on a better understanding of the role of this epigenetic modification assessment for regulatory T cells isolation and expansion for future tolerance induction-oriented therapies. CONCLUSIONS Studies included in this review are heterogeneous in study design, biological samples, and outcome. More coordinated investigations are needed to affirm DNA methylation as a clinically relevant biomarker important for prevention, monitoring, and intervention.
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Affiliation(s)
- Iacopo Cristoferi
- Division of HPB and Transplant Surgery, Department of Surgery, Erasmus MC, University Medical Center Rotterdam, Doctor Molewaterplein 40, 3015GD, Rotterdam, the Netherlands.
- Department of Pathology and Clinical Bioinformatics, Erasmus MC, University Medical Center Rotterdam, Doctor Molewaterplein 40, 3015GD, Rotterdam, the Netherlands.
- Erasmus MC Transplant Institute, Erasmus MC, University Medical Center Rotterdam, Doctor Molewaterplein 40, 3015GD, Rotterdam, the Netherlands.
| | - Tommaso Antonio Giacon
- Kidney and Pancreas Transplantation Unit, Department of Surgical, Oncological and Gastroenterological Sciences, Padua University Hospital, Via Giustiniani 2, 35128, Padua, Italy
- Occupational Medicine, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, Padua University, Via Giustiniani 2, 35128, Padua, Italy
- Environmental and Respiratory Physiology Laboratory, Department of Biomedical Sciences, Padua University, Via Marzolo 3, 35131, Padua, Italy
- Institute of Anaesthesia and Intensive Care, Department of Medicine - DIMED, Padua University Hospital, Via Cesare Battisti 267, 35128, Padua, Italy
| | - Karin Boer
- Erasmus MC Transplant Institute, Erasmus MC, University Medical Center Rotterdam, Doctor Molewaterplein 40, 3015GD, Rotterdam, the Netherlands
- Division of Nephrology and Transplantation, Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Doctor Molewaterplein 40, 3015GD, Rotterdam, The Netherlands
| | - Myrthe van Baardwijk
- Division of HPB and Transplant Surgery, Department of Surgery, Erasmus MC, University Medical Center Rotterdam, Doctor Molewaterplein 40, 3015GD, Rotterdam, the Netherlands
- Department of Pathology and Clinical Bioinformatics, Erasmus MC, University Medical Center Rotterdam, Doctor Molewaterplein 40, 3015GD, Rotterdam, the Netherlands
- Erasmus MC Transplant Institute, Erasmus MC, University Medical Center Rotterdam, Doctor Molewaterplein 40, 3015GD, Rotterdam, the Netherlands
| | - Flavia Neri
- Kidney and Pancreas Transplantation Unit, Department of Surgical, Oncological and Gastroenterological Sciences, Padua University Hospital, Via Giustiniani 2, 35128, Padua, Italy
| | - Manuela Campisi
- Occupational Medicine, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, Padua University, Via Giustiniani 2, 35128, Padua, Italy
| | - Hendrikus J A N Kimenai
- Division of HPB and Transplant Surgery, Department of Surgery, Erasmus MC, University Medical Center Rotterdam, Doctor Molewaterplein 40, 3015GD, Rotterdam, the Netherlands
- Erasmus MC Transplant Institute, Erasmus MC, University Medical Center Rotterdam, Doctor Molewaterplein 40, 3015GD, Rotterdam, the Netherlands
| | - Marian C Clahsen-van Groningen
- Department of Pathology and Clinical Bioinformatics, Erasmus MC, University Medical Center Rotterdam, Doctor Molewaterplein 40, 3015GD, Rotterdam, the Netherlands
- Erasmus MC Transplant Institute, Erasmus MC, University Medical Center Rotterdam, Doctor Molewaterplein 40, 3015GD, Rotterdam, the Netherlands
- Institute of Experimental Medicine and Systems Biology, RWTH Aachen University, Pauwelsstraße 30, 52074, Aachen, Germany
| | - Sofia Pavanello
- Occupational Medicine, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, Padua University, Via Giustiniani 2, 35128, Padua, Italy
| | - Lucrezia Furian
- Kidney and Pancreas Transplantation Unit, Department of Surgical, Oncological and Gastroenterological Sciences, Padua University Hospital, Via Giustiniani 2, 35128, Padua, Italy
| | - Robert C Minnee
- Division of HPB and Transplant Surgery, Department of Surgery, Erasmus MC, University Medical Center Rotterdam, Doctor Molewaterplein 40, 3015GD, Rotterdam, the Netherlands
- Erasmus MC Transplant Institute, Erasmus MC, University Medical Center Rotterdam, Doctor Molewaterplein 40, 3015GD, Rotterdam, the Netherlands
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20
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Subbalakshmi AR, Sahoo S, McMullen I, Saxena AN, Venugopal SK, Somarelli JA, Jolly MK. KLF4 Induces Mesenchymal-Epithelial Transition (MET) by Suppressing Multiple EMT-Inducing Transcription Factors. Cancers (Basel) 2021; 13:5135. [PMID: 34680284 PMCID: PMC8533753 DOI: 10.3390/cancers13205135] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Revised: 10/06/2021] [Accepted: 10/08/2021] [Indexed: 12/24/2022] Open
Abstract
Epithelial-Mesenchymal Plasticity (EMP) refers to reversible dynamic processes where cells can transition from epithelial to mesenchymal (EMT) or from mesenchymal to epithelial (MET) phenotypes. Both these processes are modulated by multiple transcription factors acting in concert. While EMT-inducing transcription factors (TFs)-TWIST1/2, ZEB1/2, SNAIL1/2/3, GSC, and FOXC2-are well-characterized, the MET-inducing TFs are relatively poorly understood (OVOL1/2 and GRHL1/2). Here, using mechanism-based mathematical modeling, we show that transcription factor KLF4 can delay the onset of EMT by suppressing multiple EMT-TFs. Our simulations suggest that KLF4 overexpression can promote a phenotypic shift toward a more epithelial state, an observation suggested by the negative correlation of KLF4 with EMT-TFs and with transcriptomic-based EMT scoring metrics in cancer cell lines. We also show that the influence of KLF4 in modulating the EMT dynamics can be strengthened by its ability to inhibit cell-state transitions at the epigenetic level. Thus, KLF4 can inhibit EMT through multiple parallel paths and can act as a putative MET-TF. KLF4 associates with the patient survival metrics across multiple cancers in a context-specific manner, highlighting the complex association of EMP with patient survival.
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Affiliation(s)
- Ayalur Raghu Subbalakshmi
- Centre for BioSystems Science and Engineering, Indian Institute of Science, Bangalore 560012, India; (A.R.S.); (S.S.); (S.K.V.)
| | - Sarthak Sahoo
- Centre for BioSystems Science and Engineering, Indian Institute of Science, Bangalore 560012, India; (A.R.S.); (S.S.); (S.K.V.)
| | | | | | - Sudhanva Kalasapura Venugopal
- Centre for BioSystems Science and Engineering, Indian Institute of Science, Bangalore 560012, India; (A.R.S.); (S.S.); (S.K.V.)
| | - Jason A. Somarelli
- Department of Medicine, Duke University, Durham, NC 27708, USA;
- Duke Cancer Institute, Duke University, Durham, NC 27708, USA
| | - Mohit Kumar Jolly
- Centre for BioSystems Science and Engineering, Indian Institute of Science, Bangalore 560012, India; (A.R.S.); (S.S.); (S.K.V.)
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21
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Wei S, Tao J, Xu J, Chen X, Wang Z, Zhang N, Zuo L, Jia Z, Chen H, Sun H, Yan Y, Zhang M, Lv H, Kong F, Duan L, Ma Y, Liao M, Xu L, Feng R, Liu G, Project TEWAS, Jiang Y. Ten Years of EWAS. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2021; 8:e2100727. [PMID: 34382344 PMCID: PMC8529436 DOI: 10.1002/advs.202100727] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 05/11/2021] [Indexed: 06/13/2023]
Abstract
Epigenome-wide association study (EWAS) has been applied to analyze DNA methylation variation in complex diseases for a decade, and epigenome as a research target has gradually become a hot topic of current studies. The DNA methylation microarrays, next-generation, and third-generation sequencing technologies have prepared a high-quality platform for EWAS. Here, the progress of EWAS research is reviewed, its contributions to clinical applications, and mainly describe the achievements of four typical diseases. Finally, the challenges encountered by EWAS and make bold predictions for its future development are presented.
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Affiliation(s)
- Siyu Wei
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityHarbin150081China
- The EWAS ProjectHarbinChina
| | - Junxian Tao
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityHarbin150081China
- The EWAS ProjectHarbinChina
| | - Jing Xu
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityHarbin150081China
- The EWAS ProjectHarbinChina
| | - Xingyu Chen
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityHarbin150081China
| | - Zhaoyang Wang
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityHarbin150081China
| | - Nan Zhang
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityHarbin150081China
| | - Lijiao Zuo
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityHarbin150081China
| | - Zhe Jia
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityHarbin150081China
| | - Haiyan Chen
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityHarbin150081China
| | - Hongmei Sun
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityHarbin150081China
| | - Yubo Yan
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityHarbin150081China
| | - Mingming Zhang
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityHarbin150081China
| | - Hongchao Lv
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityHarbin150081China
| | - Fanwu Kong
- The EWAS ProjectHarbinChina
- Department of NephrologyThe Second Affiliated HospitalHarbin Medical UniversityHarbin150001China
| | - Lian Duan
- The EWAS ProjectHarbinChina
- The First Affiliated Hospital of Wenzhou Medical UniversityWenzhou325000China
| | - Ye Ma
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityHarbin150081China
- The EWAS ProjectHarbinChina
| | - Mingzhi Liao
- The EWAS ProjectHarbinChina
- College of Life SciencesNorthwest A&F UniversityYanglingShanxi712100China
| | - Liangde Xu
- The EWAS ProjectHarbinChina
- School of Biomedical EngineeringWenzhou Medical UniversityWenzhou325035China
| | - Rennan Feng
- The EWAS ProjectHarbinChina
- Department of Nutrition and Food HygienePublic Health CollegeHarbin Medical UniversityHarbin150081China
| | - Guiyou Liu
- The EWAS ProjectHarbinChina
- Beijing Institute for Brain DisordersCapital Medical UniversityBeijing100069China
| | | | - Yongshuai Jiang
- College of Bioinformatics Science and TechnologyHarbin Medical UniversityHarbin150081China
- The EWAS ProjectHarbinChina
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22
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Minegishi R, Gotoh O, Tanaka N, Maruyama R, Chang JT, Mori S. A method of sample-wise region-set enrichment analysis for DNA methylomics. Epigenomics 2021; 13:1081-1093. [PMID: 34241544 DOI: 10.2217/epi-2021-0065] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Aim: Gene set analysis has commonly been used to interpret DNA methylome data. However, summarizing the DNA methylation level of a gene is challenging due to variability in the number, density and methylation levels of CpG sites, and the numerous intergenic CpGs. Instead, we propose to use region sets to annotate the DNA methylome. Methods: We developed single sample region-set enrichment analysis for DNA methylome (methyl-ssRSEA) to conduct sample-wise, region-set enrichment analysis. Results: Methyl-ssRSEA can handle both microarray- and sequencing-based platforms and reproducibly recover the known biology from the methylation profiles of peripheral blood cells and breast cancers. The performance was superior to existing tools for region-set analysis in discriminating blood cell types. Conclusion: Methyl-ssRSEA offers a novel way to functionally interpret the DNA methylome in the cell.
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Affiliation(s)
- Ryu Minegishi
- Project for Development of Innovative Research on Cancer Therapeutics, Cancer Precision Medicine Center, Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Osamu Gotoh
- Project for Development of Innovative Research on Cancer Therapeutics, Cancer Precision Medicine Center, Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Norio Tanaka
- Project for Development of Innovative Research on Cancer Therapeutics, Cancer Precision Medicine Center, Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Reo Maruyama
- Project for Cancer Epigenomics, Cancer Institute, Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Jeffrey T Chang
- Department of Integrative Biology & Pharmacology, University of Texas Health Science Center, Houston, TX 77030, USA
| | - Seiichi Mori
- Project for Development of Innovative Research on Cancer Therapeutics, Cancer Precision Medicine Center, Japanese Foundation for Cancer Research, Tokyo, Japan
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23
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Combinatorial therapy in tumor microenvironment: Where do we stand? Biochim Biophys Acta Rev Cancer 2021; 1876:188585. [PMID: 34224836 DOI: 10.1016/j.bbcan.2021.188585] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 05/28/2021] [Accepted: 06/23/2021] [Indexed: 01/09/2023]
Abstract
The tumor microenvironment plays a pivotal role in tumor initiation and progression by creating a dynamic interaction with cancer cells. The tumor microenvironment consists of various cellular components, including endothelial cells, fibroblasts, pericytes, adipocytes, immune cells, cancer stem cells and vasculature, which provide a sustained environment for cancer cell proliferation. Currently, targeting tumor microenvironment is increasingly being explored as a novel approach to improve cancer therapeutics, as it influences the growth and expansion of malignant cells in various ways. Despite continuous advancements in targeted therapies for cancer treatment, drug resistance, toxicity and immune escape mechanisms are the basis of treatment failure and cancer escape. Targeting tumor microenvironment efficiently with approved drugs and combination therapy is the solution to this enduring challenge that involves combining more than one treatment modality such as chemotherapy, surgery, radiotherapy, immunotherapy and nanotherapy that can effectively and synergistically target the critical pathways associated with disease pathogenesis. This review shed light on the composition of the tumor microenvironment, interaction of different components within tumor microenvironment with tumor cells and associated hallmarks, the current status of combinatorial therapies being developed, and various growing advancements. Furthermore, computational tools can also be used to monitor the significance and outcome of therapies being developed. We addressed the perceived barriers and regulatory hurdles in developing a combinatorial regimen and evaluated the present status of these therapies in the clinic. The accumulating depth of knowledge about the tumor microenvironment in cancer may facilitate further development of effective treatment modalities. This review presents the tumor microenvironment as a sweeping landscape for developing novel cancer therapies.
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24
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Koo HK, Morrow J, Kachroo P, Tantisira K, Weiss ST, Hersh CP, Silverman EK, DeMeo DL. Sex-specific associations with DNA methylation in lung tissue demonstrate smoking interactions. Epigenetics 2021; 16:692-703. [PMID: 32962511 PMCID: PMC8143227 DOI: 10.1080/15592294.2020.1819662] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Revised: 08/08/2020] [Accepted: 08/18/2020] [Indexed: 01/01/2023] Open
Abstract
Cigarette smoking impacts DNA methylation, but the investigation of sex-specific features of lung tissue DNA methylation in smokers has been limited. Women appear more susceptible to cigarette smoke, and often develop more severe lung disease at an earlier age with less smoke exposure. We aimed to analyse whether there are sex differences in DNA methylation in lung tissue and whether these DNA methylation marks interact with smoking. We collected lung tissue samples from former smokers who underwent lung tissue resection. One hundred thirty samples from white subjects were included for this analysis. Regression models for sex as a predictor of methylation were adjusted for age, presence of COPD, smoking variables and technical batch variables revealed 710 associated sites. 294 sites demonstrated robust sex-specific methylation associations in foetal lung tissue. Pathway analysis identified 6 nominally significant pathways including the mitophagy pathway. Three CpG sites demonstrated a suggested interaction between sex and pack-years of smoking: GPR132, ANKRD44 and C19orf60. All of them were nominally significant in both male- and female-specific models, and the effect estimates were in opposite directions for male and female; GPR132 demonstrated significant association between DNA methylation and gene expression in lung tissue (P < 0.05). Sex-specific associations with DNA methylation in lung tissue are wide-spread and may reveal genes and pathways relevant to sex differences for lung damaging effects of cigarette smoking.
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Affiliation(s)
- Hyeon-Kyoung Koo
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Division of Pulmonary and Critical Care Medicine, Ilsan Paik Hospital, Inje University College of Medicine, Ilsan, Republic of Korea
| | - Jarrett Morrow
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, USA
| | - Priyadarshini Kachroo
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, USA
| | - Kelan Tantisira
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Boston, MA, USA
| | - Scott T Weiss
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, USA
| | - Craig P Hersh
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Boston, MA, USA
| | - Edwin K Silverman
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Boston, MA, USA
| | - Dawn L DeMeo
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Boston, MA, USA
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25
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Villicaña S, Bell JT. Genetic impacts on DNA methylation: research findings and future perspectives. Genome Biol 2021; 22:127. [PMID: 33931130 PMCID: PMC8086086 DOI: 10.1186/s13059-021-02347-6] [Citation(s) in RCA: 124] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Accepted: 04/09/2021] [Indexed: 12/17/2022] Open
Abstract
Multiple recent studies highlight that genetic variants can have strong impacts on a significant proportion of the human DNA methylome. Methylation quantitative trait loci, or meQTLs, allow for the exploration of biological mechanisms that underlie complex human phenotypes, with potential insights for human disease onset and progression. In this review, we summarize recent milestones in characterizing the human genetic basis of DNA methylation variation over the last decade, including heritability findings and genome-wide identification of meQTLs. We also discuss challenges in this field and future areas of research geared to generate insights into molecular processes underlying human complex traits.
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Affiliation(s)
- Sergio Villicaña
- Department of Twin Research and Genetic Epidemiology, St. Thomas’ Hospital, King’s College London, 3rd Floor, South Wing, Block D, London, SE1 7EH UK
| | - Jordana T. Bell
- Department of Twin Research and Genetic Epidemiology, St. Thomas’ Hospital, King’s College London, 3rd Floor, South Wing, Block D, London, SE1 7EH UK
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26
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Yin J, Li F, Zhou Y, Mou M, Lu Y, Chen K, Xue J, Luo Y, Fu J, He X, Gao J, Zeng S, Yu L, Zhu F. INTEDE: interactome of drug-metabolizing enzymes. Nucleic Acids Res 2021; 49:D1233-D1243. [PMID: 33045737 PMCID: PMC7779056 DOI: 10.1093/nar/gkaa755] [Citation(s) in RCA: 80] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Revised: 08/19/2020] [Accepted: 09/22/2020] [Indexed: 12/15/2022] Open
Abstract
Drug-metabolizing enzymes (DMEs) are critical determinant of drug safety and efficacy, and the interactome of DMEs has attracted extensive attention. There are 3 major interaction types in an interactome: microbiome-DME interaction (MICBIO), xenobiotics-DME interaction (XEOTIC) and host protein-DME interaction (HOSPPI). The interaction data of each type are essential for drug metabolism, and the collective consideration of multiple types has implication for the future practice of precision medicine. However, no database was designed to systematically provide the data of all types of DME interactions. Here, a database of the Interactome of Drug-Metabolizing Enzymes (INTEDE) was therefore constructed to offer these interaction data. First, 1047 unique DMEs (448 host and 599 microbial) were confirmed, for the first time, using their metabolizing drugs. Second, for these newly confirmed DMEs, all types of their interactions (3359 MICBIOs between 225 microbial species and 185 DMEs; 47 778 XEOTICs between 4150 xenobiotics and 501 DMEs; 7849 HOSPPIs between 565 human proteins and 566 DMEs) were comprehensively collected and then provided, which enabled the crosstalk analysis among multiple types. Because of the huge amount of accumulated data, the INTEDE made it possible to generalize key features for revealing disease etiology and optimizing clinical treatment. INTEDE is freely accessible at: https://idrblab.org/intede/.
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Affiliation(s)
- Jiayi Yin
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Fengcheng Li
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Ying Zhou
- The First Affiliated Hospital, Zhejiang University, Hangzhou 310000, China
| | - Minjie Mou
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yinjing Lu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Kangli Chen
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Jia Xue
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yongchao Luo
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Jianbo Fu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Xu He
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Jianqing Gao
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Hangzhou 310018, China
| | - Su Zeng
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Hangzhou 310018, China
| | - Lushan Yu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Hangzhou 310018, China
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27
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Juvinao-Quintero DL, Starling AP, Cardenas A, Powe CE, Perron P, Bouchard L, Dabelea D, Hivert MF. Epigenome-wide association study of maternal hemoglobin A1c in pregnancy and cord blood DNA methylation. Epigenomics 2021; 13:203-218. [PMID: 33406918 DOI: 10.2217/epi-2020-0279] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Abstract
Background: Gestational hyperglycemia is associated with adverse perinatal outcomes and long-term offspring metabolic programming, likely through dysregulation of DNA methylation (DNAm). Materials & methods: We tested associations between maternal HbA1c and cord blood DNAm among 412 mother-child pairs in the genetics of glucose regulation in gestation and growth (Gen3G) and implemented Mendelian randomization to infer causality. We sought replication in an independent sample from Healthy Start. Results: Higher second trimester HbA1c levels were associated with lower DNAm at cg21645848 (p = 3.9 × 10-11) near URGCP. Mendelian randomization and replication analyses showed same direction of effect between HbA1c and DNAm at cg21645848, but did not reach statistical significance. Conclusion: We found that higher maternal glycemia reflected by HbA1c is associated with cord blood DNAm at URGCP, a gene related with inflammatory pathways.
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Affiliation(s)
- Diana L Juvinao-Quintero
- Department of Population Medicine, Harvard Pilgrim Health Care Institute, Harvard Medical School, Boston, MA 02215, USA
| | - Anne P Starling
- Department of Epidemiology & Lifecourse Epidemiology of Adiposity & Diabetes (LEAD) Center, Colorado School of Public Health, University of Colorado, Anschutz Medical Campus, CO 80045, USA
| | - Andres Cardenas
- Division of Environmental Health Sciences, School of Public Health & Center for Computational Biology, University of California, Berkeley, CA 94720-7360, USA
| | - Camille E Powe
- Diabetes Unit, Massachusetts General Hospital, Boston, MA 02114, USA.,Harvard Medical School, Boston, MA 02115, USA.,Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Patrice Perron
- Centre de Recherche du CHUS, Sherbrooke, QC J1H 5N4, CA.,Department of Medicine, Université de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
| | - Luigi Bouchard
- Centre de Recherche du CHUS, Sherbrooke, QC J1H 5N4, CA.,Department of Medical Biology, CIUSSS Saguenay-Lac-Saint-Jean, Hôpital Universitaire de Chicoutimi, Saguenay, QC G7H 5H6, Canada.,Department of Biochemistry & Functional Genomics, Université de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada
| | - Dana Dabelea
- Department of Epidemiology & Lifecourse Epidemiology of Adiposity & Diabetes (LEAD) Center, Colorado School of Public Health, University of Colorado, Anschutz Medical Campus, CO 80045, USA
| | - Marie-France Hivert
- Department of Population Medicine, Harvard Pilgrim Health Care Institute, Harvard Medical School, Boston, MA 02215, USA.,Diabetes Unit, Massachusetts General Hospital, Boston, MA 02114, USA
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28
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Ryan CP. "Epigenetic clocks": Theory and applications in human biology. Am J Hum Biol 2020; 33:e23488. [PMID: 32845048 DOI: 10.1002/ajhb.23488] [Citation(s) in RCA: 58] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2020] [Revised: 07/23/2020] [Accepted: 07/24/2020] [Indexed: 12/20/2022] Open
Abstract
All humans age, but how we age-and how fast-differs considerably from person to person. This deviation between apparent age and chronological age is often referred to as "biological age" (BA) and until recently robust tools for studying BA have been scarce. "Epigenetic clocks" are starting to change this. Epigenetic clocks use predictable changes in the epigenome, usually DNA methylation, to estimate chronological age with unprecedented accuracy. More importantly, deviations between epigenetic age and chronological age predict a broad range of health outcomes and mortality risks better than chronological age alone. Thus, epigenetic clocks appear to capture fundamental molecular processes tied to BA and can serve as powerful tools for studying health, development, and aging across the lifespan. In this article, I review epigenetic clocks, especially as they relate to key theoretical and applied issues in human biology. I first provide an overview of how epigenetic clocks are constructed and what we know about them. I then discuss emerging applications of particular relevance to human biologists-those related to reproduction, life-history, stress, and the environment. I conclude with an overview of the methods necessary for implementing epigenetic clocks, including considerations of study design, sample collection, and technical considerations for processing and interpreting epigenetic clocks. The goal of this review is to highlight some of the ways that epigenetic clocks can inform questions in human biology, and vice versa, and to provide human biologists with the foundational knowledge necessary to successfully incorporate epigenetic clocks into their research.
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Affiliation(s)
- Calen P Ryan
- Department of Anthropology, Northwestern University, Evanston, Illinois, USA
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29
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Chen H, Qin Q, Xu Z, Chen T, Yao X, Xu B, Sun X. DNA methylation data-based prognosis-subtype distinctions in patients with esophageal carcinoma by bioinformatic studies. J Cell Physiol 2020; 236:2126-2138. [PMID: 32830322 DOI: 10.1002/jcp.29999] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Accepted: 07/24/2020] [Indexed: 02/06/2023]
Abstract
Esophageal carcinoma (ESCA) is caused by the accumulation of genetic and epigenetic alterations in esophageal mucosa. Of note, the earliest and the most frequent molecular behavior in the complicated pathogenesis of ESCA is DNA methylation. In the present study, we downloaded data of 178 samples from The Cancer Genome Atlas (TCGA) database to explore specific DNA methylation sites that affect prognosis in ESCA patients. Consequently, we identified 1,098 CpGs that were significantly associated with patient prognosis. Hence, these CpGs were used for consensus clustering of the 178 samples into seven clusters. Specifically, the samples in each group were different in terms of age, gender, tumor stage, histological type, metastatic status, and patient prognosis. We further analyzed 1,224 genes in the corresponding promoter regions of the 1,098 methylation sites, and enriched these genes in biological pathways with close correlation to cellular metabolism, enzymatic synthesis, and mitochondrial autophagy. In addition, nine representative specific methylation sites were screened using the weighted gene coexpression network analysis. Finally, a prognostic prediction model for ESCA patients was built in both training and validation cohorts. In summary, our study revealed that classification based on specific DNA methylation sites could reflect ESCA heterogeneity and contribute to the improvement of individualized treatment and precise prognostic prediction.
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Affiliation(s)
- Hui Chen
- Department of Radiation Oncology, Nanjing Medical University First Affiliated Hospital, Jiangsu Province Hospital, Nanjing, Jiangsu, China
| | - Qin Qin
- Department of Radiation Oncology, Nanjing Medical University First Affiliated Hospital, Jiangsu Province Hospital, Nanjing, Jiangsu, China
| | - Zhipeng Xu
- Department of Urology, Affiliated Zhongda Hospital of Southeast University, Nanjing, Jiangsu, China
| | - Tingting Chen
- Department of Oncology, Yangzhou University Affiliated Northern Jiangsu People's Hospital, Yangzhou, Jiangsu, China
| | - Xijuan Yao
- Department of Radiation Oncology, Nanjing Medical University First Affiliated Hospital, Jiangsu Province Hospital, Nanjing, Jiangsu, China
| | - Bing Xu
- Department of Radiation Oncology, Nanjing Medical University First Affiliated Hospital, Jiangsu Province Hospital, Nanjing, Jiangsu, China
| | - Xinchen Sun
- Department of Radiation Oncology, Nanjing Medical University First Affiliated Hospital, Jiangsu Province Hospital, Nanjing, Jiangsu, China
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30
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Zindler T, Frieling H, Neyazi A, Bleich S, Friedel E. Simulating ComBat: how batch correction can lead to the systematic introduction of false positive results in DNA methylation microarray studies. BMC Bioinformatics 2020; 21:271. [PMID: 32605541 PMCID: PMC7328269 DOI: 10.1186/s12859-020-03559-6] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2020] [Accepted: 05/26/2020] [Indexed: 12/04/2022] Open
Abstract
Background Systematic technical effects—also called batch effects—are a considerable challenge when analyzing DNA methylation (DNAm) microarray data, because they can lead to false results when confounded with the variable of interest. Methods to correct these batch effects are error-prone, as previous findings have shown. Results Here, we demonstrate how using the R function ComBat to correct simulated Infinium HumanMethylation450 BeadChip (450 K) and Infinium MethylationEPIC BeadChip Kit (EPIC) DNAm data can lead to a large number of false positive results under certain conditions. We further provide a detailed assessment of the consequences for the highly relevant problem of p-value inflation with subsequent false positive findings after application of the frequently used ComBat method. Using ComBat to correct for batch effects in randomly generated samples produced alarming numbers of false discovery rate (FDR) and Bonferroni-corrected (BF) false positive results in unbalanced as well as in balanced sample distributions in terms of the relation between the outcome of interest variable and the technical position of the sample during the probe measurement. Both sample size and number of batch factors (e.g. number of chips) were systematically simulated to assess the probability of false positive findings. The effect of sample size was simulated using n = 48 up to n = 768 randomly generated samples. Increasing the number of corrected factors led to an exponential increase in the number of false positive signals. Increasing the number of samples reduced, but did not completely prevent, this effect. Conclusions Using the approach described, we demonstrate, that using ComBat for batch correction in DNAm data can lead to false positive results under certain conditions and sample distributions. Our results are thus contrary to previous publications, considering a balanced sample distribution as unproblematic when using ComBat. We do not claim completeness in terms of reporting all technical conditions and possible solutions of the occurring problems as we approach the problem from a clinician’s perspective and not from that of a computer scientist. With our approach of simulating data, we provide readers with a simple method to assess the probability of false positive findings in DNAm microarray data analysis pipelines.
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Affiliation(s)
- Tristan Zindler
- Department of Psychiatry, Social Psychiatry and Psychotherapy, Hannover Medical School, Hannover, Germany.
| | - Helge Frieling
- Department of Psychiatry, Social Psychiatry and Psychotherapy, Hannover Medical School, Hannover, Germany
| | - Alexandra Neyazi
- Department of Psychiatry, Social Psychiatry and Psychotherapy, Hannover Medical School, Hannover, Germany
| | - Stefan Bleich
- Department of Psychiatry, Social Psychiatry and Psychotherapy, Hannover Medical School, Hannover, Germany
| | - Eva Friedel
- Department of Psychiatry and Psychotherapy, Charité Campus Mitte (CCM), Charité-Universitätsmedizin Berlin, Berlin, Germany.,Berlin Institute of Health (BIH), 10178, Berlin, Germany
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Abstract
The remarkable success of cancer immunotherapies, especially the checkpoint blocking antibodies, in a subset of patients has reinvigorated the study of tumor-immune crosstalk and its role in heterogeneity of response. High-throughput sequencing and imaging technologies can help recapitulate various aspects of the tumor ecosystem. Computational approaches provide an arsenal of tools to efficiently analyze, quantify and integrate multiple parameters of tumor immunity mined from these diverse but complementary high-throughput datasets. This chapter describes numerous such computational approaches in tumor immunology that leverage high-throughput data from diverse sources (genomic, transcriptomics, epigenomics and digitized histopathology images) to systematically interrogate tumor immunity in context of its microenvironment, and to identify mechanisms that confer resistance or sensitivity to cancer therapies, in particular immunotherapy.
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Sala C, Di Lena P, Fernandes Durso D, Prodi A, Castellani G, Nardini C. Evaluation of pre-processing on the meta-analysis of DNA methylation data from the Illumina HumanMethylation450 BeadChip platform. PLoS One 2020; 15:e0229763. [PMID: 32155174 PMCID: PMC7064179 DOI: 10.1371/journal.pone.0229763] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2019] [Accepted: 02/13/2020] [Indexed: 01/06/2023] Open
Abstract
Introduction Meta-analysis is a powerful means for leveraging the hundreds of experiments being run worldwide into more statistically powerful analyses. This is also true for the analysis of omic data, including genome-wide DNA methylation. In particular, thousands of DNA methylation profiles generated using the Illumina 450k are stored in the publicly accessible Gene Expression Omnibus (GEO) repository. Often, however, the intensity values produced by the BeadChip (raw data) are not deposited, therefore only pre-processed values -obtained after computational manipulation- are available. Pre-processing is possibly different among studies and may then affect meta-analysis by introducing non-biological sources of variability. Material and methods To systematically investigate the effect of pre-processing on meta-analysis, we analysed four different collections of DNA methylation samples (datasets), each composed of two subsets, for which raw data from controls (i.e. healthy subjects) and cases (i.e. patients) are available. We pre-processed the data from each dataset with nine among the most common pipelines found in literature. Moreover, we evaluated the performance of regRCPqn, a modification of the RCP algorithm that aims to improve data consistency. For each combination of pre-processing (9 × 9), we first evaluated the between-sample variability among control subjects and, then, we identified genomic positions that are differentially methylated between cases and controls (differential analysis). Results and conclusion The pre-processing of DNA methylation data affects both the between-sample variability and the loci identified as differentially methylated, and the effects of pre-processing are strongly dataset-dependent. By contrast, application of our renormalization algorithm regRCPqn: (i) reduces variability and (ii) increases agreement between meta-analysed datasets, both critical components of data harmonization.
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Affiliation(s)
- Claudia Sala
- Department of Physics and Astronomy, University of Bologna, Bologna, Italy
- * E-mail: (CS); (CN)
| | - Pietro Di Lena
- Department of Computer Science and Engineering, University of Bologna, Bologna, Italy
| | - Danielle Fernandes Durso
- Division of Infectious Diseases and Immunology, University of Massachusetts Medical School, Worcester, Massachusetts, United States of America
| | - Andrea Prodi
- Smart Cities Living Lab, Institute of Organic Synthesis and Photoreactivity, CNR, Bologna, Italy
| | - Gastone Castellani
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Bologna, Italy
- Interdepartmental Center “L. Galvani”, University of Bologna, Bologna, Italy
| | - Christine Nardini
- Department of Laboratory Medicine, Karolinska Institutet, Stockholm, Sweden
- CNR IAC “Mauro Picone”, Roma, Italy
- Sol Group, Monza, Italy
- * E-mail: (CS); (CN)
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Li JB, Liu ZX, Zhang R, Ma SP, Lin T, Li YX, Yang SH, Zhang WC, Wang YP. Sp1 contributes to overexpression of stanniocalcin 2 through regulation of promoter activity in colon adenocarcinoma. World J Gastroenterol 2019; 25:2776-2787. [PMID: 31236000 PMCID: PMC6580349 DOI: 10.3748/wjg.v25.i22.2776] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/27/2019] [Revised: 04/22/2019] [Accepted: 04/29/2019] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Aberrant expression of stanniocalcin 2 (STC2) is implicated in colon adenocarcinoma (COAD). A previous study identified that STC2 functions as a tumor promoter to drive development of some cancers, but the role of its overexpression in the development of COAD remains unclear. AIM To evaluate the regulation mechanism of STC2 overexpression in COAD. METHODS The expression of STC2 in COAD was assessed by TCGA COAD database and GEO (GSE50760). Methylation level of the STC2 promoter was evaluated with beta value in UALCAN platform, and the correlation between STC2 expression and survival rate was investigated with TCGA COAD. Transcription binding site prediction was conducted by TRANSFAC and LASAGNA, and a luciferase reporter system was used to identify STC2 promoter activity in several cell lines, including HEK293T, NCM460, HT29, SW480, and HCT116. Western blotting was performed to evaluate the role of Sp1 on the expression of STC2. RESULTS The central finding of this work is that STC2 is overexpressed in COAD tissues and positively correlated with poor prognosis. Importantly, the binding site of the transcription factor Sp1 is widely located in the promoter region of STC2. A luciferase reporter system was successfully constructed to analyze the transcription activity of STC2, and knocking down the expression of Sp1 significantly inhibited the transcription activity of STC2. Furthermore, inhibition of Sp1 remarkably decreased protein levels of STC2. CONCLUSION Our data provide evidence that the transcription factor Sp1 is essential for the overexpression of STC2 in COAD through activation of promoter activity. Taken together, our finding provides new insights into the mechanism of oncogenic function of COAD by STC2.
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Affiliation(s)
- Ji-Bin Li
- Department of Colorectal Surgery, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang 110042, Liaoning Province, China
| | - Zhe-Xian Liu
- Department of Colorectal Surgery, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang 110042, Liaoning Province, China
| | - Rui Zhang
- Department of Colorectal Surgery, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang 110042, Liaoning Province, China
| | - Si-Ping Ma
- Department of Colorectal Surgery, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang 110042, Liaoning Province, China
| | - Tao Lin
- Department of Colorectal Surgery, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang 110042, Liaoning Province, China
| | - Yan-Xi Li
- Department of Colorectal Surgery, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang 110042, Liaoning Province, China
| | - Shi-Hua Yang
- Department of Colorectal Surgery, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang 110042, Liaoning Province, China
- China Medical University, Shenyang 110000, Liaoning Province, China
| | - Wan-Chuan Zhang
- Department of Colorectal Surgery, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang 110042, Liaoning Province, China
- China Medical University, Shenyang 110000, Liaoning Province, China
| | - Yong-Peng Wang
- Department of Colorectal Surgery, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang 110042, Liaoning Province, China
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