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Risha KS, Rasal KD, Reang D, Iquebal MA, Sonwane A, Brahmane M, Chaudhari A, Nagpure N. DNA Methylation Profiling in Genetically Selected Clarias magur (Hamilton, 1822) Provides Insights into the Epigenetic Regulation of Growth and Development. MARINE BIOTECHNOLOGY (NEW YORK, N.Y.) 2024; 26:776-789. [PMID: 39037491 DOI: 10.1007/s10126-024-10346-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/29/2024] [Accepted: 07/13/2024] [Indexed: 07/23/2024]
Abstract
DNA methylation is an epigenetic alteration that impacts gene expression without changing the DNA sequence affecting an organism's phenotype. This study utilized a reduced representation bisulfite sequencing (RRBS) approach to investigate the patterns of DNA methylation in genetically selected Clarias magur stocks. RRBS generated 249.22 million reads, with an average of 490,120 methylation sites detected in various parts of genes, including exons, introns, and intergenic regions. A total of 896 differentially methylated regions (DMRs) were identified; 356 and 540 were detected as hyper-methylated and hypo-methylated regions, respectively. The DMRs and their association with overlapping genes were explored using whole genome data of magur, which revealed 205 genes in exonic, 210 in intronic, and 480 in intergenic regions. The analysis identified the maximum number of genes enriched in biological processes such as RNA biosynthetic process, response to growth factors, nervous system development, neurogenesis, and anatomical structure morphogenesis. Differentially methylated genes (DMGs) such as myrip, mylk3, mafb, egr3, ndnf, meis2a, foxn3, bmp1a, plxna3, fgf6, sipa1l1, mcu, cnot8, trim55b, and myof were associated with growth and development. The selected DMGs were analyzed using real-time PCR, which showed altered mRNA expression levels. This work offers insights into the epigenetic mechanisms governing growth performance regulation in magur stocks. This work provides a valuable resource of epigenetic data that could be integrated into breeding programs to select high-performing individuals.
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Affiliation(s)
- K Shasti Risha
- Fish Genetics and Biotechnology, ICAR - Central Institute of Fisheries Education, Mumbai, Maharashtra, 400061, India
| | - Kiran D Rasal
- Fish Genetics and Biotechnology, ICAR - Central Institute of Fisheries Education, Mumbai, Maharashtra, 400061, India.
| | - Dhalongsaih Reang
- Fish Genetics and Biotechnology, ICAR - Central Institute of Fisheries Education, Mumbai, Maharashtra, 400061, India
| | - Mir Asif Iquebal
- Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Arvind Sonwane
- Fish Genetics and Biotechnology, ICAR - Central Institute of Fisheries Education, Mumbai, Maharashtra, 400061, India
| | - Manoj Brahmane
- Fish Genetics and Biotechnology, ICAR - Central Institute of Fisheries Education, Mumbai, Maharashtra, 400061, India
| | - Aparna Chaudhari
- Fish Genetics and Biotechnology, ICAR - Central Institute of Fisheries Education, Mumbai, Maharashtra, 400061, India
| | - Naresh Nagpure
- Fish Genetics and Biotechnology, ICAR - Central Institute of Fisheries Education, Mumbai, Maharashtra, 400061, India
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Schuurmans IK, Mulder RH, Baltramonaityte V, Lahtinen A, Qiuyu F, Rothmann LM, Staginnus M, Tuulari J, Burt SA, Buss C, Craig JM, Donald KA, Felix JF, Freeman TP, Grassi-Oliveira R, Huels A, Hyde LW, Jones SA, Karlsson H, Karlsson L, Koen N, Lawn W, Mitchell C, Monk CS, Mooney MA, Muetzel R, Nigg JT, Belangero SIN, Notterman D, O'Connor T, O'Donnell KJ, Pan PM, Paunio T, Ryabinin P, Saffery R, Salum GA, Seal M, Silk TJ, Stein DJ, Zar H, Walton E, Cecil CAM. Consortium Profile: The Methylation, Imaging and NeuroDevelopment (MIND) Consortium. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.06.23.24309353. [PMID: 38978656 PMCID: PMC11230303 DOI: 10.1101/2024.06.23.24309353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Epigenetic processes, such as DNA methylation, show potential as biological markers and mechanisms underlying gene-environment interplay in the prediction of mental health and other brain-based phenotypes. However, little is known about how peripheral epigenetic patterns relate to individual differences in the brain itself. An increasingly popular approach to address this is by combining epigenetic and neuroimaging data; yet, research in this area is almost entirely comprised of cross-sectional studies in adults. To bridge this gap, we established the Methylation, Imaging and NeuroDevelopment (MIND) Consortium, which aims to bring a developmental focus to the emerging field of Neuroimaging Epigenetics by (i) promoting collaborative, adequately powered developmental research via multi-cohort analyses; (ii) increasing scientific rigor through the establishment of shared pipelines and open science practices; and (iii) advancing our understanding of DNA methylation-brain dynamics at different developmental periods (from birth to emerging adulthood), by leveraging data from prospective, longitudinal pediatric studies. MIND currently integrates 15 cohorts worldwide, comprising (repeated) measures of DNA methylation in peripheral tissues (blood, buccal cells, and saliva) and neuroimaging by magnetic resonance imaging across up to five time points over a period of up to 21 years (Npooled DNAm = 11,299; Npooled neuroimaging = 10,133; Npooled combined = 4,914). By triangulating associations across multiple developmental time points and study types, we hope to generate new insights into the dynamic relationships between peripheral DNA methylation and the brain, and how these ultimately relate to neurodevelopmental and psychiatric phenotypes.
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Fan W, Xing Y, Yan S, Liu W, Ning J, Tian F, Wang X, Zhan Y, Luo L, Cao M, Huang J, Cai L. DUSP5 regulated by YTHDF1-mediated m6A modification promotes epithelial-mesenchymal transition and EGFR-TKI resistance via the TGF-β/Smad signaling pathway in lung adenocarcinoma. Cancer Cell Int 2024; 24:208. [PMID: 38872157 DOI: 10.1186/s12935-024-03382-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Accepted: 05/23/2024] [Indexed: 06/15/2024] Open
Abstract
BACKGROUND Lung adenocarcinoma (LUAD) patients have a dismal survival rate because of cancer metastasis and drug resistance. The study aims to identify the genes that concurrently modulate EMT, metastasis and EGFR-TKI resistance, and to investigate the underlying regulatory mechanisms. METHODS Cox regression and Kaplan-Meier analyses were applied to identify prognostic oncogenes in LUAD. Gene set enrichment analysis (GSEA) was used to indicate the biological functions of the gene. Wound-healing and Transwell assays were used to detect migratory and invasive ability. EGFR-TKI sensitivity was evaluated by assessing the proliferation, clonogenic survival and metastatic capability of cancer cells with treatment with gefitinib. Methylated RNA immunoprecipitation (MeRIP) and RNA immunoprecipitation (RIP) analyses established the level of m6A modification present on the target gene and the protein's capability to interact with RNA, respectively. Single-sample gene set enrichment (ssGSEA) algorithm used to investigate levels of immune cell infiltration. RESULTS Our study identified dual-specificity phosphatase 5 (DUSP5) as a novel and powerful predictor of adverse outcomes for LUAD by using public datasets. Functional enrichment analysis found that DUSP5 was positively enriched in EMT and transforming growth factor-beta (TGF-β) signaling pathway, a prevailing pathway involved in the induction of EMT. As expected, DUSP5 knockdown suppressed EMT via inhibiting the canonical TGF-β/Smad signaling pathway in in vitro experiments. Consistently, knockdown of DUSP5 was first found to inhibit migratory ability and invasiveness of LUAD cells in in vitro and prevent lung metastasis in in vivo. DUSP5 knockdown re-sensitized gefitinib-resistant LUAD cells to gefitinib, accompanying reversion of EMT progress. In LUAD tissue samples, we found 14 cytosine-phosphate-guanine (CpG) sites of DUSP5 that were negatively associated with DUSP5 gene expression. Importantly, 5'Azacytidine (AZA), an FDA-approved DNA methyltransferase inhibitor, restored DUSP5 expression. Moreover, RIP experiments confirmed that YTH N6-methyladenosine RNA binding protein 1 (YTHDF1), a m6A reader protein, could bind DUSP5 mRNA. YTHDF1 promoted DUSP5 expression and the malignant phenotype of LUAD cells. In addition, the DUSP5-derived genomic model revealed the two clusters with distinguishable immune features and tumor mutational burden (TMB). CONCLUSIONS Briefly, our study discovered DUSP5 which was regulated by epigenetic modification, might be a potential therapeutic target, especially in LUAD patients with acquired EGFR-TKI resistance.
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Affiliation(s)
- Weina Fan
- The Fourth Department of Medical Oncology, Harbin Medical University Cancer Hospital, Haping Road 150, Harbin, 150081, China
| | - Ying Xing
- The Fourth Department of Medical Oncology, Harbin Medical University Cancer Hospital, Haping Road 150, Harbin, 150081, China
| | - Shi Yan
- The Fourth Department of Medical Oncology, Harbin Medical University Cancer Hospital, Haping Road 150, Harbin, 150081, China
| | - Wei Liu
- The Fourth Department of Medical Oncology, Harbin Medical University Cancer Hospital, Haping Road 150, Harbin, 150081, China
| | - Jinfeng Ning
- Department of Thoracic Surgery, Harbin Medical University Cancer Hospital, Harbin, China
| | - Fanglin Tian
- The Fourth Department of Medical Oncology, Harbin Medical University Cancer Hospital, Haping Road 150, Harbin, 150081, China
| | - Xin Wang
- The Fourth Department of Medical Oncology, Harbin Medical University Cancer Hospital, Haping Road 150, Harbin, 150081, China
| | - Yuning Zhan
- The Fourth Department of Medical Oncology, Harbin Medical University Cancer Hospital, Haping Road 150, Harbin, 150081, China
| | - Lixin Luo
- The Fourth Department of Medical Oncology, Harbin Medical University Cancer Hospital, Haping Road 150, Harbin, 150081, China
| | - Mengru Cao
- The Fourth Department of Medical Oncology, Harbin Medical University Cancer Hospital, Haping Road 150, Harbin, 150081, China.
| | - Jian Huang
- The Fourth Department of Medical Oncology, Harbin Medical University Cancer Hospital, Haping Road 150, Harbin, 150081, China.
| | - Li Cai
- The Fourth Department of Medical Oncology, Harbin Medical University Cancer Hospital, Haping Road 150, Harbin, 150081, China.
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Davyson E, Shen X, Huider F, Adams M, Borges K, McCartney D, Barker L, Van Dongen J, Boomsma D, Weihs A, Grabe H, Kühn L, Teumer A, Völzke H, Zhu T, Kaprio J, Ollikainen M, David FS, Meinert S, Stein F, Forstner AJ, Dannlowski U, Kircher T, Tapuc A, Czamara D, Binder EB, Brückl T, Kwong A, Yousefi P, Wong C, Arseneault L, Fisher HL, Mill J, Cox S, Redmond P, Russ TC, van den Oord E, Aberg KA, Penninx B, Marioni RE, Wray NR, McIntosh AM. Antidepressant Exposure and DNA Methylation: Insights from a Methylome-Wide Association Study. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.01.24306640. [PMID: 38746357 PMCID: PMC11092700 DOI: 10.1101/2024.05.01.24306640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Importance Understanding antidepressant mechanisms could help design more effective and tolerated treatments. Objective Identify DNA methylation (DNAm) changes associated with antidepressant exposure. Design Case-control methylome-wide association studies (MWAS) of antidepressant exposure were performed from blood samples collected between 2006-2011 in Generation Scotland (GS). The summary statistics were tested for enrichment in specific tissues, gene ontologies and an independent MWAS in the Netherlands Study of Depression and Anxiety (NESDA). A methylation profile score (MPS) was derived and tested for its association with antidepressant exposure in eight independent cohorts, alongside prospective data from GS. Setting Cohorts; GS, NESDA, FTC, SHIP-Trend, FOR2107, LBC1936, MARS-UniDep, ALSPAC, E-Risk, and NTR. Participants Participants with DNAm data and self-report/prescription derived antidepressant exposure. Main Outcomes and Measures Whole-blood DNAm levels were assayed by the EPIC/450K Illumina array (9 studies, N exposed = 661, N unexposed = 9,575) alongside MBD-Seq in NESDA (N exposed = 398, N unexposed = 414). Antidepressant exposure was measured by self- report and/or antidepressant prescriptions. Results The self-report MWAS (N = 16,536, N exposed = 1,508, mean age = 48, 59% female) and the prescription-derived MWAS (N = 7,951, N exposed = 861, mean age = 47, 59% female), found hypermethylation at seven and four DNAm sites (p < 9.42x10 -8 ), respectively. The top locus was cg26277237 ( KANK1, p self-report = 9.3x10 -13 , p prescription = 6.1x10 -3 ). The self-report MWAS found a differentially methylated region, mapping to DGUOK-AS1 ( p adj = 5.0x10 -3 ) alongside significant enrichment for genes expressed in the amygdala, the "synaptic vesicle membrane" gene ontology and the top 1% of CpGs from the NESDA MWAS (OR = 1.39, p < 0.042). The MPS was associated with antidepressant exposure in meta-analysed data from external cohorts (N studies = 9, N = 10,236, N exposed = 661, f3 = 0.196, p < 1x10 -4 ). Conclusions and Relevance Antidepressant exposure is associated with changes in DNAm across different cohorts. Further investigation into these changes could inform on new targets for antidepressant treatments. 3 Key Points Question: Is antidepressant exposure associated with differential whole blood DNA methylation?Findings: In this methylome-wide association study of 16,536 adults across Scotland, antidepressant exposure was significantly associated with hypermethylation at CpGs mapping to KANK1 and DGUOK-AS1. A methylation profile score trained on this sample was significantly associated with antidepressant exposure (pooled f3 [95%CI]=0.196 [0.105, 0.288], p < 1x10 -4 ) in a meta-analysis of external datasets. Meaning: Antidepressant exposure is associated with hypermethylation at KANK1 and DGUOK-AS1 , which have roles in mitochondrial metabolism and neurite outgrowth. If replicated in future studies, targeting these genes could inform the design of more effective and better tolerated treatments for depression.
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Bell CG. Epigenomic insights into common human disease pathology. Cell Mol Life Sci 2024; 81:178. [PMID: 38602535 PMCID: PMC11008083 DOI: 10.1007/s00018-024-05206-2] [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: 01/19/2024] [Revised: 03/11/2024] [Accepted: 03/13/2024] [Indexed: 04/12/2024]
Abstract
The epigenome-the chemical modifications and chromatin-related packaging of the genome-enables the same genetic template to be activated or repressed in different cellular settings. This multi-layered mechanism facilitates cell-type specific function by setting the local sequence and 3D interactive activity level. Gene transcription is further modulated through the interplay with transcription factors and co-regulators. The human body requires this epigenomic apparatus to be precisely installed throughout development and then adequately maintained during the lifespan. The causal role of the epigenome in human pathology, beyond imprinting disorders and specific tumour suppressor genes, was further brought into the spotlight by large-scale sequencing projects identifying that mutations in epigenomic machinery genes could be critical drivers in both cancer and developmental disorders. Abrogation of this cellular mechanism is providing new molecular insights into pathogenesis. However, deciphering the full breadth and implications of these epigenomic changes remains challenging. Knowledge is accruing regarding disease mechanisms and clinical biomarkers, through pathogenically relevant and surrogate tissue analyses, respectively. Advances include consortia generated cell-type specific reference epigenomes, high-throughput DNA methylome association studies, as well as insights into ageing-related diseases from biological 'clocks' constructed by machine learning algorithms. Also, 3rd-generation sequencing is beginning to disentangle the complexity of genetic and DNA modification haplotypes. Cell-free DNA methylation as a cancer biomarker has clear clinical utility and further potential to assess organ damage across many disorders. Finally, molecular understanding of disease aetiology brings with it the opportunity for exact therapeutic alteration of the epigenome through CRISPR-activation or inhibition.
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Affiliation(s)
- Christopher G Bell
- William Harvey Research Institute, Barts & The London Faculty of Medicine, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK.
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Sun C, Cheng X, Xu J, Chen H, Tao J, Dong Y, Wei S, Chen R, Meng X, Ma Y, Tian H, Guo X, Bi S, Zhang C, Kang J, Zhang M, Lv H, Shang Z, Lv W, Zhang R, Jiang Y. A review of disease risk prediction methods and applications in the omics era. Proteomics 2024:e2300359. [PMID: 38522029 DOI: 10.1002/pmic.202300359] [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: 09/15/2023] [Revised: 03/08/2024] [Accepted: 03/12/2024] [Indexed: 03/25/2024]
Abstract
Risk prediction and disease prevention are the innovative care challenges of the 21st century. Apart from freeing the individual from the pain of disease, it will lead to low medical costs for society. Until very recently, risk assessments have ushered in a new era with the emergence of omics technologies, including genomics, transcriptomics, epigenomics, proteomics, and so on, which potentially advance the ability of biomarkers to aid prediction models. While risk prediction has achieved great success, there are still some challenges and limitations. We reviewed the general process of omics-based disease risk model construction and the applications in four typical diseases. Meanwhile, we highlighted the problems in current studies and explored the potential opportunities and challenges for future clinical practice.
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Affiliation(s)
- Chen Sun
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Xiangshu Cheng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Jing Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Haiyan Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Junxian Tao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Yu Dong
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Siyu Wei
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Rui Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Xin Meng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yingnan Ma
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Hongsheng Tian
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Xuying Guo
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Shuo Bi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Chen Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Jingxuan Kang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Mingming Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Hongchao Lv
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Zhenwei Shang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Wenhua Lv
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Ruijie Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yongshuai Jiang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
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Raffington L. Utilizing epigenetics to study the shared nature of development and biological aging across the lifespan. NPJ SCIENCE OF LEARNING 2024; 9:24. [PMID: 38509146 PMCID: PMC10954727 DOI: 10.1038/s41539-024-00239-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Accepted: 03/13/2024] [Indexed: 03/22/2024]
Abstract
Recently, biological aging has been quantified in DNA-methylation samples of older adults and applied as so-called "methylation profile scores" (MPSs) in separate target samples, including samples of children. This nascent research indicates that (1) biological aging can be quantified early in the life course, decades before the onset of aging-related disease, (2) is affected by common environmental predictors of childhood development, and (3) shows overlap with "developmental processes" (e.g., puberty). Because the MPSs were computed using algorithms developed in adults, these studies indicate a molecular link between childhood environments, development, and adult biological aging. Yet, if MPSs can be used to connect development and aging, previous research has only traveled one way, deriving MPSs developed in adults and applying them to samples of children. Researchers have not yet quantified epigenetic measures that reflect the pace of child development, and tested whether resulting MPSs are associated with physical and psychological aging. In this perspective I posit that combining measures of biological aging with new quantifications of child development has the power to address fundamental questions about life span: How are development and experience in childhood related to biological aging in adulthood? And what is aging?
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Affiliation(s)
- Laurel Raffington
- Max Planck Research Group Biosocial-Biology, Social Disparities, and Development, Max Planck Institute for Human Development, Lentzeallee 94, 14195, Berlin, Germany.
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Langford N, Fargeot L, Blanchet S. Spatial covariation between genetic and epigenetic diversity in wild plant and animal populations: a meta-analysis. J Exp Biol 2024; 227:jeb246009. [PMID: 38449323 DOI: 10.1242/jeb.246009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2024]
Abstract
Epigenetic variation may be crucial in understanding the structure of wild populations, thereby aiding in their management and conservation. However, the relationship between epigenetic and genetic variation remains poorly understood, especially in wild populations. To address this, we conducted a meta-analysis of studies that examined the genetic and epigenetic structures of wild plant and animal populations. We aimed to determine whether epigenetic variation is spatially independent of genetic variation in the wild and to highlight the conditions under which epigenetic variation might be informative. We show a significant positive correlation between genetic and epigenetic pairwise differentiation, indicating that in wild populations, epigenetic diversity is closely linked to genetic differentiation. The correlation was weaker for population pairs that were weakly differentiated genetically, suggesting that in such cases, epigenetic marks might be independent of genetic marks. Additionally, we found that global levels of genetic and epigenetic differentiation were similar across plant and animal populations, except when populations were weakly differentiated genetically. In such cases, epigenetic differentiation was either higher or lower than genetic differentiation. Our results suggest that epigenetic information is particularly relevant in populations that have recently diverged genetically or are connected by gene flow. Future studies should consider the genetic structure of populations when inferring the role of epigenetic diversity in local adaptation in wild populations. Furthermore, there is a need to identify the factors that sustain the links between genetic and epigenetic diversity to improve our understanding of the interplay between these two forms of variation in wild populations.
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Affiliation(s)
- Nadia Langford
- Centre National de la Recherche Scientifique (CNRS), Université Paul Sabatier (UPS); Station d'Ecologie Théorique et Expérimentale, UAR 2029, F-09200 Moulis, France
| | - Laura Fargeot
- Centre National de la Recherche Scientifique (CNRS), Université Paul Sabatier (UPS); Station d'Ecologie Théorique et Expérimentale, UAR 2029, F-09200 Moulis, France
| | - Simon Blanchet
- Centre National de la Recherche Scientifique (CNRS), Université Paul Sabatier (UPS); Station d'Ecologie Théorique et Expérimentale, UAR 2029, F-09200 Moulis, France
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9
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Wani A, Katrinli S, Zhao X, Daskalakis N, Zannas A, Aiello A, Baker D, Boks M, Brick L, Chen CY, Dalvie S, Fortier C, Geuze E, Hayes J, Kessler R, King A, Koen N, Liberzon I, Lori A, Luykx J, Maihofer A, Milberg W, Miller M, Mufford M, Nugent N, Rauch S, Ressler K, Risbrough V, Rutten B, Stein D, Stein M, Ursano R, Verfaellie M, Ware E, Wildman D, Wolf E, Nievergelt C, Logue M, Smith A, Uddin M, Vermetten E, Vinkers C. Blood-based DNA methylation and exposure risk scores predict PTSD with high accuracy in military and civilian cohorts. RESEARCH SQUARE 2024:rs.3.rs-3952163. [PMID: 38410438 PMCID: PMC10896387 DOI: 10.21203/rs.3.rs-3952163/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/28/2024]
Abstract
Background Incorporating genomic data into risk prediction has become an increasingly useful approach for rapid identification of individuals most at risk for complex disorders such as PTSD. Our goal was to develop and validate Methylation Risk Scores (MRS) using machine learning to distinguish individuals who have PTSD from those who do not. Methods Elastic Net was used to develop three risk score models using a discovery dataset (n = 1226; 314 cases, 912 controls) comprised of 5 diverse cohorts with available blood-derived DNA methylation (DNAm) measured on the Illumina Epic BeadChip. The first risk score, exposure and methylation risk score (eMRS) used cumulative and childhood trauma exposure and DNAm variables; the second, methylation-only risk score (MoRS) was based solely on DNAm data; the third, methylation-only risk scores with adjusted exposure variables (MoRSAE) utilized DNAm data adjusted for the two exposure variables. The potential of these risk scores to predict future PTSD based on pre-deployment data was also assessed. External validation of risk scores was conducted in four independent cohorts. Results The eMRS model showed the highest accuracy (92%), precision (91%), recall (87%), and f1-score (89%) in classifying PTSD using 3730 features. While still highly accurate, the MoRS (accuracy = 89%) using 3728 features and MoRSAE (accuracy = 84%) using 4150 features showed a decline in classification power. eMRS significantly predicted PTSD in one of the four independent cohorts, the BEAR cohort (beta = 0.6839, p-0.003), but not in the remaining three cohorts. Pre-deployment risk scores from all models (eMRS, beta = 1.92; MoRS, beta = 1.99 and MoRSAE, beta = 1.77) displayed a significant (p < 0.001) predictive power for post-deployment PTSD. Conclusion Results, especially those from the eMRS, reinforce earlier findings that methylation and trauma are interconnected and can be leveraged to increase the correct classification of those with vs. without PTSD. Moreover, our models can potentially be a valuable tool in predicting the future risk of developing PTSD. As more data become available, including additional molecular, environmental, and psychosocial factors in these scores may enhance their accuracy in predicting the condition and, relatedly, improve their performance in independent cohorts.
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Affiliation(s)
- Agaz Wani
- University of South Florida College of Public Health, Genomics Program
| | - Seyma Katrinli
- Emory University Department of Gynecology and Obstetrics
| | - Xiang Zhao
- Boston University School of Public Health
| | | | - Anthony Zannas
- University of North Carolina at Chapel Hill, Carolina Stress Initiative
| | - Allison Aiello
- Robert N Butler Columbia Aging Center, Columbia University
| | - Dewleen Baker
- University of California San Diego, Department of Psychiatry
| | - Marco Boks
- Brain Center University Medical Center Utrecht, Department of Psychiatry
| | | | | | | | | | - Elbert Geuze
- Netherlands Ministry of Defence, Brain Research and Innovation Centre
| | | | - Ronald Kessler
- Harvard Medical School, Department of Health Care Policy
| | - Anthony King
- The Ohio State University, College of Medicine, Institute for Behavioral Medicine Research
| | - Nastassja Koen
- University of Cape Town, Department of Psychiatry & Mental Health
| | - Israel Liberzon
- Texas A&M University College of Medicine, Department of Psychiatry and Behavioral Sciences
| | - Adriana Lori
- Emory University, Department of Psychiatry and Behavioral Sciences
| | - Jurjen Luykx
- UMC Utrecht Brain Center Rudolf Magnus, Department of Psychiatry
| | | | | | - Mark Miller
- Boston University School of Medicine, Psychiatry
| | | | - Nicole Nugent
- Alpert Brown Medical School, Department of Emergency Medicine
| | - Sheila Rauch
- Emory University, Department of Psychiatry & Behavioral Sciences
| | | | | | - Bart Rutten
- Maastricht Universitair Medisch Centrum, School for Mental Health and Neuroscience, Department of Psychiatry and Neuropsychology
| | - Dan Stein
- University of Cape Town, Department of Psychiatry & Mental Health
| | - Murrary Stein
- University of California San Diego, Department of Psychiatry
| | - Robert Ursano
- Uniformed Services University, Department of Psychiatry
| | | | - Erin Ware
- University of Michigan, Population Studies Center
| | - Derek Wildman
- University of South Florida College of Public Health, Genomics Program
| | - Erika Wolf
- VA Boston Healthcare System, National Center for PTSD
| | | | - Mark Logue
- Boston University School of Public Health
| | - Alicia Smith
- Emory University Department of Gynecology and Obstetrics
| | - Monica Uddin
- University of South Florida College of Public Health, Genomics Program
| | - Eric Vermetten
- Leiden University Medical Center, Department of Psychiatry
| | - Christiaan Vinkers
- Amsterdam Neuroscience, Mood, Anxiety, Psychosis, Sleep & Stress Program
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10
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Linares-Pineda TM, Fragoso-Bargas N, Picón MJ, Molina-Vega M, Jenum AK, Sletner L, Lee-Ødegård S, Opsahl JO, Moen GH, Qvigstad E, Prasad RB, Birkeland KI, Morcillo S, Sommer C. DNA methylation risk score for type 2 diabetes is associated with gestational diabetes. Cardiovasc Diabetol 2024; 23:68. [PMID: 38350951 PMCID: PMC10865541 DOI: 10.1186/s12933-024-02151-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Accepted: 02/02/2024] [Indexed: 02/15/2024] Open
Abstract
BACKGROUND Gestational diabetes mellitus (GDM) and type 2 diabetes mellitus (T2DM) share many pathophysiological factors including genetics, but whether epigenetic marks are shared is unknown. We aimed to test whether a DNA methylation risk score (MRS) for T2DM was associated with GDM across ancestry and GDM criteria. METHODS In two independent pregnancy cohorts, EPIPREG (n = 480) and EPIDG (n = 32), DNA methylation in peripheral blood leukocytes was measured at a gestational age of 28 ± 2. We constructed an MRS in EPIPREG and EPIDG based on CpG hits from a published epigenome-wide association study (EWAS) of T2DM. RESULTS With mixed models logistic regression of EPIPREG and EPIDG, MRS for T2DM was associated with GDM: odd ratio (OR)[95% CI]: 1.3 [1.1-1.8], P = 0.002 for the unadjusted model, and 1.4 [1.1-1.7], P = 0.00014 for a model adjusted by age, pre-pregnant BMI, family history of diabetes and smoking status. Also, we found 6 CpGs through a meta-analysis (cg14020176, cg22650271, cg14870271, cg27243685, cg06378491, cg25130381) associated with GDM, and some of their methylation quantitative loci (mQTLs) were related to T2DM and GDM. CONCLUSION For the first time, we show that DNA methylation marks for T2DM are also associated with GDM, suggesting shared epigenetic mechanisms between GDM and T2DM.
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Affiliation(s)
- Teresa M Linares-Pineda
- Department of Endocrinology and Nutrition, Instituto de Investigación Biomédica Málaga (IBIMA)- Plataforma Bionand, University Hospital Virgen de la Victoria, Málaga, Spain
- Department of Biochemistry and Molecular Biology 2, University of Granada, Granada, Spain
- Centre for Biomedical Research Network on Obesity Physiopathology and Nutrition (CIBEROBN), Madrid, Spain
| | - Nicolas Fragoso-Bargas
- Department of Endocrinology, Morbid Obesity and Preventive Medicine, Oslo University Hospital, Oslo, 0424, Norway
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
- Mohn Center for Diabetes Precision Medicine, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - María José Picón
- Department of Endocrinology and Nutrition, Instituto de Investigación Biomédica Málaga (IBIMA)- Plataforma Bionand, University Hospital Virgen de la Victoria, Málaga, Spain
| | - Maria Molina-Vega
- Department of Endocrinology and Nutrition, Instituto de Investigación Biomédica Málaga (IBIMA)- Plataforma Bionand, University Hospital Virgen de la Victoria, Málaga, Spain
| | - Anne Karen Jenum
- General Practice Research Unit (AFE), Department of General Practice, Institute of Health and Society, University of Oslo, Oslo, Norway
| | - Line Sletner
- Department of Pediatric and Adolescents Medicine, Akershus University Hospital, Lørenskog, Norway
| | - Sindre Lee-Ødegård
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Julia O Opsahl
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
- Haukeland University Hospital, Bergen, Norway
| | - Gunn-Helen Moen
- Department of Endocrinology, Morbid Obesity and Preventive Medicine, Oslo University Hospital, Oslo, 0424, Norway
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
- Institute of Molecular Bioscience, The University of Queensland, Brisbane, Australia
- K. G Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
- Frazer Institute, The University of Queensland, Woolloongabba, QLD, 4102, Australia
| | - Elisabeth Qvigstad
- Department of Endocrinology, Morbid Obesity and Preventive Medicine, Oslo University Hospital, Oslo, 0424, Norway
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Rashmi B Prasad
- Lund University Diabetes Centre, Malmo, Sweden
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Kåre I Birkeland
- Department of Endocrinology, Morbid Obesity and Preventive Medicine, Oslo University Hospital, Oslo, 0424, Norway
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Sonsoles Morcillo
- Department of Endocrinology and Nutrition, Instituto de Investigación Biomédica Málaga (IBIMA)- Plataforma Bionand, University Hospital Virgen de la Victoria, Málaga, Spain
- Centre for Biomedical Research Network on Obesity Physiopathology and Nutrition (CIBEROBN), Madrid, Spain
| | - Christine Sommer
- Department of Endocrinology, Morbid Obesity and Preventive Medicine, Oslo University Hospital, Oslo, 0424, Norway.
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11
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Yan W, Tan L, Mengshan L, Weihong Z, Sheng S, Jun W, Fu-An W. Time series-based hybrid ensemble learning model with multivariate multidimensional feature coding for DNA methylation prediction. BMC Genomics 2023; 24:758. [PMID: 38082253 PMCID: PMC10712061 DOI: 10.1186/s12864-023-09866-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 12/02/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND DNA methylation is a form of epigenetic modification that impacts gene expression without modifying the DNA sequence, thereby exerting control over gene function and cellular development. The prediction of DNA methylation is vital for understanding and exploring gene regulatory mechanisms. Currently, machine learning algorithms are primarily used for model construction. However, several challenges remain to be addressed, including limited prediction accuracy, constrained generalization capability, and insufficient learning capacity. RESULTS In response to the aforementioned challenges, this paper leverages the similarities between DNA sequences and time series to introduce a time series-based hybrid ensemble learning model, called Multi2-Con-CAPSO-LSTM. The model utilizes multivariate and multidimensional encoding approach, combining three types of time series encodings with three kinds of genetic feature encodings, resulting in a total of nine types of feature encoding matrices. Convolutional Neural Networks are utilized to extract features from DNA sequences, including temporal, positional, physicochemical, and genetic information, thereby creating a comprehensive feature matrix. The Long Short-Term Memory model is then optimized using the Chaotic Accelerated Particle Swarm Optimization algorithm for predicting DNA methylation. CONCLUSIONS Through cross-validation experiments conducted on 17 species involving three types of DNA methylation (6 mA, 5hmC, and 4mC), the results demonstrate the robust predictive capabilities of the Multi2-Con-CAPSO-LSTM model in DNA methylation prediction across various types and species. Compared with other benchmark models, the Multi2-Con-CAPSO-LSTM model demonstrates significant advantages in sensitivity, specificity, accuracy, and correlation. The model proposed in this paper provides valuable insights and inspiration across various disciplines, including sequence alignment, genetic evolution, time series analysis, and structure-activity relationships.
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Affiliation(s)
- Wu Yan
- School of Biotechnology, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu, 212018, China.
- School of Mathematics and Computer Science, Gannan Normal University, Ganzhou, Jiangxi, 341000, China.
- Sericultural Research Institute, Chinese Academy of Agricultural Sciences, Zhenjiang, Jiangsu, 212018, China.
| | - Li Tan
- College of Physics and Electronic Information, Gannan Normal University, Ganzhou, Jiangxi, 341000, China
| | - Li Mengshan
- College of Physics and Electronic Information, Gannan Normal University, Ganzhou, Jiangxi, 341000, China.
| | - Zhou Weihong
- School of Biotechnology, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu, 212018, China
- Sericultural Research Institute, Chinese Academy of Agricultural Sciences, Zhenjiang, Jiangsu, 212018, China
| | - Sheng Sheng
- School of Biotechnology, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu, 212018, China
- Sericultural Research Institute, Chinese Academy of Agricultural Sciences, Zhenjiang, Jiangsu, 212018, China
| | - Wang Jun
- School of Biotechnology, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu, 212018, China
- Sericultural Research Institute, Chinese Academy of Agricultural Sciences, Zhenjiang, Jiangsu, 212018, China
| | - Wu Fu-An
- School of Biotechnology, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu, 212018, China.
- Sericultural Research Institute, Chinese Academy of Agricultural Sciences, Zhenjiang, Jiangsu, 212018, China.
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12
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García-Álvarez NC, Riezu-Boj JI, Martínez JA, García-Calzón S, Milagro FI. A Predictive Tool Based on DNA Methylation Data for Personalized Weight Loss through Different Dietary Strategies: A Pilot Study. Nutrients 2023; 15:5023. [PMID: 38140282 PMCID: PMC10746100 DOI: 10.3390/nu15245023] [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: 10/26/2023] [Revised: 11/28/2023] [Accepted: 12/02/2023] [Indexed: 12/24/2023] Open
Abstract
BACKGROUND AND AIMS Obesity is a public health problem. The usual treatment is a reduction in calorie intake and an increase in energy expenditure, but not all individuals respond equally to these treatments. Epigenetics could be a factor that contributes to this heterogeneity. The aim of this research was to determine the association between DNA methylation at baseline and the percentage of BMI loss (%BMIL) after two dietary interventions, in order to design a prediction model to evaluate %BMIL based on methylation data. METHODS AND RESULTS Spanish participants with overweight or obesity (n = 306) were randomly assigned to two lifestyle interventions with hypocaloric diets: one moderately high in protein (MHP) and the other low in fat (LF) for 4 months (Obekit study; ClinicalTrials.gov ID: NCT02737267). Basal DNA methylation was analyzed in white blood cells using the Infinium MethylationEPIC array. After identifying those methylation sites associated with %BMIL (p < 0.05 and SD > 0.1), two weighted methylation sub-scores were constructed for each diet: 15 CpGs were used for the MHP diet and 11 CpGs for the LF diet. Afterwards, a total methylation score was made by subtracting the previous sub-scores. These data were used to design a prediction model for %BMIL through a linear mixed effect model with the interaction between diet and total score. CONCLUSION Overall, DNA methylation predicts the %BMIL of two 4-month hypocaloric diets and was able to determine which type of diet is the most appropriate for each individual. The results of this pioneer study confirm that epigenetic biomarkers may be further used for precision nutrition and the design of personalized dietary strategies against obesity.
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Affiliation(s)
- Nereyda Carolina García-Álvarez
- Center for Nutrition Research, Department of Nutrition, Food Science and Physiology, Faculty of Pharmacy and Nutrition, University of Navarra, 31008 Pamplona, Spain; (N.C.G.-Á.); (J.I.R.-B.); (J.A.M.); (S.G.-C.)
| | - José Ignacio Riezu-Boj
- Center for Nutrition Research, Department of Nutrition, Food Science and Physiology, Faculty of Pharmacy and Nutrition, University of Navarra, 31008 Pamplona, Spain; (N.C.G.-Á.); (J.I.R.-B.); (J.A.M.); (S.G.-C.)
- Navarra Institute for Health Research (IdiSNA), 31008 Pamplona, Spain
| | - J. Alfredo Martínez
- Center for Nutrition Research, Department of Nutrition, Food Science and Physiology, Faculty of Pharmacy and Nutrition, University of Navarra, 31008 Pamplona, Spain; (N.C.G.-Á.); (J.I.R.-B.); (J.A.M.); (S.G.-C.)
| | - Sonia García-Calzón
- Center for Nutrition Research, Department of Nutrition, Food Science and Physiology, Faculty of Pharmacy and Nutrition, University of Navarra, 31008 Pamplona, Spain; (N.C.G.-Á.); (J.I.R.-B.); (J.A.M.); (S.G.-C.)
- Navarra Institute for Health Research (IdiSNA), 31008 Pamplona, Spain
| | - Fermín I. Milagro
- Center for Nutrition Research, Department of Nutrition, Food Science and Physiology, Faculty of Pharmacy and Nutrition, University of Navarra, 31008 Pamplona, Spain; (N.C.G.-Á.); (J.I.R.-B.); (J.A.M.); (S.G.-C.)
- Navarra Institute for Health Research (IdiSNA), 31008 Pamplona, Spain
- Centro de Investigación Biomédica en Red de la Fisiopatología de la Obesidad y la Nutrición (CIBERobn), Carlos III Health Institute, 28029 Madrid, Spain
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13
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Lu X, Vano YA, Su X, Helleux A, Lindner V, Mouawad R, Spano JP, Rouprêt M, Compérat E, Verkarre V, Sun CM, Bennamoun M, Lang H, Barthelemy P, Cheng W, Xu L, Davidson I, Yan F, Fridman WH, Sautes-Fridman C, Oudard S, Malouf GG. Silencing of genes by promoter hypermethylation shapes tumor microenvironment and resistance to immunotherapy in clear-cell renal cell carcinomas. Cell Rep Med 2023; 4:101287. [PMID: 37967556 PMCID: PMC10694769 DOI: 10.1016/j.xcrm.2023.101287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 07/21/2023] [Accepted: 10/19/2023] [Indexed: 11/17/2023]
Abstract
The efficacy of immune checkpoint inhibitors varies in clear-cell renal cell carcinoma (ccRCC), with notable primary resistance among patients. Here, we integrate epigenetic (DNA methylation) and transcriptome data to identify a ccRCC subtype characterized by cancer-specific promoter hypermethylation and epigenetic silencing of Polycomb targets. We develop and validate an index of methylation-based epigenetic silencing (iMES) that predicts primary resistance to immune checkpoint inhibition (ICI) in the BIONIKK trial. High iMES is associated with VEGF pathway silencing, endothelial cell depletion, immune activation/suppression, EZH2 activation, BAP1/SETD2 deficiency, and resistance to ICI. Combination therapy with hypomethylating agents or tyrosine kinase inhibitors may benefit patients with high iMES. Intriguingly, tumors with low iMES exhibit increased endothelial cells and improved ICI response, suggesting the importance of angiogenesis in ICI treatment. We also develop a transcriptome-based analogous system for extended applicability of iMES. Our study underscores the interplay between epigenetic alterations and tumor microenvironment in determining immunotherapy response.
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Affiliation(s)
- Xiaofan Lu
- Department of Cancer and Functional Genomics, Institute of Genetics and Molecular and Cellular Biology, CNRS/INSERM/UNISTRA, 67400 Illkirch, France
| | - Yann-Alexandre Vano
- Department of Medical Oncology, Hôpital Européen Georges Pompidou, Institut du Cancer Paris CARPEM, AP-HP, Université Paris Cité, Paris, France; Centre de Recherche Cordeliers, INSERM 1138, Université de Paris Cité, Sorbonne Université, Equipe labellisée Ligue contre le Cancer, 75006 Paris, France
| | - Xiaoping Su
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Alexandra Helleux
- Department of Cancer and Functional Genomics, Institute of Genetics and Molecular and Cellular Biology, CNRS/INSERM/UNISTRA, 67400 Illkirch, France
| | - Véronique Lindner
- Department of Pathology, Strasbourg University Hospital, Strasbourg, France
| | - Roger Mouawad
- Department of Medical Oncology, Sorbonne University, AP-HP, Hôpital Pitié-Salpêtrière, Paris, France
| | - Jean-Philippe Spano
- Department of Medical Oncology, Sorbonne University, AP-HP, Hôpital Pitié-Salpêtrière, Paris, France
| | - Morgan Rouprêt
- Sorbonne University, GRC 5 P, UKredictive Onco-Uro, AP-HP, Urology, Pitié-Salpêtrière Hospital, 75013 Paris, France
| | - Eva Compérat
- Department of Pathology, Sorbonne University, AP-HP, Hôpital Tenon, Paris, France
| | - Virginie Verkarre
- Department of Pathology, Hôpital Européen Georges Pompidou, Institut du Cancer Paris CARPEM, AP-HP, Université Paris Cité, Paris, France
| | - Cheng-Ming Sun
- Centre de Recherche Cordeliers, INSERM 1138, Université de Paris Cité, Sorbonne Université, Equipe labellisée Ligue contre le Cancer, 75006 Paris, France
| | - Mostefa Bennamoun
- Department of Medical Oncology, Institut Mutualiste Montsouris, Paris, France
| | - Hervé Lang
- Department of Urology, Strasbourg University Hospital, Strasbourg, France
| | - Philippe Barthelemy
- Department of Medical Oncology, Strasbourg University, Institut de Cancérologie de Strasbourg, Strasbourg, France
| | - Wenxuan Cheng
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, P.R. China
| | - Li Xu
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, P.R. China
| | - Irwin Davidson
- Department of Cancer and Functional Genomics, Institute of Genetics and Molecular and Cellular Biology, CNRS/INSERM/UNISTRA, 67400 Illkirch, France
| | - Fangrong Yan
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, P.R. China
| | - Wolf Hervé Fridman
- Centre de Recherche Cordeliers, INSERM 1138, Université de Paris Cité, Sorbonne Université, Equipe labellisée Ligue contre le Cancer, 75006 Paris, France
| | - Catherine Sautes-Fridman
- Centre de Recherche Cordeliers, INSERM 1138, Université de Paris Cité, Sorbonne Université, Equipe labellisée Ligue contre le Cancer, 75006 Paris, France
| | - Stéphane Oudard
- Centre de Recherche Cordeliers, INSERM 1138, Université de Paris Cité, Sorbonne Université, Equipe labellisée Ligue contre le Cancer, 75006 Paris, France
| | - Gabriel G Malouf
- Department of Cancer and Functional Genomics, Institute of Genetics and Molecular and Cellular Biology, CNRS/INSERM/UNISTRA, 67400 Illkirch, France; Department of Medical Oncology, Strasbourg University, Institut de Cancérologie de Strasbourg, Strasbourg, France.
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14
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Salvioli S, Basile MS, Bencivenga L, Carrino S, Conte M, Damanti S, De Lorenzo R, Fiorenzato E, Gialluisi A, Ingannato A, Antonini A, Baldini N, Capri M, Cenci S, Iacoviello L, Nacmias B, Olivieri F, Rengo G, Querini PR, Lattanzio F. Biomarkers of aging in frailty and age-associated disorders: State of the art and future perspective. Ageing Res Rev 2023; 91:102044. [PMID: 37647997 DOI: 10.1016/j.arr.2023.102044] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 08/24/2023] [Accepted: 08/25/2023] [Indexed: 09/01/2023]
Abstract
According to the Geroscience concept that organismal aging and age-associated diseases share the same basic molecular mechanisms, the identification of biomarkers of age that can efficiently classify people as biologically older (or younger) than their chronological (i.e. calendar) age is becoming of paramount importance. These people will be in fact at higher (or lower) risk for many different age-associated diseases, including cardiovascular diseases, neurodegeneration, cancer, etc. In turn, patients suffering from these diseases are biologically older than healthy age-matched individuals. Many biomarkers that correlate with age have been described so far. The aim of the present review is to discuss the usefulness of some of these biomarkers (especially soluble, circulating ones) in order to identify frail patients, possibly before the appearance of clinical symptoms, as well as patients at risk for age-associated diseases. An overview of selected biomarkers will be discussed in this regard, in particular we will focus on biomarkers related to metabolic stress response, inflammation, and cell death (in particular in neurodegeneration), all phenomena connected to inflammaging (chronic, low-grade, age-associated inflammation). In the second part of the review, next-generation markers such as extracellular vesicles and their cargos, epigenetic markers and gut microbiota composition, will be discussed. Since recent progresses in omics techniques have allowed an exponential increase in the production of laboratory data also in the field of biomarkers of age, making it difficult to extract biological meaning from the huge mass of available data, Artificial Intelligence (AI) approaches will be discussed as an increasingly important strategy for extracting knowledge from raw data and providing practitioners with actionable information to treat patients.
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Affiliation(s)
- Stefano Salvioli
- Department of Medical and Surgical Science, University of Bologna, Bologna, Italy; IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy.
| | | | - Leonardo Bencivenga
- Department of Translational Medical Sciences, University of Naples Federico II, Napoli, Italy
| | - Sara Carrino
- Department of Medical and Surgical Science, University of Bologna, Bologna, Italy
| | - Maria Conte
- Department of Medical and Surgical Science, University of Bologna, Bologna, Italy
| | - Sarah Damanti
- IRCCS Ospedale San Raffaele and Vita-Salute San Raffaele University, Milano, Italy
| | - Rebecca De Lorenzo
- IRCCS Ospedale San Raffaele and Vita-Salute San Raffaele University, Milano, Italy
| | - Eleonora Fiorenzato
- Parkinson's Disease and Movement Disorders Unit, Center for Rare Neurological Diseases (ERN-RND), Department of Neurosciences, University of Padova, Padova, Italy
| | - Alessandro Gialluisi
- Department of Epidemiology and Prevention, IRCCS NEUROMED, Pozzilli, Italy; EPIMED Research Center, Department of Medicine and Surgery, University of Insubria, Varese, Italy
| | - Assunta Ingannato
- Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Florence, Italy; IRCCS Fondazione Don Carlo Gnocchi, Florence, Italy
| | - Angelo Antonini
- Parkinson's Disease and Movement Disorders Unit, Center for Rare Neurological Diseases (ERN-RND), Department of Neurosciences, University of Padova, Padova, Italy; Center for Neurodegenerative Disease Research (CESNE), Department of Neurosciences, University of Padova, Padova, Italy
| | - Nicola Baldini
- IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy; Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Miriam Capri
- Department of Medical and Surgical Science, University of Bologna, Bologna, Italy
| | - Simone Cenci
- IRCCS Ospedale San Raffaele and Vita-Salute San Raffaele University, Milano, Italy
| | - Licia Iacoviello
- Department of Epidemiology and Prevention, IRCCS NEUROMED, Pozzilli, Italy; EPIMED Research Center, Department of Medicine and Surgery, University of Insubria, Varese, Italy
| | - Benedetta Nacmias
- Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Florence, Italy; IRCCS Fondazione Don Carlo Gnocchi, Florence, Italy
| | - Fabiola Olivieri
- Department of Clinical and Molecular Sciences, Università Politecnica Delle Marche, Ancona, Italy; Clinic of Laboratory and Precision Medicine, IRCCS INRCA, Ancona, Italy
| | - Giuseppe Rengo
- Department of Translational Medical Sciences, University of Naples Federico II, Napoli, Italy; Istituti Clinici Scientifici Maugeri IRCCS, Scientific Institute of Telese Terme, Telese Terme, Italy
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