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Cited by 155Epigenetics and DNA MethylationGenetics, Aging, and Longevity in Model OrganismsGenetic Syndromes and Imprinting

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Age-related DNA methylation changes are sex-specific: a comprehensive assessment

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Yusipov, Igor, Bacalini, Maria Giulia, Kalyakulina, Alena, Krivonosov, Mikhail, Pirazzini, Chiara, Gensous, Noémie, Ravaioli, Francesco, Milazzo, Maddalena, Giuliani, Cristina, Vedunova, Maria, Fiorito, Giovanni, Gagliardi, Amedeo, Polidoro, Silvia, Garagnani, Paolo, Ivanchenko, Mikhail, Franceschi, Claudio·2020·Aging
DOI 10.18632/aging.202251PMC PMC7762479
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Abstract

Abstract In humans, females live longer than males but experience a worse longevity, as genome-wide autosomal DNA methylation differences between males and females have been reported. So far, few studies have investigated if DNA methylation is differently affected by aging in males and females. We performed a meta-analysis of 4 large whole blood datasets, comparing 4 aspects of epigenetic age-dependent remodeling between the two sexes: differential methylation, variability, epimutations and entropy. We reported that a large fraction (43%) of sex-associated probes undergoes age-associated DNA methylation changes, and that a limited number of probes shows age-by-sex interaction. We experimentally validated 2 regions mapping in FIGN and PRR4 genes, and showed sex-specific deviations of their methylation patterns in models of decelerated (centenarians) and accelerated (Down syndrome) aging. While we did not find sex differences in the age-associated increase in epimutations and in entropy, we showed that the number of probes showing age-related increase in methylation variability is 15 times higher in males compared to females. Our results can offer new epigenetic tools to study the interaction between aging and sex and can pave the way to the identification of molecular triggers of sex differences in longevity and age-related diseases prevalence.

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Figure 1 Identification of sex- and age-associated differentially methylated positions (saDMPs). (A) The procedure used to identify saDMPs and snaDMPs. (B) Scheme of the number of sDMPs, aDMPs and saDMPs, divided according to the direction of methylation changes respect to sex (hyper- or hypo-methylated in males compared to females) and age (hyper- or hypo-methylated with increasing age). (C) Scatter plots of a selection of saDMPs: cg01620164 is hypomethylated in males and undergoes age-associated hypomethylation; cg03890691 is hypomethylated in males and undergoes age-associated hypermethylation; cg07128102 is hypermethylated in males and undergoes age-associated hypomethylation; cg04628369 is hypermethylated in males and undergoes age-associated hypermethylation.
Figure 1 Identification of sex- and age-associated differentially methylated positions (saDMPs). (A) The procedure used to identify saDMPs and snaDMPs. (B) Scheme of the number of sDMPs, aDMPs and saDMPs, divided according to the direction of methylation changes respect to sex (hyper- or hypo-methylated in males compared to females) and age (hyper- or hypo-methylated with increasing age). (C) Scatter plots of a selection of saDMPs: cg01620164 is hypomethylated in males and undergoes age-associated hypomethylation; cg03890691 is hypomethylated in males and undergoes age-associated hypermethylation; cg07128102 is hypermethylated in males and undergoes age-associated hypomethylation; cg04628369 is hypermethylated in males and undergoes age-associated hypermethylation.CC BY · PubMed Central
Figure 2 Validation of FIGN and PRR4 loci by EpiTYPER. (A) Methylation of CpG unit 9 in FIGN amplicon vs age. (B) Methylation of CpG unit 3 in PRR4 amplicon vs age. (A and B) For each CpG unit, DNA methylation in controls (general population), centenarians, centenarian's offspring and persons with Down syndrome is reported vs the age of the subjects. Males are in blue, females are in red. Linear regression between DNA methylation and age was calculated separately for males and females in control subjects and was reported in each plot. (C) Boxplots of DNA methylation of CpG unit 9 in FIGN amplicon in aged controls and centenarians. (D) Boxplots of DNA methylation of CpG unit 3 in PRR4 amplicon in healthy controls and persons with Down syndrome.
Figure 2 Validation of FIGN and PRR4 loci by EpiTYPER. (A) Methylation of CpG unit 9 in FIGN amplicon vs age. (B) Methylation of CpG unit 3 in PRR4 amplicon vs age. (A and B) For each CpG unit, DNA methylation in controls (general population), centenarians, centenarian's offspring and persons with Down syndrome is reported vs the age of the subjects. Males are in blue, females are in red. Linear regression between DNA methylation and age was calculated separately for males and females in control subjects and was reported in each plot. (C) Boxplots of DNA methylation of CpG unit 9 in FIGN amplicon in aged controls and centenarians. (D) Boxplots of DNA methylation of CpG unit 3 in PRR4 amplicon in healthy controls and persons with Down syndrome.CC BY · PubMed Central
Figure 3 Probability density function (PDF) of standard deviation values calculated in the GSE87571 dataset for 3 age classes, considering males and females together (A) or separated (B).
Figure 3 Probability density function (PDF) of standard deviation values calculated in the GSE87571 dataset for 3 age classes, considering males and females together (A) or separated (B).CC BY · PubMed Central
Figure 4 Identification of sex-specific age-associated variably methylated positions (saVMPs) (A) The approach used to identify saVMPs. (B–E) Some examples of saVMPs showing age-associated increase in variability in males (B, C), age-associated decrease in variability in males (D) or age-associated increase in variability in females (E). x axis corresponds to age of subjects, y axis to methylation levels.
Figure 4 Identification of sex-specific age-associated variably methylated positions (saVMPs) (A) The approach used to identify saVMPs. (B–E) Some examples of saVMPs showing age-associated increase in variability in males (B, C), age-associated decrease in variability in males (D) or age-associated increase in variability in females (E). x axis corresponds to age of subjects, y axis to methylation levels.CC BY · PubMed Central
Figure 5 (A) Number of epimutations (log scale) in dependence on age in females (red) and males (blue). (B) Shannon entropy for 4 considered datasets: GSE40279, GSE87571, EPIC, GSE55763.
Figure 5 (A) Number of epimutations (log scale) in dependence on age in females (red) and males (blue). (B) Shannon entropy for 4 considered datasets: GSE40279, GSE87571, EPIC, GSE55763.CC BY · PubMed Central

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  1. Weisberg, 1983, Biometrika, 70, 1, 10.2307/2335938 · DOI

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Authors
Yusipov, I., Bacalini, M. G., Kalyakulina, A., Krivonosov, M., Pirazzini, C., Gensous, N., Ravaioli, F., Milazzo, M., Giuliani, C., Vedunova, M., Fiorito, G., Gagliardi, A., Polidoro, S., Garagnani, P., Ivanchenko, M., & Franceschi, C.
Affiliations
  • N. I. Lobachevsky State University of Nizhny Novgorod (RU)
  • Istituto delle Scienze Neurologiche di Bologna (IT)
  • University of Bologna (IT)
  • University of Oxford (GB)
  • University of Sassari (IT)
  • Italian institute for Genomic Medicine (IT)
  • Karolinska University Hospital (SE)
Corresponding author
Bacalini, Maria Giulia
ORCID
Venue
Aging
Publisher
Impact Journals, LLC
Published
2020-12-03
Volume
12
Issue
23
Pages
24057-24080
ISSN
1945-4589 (ISSN-L)
DOI
10.18632/aging.202251
PMC
PMC7762479
OpenAlex
W3108393015
Cited by
148
References
69
Citations by year
2026: 13 · 2025: 27 · 2024: 37 · 2023: 19 · 2022: 41 · 2021: 11
FWCI
1.84
Type
journal article
Language
en
Open access
Locations
  • Aging (publishedVersion)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) (submittedVersion, cc-by)
  • UnissResearch (Università degli Studi di Sassari) (submittedVersion)
Funders
  • EU Joint Programme – Neurodegenerative Disease Research
  • European Commission (H2020, 634821)
  • Ministry of Education and Science of the Russian Federation (075-15-2019-871)
  • Horizon 2020 Framework Programme (634821)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Aging › Genetics, Aging, and Longevity in Model Organisms
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Syndromes and Imprinting
Keywords
DNA methylation, sex differences, epigenetics, centenarians, Down syndrome, epimutations, longevity, age-related diseases
MeSH terms
ATPases Associated with Diverse Cellular Activities, Adolescent, Adult, Age Factors, Aged, Aged, 80 and over, Aging, Down Syndrome, Female, Humans, Longevity, Male, Microtubule-Associated Proteins, Middle Aged, Sex Factors, CpG Islands, DNA Methylation, Databases, Genetic, Epigenesis, Genetic, Proline-Rich Protein Domains, Young Adult

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