scholarly journals Epigenetic clock and DNA methylation analysis of porcine models of aging and obesity

GeroScience ◽  
2021 ◽  
Author(s):  
Kyle M. Schachtschneider ◽  
Lawrence B. Schook ◽  
Jennifer J. Meudt ◽  
Dhanansayan Shanmuganayagam ◽  
Joseph A. Zoller ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Tissue Type ◽  
Epigenetic Clock ◽  
Miniature Swine ◽  
Large White ◽  
Methylation Array ◽  
Domestic Pigs ◽  
Kidney Liver ◽  
Crossbred Pigs

AbstractDNA-methylation profiles have been used successfully to develop highly accurate biomarkers of age, epigenetic clocks, for many species. Using a custom methylation array, we generated DNA methylation data from n = 238 porcine tissues including blood, bladder, frontal cortex, kidney, liver, and lung, from domestic pigs (Sus scrofa domesticus) and minipigs (Wisconsin Miniature Swine™). Samples used in this study originated from Large White X Landrace crossbred pigs, Large White X Minnesota minipig crossbred pigs, and Wisconsin Miniature Swine™. We present 4 epigenetic clocks for pigs that are distinguished by their compatibility with tissue type (pan-tissue and blood clock) and species (pig and human). Two dual-species human-pig pan-tissue clocks accurately measure chronological age and relative age, respectively. We also characterized CpGs that differ between minipigs and domestic pigs. Strikingly, several genes implicated by our epigenetic studies of minipig status overlap with genes (ADCY3, TFAP2B, SKOR1, and GPR61) implicated by genetic studies of body mass index in humans. In addition, CpGs with different levels of methylation between the two pig breeds were identified proximal to genes involved in blood LDL levels and cholesterol synthesis, of particular interest given the minipig’s increased susceptibility to cardiovascular disease compared to domestic pigs. Thus, breed-specific differences of domestic and minipigs may potentially help to identify biological mechanisms underlying weight gain and aging-associated diseases. Our porcine clocks are expected to be useful for elucidating the role of epigenetics in aging and obesity, and the testing of anti-aging interventions.

scholarly journals Epigenetic clock and DNA methylation analysis of porcine models of aging and obesity

2020 ◽  
Author(s):  
Kyle M. Schachtschneider ◽  
Lawrence B Schook ◽  
Jennifer J. Meudt ◽  
Dhanansayan Shanmuganayagam ◽  
Joseph A. Zoller ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Tissue Type ◽  
Epigenetic Clock ◽  
Miniature Swine ◽  
Methylation Array ◽  
Domestic Pigs ◽  
Relative Age ◽  
Sus Scrofa Domesticus ◽  
Kidney Liver

AbstractDNA-methylation profiles have been used successfully to develop highly accurate biomarkers of age, epigenetic clocks, for many species. Using a custom methylation array, we generated DNA methylation data from n=238 porcine tissues including blood, bladder, frontal cortex, kidney, liver and lung, from domestic pigs (Sus scrofa domesticus) and minipigs (Wisconsin Miniature Swine™). We present 4 epigenetic clocks for pigs that are distinguished by their compatibility with tissue type (pan-tissue and blood clock) and species (pig and human). Two dual-species human-pig pan-tissue clocks accurately measure chronological age and relative age, respectively. We also characterized CpGs that differ between minipigs and domestic pigs. Strikingly, several genes implicated by our epigenetic studies of minipig status overlap with genes (ADCY3, TFAP2B, SKOR1, and GPR61) implicated by genetic studies of body mass index in humans. In addition, CpGs with different levels of methylation between the two pig breeds were identified proximal to genes involved in blood LDL levels and cholesterol synthesis, of particular interest given the minipig’s increased susceptibility to cardiovascular disease compared to domestic pigs. Thus, inbred differences of domestic and minipigs may potentially help to identify biological mechanisms underlying weight gain and aging-associated diseases. Our porcine clocks are expected to be useful for elucidating the role of epigenetics in aging and obesity, and the testing of anti-aging interventions.

scholarly journals DNA Methylation Networks Underlying Mammalian Traits

2021 ◽  
Author(s):  
A. Haghani ◽  
A.T. Lu ◽  
C.Z. Li ◽  
T.R. Robeck ◽  
K. Belov ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Growth Hormone ◽  
Phylogenetic Tree ◽  
Hormone Receptors ◽  
Tissue Type ◽  
Mammalian Evolution ◽  
Order Analysis ◽  
Species Lifespan ◽  
Different Tissues

SummaryEpigenetics has hitherto been studied and understood largely at the level of individual organisms. Here, we report a multi-faceted investigation of DNA methylation across 11,117 samples from 176 different species. We performed an unbiased clustering of individual cytosines into 55 modules and identified 31 modules related to primary traits including age, species lifespan, sex, adult species weight, tissue type and phylogenetic order. Analysis of the correlation between DNA methylation and species allowed us to construct phyloepigenetic trees for different tissues that parallel the phylogenetic tree. In addition, while some stable cytosines reflect phylogenetic signatures, others relate to age and lifespan, and in many cases responding to anti-aging interventions in mice such as caloric restriction and ablation of growth hormone receptors. Insights uncovered by this investigation have important implications for our understanding of the role of epigenetics in mammalian evolution, aging and lifespan.

scholarly journals Epigenetic clock and DNA methylation studies of roe deer in the wild

2020 ◽  
Author(s):  
Jean-François Lemaître ◽  
Benjamin Rey ◽  
Jean-Michel Gaillard ◽  
Corinne Régis ◽  
Emmanuelle Gilot ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Evolutionary Biology ◽  
Roe Deer ◽  
Chronological Age ◽  
Epigenetic Clock ◽  
Aging Effects ◽  
Methylation Array ◽  
Biomarkers Of Aging ◽  
Epigenetic Age ◽  
The Relationship

AbstractDNA methylation-based biomarkers of aging (epigenetic clocks) promise to lead to new insights in the evolutionary biology of ageing. Relatively little is known about how the natural environment affects epigenetic aging effects in wild species. In this study, we took advantage of a unique long-term (>40 years) longitudinal monitoring of individual roe deer (Capreolus capreolus) living in two wild populations (Chizé and Trois Fontaines, France) facing different ecological contexts to investigate the relationship between chronological age and levels of DNA methylation (DNAm). We generated novel DNA methylation data from n=90 blood samples using a custom methylation array (HorvathMammalMethylChip40). We present three DNA methylation-based estimators of age (DNAm or epigenetic age), which were trained in males, females, and both sexes combined. We investigated how sex differences influenced the relationship between DNAm age and chronological age through the use of sex-specific epigenetic clocks. Our results highlight that both populations and sex influence the epigenetic age, with the bias toward a stronger male average age acceleration (i.e. differences between epigenetic age and chronological ages) particularly pronounced in the population facing harsh environmental conditions. Further, we identify the main sites of epigenetic alteration that have distinct aging patterns across the two sexes. These findings open the door to promising avenues of research at the crossroad of evolutionary biology and biogerontology.

scholarly journals Recalibrating the epigenetic clock: implications for assessing biological age in the human cortex

Brain ◽  
2020 ◽  
Author(s):  
Gemma L Shireby ◽  
Jonathan P Davies ◽  
Paul T Francis ◽  
Joe Burrage ◽  
Emma M Walker ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Age Distribution ◽  
Biological Age ◽  
Supervised Machine Learning ◽  
Tissue Type ◽  
Chronological Age ◽  
Epigenetic Clock ◽  
Methylation Data ◽  
Human Cortex ◽  
Dna Methylation Age

Abstract Human DNA methylation data have been used to develop biomarkers of ageing, referred to as ‘epigenetic clocks’, which have been widely used to identify differences between chronological age and biological age in health and disease including neurodegeneration, dementia and other brain phenotypes. Existing DNA methylation clocks have been shown to be highly accurate in blood but are less precise when used in older samples or in tissue types not included in training the model, including brain. We aimed to develop a novel epigenetic clock that performs optimally in human cortex tissue and has the potential to identify phenotypes associated with biological ageing in the brain. We generated an extensive dataset of human cortex DNA methylation data spanning the life course (n = 1397, ages = 1 to 108 years). This dataset was split into ‘training’ and ‘testing’ samples (training: n = 1047; testing: n = 350). DNA methylation age estimators were derived using a transformed version of chronological age on DNA methylation at specific sites using elastic net regression, a supervised machine learning method. The cortical clock was subsequently validated in a novel independent human cortex dataset (n = 1221, ages = 41 to 104 years) and tested for specificity in a large whole blood dataset (n = 1175, ages = 28 to 98 years). We identified a set of 347 DNA methylation sites that, in combination, optimally predict age in the human cortex. The sum of DNA methylation levels at these sites weighted by their regression coefficients provide the cortical DNA methylation clock age estimate. The novel clock dramatically outperformed previously reported clocks in additional cortical datasets. Our findings suggest that previous associations between predicted DNA methylation age and neurodegenerative phenotypes might represent false positives resulting from clocks not robustly calibrated to the tissue being tested and for phenotypes that become manifest in older ages. The age distribution and tissue type of samples included in training datasets need to be considered when building and applying epigenetic clock algorithms to human epidemiological or disease cohorts.

scholarly journals Examining the Vanishing Twin Hypothesis of Neural Tube Defects: Application of an Epigenetic Predictor for Monozygotic Twinning

2021 ◽  
pp. 1-5
Author(s):  
Jenny van Dongen ◽  
Scott D. Gordon ◽  
Veronika V. Odintsova ◽  
Allan F. McRae ◽  
Wendy P. Robinson ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Neural Tube ◽  
Neural Tube Defects ◽  
Methylation Array ◽  
Chorionic Villi ◽  
Monozygotic Twinning ◽  
Dizygotic Twins ◽  
Dna Methylation Array ◽  
Mz Twins

Abstract Strong associations between neural tube defects (NTDs) and monozygotic (MZ) twinning have long been noted, and it has been suggested that NTD cases who do not present as MZ twins may be the survivors of MZ twinning events. We have recently shown that MZ twins carry a strong, distinctive DNA methylation signature and have developed an algorithm based on genomewide DNA methylation array data that distinguishes MZ twins from dizygotic twins and other relatives at well above chance level. We have applied this algorithm to published methylation data from five fetal tissues (placental chorionic villi, kidney, spinal cord, brain and muscle) collected from spina bifida cases (n = 22), anencephalic cases (n = 15) and controls (n = 19). We see no difference in signature between cases and controls, providing no support for a common etiological role of MZ twinning in NTDs. The strong associations therefore continue to await elucidation.

scholarly journals Epigenetic clock and methylation studies in gray short-tailed opossums

2021 ◽  
Author(s):  
Steve Horvath ◽  
Amin Haghani ◽  
Joseph Alan Zoller ◽  
Ken Raj ◽  
Ishani Sinha ◽  
...  
Keyword(s):  
Experimental Manipulation ◽  
Monodelphis Domestica ◽  
Tissue Type ◽  
Epigenetic Clock ◽  
Methylation Data ◽  
Methylation Array ◽  
Relative Age ◽  
Unique Biology ◽  
Age Related ◽  
Age Related Changes

The opossum (Monodelphis domestica), with its sequenced genome, ease of laboratory care and experimental manipulation, and unique biology, is the most used laboratory marsupial. Using the mammalian methylation array, we generated DNA methylation data from n=100 opossum tissues including blood, liver, and tail. We contrast age-related changes in the opossum methylome to those of C57BL/6J mice. We present several epigenetic clocks for opossums that are distinguished by their compatibility with tissue type (pan-tissue and blood clock) and species (opossum and human). Two dual-species human-opossum pan-tissue clocks accurately measure chronological age and relative age, respectively. These human-opossum epigenetic clocks are expected to provide a significant boost to the attractiveness of opossum as a biological model.

scholarly journals Recalibrating the Epigenetic Clock: Implications for Assessing Biological Age in the Human Cortex

2020 ◽  
Author(s):  
Gemma L Shireby ◽  
Jonathan P Davies ◽  
Paul T Francis ◽  
Joe Burrage ◽  
Emma M Walker ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Age Distribution ◽  
Biological Age ◽  
Supervised Machine Learning ◽  
Tissue Type ◽  
Chronological Age ◽  
Epigenetic Clock ◽  
Methylation Data ◽  
Human Cortex ◽  
Dna Methylation Age

AbstractHuman DNA-methylation data have been used to develop biomarkers of ageing - referred to ‘epigenetic clocks’ - that have been widely used to identify differences between chronological age and biological age in health and disease including neurodegeneration, dementia and other brain phenotypes. Existing DNA methylation clocks are highly accurate in blood but are less precise when used in older samples or on brain tissue. We aimed to develop a novel epigenetic clock that performs optimally in human cortex tissue and has the potential to identify phenotypes associated with biological ageing in the brain. We generated an extensive dataset of human cortex DNA methylation data spanning the life-course (n = 1,397, ages = 1 to 104 years). This dataset was split into ‘training’ and ‘testing’ samples (training: n = 1,047; testing: n = 350). DNA methylation age estimators were derived using a transformed version of chronological age on DNA methylation at specific sites using elastic net regression, a supervised machine learning method. The cortical clock was subsequently validated in a novel human cortex dataset (n = 1,221, ages = 41 to 104 years) and tested for specificity in a large whole blood dataset (n = 1,175, ages = 28 to 98 years). We identified a set of 347 DNA methylation sites that, in combination optimally predict age in the human cortex. The sum of DNA methylation levels at these sites weighted by their regression coefficients provide the cortical DNA methylation clock age estimate. The novel clock dramatically out-performed previously reported clocks in additional cortical datasets. Our findings suggest that previous associations between predicted DNA methylation age and neurodegenerative phenotypes might represent false positives resulting from clocks not robustly calibrated to the tissue being tested and for phenotypes that become manifest in older ages. The age distribution and tissue type of samples included in training datasets need to be considered when building and applying epigenetic clock algorithms to human epidemiological or disease cohorts.

DNA methylation differences in cortical grey and white matter in schizophrenia

Epigenomics ◽  
2021 ◽  
Author(s):  
Amber Berdenis van Berlekom ◽  
Nina Notman ◽  
Marjolein AM Sneeboer ◽  
Gijsje JLJ Snijders ◽  
Lotte C Houtepen ◽  
...  
Keyword(s):  
Dna Methylation ◽  
White Matter ◽  
Postmortem Brain ◽  
Tissue Type ◽  
Cortical Tissue ◽  
Methylation Array ◽  
Genome Wide ◽  
Postmortem Brain Tissue ◽  
And Control ◽  
Dna Methylation Analysis

Aim: Identify grey- and white-matter-specific DNA-methylation differences between schizophrenia (SCZ) patients and controls in postmortem brain cortical tissue. Materials & methods: Grey and white matter were separated from postmortem brain tissue of the superior temporal and medial frontal gyrus from SCZ (n = 10) and control (n = 11) cases. Genome-wide DNA-methylation analysis was performed using the Infinium EPIC Methylation Array (Illumina, CA, USA). Results: Four differentially methylated regions associated with SCZ status and tissue type (grey vs white matter) were identified within or near KLF9, SFXN1, SPRED2 and ALS2CL genes. Gene-expression analysis showed differential expression of KLF9 and SFXN1 in SCZ. Conclusion: Our data show distinct differences in DNA methylation between grey and white matter that are unique to SCZ, providing new leads to unravel the pathogenesis of SCZ.

scholarly journals Analysis of epigenetic aging in vivo and in vitro: Factors controlling the speed and direction

2020 ◽  
Vol 245 (17) ◽  
pp. 1543-1551 ◽  
Author(s):  
Mieko Matsuyama ◽  
Arne Søraas ◽  
Sarah Yu ◽  
Kyuhyeon Kim ◽  
Evi X Stavrou ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Biological Significance ◽  
Methylation Pattern ◽  
Tissue Type ◽  
Epigenetic Clock ◽  
Whole Genome ◽  
Aging Research ◽  
Epigenetic Aging

The mechanism of aging is not yet fully understood. It has been recognized that there are age-dependent changes in the DNA methylation pattern of the whole genome. To date, there are several DNA methylation-based estimators of the chronological age. A majority of the estimators use the DNA methylation data from a single tissue type, such as blood. In 2013, for the first time, Steve Horvath reported the DNA methylation-based age estimator (353 CpGs were used) that could be applied to multiple tissues. A refined, more sensitive version that uses 391 CpGs was subsequently developed and validated in human cells, including fibroblasts. In this review, the age predicted by DNA methylation-based age estimator is referred to as DNAmAge, and the biological process controlling the progression of DNAmAge is referred to as the epigenetic aging in this minireview. The concepts of DNAmAge and epigenetic aging provide us opportunities to discover previously unrecognized biological events controlling aging. In this article, we discuss the frequently asked questions regarding DNAmAge and the epigenetic aging by introducing recent studies of ours and others. We focus on addressing the following questions: (1) Is there any synchronization of DNAmAge between cells in a human body?, (2) Can we use in vitro (cell culture) systems to study the epigenetic aging?, (3) Is there an age limit of DNAmAge?, and (4) Is it possible to change the speed and direction of the epigenetic aging? We describe our current understandings to these questions and outline potential future directions. Impact statement Aging is associated with DNA methylation (DNAm) changes. Recent advancement of the whole-genome DNAm analysis technology allowed scientists to develop DNAm-based age estimators. A majority of these estimators use DNAm data from a single tissue type such as blood. In 2013, a multi-tissue age estimator using DNAm pattern of 353 CpGs was developed by Steve Horvath. This estimator was named “epigenetic clock”, and the improved version using DNAm pattern of 391 CpGs was developed in 2018. The estimated age by epigenetic clock is named DNAmAge. DNAmAge can be used as a biomarker of aging predicting the risk of age-associated diseases and mortality. Although the DNAm-based age estimators were developed, the mechanism of epigenetic aging is still enigmatic. The biological significance of epigenetic aging is not well understood, either. This minireview discusses the current understanding of the mechanism of epigenetic aging and the future direction of aging research.

scholarly journals DNA methylation study of age and sex in baboons and four other primates

2020 ◽  
Author(s):  
Steve Horvath ◽  
Amin Haghani ◽  
Joseph A. Zoller ◽  
Jason Ernst ◽  
Matteo Pellegrini ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Cerebral Cortex ◽  
Mammalian Species ◽  
Papio Hamadryas ◽  
Primate Species ◽  
Epigenetic Clock ◽  
Fetal Life ◽  
Methylation Array ◽  
Depth Analysis ◽  
Age And Sex

ABSTRACTDNA methylation data have been successfully used to develop highly accurate estimators of age (“epigenetic clocks”) in several mammalian species. With a view of extending epigenetic clocks to primates, we analyzed DNA methylation profiles from five primate species; Papio hamadryas (baboons), Callithrix jacchus (common marmoset), Chlorocebus sabaeus (vervet monkey), Macaca mulatta (rhesus macaque), and Homo sapiens (human). From these we present here, a highly accurate primate epigenetic clock. This clock is based on methylation profiles of CpGs that are highly conserved and are located on a custom methylation array (HorvathMammalMethylChip40). Furthermore, we carried out in-depth analysis of the baboon, as it is evolutionarily the closest primate to humans that can be employed in biomedical research. We present five epigenetic clocks for baboons (Olive-yellow baboon hybrid), one of which, the pan tissue epigenetic clock, was trained on seven tissue types (fetal cerebral cortex, adult cerebral cortex, cerebellum, adipose, heart, liver, and skeletal muscle) with ages ranging from late fetal life to 22.8 years of age. To facilitate translational capability, we constructed two dual-species, human-baboon clocks, whereby one measures ages of both species in units of years, while the other reports ages relative to the maximum lifespan of the species. Although the primate clock applies to all five primate species, the baboon-specific clocks exhibit only moderate age correlations with other primates. We also provide detailed gene and pathway analyses of individual CpGs that relate to age and sex across different primate species. Ten out of 739 sex related CpGs in primate species are located near 9 autosomal genes (including FAM217A, CDYL, POU3F2, and UHRF2). Overall, this study sheds light on epigenetic aging mechanisms in primates, and the potential influence of sex.

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