Universal DNA methylation age across mammalian tissues

Aging, often considered a result of random cellular damage, can be accurately estimated using DNA methylation profiles, the foundation of pan-tissue epigenetic clocks. Here, we demonstrate the development of universal pan-mammalian clocks, using 11,754 methylation arrays from our Mammalian Methylation Consortium, which encompass 59 tissue types across 185 mammalian species. These predictive models estimate mammalian tissue age with high accuracy (r > 0.96). Age deviations correlate with human mortality risk, mouse somatotropic axis mutations and caloric restriction. We identified specific cytosines with methylation levels that change with age across numerous species. These sites, highly enriched in polycomb repressive complex 2-binding locations, are near genes implicated in mammalian development, cancer, obesity and longevity. Our findings offer new evidence suggesting that aging is evolutionarily conserved and intertwined with developmental processes across all mammals.Publisher PDFPeer reviewe

Universal DNA methylation age across mammalian tissues.

Aging, often considered a result of random cellular damage, can be accurately estimated using DNA methylation profiles, the foundation of pan-tissue epigenetic clocks. Here, we demonstrate the development of universal pan-mammalian clocks, using 11,754 methylation arrays from our Mammalian Methylation Consortium, which encompass 59 tissue types across 185 mammalian species. These predictive models estimate mammalian tissue age with high accuracy (r > 0.96). Age deviations correlate with human mortality risk, mouse somatotropic axis mutations and caloric restriction. We identified specific cytosines with methylation levels that change with age across numerous species. These sites, highly enriched in polycomb repressive complex 2-binding locations, are near genes implicated in mammalian development, cancer, obesity and longevity. Our findings offer new evidence suggesting that aging is evolutionarily conserved and intertwined with developmental processes across all mammals

Universal DNA methylation age across mammalian tissues

DATA AVAILABILITY STATEMENT : The individual-level data from the Mammalian Methylation Consortium can be accessed from several online locations. All data from the Mammalian Methylation Consortium are posted on Gene Expression Omnibus (complete dataset, GSE223748). Subsets of the datasets can also be downloaded from accession numbers GSE174758, GSE184211, GSE184213, GSE184215, GSE184216, GSE184218, GSE184220, GSE184221, GSE184224, GSE190660, GSE190661, GSE190662, GSE190663, GSE190664, GSE174544, GSE190665, GSE174767, GSE184222, GSE184223, GSE174777, GSE174778, GSE173330, GSE164127, GSE147002, GSE147003, GSE147004, GSE223943 and GSE223944. Additional details can be found in Supplementary Note 2. The mammalian data can also be downloaded from the Clock Foundation webpage: https://clockfoundation.org/MammalianMethylationConsortium. The mammalian methylation array is available through the non-profit Epigenetic Clock Development Foundation (https://clockfoundation.org/). The manifest file of the mammalian array and genome annotations of CpG sites can be found on Zenodo (10.5281/zenodo.7574747). All other data supporting the findings of this study are available from the corresponding author upon reasonable request. The chip manifest files, genome annotations of CpG sites and the software code for universal pan-mammalian clocks can be found on GitHub95 at https://github.com/shorvath/MammalianMethylationConsortium/tree/v2.0.0. The individual R code for the universal pan-mammalian clocks, EWAS analysis and functional enrichment studies can be also found in the Supplementary Code.SUPPLEMENTARY MATERIAL 1 : Supplementary Tables 1–3 and Notes 1–6.SUPPLEMENTARY MATERIAL 2 : Reporting SummarySUPPLEMENTARY MATERIAL 3 : Supplementary Data 1–14.SUPPLEMENTARY MATERIAL 4 : Supplementary Code.Aging, often considered a result of random cellular damage, can be accurately estimated using DNA methylation profiles, the foundation of pan-tissue epigenetic clocks. Here, we demonstrate the development of universal pan-mammalian clocks, using 11,754 methylation arrays from our Mammalian Methylation Consortium, which encompass 59 tissue types across 185 mammalian species. These predictive models estimate mammalian tissue age with high accuracy (r > 0.96). Age deviations correlate with human mortality risk, mouse somatotropic axis mutations and caloric restriction. We identified specific cytosines with methylation levels that change with age across numerous species. These sites, highly enriched in polycomb repressive complex 2-binding locations, are near genes implicated in mammalian development, cancer, obesity and longevity. Our findings offer new evidence suggesting that aging is evolutionarily conserved and intertwined with developmental processes across all mammals.https://www.nature.com/nataginghj2024Zoology and EntomologySDG-15:Life on lan

The potential role of miRNAs in calcification of cardiovascular diseases

MicroRNA is a class of endogenous noncoding 17-25 nucleotides RNAs that regulate gene expression. Recently, more and more works have been appeared confirming the important role of miRNAs in the development and progression of cardiovascular diseases. Calcification mechanisms include impaired regulation of calcium and phosphate metabolism, activation of the signaling pathways that regulate bone formation, and suppression of the signaling pathways responsible for maintaining the smooth muscle cell phenotype. The involvement of microRNAs was demonstrated for each of these mechanisms, which emphasizes the significant contribution of microRNAs to the development of calcification of blood vessels. This review summarizes the scientific data on microRNAs that are proven to be involved in the development of in vitro and in vivo calcification of their targets, as well as the latest achievements in microRNA studies in the context of vascular calcification. We also discuss the possibility of their use for early diagnostics and treatment of calcification in cardiovascular diseases

Methanol May Function as a Cross-Kingdom Signal

<div><p>Recently, we demonstrated that leaf wounding results in the synthesis of pectin methylesterase (PME), which causes the plant to release methanol into the air. Methanol emitted by a wounded plant increases the accumulation of methanol-inducible gene mRNA and enhances antibacterial resistance as well as cell-to-cell communication, which facilitates virus spreading in neighboring plants. We concluded that methanol is a signaling molecule involved in within-plant and plant-to-plant communication. Methanol is considered to be a poison in humans because of the alcohol dehydrogenase (ADH)-mediated conversion of methanol into toxic formaldehyde. However, recent data showed that methanol is a natural compound in normal, healthy humans. These data call into question whether human methanol is a metabolic waste product or whether methanol has specific function in humans.</p> <p>Here, to reveal human methanol-responsive genes (MRGs), we used suppression subtractive hybridization cDNA libraries of HeLa cells lacking ADH and exposed to methanol. This design allowed us to exclude genes involved in formaldehyde and formic acid detoxification from our analysis. We identified MRGs and revealed a correlation between increases in methanol content in the plasma and changes in human leukocyte MRG mRNA levels after fresh salad consumption by volunteers. Subsequently, we showed that the methanol generated by the pectin/PME complex in the gastrointestinal tract of mice induces the up- and downregulation of brain MRG mRNA. We used an adapted Y-maze to measure the locomotor behavior of the mice while breathing wounded plant vapors in two-choice assays. We showed that mice prefer the odor of methanol to other plant volatiles and that methanol changed MRG mRNA accumulation in the mouse brain.</p> <p>We hypothesize that the methanol emitted by wounded plants may have a role in plant-animal signaling. The known positive effect of plant food intake on human health suggests a role for physiological methanol in human gene regulation.</p> </div

Citrus pectin preparation contains methanol-generating PME.

<p>(<b>A</b>) Detection of PME activity in plant material. Visual detection of PME activity in agarose gels containing 90% methylesterified pectin and stained by ruthenium red. The wells were loaded with citrus pectin (Nittary Pharmaceuticals, VitaLine Inc.) extract before (1–3) and after (4–6) heating (70°C, 10 min), the cell wall extract of carrot (7–9), cabbage head (<i>Brassica oleracea</i>) (10–12), sauerkraut (13–15), sauerkraut brine (16), the cell wall supernatant fraction of sauerkraut (17), the cell wall extract of dried plum (prune) (18–20) and red beet (21–23). Well #24 was loaded with pectinesterase from orange peel containing 20 nkatals PME. Plant material was extracted with Na-citrate buffer, pH 7.0, without NaCl (1, 4, 7, 10, 13, 18, 21) or with 0.15 M (2, 5, 8, 11, 14, 19, 22) or 1 M (3, 6, 9, 12, 15, 16, 17, 20, 23, 24,) NaCl. (B) citrus pectin(PME+) generates methanol. The methanol content in the pectin suspension in water after a 2-h and an 18-h incubation at 28°C. The data represent five independent experiments, and the standard error bars are indicated. Student's <i>t</i>-test <i>P</i>-values are indicated.</p

Relative quantity of (A) <i>mTax1BP1</i>, (B) <i>mSNX2</i>7 and (C) <i>mCycA2</i> mRNA in mouse organs as determined by qRT-PCR after methanol and pectin(PME+) ingestion.

<p>The <i>mCycA2</i> data (C) are plotted on a semi-logarithmic scale. The standard errors and Student's <i>t</i>-test <i>P</i>-values are indicated.</p

Methanol content in mouse serum 2 h after direct administration of pectin into the stomach.

<p>The mice were randomly divided into groups of ten. Each mouse in the treatment groups received 20 mg of pectin(PME+) or pectin(PME−) directly into the stomach by gavage. After 10, 30, 60 and 120 min, a blood sample was obtained and analyzed for methanol content by gas chromatography. The control groups received water or 0.5% glucose solution. The data represent five independent experiments, and standard error bars are indicated. ***, <i>P</i><0.001 (Student's <i>t</i>-test).</p

Methanol content in the plasma of volunteers 3 h after fresh salad or turkey meat intake.

<p>The data obtained in May 2011 are presented as the means ± SE. Student's <i>t</i>-test <i>P</i>-values to determine the statistical significance of the differences in methanol content before and after food intake are indicated.</p

Relative quantity of <i>mGAPDH</i> mRNA in mouse organs after methanol and pectin(PME+) ingestion as determined by qRT-PCR: A – liver, B – heart, C – spleen and D – brain.

<p>Relative quantity of <i>mGAPDH</i> mRNA in mouse organs after methanol and pectin(PME+) ingestion as determined by qRT-PCR: A – liver, B – heart, C – spleen and D – brain.</p