DNA methylation and chromatin accessibility predict age in the domestic dog

Across mammals, the epigenome is highly predictive of chronological age. These “epigenetic clocks,” most of which have been built using DNA methylation (DNAm) profiles, have gained traction as biomarkers of aging and organismal health. While the ability of DNAm to predict chronological age has been repeatedly demonstrated, the ability of other epigenetic features to predict age remains unclear. Here, we use two types of epigenetic information—DNAm, and chromatin accessibility as measured by ATAC-seq—to develop age predictors in peripheral blood mononuclear cells sampled from a population of domesticated dogs. We measured DNAm and ATAC-seq profiles for 71 dogs, building separate predictive clocks from each, as well as the combined dataset. We also use fluorescence-assisted cell sorting to quantify major lymphoid populations for each sample. We found that chromatin accessibility can accurately predict chronological age (R²_ATAC = 26%), though less accurately than the DNAm clock (R²_DNAm = 33%), and the clock built from the combined datasets was comparable to both (R²_combined = 29%). We also observed various populations of CD62L+ T cells significantly correlated with dog age. Finally, we found that all three clocks selected features that were in or near at least two protein-coding genes: BAIAP2 and SCARF2, both previously implicated in processes related to cognitive or neurological impairment. Taken together, these results highlight the potential of chromatin accessibility as a complementary epigenetic resource for modeling and investigating biologic age.     

Bloom syndrome patients and mice display accelerated epigenetic aging

Bloom syndrome (BSyn) is an autosomal recessive disorder caused by variants in the BLM gene, which is involved in genome stability. Patients with BSyn present with poor growth, sun sensitivity, mild immunodeficiency, diabetes, and increased risk of cancer, most commonly leukemias. Interestingly, patients with BSyn do not have other signs of premature aging such as early, progressive hair loss and cataracts. We set out to determine epigenetic age in BSyn, which can be a better predictor of health and disease over chronological age. Our results show for the first time that patients with BSyn have evidence of accelerated epigenetic aging across several measures in blood lymphocytes, as compared to carriers. Additionally, homozygous Blm mice exhibit accelerated methylation age in multiple tissues, including brain, blood, kidney, heart, and skin, according to the brain methylation clock. Overall, we find that Bloom syndrome is associated with accelerated epigenetic aging effects in multiple tissues and more generally a strong effect on CpG methylation levels.     

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

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 statementAging 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.     

Age‐associated microRNA expression in human peripheral blood is associated with all‐cause mortality and age‐related traits

Recent studies provide evidence of correlations of DNA methylation and expression of protein‐coding genes with human aging. The relations of microRNA expression with age and age‐related clinical outcomes have not been characterized thoroughly. We explored associations of age with whole‐blood microRNA expression in 5221 adults and identified 127 microRNAs that were differentially expressed by age at P < 3.3 × 10^?4 (Bonferroni‐corrected). Most microRNAs were underexpressed in older individuals. Integrative analysis of microRNA and mRNA expression revealed changes in age‐associated mRNA expression possibly driven by age‐associated microRNAs in pathways that involve RNA processing, translation, and immune function. We fitted a linear model to predict ‘microRNA age’ that incorporated expression levels of 80 microRNAs. MicroRNA age correlated modestly with predicted age from DNA methylation (r = 0.3) and mRNA expression (r = 0.2), suggesting that microRNA age may complement mRNA and epigenetic age prediction models. We used the difference between microRNA age and chronological age as a biomarker of accelerated aging (Δage) and found that Δage was associated with all‐cause mortality (hazards ratio 1.1 per year difference, P = 4.2 × 10^?5 adjusted for sex and chronological age). Additionally, Δage was associated with coronary heart disease, hypertension, blood pressure, and glucose levels. In conclusion, we constructed a microRNA age prediction model based on whole‐blood microRNA expression profiling. Age‐associated microRNAs and their targets have potential utility to detect accelerated aging and to predict risks for age‐related diseases.     

Reversal of epigenetic aging and immunosenescent trends in humans

Epigenetic “clocks” can now surpass chronological age in accuracy for estimating biological age. Here, we use four such age estimators to show that epigenetic aging can be reversed in humans. Using a protocol intended to regenerate the thymus, we observed protective immunological changes, improved risk indices for many age‐related diseases, and a mean epigenetic age approximately 1.5 years less than baseline after 1 year of treatment (?2.5‐year change compared to no treatment at the end of the study). The rate of epigenetic aging reversal relative to chronological age accelerated from ?1.6 year/year from 0–9 month to ?6.5 year/year from 9–12 month. The GrimAge predictor of human morbidity and mortality showed a 2‐year decrease in epigenetic vs. chronological age that persisted six months after discontinuing treatment. This is to our knowledge the first report of an increase, based on an epigenetic age estimator, in predicted human lifespan by means of a currently accessible aging intervention.     

Meta‐analysis on blood transcriptomic studies identifies consistently coexpressed protein–protein interaction modules as robust markers of human aging

The bodily decline that occurs with advancing age strongly impacts on the prospects for future health and life expectancy. Despite the profound role of age in disease etiology, knowledge about the molecular mechanisms driving the process of aging in humans is limited. Here, we used an integrative network-based approach for combining multiple large-scale expression studies in blood (2539 individuals) with protein–protein Interaction (PPI) data for the detection of consistently coexpressed PPI modules that may reflect key processes that change throughout the course of normative aging. Module detection followed by a meta-analysis on chronological age identified fifteen consistently coexpressed PPI modules associated with chronological age, including a highly significant module (P = 3.5 × 10⁻³⁸) enriched for ‘T-cell activation’ marking age-associated shifts in lymphocyte blood cell counts (R² = 0.603; P = 1.9 × 10⁻¹⁰). Adjusting the analysis in the compendium for the ‘T-cell activation’ module showed five consistently coexpressed PPI modules that robustly associated with chronological age and included modules enriched for ‘Translational elongation’, ‘Cytolysis’ and ‘DNA metabolic process’. In an independent study of 3535 individuals, four of five modules consistently associated with chronological age, underpinning the robustness of the approach. We found three of five modules to be significantly enriched with aging-related genes, as defined by the GenAge database, and association with prospective survival at high ages for one of the modules including ASF1A. The hereby-detected age-associated and consistently coexpressed PPI modules therefore may provide a molecular basis for future research into mechanisms underlying human aging.     

The migration of mitochondrial DNA fragments to the nucleus affects the chronological aging process of Saccharomyces cerevisiae

Migration of fragmented mitochondrial DNA (mtDNA) to the nucleus has been shown to occur in multiple species including yeast, plants, and mammals. Several human diseases, including Pallister–Hall syndrome and mucolipidosis, can be initiated by mtDNA insertion mutagenesis of nuclear DNA. In yeast, we demonstrated that the rate of mtDNA fragments translocating to the nucleus increases during chronological aging. The yeast chronological lifespan (CLS) is determined by the survival of nondividing cell populations. Whereas yeast strains with elevated migration rates of mtDNA fragments to the nucleus showed accelerated chronological aging, strains with decreased mtDNA transfer rates to the nucleus exhibited an extended CLS. Although one of the most popular theories of aging is the free radical theory, migration of mtDNA fragments to the nucleus may also contribute to the chronological aging process by possibly increasing nuclear genomic instability in cells with advanced age.     

The secretome atlas of two mouse models of progeria

Hutchinson-Gilford progeria syndrome (HGPS) is a rare genetic disease caused by nuclear envelope alterations that lead to accelerated aging and premature death. Several studies have linked health and longevity to cell-extrinsic mechanisms, highlighting the relevance of circulating factors in the aging process as well as in age-related diseases. We performed a global plasma proteomic analysis in two preclinical progeroid models (Lmna^G609G/G609G and Zmpste24^−/− mice) using aptamer-based proteomic technology. Pathways related to the extracellular matrix, growth factor response and calcium ion binding were among the most enriched in the proteomic signature of progeroid samples compared to controls. Despite the global downregulation trend found in the plasma proteome of progeroid mice, several proteins associated with cardiovascular disease, the main cause of death in HGPS, were upregulated. We also developed a chronological age predictor using plasma proteome data from a cohort of healthy mice (aged 1–30 months), that reported an age acceleration when applied to progeroid mice, indicating that these mice exhibit an “old” plasma proteomic signature. Furthermore, when compared to naturally-aged mice, a great proportion of differentially expressed circulating proteins in progeroid mice were specific to premature aging, highlighting secretome-associated differences between physiological and accelerated aging. This is the first large-scale profiling of the plasma proteome in progeroid mice, which provides an extensive list of candidate circulating plasma proteins as potential biomarkers and/or therapeutic targets for further exploration and hypothesis generation in the context of both physiological and premature aging.     

Molecular and physiological manifestations and measurement of aging in humans

Biological aging is associated with a reduction in the reparative and regenerative potential in tissues and organs. This reduction manifests as a decreased physiological reserve in response to stress (termed homeostenosis) and a time‐dependent failure of complex molecular mechanisms that cumulatively create disorder. Aging inevitably occurs with time in all organisms and emerges on a molecular, cellular, organ, and organismal level with genetic, epigenetic, and environmental modulators. Individuals with the same chronological age exhibit differential trajectories of age‐related decline, and it follows that we should assess biological age distinctly from chronological age. In this review, we outline mechanisms of aging with attention to well‐described molecular and cellular hallmarks and discuss physiological changes of aging at the organ‐system level. We suggest methods to measure aging with attention to both molecular biology (e.g., telomere length and epigenetic marks) and physiological function (e.g., lung function and echocardiographic measurements). Finally, we propose a framework to integrate these molecular and physiological data into a composite score that measures biological aging in humans. Understanding the molecular and physiological phenomena that drive the complex and multifactorial processes underlying the variable pace of biological aging in humans will inform how researchers assess and investigate health and disease over the life course. This composite biological age score could be of use to researchers seeking to characterize normal, accelerated, and exceptionally successful aging as well as to assess the effect of interventions aimed at modulating human aging.     

Proteomics in aging research: A roadmap to clinical,translational research

The identification of plasma proteins that systematically change with age and, independent of chronological age, predict accelerated decline of health is an expanding area of research. Circulating proteins are ideal translational “omics” since they are final effectors of physiological pathways and because physicians are accustomed to use information of plasma proteins as biomarkers for diagnosis, prognosis, and tracking the effectiveness of treatments. Recent technological advancements, including mass spectrometry (MS)‐based proteomics, multiplexed proteomic assay using modified aptamers (SOMAscan), and Proximity Extension Assay (PEA, O‐Link), have allowed for the assessment of thousands of proteins in plasma or other biological matrices, which are potentially translatable into new clinical biomarkers and provide new clues about the mechanisms by which aging is associated with health deterioration and functional decline. We carried out a detailed literature search for proteomic studies performed in different matrices (plasma, serum, urine, saliva, tissues) and species using multiple platforms. Herein, we identified 232 proteins that were age‐associated across studies. Enrichment analysis of the 232 age‐associated proteins revealed metabolic pathways previously connected with biological aging both in animal models and in humans, most remarkably insulin‐like growth factor (IGF) signaling, mitogen‐activated protein kinases (MAPK), hypoxia‐inducible factor 1 (HIF1), cytokine signaling, Forkhead Box O (FOXO) metabolic pathways, folate metabolism, advance glycation end products (AGE), and receptor AGE (RAGE) metabolic pathway. Information on these age‐relevant proteins, likely expanded and validated in longitudinal studies and examined in mechanistic studies, will be essential for patient stratification and the development of new treatments aimed at improving health expectancy.     

MOABS: model based analysis of bisulfite sequencing data

Background

Human aging is associated with DNA methylation changes at specific sites in the genome. These epigenetic modifications may be used to track donor age for forensic analysis or to estimate biological age.

Results

We perform a comprehensive analysis of methylation profiles to narrow down 102 age-related CpG sites in blood. We demonstrate that most of these age-associated methylation changes are reversed in induced pluripotent stem cells (iPSCs). Methylation levels at three age-related CpGs - located in the genes ITGA2B, ASPA and PDE4C - were subsequently analyzed by bisulfite pyrosequencing of 151 blood samples. This epigenetic aging signature facilitates age predictions with a mean absolute deviation from chronological age of less than 5 years. This precision is higher than age predictions based on telomere length. Variation of age predictions correlates moderately with clinical and lifestyle parameters supporting the notion that age-associated methylation changes are associated more with biological age than with chronological age. Furthermore, patients with acquired aplastic anemia or dyskeratosis congenita - two diseases associated with progressive bone marrow failure and severe telomere attrition - are predicted to be prematurely aged.

Conclusions

Our epigenetic aging signature provides a simple biomarker to estimate the state of aging in blood. Age-associated DNA methylation changes are counteracted in iPSCs. On the other hand, over-estimation of chronological age in bone marrow failure syndromes is indicative for exhaustion of the hematopoietic cell pool. Thus, epigenetic changes upon aging seem to reflect biological aging of blood.     

Nanopore sequencing identifies a higher frequency and expanded spectrum of mitochondrial DNA deletion mutations in human aging

Mitochondrial DNA (mtDNA) deletion mutations cause many human diseases and are linked to age-induced mitochondrial dysfunction. Mapping the mutation spectrum and quantifying mtDNA deletion mutation frequency is challenging with next-generation sequencing methods. We hypothesized that long-read sequencing of human mtDNA across the lifespan would detect a broader spectrum of mtDNA rearrangements and provide a more accurate measurement of their frequency. We employed nanopore Cas9-targeted sequencing (nCATS) to map and quantitate mtDNA deletion mutations and develop analyses that are fit-for-purpose. We analyzed total DNA from vastus lateralis muscle in 15 males ranging from 20 to 81 years of age and substantia nigra from three 20-year-old and three 79-year-old men. We found that mtDNA deletion mutations detected by nCATS increased exponentially with age and mapped to a wider region of the mitochondrial genome than previously reported. Using simulated data, we observed that large deletions are often reported as chimeric alignments. To address this, we developed two algorithms for deletion identification which yield consistent deletion mapping and identify both previously reported and novel mtDNA deletion breakpoints. The identified mtDNA deletion frequency measured by nCATS correlates strongly with chronological age and predicts the deletion frequency as measured by digital PCR approaches. In substantia nigra, we observed a similar frequency of age-related mtDNA deletions to those observed in muscle samples, but noted a distinct spectrum of deletion breakpoints. NCATS-mtDNA sequencing allows the identification of mtDNA deletions on a single-molecule level, characterizing the strong relationship between mtDNA deletion frequency and chronological aging.     

Biological age and tempos of aging in women over 60 in connection with their morphofunctional characteristics

Background

The study of aging processes and the changes in morphological, physiological, and functional characteristics that are associated with aging is of great interest not only for researchers, but also for the general public. The aim of the present paper is to study the biological age and tempos of aging in women older than 60 years, including long-lived females (over 90-years-old), and their associations with morphofunctional characteristics.

Results

Somatic traits, body mass components, and functional characteristics were investigated in 119 elderly (between 60 and 74-years-old) and long-lived (over 90-years-old) women in Tiraspol. With the special PC software ‘Diagnostics of Aging: BioAge’ (National Gerontological Center, Moscow, Russia) the biological age and tempos of aging were evaluated in the study participants. The results show close connections between morphofunctional changes, particularly in body mass components, and biological age. The software demonstrated its validity in the estimation of biological age in the group of elderly women. In the homogenous (according to their chronological age) group of women, three subgroups were separated with different tempos of aging: those with lower rates of aging (biological age less than chronological age by two years or more); those consistent with their chronological age, and those with accelerated tempos of aging (biological age higher than chronological age by two years or more).

Conclusions

Morphofunctional characteristics in the studied groups of women demonstrate the trends of age-involutive changes which can be traced through all groups, from those with slow rates of aging, to those with average rates, to those with accelerated tempos of aging, and finally in long-lived women. The results of comparative analysis show that women with accelerated aging are characterized with such traits as lower skeletal muscle mass, lower hand grip strength, and higher metabolic rate. Canonical discriminant analysis revealed a number of morphofunctional characteristics which differentiate the early-aging women from women with average rates of aging: higher BMI values, excessive fat mass, lower skeletal muscle mass and low values of hand grip strength. Thus the presence of such characteristics in elderly women can be considered as additional risk factor towards the early onset of the aging process.     

Life spanning murine gene expression profiles in relation to chronological and pathological aging in multiple organs

Aging and age‐related pathology is a result of a still incompletely understood intricate web of molecular and cellular processes. We present a C57BL/6J female mice in vivo aging study of five organs (liver, kidney, spleen, lung, and brain), in which we compare genome‐wide gene expression profiles during chronological aging with pathological changes throughout the entire murine life span (13, 26, 52, 78, 104, and 130 weeks). Relating gene expression changes to chronological aging revealed many differentially expressed genes (DEGs), and altered gene sets (AGSs) were found in most organs, indicative of intraorgan generic aging processes. However, only ≤ 1% of these DEGs are found in all organs. For each organ, at least one of 18 tested pathological parameters showed a good age‐predictive value, albeit with much inter‐ and intraindividual (organ) variation. Relating gene expression changes to pathology‐related aging revealed correlated genes and gene sets, which made it possible to characterize the difference between biological and chronological aging. In liver, kidney, and brain, a limited number of overlapping pathology‐related AGSs were found. Immune responses appeared to be common, yet the changes were specific in most organs. Furthermore, changes were observed in energy homeostasis, reactive oxygen species, cell cycle, cell motility, and DNA damage. Comparison of chronological and pathology‐related AGSs revealed substantial overlap and interesting differences. For example, the presence of immune processes in liver pathology‐related AGSs that were not detected in chronological aging. The many cellular processes that are only found employing aging‐related pathology could provide important new insights into the progress of aging.