Using DNA Methylation Profiling to Evaluate Biological Age and Longevity Interventions

Blood and Bone-Derived DNA Methylation Ages Predict Mortality After Geriatric Hip Fracture: A Pilot Study

BACKGROUND

The purpose of this study was to (1) perform the first analysis of bone-derived DNA methylation, (2) compare DNA methylation clocks derived from bone with those derived from whole blood, and (3) establish a relationship between DNA methylation age and 1-year mortality within the geriatric hip fracture population.

METHODS

Patients ≥65 years old who presented to a Level-I trauma center with a hip fracture were prospectively enrolled from 2020 to 2021. Preoperative whole blood and intraoperative bone samples were collected. Following DNA extraction, RRBS (reduced representation bisulfite sequencing) libraries for methylation clock analysis were prepared. Sequencing data were analyzed using computational algorithms previously described by Horvath et al. to build a regression model of methylation (biological) age for each tissue type. Student t tests were used to analyze differences (Δ) in methylation age versus chronological age. Correlation between blood and bone methylation ages was expressed using the Pearson R coefficient.

RESULTS

Blood and bone samples were collected from 47 patients. DNA extraction, sequencing, and methylation analysis were performed on 24 specimens from 12 subjects. Mean age at presentation was 85.4 ± 8.65 years. There was no difference in DNA extraction yield between the blood and bone samples (p = 0.935). The mean follow-up duration was 12.4 ± 4.3 months. The mortality cohort (4 patients, 33%) showed a mean ΔAgeBone of 18.33 ± 6.47 years and mean ΔAgeBlood of 16.93 ± 4.02 years. In comparison, the survival cohort showed a significantly lower mean ΔAgeBone and ΔAgeBlood (7.86 ± 6.7 and 7.31 ± 7.71 years; p = 0.026 and 0.039, respectively). Bone-derived methylation age was strongly correlated with blood-derived methylation age (R = 0.81; p = 0.0016).

CONCLUSIONS

Bone-derived DNA methylation clocks were found to be both feasible and strongly correlated with those derived from whole blood within a geriatric hip fracture population. Mortality was independently associated with the DNA methylation age, and that age was approximately 17 years greater than chronological age in the mortality cohort. The results of the present study suggest that prevention of advanced DNA methylation may play a key role in decreasing mortality following hip fracture.

LEVEL OF EVIDENCE

Prognostic Level I. See Instructions for Authors for a complete description of levels of evidence.

Epistemic uncertainty challenges aging clock reliability in predicting rejuvenation effects

Epigenetic aging clocks have been widely used to validate rejuvenation effects during cellular reprogramming. However, these predictions are unverifiable because the true biological age of reprogrammed cells remains unknown. We present an analytical framework to consider rejuvenation predictions from the uncertainty perspective. Our analysis reveals that the DNA methylation profiles across reprogramming are poorly represented in the aging data used to train clock models, thus introducing high epistemic uncertainty in age estimations. Moreover, predictions of different published clocks are inconsistent, with some even suggesting zero or negative rejuvenation. While not questioning the possibility of age reversal, we show that the high clock uncertainty challenges the reliability of rejuvenation effects observed during in vitro reprogramming before pluripotency and throughout embryogenesis. Conversely, our method reveals a significant age increase after in vivo reprogramming. We recommend including uncertainty estimation in future aging clock models to avoid the risk of misinterpreting the results of biological age prediction.

Profiling the transcriptomic age of single-cells in humans

Although aging clocks predicting the age of individual organisms have been extensively studied, the age of individual cells remained largely unexplored. Most recently single-cell omics clocks were developed for the mouse, however, extensive profiling the age of human cells is still lacking. To fill this gap, here we use available scRNA-seq data of 1,058,909 blood cells of 508 healthy, human donors (between 19 and 75 years), for developing single-cell transcriptomic clocks and predicting the age of human blood cells. By the application of the proposed cell-type-specific single-cell clocks, our main observations are that (i) transcriptomic age is associated with cellular senescence; (ii) the transcriptomic age of classical monocytes as well as naive B and T cells is decreased in moderate COVID-19 followed by an increase for some cell types in severe COVID-19; and (iii) the human embryo cells transcriptomically rejuvenated at the morulae and blastocyst stages. In summary, here we demonstrate that single-cell transcriptomic clocks are useful tools to investigate aging and rejuvenation at the single-cell level.

A cellular identity crisis? Plasticity changes during aging and rejuvenation

Cellular plasticity in adult multicellular organisms is a protective mechanism that allows certain tissues to regenerate in response to injury. Considering that aging involves exposure to repeated injuries over a lifetime, it is conceivable that cell identity itself is more malleable-and potentially erroneous-with age. In this review, we summarize and critically discuss the available evidence that cells undergo age-related shifts in identity, with an emphasis on those that contribute to age-associated pathologies, including neurodegeneration and cancer. Specifically, we focus on reported instances of programs associated with dedifferentiation, biased differentiation, acquisition of features from alternative lineages, and entry into a preneoplastic state. As some of the most promising approaches to rejuvenate cells reportedly also elicit transient changes to cell identity, we further discuss whether cell state change and rejuvenation can be uncoupled to yield more tractable therapeutic strategies.

From Reactive to Proactive - The Future Life Design to Promote Health and Extend the Human Lifespan

Disease treatment and prevention have improved the human lifespan. Current studies on aging, such as the biological clock and senolytic drugs have focused on the medical treatments of various disorders and health maintenance. However, to efficiently extend the human lifespan to its theoretical maximum, medicine can take a further proactive approach and identify the inapparent disorders that affect the gestation, body growth, and reproductive stages of the so-called "healthy" population. The goal is to upgrade the standard health status to a new level by targeting the inapparent disorders. Thus, future research can shift from reaction, response, and prevention to proactive, quality promotion and vigor prolonging; from single disease-oriented to multiple dimension protocol for a healthy body; from treatment of symptom onset to keep away from disorders; and from the healthy aging management to a healthy promotion design beginning at the birth.

Age-associated clonal B cells drive B cell lymphoma in mice

Although cancer is an age-related disease, how the processes of aging contribute to cancer progression is not well understood. In this study, we uncovered how mouse B cell lymphoma develops as a consequence of a naturally aged system. We show here that this malignancy is associated with an age-associated clonal B cell (ACBC) population that likely originates from age-associated B cells. Driven by c-Myc activation, promoter hypermethylation and somatic mutations, IgM+ ACBCs clonally expand independently of germinal centers and show increased biological age. ACBCs become self-sufficient and support malignancy when transferred into young recipients. Inhibition of mTOR or c-Myc in old mice attenuates pre-malignant changes in B cells during aging. Although the etiology of mouse and human B cell lymphomas is considered distinct, epigenetic changes in transformed mouse B cells are enriched for changes observed in human B cell lymphomas. Together, our findings characterize the spontaneous progression of cancer during aging through both cell-intrinsic and microenvironmental changes and suggest interventions for its prevention.

High-dimensional Ageome Representations of Biological Aging across Functional Modules

The aging process involves numerous molecular changes that lead to functional decline and increased disease and mortality risk. While epigenetic aging clocks have shown accuracy in predicting biological age, they typically provide single estimates for the samples and lack mechanistic insights. In this study, we challenge the paradigm that aging can be sufficiently described with a single biological age estimate. We describe Ageome, a computational framework for measuring the epigenetic age of thousands of molecular pathways simultaneously in mice and humans. Ageome is based on the premise that an organism's overall biological age can be approximated by the collective ages of its functional modules, which may age at different rates and have different biological ages. We show that, unlike conventional clocks, Ageome provides a high-dimensional representation of biological aging across cellular functions, enabling comprehensive assessment of aging dynamics within an individual, in a population, and across species. Application of Ageome to longevity intervention models revealed distinct patterns of pathway-specific age deceleration. Notably, cell reprogramming, while rejuvenating cells, also accelerated aging of some functional modules. When applied to human cohorts, Ageome demonstrated heterogeneity in predictive power for mortality risk, and some modules showed better performance in predicting the onset of age-related diseases, especially cancer, compared to existing clocks. Together, the Ageome framework offers a comprehensive and interpretable approach for assessing aging, providing insights into mechanisms and targets for intervention.

Fundamental equations linking methylation dynamics to maximum lifespan in mammals

We describe a framework that addresses concern that the rate of change in any aging biomarker displays a trivial inverse relation with maximum lifespan. We apply this framework to methylation data from the Mammalian Methylation Consortium. We study the relationship of lifespan with the average rate of change in methylation (AROCM) from two datasets: one with 90 dog breeds and the other with 125 mammalian species. After examining 54 chromatin states, we conclude three key findings: First, a reciprocal relationship exists between the AROCM in bivalent promoter regions and maximum mammalian lifespan: AROCM ∝ 1/MaxLifespan. Second, the correlation between average methylation and age bears no relation to maximum lifespan, Cor(Methyl,Age) ⊥ MaxLifespan. Third, the rate of methylation change in young animals is related to that in old animals: Young animals' AROCM ∝ Old AROCM. These findings critically hinge on the chromatin context, as different results emerge in other chromatin contexts.

Characterization of a natural model of adult mice with different rate of aging

Aging is a heterogeneous process, so individuals of the same age may be aging at a different rate. A natural model of premature aging in mice have been proposed based on the poor response to the T-maze. Those that take longer to cross the intersection are known as Prematurely Aging Mice (PAM), while those that show an exceptional response are known as Exceptional non-PAM (E-NPAM), being the rest non-PAM (NPAM). Although many aspects of PAM and E-NPAM have been described, some aspects of their brain aging have not been studied. Similarly, it is known that PAM, NPAM and E-NPAM show a different rate of aging and longevity, but the differences between these three groups in behavior, immune function and oxidative-inflammatory state are unknown. The present study aims to deepen the study of brain aging in PAM and E-NPAM, and to study the differences in behavior, immunity, and oxidative-inflammatory state of peritoneal leukocytes between PAM, NPAM and E-NPAM. Results show deteriorated brains in PAM. Moreover, NPAM show an oxidative state similar to E-NPAM, an anxiety similar to PAM, and an intermediate immunity and lifespan between PAM and E-NPAM. In conclusion, immune function seems to be more associated with the longevity achieved.

Dissecting the impact of differentiation stage, replicative history, and cell type composition on epigenetic clocks

Epigenetic clocks, built on DNA methylation patterns of bulk tissues, are powerful age predictors, but their biological basis remains incompletely understood. Here, we conducted a comparative analysis of epigenetic age in murine muscle, epithelial, and blood cell types across lifespan. Strikingly, our results show that cellular subpopulations within these tissues, including adult stem and progenitor cells as well as their differentiated progeny, exhibit different epigenetic ages. Accordingly, we experimentally demonstrate that clocks can be skewed by age-associated changes in tissue composition. Mechanistically, we provide evidence that the observed variation in epigenetic age among adult stem cells correlates with their proliferative state, and, fittingly, forced proliferation of stem cells leads to increases in epigenetic age. Collectively, our analyses elucidate the impact of cell type composition, differentiation state, and replicative potential on epigenetic age, which has implications for the interpretation of existing clocks and should inform the development of more sensitive clocks.

scEpiAge: an age predictor highlighting single-cell ageing heterogeneity in mouse blood

Ageing is the accumulation of changes and decline of function of organisms over time. The concept and biomarkers of biological age have been established, notably DNA methylation-based clocks. The emergence of single-cell DNA methylation profiling methods opens the possibility of studying the biological age of individual cells. Here, we generate a large single-cell DNA methylation and transcriptome dataset from mouse peripheral blood samples, spanning a broad range of ages. The number of genes expressed increases with age, but gene-specific changes are small. We next develop scEpiAge, a single-cell DNA methylation age predictor, which can accurately predict age in (very sparse) publicly available datasets, and also in single cells. DNA methylation age distribution is wider than technically expected, indicating epigenetic age heterogeneity and functional differences. Our work provides a foundation for single-cell and sparse data epigenetic age predictors, validates their functionality and highlights epigenetic heterogeneity during ageing.

Biomarkers for aging of blood - how transferable are they between mice and humans?

Aging significantly impacts the hematopoietic system, reducing its regenerative capacity and ability to restore homeostasis after stress. Mouse models have been invaluable in studying this process due to their shorter lifespan and the ability to explore genetic, treatment, and environmental influences on aging. However, not all aspects of aging are mirrored between species. This review compares three key aging biomarkers in the hematopoietic systems of mice and humans: myeloid bias, telomere attrition, and epigenetic clocks. Myeloid bias, marked by an increased fraction of myeloid cells and decreased lymphoid cells, is a significant aging marker in mice but is scarcely observed in humans after childhood. Conversely, telomere length is a robust aging biomarker in humans, whereas mice exhibit significantly different telomere dynamics, making telomere length less reliable in the murine system. Epigenetic clocks, based on DNA methylation changes at specific genomic regions, provide precise estimates of chronologic age in both mice and humans. Notably, age-associated regions in mice and humans occur at homologous genomic locations. Epigenetic clocks, depending on the epigenetic signatures used, also capture aspects of biological aging, offering powerful tools to assess genetic and environmental impacts on aging. Taken together, not all blood aging biomarkers are transferable between mice and humans. When using murine models to extrapolate human aging, it may be advantageous to focus on aging phenomena observed in both species. In conclusion, although mouse models offer significant insights, selecting appropriate biomarkers is crucial for translating findings to human aging.

Targeting neuronal epigenomes for brain rejuvenation

Aging is associated with a progressive decline of brain function, and the underlying causes and possible interventions to prevent this cognitive decline have been the focus of intense investigation. The maintenance of neuronal function over the lifespan requires proper epigenetic regulation, and accumulating evidence suggests that the deterioration of the neuronal epigenetic landscape contributes to brain dysfunction during aging. Epigenetic aging of neurons may, however, be malleable. Recent reports have shown age-related epigenetic changes in neurons to be reversible and targetable by rejuvenation strategies that can restore brain function during aging. This review discusses the current evidence that identifies neuronal epigenetic aging as a driver of cognitive decline and a promising target of brain rejuvenation strategies, and it highlights potential approaches for the specific manipulation of the aging neuronal epigenome to restore a youthful epigenetic state in the brain.

Frailty and biological age. Which best describes our aging and longevity?

Frailty and Biological Age are two closely related concepts; however, frailty is a multisystem geriatric syndrome that applies to elderly subjects, whereas biological age is a gerontologic way to describe the rate of aging of each individual, which can be used from the beginning of the aging process, in adulthood. If frailty reaches less consensus on the definition, it is a term much more widely used than this of biological age, which shows a clearer definition but is scarcely employed in social and medical fields. In this review, we suggest that this Biological Age is the best to describe how we are aging and determine our longevity, and several examples support our proposal.

Epigenetic clock in the aorta and age-related endothelial dysfunction in mice

While epigenetic age (EA) of mouse blood can be determined using DNA methylation analysis at three CpG sites in the Prima1, Hsf4 and Kcns1 genes it is not known whether this approach is useful for predicting vascular biological age. In this study we validated the 3-CpG estimator for age prediction in mouse blood, developed a new predictive model for EA in mouse aorta, and assessed whether epigenetic age acceleration (EAA) measured with blood and aorta samples correlates with age-dependent endothelial dysfunction. Endothelial function was characterized in vivo by MRI in 8-96-week-old C57BL/6 mice. Arterial stiffness was measured by USG-doppler. EA-related changes within 41 CpG sites in Prima1, Kcns1 and Hsf4 loci, were analyzed in the aorta and blood using bisulfite amplicon high-throughput sequencing. Progressive age-dependent endothelial dysfunction and changes in arterial stiffness were observed in 36-96-week-old C57BL/6 mice. Methylation levels of the investigated loci correlated with chronological age in blood and the aorta. The new model for EA estimation in aorta included three cytosines located in the Kcns1 and Hsf4, explained R2 = 87.8% of the variation in age, and predicted age with an mean absolute error of 9.6 weeks in the independent test set. EAA in the aorta was associated with endothelial dysfunction in the abdominal aorta and femoral artery what was consistent with the EAA direction estimated in blood samples. The rate of vascular biological ageing in mice, reflected by the age-dependent systemic endothelial dysfunction, could be estimated using DNA methylation measurements at three loci in aorta and blood samples.

Rejuvenation of the Aging Heart: Molecular Determinants and Applications

In Canada and worldwide, the elderly population (ie, individuals > 65 years of age) is increasing disproportionately relative to the total population. This is expected to have a substantial impact on the health care system, as increased aged is associated with a greater incidence of chronic noncommunicable diseases. Within the elderly population, cardiovascular disease is a leading cause of death, therefore developing therapies that can prevent or slow disease progression in this group is highly desirable. Historically, aging research has focused on the development of anti-aging therapies that are implemented early in life and slow the age-dependent decline in cell and organ function. However, accumulating evidence supports that late-in-life therapies can also benefit the aged cardiovascular system by limiting age-dependent functional decline. Moreover, recent studies have demonstrated that rejuvenation (ie, reverting cellular function to that of a younger phenotype) of the already aged cardiovascular system is possible, opening new avenues to develop therapies for older individuals. In this review, we first provide an overview of the functional changes that occur in the cardiomyocyte with aging and how this contributes to the age-dependent decline in heart function. We then discuss the various anti-aging and rejuvenation strategies that have been pursued to improve the function of the aged cardiomyocyte, with a focus on therapies implemented late in life. These strategies include 1) established systemic approaches (caloric restriction, exercise), 2) pharmacologic approaches (mTOR, AMPK, SIRT1, and autophagy-targeting molecules), and 3) emerging rejuvenation approaches (partial reprogramming, parabiosis/modulation of circulating factors, targeting endogenous stem cell populations, and senotherapeutics). Collectively, these studies demonstrate the exciting potential and limitations of current rejuvenation strategies and highlight future areas of investigation that will contribute to the development of rejuvenation therapies for the aged heart.

The role of ribosomal DNA methylation in embryonic development, aging and diseases

The ribosomal DNA (rDNA) constitutes a remarkably conserved DNA sequence within species, located in the area of the nucleolus, and responsible for coding three major types of rRNAs (18S, 5.8S and 28S). While historical investigations into rDNA focused on its structure and coding capabilities, recent research has turned to explore its functional roles in various biological processes. In this review, we summarize the main findings of rDNA methylation with embryonic development, aging and diseases in multiple species, including epigenetic alterations, related biological processes and potential applications of rDNA methylation. We present an overview of current related research and identify gaps in this field.

Epigenetic aging studies of pair bonding in prairie voles

The quality of romantic relationships can predict health consequences related to aging. DNA methylation-based biomarkers of aging accurately estimate chronological age. We developed several highly accurate epigenetic aging clocks, based on highly conserved mammalian CpGs, for the socially monogamous prairie vole (Microtus ochrogaster). In addition, our dual-species human-vole clock accurately measured relative age and illustrates high species conservation of epigenetic aging effects. Next, we assessed how pair bonding impacts epigenetic aging. We did not find evidence that pair-bonded voles exhibit accelerated or decelerated epigenetic aging effects in blood, ear, liver, or brain tissue. Our epigenome wide association study identified CpGs in five genes strongly associated with pair bonding: Foxp4, Phf2, Mms22l, Foxb1, and Eif1ad. Overall, we present accurate DNA methylation-based estimators of age for a species of great interest to researchers studying monogamy in animals. We did not find any evidence that sex-naive animals age differently from pair-bonded animals.

Spatiotemporal transcriptomic profiling and modeling of mouse brain at single-cell resolution reveals cell proximity effects of aging and rejuvenation

Old age is associated with a decline in cognitive function and an increase in neurodegenerative disease risk1. Brain aging is complex and accompanied by many cellular changes2-20. However, the influence that aged cells have on neighboring cells and how this contributes to tissue decline is unknown. More generally, the tools to systematically address this question in aging tissues have not yet been developed. Here, we generate spatiotemporal data at single-cell resolution for the mouse brain across lifespan, and we develop the first machine learning models based on spatial transcriptomics ('spatial aging clocks') to reveal cell proximity effects during brain aging and rejuvenation. We collect a single-cell spatial transcriptomics brain atlas of 4.2 million cells from 20 distinct ages and across two rejuvenating interventions-exercise and partial reprogramming. We identify spatial and cell type-specific transcriptomic fingerprints of aging, rejuvenation, and disease, including for rare cell types. Using spatial aging clocks and deep learning models, we find that T cells, which infiltrate the brain with age, have a striking pro-aging proximity effect on neighboring cells. Surprisingly, neural stem cells have a strong pro-rejuvenating effect on neighboring cells. By developing computational tools to identify mediators of these proximity effects, we find that pro-aging T cells trigger a local inflammatory response likely via interferon-γ whereas pro-rejuvenating neural stem cells impact the metabolism of neighboring cells possibly via growth factors (e.g. vascular endothelial growth factor) and extracellular vesicles, and we experimentally validate some of these predictions. These results suggest that rare cells can have a drastic influence on their neighbors and could be targeted to counter tissue aging. We anticipate that these spatial aging clocks will not only allow scalable assessment of the efficacy of interventions for aging and disease but also represent a new tool for studying cell-cell interactions in many spatial contexts.

PRC2-AgeIndex as a universal biomarker of aging and rejuvenation

DNA methylation (DNAm) is one of the most reliable biomarkers of aging across mammalian tissues. While the age-dependent global loss of DNAm has been well characterized, DNAm gain is less characterized. Studies have demonstrated that CpGs which gain methylation with age are enriched in Polycomb Repressive Complex 2 (PRC2) targets. However, whole-genome examination of all PRC2 targets as well as determination of the pan-tissue or tissue-specific nature of these associations is lacking. Here, we show that low-methylated regions (LMRs) which are highly bound by PRC2 in embryonic stem cells (PRC2 LMRs) gain methylation with age in all examined somatic mitotic cells. We estimated that this epigenetic change represents around 90% of the age-dependent DNAm gain genome-wide. Therefore, we propose the "PRC2-AgeIndex," defined as the average DNAm in PRC2 LMRs, as a universal biomarker of cellular aging in somatic cells which can distinguish the effect of different anti-aging interventions.

Towards Healthy Longevity: Comprehensive Insights from Molecular Targets and Biomarkers to Biological Clocks

Aging is a complex and time-dependent decline in physiological function that affects most organisms, leading to increased risk of age-related diseases. Investigating the molecular underpinnings of aging is crucial to identify geroprotectors, precisely quantify biological age, and propose healthy longevity approaches. This review explores pathways that are currently being investigated as intervention targets and aging biomarkers spanning molecular, cellular, and systemic dimensions. Interventions that target these hallmarks may ameliorate the aging process, with some progressing to clinical trials. Biomarkers of these hallmarks are used to estimate biological aging and risk of aging-associated disease. Utilizing aging biomarkers, biological aging clocks can be constructed that predict a state of abnormal aging, age-related diseases, and increased mortality. Biological age estimation can therefore provide the basis for a fine-grained risk stratification by predicting all-cause mortality well ahead of the onset of specific diseases, thus offering a window for intervention. Yet, despite technological advancements, challenges persist due to individual variability and the dynamic nature of these biomarkers. Addressing this requires longitudinal studies for robust biomarker identification. Overall, utilizing the hallmarks of aging to discover new drug targets and develop new biomarkers opens new frontiers in medicine. Prospects involve multi-omics integration, machine learning, and personalized approaches for targeted interventions, promising a healthier aging population.

Aging clocks based on accumulating stochastic variation

Aging clocks have provided one of the most important recent breakthroughs in the biology of aging, and may provide indicators for the effectiveness of interventions in the aging process and preventive treatments for age-related diseases. The reproducibility of accurate aging clocks has reinvigorated the debate on whether a programmed process underlies aging. Here we show that accumulating stochastic variation in purely simulated data is sufficient to build aging clocks, and that first-generation and second-generation aging clocks are compatible with the accumulation of stochastic variation in DNA methylation or transcriptomic data. We find that accumulating stochastic variation is sufficient to predict chronological and biological age, indicated by significant prediction differences in smoking, calorie restriction, heterochronic parabiosis and partial reprogramming. Although our simulations may not explicitly rule out a programmed aging process, our results suggest that stochastically accumulating changes in any set of data that have a ground state at age zero are sufficient for generating aging clocks.

Nature of epigenetic aging from a single-cell perspective

Age-related changes in DNA methylation (DNAm) form the basis of the most robust predictors of age-epigenetic clocks-but a clear mechanistic understanding of exactly which aspects of aging are quantified by these clocks is lacking. Here, to clarify the nature of epigenetic aging, we juxtapose the dynamics of tissue and single-cell DNAm in mice. We compare these changes during early development with those observed during adult aging in mice, and corroborate our analyses with a single-cell RNA sequencing analysis within the same multiomics dataset. We show that epigenetic aging involves co-regulated changes as well as a major stochastic component, and this is consistent with transcriptional patterns. We further support the finding of stochastic epigenetic aging by direct tissue and single-cell DNAm analyses and modeling of aging DNAm trajectories with a stochastic process akin to radiocarbon decay. Finally, we describe a single-cell algorithm for the identification of co-regulated and stochastic CpG clusters showing consistent transcriptomic coordination patterns. Together, our analyses increase our understanding of the basis of epigenetic clocks and highlight potential opportunities for targeting aging and evaluating longevity interventions.

Targeting the epigenetically older individuals for geroprotective trials: the use of DNA methylation clocks

Chronological age is the most important risk factor for the incidence of age-related diseases. The pace of ageing determines the magnitude of that risk and can be expressed as biological age. Targeting fundamental pathways of human aging with geroprotectors has the potential to lower the biological age and therewith prolong the healthspan, the period of life one spends in good health. Target populations for geroprotective interventions should be chosen based on the ageing mechanisms being addressed and the expected effect of the geroprotector on the primary outcome. Biomarkers of ageing, such as DNA methylation age, can be used to select populations for geroprotective interventions and as a surrogate outcome. Here, the use of DNA methylation clocks for selecting target populations for geroprotective intervention is explored.

A Youthful Touch: Reversal of Aging Hallmarks by Cell Reprogramming

BACKGROUND

With the elderly population projected to double by 2050, there is an urgent need to address the increasing prevalence of age-related debilitating diseases and ultimately minimize discrepancies between the rising lifespan and stagnant health span. Cellular reprogramming by overexpression of Oct3/4, Klf4, Sox2, and cMyc (OKSM) transcription factors is gaining attention in this context thanks to demonstrated rejuvenating effects in human cell cultures and live mice, many of which can be uncoupled from dedifferentiation and loss of cell identity.

SUMMARY

Here, we review current evidence of the impact of cell reprogramming on established aging hallmarks and the underlying mechanisms that mediate these effects. We also provide a critical assessment of the challenges in translating these findings and, overall, cell reprogramming technologies into clinically translatable antiaging interventions.

KEY MESSAGES

Cellular reprogramming has the potential to reverse at least partially some key hallmarks of aging. However, further research is necessary to determine the biological significance and duration of such changes and to ensure the safety of cell reprogramming as a rejuvenation approach. With this review, we hope to stimulate new research directions in the quest to extend health span effectively.

Age Prediction Using DNA Methylation Heterogeneity Metrics

Dynamic changes in genomic DNA methylation patterns govern the epigenetic developmental programs and accompany the organism's aging. Epigenetic clock (eAge) algorithms utilize DNA methylation to estimate the age and risk factors for diseases as well as analyze the impact of various interventions. High-throughput bisulfite sequencing methods, such as reduced-representation bisulfite sequencing (RRBS) or whole genome bisulfite sequencing (WGBS), provide an opportunity to identify the genomic regions of disordered or heterogeneous DNA methylation, which might be associated with cell-type heterogeneity, DNA methylation erosion, and allele-specific methylation. We systematically evaluated the applicability of five scores assessing the variability of methylation patterns by evaluating within-sample heterogeneity (WSH) to construct human blood epigenetic clock models using RRBS data. The best performance was demonstrated by the model based on a metric designed to assess DNA methylation erosion with an MAE of 3.686 years. We also trained a prediction model that uses the average methylation level over genomic regions. Although this region-based model was relatively more efficient than the WSH-based model, the latter required the analysis of just a few short genomic regions and, therefore, could be a useful tool to design a reduced epigenetic clock that is analyzed by targeted next-generation sequencing.

MammalMethylClock R package: software for DNA methylation-based epigenetic clocks in mammals

MOTIVATION

Epigenetic clocks are prediction methods based on DNA methylation levels in a given species or set of species. Defined as multivariate regression models, these DNA methylation-based biomarkers of age or mortality risk are useful in species conservation efforts and in preclinical studies.

RESULTS

We present an R package called MammalMethylClock for the construction, assessment, and application of epigenetic clocks in different mammalian species. The R package includes the utility for implementing pre-existing mammalian clocks from the Mammalian Methylation Consortium.

AVAILABILITY AND IMPLEMENTATION

The source code and documentation manual for MammalMethylClock, and clock coefficient .csv files that are included within this software package, can be found on Zenodo at https://doi.org/10.5281/zenodo.10971037.

Prognosis algorithms for acute decompensation of cirrhosis and ACLF

Accurate prediction of survival in patients with cirrhosis is crucial, as patients who are unlikely to survive in the short-term need to be oriented to liver transplantation and to novel therapeutic approaches. Patients with acute decompensation of cirrhosis without or with organ dysfunction/failure, the so-called acute-on-chronic liver failure (ACLF), have a particularly high short-term mortality. Recognizing the specificity of this clinical situation, dedicated classifications and scores have been developed over the last 15 years, including variables (e.g. organ failures and systemic inflammation) not part of the formerly available cirrhosis severity scores, namely Child-Pugh score or MELD. For patients with acute decompensation of cirrhosis, it led to the development of a dedicated score, the Clif-C-AD score, independently validated. For more severe patients, three different scoring systems have been proposed, by European, Asian and North American societies namely Clif-C-ACLF, AARC score and NASCELD-ACLF respectively. These scores have been validated, and are widely used across the world. The differences and similarities between these scores, as well as their validation and limitations are discussed here. Even if these scores and classifications have been a step forward in favouring homogeneity between studies, and in helping making decisions for individual patients, their predictive value for mortality can still be improved as their area under the ROC curve does not exceed .8. Novel scores including biomarkers reflecting the pathophysiology of acute decompensation of cirrhosis might help reach that goal.

A cost-effective blood DNA methylation-based age estimation method in domestic cats, Tsushima leopard cats (Prionailurus bengalensis euptilurus) and Panthera species, using targeted bisulphite sequencing and machine learning models

Individual age can be used to design more efficient and suitable management plans in both in situ and ex situ conservation programmes for targeted wildlife species. DNA methylation is a promising marker of epigenetic ageing that can accurately estimate age from small amounts of biological material, which can be collected in a minimally invasive manner. In this study, we sequenced five targeted genetic regions and used 8-23 selected CpG sites to build age estimation models using machine learning methods at only about $3-7 per sample. Blood samples of seven Felidae species were used, ranging from small to big, and domestic to endangered species: domestic cats (Felis catus, 139 samples), Tsushima leopard cats (Prionailurus bengalensis euptilurus, 84 samples) and five Panthera species (96 samples). The models achieved satisfactory accuracy, with the mean absolute error of the most accurate models recorded at 1.966, 1.348 and 1.552 years in domestic cats, Tsushima leopard cats and Panthera spp. respectively. We developed the models in domestic cats and Tsushima leopard cats, which were applicable to individuals regardless of health conditions; therefore, these models are applicable to samples collected from individuals with diverse characteristics, which is often the case in conservation. We also showed the possibility of developing universal age estimation models for the five Panthera spp. using only two of the five genetic regions. We do not recommend building a common age estimation model for all the target species using our markers, because of the degraded performance of models that included all species.

pyaging: a Python-based compendium of GPU-optimized aging clocks

MOTIVATION

Aging is intricately linked to diseases and mortality. It is reflected in molecular changes across various tissues which can be leveraged for the development of biomarkers of aging using machine learning models, known as aging clocks. Despite advancements in the field, a significant challenge remains: the lack of robust, Python-based software tools for integrating and comparing these diverse models. This gap highlights the need for comprehensive solutions that can handle the complexity and variety of data in aging research.

RESULTS

To address this gap, I introduce pyaging, a comprehensive open-source Python package designed to facilitate aging research. pyaging harmonizes dozens of aging clocks, covering a range of molecular data types such as DNA methylation, transcriptomics, histone mark ChIP-Seq, and ATAC-Seq. The package is not limited to traditional model types; it features a diverse array, from linear and principal component models to neural networks and automatic relevance determination models. Thanks to a PyTorch-based backend that enables GPU acceleration, pyaging is capable of rapid inference, even when dealing with large datasets and complex models. In addition, the package's support for multi-species analysis extends its utility across various organisms, including humans, various mammals, and Caenorhabditis elegans.

AVAILABILITY AND IMPLEMENTATION

pyaging is accessible on GitHub, at https://github.com/rsinghlab/pyaging, and the distribution is available on PyPi, at https://pypi.org/project/pyaging/. The software is also archived on Zenodo, at https://zenodo.org/doi/10.5281/zenodo.10335011.

Multi-omics characterization of partial chemical reprogramming reveals evidence of cell rejuvenation

Partial reprogramming by cyclic short-term expression of Yamanaka factors holds promise for shifting cells to younger states and consequently delaying the onset of many diseases of aging. However, the delivery of transgenes and potential risk of teratoma formation present challenges for in vivo applications. Recent advances include the use of cocktails of compounds to reprogram somatic cells, but the characteristics and mechanisms of partial cellular reprogramming by chemicals remain unclear. Here, we report a multi-omics characterization of partial chemical reprogramming in fibroblasts from young and aged mice. We measured the effects of partial chemical reprogramming on the epigenome, transcriptome, proteome, phosphoproteome, and metabolome. At the transcriptome, proteome, and phosphoproteome levels, we saw widescale changes induced by this treatment, with the most notable signature being an upregulation of mitochondrial oxidative phosphorylation. Furthermore, at the metabolome level, we observed a reduction in the accumulation of aging-related metabolites. Using both transcriptomic and epigenetic clock-based analyses, we show that partial chemical reprogramming reduces the biological age of mouse fibroblasts. We demonstrate that these changes have functional impacts, as evidenced by changes in cellular respiration and mitochondrial membrane potential. Taken together, these results illuminate the potential for chemical reprogramming reagents to rejuvenate aged biological systems and warrant further investigation into adapting these approaches for in vivo age reversal.

Reversal of biological age in multiple rat organs by young porcine plasma fraction

Young blood plasma is known to confer beneficial effects on various organs in mice and rats. However, it was not known whether plasma from young adult pigs rejuvenates old rat tissues at the epigenetic level; whether it alters the epigenetic clock, which is a highly accurate molecular biomarker of aging. To address this question, we developed and validated six different epigenetic clocks for rat tissues that are based on DNA methylation values derived from n = 613 tissue samples. As indicated by their respective names, the rat pan-tissue clock can be applied to DNA methylation profiles from all rat tissues, while the rat brain, liver, and blood clocks apply to the corresponding tissue types. We also developed two epigenetic clocks that apply to both human and rat tissues by adding n = 1366 human tissue samples to the training data. We employed these six rat clocks to investigate the rejuvenation effects of a porcine plasma fraction treatment in different rat tissues. The treatment more than halved the epigenetic ages of blood, heart, and liver tissue. A less pronounced, but statistically significant, rejuvenation effect could be observed in the hypothalamus. The treatment was accompanied by progressive improvement in the function of these organs as ascertained through numerous biochemical/physiological biomarkers, behavioral responses encompassing cognitive functions. An immunoglobulin G (IgG) N-glycosylation pattern shift from pro- to anti-inflammatory also indicated reversal of glycan aging. Overall, this study demonstrates that a young porcine plasma-derived treatment markedly reverses aging in rats according to epigenetic clocks, IgG glycans, and other biomarkers of aging.

Causality-enriched epigenetic age uncouples damage and adaptation

Machine learning models based on DNA methylation data can predict biological age but often lack causal insights. By harnessing large-scale genetic data through epigenome-wide Mendelian randomization, we identified CpG sites potentially causal for aging-related traits. Neither the existing epigenetic clocks nor age-related differential DNA methylation are enriched in these sites. These CpGs include sites that contribute to aging and protect against it, yet their combined contribution negatively affects age-related traits. We established a new framework to introduce causal information into epigenetic clocks, resulting in DamAge and AdaptAge-clocks that track detrimental and adaptive methylation changes, respectively. DamAge correlates with adverse outcomes, including mortality, while AdaptAge is associated with beneficial adaptations. These causality-enriched clocks exhibit sensitivity to short-term interventions. Our findings provide a detailed landscape of CpG sites with putative causal links to lifespan and healthspan, facilitating the development of aging biomarkers, assessing interventions, and studying reversibility of age-associated changes.

DNA methylation clocks for clawed frogs reveal evolutionary conservation of epigenetic aging

To address how conserved DNA methylation-based epigenetic aging is in diverse branches of the tree of life, we generated DNA methylation data from African clawed frogs (Xenopus laevis) and Western clawed frogs (Xenopus tropicalis) and built multiple epigenetic clocks. Dual species clocks were developed that apply to both humans and frogs (human-clawed frog clocks), supporting that epigenetic aging processes are evolutionary conserved outside mammals. Highly conserved positively age-related CpGs are located in neural-developmental genes such as uncx, tfap2d as well as nr4a2 implicated in age-associated disease. We conclude that signatures of epigenetic aging are evolutionary conserved between frogs and mammals and that the associated genes relate to neural processes, altogether opening opportunities to employ Xenopus as a model organism to study aging.

TIME-seq reduces time and cost of DNA methylation measurement for epigenetic clock construction

Epigenetic 'clocks' based on DNA methylation have emerged as the most robust and widely used aging biomarkers, but conventional methods for applying them are expensive and laborious. Here we develop tagmentation-based indexing for methylation sequencing (TIME-seq), a highly multiplexed and scalable method for low-cost epigenetic clocks. Using TIME-seq, we applied multi-tissue and tissue-specific epigenetic clocks in over 1,800 mouse DNA samples from eight tissue and cell types. We show that TIME-seq clocks are accurate and robust, enriched for polycomb repressive complex 2-regulated loci, and benchmark favorably against conventional methods despite being up to 100-fold less expensive. Using dietary treatments and gene therapy, we find that TIME-seq clocks reflect diverse interventions in multiple tissues. Finally, we develop an economical human blood clock (R > 0.96, median error = 3.39 years) in 1,056 demographically representative individuals. These methods will enable more efficient epigenetic clock measurement in larger-scale human and animal studies.

Epigenetic drift underlies epigenetic clock signals, but displays distinct responses to lifespan interventions, development, and cellular dedifferentiation

Changes in DNA methylation with age are observed across the tree of life. The stereotypical nature of these changes can be modeled to produce epigenetic clocks capable of predicting chronological age with unprecedented accuracy. Despite the predictive ability of epigenetic clocks and their utility as biomarkers in clinical applications, the underlying processes that produce clock signals are not fully resolved, which limits their interpretability. Here, we develop a computational approach to spatially resolve the within read variability or "disorder" in DNA methylation patterns and test if age-associated changes in DNA methylation disorder underlie signals comprising epigenetic clocks. We find that epigenetic clock loci are enriched in regions that both accumulate and lose disorder with age, suggesting a link between DNA methylation disorder and epigenetic clocks. We then develop epigenetic clocks that are based on regional disorder of DNA methylation patterns and compare their performance to other epigenetic clocks by investigating the influences of development, lifespan interventions, and cellular dedifferentiation. We identify common responses as well as critical differences between canonical epigenetic clocks and those based on regional disorder, demonstrating a fundamental decoupling of epigenetic aging processes. Collectively, we identify key linkages between epigenetic disorder and epigenetic clocks and demonstrate the multifaceted nature of epigenetic aging in which stochastic processes occurring at non-random loci produce predictable outcomes.

The ticking of aging clocks

Computational models that measure biological age and aging rate regardless of chronological age are called aging clocks. The underlying counting mechanisms of the intrinsic timers of these clocks are still unclear. Molecular mediators and determinants of aging rate point to the key roles of DNA damage, epigenetic drift, and inflammation. Persistent DNA damage leads to cellular senescence and the senescence-associated secretory phenotype (SASP), which induces cytotoxic immune cell infiltration; this further induces DNA damage through reactive oxygen and nitrogen species (RONS). I discuss the possibility that DNA damage (or the response to it, including epigenetic changes) is the fundamental counting unit of cell cycles and cellular senescence, that ultimately accounts for cell composition changes and functional decline in tissues, as well as the key intervention points.

Epigenetic Mechanisms in Hematologic Aging and Premalignant Conditions

Hematopoietic stem cells (HSCs) are essential for maintaining overall health by continuously generating blood cells throughout an individual's lifespan. However, as individuals age, the hematopoietic system undergoes significant functional decline, rendering them more susceptible to age-related diseases. Growing research evidence has highlighted the critical role of epigenetic regulation in this age-associated decline. This review aims to provide an overview of the diverse epigenetic mechanisms involved in the regulation of normal HSCs during the aging process and their implications in aging-related diseases. Understanding the intricate interplay of epigenetic mechanisms that contribute to aging-related changes in the hematopoietic system holds great potential for the development of innovative strategies to delay the aging process. In fact, interventions targeting epigenetic modifications have shown promising outcomes in alleviating aging-related phenotypes and extending lifespan in various animal models. Small molecule-based therapies and reprogramming strategies enabling epigenetic rejuvenation have emerged as effective approaches for ameliorating or even reversing aging-related conditions. By acquiring a deeper understanding of these epigenetic mechanisms, it is anticipated that interventions can be devised to prevent or mitigate the rates of hematologic aging and associated diseases later in life. Ultimately, these advancements have the potential to improve overall health and enhance the quality of life in aging individuals.

How is Big Data reshaping preclinical aging research?

The exponential scientific and technological progress during the past 30 years has favored the comprehensive characterization of aging processes with their multivariate nature, leading to the advent of Big Data in preclinical aging research. Spanning from molecular omics to organism-level deep phenotyping, Big Data demands large computational resources for storage and analysis, as well as new analytical tools and conceptual frameworks to gain novel insights leading to discovery. Systems biology has emerged as a paradigm that utilizes Big Data to gain insightful information enabling a better understanding of living organisms, visualized as multilayered networks of interacting molecules, cells, tissues and organs at different spatiotemporal scales. In this framework, where aging, health and disease represent emergent states from an evolving dynamic complex system, context given by, for example, strain, sex and feeding times, becomes paramount for defining the biological trajectory of an organism. Using bioinformatics and artificial intelligence, the systems biology approach is leading to remarkable advances in our understanding of the underlying mechanism of aging biology and assisting in creative experimental study designs in animal models. Future in-depth knowledge acquisition will depend on the ability to fully integrate information from different spatiotemporal scales in organisms, which will probably require the adoption of theories and methods from the field of complex systems. Here we review state-of-the-art approaches in preclinical research, with a focus on rodent models, that are leading to conceptual and/or technical advances in leveraging Big Data to understand basic aging biology and its full translational potential.

Multi-omics characterization of partial chemical reprogramming reveals evidence of cell rejuvenation

Partial reprogramming by cyclic short-term expression of Yamanaka factors holds promise for shifting cells to younger states and consequently delaying the onset of many diseases of aging. However, the delivery of transgenes and potential risk of teratoma formation present challenges for in vivo applications. Recent advances include the use of cocktails of compounds to reprogram somatic cells, but the characteristics and mechanisms of partial cellular reprogramming by chemicals remain unclear. Here, we report a multi-omics characterization of partial chemical reprogramming in fibroblasts from young and aged mice. We measured the effects of partial chemical reprogramming on the epigenome, transcriptome, proteome, phosphoproteome, and metabolome. At the transcriptome, proteome, and phosphoproteome levels, we saw widescale changes induced by this treatment, with the most notable signature being an upregulation of mitochondrial oxidative phosphorylation. Furthermore, at the metabolome level, we observed a reduction in the accumulation of aging-related metabolites. Using both transcriptomic and epigenetic clock-based analyses, we show that partial chemical reprogramming reduces the biological age of mouse fibroblasts. We demonstrate that these changes have functional impacts, as evidenced by changes in cellular respiration and mitochondrial membrane potential. Taken together, these results illuminate the potential for chemical reprogramming reagents to rejuvenate aged biological systems and warrant further investigation into adapting these approaches for in vivo age reversal.

The rate of epigenetic drift scales with maximum lifespan across mammals

Epigenetic drift or "disorder" increases across the mouse lifespan and is suggested to underlie epigenetic clock signals. While the role of epigenetic drift in determining maximum lifespan across species has been debated, robust tests of this hypothesis are lacking. Here, we test if epigenetic disorder at various levels of genomic resolution explains maximum lifespan across four mammal species. We show that epigenetic disorder increases with age in all species and at all levels of genomic resolution tested. The rate of disorder accumulation occurs faster in shorter lived species and corresponds to species adjusted maximum lifespan. While the density of cytosine-phosphate-guanine dinucleotides ("CpGs") is negatively associated with the rate of age-associated disorder accumulation, it does not fully explain differences across species. Our findings support the hypothesis that the rate of epigenetic drift explains maximum lifespan and provide partial support for the hypothesis that CpG density buffers against epigenetic drift.

Microgravity Effects and Aging Physiology: Similar Changes or Common Mechanisms?

Despite the use of countermeasures (including intense physical activity), cosmonauts and astronauts develop muscle atony and atrophy, cardiovascular system failure, osteopenia, etc. All these changes, reminiscent of age-related physiological changes, occur in a healthy person in microgravity quite quickly - within a few months. Adaptation to the lost of gravity leads to the symptoms of aging, which are compensated after returning to Earth. The prospect of interplanetary flights raises the question of gravity thresholds, below which the main physiological systems will decrease their functional potential, similar to aging, and affect life expectancy. An important role in the aging process belongs to the body's cellular reserve - progenitor cells, which are involved in physiological remodeling and regenerative/reparative processes of all physiological systems. With age, progenitor cell count and their regenerative potential decreases. Moreover, their paracrine profile becomes pro-inflammatory during replicative senescence, disrupting tissue homeostasis. Mesenchymal stem/stromal cells (MSCs) are mechanosensitive, and therefore deprivation of gravitational stimulus causes serious changes in their functional status. The review compares the cellular effects of microgravity and changes developing in senescent cells, including stromal precursors.

Modeling the consequences of age-linked rDNA hypermethylation with dCas9-directed DNA methylation in human cells

Ribosomal DNA (rDNA) genes encode the structural RNAs of the ribosome and are present in hundreds of copies in mammalian genomes. Age-linked DNA hypermethylation throughout the rDNA constitutes a robust "methylation clock" that accurately reports age, yet the consequences of hypermethylation on rDNA function are unknown. We confirmed that pervasive hypermethylation of rDNA occurs during mammalian aging and senescence while rDNA copy number remains stable. We found that DNA methylation is exclusively found on the promoters and gene bodies of inactive rDNA. To model the effects of age-linked methylation on rDNA function, we directed de novo DNA methylation to the rDNA promoter or gene body with a nuclease-dead Cas9 (dCas9) - DNA methyltransferase fusion enzyme in human cells. Hypermethylation at each target site had no detectable effect on rRNA transcription, nucleolar morphology, or cellular growth rate. Instead, human UBF and Pol I remain bound to rDNA promoters in the presence of increased DNA methylation. These data suggest that promoter methylation is not sufficient to impair transcription of the human rDNA and imply that the human rDNA transcription machinery may be resilient to age-linked rDNA hypermethylation.

Reappraisal of the Concept of Accelerated Aging in Neurodegeneration and Beyond

BACKGROUND

Genetic and epigenetic changes, oxidative stress and inflammation influence the rate of aging, which diseases, lifestyle and environmental factors can further accelerate. In accelerated aging (AA), the biological age exceeds the chronological age.

OBJECTIVE

The objective of this study is to reappraise the AA concept critically, considering its weaknesses and limitations.

METHODS

We reviewed more than 300 recent articles dealing with the physiology of brain aging and neurodegeneration pathophysiology.

RESULTS

(1) Application of the AA concept to individual organs outside the brain is challenging as organs of different systems age at different rates. (2) There is a need to consider the deceleration of aging due to the potential use of the individual structure-functional reserves. The latter can be restored by pharmacological and/or cognitive therapy, environment, etc. (3) The AA concept lacks both standardised terminology and methodology. (4) Changes in specific molecular biomarkers (MBM) reflect aging-related processes; however, numerous MBM candidates should be validated to consolidate the AA theory. (5) The exact nature of many potential causal factors, biological outcomes and interactions between the former and the latter remain largely unclear.

CONCLUSIONS

Although AA is commonly recognised as a perspective theory, it still suffers from a number of gaps and limitations that assume the necessity for an updated AA concept.

The impact of life stress on hallmarks of aging and accelerated senescence: Connections in sickness and in health

Chronic stress is a risk factor for numerous aging-related diseases and has been shown to shorten lifespan in humans and other social mammals. Yet how life stress causes such a vast range of diseases is still largely unclear. In recent years, the impact of stress on health and aging has been increasingly associated with the dysregulation of the so-called hallmarks of aging. These are basic biological mechanisms that influence intrinsic cellular functions and whose alteration can lead to accelerated aging. Here, we review correlational and experimental literature (primarily focusing on evidence from humans and murine models) on the contribution of life stress - particularly stress derived from adverse social environments - to trigger hallmarks of aging, including cellular senescence, sterile inflammation, telomere shortening, production of reactive oxygen species, DNA damage, and epigenetic changes. We also evaluate the validity of stress-induced senescence and accelerated aging as an etiopathological proposition. Finally, we highlight current gaps of knowledge and future directions for the field, and discuss perspectives for translational geroscience.

New Possibilities for Evaluating the Development of Age-Related Pathologies Using the Dynamical Network Biomarkers Theory

Aging is the slowest process in a living organism. During this process, mortality rate increases exponentially due to the accumulation of damage at the cellular level. Cellular senescence is a well-established hallmark of aging, as well as a promising target for preventing aging and age-related diseases. However, mapping the senescent cells in tissues is extremely challenging, as their low abundance, lack of specific markers, and variability arise from heterogeneity. Hence, methodologies for identifying or predicting the development of senescent cells are necessary for achieving healthy aging. A new wave of bioinformatic methodologies based on mathematics/physics theories have been proposed to be applied to aging biology, which is altering the way we approach our understand of aging. Here, we discuss the dynamical network biomarkers (DNB) theory, which allows for the prediction of state transition in complex systems such as living organisms, as well as usage of Raman spectroscopy that offers a non-invasive and label-free imaging, and provide a perspective on potential applications for the study of aging.

Challenges in anti-aging medicine-trends in biomarker discovery and therapeutic interventions for a healthy lifespan

We are facing a growing aging population, along with increasing pressure on health systems, caused by the impact of chronic co-morbidities (i.e. cancer, cardiovascular and neurodegenerative diseases) and functional disabilities as people age. Relatively simple preventive lifestyle interventions, such as dietary restriction and physical exercise, are important contributors to active and healthy aging in the general population. However, as shown in model organisms or in 'in vitro' conditions, lifestyle-independent interventions may have additional health benefits and can even be conceived as possible reversers of the aging process. Thus, pharmaceutical laboratories, research institutes, and universities are putting more and more effort into finding new molecular pathways and druggable targets to develop gerotherapeutics. One approach is to target the driving mechanisms of aging, some of which, like cellular senescence and impaired autophagy, we discussed in an update on the biology of aging at AgingFit 2023 in Lille, France. We underline the importance of carefully and extensively testing senotherapeutics, given the pleiotropism and heterogeneity of targeted senescent cells within different organs, at different time frames. Other druggable targets emerging from new putative mechanisms, like those based on transcriptome imbalance, nucleophagy, protein phosphatase depletion, glutamine metabolism, or seno-antigenicity, have been evidenced by recent preclinical studies in classical models of aging but need to be validated in humans. Finally, we highlight several approaches in the discovery of biomarkers of healthy aging, as well as for the prediction of neurodegenerative diseases and the evaluation of rejuvenation strategies.

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.

DNA methylation markers of age(ing) in non-model animals

Inferring the chronological and biological age of individuals is fundamental to population ecology and our understanding of ageing itself, its evolution, and the biological processes that affect or even cause ageing. Epigenetic clocks based on DNA methylation (DNAm) at specific CpG sites show a strong correlation with chronological age in humans, and discrepancies between inferred and actual chronological age predict morbidity and mortality. Recently, a growing number of epigenetic clocks have been developed in non-model animals and we here review these studies. We also conduct a meta-analysis to assess the effects of different aspects of experimental protocol on the performance of epigenetic clocks for non-model animals. Two measures of performance are usually reported, the R2 of the association between the predicted and chronological age, and the mean/median absolute deviation (MAD) of estimated age from chronological age, and we argue that only the MAD reflects accuracy. R2 for epigenetic clocks based on the HorvathMammalMethylChip4 was higher and the MAD scaled to age range lower, compared with other DNAm quantification approaches. Scaled MAD tended to be lower among individuals in captive populations, and decreased with an increasing number of CpG sites. We conclude that epigenetic clocks can predict chronological age with relatively high accuracy, suggesting great potential in ecological epigenetics. We discuss general aspects of epigenetic clocks in the hope of stimulating further DNAm-based research on ageing, and perhaps more importantly, other key traits.