BiT age: A transcriptome-based aging clock near the theoretical limit of accuracy

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.

Longevity biotechnology: bridging AI, biomarkers, geroscience and clinical applications for healthy longevity

The recent unprecedented progress in ageing research and drug discovery brings together fundamental research and clinical applications to advance the goal of promoting healthy longevity in the human population. We, from the gathering at the Aging Research and Drug Discovery Meeting in 2023, summarised the latest developments in healthspan biotechnology, with a particular emphasis on artificial intelligence (AI), biomarkers and clocks, geroscience, and clinical trials and interventions for healthy longevity. Moreover, we provide an overview of academic research and the biotech industry focused on targeting ageing as the root of age-related diseases to combat multimorbidity and extend healthspan. We propose that the integration of generative AI, cutting-edge biological technology, and longevity medicine is essential for extending the productive and healthy human lifespan.

Improved resilience and proteostasis mediate longevity upon DAF-2 degradation in old age

Little is known about the possibility of reversing age-related biological changes when they have already occurred. To explore this, we have characterized the effects of reducing insulin/IGF-1 signaling (IIS) during old age. Reduction of IIS throughout life slows age-related decline in diverse species, most strikingly in the nematode Caenorhabditis elegans. Here we show that even at advanced ages, auxin-induced degradation of DAF-2 in single tissues, including neurons and the intestine, is still able to markedly increase C. elegans lifespan. We describe how reversibility varies among senescent changes. While senescent pathologies that develop in mid-life were not reversed, there was a rejuvenation of the proteostasis network, manifesting as a restoration of the capacity to eliminate otherwise intractable protein aggregates that accumulate with age. Moreover, resistance to several stressors was restored. These results support several new conclusions. (1) Loss of resilience is not solely a consequence of pathologies that develop in earlier life. (2) Restoration of proteostasis and resilience by inhibiting IIS is a plausible cause of the increase in lifespan. And (3), most interestingly, some aspects of the age-related transition from resilience to frailty can be reversed to a certain extent. This raises the possibility that the effect of IIS and related pathways on resilience and frailty during aging in higher animals might possess some degree of reversibility.

BayesAge 2.0: A Maximum Likelihood Algorithm to Predict Transcriptomic Age

Aging is a complex biological process influenced by various factors, including genetic and environmental influences. In this study, we present BayesAge 2.0, an improved version of our maximum likelihood algorithm designed for predicting transcriptomic age (tAge) from RNA-seq data. Building on the original BayesAge framework, which was developed for epigenetic age prediction, BayesAge 2.0 integrates a Poisson distribution to model count-based gene expression data and employs LOWESS smoothing to capture non-linear gene-age relationships. BayesAge 2.0 provides significant improvements over traditional linear models, such as Elastic Net regression. Specifically, it addresses issues of age bias in predictions, with minimal age-associated bias observed in residuals. Its computational efficiency further distinguishes it from traditional models, as reference construction and cross-validation are completed more quickly compared to Elastic Net regression, which requires extensive hyperparameter tuning. Overall, BayesAge 2.0 represents a notable advance in transcriptomic age prediction, offering a robust, accurate, and efficient tool for aging research and biomarker development.

Systematic mapping of organism-scale gene-regulatory networks in aging using population asynchrony

In aging, physiologic networks decline in function at rates that differ between individuals, producing a wide distribution of lifespan. Though 70% of human lifespan variance remains unexplained by heritable factors, little is known about the intrinsic sources of physiologic heterogeneity in aging. To understand how complex physiologic networks generate lifespan variation, new methods are needed. Here, we present Asynch-seq, an approach that uses gene-expression heterogeneity within isogenic populations to study the processes generating lifespan variation. By collecting thousands of single-individual transcriptomes, we capture the Caenorhabditis elegans "pan-transcriptome"-a highly resolved atlas of non-genetic variation. We use our atlas to guide a large-scale perturbation screen that identifies the decoupling of total mRNA content between germline and soma as the largest source of physiologic heterogeneity in aging, driven by pleiotropic genes whose knockdown dramatically reduces lifespan variance. Our work demonstrates how systematic mapping of physiologic heterogeneity can be applied to reduce inter-individual disparities in aging.

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.

A lipidomic based metabolic age score captures cardiometabolic risk independent of chronological age

BACKGROUND

Metabolic ageing biomarkers may capture the age-related shifts in metabolism, offering a precise representation of an individual's overall metabolic health.

METHODS

Utilising comprehensive lipidomic datasets from two large independent population cohorts in Australia (n = 14,833, including 6630 males, 8203 females), we employed different machine learning models, to predict age, and calculated metabolic age scores (mAge). Furthermore, we defined the difference between mAge and age, termed mAgeΔ, which allow us to identify individuals sharing similar age but differing in their metabolic health status.

FINDINGS

Upon stratification of the population into quintiles by mAgeΔ, we observed that participants in the top quintile group (Q5) were more likely to have cardiovascular disease (OR = 2.13, 95% CI = 1.62-2.83), had a 2.01-fold increased risk of 12-year incident cardiovascular events (HR = 2.01, 95% CI = 1.45-2.57), and a 1.56-fold increased risk of 17-year all-cause mortality (HR = 1.56, 95% CI = 1.34-1.79), relative to the individuals in the bottom quintile group (Q1). Survival analysis further revealed that men in the Q5 group faced the challenge of reaching a median survival rate due to cardiovascular events more than six years earlier and reaching a median survival rate due to all-cause mortality more than four years earlier than men in the Q1 group.

INTERPRETATION

Our findings demonstrate that the mAge score captures age-related metabolic changes, predicts health outcomes, and has the potential to identify individuals at increased risk of metabolic diseases.

FUNDING

The specific funding of this article is provided in the acknowledgements section.

Coordinate-wise monotonic transformations enable privacy-preserving age estimation with 3D face point cloud

The human face is a valuable biomarker of aging, but the collection and use of its image raise significant privacy concerns. Here we present an approach for facial data masking that preserves age-related features using coordinate-wise monotonic transformations. We first develop a deep learning model that estimates age directly from non-registered face point clouds with high accuracy and generalizability. We show that the model learns a highly indistinguishable mapping using faces treated with coordinate-wise monotonic transformations, indicating that the relative positioning of facial information is a low-level biomarker of facial aging. Through visual perception tests and computational 3D face verification experiments, we demonstrate that transformed faces are significantly more difficult to perceive for human but not for machines, except when only the face shape information is accessible. Our study leads to a facial data protection guideline that has the potential to broaden public access to face datasets with minimized privacy risks.

A miRNA-based epigenetic molecular clock for biological skin-age prediction

Skin aging is one of the visible characteristics of the aging process in humans. In recent years, different biological clocks have been generated based on protein or epigenetic markers, but few have focused on biological age in the skin. Arrest the aging process or even being able to restore an organism from an older to a younger stage is one of the main challenges in the last 20 years in biomedical research. We have implemented several machine learning models, including regression and classification algorithms, in order to create an epigenetic molecular clock based on miRNA expression profiles of healthy subjects to predict biological age-related to skin. Our best models are capable of classifying skin samples according to age groups (18-28; 29-39; 40-50; 51-60 or 61-83 years old) with an accuracy of 80% or predict age with a mean absolute error of 10.89 years using the expression levels of 1856 unique miRNAs. Our results suggest that this kind of epigenetic clocks arises as a promising tool with several applications in the pharmaco-cosmetic industry.

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.

Ageing as a two-phase process: theoretical framework

Human ageing, along with the ageing of conventional model organisms, is depicted as a continuous and progressive decline of biological capabilities accompanied by an exponentially increasing mortality risk. However, not all organisms experience ageing identically and our understanding of the phenomenon is coloured by human-centric views. Ageing is multifaceted and influences a diverse range of species in varying ways. Some undergo swift declines post-reproduction, while others exhibit insubstantial changes throughout their existence. This vast array renders defining universally applicable "ageing attributes" a daunting task. It is nonetheless essential to recognize that not all ageing features are organism-specific. These common attributes have paved the way for identifying "hallmarks of ageing," processes that are intertwined with age, amplified during accelerated ageing, and manipulations of which can potentially modulate or even reverse the ageing process. Yet, a glaring observation is that individuals within a single population age at varying rates. To address this, demographers have coined the term 'frailty'. Concurrently, scientific advancements have ushered in the era of molecular clocks. These innovations enable a distinction between an individual's chronological age (time since birth) and biological age (physiological status and mortality risk). In 2011, the "Smurf" phenotype was unveiled in Drosophila, delineating an age-linked escalation in intestinal permeability that presages imminent mortality. It not only acts as a predictor of natural death but identifies individuals exhibiting traits normally described as age-related. Subsequent studies have revealed the phenotype in organisms like nematodes, zebrafish, and mice, invariably acting as a death predictor. Collectively, these findings have steered our conception of ageing towards a framework where ageing is not linear and continuous but marked by two distinct, necessary phases, discernible in vivo, courtesy of the Smurf phenotype. This framework includes a mathematical enunciation of longevity trends based on three experimentally measurable parameters. It facilitates a fresh perspective on the evolution of ageing as a function. In this article, we aim to delineate and explore the foundational principles of this innovative framework, emphasising its potential to reshape our understanding of ageing, challenge its conventional definitions, and recalibrate our comprehension of its evolutionary trajectory.

T-cell lymphocytes' aging clock: telomeres, telomerase and aging

Aging is the decline of physiological capabilities required for life maintenance and reproduction over time. The human immune cells, including T-cells lymphocytes, undergo dramatic aging-related changes, including those related to telomeres and telomerase. It was demonstrated that telomeres and telomerase play crucial roles in T-cell differentiation, aging, and diseases, including a well-documented link between short telomeres and telomerase activation demonstrated in several T-cells malignancies. Herein, we provide a comprehensive review of the literature regarding T-cells' telomeres and telomerase in health and age related-diseases.

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.

DNA repair-deficient premature aging models display accelerated epigenetic age

Several premature aging mouse models have been developed to study aging and identify interventions that can delay age-related diseases. Yet, it is still unclear whether these models truly recapitulate natural aging. Here, we analyzed DNA methylation in multiple tissues of four previously reported mouse models of premature aging (Ercc1, LAKI, Polg, and Xpg). We estimated DNA methylation (DNAm) age of these samples using the Horvath clock. The most pronounced increase in DNAm age could be observed in Ercc1 mice, a strain which exhibits a deficit in DNA nucleotide excision repair. Similarly, we detected an increase in epigenetic age in fibroblasts isolated from patients with progeroid syndromes associated with mutations in DNA excision repair genes. These findings highlight that mouse models with deficiencies in DNA repair, unlike other premature aging models, display accelerated epigenetic age, suggesting a strong connection between DNA damage and epigenetic dysregulation during aging.

Glutathione amino acid precursors protect skin from UVB-induced damage and improve skin tone

BACKGROUND

UV radiation exposure causes skin irritation, erythema, darkening and barrier disruption by inducing oxidative stress and inflammation. Glutathione, a master antioxidant, plays an important role in the antioxidant defence network of the skin.

OBJECTIVE

This study aimed to assess the in vitro protective effects of the glutathione amino acid precursors blend (GAP) on transcriptomic and phenotypic endpoints against UVB-induced challenges.

METHODS

Normal human epidermal melanocytes (NHEMs) were exposed to GAP, ascorbic acid (AA) and its derivatives. Viability was assessed using the CCK8 method. Melakutis®, a pigmented living skin equivalent (pLSE) model, underwent repeated 50 mJ/cm2 UVB irradiation with or without GAP treatment. Images of the model were captured with consistent camera parameters, and the model's light intensity was measured using a spectrophotometer. Melanin content was determined by measuring absorbance at 405 nm. Confirmation of melanin deposition and distribution was achieved through Fontana-Masson staining. Transcriptomic analysis was conducted using RNA sequencing (RNA-Seq), and a machine learning approach was employed for transcriptomic aging clock analysis.

RESULTS

In NHEMs, all tested compounds exhibited over 85% viability compared to the vehicle control, indicating no heightened risk of cytotoxicity. Notably, GAP demonstrated greater efficacy in inhibiting melanin production than AA derivatives at equivalent concentrations. In pLSE models, GAP notably enhanced model lightness, and reduced melanin content and deposition following the UVB challenge, whereas AA showed minimal impact. GAP effectively counteracted UVB-induced alterations in gene expression linked to pigmentation, inflammation and aging. Moreover, recurrent UVB exposure substantially elevated the biological age of pLSE models, a phenomenon mitigated by GAP treatment.

CONCLUSIONS

In NHEMs, GAP exhibited enhanced effectiveness in inhibiting melanin production at identical tested doses in comparison to AA derivatives. Noteworthy protective effects of GAP against UVB irradiation were observed in the pLSE models, as evidenced by skin pigmentation measurements and transcriptomic changes.

Selecting mRNA markers in blood for age estimation of the donor of a biological stain

RNA has gained a substantial amount of attention within the forensic field over the last decade. There is evidence that RNAs are differentially expressed with biological age. Since RNA can be co-extracted with DNA from the same piece of evidence, RNA-based analysis appears as a promising molecular alternative for predicting the biological age and hence inferring the chronological age of a person. Using RNA-Seq data we searched for markers in blood potentially associated with age. We used our own RNA-Seq data from dried blood stains as well as publicly available RNA-Seq data from whole blood, and compared two different approaches to select candidate markers. The first approach focused on individual gene analysis with DESeq2 to select the genes most correlated with age, while the second approach employed lasso regression to select a set of genes for optimal prediction of age. We present two lists with 270 candidate markers, one for each approach.

CellBiAge: Improved single-cell age classification using data binarization

Aging is a major risk factor for many diseases. Accurate methods for predicting age in specific cell types are essential to understand the heterogeneity of aging and to assess rejuvenation strategies. However, classifying organismal age at single-cell resolution using transcriptomics is challenging due to sparsity and noise. Here, we developed CellBiAge, a robust and easy-to-implement machine learning pipeline, to classify the age of single cells in the mouse brain using single-cell transcriptomics. We show that binarization of gene expression values for the top highly variable genes significantly improved test performance across different models, techniques, sexes, and brain regions, with potential age-related genes identified for model prediction. Additionally, we demonstrate CellBiAge's ability to capture exercise-induced rejuvenation in neural stem cells. This study provides a broadly applicable approach for robust classification of organismal age of single cells in the mouse brain, which may aid in understanding the aging process and evaluating rejuvenation methods.

ExplaiNAble BioLogical Age (ENABL Age): an artificial intelligence framework for interpretable biological age

BACKGROUND

Biological age is a measure of health that offers insights into ageing. The existing age clocks, although valuable, often trade off accuracy and interpretability. We introduce ExplaiNAble BioLogical Age (ENABL Age), a computational framework that combines machine-learning models with explainable artificial intelligence (XAI) methods to accurately estimate biological age with individualised explanations.

METHODS

To construct the ENABL Age clock, we first predicted an age-related outcome (eg, all-cause or cause-specific mortality), and then rescaled these predictions to estimate biological age, using UK Biobank and National Health and Nutrition Examination Survey (NHANES) datasets. We adapted existing XAI methods to decompose individual ENABL Ages into contributing risk factors. For broad accessibility, we developed two versions: ENABL Age-L, based on blood tests, and ENABL Age-Q, based on questionnaire characteristics. Finally, we validated diverse ageing mechanisms captured by each ENABL Age clock through genome-wide association studies (GWAS) association analyses.

FINDINGS

Our ENABL Age clock was significantly correlated with chronological age (r=0·7867, p<0·0001 for UK Biobank; r=0·7126, p<0·0001 for NHANES). These clocks distinguish individuals who are healthy (ie, their ENABL Age is lower than their chronological age) from those who are unhealthy (ie, their ENABL Age is higher than their chronological age), predicting mortality more effectively than existing clocks. Groups of individuals who were unhealthy showed approximately three to 12 times higher log hazard ratio than healthy groups, as per ENABL Age. The clocks achieved high mortality prediction power with an area under the receiver operating characteristic curve of 0·8179 for 5-year mortality and 0·8115 for 10-year mortality on the UK Biobank dataset, and 0·8935 for 5-year mortality and 0·9107 for 10-year mortality on the NHANES dataset. The individualised explanations that revealed the contribution of specific characteristics to ENABL Age provided insights into the important characteristics for ageing. An association analysis with risk factors and ageing-related morbidities and GWAS results on ENABL Age clocks trained on different mortality causes showed that each clock captures distinct ageing mechanisms.

INTERPRETATION

ENABL Age brings an important leap forward in the application of XAI for interpreting biological age clocks. ENABL Age also carries substantial potential in practical settings, assisting medical professionals in untangling the complexity of ageing mechanisms, and potentially becoming a valuable tool in informed clinical decision-making processes.

FUNDING

National Science Foundation and National Institutes of Health.

Measuring healthy ageing: current and future tools

Human ageing is a complex, multifactorial process characterised by physiological damage, increased risk of age-related diseases and inevitable functional deterioration. As the population of the world grows older, placing significant strain on social and healthcare resources, there is a growing need to identify reliable and easy-to-employ markers of healthy ageing for early detection of ageing trajectories and disease risk. Such markers would allow for the targeted implementation of strategies or treatments that can lessen suffering, disability, and dependence in old age. In this review, we summarise the healthy ageing scores reported in the literature, with a focus on the past 5 years, and compare and contrast the variables employed. The use of approaches to determine biological age, molecular biomarkers, ageing trajectories, and multi-omics ageing scores are reviewed. We conclude that the ideal healthy ageing score is multisystemic and able to encompass all of the potential alterations associated with ageing. It should also be longitudinal and able to accurately predict ageing complications at an early stage in order to maximize the chances of successful early intervention.

Smurfness-based two-phase model of ageing helps deconvolve the ageing transcriptional signature

Ageing is characterised at the molecular level by six transcriptional 'hallmarks of ageing', that are commonly described as progressively affected as time passes. By contrast, the 'Smurf' assay separates high-and-constant-mortality risk individuals from healthy, zero-mortality risk individuals, based on increased intestinal permeability. Performing whole body total RNA sequencing, we found that Smurfness distinguishes transcriptional changes associated with chronological age from those associated with biological age. We show that transcriptional heterogeneity increases with chronological age in non-Smurf individuals preceding the other five hallmarks of ageing that are specifically associated with the Smurf state. Using this approach, we also devise targeted pro-longevity genetic interventions delaying entry in the Smurf state. We anticipate that increased attention to the evolutionary conserved Smurf phenotype will bring about significant advances in our understanding of the mechanisms of ageing.

Epigenetic context predicts gene expression variation and reproductive traits across genetically identical individuals

In recent decades, genome-wide association studies (GWAS) have been the major approach to understand the biological basis of individual differences in traits and diseases. However, GWAS approaches have proven to have limited predictive power to explain individual differences, particularly for complex traits and diseases in which environmental factors play a substantial role in their etiology. Indeed, individual differences persist even in genetically identical individuals, although fully separating genetic and environmental causation is difficult or impossible in most organisms. To understand the basis of individual differences in the absence of genetic differences, we measured two quantitative reproductive traits in 180 genetically identical young adult Caenorhabditis elegans roundworms in a shared environment and performed single-individual transcriptomics on each worm. We identified hundreds of genes for which expression variation was strongly associated with reproductive traits, some of which depended on prior environmental experience and some of which was random. Multiple small sets of genes together were highly predictive of reproductive traits across individuals, explaining on average over half and over a quarter of variation in the two traits. We manipulated mRNA levels of predictive genes using RNA interference to identify a set of causal genes, demonstrating the utility of this approach for both prediction and understanding underlying biology. Finally, we found that the chromatin environment of predictive genes was enriched for H3K27 trimethylation, suggesting that individual gene expression differences underlying critical traits may be driven in part by chromatin structure. Together, this work shows that individual differences in gene expression that arise independently of underlying genetic differences are both predictive and causal in shaping reproductive traits at levels that equal or exceed genetic variation.

Identifying novel age-modulating compounds and quantifying cellular aging using novel computational framework for evaluating transcriptional age

The differentiation of human pluripotent stem cells (hPSCs) provides access to most cell types and tissues. However, hPSC-derived lineages capture a fetal-stage of development and methods to accelerate progression to an aged identity are limited. Understanding the factors driving cellular age and rejuvenation is also essential for efforts aimed at extending human life and health span. A prerequisite for such studies is the development of methods to score cellular age and simple readouts to assess the relative impact of various age modifying strategies. Here we established a transcriptional score (RNAge) in young versus old primary fibroblasts, frontal cortex and substantia nigra tissue. We validated the score in independent RNA-seq datasets and demonstrated a strong cell and tissue specificity. In fibroblasts we observed a reset of RNAge during iPSC reprogramming while direct reprogramming of aged fibroblasts to induced neurons (iN) resulted in the maintenance of both a neuronal and a fibroblast aging signature. Increased RNAge in hPSC-derived neurons was confirmed for several age-inducing strategies such as SATB1 loss, progerin expression or chemical induction of senescence (SLO). Using RNAge as a probe set, we next performed an in-silico screen using the LINCS L1000 dataset. We identified and validated several novel age-inducing and rejuvenating compounds, and we observed that RNAage captures age-related changes associated with distinct cellular hallmarks of age. Our study presents a simple tool to score age manipulations and identifies compounds that greatly expand the toolset of age-modifying strategies in hPSC derived lineages.

N6-Methyladenine Progressively Accumulates in Mitochondrial DNA during Aging

N6-methyladenine (6mA) in the DNA is a conserved epigenetic mark with various cellular, physiological and developmental functions. Although the presence of 6mA was discovered a few years ago in the nuclear genome of distantly related animal taxa and just recently in mammalian mitochondrial DNA (mtDNA), accumulating evidence at present seriously questions the presence of N6-adenine methylation in these genetic systems, attributing it to methodological errors. In this paper, we present a reliable, PCR-based method to determine accurately the relative 6mA levels in the mtDNA of Caenorhabditis elegans, Drosophila melanogaster and dogs, and show that these levels gradually increase with age. Furthermore, daf-2(-)-mutant worms, which are defective for insulin/IGF-1 (insulin-like growth factor) signaling and live twice as long as the wild type, display a half rate at which 6mA progressively accumulates in the mtDNA as compared to normal values. Together, these results suggest a fundamental role for mtDNA N6-adenine methylation in aging and reveal an efficient diagnostic technique to determine age using DNA.

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 (LmnaG609G/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.

Transcriptomics age acceleration in prolonged treated HIV infection

Biological aging in people with HIV (PWH) with prolonged successful antiretroviral therapy (ART) is convoluted and poorly defined. Here, we aimed to investigate the transcriptomics age estimator (TAE) in a cohort of 178 PWH on prolonged successful ART with immune reconstitution and viral suppression from the Copenhagen Comorbidity (COCOMO) cohort. We also used 143 clinical, demographical, and lifestyle factors to identify the confounders potentially responsible or associated with age acceleration. Among the PWH, 43% had an accelerated aging process (AAP), and 21% had decelerated aging process (DAP). DAP is linked with older age, European ancestry, and higher use of tenofovir disoproxil/alafenamide fumarate. A directionally class-based gene set enrichment analysis identified the upregulation of inflammatory pathways (e.g., cytokine and Retinoic acid-inducible gene I (RIG-I)-like receptor signaling pathways) and immune response like T-cell receptor signaling, antigen processing, and presentation in AAP and the downregulation of metabolic processes like oxidative phosphorylation, pyruvate metabolism.

Aging measures and cancer: Findings from the Health and Retirement Study

Background

Compared to cancer-free persons, cancer survivors of the same chronological age (CA) have increased physiological dysfunction, i.e., higher biological age (BA), which may lead to higher morbidity and mortality. We estimated BA using eight aging metrics: BA computed by Klemera Doubal method (KDM-BA), phenotypic age (PhenoAge), five epigenetic clocks (ECs, Horvath, Hannum, Levine, GrimAge, and pace of aging (POA)), and subjective age (SA). We tested if aging constructs were associated with total cancer prevalence and all-cause mortality in cancer survivors and controls, i.e., cancer-free persons, in the Health and Retirement Study (HRS), a large population-based study.

Methods

In 2016, data on BA-KDM, PhenoAge, and SA were available for 946 cancer survivors and 4,555 controls; data for the five ECs were available for 582 cancer survivors and 2,805 controls. Weighted logistic regression was used to estimate the association between each aging construct and cancer prevalence (odds ratio, OR, 95%CI). Weighted Cox proportional hazards regression was used to estimate the associations between each aging construct and cancer incidence as well as all-cause mortality (hazard ratio, HR, 95%CI). To study all BA metrics (except for POA) independent of CA, we estimated age acceleration as residuals of BA regressed on CA.

Results

Age acceleration for each aging construct and POA were higher in cancer survivors than controls. In a multivariable-adjusted model, five aging constructs (age acceleration for Hannum, Horvath, Levine, GrimAge, and SA) were associated with cancer prevalence. Among all cancer survivors, age acceleration for PhenoAge and four ECs (Hannum, Horvath, Levine, and GrimAge), was associated with higher all-cause mortality over 4 years of follow-up. PhenoAge, Hannum, and GrimAge were also associated with all-cause mortality in controls. The highest HR was observed for GrimAge acceleration in cancer survivors: 2.03 (95% CI, 1.58-2.60). In contrast, acceleration for KDM-BA and POA was significantly associated with mortality in controls but not in cancer survivors. When all eight aging constructs were included in the same model, two of them (Levine and GrimAge) were significantly associated with mortality among cancers survivors. None of the aging constructs were associated with cancer incidence.

Conclusion

Variations in the associations between aging constructs and mortality in cancer survivors and controls suggests that aging constructs may capture different aspects of aging and that cancer survivors may be experiencing age-related physiologic dysfunctions differently than controls. Future work should evaluate how these aging constructs predict mortality for specific cancer types.

The shelterin protein expansion of telomere dynamics: Linking early life adversity, life history, and the hallmarks of aging

Aging is characterized by functional decline occurring alongside changes to several hallmarks of aging. One of the hallmarks includes attrition of repeated DNA sequences found at the ends of chromosomes called telomeres. While telomere attrition is linked to morbidity and mortality, whether and how it causally contributes to lifelong rates of functional decline is unclear. In this review, we propose the shelterin-telomere hypothesis of life history, in which telomere-binding shelterin proteins translate telomere attrition into a range of physiological outcomes, the extent of which may be modulated by currently understudied variation in shelterin protein levels. Shelterin proteins may expand the breadth and timing of consequences of telomere attrition, e.g., by translating early life adversity into acceleration of the aging process. We consider how the pleiotropic roles of shelterin proteins provide novel insights into natural variation in physiology, life history, and lifespan. We highlight key open questions that encourage the integrative, organismal study of shelterin proteins that enhances our understanding of the contribution of the telomere system to aging.

Small immunological clocks identified by deep learning and gradient boosting

Background

The aging process affects all systems of the human body, and the observed increase in inflammatory components affecting the immune system in old age can lead to the development of age-associated diseases and systemic inflammation.

Results

We propose a small clock model SImAge based on a limited number of immunological biomarkers. To regress the chronological age from cytokine data, we first use a baseline Elastic Net model, gradient-boosted decision trees models, and several deep neural network architectures. For the full dataset of 46 immunological parameters, DANet, SAINT, FT-Transformer and TabNet models showed the best results for the test dataset. Dimensionality reduction of these models with SHAP values revealed the 10 most age-associated immunological parameters, taken to construct the SImAge small immunological clock. The best result of the SImAge model shown by the FT-Transformer deep neural network model has mean absolute error of 6.94 years and Pearson ρ = 0.939 on the independent test dataset. Explainable artificial intelligence methods allow for explaining the model solution for each individual participant.

Conclusions

We developed an approach to construct a model of immunological age based on just 10 immunological parameters, coined SImAge, for which the FT-Transformer deep neural network model had proved to be the best choice. The model shows competitive results compared to the published studies on immunological profiles, and takes a smaller number of features as an input. Neural network architectures outperformed gradient-boosted decision trees, and can be recommended in the further analysis of immunological profiles.

Measuring healthy ageing: current and future tools

Human ageing is a complex, multifactorial process characterised by physiological damage, increased risk of age-related diseases and inevitable functional deterioration. As the population of the world grows older, placing significant strain on social and healthcare resources, there is a growing need to identify reliable and easy-to-employ markers of healthy ageing for early detection of ageing trajectories and disease risk. Such markers would allow for the targeted implementation of strategies or treatments that can lessen suffering, disability, and dependence in old age. In this review, we summarise the healthy ageing scores reported in the literature, with a focus on the past 5 years, and compare and contrast the variables employed. The use of approaches to determine biological age, molecular biomarkers, ageing trajectories, and multi-omics ageing scores are reviewed. We conclude that the ideal healthy ageing score is multisystemic and able to encompass all of the potential alterations associated with ageing. It should also be longitudinal and able to accurately predict ageing complications at an early stage in order to maximize the chances of successful early intervention.

Current Trends in Anti-Aging Strategies

The process of aging manifests from a highly interconnected network of biological cascades resulting in the degradation and breakdown of every living organism over time. This natural development increases risk for numerous diseases and can be debilitating. Academic and industrial investigators have long sought to impede, or potentially reverse, aging in the hopes of alleviating clinical burden, restoring functionality, and promoting longevity. Despite widespread investigation, identifying impactful therapeutics has been hindered by narrow experimental validation and the lack of rigorous study design. In this review, we explore the current understanding of the biological mechanisms of aging and how this understanding both informs and limits interpreting data from experimental models based on these mechanisms. We also discuss select therapeutic strategies that have yielded promising data in these model systems with potential clinical translation. Lastly, we propose a unifying approach needed to rigorously vet current and future therapeutics and guide evaluation toward efficacious therapies.

Biomarkers of aging through the life course: A Recent Literature Update

Purpose of review

The development of biomarkers of aging has greatly advanced epidemiological studies of aging processes. However, much debate remains on the timing of aging onset and the causal relevance of these biomarkers. In this review, we discuss the most recent biomarkers of aging that have been applied across the life course.

Recent findings

The most recently developed aging biomarkers that have been applied across the life course can be designated into three categories: epigenetic clocks, epigenetic markers of chronic inflammation, and mitochondrial DNA copy number. While these have been applied at different life stages, the development, validation, and application of these markers has been largely centered on populations of older adults. Few studies have examined trajectories of aging biomarkers across the life course. As the wealth of molecular and biochemical data increases, emerging biomarkers may be able to capture complex and system-specific aging processes. Recently developed biomarkers include novel epigenetic clocks; clocks based on ribosomal DNA, transcriptomic profiles, proteomics, metabolomics, and inflammatory markers; clonal hematopoiesis of indeterminate potential gene mutations; and multi-omics approaches.

Summary

Attention should be placed on aging at early and middle life stages to better understand trajectories of aging biomarkers across the life course. Additionally, novel biomarkers will provide greater insight into aging processes. The specific mechanisms of aging reflected by these biomarkers should be considered when interpreting results.

High-Intensity interval training reduces transcriptomic age: A randomized controlled trial

While the relationship between exercise and life span is well-documented, little is known about the effects of specific exercise protocols on modern measures of biological age. Transcriptomic age (TA) predictors provide an opportunity to test the effects of high-intensity interval training (HIIT) on biological age utilizing whole-genome expression data. A single-site, single-blinded, randomized controlled clinical trial design was utilized. Thirty sedentary participants (aged 40-65) were assigned to either a HIIT group or a no-exercise control group. After collecting baseline measures, HIIT participants performed three 10 × 1 HIIT sessions per week for 4 weeks. Each session lasted 23 min, and total exercise duration was 276 min over the course of the 1-month exercise protocol. TA, PSS-10 score, PSQI score, PHQ-9 score, and various measures of body composition were all measured at baseline and again following the conclusion of exercise/control protocols. Transcriptomic age reduction of 3.59 years was observed in the exercise group while a 3.29-years increase was observed in the control group. Also, PHQ-9, PSQI, BMI, body fat mass, and visceral fat measures were all improved in the exercise group. A hypothesis-generation gene expression analysis suggested exercise may modify autophagy, mTOR, AMPK, PI3K, neurotrophin signaling, insulin signaling, and other age-related pathways. A low dose of HIIT can reduce an mRNA-based measure of biological age in sedentary adults between the ages of 40 and 65 years old. Other changes in gene expression were relatively modest, which may indicate a focal effect of exercise on age-related biological processes.

Biomarkers of aging

Aging biomarkers are a combination of biological parameters to (i) assess age-related changes, (ii) track the physiological aging process, and (iii) predict the transition into a pathological status. Although a broad spectrum of aging biomarkers has been developed, their potential uses and limitations remain poorly characterized. An immediate goal of biomarkers is to help us answer the following three fundamental questions in aging research: How old are we? Why do we get old? And how can we age slower? This review aims to address this need. Here, we summarize our current knowledge of biomarkers developed for cellular, organ, and organismal levels of aging, comprising six pillars: physiological characteristics, medical imaging, histological features, cellular alterations, molecular changes, and secretory factors. To fulfill all these requisites, we propose that aging biomarkers should qualify for being specific, systemic, and clinically relevant.

A molecular phenology scale of grape berry development

Fruit growth and development consist of a continuous succession of physical, biochemical, and physiological changes driven by a genetic program that dynamically responds to environmental cues. Establishing recognizable stages over the whole fruit lifetime represents a fundamental requirement for research and fruit crop cultivation. This is especially relevant in perennial crops like grapevine (Vitis vinifera L.) to scale the development of its fruit across genotypes and growing conditions. In this work, molecular-based information from several grape berry transcriptomic datasets was exploited to build a molecular phenology scale (MPhS) and to map the ontogenic development of the fruit. The proposed statistical pipeline consisted of an unsupervised learning procedure yielding an innovative combination of semiparametric, smoothing, and dimensionality reduction tools. The transcriptomic distance between fruit samples was precisely quantified by means of the MPhS that also enabled to highlight the complex dynamics of the transcriptional program over berry development through the calculation of the rate of variation of MPhS stages by time. The MPhS allowed the alignment of time-series fruit samples proving to be a complementary method for mapping the progression of grape berry development with higher detail compared to classic time- or phenotype-based approaches.

Senescence-associated transcriptional derepression in subtelomeres is determined in a chromosome-end-specific manner

Aging is a continuous process leading to physiological deterioration with age. One of the factors contributing to aging is telomere shortening, causing alterations in the protein protective complex named shelterin and replicative senescence. Here, we address the question of the link between this telomere shortening and the transcriptional changes occurring in senescent cells. We found that in replicative senescent cells, the genes whose expression escaped repression are enriched in subtelomeres. The shelterin protein TRF2 and the nuclear lamina factor Lamin B1, both downregulated in senescent cells, are involved in the regulation of some but not all of these subtelomeric genes, suggesting complex mechanisms of transcriptional regulation. Indeed, the subtelomeres containing these derepressed genes are enriched in factors of polycomb repression (EZH2 and H3K27me3), insulation (CTCF and MAZ), and cohesion (RAD21 and SMC3) while being associated with the open A-type chromatin compartment. These findings unveil that the subtelomere transcriptome associated with senescence is determined in a chromosome-end-specific manner according to the type of higher-order chromatin structure.

The RNA-Seq data analysis shows how the ontogenesis defines aging

This paper presents a global statistical analysis of the RNA-Seq results of the entire Mus musculus genome. We explain aging by a gradual redistribution of limited resources between two major tasks of the organism: its self-sustenance based on the function of the housekeeping gene group (HG) and functional differentiation provided by the integrative gene group (IntG). All known disorders associated with aging are the result of a deficiency in the repair processes provided by the cellular infrastructure. Understanding exactly how this deficiency arises is our primary goal. Analysis of RNA production data of 35,630 genes, from which 5,101 were identified as HG genes, showed that RNA production levels in the HG and IntG genes had statistically significant differences (p-value <0.0001) throughout the entire observation period. In the reproductive period of life, which has the lowest actual mortality risk for Mus musculus, changes in the age dynamics of RNA production occur. The statistically significant dynamics of the decrease of RNA production in the HG group in contrast to the IntG group was determined (p-value = 0.0045). The trend toward significant shift in the HG/IntG ratio occurs after the end of the reproductive period, coinciding with the beginning of the mortality rate increase in Mus musculus indirectly supports our hypothesis. The results demonstrate a different orientation of the impact of ontogenesis regulatory mechanisms on the groups of genes representing cell infrastructures and their organismal functions, making the chosen direction promising for further research and understanding the mechanisms of aging.

An Updated Overview on the Role of Small Molecules and Natural Compounds in the "Young Science" of Rejuvenation

Aging is a gradual process that occurs over time which leads to a progressive decline of cells and tissues. Telomere shortening, genetic instability, epigenetic alteration, and the accumulation of misfolded proteins represent the main hallmarks that cause perturbed cellular functions; this occurs in conjunction with the progression of the so-called "aging clocks". Rejuvenation aims to influence the natural evolution of such aging clocks and to enhance regenerative capacity, thus overcoming the limitations of common anti-aging interventions. Current rejuvenation processes are based on heterochronic parabiosis, cell damage dilution through asymmetrical cell division, the excretion of extracellular vesicles, the modulation of genetic instability involving G-quadruplexes and DNA methylation, and cell reprogramming using Yamanaka factors and the actions of antioxidant species. In this context, we reviewed the most recent contributions that report on small molecules acting as senotherapeutics; these molecules act by promoting one or more of the abovementioned processes. Candidate drugs and natural compounds that are being studied as potential rejuvenation therapies act by interfering with CDGSH iron-sulfur domain 2 (CISD2) expression, G-quadruplex structures, DNA methylation, and mitochondrial decay. Moreover, direct and indirect antioxidants have been reported to counteract or revert aging through a combination of mixed mechanisms.

Biological Aging in People Living with HIV on Successful Antiretroviral Therapy: Do They Age Faster?

PURPOSE OF REVIEW

In the absence of a prophylactic/therapeutic vaccine or cure, the most amazing achievement in the battle against HIV was the discovery of effective, well-tolerated combination antiretroviral therapy (cART). The primary research question remains whether PLWH on prolonged successful therapy has accelerated, premature, or accentuated biological aging. In this review, we discuss the current understanding of the immunometabolic profile in PLWH, potentially associated with biological aging, and a better understanding of the mechanisms and temporal dynamics of biological aging in PLWH.

RECENT FINDINGS

Biological aging, defined by the epigenetic alterations analyzed by the DNA methylation pattern, has been reported in PLWH with cART that points towards epigenetic age acceleration. The hastened development of specific clinical geriatric syndromes like cardiovascular diseases, metabolic syndrome, cancers, liver diseases, neurocognitive diseases, persistent low-grade inflammation, and a shift toward glutamate metabolism in PLWH may potentiate a metabolic profile at-risk for accelerated aging.

Cell-type-specific aging clocks to quantify aging and rejuvenation in neurogenic regions of the brain

The diversity of cell types is a challenge for quantifying aging and its reversal. Here we develop 'aging clocks' based on single-cell transcriptomics to characterize cell-type-specific aging and rejuvenation. We generated single-cell transcriptomes from the subventricular zone neurogenic region of 28 mice, tiling ages from young to old. We trained single-cell-based regression models to predict chronological age and biological age (neural stem cell proliferation capacity). These aging clocks are generalizable to independent cohorts of mice, other regions of the brains, and other species. To determine if these aging clocks could quantify transcriptomic rejuvenation, we generated single-cell transcriptomic datasets of neurogenic regions for two interventions-heterochronic parabiosis and exercise. Aging clocks revealed that heterochronic parabiosis and exercise reverse transcriptomic aging in neurogenic regions, but in different ways. This study represents the first development of high-resolution aging clocks from single-cell transcriptomic data and demonstrates their application to quantify transcriptomic rejuvenation.

Measuring biological age using omics data

Age is the key risk factor for diseases and disabilities of the elderly. Efforts to tackle age-related diseases and increase healthspan have suggested targeting the ageing process itself to 'rejuvenate' physiological functioning. However, achieving this aim requires measures of biological age and rates of ageing at the molecular level. Spurred by recent advances in high-throughput omics technologies, a new generation of tools to measure biological ageing now enables the quantitative characterization of ageing at molecular resolution. Epigenomic, transcriptomic, proteomic and metabolomic data can be harnessed with machine learning to build 'ageing clocks' with demonstrated capacity to identify new biomarkers of biological ageing.

Three Tiers to biological escape velocity: The quest to outwit aging

As longevity companies emerge with new products and the fields of anti-aging research develop new cutting-edge therapies, three distinct classes of longevity methodologies emerge. This discussion finds that there are three clear classes (Tiers) of longevity systems that are currently under development, and all three will be paramount to achieve biological escape velocity (where tissues can be repaired faster than aging can damage them). These classes are referred to as Tier 1, Tier 2, and Tier 3 treatments and are described in detail below. These three Tiers are required for easy identification for pharmaceutical companies and research companies to determine the type of therapy they may choose to deliver being noninvasive, invasive, time consuming, or simple end user products. Specific targets and goals need to be defined clearly from an early perspective in the development of these technologies for future precision medicines. This allows consumers of future anti-aging technologies to consider which Tier a particular therapy may be, delivering a more informed choice.

Biological Age Predictors: The Status Quo and Future Trends

There is no single universal biomarker yet to estimate overall health status and longevity prospects. Moreover, a consensual approach to the very concept of aging and the means of its assessment are yet to be developed. Markers of aging could facilitate effective health control, more accurate life expectancy estimates, and improved health and quality of life. Clinicians routinely use several indicators that could be biomarkers of aging. Duly validated in a large cohort, models based on a combination of these markers could provide a highly accurate assessment of biological age and the pace of aging. Biological aging is a complex characteristic of chronological age (usually), health-to-age concordance, and medically estimated life expectancy. This study is a review of the most promising techniques that could soon be used in routine clinical practice. Two main selection criteria were applied: a sufficient sample size and reliability based on validation. The selected biological age calculators were grouped according to the type of biomarker used: (1) standard clinical and laboratory markers; (2) molecular markers; and (3) epigenetic markers. The most accurate were the calculators, which factored in a variety of biomarkers. Despite their demonstrated effectiveness, most of them require further improvement and cannot yet be considered for use in standard clinical practice. To illustrate their clinical application, we reviewed their use during the COVID-19 pandemic.

Epigenetic aging: Biological age prediction and informing a mechanistic theory of aging

Numerous studies have shown that epigenetic age-an individual's degree of aging based on patterns of DNA methylation-can be computed and is associated with an array of factors including diet, lifestyle, genetics, and disease. One can expect that still further associations will emerge with additional aging research, but to what end? Prediction of age was an important first step, but-in our view-the focus must shift from chasing increasingly accurate age computations to understanding the links between the epigenome and the mechanisms and physiological changes of aging. Here, we outline emerging areas of epigenetic aging research that prioritize biological understanding and clinical application. First, we survey recent progress in epigenetic clocks, which are beginning to predict not only chronological age but aging outcomes such as all-cause mortality and onset of disease, or which integrate aging signals across multiple biological processes. Second, we discuss research that exemplifies how investigation of the epigenome is building a mechanistic theory of aging and informing clinical practice. Such examples include identifying methylation sites and the genes most strongly predictive of aging-a subset of which have shown strong potential as biomarkers of neurodegenerative disease and cancer; relating epigenetic clock predictions to hallmarks of aging; and using longitudinal studies of DNA methylation to characterize human disease, resulting in the discovery of epigenetic indications of type 1 diabetes and the propensity for psychotic experiences.

A hierarchical process model links behavioral aging and lifespan in C. elegans

Aging involves a transition from youthful vigor to geriatric infirmity and death. Individuals who remain vigorous longer tend to live longer, and within isogenic populations of C. elegans the timing of age-associated vigorous movement cessation (VMC) is highly correlated with lifespan. Yet, many mutations and interventions in aging alter the proportion of lifespan spent moving vigorously, appearing to “uncouple” youthful vigor from lifespan. To clarify the relationship between vigorous movement cessation, death, and the physical declines that determine their timing, we developed a new version of the imaging platform called “The Lifespan Machine”. This technology allows us to compare behavioral aging and lifespan at an unprecedented scale. We find that behavioral aging involves a time-dependent increase in the risk of VMC, reminiscent of the risk of death. Furthermore, we find that VMC times are inversely correlated with remaining lifespan across a wide range of genotypes and environmental conditions. Measuring and modelling a variety of lifespan-altering interventions including a new RNA-polymerase II auxin-inducible degron system, we find that vigorous movement and lifespan are best described as emerging from the interplay between at least two distinct physical declines whose rates co-vary between individuals. In this way, we highlight a crucial limitation of predictors of lifespan like VMC—in organisms experiencing multiple, distinct, age-associated physical declines, correlations between mid-life biomarkers and late-life outcomes can arise from the contextual influence of confounding factors rather than a reporting by the biomarker of a robustly predictive biological age.

Aging produces a variety of outcomes—declines in various measures of health and eventually death. By studying the relationship between two outcomes of aging in the same individual, we can learn about the underlying aging processes that cause them. Here, we consider the relationship between death and an outcome often used to quantify health in C. elegans—vigorous movement cessation which describes the age-associated loss of an individuals’ ability to move long distances. We develop an automated imaging platform that allows us to precisely compare this pair of outcomes in each individual across large populations. We find that individuals who remain vigorous longer subsequently have a shorter remaining lifespan—a pattern that holds even after vigorous movement and lifespan timing are both altered by several different mutations and interventions in aging. Modelling our data using a combination of simulation and analytic studies, we demonstrate how the relative timing of vigorous movement cessation and death suggest that these two outcomes are driven by distinct aging processes. Our data and analyses demonstrate how two outcomes of aging can be correlated across individuals with the timing of one predicting the timing of the other, but nevertheless be driven by mostly distinct underlying physical declines.

Progeria and Aging-Omics Based Comparative Analysis

Since ancient times aging has also been regarded as a disease, and humankind has always strived to extend the natural lifespan. Analyzing the genes involved in aging and disease allows for finding important indicators and biological markers for pathologies and possible therapeutic targets. An example of the use of omics technologies is the research regarding aging and the rare and fatal premature aging syndrome progeria (Hutchinson-Gilford progeria syndrome, HGPS). In our study, we focused on the in silico analysis of differentially expressed genes (DEGs) in progeria and aging, using a publicly available RNA-Seq dataset (GEO dataset GSE113957) and a variety of bioinformatics tools. Despite the GSE113957 RNA-Seq dataset being well-known and frequently analyzed, the RNA-Seq data shared by Fleischer et al. is far from exhausted and reusing and repurposing the data still reveals new insights. By analyzing the literature citing the use of the dataset and subsequently conducting a comparative analysis comparing the RNA-Seq data analyses of different subsets of the dataset (healthy children, nonagenarians and progeria patients), we identified several genes involved in both natural aging and progeria (KRT8, KRT18, ACKR4, CCL2, UCP2, ADAMTS15, ACTN4P1, WNT16, IGFBP2). Further analyzing these genes and the pathways involved indicated their possible roles in aging, suggesting the need for further in vitro and in vivo research. In this paper, we (1) compare "normal aging" (nonagenarians vs. healthy children) and progeria (HGPS patients vs. healthy children), (2) enlist genes possibly involved in both the natural aging process and progeria, including the first mention of IGFBP2 in progeria, (3) predict miRNAs and interactomes for WNT16 (hsa-mir-181a-5p), UCP2 (hsa-mir-26a-5p and hsa-mir-124-3p), and IGFBP2 (hsa-mir-124-3p, hsa-mir-126-3p, and hsa-mir-27b-3p), (4) demonstrate the compatibility of well-established R packages for RNA-Seq analysis for researchers interested but not yet familiar with this kind of analysis, and (5) present comparative proteomics analyses to show an association between our RNA-Seq data analyses and corresponding changes in protein expression.

Brain aging is faithfully modelled in organotypic brain slices and accelerated by prions

Mammalian models are essential for brain aging research. However, the long lifespan and poor amenability to genetic and pharmacological perturbations have hindered the use of mammals for dissecting aging-regulatory molecular networks and discovering new anti-aging interventions. To circumvent these limitations, we developed an ex vivo model system that faithfully mimics the aging process of the mammalian brain using cultured mouse brain slices. Genome-wide gene expression analyses showed that cultured brain slices spontaneously upregulated senescence-associated genes over time and reproduced many of the transcriptional characteristics of aged brains. Treatment with rapamycin, a classical anti-aging compound, largely abolished the time-dependent transcriptional changes in naturally aged brain slice cultures. Using this model system, we discovered that prions drastically accelerated the development of age-related molecular signatures and the pace of brain aging. We confirmed this finding in mouse models and human victims of Creutzfeldt-Jakob disease. These data establish an innovative, eminently tractable mammalian model of brain aging, and uncover a surprising acceleration of brain aging in prion diseases.

LipidClock: A Lipid-Based Predictor of Biological Age

Complexity is a fundamental feature of biological systems. Omics techniques like lipidomics can simultaneously quantify many thousands of molecules, thereby directly capturing the underlying biological complexity. However, this approach transfers the original biological complexity to the resulting datasets, posing challenges in data reduction and analysis. Aging is a prime example of a process that exhibits complex behaviour across multiple scales of biological organisation. The aging process is characterised by slow, cumulative and detrimental changes that are driven by intrinsic biological stochasticity and mediated through non-linear interactions and feedback within and between these levels of organization (ranging from metabolites, macromolecules, organelles and cells to tissue and organs). Only collectively and over long timeframes do these changes manifest as the exponential increases in morbidity and mortality that define biological aging, making aging a problem more difficult to study than the aetiologies of specific diseases. But aging's time dependence can also be exploited to extract key insights into its underlying biology. Here we explore this idea by using data on changes in lipid composition across the lifespan of an organism to construct and test a LipidClock to predict biological age in the nematode Caenorhabdits elegans. The LipidClock consist of a feature transformation via Principal Component Analysis followed by Elastic Net regression and yields and Mean Absolute Error of 1.45 days for wild type animals and 4.13 days when applied to mutant strains with lifespans that are substantially different from that of wild type. Gompertz aging rates predicted by the LipidClock can be used to simulate survival curves that are in agreement with those from lifespan experiments.

Multi-omic rejuvenation of human cells by maturation phase transient reprogramming

Ageing is the gradual decline in organismal fitness that occurs over time leading to tissue dysfunction and disease. At the cellular level, ageing is associated with reduced function, altered gene expression and a perturbed epigenome. Recent work has demonstrated that the epigenome is already rejuvenated by the maturation phase of somatic cell reprogramming, which suggests full reprogramming is not required to reverse ageing of somatic cells. Here we have developed the first "maturation phase transient reprogramming" (MPTR) method, where reprogramming factors are selectively expressed until this rejuvenation point then withdrawn. Applying MPTR to dermal fibroblasts from middle-aged donors, we found that cells temporarily lose and then reacquire their fibroblast identity, possibly as a result of epigenetic memory at enhancers and/or persistent expression of some fibroblast genes. Excitingly, our method substantially rejuvenated multiple cellular attributes including the transcriptome, which was rejuvenated by around 30 years as measured by a novel transcriptome clock. The epigenome was rejuvenated to a similar extent, including H3K9me3 levels and the DNA methylation ageing clock. The magnitude of rejuvenation instigated by MPTR appears substantially greater than that achieved in previous transient reprogramming protocols. In addition, MPTR fibroblasts produced youthful levels of collagen proteins, and showed partial functional rejuvenation of their migration speed. Finally, our work suggests that optimal time windows exist for rejuvenating the transcriptome and the epigenome. Overall, we demonstrate that it is possible to separate rejuvenation from complete pluripotency reprogramming, which should facilitate the discovery of novel anti-ageing genes and therapies.

Utilization of Host and Microbiome Features in Determination of Biological Aging

The term 'old age' generally refers to a period characterized by profound changes in human physiological functions and susceptibility to disease that accompanies the final years of a person's life. Despite the conventional definition of old age as exceeding the age of 65 years old, quantifying aging as a function of life years does not necessarily reflect how the human body ages. In contrast, characterizing biological (or physiological) aging based on functional parameters may better reflect a person's temporal physiological status and associated disease susceptibility state. As such, differentiating 'chronological aging' from 'biological aging' holds the key to identifying individuals featuring accelerated aging processes despite having a young chronological age and stratifying them to tailored surveillance, diagnosis, prevention, and treatment. Emerging evidence suggests that the gut microbiome changes along with physiological aging and may play a pivotal role in a variety of age-related diseases, in a manner that does not necessarily correlate with chronological age. Harnessing of individualized gut microbiome data and integration of host and microbiome parameters using artificial intelligence and machine learning pipelines may enable us to more accurately define aging clocks. Such holobiont-based estimates of a person's physiological age may facilitate prediction of age-related physiological status and risk of development of age-associated diseases.