Stability analysis of a model gene network links aging, stress resistance, and negligible senescence

Transcriptional drift in aging cells: A global decontroller

As cells age, they undergo a remarkable global change: In transcriptional drift, hundreds of genes become overexpressed while hundreds of others become underexpressed. Using archetype modeling and Gene Ontology analysis on data from aging Caenorhabditis elegans worms, we find that the up-regulated genes code for sensory proteins upstream of stress responses and down-regulated genes are growth- and metabolism-related. We observe similar trends within human fibroblasts, suggesting that this process is conserved in higher organisms. We propose a simple mechanistic model for how such global coordination of multiprotein expression levels may be achieved by the binding of a single factor that concentrates with age in C. elegans. A key implication is that a cell's own responses are part of its aging process, so unlike wear-and-tear processes, intervention might be able to modulate these effects.

Model biological systems demonstrate the inducibility of pathways that strongly reduce cryoprotectant toxicity

Cryoprotectant toxicity is a limiting factor for the cryopreservation of many living systems. We were moved to address this problem by the potential of organ vitrification to relieve the severe shortage of viable donor organs available for human transplantation. The M22 vitrification solution is presently the only solution that has enabled the vitrification and subsequent transplantation with survival of large mammalian organs, but its toxicity remains an obstacle to organ stockpiling for transplantation. We therefore undertook a series of exploratory studies to identify potential pretreatment interventions that might reduce the toxic effects of M22. Hormesis, in which a living system becomes more resistant to toxic stress after prior subtoxic exposure to a related stress, was investigated as a potential remedy for M22 toxicity in yeast, in the nematode worm C. elegans, and in mouse kidney slices. In yeast, heat shock pretreatment increased survival by 18-fold after exposure to formamide and by over 9-fold after exposure to M22 at 30 °C; at 0 °C and with two-step addition, treatment with 90% M22 resulted in 100% yeast survival. In nematodes, surveying a panel of pretreatment interventions revealed 3 that conferred nearly total protection from acute whole-worm M22-induced damage. One of these protective pretreatments (exposure to hydrogen peroxide) was applied to mouse kidney slices in vitro and was found to strongly protect nuclear and plasma membrane integrity in both cortical and medullary renal cells exposed to 75-100% M22 at room temperature for 40 min. These studies demonstrate for the first time that endogenous cellular defenses, conserved from yeast to mammals, can be marshalled to substantially ameliorate the toxic effects of one of the most toxic single cryoprotectants and the toxicity of the most concentrated vitrification solution so far described for whole organs.

Transcriptional drift in aging cells: A global de-controller

As cells age, they undergo a remarkable global change: In transcriptional drift, hundreds of genes become overexpressed while hundreds of others become underexpressed. Using archetype modeling and Gene Ontology analysis on data from aging Caenorhabditis elegans worms, we find that the upregulated genes code for sensory proteins upstream of stress responses and downregulated genes are growth- and metabolism-related. We propose a simple mechanistic model for how such global coordination of multi-protein expression levels may be achieved by the binding of a single ligand that concentrates with age. A key implication is that a cell's own responses are part of its aging process, so unlike for wear-and-tear processes, intervention might be able to modulate these effects.

Methods for Assessment of Nucleotide Excision Repair Efficiency

Nucleotide excision repair (NER) is responsible for removing a wide variety of bulky adducts from DNA, thus contributing to the maintenance of genome stability. The efficiency with which proteins of the NER system recognize and remove bulky adducts depends on many factors and is of great clinical and diagnostic significance. The review examines current concepts of the NER system molecular basis in eukaryotic cells and analyzes methods for the assessment of the NER-mediated DNA repair efficiency both in vitro and ex vivo.

Dynamic modeling of the cellular senescence gene regulatory network

Cellular senescence is a cell fate that prominently impacts physiological and pathophysiological processes. Diverse cellular stresses induce it, and dramatic gene expression changes accompany it. However, determining the interactions comprising the gene regulatory network (GRN) governing senescence remains challenging. Recent advances in signal processing techniques provide opportunities to reconstruct GRNs. Here, we describe a GRN for senescence integrating time-series transcriptome and transcription factor depletion datasets. Specifically, we infer a set of differential equations using the "Sparse Identification of Nonlinear Dynamics" (SINDy) algorithm, discriminate genes with potential hidden regulators, validate the inferred GRN for time-points not included in the training data, and comprehensively benchmark our approach. Our work is a proof of concept for a data-driven GRN reconstruction method, consolidating an iterative, powerful mathematical platform for senescence modeling that can be used to test hypotheses in silico and has the potential for future discoveries of clinical impact.

Unsupervised learning of aging principles from longitudinal data

Age is the leading risk factor for prevalent diseases and death. However, the relation between age-related physiological changes and lifespan is poorly understood. We combined analytical and machine learning tools to describe the aging process in large sets of longitudinal measurements. Assuming that aging results from a dynamic instability of the organism state, we designed a deep artificial neural network, including auto-encoder and auto-regression (AR) components. The AR model tied the dynamics of physiological state with the stochastic evolution of a single variable, the "dynamic frailty indicator" (dFI). In a subset of blood tests from the Mouse Phenome Database, dFI increased exponentially and predicted the remaining lifespan. The observation of the limiting dFI was consistent with the late-life mortality deceleration. dFI changed along with hallmarks of aging, including frailty index, molecular markers of inflammation, senescent cell accumulation, and responded to life-shortening (high-fat diet) and life-extending (rapamycin) treatments.

Stability of Gene Regulatory Networks Modeled by Generalized Proportional Caputo Fractional Differential Equations

A model of gene regulatory networks with generalized proportional Caputo fractional derivatives is set up, and stability properties are studied. Initially, some properties of absolute value Lyapunov functions and quadratic Lyapunov functions are discussed, and also, their application to fractional order systems and the advantage of quadratic functions are pointed out. The equilibrium of the generalized proportional Caputo fractional model and its generalized exponential stability are defined, and sufficient conditions for the generalized exponential stability and asymptotic stability of the equilibrium are obtained. As a special case, the stability of the equilibrium of the Caputo fractional model is discussed. Several examples are provided to illustrate our theoretical results and the influence of the type of fractional derivative on the stability behavior of the equilibrium.

Damage Repair versus Aging in an Individual-Based Model of Biofilms

Damage is an inevitable consequence of life. For unicellular organisms, this leads to a trade-off between allocating resources into damage repair or into growth coupled with segregation of damage upon cell division, i.e., aging and senescence. Few studies considered repair as an alternative to senescence. None considered biofilms, where the majority of unicellular organisms live, although fitness advantages in well-mixed systems often turn into disadvantages in spatially structured systems such as biofilms. We compared the fitness consequences of aging versus an adaptive repair mechanism based on sensing damage, using an individual-based model of a generic unicellular organism growing in biofilms. We found that senescence is not beneficial provided that growth is limited by substrate availability. Instead, it is useful as a stress response to deal with damage that failed to be repaired when (i) extrinsic mortality was high; (ii) a degree of multicellularity was present; and (iii) damage segregation was effective.

The extent of senescence due to damage accumulation—or aging—is evidently evolvable as it differs hugely between species and is not universal, suggesting that its fitness advantages depend on life history and environment. In contrast, repair of damage is present in all organisms studied. Despite the fundamental trade-off between investing resources into repair or into growth, repair and segregation of damage have not always been considered alternatives. For unicellular organisms, unrepaired damage could be divided asymmetrically between daughter cells, leading to senescence of one and rejuvenation of the other. Repair of “unicells” has been predicted to be advantageous in well-mixed environments such as chemostats. Most microorganisms, however, live in spatially structured systems, such as biofilms, with gradients of environmental conditions and cellular physiology as well as a clonal population structure. To investigate whether this clonal structure might favor senescence by damage segregation (a division-of-labor strategy akin to the germline-soma division in multicellular organisms), we used an individual-based computational model and developed an adaptive repair strategy where cells respond to their current intracellular damage levels by investing into repair machinery accordingly. Our simulations showed that the new adaptive repair strategy was advantageous provided that growth was limited by substrate availability, which is typical for biofilms. Thus, biofilms do not favor a germline-soma-like division of labor between daughter cells in terms of damage segregation. We suggest that damage segregation is beneficial only when extrinsic mortality is high, a degree of multicellularity is present, and an active mechanism makes segregation effective.

IMPORTANCE Damage is an inevitable consequence of life. For unicellular organisms, this leads to a trade-off between allocating resources into damage repair or into growth coupled with segregation of damage upon cell division, i.e., aging and senescence. Few studies considered repair as an alternative to senescence. None considered biofilms, where the majority of unicellular organisms live, although fitness advantages in well-mixed systems often turn into disadvantages in spatially structured systems such as biofilms. We compared the fitness consequences of aging versus an adaptive repair mechanism based on sensing damage, using an individual-based model of a generic unicellular organism growing in biofilms. We found that senescence is not beneficial provided that growth is limited by substrate availability. Instead, it is useful as a stress response to deal with damage that failed to be repaired when (i) extrinsic mortality was high; (ii) a degree of multicellularity was present; and (iii) damage segregation was effective.

Unique age-related transcriptional signature in the nervous system of the long-lived red sea urchin Mesocentrotus franciscanus

The red sea urchin, Mesocentrotus franciscanus, is one the earth’s longest-lived animals, reported to live more than 100 years with indeterminate growth, life-long reproduction and no increase in mortality rate with age. To gain insight into mechanisms associated with longevity and negligible senescence, age-related transcriptional profiles were examined in tissues of the red sea urchin. Genome-wide transcriptional profiling using RNA-Seq revealed few age-related changes in gene expression in muscle and esophagus tissue. In contrast, radial nerve showed an unexpected level of complexity with the expression of 3,370 genes significantly altered more than two-fold with age, including genes involved in nerve function, signaling, metabolism, transcriptional regulation and chromatin modification. There was an age-related upregulation in expression of genes involved in synaptogenesis, axonogenesis and neuroprotection suggesting preservation of neuronal processes with age. There was also an upregulation in expression of positive regulators and key components of the AMPK pathway, autophagy, proteasome function, and the unfolded protein response. This unique age-related gene expression profile in the red sea urchin nervous system may play a role in mitigating the detrimental effects of aging in this long-lived animal.

A universal transcriptomic signature of age reveals the temporal scaling of Caenorhabditis elegans aging trajectories

We collected 60 age-dependent transcriptomes for C. elegans strains including four exceptionally long-lived mutants (mean adult lifespan extended 2.2- to 9.4-fold) and three examples of lifespan-increasing RNAi treatments. Principal Component Analysis (PCA) reveals aging as a transcriptomic drift along a single direction, consistent across the vastly diverse biological conditions and coinciding with the first principal component, a hallmark of the criticality of the underlying gene regulatory network. We therefore expected that the organism's aging state could be characterized by a single number closely related to vitality deficit or biological age. The "aging trajectory", i.e. the dependence of the biological age on chronological age, is then a universal stochastic function modulated by the network stiffness; a macroscopic parameter reflecting the network topology and associated with the rate of aging. To corroborate this view, we used publicly available datasets to define a transcriptomic biomarker of age and observed that the rescaling of age by lifespan simultaneously brings together aging trajectories of transcription and survival curves. In accordance with the theoretical prediction, the limiting mortality value at the plateau agrees closely with the mortality rate doubling exponent estimated at the cross-over age near the average lifespan. Finally, we used the transcriptomic signature of age to identify possible life-extending drug compounds and successfully tested a handful of the top-ranking molecules in C. elegans survival assays and achieved up to a +30% extension of mean lifespan.

Artificial intelligence for aging and longevity research: Recent advances and perspectives

The applications of modern artificial intelligence (AI) algorithms within the field of aging research offer tremendous opportunities. Aging is an almost universal unifying feature possessed by all living organisms, tissues, and cells. Modern deep learning techniques used to develop age predictors offer new possibilities for formerly incompatible dynamic and static data types. AI biomarkers of aging enable a holistic view of biological processes and allow for novel methods for building causal models-extracting the most important features and identifying biological targets and mechanisms. Recent developments in generative adversarial networks (GANs) and reinforcement learning (RL) permit the generation of diverse synthetic molecular and patient data, identification of novel biological targets, and generation of novel molecular compounds with desired properties and geroprotectors. These novel techniques can be combined into a unified, seamless end-to-end biomarker development, target identification, drug discovery and real world evidence pipeline that may help accelerate and improve pharmaceutical research and development practices. Modern AI is therefore expected to contribute to the credibility and prominence of longevity biotechnology in the healthcare and pharmaceutical industry, and to the convergence of countless areas of research.

Hacking Aging: A Strategy to Use Big Data From Medical Studies to Extend Human Life

Age is the most important single factor associated with chronic diseases and ultimately, death. The mortality rate in humans doubles approximately every eight years, as described by the Gompertz law of mortality. The incidence of specific diseases, such as cancer or stroke, also accelerates after the age of about 40 and doubles at a rate that mirrors the mortality-rate doubling time. It is therefore, entirely plausible to think that there is a single underlying process, the driving force behind the progressive reduction of the organism's health leading to the increased susceptibility to diseases and death; aging. There is, however, no fundamental law of nature requiring exponential morbidity and mortality risk trajectories. The acceleration of mortality is thus the most important characteristics of the aging process. It varies dramatically even among closely related mammalian species and hence appears to be a tunable phenotype. Here, we follow how big data from large human medical studies, and analytical approaches borrowed from physics of complex dynamic systems can help to reverse engineer the underlying biology behind Gompertz mortality law. With such an approach we hope to generate predictive models of aging for systematic discovery of biomarkers of aging followed by identification of novel therapeutic targets for future anti-aging interventions.

Fountain of youth for squamous cell carcinomas? On the epigenetic age of non-small cell lung cancer and corresponding tumor-free lung tissues

Aging affects the core processes of almost every organism, and the functional decline at the cellular and tissue levels influences disease development. Recently, it was shown that the methylation of certain CpG dinucleotides correlates with chronological age and that this epigenetic clock can be applied to study aging‐related effects. We investigated these molecular age loci in non‐small cell lung cancer (NSCLC) tissues from patients with adenocarcinomas (AC) and squamous cell carcinomas (SQC) as well as in matched tumor‐free lung tissue. In both NSCLC subtypes, the calculated epigenetic age did not correlate with the chronological age. In particular, SQC exhibited rejuvenation compared to the corresponding normal lung tissue as well as with the chronological age of the donor. Moreover, the younger epigenetic pattern was associated with a trend toward stem cell‐like gene expression patterns. These findings show deep phenotypic differences between the tumor entities AC and SQC, which might be useful for novel therapeutic and diagnostic approaches.

What's new?

Chronological age is correlated with the methylation status of CpG sites in the genome, enabling the study of aging‐related phenomena. Here, investigation of molecular age loci in cells from patients with non‐small cell lung cancer (NSCLC) reveals remarkable differences in NSCLC cell epigenetic age compared to the host's chronological age. Adenocarcinomas showed a higher epigenetic age than squamous cell carcinomas (SQC). Reduced SQC epigenetic age was accompanied by increased expression of stem cell gene signatures, suggesting an increased abundance of stem cells in SQC. Elevated stem cell levels could have clinical implications, as stems cells often show therapeutic resistance.

Intercellular competition and the inevitability of multicellular aging

Current theories attribute aging to a failure of selection, due to either pleiotropic constraints or declining strength of selection after the onset of reproduction. These theories implicitly leave open the possibility that if senescence-causing alleles could be identified, or if antagonistic pleiotropy could be broken, the effects of aging might be ameliorated or delayed indefinitely. These theories are built on models of selection between multicellular organisms, but a full understanding of aging also requires examining the role of somatic selection within an organism. Selection between somatic cells (i.e., intercellular competition) can delay aging by purging nonfunctioning cells. However, the fitness of a multicellular organism depends not just on how functional its individual cells are but also on how well cells work together. While intercellular competition weeds out nonfunctional cells, it may also select for cells that do not cooperate. Thus, intercellular competition creates an inescapable double bind that makes aging inevitable in multicellular organisms.

Genome Stability Maintenance in Naked Mole-Rat

The naked mole-rat (Heterocephalus glaber) is one of the most promising models used to study genome maintenance systems, including the effective repair of damage to DNA. The naked mole-rat is the longest lived rodent species, which is extraordinarily resistant to cancer and has a number of other unique phenotypic traits. For at least 80% of its lifespan, this animal shows no signs of aging or any increased likelihood of death and retains the ability to reproduce. The naked mole-rat draws the heightened attention of researchers who study the molecular basis of lengthy lifespan and cancer resistance. Despite the fact that the naked mole-rat lives under genotoxic stress conditions (oxidative, etc.), the main characteristics of its genome and proteome are a high stability and effective functioning. Replicative senescence in the somatic cells of naked mole-rats is missing, while an additional p53/pRb-dependent mechanism of early contact inhibition has been revealed in its fibroblasts, which controls cell proliferation and its mechanism of arf-dependent aging. The unique traits of phenotypic and molecular adaptations found in the naked mole-rat speak to a high stability and effective functioning of the molecular machinery that counteract damage accumulation in its genome. This review analyzes existing results in the study of the molecular basis of longevity and high cancer resistance in naked mole-rats.

AxGxE: Using Flies to Interrogate the Complex Etiology of Neurodegenerative Disease

Progressive and late-onset neurological disorders such as Parkinson's disease and Alzheimer's disease affect up to 50 million people globally-a number postulated to double every 20 years in a continually aging population. While predisposing allelic variants in several genes clearly confer risk, individual age and specific environmental influences are equally important discriminators of disease onset age and progression. However, none of these factors can independently predict disease with significant precision. Therefore, we must actively develop models that accommodate contributions from all factors, potentially resulting in an A × G × E (age-gene-environment) metric that reflects individual cumulative risk and reliably forecasts disease outcomes. This effort can only be enabled by a deep quantitative understanding of the contribution of these factors to neurodegenerative disease, both individually and in combination. This is also an important consideration because neuronal loss typically precedes clinical presentation and disease-modifying therapies are contingent on early diagnosis that is likely to be informed by an accurate estimation of individual risk. Although epidemiological studies continue to make strong advances in these areas with the advent of powerful "omics"-based approaches, systematic phenotypic modeling of AxGxE interactions is currently more feasible in model organisms such as Drosophila melanogaster where all three parameters can be manipulated with manageable experimental burden. Here, we outline the advantages of using fruit flies for investigating these complex interactions and highlight potential approaches that might help synthesize existing information from diverse fields into a cogent description of age-dependent, environmental, and genetic risk factors in the pathophysiology of neurological disorders.