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Biological clocks have been developed at different molecular levels and were found to be more advanced in the presence of somatic illness and mental disorders. However, it is unclear whether different biological clocks reflect similar aging processes and determinants. In ~3000 subjects, we examined whether five biological clocks (telomere length, epigenetic, transcriptomic, proteomic, and metabolomic clocks) were interrelated and associated to somatic and mental health determinants. Correlations between biological aging indicators were small (all r < 0.2), indicating little overlap. The most consistent associations of advanced biological aging were found for male sex, higher body mass index (BMI), metabolic syndrome, smoking, and depression. As compared to the individual clocks, a composite index of all five clocks showed most pronounced associations with health determinants. The large effect sizes of the composite index and the low correlation between biological aging indicators suggest that one's biological age is best reflected by combining aging measures from multiple cellular levels.
Epigenetic alterations are a hallmark of aging and age-related diseases. Computational models using DNA methylation data can create "epigenetic clocks" which are proposed to reflect "biological" aging. Thus, it is important to understand the relationship between predictive clock sites and aging biology. To do this, we examined over 450,000 methylation sites from 9,699 samples. We found ~20% of the measured genomic cytosines can be used to make many different epigenetic clocks whose age prediction performance surpasses that of telomere length. Of these predictive sites, the average methylation change over a lifetime was small (~1.5%) and these sites were under-represented in canonical regions of epigenetic regulation. There was only a weak association between "accelerated" epigenetic aging and disease. We also compare tissue-specific and pan-tissue clock performance. This is critical to applying clocks both to new sample sets in basic research, as well as understanding if clinically available tissues will be feasible samples to evaluate "epigenetic aging" in unavailable tissues (e.g., brain). Despite the reproducible and accurate age predictions from DNA methylation data, these findings suggest they may have limited utility as currently designed in understanding the molecular biology of aging and may not be suitable as surrogate endpoints in studies of anti-aging interventions. Purpose-built clocks for specific tissues age ranges or phenotypes may perform better for their specific purpose. However, if purpose-built clocks are necessary for meaningful predictions, then the utility of clocks and their application in the field needs to be considered in that context.
The concept of age acceleration, the difference between biological age and chronological age, is of growing interest, particularly with respect to age-related disorders, such as Alzheimer's Disease (AD). Whilst studies have reported associations with AD risk and related phenotypes, there remains a lack of consensus on these associations. Here we aimed to comprehensively investigate the relationship between five recognised measures of age acceleration, based on DNA methylation patterns (DNAm age), and cross-sectional and longitudinal cognition and AD-related neuroimaging phenotypes (volumetric MRI and Amyloid-β PET) in the Australian Imaging, Biomarkers and Lifestyle (AIBL) and the Alzheimer's Disease Neuroimaging Initiative (ADNI). Significant associations were observed between age acceleration using the Hannum epigenetic clock and cross-sectional hippocampal volume in AIBL and replicated in ADNI. In AIBL, several other findings were observed cross-sectionally, including a significant association between hippocampal volume and the Hannum and Phenoage epigenetic clocks. Further, significant associations were also observed between hippocampal volume and the Zhang and Phenoage epigenetic clocks within Amyloid-β positive individuals. However, these were not validated within the ADNI cohort. No associations between age acceleration and other Alzheimer's disease-related phenotypes, including measures of cognition or brain Amyloid-β burden, were observed, and there was no association with longitudinal change in any phenotype. This study presents a link between age acceleration, as determined using DNA methylation, and hippocampal volume that was statistically significant across two highly characterised cohorts. The results presented in this study contribute to a growing literature that supports the role of epigenetic modifications in ageing and AD-related phenotypes.
Adipocytes are energy stores of the body which also play a role in physiological regulation and homeostasis through their endocrine activity. Adipocyte circadian clocks drive rhythms in gene expression, and dysregulation of these circadian rhythms associates with pathological conditions such as diabetes. However, although the role of circadian rhythms in adipose cells and related tissues has been studied from phsyiological and molecular perspectives, they have not yet been explored from an electrical perspective. Research into electro-chronobiology has revealed that electrical properties have important roles in peripheral clock regulation independently of transcription-translation feedback loops. We have used dielectrophoresis to study electrophysiological rhythms in pre-adipocytes - representing an adipocyte precursor and nucleated cell-based model, using serum shocking as the cellular method of clock entrainment. The results revealed significant electrophysiological rhythms, culminating in circadian (ca. 24 hourly) cycles in effective membrane capacitance and radius properties, whereas effective membrane conductance was observed to express ultradian (ca. 14 hourly) rhythms. These data shed new light into pre-adipocyte electrical behaviour and present a potential target for understanding and manipulation of metabolic physiology.
A hallmark of self-organisation in living systems is their capacity to stabilise their own dynamics, often appearing to anticipate and act upon potential outcomes. Caulerpa brachypus is a marine green alga consisting of differentiated organs resembling leaves, stems and roots. While an individual can exceed a metre in size, it is a single multinucleated giant cell. Thus Caulerpa presents the mystery of morphogenesis on macroscopic scales in the absence of cellularization. The experiments reported here reveal self-organised waves of greenness - chloroplasts - that propagate throughout the alga in anticipation of the day-night light cycle. Using dynamical systems analysis we show that these waves are coupled to a self-sustained oscillator, and demonstrate their entrainment to light. Under constant conditions light intensity affects the natural period and drives transition to temporal disorder. Moreover, we find distinct morphologies depending on light temporal patterns, suggesting waves of chlorophyll could link biological oscillators to metabolism and morphogenesis in this giant single-celled organism.
As major contributors to global oxygen levels and producers of fatty acids, carotenoids, sterols, and phycocolloids, algae have significant ecological and commercial roles. Early algal models have contributed much to our understanding of circadian clocks at physiological and biochemical levels. The genetic and molecular approaches that identified clock components in other taxa have not been as widely applied to algae. We review results from seven species: the chlorophytes Chlamydomonas reinhardtii, Ostreococcus tauri, and Acetabularia spp.; the dinoflagellates Lingulodinium polyedrum and Symbiodinium spp.; the euglenozoa Euglena gracilis; and the red alga Cyanidioschyzon merolae. The relative simplicity, experimental tractability, and ecological and evolutionary diversity of algal systems may now make them particularly useful in integrating quantitative data from "omic" technologies (e.g., genomics, transcriptomics, metabolomics, and proteomics) with computational and mathematical methods.
Epigenetic clocks based on DNA methylation (DNAm) can accurately predict chronological age and are thought to capture biological aging. A variety of epigenetic clocks have been developed for different tissue types and age ranges, but none have focused on postnatal age prediction for preterm infants. Epigenetic estimators of biological age might be especially informative in epidemiologic studies of neonates since DNAm is highly dynamic during the neonatal period and this is a key developmental window. Additionally, markers of biological aging could be particularly important for those born preterm since they are at heightened risk of developmental impairments. We aimed to fill this gap by developing epigenetic clocks for neonatal aging in preterm infants. As part of the Neonatal Neurobehavior and Outcomes in Very Preterm Infants (NOVI) study, buccal cells were collected at NICU discharge to profile DNAm levels in 542 very preterm infants. We applied elastic net regression to identify four epigenetic clocks (NEOage Clocks) predictive of post-menstrual and postnatal age, compatible with the Illumina EPIC and 450K arrays. We observed high correlations between predicted and reported ages (0.93 - 0.94) with root mean squared errors (1.28 - 1.63 weeks). Epigenetic estimators of neonatal aging in preterm infants can be useful tools to evaluate biological maturity and associations with neonatal and long-term morbidities.
Studying chronobiology in reef-building corals is challenging due to the tightly coupled symbiosis with their photosynthetic algae, Symbiodiniaceae. Although symbiosis requires metabolic synchronization and coordination of cellular processes in the holobiont, the cross-talk between the host and symbiont's clocks is still puzzling. Here, we use the mesophotic coral Euphyllia paradivisa to examine temporal gene expression patterns in symbiotic and aposymbiotic morphs exposed to natural light/dark cycles and constant darkness. Our comparative transcriptomic analyses revealed circadian and circatidal cycles of gene expression with a predominant diel pattern in both coral morphs. We found a substantial number of transcripts consistently rhythmic under both light conditions, including genes likely involved in the cnidarians' circadian clock, thus indicating that an endogenous clock, which can oscillate independently from the Symbiodiniaceae clock, exists in E. paradivisa. The analysis further manifests the remarkable impacts of symbiosis on transcriptional rhythms and implies that the algae's presence influences the host's biorhythm.
Circadian rhythms play an essential part in many biological processes, and only three prokaryotic proteins are required to constitute a true post-translational circadian oscillator1. The evolutionary history of the three Kai proteins indicates that KaiC is the oldest member and a central component of the clock2. Subsequent additions of KaiB and KaiA regulate the phosphorylation state of KaiC for time synchronization. The canonical KaiABC system in cyanobacteria is well understood3-6, but little is known about more ancient systems that only possess KaiBC. However, there are reports that they might exhibit a basic, hourglass-like timekeeping mechanism7-9. Here we investigate the primordial circadian clock in Rhodobacter sphaeroides, which contains only KaiBC, to elucidate its inner workings despite missing KaiA. Using a combination of X-ray crystallography and cryogenic electron microscopy, we find a new dodecameric fold for KaiC, in which two hexamers are held together by a coiled-coil bundle of 12 helices. This interaction is formed by the carboxy-terminal extension of KaiC and serves as an ancient regulatory moiety that is later superseded by KaiA. A coiled-coil register shift between daytime and night-time conformations is connected to phosphorylation sites through a long-range allosteric network that spans over 140 Å. Our kinetic data identify the difference in the ATP-to-ADP ratio between day and night as the environmental cue that drives the clock. They also unravel mechanistic details that shed light on the evolution of self-sustained oscillators.
Ageing is often characterised by progressive accumulation of damage, and it is one of the most important risk factors for chronic disease development. Epigenetic mechanisms including DNA methylation could functionally contribute to organismal aging, however the key functions and biological processes may govern ageing are still not understood. Although age predictors called epigenetic clocks can accurately estimate the biological age of an individual based on cellular DNA methylation, their models have limited ability to explain the prediction algorithm behind and underlying key biological processes controlling ageing. Here we present XAI-AGE, a biologically informed, explainable deep neural network model for accurate biological age prediction across multiple tissue types. We show that XAI-AGE outperforms the first-generation age predictors and achieves similar results to deep learning-based models, while opening up the possibility to infer biologically meaningful insights of the activity of pathways and other abstract biological processes directly from the model.
DNA methylation profiles have been used to develop biomarkers of aging known as epigenetic clocks, which predict chronological age with remarkable accuracy and show promise for inferring health status as an indicator of biological age. Epigenetic clocks were first built to monitor human aging, but their underlying principles appear to be evolutionarily conserved, as they have now been successfully developed for many mammalian species. Here, we describe reliable and highly accurate epigenetic clocks shown to apply to 93 domestic dog breeds. The methylation profiles were generated using the mammalian methylation array, which utilizes DNA sequences that are conserved across all mammalian species. Canine epigenetic clocks were constructed to estimate age and also average time to death. We also present two highly accurate human–dog dual species epigenetic clocks (R = 0.97), which may facilitate the ready translation from canine to human use (or vice versa) of antiaging treatments being developed for longevity and preventive medicine. Finally, epigenome-wide association studies here reveal individual methylation sites that may underlie the inverse relationship between breed weight and lifespan. Overall, we describe robust biomarkers to measure aging and, potentially, health status in canines.
In this issue of Cancer Cell, Kettner et al. identify the disruption of normal circadian rhythmicity as an independent risk factor for hepatocellular carcinoma (HCC) in experimental animals and reveal opposing roles for the nuclear receptors FXR and CAR in disease progression from non-alcoholic fatty liver disease (NAFLD) to HCC.
The multispecies coalescent (MSC) model accommodates both species divergences and within-species coalescent and provides a natural framework for phylogenetic analysis of genomic data when the gene trees vary across the genome. The MSC model implemented in the program bpp assumes a molecular clock and the Jukes-Cantor model, and is suitable for analyzing genomic data from closely related species. Here we extend our implementation to more general substitution models and relaxed clocks to allow the rate to vary among species. The MSC-with-relaxed-clock model allows the estimation of species divergence times and ancestral population sizes using genomic sequences sampled from contemporary species when the strict clock assumption is violated, and provides a simulation framework for evaluating species tree estimation methods. We conducted simulations and analyzed two real datasets to evaluate the utility of the new models. We confirm that the clock-JC model is adequate for inference of shallow trees with closely related species, but it is important to account for clock violation for distant species. Our simulation suggests that there is valuable phylogenetic information in the gene-tree branch lengths even if the molecular clock assumption is seriously violated, and the relaxed-clock models implemented in bpp are able to extract such information. Our Markov chain Monte Carlo algorithms suffer from mixing problems when used for species tree estimation under the relaxed clock and we discuss possible improvements. We conclude that the new models are currently most effective for estimating population parameters such as species divergence times when the species tree is fixed.
The gene networks that comprise the circadian clock modulate biological function across a range of scales, from gene expression to performance and adaptive behaviour. The clock functions by generating endogenous rhythms that can be entrained to the external 24-h day-night cycle, enabling organisms to optimally time biochemical processes relative to dawn and dusk. In recent years, computational models based on differential equations have become useful tools for dissecting and quantifying the complex regulatory relationships underlying the clock's oscillatory dynamics. However, optimizing the large parameter sets characteristic of these models places intense demands on both computational and experimental resources, limiting the scope of in silico studies. Here, we develop an approach based on Boolean logic that dramatically reduces the parametrization, making the state and parameter spaces finite and tractable. We introduce efficient methods for fitting Boolean models to molecular data, successfully demonstrating their application to synthetic time courses generated by a number of established clock models, as well as experimental expression levels measured using luciferase imaging. Our results indicate that despite their relative simplicity, logic models can (i) simulate circadian oscillations with the correct, experimentally observed phase relationships among genes and (ii) flexibly entrain to light stimuli, reproducing the complex responses to variations in daylength generated by more detailed differential equation formulations. Our work also demonstrates that logic models have sufficient predictive power to identify optimal regulatory structures from experimental data. By presenting the first Boolean models of circadian circuits together with general techniques for their optimization, we hope to establish a new framework for the systematic modelling of more complex clocks, as well as other circuits with different qualitative dynamics. In particular, we anticipate that the ability of logic models to provide a computationally efficient representation of system behaviour could greatly facilitate the reverse-engineering of large-scale biochemical networks.
Epigenetic clocks, developed using DNA methylation data, have been widely used to quantify biological aging in multiple tissues/cells. However, many existing epigenetic clocks are weakly correlated with each other, suggesting they may capture different biological processes. We utilize multi-omics data from diverse human tissue/cells to identify shared features across eleven existing epigenetic clocks. Despite the striking lack of overlap in CpGs, multi-omics analysis suggested five clocks (Horvath1, Horvath2, Levine, Hannum, and Lin) share transcriptional associations conserved across purified CD14+ monocytes and dorsolateral prefrontal cortex. The pathways enriched in the shared transcriptional association suggested links between epigenetic aging and metabolism, immunity, and autophagy. Results from in vitro experiments showed that two clocks (Levine and Lin) were accelerated in accordance with two hallmarks of aging-cellular senescence and mitochondrial dysfunction. Finally, using multi-tissue data to deconstruct the epigenetic clock signals, we developed a meta-clock that demonstrated improved prediction for mortality and robustly related to hallmarks of aging in vitro than single clocks.
Immune function and redox markers are used for estimating the aging rate, namely biological age (BA). However, it is unknown if this BA and its changes can be reflected in longevity. Thus, we must quantify BA in experimental animals. In peritoneal immune cells of 202 female mice (ICR/CD1) in different ages, 10 immune and 6 redox parameters were evaluated to construct two mathematical models for BA quantification in mice by multiple linear regression. Immune and redox parameters were selected as independent variables and chronological age as dependent, developing two models: the Immunity and the Redox Clocks, reaching both an adjusted R2 of 80.9% and a standard error of 6.38 and 8.57 weeks, respectively. Both models were validated in a different group of healthy mice obtaining a Pearson's correlation coefficient of 0.844 and 0.800 (p < 0.001) between chronological and BA. Furthermore, they were applied to adult prematurely aging mice, which showed a higher BA than non-prematurely aging mice. Moreover, after positive and negative lifestyle interventions, mice showed a lower and higher BA, respectively, than their age-matched controls. In conclusion, the Immunity and Redox Clocks allow BA quantification in mice and both the ImmunolAge and RedoxAge in mice relate to lifespan.
How do cells perceive time? Do cells use temporal information to regulate the production/degradation of their enzymes, membranes, and organelles? Does controlling biological time influence cytoskeletal organization and cellular architecture in ways that confer evolutionary and physiological advantages? Potential answers to these fundamental questions of cell biology have historically revolved around the discussion of 'master' temporal programs, such as the principal cyclin-dependent kinase/cyclin cell division oscillator and the circadian clock. In this review, we provide an overview of the recent evidence supporting an emerging concept of 'autonomous clocks,' which under normal conditions can be entrained by the cell cycle and/or the circadian clock to run at their pace, but can also run independently to serve their functions if/when these major temporal programs are halted/abrupted. We begin the discussion by introducing recent developments in the study of such clocks and their roles at different scales and complexities. We then use current advances to elucidate the logic and molecular architecture of temporal networks that comprise autonomous clocks, providing important clues as to how these clocks may have evolved to run independently and, sometimes at the cost of redundancy, have strongly coupled to run under the full command of the cell cycle and/or the circadian clock. Next, we review a list of important recent findings that have shed new light onto potential hallmarks of autonomous clocks, suggestive of prospective theoretical and experimental approaches to further accelerate their discovery. Finally, we discuss their roles in health and disease, as well as possible therapeutic opportunities that targeting the autonomous clocks may offer.
Aging is a complex process with interindividual variability, which can be measured by aging biological clocks. Aging clocks are machine-learning algorithms guided by biological information and associated with mortality risk and a wide range of health outcomes. One of these aging clocks are transcriptomic clocks, which uses gene expression data to predict biological age; however, their functional role is unknown. Here, we profiled two transcriptomic clocks (RNAAgeCalc and knowledge-based deep neural network clock) in a large dataset of human postmortem prefrontal cortex (PFC) samples. We identified that deep-learning transcriptomic clock outperforms RNAAgeCalc to predict transcriptomic age in the human PFC. We identified associations of transcriptomic clocks with psychiatric-related traits. Further, we applied system biology algorithms to identify common gene networks among both clocks and performed pathways enrichment analyses to assess its functionality and prioritize genes involved in the aging processes. Identified gene networks showed enrichment for diseases of signal transduction by growth factor receptors and second messenger pathways. We also observed enrichment of genome-wide signals of mental and physical health outcomes and identified genes previously associated with human brain aging. Our findings suggest a link between transcriptomic aging and health disorders, including psychiatric traits. Further, it reveals functional genes within the human PFC that may play an important role in aging and health risk.
Epigenetic clocks allow us to accurately predict the age and future health of individuals based on the methylation status of specific CpG sites in the genome and are a powerful tool to measure the effectiveness of longevity interventions. There is a growing need for methods to efficiently construct epigenetic clocks. The most common approach is to create clocks using elastic net regression modelling of all measured CpG sites, without first identifying specific features or CpGs of interest. The addition of feature selection approaches provides the opportunity to optimise the identification of predictive CpG sites. Here, we apply novel feature selection methods and combinatorial approaches including newly adapted neural networks, genetic algorithms, and 'chained' combinations. Human whole blood methylation data of ~470,000 CpGs was used to develop clocks that predict age with R2 correlation scores of greater than 0.73, the most predictive of which uses 35 CpG sites for a R2 correlation score of 0.87. The five most frequent sites across all clocks were modelled to build a clock with a R2 correlation score of 0.83. These two clocks are validated on two external datasets where they maintain excellent predictive accuracy. When compared with three published epigenetic clocks (Hannum, Horvath, Weidner) also applied to these validation datasets, our clocks outperformed all three models. We identified gene regulatory regions associated with selected CpGs as possible targets for future aging studies. Thus, our feature selection algorithms build accurate, generalizable clocks with a low number of CpG sites, providing important tools for the field.
DNA methylation changes that accrue in the stem cell pool of an adult tissue in line with the cumulative number of cell divisions may contribute to the observed variation in cancer risk among tissues and individuals. Thus, the construction of epigenetic "mitotic" clocks that can measure the lifetime number of stem cell divisions is of paramount interest.
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