Biology subjects

Kane, A. E.

Publications and source records attributed to Kane, A. E..

9 recordsLinked to original sources

bioRxiv2025

DNA methylation signatures of frailty beyond age: a longitudinal study of female and male mice

Frailty is an age-related geriatric syndrome with largely unknown mechanisms. We conducted a longitudinal study of aging C57BL/6JNIA mice (females; n = 40, male; n = 49), measured frailty index and derived DNA methylation data from PBMCs. We selected frailty-related differentially methylated CpGs and determined differentially methylated regions (DMRs), focusing on both age-independent and -dependent frailty, and using both mixed-sex and sex-stratified subgroups. We propose a joint set of 925 frailty-related DMRs, perform an association study with frailty outcomes, build epigenetic frailty clocks and validate in mice with interventions. Notably, age-independent frailty DMRs are enriched in nervous and endocrine pathways, distinct from signaling and lipid metabolism pathways identified from age-dependent DMRs. We observe hypermethylation in signaling pathways and hypomethylation in lipid metabolism and cytochrome P450 pathways with frailty progression. 36 DMRs show consistent associations in validation. These findings highlight distinct epigenetic signatures underlying frailty and aging, with potential sex-specific mechanisms.

molecular biology↗

bioRxiv2025

Multiomic clocks to predict phenotypic age in mice

Biological age refers to a persons overall health in aging, as distinct from their chronological age. Diverse measures of biological age, referred to as "clocks", have been developed in recent years and enable risk assessments, and an estimation of the efficacy of longevity interventions in animals and humans. While most clocks are trained to predict chronological age, clocks have been developed to predict more complex composite biological age outcomes, at least in humans. These composite outcomes can be made up of a combination of phenotypic data, chronological age, and disease or mortality risk. Here, we develop the first such composite biological age measure for mice: the mouse phenotypic age model (Mouse PhenoAge). This outcome is based on frailty measures, complete blood counts, and mortality risk in a longitudinally assessed cohort of male and female C57BL/6 mice. We then develop clocks to predict Mouse PhenoAge, based on multi-omic models using metabolomic and DNA methylation data. Our models accurately predict Mouse PhenoAge, and residuals of the models are associated with remaining lifespan, even for mice of the same chronological age. These methods offer novel ways to accurately predict mortality in laboratory mice thus reducing the need for lengthy and costly survival studies.

systems biology↗

bioRxiv2025

Metabolomics biomarkers of frailty: a longitudinal study of aging female and male mice

Frailty is an age-related geriatric syndrome, for which the mechanisms remain largely unknown. We performed a longitudinal study of aging female (n = 40) and male (n = 47) C57BL/6NIA mice, measured frailty index and derived metabolomics data from plasma samples. We identify differentially abundant metabolites related to aging, determine frailty related metabolites via a machine learning approach, and generate a union set of frailty features, both in the whole cohort and in sex-stratified subgroups. Using the features, we perform an association study and build a metabolomics-based frailty clock. We find that frailty related metabolites are enriched for amino acid metabolism and metabolism of cofactors and vitamins, include ergothioneine, tryptophan, and alpha-ketoglutarate, and present sex dimorphism. We identify B vitamin metabolism related flavin adenine dinucleotide and pyridoxate as female-specific frailty biomarkers, and lipid metabolism related sphingomyelins, glycerophosphoethanolamine and glycerophosphocholine as male-specific frailty biomarkers. These associations are confirmed in a validation cohort, with ergothioneine and perfluorooctanesulfonate identified as robust frailty biomarkers. In summary, our results identify sex-specific metabolite biomarkers of frailty in aging, and shed light on potential mechanisms involved in frailty.

biochemistry↗

bioRxiv2024

Long-term NMN treatment increases lifespan and healthspan in mice in a sex dependent manner

Nicotinamide adenine dinucleotide (NAD) is essential for many enzymatic reactions, including those involved in energy metabolism, DNA repair and the activity of sirtuins, a family of defensive deacylases. During aging, levels of NAD+ can decrease by up to 50% in some tissues, the repletion of which provides a range of health benefits in both mice and humans. Whether or not the NAD+ precursor nicotinamide mononucleotide (NMN) extends lifespan in mammals is not known. Here we investigate the effect of long-term administration of NMN on the health, cancer burden, frailty and lifespan of male and female mice. Without increasing tumor counts or severity in any tissue, NMN treatment of males and females increased activity, maintained more youthful gene expression patterns, and reduced overall frailty. Reduced frailty with NMN treatment was associated with increases in levels of Anerotruncus colihominis, a gut bacterium associated with lower inflammation in mice and increased longevity in humans. NMN slowed the accumulation of adipose tissue later in life and improved metabolic health in male but not female mice, while in females but not males, NMN increased median lifespan by 8.5%, possible due to sex-specific effects of NMN on NAD+ metabolism. Together, these data show that chronic NMN treatment delays frailty, alters the microbiome, improves male metabolic health, and increases female mouse lifespan, without increasing cancer burden. These results highlight the potential of NAD+ boosters for treating age-related conditions and the importance of using both sexes for interventional lifespan studies.

pharmacology and toxicology↗

bioRxiv2022

Novel feature selection methods for construction of accurate epigenetic clocks

Epigenetic clocks allow the accurate prediction of age based on the methylation status of specific CpG sites in a variety of tissues. These predictive models can be used to distinguish the biological age of an organism from its chronological age, and are a powerful tool to measure the effectiveness of aging 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 reduce the cost and time of clock development by decreasing the number of CpG sites included in clocks. Here, we apply both classic feature selection methods and novel combinatorial methods to the development of epigenetic clocks. We perform feature selection on the human whole blood methylation dataset of [~]470,000 CpG features published by Hannum and colleagues (2015). We develop clocks to predict age, using a variety of feature selection approaches, and all clocks have R2 correlation scores of greater than 0.73. The most predictive clock uses 35 CpG sites for a R2 correlation score of 0.87. The five most frequent sites across all clocks are also 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 and outperform Hannum et als model in accuracy of age prediction despite using significantly less CpGs. We also identify the associated gene regulatory regions of these CpG sites, which may be possible targets for future aging studies. These novel feature selection algorithms will lower the number of sites needed to be sequenced to build clocks and allow conventionally expensive aging epigenetic studies to cost a fraction of what it would normally.

bioinformatics↗

bioRxiv2021

Ultra-cheap and scalable epigenetic age predictions with TIME-Seq

Epigenetic "clocks" based on DNA methylation (DNAme) have emerged as the most robust and widely employed aging biomarkers, but conventional methods for applying them are expensive and laborious. Here, we develop Tagmentation-based Indexing for Methylation Sequencing (TIME-Seq), a highly multiplexed and scalable method for low-cost epigenetic clocks. Using TIME-Seq, we applied multi-tissue and tissue-specific epigenetic clocks to over 1,600 mouse DNA samples. We also discovered a novel approach for age prediction from shallow sequencing (e.g., 10,000 reads) by adapting scAge for bulk measurements. In benchmarking experiments, TIME-Seq performed favorably against prevailing methods and could quantify the effects of interventions thought to accelerate, slow, and reverse aging in mice. Finally, we built and validated a highly accurate human blood clock from 1,056 demographically representative individuals. Our methods increase the scalability and reduce the cost of epigenetic age predictions by more than 100-fold, enabling accurate aging biomarkers to be applied in more large-scale animal and human studies.

genomics↗

bioRxiv2021

Gut Microbiota predicts Healthy Late-life Aging in Male Mice

Calorie restriction (CR) extends lifespan and retards age-related chronic diseases in most species. There is growing evidence that the gut microbiota has a pivotal role in host health and age-related pathological conditions. Yet, it is still unclear how CR and the gut microbiota are related to healthy aging. Here we report findings from a small longitudinal study of male C57BL/6 mice maintained on either ad libitum or mild (15%) CR diets from 21 months of age and tracked until natural death. We demonstrate that CR results in a significant reduction in frailty index (FI), a well-established indicator of aging. We observed significant alterations in bacterial load, diversity, and compositional patterns of the mouse gut microbiota during the aging process. Interrogating the FI-related microbial features using machine learning techniques, we show that gut microbial signatures from 21-month-old mice can predict the healthy aging of 30-month-old mice with reasonable accuracy. This study deepens our understanding of the links between CR, gut microbiota, and frailty in the aging process of mice.

microbiology↗

bioRxiv2019

Age and life expectancy clocks based on machine learning analysis of mouse frailty

The identification of genes and interventions that slow or reverse aging is hampered by the lack of non-invasive metrics that can predict life expectancy of pre-clinical models. Frailty Indices (FIs) in mice are composite measures of health that are cost-effective and non-invasive, but whether they can accurately predict health and lifespan is not known. Here, mouse FIs were scored longitudinally until death and machine learning was employed to develop two clocks. A random forest regression was trained on FI components for chronological age to generate the FRIGHT (Frailty Inferred Geriatric Health Timeline) clock, a strong predictor of chronological age. A second model was trained on remaining lifespan to generate the AFRAID (Analysis of Frailty and Death) clock, which accurately predicts life expectancy and the efficacy of a lifespan-extending intervention up to a year in advance. Adoption of these clocks should accelerate the identification of novel longevity genes and aging interventions.

physiology↗

bioRxiv2019

DNA Break-Induced Epigenetic Drift as a Cause of Mammalian Aging

There are numerous hallmarks of aging in mammals, but no unifying cause has been identified. In budding yeast, aging is associated with a loss of epigenetic information that occurs in response to genome instability, particularly DNA double-strand breaks (DSBs). Mammals also undergo predictable epigenetic changes with age, including alterations to DNA methylation patterns that serve as epigenetic \"age\" clocks, but what drives these changes is not known. Using a transgenic mouse system called \"ICE\" (for inducible changes to the epigenome), we show that a tissues response to non-mutagenic DSBs reorganizes the epigenome and accelerates physiological, cognitive, and molecular changes normally seen in older mice, including advancement of the epigenetic clock. These findings implicate DSB-induced epigenetic drift as a conserved cause of aging from yeast to mammals.\n\nOne Sentence SummaryDNA breaks induce epigenomic changes that accelerate the aging clock in mammals

molecular biology↗