Biology subjects

Thrush, K.

Publications and source records attributed to Thrush, K..

5 recordsLinked to original sources

bioRxiv2024

Age-Invariant Genes: Multi-Tissue Identification and Characterization of Murine Reference Genes

Studies of the aging transcriptome focus on genes that change with age. But what can we learn from age-invariant genes--those that remain unchanged throughout the aging process? These genes also have a practical application: they serve as reference genes (often called housekeeping genes) in expression studies. Reference genes have mostly been identified and validated in young organisms, and no systematic investigation has been done across the lifespan. Here, we build upon a common pipeline for identifying reference genes in RNA-seq datasets to identify age-invariant genes across seventeen C57BL/6 mouse tissues (brain, lung, bone marrow, muscle, white blood cells, heart, small intestine, kidney, liver, pancreas, skin, brown, gonadal, marrow, and subcutaneous adipose tissue) spanning 1 to 21+ months of age. We identify 9 pan-tissue age-invariant genes and many tissue-specific age-invariant genes. These genes are stable across the lifespan and are validated in independent bulk RNA-seq datasets and RT-qPCR. We find age-invariant genes have shorter transcripts on average and are enriched for CpG islands. Interestingly, pathway enrichment analysis for age-invariant genes identifies an overrepresentation of molecular functions associated with some, but not all, hallmarks of aging. Thus, though hallmarks of aging typically involve changes in cell maintenance mechanisms, select genes associated with these hallmarks resist fluctuations in expression with age. Finally, our analysis concludes no classical reference gene is appropriate for aging studies in all tissues. Instead, we provide tissue-specific and pan-tissue genes for assays utilizing reference gene normalization (i.e., RT-qPCR) that can be applied to animals across the lifespan.

genomics↗

bioRxiv2022

Revisiting the bad luck hypothesis: Cancer risk and aging are linked to replication-driven changes to the epigenome

Aging is the leading risk factor for cancer. While its been proposed that the age-related accumulation of somatic mutations drives this relationship, it is likely not the full story. Here, we show that both aging and cancer share a common epigenetic replication signature, which we modeled from DNA methylation data in extensively passaged immortalized human cells in vitro and tested on clinical tissues. This epigenetic signature of replication - termed CellDRIFT - increased with age across multiple tissues, distinguished tumor from normal tissue, and was escalated in normal breast tissue from cancer patients. Additionally, within-person tissue differences were correlated with both predicted lifetime tissue-specific stem cell divisions and tissue-specific cancer risk. Overall, our findings suggest that age-related replication drives epigenetic changes in cells, pushing them towards a more tumorigenic state. One sentence summaryCellular replication leaves an epigenetic fingerprint that may partially underly the age-associated increase in cancer risk.

cancer biology↗

bioRxiv2022

R methylCIPHER: A Methylation Clock Investigational Package for Hypothesis-Driven Evaluation & Research

BackgroundEpigenetic clocks are promising tools for the study of aging in humans. The clocks quantify biological aging above and beyond chronological age, demonstrate systematic associations with risk factors that accelerate aging, and predict age-related morbidity and mortality. There is interest in using them as surrogate endpoints in intervention studies. However, the large number of clocks, decentralized publication and explosive popularity in the last decade has made for poor accessibility and standardization. This has hampered the abilities of new researchers to conduct truly hypothesis driven research--whether by not knowing about the best available clocks for a given question, or by systematically testing many or all as they become available. ResultsWe report a centralized R package which can be installed and run locally on the users machine, and provides a standardized syntax for epigenetic clock calculation. The package includes a set of helper functions to assist with navigating clock literature and selecting clocks for analysis, as well as affording the user with the details of clock calculation. We describe each clocks resilience to missing CpG information, combined with functionality to assess the need for imputation in the users own data. Furthermore, we demonstrate that while CpGs may not be shared among clocks with similar outputs, many clocks have highly correlated outputs. ConclusionsDue to the previous decentralization of epigenetic clocks, gathering code and performing systematic analysis, particularly in protected datasets, has required significant information gathering effort. Here, we offer an R package with standardized implementation and potential for future growth and clock incorporation to assist with hypothesis driven investigation of aging as measured by epigenetic clocks. We show the potential of this package to drive the user to think globally about signals captured by epigenetic clocks, as well as to properly identify the potential and limitations of each clock in their current research.

bioinformatics↗

bioRxiv2022

Clock Work: Deconstructing the Epigenetic Clock Signals in Aging, Disease, and Reprogramming

Epigenetic clocks have come to be regarded as powerful tools for estimating aging. However, a major drawback in their application is our lack of mechanistic understanding. We hypothesize that uncovering the underlying biology is difficult due to the fact that epigenetic clocks are multifactorial composites: They are comprised of disparate parts, each with their own causal mechanism and functional consequences. Thus, only by deconstructing epigenetic clock signals will it be possible to glean biological insight. Here we clustered 5,717 clock CpGs into twelve distinct modules using multi-tissue and in-vitro datasets. We show that epigenetic clocks are comprised of different proportions of modules, which may explain their discordance when simultaneously applied in a given study. We also observe that epigenetic reprogramming does not reset the entire clock and instead the observed rejuvenation is driven by a subset of modules. Overall, two modules stand-out in terms of their unique features. The first is one of the most responsive to epigenetic reprogramming; is the strongest predictor of all-cause mortality; and shows increases with in vitro passaging up until senescence burden begins to emerge. The light-second module is moderately responsive to reprogramming; is very accelerated in tumor vs. normal tissues; and tracks with passaging in vitro even as population doublings decelerate. Overall, we show that clock deconstruction can identify unique DNAm alterations and facilitate our mechanistic understanding of epigenetic clocks.

genetics↗

bioRxiv2020

A rat epigenetic clock recapitulates phenotypic aging and co-localizes with heterochromatin-associated histone modifications

Aging has been shown to be a strong driver of DNA methylation changes, leading to the development of robust biomarkers in humans and more recently, in mice. This study aimed to generate a novel epigenetic clock in rats--a model with unique physical, physiological, and biochemical advantages for studying mammalian aging. Additionally, we incorporated behavioral data, unsupervised machine learning, and network analysis to identify epigenetic signals that not only track with age, but also relate to phenotypic aging and reflect higher-order molecular aging changes. We used DNAm data from reduced representation bisulfite sequencing (RRBS) to train an epigenetic age (DNAmAge) measure in Fischer 344 CDF (F344) rats. In an independent sample of n=32 F344 rats, we found that this measure correlated with age at (r=0.93), and related to physical functioning (5.9e-3), after adjusting for age and differential cell counts. DNAmAge was also found to correlate with age in C57BL/6 mice (r=0.79), and was decreased in response to caloric restriction (CR), such that the longer the animal was on a CR diet, the greater the decrease in DNAm. We also observed resetting of DNAm when kidney and lung fibroblasts when converted to induced pluripotent stem cells (iPSCs). Using weighted gene correlation network analysis (WGCNA) we identified two modules that appeared to drive our DNAmAge measure. These two modules contained CpGs in intergenic regions that showed substantial overlap with histone marks H3K9me3, H3K27me3, and E2F1 transcriptional factor binding. In moving forward, our ability to unravel the complex signals linking DNA methylation changes to functional aging would require experimental studies in model systems in which longitudinal epigenetic changes can be related to other molecular and physiological hallmarks of aging.

molecular biology↗