TIME-Seq Enables Scalable and Inexpensive Epigenetic Age Predictions

Griffin, Patrick; Kane, Alice E.; Trapp, Alexandre; Li, Jien; McNamara, Maeve S.; Meer, Margarita; MacArthur, Michael R.; Mitchell, Sarah J.; Mueller, Amber L.; Carmody, Colleen; Vera, Daniel; Kerepesi, Csaba; Hooten, Nicole Noren; Mitchell, James R.; Evans, Michele K.; Gladyshev, Vadim N.; Sinclair, David

doi:10.1101/2021.10.25.465725

Cited by 13 publications

(10 citation statements)

References 67 publications

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“…These methods can be applied to a range of future studies developing epigenetic clocks including across new tissue types, or by examining a limited subset of CpGs in mutual overlap between bulk methylation and single cell datasets (Trapp et al, 2021). Parallelized, highly cost-reduced methods targeting specific CpG regions (Griffin et al 2021) are another prime example. Lastly, these methods are not limited to the identification of CpG sites as features, and this pipeline could be used to identify features for biomarkers or clocks developed from a range of datasets (eg.…”

Section: Discussionmentioning

confidence: 99%

Novel feature selection methods for construction of accurate epigenetic clocks

Kane

Mueller

et al. 2022

Preprint

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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 al’s 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.

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Section: Discussionmentioning

confidence: 99%

Novel feature selection methods for construction of accurate epigenetic clocks

Kane

Mueller

et al. 2022

Preprint

Self Cite

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“…Future studies that have serial blood collection of participants throughout the course of mRNA- vaccination and even following booster shots will be extremely valuable for epigenetic clock investigations. Recent technological advancements based on Tagmentation-based Indexing of Methylation Sequencing (TIME-Seq) have scaled and reduced the cost of epigenetic age predictions permitting methodology for a more comprehensive study follow-up to our findings (Griffin et al, 2021). We also acknowledge the limited clinical data for participants and that the SARS-CoV-2 infection DNA methylation dataset may be relevant to a early genetic lineage not reflecting emerging variants being monitored nor variants of concern such as Delta (B.1.617.2) .…”

Section: Discussionmentioning

confidence: 99%

Longitudinal study of DNA methylation and epigenetic clocks prior to and following test-confirmed COVID-19 and mRNA vaccination

Pang

Higgins‐Chen

Comite

et al. 2021

Preprint

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The host epigenetic landscape is rapidly changed during SARS-CoV-2 infection and evidence suggests that severe COVID-19 is associated with durable scars to the epigenome. Specifically, aberrant DNA methylation changes in immune cells and alterations to epigenetic clocks in blood relate to severe COVID-19. However, a longitudinal assessment of DNA methylation states and epigenetic clocks in blood from healthy individuals prior to and following test-confirmed non-hospitalized COVID-19 has not been performed. Moreover, the impact of mRNA COVID-19 vaccines upon the host epigenome remains understudied. Here, we first examined DNA methylation states in blood of 21 participants prior to and following test confirmed COVID-19 diagnosis at a median timeframe of 8.35 weeks. 261 CpGs were identified as differentially methylated following COVID-19 diagnosis in blood at an FDR adjusted P value <0.05. These CpGs were enriched in gene body and northern and southern shelf regions of genes involved in metabolic pathways. Integrative analysis revealed overlap among genes identified in transcriptional SARS-CoV-2 infection datasets. Principal component-based epigenetic clock estimates of PhenoAge and GrimAge significantly increased in people over 50 following infection by an average of 2.1 and 0.84 years. In contrast, PCPhenoAge significantly decreased in people under 50 following infection by an average of 2.06 years. This observed divergence in epigenetic clocks following COVID-19 was related to age and immune cell-type compositional changes in CD4+ T cells, B cells, granulocytes, plasmablasts, exhausted T cells, and naïve T cells. Complementary longitudinal epigenetic clock analyses of 36 participants prior to and following Pfizer and Moderna mRNA-based COVID-19 vaccination revealed vaccination significantly reduced principal component-based Horvath epigenetic clock estimates in people over 50 by an average of 3.91 years for those that received Moderna. This reduction in epigenetic clock estimates was significantly related to chronological age and immune cell-type compositional changes in B cells and plasmablasts pre- and post-vaccination. These findings suggest the potential utility of epigenetic clocks as a biomarker of COVID-19 vaccine responses. Future research will need to unravel the significance and durability of short-term changes in epigenetic age related to COVID-19 exposure and mRNA vaccination.

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“…Age prediction by DNAm beta values were performed by either GLM model trained according to Horvath et al . 25 or a modified scAge code (TIMEseq model) 30,63 . The training cohort did not include donor aged 0-18 years thus no log-transformation was performed to the younger age.…”

Section: Methodsmentioning

confidence: 99%

Tracking single cell evolution via clock-like chromatin accessibility

Wang

Jin²,

Wang

et al. 2022

Preprint

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Single cell chromatin accessibility sequencing (scATAC) reconstructs developmental trajectory by phenotypic similarity. However, inferring the exact developmental trajectory is challenging. Here we show a simple, accurate and phenotypic-neutral measure of cell developmental hierarchy – the fraction of accessible clock-like differential methylation loci (ClockDML). As cell undergone mitosis, heterogeneity of chromatin accessibility on ClockDML reduced, providing a measure of mitotic age. We developed a method, EpiTrace, that counts the fraction of opened ClockDML from scATAC data to determine cell age and perform lineage tracing. EpiTrace derived cell age shows concordance to known developmental hierarchies, correlates well with DNA methylation-based clocks, and is complementary with mutation-based lineage tracing, RNA velocity, and stemness predictions. Applying EpiTrace to human scATAC data revealed a multitude of novel biological insights with clinically relevant implications, ranging from hematopoiesis, organ development, tumor biology and immunity to cortical gyrification. Our work discovered a universal epigenomic hallmark during cellular development, which facilitates the study of cellular hierarchies and organismal aging.

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TIME-Seq Enables Scalable and Inexpensive Epigenetic Age Predictions

Cited by 13 publications

References 67 publications

Novel feature selection methods for construction of accurate epigenetic clocks

Novel feature selection methods for construction of accurate epigenetic clocks

Longitudinal study of DNA methylation and epigenetic clocks prior to and following test-confirmed COVID-19 and mRNA vaccination

Tracking single cell evolution via clock-like chromatin accessibility

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