scholarly journals Adapting Blood DNA Methylation Aging Clocks for Use in Saliva Samples With Cell-type Deconvolution

2021 ◽  
Vol 2 ◽  
Author(s):  
Fedor Galkin ◽  
Kirill Kochetov ◽  
Polina Mamoshina ◽  
Alex Zhavoronkov

DeepMAge is a deep-learning DNA methylation aging clock that measures the organismal pace of aging with the information from human epigenetic profiles. In blood samples, DeepMAge can predict chronological age within a 2.8 years error margin, but in saliva samples, its performance is drastically reduced since aging clocks are restricted by the training set domain. However, saliva is an attractive fluid for genomic studies due to its availability, compared to other tissues, including blood. In this article, we display how cell type deconvolution and elastic net can be used to expand the domain of deep aging clocks to other tissues. Using our approach, DeepMAge’s error in saliva samples was reduced from 20.9 to 4.7 years with no retraining.

2020 ◽  
Author(s):  
Lacey W. Heinsberg ◽  
Mitali Ray ◽  
Yvette P. Conley ◽  
James M. Roberts ◽  
Arun Jeyabalan ◽  
...  

ABSTRACTBackgroundPreeclampsia is a leading cause of maternal and neonatal morbidity and mortality. Chronological age and race are associated with increased risk of preeclampsia; however, the pathophysiology of preeclampsia and how these risk factors impact its development, are not entirely understood. This gap precludes clinical interventions to prevent preeclampsia occurrence or to address stark racial disparities in maternal and neonatal outcomes. Of note, cellular aging rates can differ between individuals and chronological age is often a poor surrogate of biological age. DNA methylation age provides a marker of biological aging, and those with a DNA methylation age greater than their chronological age have ‘age acceleration’. Examining age acceleration in the context of preeclampsia status, and race, could strengthen our understanding of preeclampsia pathophysiology, inform future interventions to improve maternal/neonatal outcomes, and provide insight to racial disparities across pregnancy.ObjectivesThe purpose of this exploratory study was to examine associations between age acceleration, preeclampsia status, and race across pregnancy.Study designThis was a longitudinal, observational, case-control study of 56 pregnant individuals who developed preeclampsia (n=28) or were normotensive controls (n=28). Peripheral blood samples were collected at trimester-specific time points and genome-wide DNA methylation data were generated using the Infinium MethylationEPIC Beadchip. DNA methylation age was estimated using the Elastic Net ‘Improved Precision’ clock and age acceleration was computed as Δage, the difference between DNA methylation age and chronological age. DNA methylation age was compared with chronological age using scatterplots and Pearson correlations, while considering preeclampsia status and race. The relationships between preeclampsia status, race, and Δage were formally tested using multiple linear regression, while adjusting for pre-pregnancy body mass index, chronological age, and (chronological age)2. Regressions were performed both with and without consideration of cell-type heterogeneity.ResultsWe observed strong correlations between chronological age and DNA methylation age in all trimesters, ranging from R=0.91-0.95 in cases and R=0.86-0.90 in controls. We observed significantly stronger correlations between chronological age and DNA methylation age in White versus Black participants ranging from R=0.89-0.98 in White participants and R=0.77-0.83 in Black participants. We observed no association between Δage and preeclampsia status within trimesters. However, even while controlling for covariates, Δage was higher in trimester 1 in participants with higher pre-pregnancy BMI (β=0.12, 95% CI=0.02 to 0.22, p=0.02) and lower in Black participants relative to White participants in trimesters 2 (β=−2.68, 95% CI=−4.43 to −0.94, p=0.003) and 3 (β=−2.10, 95% CI=−4.03 to −0.17, p=0.03). When controlling for cell-type heterogeneity, the observations with BMI in trimester 1 and race in trimester 2 persisted.ConclusionsWe report no association between Δage and preeclampsia status, although there were associations with pre-pregnancy BMI and race. In particular, our findings in a small sample demonstrate the need for additional studies to not only investigate the complex pathophysiology of preeclampsia, but also the relationship between race and biological aging, which could provide further insight into racial disparities in pregnancy and birth. Future efforts to confirm these findings in larger samples, including exploration and applications of other epigenetic clocks, is needed.


BMC Genomics ◽  
2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Yunsung Lee ◽  
Kristine L. Haftorn ◽  
William R. P. Denault ◽  
Haakon E. Nustad ◽  
Christian M. Page ◽  
...  

Abstract Background Epigenetic clocks have been recognized for their precise prediction of chronological age, age-related diseases, and all-cause mortality. Existing epigenetic clocks are based on CpGs from the Illumina HumanMethylation450 BeadChip (450 K) which has now been replaced by the latest platform, Illumina MethylationEPIC BeadChip (EPIC). Thus, it remains unclear to what extent EPIC contributes to increased precision and accuracy in the prediction of chronological age. Results We developed three blood-based epigenetic clocks for human adults using EPIC-based DNA methylation (DNAm) data from the Norwegian Mother, Father and Child Cohort Study (MoBa) and the Gene Expression Omnibus (GEO) public repository: 1) an Adult Blood-based EPIC Clock (ABEC) trained on DNAm data from MoBa (n = 1592, age-span: 19 to 59 years), 2) an extended ABEC (eABEC) trained on DNAm data from MoBa and GEO (n = 2227, age-span: 18 to 88 years), and 3) a common ABEC (cABEC) trained on the same training set as eABEC but restricted to CpGs common to 450 K and EPIC. Our clocks showed high precision (Pearson correlation between chronological and epigenetic age (r) > 0.94) in independent cohorts, including GSE111165 (n = 15), GSE115278 (n = 108), GSE132203 (n = 795), and the Epigenetics in Pregnancy (EPIPREG) study of the STORK Groruddalen Cohort (n = 470). This high precision is unlikely due to the use of EPIC, but rather due to the large sample size of the training set. Conclusions Our ABECs predicted adults’ chronological age precisely in independent cohorts. As EPIC is now the dominant platform for measuring DNAm, these clocks will be useful in further predictions of chronological age, age-related diseases, and mortality.


Author(s):  
V. A. Lemesh ◽  
V. N. Kipen ◽  
M. V. Bahdanava ◽  
A. A. Burakova ◽  
A. A. Bulgak ◽  
...  

Based on the bioinformatic and statistical analysis of the GEO-projects to determine the genome-wide profile of human DNA methylation, a list of 27 CpG dinucleotides with a high predictive potential was formed to create models for prediction of the human age from blood samples. The methylation level was determined for 245 samples of individuals from the Republic of Belarus. The correlation coefficients R were calculated, and the mathematical models for determining the age of an individual were constructed. The average accuracy value of the age prediction from blood samples using 12 CpG-dinucleotides was 3.4 years (for men – 3.3, for women – 3.5). The results obtained will be used as a basis for development of calculators for predicting the age of an individual based on the biological traces for forensic experts.


2017 ◽  
Author(s):  
Elior Rahmani ◽  
Regev Schweiger ◽  
Liat Shenhav ◽  
Theodora Wingert ◽  
Ira Hofer ◽  
...  

AbstractWe introduce a Bayesian semi-supervised method for estimating cell counts from DNA methylation by leveraging an easily obtainable prior knowledge on the cell type composition distribution of the studied tissue. We show mathematically and empirically that alternative methods which attempt to infer explicit cell counts without methylation reference can only capture linear combinations of cell counts rather than provide one component per cell type. Our approach, which allows the construction of a set of components such that each component corresponds to a single cell type, therefore provides a new opportunity to investigate cell compositions in genomic studies of tissues for which it was not possible before.


2020 ◽  
Author(s):  
Julia Franzen ◽  
Selina Nüchtern ◽  
Vithurithra Tharmapalan ◽  
Margherita Vieri ◽  
Miloš Nikolić ◽  
...  

AbstractAge is a major risk factor for severe outcome of coronavirus disease 2019 (COVID-19), but it remains unclear if this is rather due to increased chronological age or biological age. During lifetime, specific DNA methylation changes are acquired in our genome that act as “epigenetic clocks” allowing to estimate donor age and to provide a surrogate marker for biological age. In this study, we followed the hypothesis that particularly patients with accelerated epigenetic age are affected by severe outcomes of COVID-19. Using four different age predictors, we did not observe accelerated age in global DNA methylation profiles of blood samples of nine COVID-19 patients. Alternatively, we used targeted bisulfite amplicon sequencing of three age-associated genomic regions to estimate donor-age of blood samples of 95 controls and seventeen COVID-19 patients. The predictions correlated well with chronological age, while COVID-19 patients even tended to be predicted younger than expected. Furthermore, lymphocytes in nineteen COVID-19 patients did not reveal significantly accelerated telomere attrition. Our results demonstrate that these biomarkers of biological age are therefore not suitable to predict a higher risk for severe COVID-19 infection in elderly patients.


2021 ◽  
Vol 22 (17) ◽  
pp. 9306
Author(s):  
Julia Franzen ◽  
Selina Nüchtern ◽  
Vithurithra Tharmapalan ◽  
Margherita Vieri ◽  
Miloš Nikolić ◽  
...  

Age is a major risk factor for severe outcome of the 2019 coronavirus disease (COVID-19). In this study, we followed the hypothesis that particularly patients with accelerated epigenetic age are affected by severe outcomes of COVID-19. We investigated various DNA methylation datasets of blood samples with epigenetic aging signatures and performed targeted bisulfite amplicon sequencing. Overall, epigenetic clocks closely correlated with the chronological age of patients, either with or without acute respiratory distress syndrome. Furthermore, lymphocytes did not reveal significantly accelerated telomere attrition. Thus, these biomarkers cannot reliably predict higher risk for severe COVID-19 infection in elderly patients.


2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Guillermo Palou-Márquez ◽  
Isaac Subirana ◽  
Lara Nonell ◽  
Alba Fernández-Sanlés ◽  
Roberto Elosua

Abstract Background The integration of different layers of omics information is an opportunity to tackle the complexity of cardiovascular diseases (CVD) and to identify new predictive biomarkers and potential therapeutic targets. Our aim was to integrate DNA methylation and gene expression data in an effort to identify biomarkers related to cardiovascular disease risk in a community-based population. We accessed data from the Framingham Offspring Study, a cohort study with data on DNA methylation (Infinium HumanMethylation450 BeadChip; Illumina) and gene expression (Human Exon 1.0 ST Array; Affymetrix). Using the MOFA2 R package, we integrated these data to identify biomarkers related to the risk of presenting a cardiovascular event. Results Four independent latent factors (9, 19, 21—only in women—and 27), driven by DNA methylation, were associated with cardiovascular disease independently of classical risk factors and cell-type counts. In a sensitivity analysis, we also identified factor 21 as associated with CVD in women. Factors 9, 21 and 27 were also associated with coronary heart disease risk. Moreover, in a replication effort in an independent study three of the genes included in factor 27 were also present in a factor identified to be associated with myocardial infarction (CDC42BPB, MAN2A2 and RPTOR). Factor 9 was related to age and cell-type proportions; factor 19 was related to age and B cells count; factor 21 pointed to human immunodeficiency virus infection-related pathways and inflammation; and factor 27 was related to lifestyle factors such as alcohol consumption, smoking and body mass index. Inclusion of factor 21 (only in women) improved the discriminative and reclassification capacity of the Framingham classical risk function and factor 27 improved its discrimination. Conclusions Unsupervised multi-omics data integration methods have the potential to provide insights into the pathogenesis of cardiovascular diseases. We identified four independent factors (one only in women) pointing to inflammation, endothelium homeostasis, visceral fat, cardiac remodeling and lifestyles as key players in the determination of cardiovascular risk. Moreover, two of these factors improved the predictive capacity of a classical risk function.


2020 ◽  
pp. 1-14
Author(s):  
Siqiang Chen ◽  
Masahiro Toyoura ◽  
Takamasa Terada ◽  
Xiaoyang Mao ◽  
Gang Xu

A textile fabric consists of countless parallel vertical yarns (warps) and horizontal yarns (wefts). While common looms can weave repetitive patterns, Jacquard looms can weave the patterns without repetition restrictions. A pattern in which the warps and wefts cross on a grid is defined in a binary matrix. The binary matrix can define which warp and weft is on top at each grid point of the Jacquard fabric. The process can be regarded as encoding from pattern to textile. In this work, we propose a decoding method that generates a binary pattern from a textile fabric that has been already woven. We could not use a deep neural network to learn the process based solely on the training set of patterns and observed fabric images. The crossing points in the observed image were not completely located on the grid points, so it was difficult to take a direct correspondence between the fabric images and the pattern represented by the matrix in the framework of deep learning. Therefore, we propose a method that can apply the framework of deep learning viau the intermediate representation of patterns and images. We show how to convert a pattern into an intermediate representation and how to reconvert the output into a pattern and confirm its effectiveness. In this experiment, we confirmed that 93% of correct pattern was obtained by decoding the pattern from the actual fabric images and weaving them again.


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