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.

Background Dyskeratosis congenita (DKC) and idiopathic aplastic anemia (AA) are bone marrow failure syndromes that share characteristics of premature aging with severe telomere attrition. Aging is also reflected by DNA methylation changes, which can be utilized to predict donor age. There is evidence that such epigenetic age predictions are accelerated in premature aging syndromes, but it is yet unclear how this is related to telomere length. DNA methylation analysis may support diagnosis of DKC and AA, which still remains a challenge for these rare diseases. Results In this study, we analyzed blood samples of 70 AA and 18 DKC patients to demonstrate that their epigenetic age predictions are overall increased, albeit not directly correlated with telomere length. Aberrant DNA methylation was observed in the gene PRDM8 in DKC and AA as well as in other diseases with premature aging phenotype, such as Down syndrome and Hutchinson-Gilford-Progeria syndrome. Aberrant DNA methylation patterns were particularly found within subsets of cell populations in DKC and AA samples as measured with barcoded bisulfite amplicon sequencing (BBA-seq). To gain insight into the functional relevance of PRDM8, we used CRISPR/Cas9 technology to generate induced pluripotent stem cells (iPSCs) with heterozygous and homozygous knockout. Loss of PRDM8 impaired hematopoietic and neuronal differentiation of iPSCs, even in the heterozygous knockout clone, but it did not impact on epigenetic age. Conclusion Taken together, our results demonstrate that epigenetic aging is accelerated in DKC and AA, independent from telomere attrition. Furthermore, aberrant DNA methylation in PRDM8 provides another biomarker for bone marrow failure syndromes and modulation of this gene in cellular subsets may be related to the hematopoietic and neuronal phenotypes observed in premature aging syndromes. Graphical abstract

Background Differential leukocyte counts are usually measured based on cellular morphology or surface marker expression. It has recently been shown that leukocyte counts can also be determined by cell-type–specific DNA methylation (DNAm). Such epigenetic leukocyte counting is applicable to small blood volumes and even frozen material, but for clinical translation, the method needs to be further refined and validated. Methods We further optimized and validated targeted DNAm assays for leukocyte deconvolution using 332 venous and 122 capillary blood samples from healthy donors. In addition, we tested 36 samples from ring trials and venous blood from 266 patients diagnosed with different hematological diseases. Deconvolution of cell types was determined with various models using DNAm values obtained by pyrosequencing or digital droplet PCR (ddPCR). Results Relative leukocyte quantification correlated with conventional blood counts for granulocytes, lymphocytes, B cells, T cells (CD4 or CD8), natural killer cells, and monocytes with pyrosequencing (r = 0.84; r = 0.82; r = 0.58; r = 0.50; r = 0.70; r = 0.61; and r = 0.59, respectively) and ddPCR measurements (r = 0.65; r = 0.79; r = 0.56; r = 0.57; r = 0.75; r = 0.49; and r = 0.46, respectively). In some patients, particularly with hematopoietic malignancies, we observed outliers in epigenetic leukocyte counts, which could be discerned if relative proportions of leukocyte subsets did not sum up to 100%. Furthermore, absolute quantification was obtained by spiking blood samples with a reference plasmid of known copy number. Conclusions Targeted DNAm analysis by pyrosequencing or ddPCR is a valid alternative to quantify leukocyte subsets, but some assays require further optimization.

Age 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.

Aging of mice can be tracked by DNA methylation changes at specific sites in the genome. In this study, we used the recently released Infinium Mouse Methylation BeadChip to compare such epigenetic modifications in C57BL/6 (B6) and DBA/2J (DBA) mice. We observed marked differences in age-associated DNA methylation in these commonly used inbred mouse strains, indicating that epigenetic clocks for one strain cannot be simply applied to other strains without further verification. In B6 mice age-associated hypomethylation prevailed with focused hypermethylation at CpG islands, whereas in DBA mice CpG islands revealed rather hypomethylation upon aging. Interestingly, the CpGs with highest age-correlation were still overlapping in B6 and DBA mice and included the genes Hsf4, Prima1, Aspa, and Wnt3a. Notably, Hsf4 and Prima1 were also top candidates in previous studies based on whole genome deep sequencing approaches. Furthermore, Hsf4, Aspa, and Wnt3a revealed highly significant age-associated DNA methylation in the homologous regions in human. Subsequently, we used pyrosequencing of the four relevant regions to establish a targeted epigenetic clock that provided very high correlation with chronological age in independent cohorts of B6 (R2 = 0.98) and DBA (R2 = 0.91). Taken together, the methylome differs extensively between B6 and DBA mice, while prominent age-associated changes are conserved among these strains and even in humans. Our new targeted epigenetic clock with 4 CpGs provides a versatile tool for other researchers analyzing aging in mice.