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The genome of multicellular organisms carries the hereditary information necessary for the development of all organs and tissues and to maintain function in adulthood. To ensure the genetic stability of the species, genomes are protected against changes in sequence information. However, genomes are not static. De novo mutations in germline cells are passed on to offspring and generate the variation needed in evolution. Moreover, postzygotic mutations occur in all somatic cells during development and aging. These somatic mutations remain limited to the individual, generating tissues that are genome mosaics. Insight into such mutations and their consequences has been limited due to their extremely low abundance, with most mutations unique for each cell. Recent advances in sequencing, including whole genome sequencing at the single-cell level, have now led to the first insights into somatic mutation burdens in human tissues. Here, we will first briefly describe the latest methodology for somatic mutation analysis, then review our current knowledge of somatic mutation burden in human tissues and, finally, briefly discuss the possible functional impact of somatic mutations on the aging process and age-related diseases, including cancer and diseases other than cancer.

Telomere attrition has been proposed as a biomarker and causal factor in aging. In addition to causing cellular senescence and apoptosis, telomere shortening has been found to affect gene expression in subtelomeric regions. Here, we analyzed the distribution of age-related differentially expressed genes from the GTEx RNA sequencing database of 54 tissue types from 979 human subjects and found significantly more upregulated than downregulated genes in subtelomeric regions as compared to the genome-wide average. Our data demonstrate spatial relationships between telomeres and gene expression in aging.

Presentation of poster 635B at TAGC 2020 online. Files include a PDF of poster (TAGC_poster_BS2020.pdf)

Representative subset (Meg3, Rian and Mirg) of maternally expressed and imprinted ncRNA locus, Dlk-Dio3, on chromosome 12 in UCSC Genome Browser overlaid with aligned reads from RNA-seq dataset. Blue = sense paired-end, red = antisense paired-end; Scale = 100 kb. (PDF 64 kb)

RNAseq validation. (a) qPCR validation of 13 selected transcripts normalized to eukaryotic 18 s rRNA using TaqMan primer/probe sets. (b) qPCR validation of 13 differentially expressed non-coding RNAs normalized to GAPDH mRNA using custom made primer for each novel ncRNA. For both, RNA-seq and qPCR values are plotted as the mean (Old, n = 3; Young, n = 3) log2FoldChange(Old/Young) value, where the red line indicates the slope of linear regression and the square of Pearson’s correlation coefficient (r). Insets are bar plots of equivalent corresponding values. (PDF 122 kb)

Representative subset (Meg3, Rian and Mirg) of maternally expressed and imprinted ncRNA locus, Dlk-Dio3, on chromosome 12 in UCSC Genome Browser overlaid with aligned reads from RNA-seq dataset. Blue = sense paired-end, red = antisense paired-end; Scale = 100 kb. (PDF 64 kb)

RNAseq validation. (a) qPCR validation of 13 selected transcripts normalized to eukaryotic 18 s rRNA using TaqMan primer/probe sets. (b) qPCR validation of 13 differentially expressed non-coding RNAs normalized to GAPDH mRNA using custom made primer for each novel ncRNA. For both, RNA-seq and qPCR values are plotted as the mean (Old, n = 3; Young, n = 3) log2FoldChange(Old/Young) value, where the red line indicates the slope of linear regression and the square of Pearson’s correlation coefficient (r). Insets are bar plots of equivalent corresponding values. (PDF 122 kb)

Maintaining the stability of the genome is critical to cell survival and normal cell growth. Genetic modification of hematopoietic cells might bear an inherent increased risk for the accumulation of DNA mutations. It frequently requires cultivation of the cells under super-physiological oxygen levels, which can result in increased oxidative damage, as well as under super-physiological concentrations of cytokines, which might interfere with DNA-damage checkpoint activation and by this means might result in an increased mutational load. We describe here a protocol for monitoring the frequency of DNA mutations in bone marrow cells post transduction or upon selection either in vitro or in vivo based on the lacZ-plasmid (pUR288) transgenic mouse (small blue mouse) mutation indicator strain.

Supplementary materials. The supplementary materials include Figures S1â S3, Tables S1â S3, and Supplementary Experimental Procedures. (PDF 473 kb)

Supplementary materials. The supplementary materials include Figures S1â S3, Tables S1â S3, and Supplementary Experimental Procedures. (PDF 473 kb)