Mutational damage to nuclear DNA occurs constantly in all cells, and not all of it is successfully repaired. Setting aside recent evidence for cycles of damage and repair to cause epigenetic changes characteristic of aging, most unrepaired mutational damage has no meaningful consequence. It occurs in somatic cells that have few cell divisions left, so will not spread, and these cells will die or become senescent and be destroyed once they reach the Hayflick limit. It occurs in genes that are not active in the tissue in question, so even in long-lived somatic cells that do not replicate, such as those of the central nervous system, most mutations will be irrelevant to function.
So how might this process significantly affect tissue function and health? Firstly, mutations or combinations of mutations to a small number of important genes can make a cell cancerous, leading to unfettered replication and a tumor if not stopped by the immune system. Secondly, mutations that take place in a stem cell or progenitor cell can spread widely into tissue, and if they happen to change function in some way, that might contribute to age-related decline. There is no good evidence for the size of this effect, however. A first step on the way towards gathering that evidence is mapping the extent of somatic mutation and its clonal expansion in aged tissues, a project that is still ongoing in the research community.
Should somatic mutation turn out to be an important contributing cause of aging, what can be done about it? Targeted destruction of damaged cells might be off the table, given the size of the mutated cell population, and in any case there is the question of how to identify an enormous number of different stochastic mutations in order to trigger a suicide gene therapy or similar in only the desired cells. This is not a simple proposition. Periodic replacement of stem cell populations seems the most viable of options, as it would make the necessary gene therapy a somewhat easier prospect - it only has to be accomplished in the transplanted stem cells, rather than throughout the body. But again, identifying and fixing tens of thousands of broken genes, even in a petri dish, is certainly not a near term prospect. Indeed, viable methods of robustly replacing stem cell populations are still only in the earliest stages of development at this time. These are tools of the 2030s and 2040s, building atop a much more developed industry of gene therapy and regenerative medicine.
In humans, somatic mutations play a key role in senescence and tumorigenesis. Pioneering work on somatic evolution in cancer has led to the characterization of cancer driver genes and mutation signatures; the interplay between chromatin, nuclear architecture, carcinogens, and the mutational landscape; the evolutionary forces acting on somatic mutations; and clinical implications of somatic mutations. Somatic mutations have been far less studied in healthy human tissues than in cancer. Early studies focused on blood as it is readily accessible and because of the known effects of immune-driven somatic mutation. Recently, somatic mutations have been characterized in tissues like the skin, brain, esophagus, and colon. These studies confirmed that cells harboring certain mutations expand clonally, and the number of clonal populations - as well as the total number of somatic mutations - increases with age. Additionally, recurrent positively selected mutations in specific genes (e.g., NOTCH1) were observed. However, a more comprehensive understanding of somatic mutations across the human body has been limited by the small number of tissues studied to date.
Most studies on somatic evolution in healthy tissues have sequenced DNA from biopsies to high coverage. However, the transcriptome also carries all the genomic information of a cell's transcribed genome, in addition to RNA-specific mutations or edits. RNA-seq has been used to identify germline DNA variants, and recently, single-cell (sc) RNA-seq was used to call DNA somatic mutations in the pancreas of several people. To systematically identify somatic mutations in the human body and to investigate their distribution and functional impact, we developed a method that leverages the genomic information carried by RNA to identify DNA somatic mutations while avoiding most sources of false positives. We applied it to infer somatic mutations across 7500 tissue samples from 36 non-cancerous tissues, allowing us to explore the landscape of somatic mutations throughout the human body. To our knowledge, this is the largest map to date of somatic mutations in non-cancerous tissues.
It has been proposed that somatic mutations contribute to aging and organ deterioration; consistently, we observed a positive correlation between age and mutation burden in most tissues. Interestingly, several brain regions are among the tissues exhibiting stronger age correlation, and somatic mutations have been shown to have a role in neurodegeneration. We observed largely tissue-specific behaviors and some pervasive observations shared across tissues. These results suggest that different cell types are subjected to different evolutionary paths that could be dependent on environmental or developmental differences. For example, while most samples exhibit tissue-specific mutation profiles, some others like transverse colon and the small intestine have similar profiles. Additionally, we observed that genes whose expression is associated with mutation load in several tissues are enriched in DNA repair, autophagy, immune response, cellular transport, cell adhesion, and viral processes, and while these functions have been implicated in mutagenesis in cancer, our results highlight how expression variation of these genes associates with mutational variation in healthy tissues.
Our findings paint a complex landscape of somatic mutation across the human body, highlighting their tissue-specific distributions and functional associations. The prevalence of cancer mutations and positive selection of cancer driver genes in non-diseased tissues suggests the possibility of a poised pre-cancerous state, which could also contribute to aging. Finally, our method for inferring somatic mutations from RNA-seq data may help accelerate the study of somatic evolution and its role in aging and disease.