Gene expression levels modulate germline mutation rates through the compound effects of transcription-coupled repair and damage

Transcriptional and mutational signatures of the Drosophila ageing germline

Ageing is a complex biological process that is accompanied by changes in gene expression and mutational load. In many species, including humans, older fathers pass on more paternally derived de novo mutations; however, the cellular basis and cell types driving this pattern are still unclear. To explore the root causes of this phenomenon, we performed single-cell RNA sequencing on testes from young and old male Drosophila and genomic sequencing (DNA sequencing) on somatic tissues from the same flies. We found that early germ cells from old and young flies enter spermatogenesis with similar mutational loads but older flies are less able to remove mutations during spermatogenesis. Mutations in old cells may also increase during spermatogenesis. Our data reveal that old and young flies have distinct mutational biases. Many classes of genes show increased postmeiotic expression in the germlines of older flies. Late spermatogenesis-biased genes have higher dN/dS (ratio of non-synonymous to synonymous substitutions) than early spermatogenesis-biased genes, supporting the hypothesis that late spermatogenesis is a source of evolutionary innovation. Surprisingly, genes biased in young germ cells show higher dN/dS than genes biased in old germ cells. Our results provide new insights into the role of the germline in de novo mutation.

Identification of lung cancer drivers by comparison of the observed and the expected numbers of missense and nonsense mutations in individual human genes

Largely, cancer development is driven by acquisition and positive selection of somatic mutations that increase proliferation and survival of tumor cells. As a result, genes related to cancer development tend to have an excess of somatic mutations in them. An excess of missense and/or nonsense mutations in a gene is an indicator of its cancer relevance. To identify genes with an excess of potentially functional missense or nonsense mutations one needs to compare the observed and expected numbers of mutations in the gene. We estimated the expected numbers of missense and nonsense mutations in individual human genes using (i) the number of potential sites for missense and nonsense mutations in individual transcripts and (ii) histology-specific nucleotide context-dependent mutation rates. To estimate mutation rates defined as the number of mutations per site per tumor we used silent mutations reported in the Catalog Of Somatic Mutations In Cancer (COSMIC). The estimates were nucleotide context dependent. We have identified 26 genes with an excess of missense and/or nonsense mutations for lung adenocarcinoma, 18 genes for small cell lung cancer, and 26 genes for squamous cell carcinoma of the lung. These genes include known genes and novel lung cancer gene candidates.