Universal constraints on protein evolution in the long-term evolution experiment with Escherichia coli

Although it is well known that abundant proteins evolve slowly across the tree of life, there is little consensus for why this is true. Here, I report that abundant proteins evolve slowly in the hypermutator populations of Lenski’s long-term evolution experiment with Escherichia coli (LTEE). Specifically, the density of all observed mutations per gene, as measured in metagenomic time series covering 60,000 generations of the LTEE, significantly anti-correlates with mRNA abundance, protein abundance, and degree of protein-protein interaction. The same pattern holds for nonsynonymous mutation density. However, synonymous mutation density, measured across the LTEE hypermutator populations, positively correlates with protein abundance. These results show that universal constraints on protein evolution are visible in data spanning three decades of experimental evolution. Therefore, it should be possible to design experiments to answer why abundant proteins evolve slowly.

Mutation distribution density in tumors reconstructs human’s lost diversity

Introductory ParagraphMutations do not accumulate uniformly across the genome. Human germline and tumor mutation density correlate poorly, and each is associated with different genomic features. Here, we analyze the genome-wide distribution of mutation densities in human and non-human Great Ape (NHGA) germlines as well as human tumors. Strikingly, non-human Great Ape germlines present higher correlation with tumors than the human germline does. This situation is mediated by a different distribution in the human germline of mutations at non-CpG sites, but not of CpG>T transitions. We propose that the impact of ancestral and historical human demographic events on human mutation density leads to this specific disruption in its expected genome-wide distribution. Tumors partially recover this distribution by the accumulation of pre-neoplastic-like somatic mutations. Our results highlight the potential utility of using Great Ape population data, rather than human controls, to establish the expected mutational background of healthy somatic cells.

The Origins and Consequences of Localized and Global Somatic Hypermutation

Cancer is a disease of the genome, but the dramatic inter-patient variability in mutation number is poorly understood. Tumours of the same type can differ by orders of magnitude in their mutation rate. To understand potential drivers and consequences of the underlying heterogeneity in mutation rate across tumours, we evaluated both local and global measures of mutation density: both single-stranded and double-stranded DNA breaks in 2,460 tumours of 38 cancer types. We find that SCNAs in thousands of genes are associated with elevated rates of point-mutations, while similarly point-mutation patterns in dozens of genes are associated with specific patterns of DNA double-stranded breaks. These candidate drivers of mutation density are enriched for known cancer drivers, and preferentially occur early in tumour evolution, appearing clonally in all cells of a tumour. To supplement this understanding of global mutation density, we developed and validated a tool called SeqKat to identify localized “rainstorms” of point-mutations (kataegis). We show that rates of kataegis differ by four orders of magnitude across tumour types, with malignant lymphomas showing the highest. Tumours with TP53 mutations were 2.6-times more likely to harbour a kataegic event than those without, and 239 SCNAs were associated with elevated rates of kataegis, including loss of the tumour-suppressor CDKN2A. We identify novel subtypes of kataegic events not associated with aberrant APOBEC activity, and find that these are localized to specific cellular regions, enriched for MYC-target genes. Kataegic events were associated with patient survival in some, but not all tumour types, highlighting a combination of global and tumour-type specific effects. Taken together, we reveal a landscape of genes driving localized and tumour-specific hyper-mutation, and reveal novel mutational processes at play in specific tumour types.

Non-canonical secondary structures arising from non-B-DNA motifs are determinants of mutagenesis

SummarySomatic mutations show variation in density across cancer genomes. Previous studies have shown that chromatin organization and replication time domains are correlated with and thus predictive of this variation 1,2,3,4,5. Here, we analyse 1,809 whole-genome sequences from nine cancer types 6,7,8 to show that a subset of repetitive DNA sequences called non-B motifs that predict non-canonical secondary structure formation 9,10,11,12 can independently account for variation in mutation density. However, combined with epigenetic factors and replication timing, the variance explained can be improved to 43-76%. Intriguingly, ~2-fold mutation enrichment is observed directly within non-B motifs, is focused on exposed structural components, and is dependent on physical properties that are optimal for secondary structure formation. Therefore, there is mounting evidence that secondary structures arising from non-B motifs are not simply associated with increased mutation density, they are possibly causally implicated. Our results suggest that they are determinants of mutagenesis and increase the likelihood of recurrent mutations in the genome 13,6. This analysis calls for caution in the interpretation of recurrent mutations and highlights the importance of taking non-B motifs, that can simply be inferred from the reference sequence, into consideration in background models of mutability henceforth.