Mutation bias shapes gene evolution in Arabidopsis thaliana
Preprint posted on June 18, 2020 https://www.biorxiv.org/content/10.1101/2020.06.17.156752v1
Article now published in Nature at http://dx.doi.org/10.1038/s41586-021-04269-6
Mutations in the DNA are one of the main drivers of genome evolution in all organisms. These mutations include transitions and transversions (single nucleotide polymorphisms, SNPs), insertions and deletions (INDELS) that could impact regulatory regions and coding sequences and affect the fitness of the organisms. It was observed that mutation rates are influenced by the DNA sequence and epigenetic features in wild populations. However, this mutation bias is affected by strong selection. Lack of studies analysing large de novo mutation catalogues in plants not subject to strong selection limit our knowledge on whether this bias is independent of selection or not. Grey Monroe and colleagues reanalysed a collection of spontaneous mutations in A. thaliana and associated them with cytogenetic features (GC content, DNA methylation, histone marks, chromatin accessibility (ATAC-seq) and gene expression) to generate a regression model and compare it with natural variation.
Figure 2 from the pre-print. (A) Schematic representation of the regression model. (B-C) contribution of different cytogenetic features to the model. (D-E) Comparison of gene-level distribution compared predicted mutation rates and polymorphism in wild populations.
The generated model weighed the contribution of each cytogenetic feature in mutation rate. Regions with high GC content had the lowest mutation rate, whereas chromatin accessibility showed the opposite trend. Histone modifications associated with active gene expression (such as H3K4me1, H3K27ac and H3H36me3) also showed lower mutation rates, whereas H3K9me1 and cytosine methylation were associated with high mutation rates. These correlations are consistent with works in mammals and yeast and suggest that the bias could be explained by the different target preference of the DNA mismatch repair machinery. In addition, the predictive model also has a similar gene-level distribution compared with polymorphisms in wild populations, with peaks in the transcription starting sites (TSS) and transcription termination sites (TTS). This suggests that the mutation bias observed in the natural population is a consequence of de novo mutation bias and not necessarily a product of selection.
Authors also analysed mutation bias in each gene feature (promoters, UTRs, exons, etc.). This is particularly interesting for coding regions which can have major impacts on fitness. They find that mutation frequency is correlated with functional constraints (synonymous vs non-synonymous mutations, gene expression level, etc.). Moreover, high mutation rates are anti-correlated with genes annotated with core biological function ontologies.
The preprint questions many concepts generally accepted in the classic theories of evolution. It is also clear and concise regarding the problems that motivate the work and the answers that the authors provide with the existing data.
This preprint is a provocative piece with many important novel findings associated with features that are frequently passed over. Their findings will have a broad impact on the evolutionary biology community, not only plant biologist. The release of the preprint sparked a great and interesting discussion on social media between the authors and readers. In a very innovative initiative, the authors also open a Google docs file in order to receive feedback from the community. Many interesting questions remain open, particularly associated with genetic and epigenetic features that were not specifically covered in the regression model and downstream analysis, such as transposable and repetitive elements or nucleosome positioning. Also, there could be important differences between SNPs and INDELS that could be missed in the analysis when both events are combined in mutations as a whole (Lujan et al., 2014). Certainly, the preprint will open pathways to future works assessing the impact of mutation bias in evolutionary events. Recently, Boukas et al. (2020) have released another pre-print addressing similar questions in humans but focused on promoter region methylation and CpG islands.
Lujan, S. A., Clausen, A. R., Clark, A. B., MacAlpine, H. K., MacAlpine, D. M., Malc, E. P., Mieczkowski, P. A., Burkholder, A. B., Fargo, D. C., Gordenin, D. A., & Kunkel, T. A. (2014). Heterogeneous polymerase fidelity and mismatch repair bias genome variation and composition. Genome research, 24(11), 1751–1764. https://doi.org/10.1101/gr.178335.114
Boukas, L, Bjornsson H. T., Hansen K. D. (2020). Purifying selection acts on germline methylation to modify the CpG mutation rate at promoters. bioRxiv 2020.07.04.187880. https://doi.org/10.1101/2020.07.04.187880
Posted on: 6th July 2020 , updated on: 14th July 2020Read preprint
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