Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Comment
. 2023 Jul;619(7971):E52-E56.
doi: 10.1038/s41586-023-06314-y. Epub 2023 Jul 26.

Re-evaluating evidence for adaptive mutation rate variation

Long Wang  1 , Alexander T Ho  2 , Laurence D Hurst  3 , Sihai Yang  4
Affiliations
Comment

Re-evaluating evidence for adaptive mutation rate variation

Long Wang et al. Nature. 2023 Jul.
No abstract available

PubMed Disclaimer

Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1. Core claims of Monroe et al. are error artefacts.
a, Top row: the profile of errors (misascribed as mutations) expected around TSS and TTS attributable to the errors associated with A/T homopolymeric runs. We simulated 2 million errors associated with A/T homopolymeric runs and used the Monroe script to generate the left plot. The data are an exact match to their somatic data calls (reproduced here as the top right plot, figure reused from Monroe et al. under Creative Commons Attribution 4.0 International License). Bottom row: germline mutations in TEs (left panel). Observed mutations in germline unmasked. bp, base pair. b, Essential genes do not have a low mutation rate. The second claim of Monroe to substantiate that mutation is lower in more functionally important sequences is that essential genes have the lowest mutation rates (their Fig. 3c). To substantiate this, they seem to have used many thousands of unfiltered calls. We repeat the analysis using filtered data, either Weng or Monroe, including indels. In neither case is there significant heterogeneity (we provide χ2 values for all comparisons, df = 3, but for Monroe CDS numbers are so small that these calculations are not valid and presented for completeness alone). Tests are one-sided in the sense that we call significance only if there is heterogeneity not if they are more similar than expected. Tests are two-sided in the sense that we ask about deviation from null in any direction. Unification of the two datasets does not alter conclusions: CDS, χ2 = 3.3; intron, χ2 = 5.76, P > 0.05 for all without multi-test correction. Error bars are s.e.m. across gene samples for which sample sizes are: essential (n = 719), morphological (n = 861), cellular or biochemical (n = 297) and environmental (n = 522) for CDS analysis (Monroe CDS and Weng CDS), and essential (n = 671), morphological (n = 789), cellular or biochemical (n = 270) and environmental (n = 452) for the intron analysis (Monroe intron and Weng intron). For representation of the underlying data, see Extended Data Fig. 2.
Extended Data Fig. 1
Extended Data Fig. 1. Mutational properties in different mutation data sets.
We consider the data from Weng et al. and Monroe et al.. We reanalysed both datasets from raw files and split the data into confident mutation calls (HQ) and low-quality calls (LQ). The samples sizes are 1743, 160, 107, 4162 in Weng high confident (W-HQ), Monroe high confident (M-HQ), Weng low quality (W-LQ), Monroe low quality (M-LQ). a is a visual representation of the frequency of each class of mutation, b are the Euclidean distances between the frequency vectors (upper section), including the two full data sets. The values below the diagonal are chi2 P values with v = 7, based on raw counts omitting any rows where both were zero (indicated *, v = 6). Those significant after Bonferonni correction are highlighted in bold. Tests are one sided in the sense that we call significance only if there is heterogeneity not if they are more similar than expected. Tests are two sided in the sense that we ask about deviation from null in any direction. c. Relative rates of different mononucleotide mutations. Above the diagonal, Euclidean distances between the 12 element vectors of relative mutation frequencies. Below the diagnonal, P from chi2 on raw counts (v = 11), with those significant after Bonferonni correction highlighted in bold. Tests are one sided in the sense that we call significance only if there is heterogeneity not if they are more similar than expected. Tests are two sided in the sense that we ask about deviation from null in any direction. d. rates of potential bleed errors in proximity to homopolymeric runs of different length (top panel A or T runs, bottom panel, G or C runs) for Weng HQ (i.e. Confident) and Monroe LQ calls. Y axis is number of homomeric runs with associated bleed type mutations. e. Distribution of mutations on chromosomes 3, 4 and 5. Centromere is shown as orange block. Weng HQ data is in blue, Monroe LQ data in red. f. Relative normalised dinucleotide mutation frequencies in the Weng et al. and Monroe et al. data. In each data set we determined the absolute number of each dinucleotide-associated mutation. We then determined the normalised rate by dividing observed rates by numbers of each dinucleotide in the genome, this providing a rate per bp. The sum rate for each set was calculated and the normalised rates divided by this sum to provide a relative normalised rate. The line of slope 1 indicates equivalence between the two data sets. In blue and red are all the dinucleotide based events that terminate either AA or TT after mutation. In blue are those mutating C/G residues, in red, A/T residues. CG starting dinucleotides are in green. For clarity most other data points are represented by dots alone. g. Predicted and observed dinucleotide frequencies. Observed dinucleotide frequencies are from intergenic sequence. Mutational equilibrium analytically derived as in ref. . Left panel, Weng et al. full data, right panel, Monroe et al. full data. To test for AA/TT concordance, we consider slopes from regression of observed and predicted, including (dashed) and omitting (solid) AA and TT. If AA and TT are unduly influential, we expect a significant difference in slopes. Difference in slopes was tested by t test with df = 26 (Monroe data, t = 3.39, P = 0.0028, Weng data, t = −0.26, P = 0.38). The test is two-sided.
Extended Data Fig. 2
Extended Data Fig. 2. Representation of Fig. 1b showing underlying data points.
Note that for most genes there are no mutations in the reduced data sets hence most data sits at y = 0.
See this image and copyright information in PMC

Comment in

  • Reply to: Re-evaluating evidence for adaptive mutation rate variation.
    Monroe JG, Murray KD, Xian W, Srikant T, Carbonell-Bejerano P, Becker C, Lensink M, Exposito-Alonso M, Klein M, Hildebrandt J, Neumann M, Kliebenstein D, Weng ML, Imbert E, Ågren J, Rutter MT, Fenster CB, Weigel D. Monroe JG, et al. Nature. 2023 Jul;619(7971):E57-E60. doi: 10.1038/s41586-023-06315-x. Nature. 2023. PMID: 37495874 Free PMC article. No abstract available.

Comment on

  • Mutation bias reflects natural selection in Arabidopsis thaliana.
    Monroe JG, Srikant T, Carbonell-Bejerano P, Becker C, Lensink M, Exposito-Alonso M, Klein M, Hildebrandt J, Neumann M, Kliebenstein D, Weng ML, Imbert E, Ågren J, Rutter MT, Fenster CB, Weigel D. Monroe JG, et al. Nature. 2022 Feb;602(7895):101-105. doi: 10.1038/s41586-021-04269-6. Epub 2022 Jan 12. Nature. 2022. PMID: 35022609 Free PMC article.

References

    1. Chuang JH, Li H. Functional bias and spatial organization of genes in mutational hot and cold regions in the human genome. PLoS Biol. 2004;2:e29. doi: 10.1371/journal.pbio.0020029. - DOI - PMC - PubMed
    1. Martincorena I, Seshasayee ASN, Luscombe NM. Evidence of non-random mutation rates suggests an evolutionary risk management strategy. Nature. 2012;485:95–98. doi: 10.1038/nature10995. - DOI - PubMed
    1. Chen XZ, Zhang JZ. No gene-specific optimization of mutation rate in Escherichia coli. Mol. Biol. Evol. 2013;30:1559–1562. doi: 10.1093/molbev/mst060. - DOI - PMC - PubMed
    1. Monroe JG, et al. Mutation bias reflects natural selection in Arabidopsis thaliana. Nature. 2022;602:101–105. doi: 10.1038/s41586-021-04269-6. - DOI - PMC - PubMed
    1. Weng, M. L. et al. Fine-grained analysis of spontaneous mutation spectrum and frequency in Arabidopsis thaliana. Genetics211, 703–714 (2019). - PMC - PubMed