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Epimutations driven by small RNAs arise frequently but most have limited duration in Caenorhabditis elegans

Nature Ecology & Evolution volume 4, pages 1539–1548 (2020)Cite this article

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Abstract

Epigenetic regulation involves changes in gene expression independent of DNA sequence variation that are inherited through cell division. In addition to a fundamental role in cell differentiation, some epigenetic changes can also be transmitted transgenerationally through meiosis. Epigenetic alterations (epimutations) could thus contribute to heritable variation within populations and be subject to evolutionary processes such as natural selection and drift. However, the rate at which epimutations arise and their typical persistence are unknown, making it difficult to evaluate their potential for evolutionary adaptation. Here, we perform a genome-wide study of epimutations in a metazoan organism. We use experimental evolution to characterize the rate, spectrum and stability of epimutations driven by small silencing RNAs in the model nematode Caenorhabditis elegans. We show that epimutations arise spontaneously at a rate approximately 25 times greater than DNA sequence changes and typically have short half-lives of two to three generations. Nevertheless, some epimutations last at least ten generations. Epimutations mediated by small RNAs may thus contribute to evolutionary processes over a short timescale but are unlikely to bring about long-term divergence in the absence of selection.

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Fig. 1: Identification of small RNA-mediated epimutations in C. elegans mutation accumulation lines.
Fig. 2: Absence of evidence for long-term inheritance of epimutations.
Fig. 3: Characterization of the short-term dynamics of epimutations.
Fig. 4: Silencing small RNA pathways show hypervariability in 22G-RNA levels.

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Data availability

High-throughput sequencing data for small RNAs and polyA-selected RNAs have been submitted to the Sequence Read Archive under accession no. PRJNA553063.

Code availability

The custom code and R markdown documents generated in this project have been uploaded to GitHub (https://github.com/belmaran/epimutations).

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Acknowledgements

We thank the London Institute of Medical Sciences genomics facility for sequencing support. We thank B. Lehner for critical comments on the manuscript. We thank M. Merkenshlager, T. Warnecke and E. Martinez-Perez for invaluable feedback on the project at various stages. We thank members of the Sarkies and Katju laboratories for helpful discussions. Work in the Sarkies laboratory is funded by the UK Medical Research Council.

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Authors and Affiliations

  1. MRC London Institute of Medical Sciences, London, UK

    Toni Beltran & Peter Sarkies

  2. Institute of Clinical Sciences, Imperial College London, London, UK

    Toni Beltran & Peter Sarkies

  3. Department of Mathematics, Faculty of Natural Sciences, Imperial College London, London, UK

    Vahid Shahrezaei

  4. Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, TX, USA

    Vaishali Katju

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  1. Toni Beltran
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Contributions

P.S. conceived the original experimental idea, which was refined in discussion with V.K. and T.B. V.K. provided the mutation accumulation lines at 25 and 100 generations. T.B. collected RNA from these lines and executed the experiment to follow 2 lineages for 13 successive generations. T.B. and P.S. designed the analyses of the data, with crucial assistance from V.S. T.B. carried out most analyses with some supplementary analyses performed by P.S. P.S., T.B., V.S. and V.K. discussed the results of these analyses. The manuscript and figures were drafted by T.B. and P.S. and edited and improved with help from V.K. and V.S.

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Correspondence to Peter Sarkies.

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Extended data

Extended Data Fig. 1 Correlation analysis of the 22G-RNA counts dataset.

a, Heatmap depicting all vs all correlation values calculated for the set of epimutated genes in MA25 and MA100 lines, after log2 transformation of the normalised counts matrix. b, Barplot outlining the number of genes epimutated in 1,2,3, up to 22 lines. Epimutated genes were defined as those with a different small RNA state (defined by k-means clustering) compared to the starting population (PeMA).

Extended Data Fig. 2 Minimal overlap of epimutations and genetic mutations across lineages.

a, Overlap between genetic mutations detected by high throughput genome sequencing after 400 generations of selfing (MA400 lines, Konrad et. al., 2019), and epimutations detected in MA25 and MA100 lines. b, Representative examples of 22G-RNA counts across lines for epimutable genes with an overlapping mutation in any one line, regardless of their epimutation status. A mutation was considered to overlap with a gene if located within the gene or 1kb flanking regions. Lines with an overlapping mutation are shown in red. c, 22G-RNA counts in the two epimutations in lineage G overlapping with genetic mutations as in b.

Extended Data Fig. 3 Examples of long-lasting epimutations and genes with reduced within-lineage variation.

a, 22G normalised counts across lines for genes showing low p-values in both the variance test (pvar) and the epistates test (pst). N indicates the number of matching states. The red line indicates the threshold separating high and low small RNA states according to k-means clustering. b, 22G signal profile for F52C9.1.2 in the parental line (PeMA), in lineage D (epimutated) and lineage F (no change in 22G-RNAs).

Extended Data Fig. 4 Comparison of methods for survival analysis of epimutations.

a, b, Comparison of methods to estimate the duration of epimutations. Duration distributions are shown in A, and survival curves in B. c, Genomic features influencing the duration of epimutations. Survival curves estimated from either the k-means or the HMM datasets are shown, showing qualitatively similar trends.

Extended Data Fig. 5 Dynamics of transgenerational 22G-RNA changes.

a, Example of a gene displaying bistable 22G-RNA levels (compare with Fig. 3d). b, Method to examine the transition in 22G-RNA levels around the epimutation. Three generations before and after the transition were selected, and a linear model was fit to the data, recovering an r2 that represents the linearity of the change. c, Histogram illustrating the distribution of r2 values obtained from the analysis in b applied to all epimutations. d, Mean normalized small RNA levels within groups spanning different linearity in 22G-RNA alterations. e, Classification into fluctuating, bistable and gradual changes in small RNAs. Grey lines show each individual epimutation and the mean across each group is shown as a thick coloured line. f, Association of different 22G-RNA dynamics with HRDE-1, piRNA and WAGO-1 target genes. The p-value for a difference in proportion between targets and non-targets according to a Fisher’s exact test is shown above each plot. g, The duration of bistable and gradual changes is longer than fluctuating epimutable genes. The p-value for a difference in proportion between <=2 or >2 generations of epimutations is shown above the plot.

Extended Data Fig. 6 Direct identification of genes with heritable variation in 22G-RNA levels.

a, Test for short-term inheritance. By comparing the intergenerational variance with the overall variance across the lineage, genes with heritable variation in 22G-RNA levels are identified. b, p-value and FDR histograms from the test for short-term inheritance, in lineages A and B. c, Overlap of genes with heritable 22G-RNA levels with small RNA pathway gene targets. d, Distribution duration of runs of consecutive generations with low intergenerational variance. These were defined as consecutive generations with a difference lower than 30% of the standard deviation in 22G-RNA levels across the lineage. e, Overlap of genes with heritable variation in 22G-RNA levels with epimutable genes in MA25 and MA100 lines, and epimutable in short-term lineages A and B. f, Examples of 22G-RNA dynamics at genes with heritable variation in 22G-RNA levels in lineages A and B.

Extended Data Fig. 7 Integration of 22G-RNA and mRNA abundance data.

a, Visualisation of changes in 22G-RNA levels against changes at the mRNA level. Genes with significantly different mRNA levels between the high and low small RNA states are shown in green. b, Correlation between the absolute change in 22G-RNA and mRNA levels, for all epimutations (top panel), and for genes with positively correlated (bottom left) and negatively correlated (bottom right) mRNA-22G changes. c, Genes with correlated changes in 22G-RNAs and mRNAs are marginally enriched in the epimutations set (blue line) compared to random subsets of genes with >10 22G-RNA normalised counts. This enrichment is highest for cases where there is a negative correlation between 22G-RNA and mRNA levels. d, e, Example of gene with negatively correlated 22G-RNA and mRNA levels. f, Visualisation of changes in 22G-RNA levels against changes at the mRNA level for genes with significantly different mRNA levels between the high and low small RNA states, coloured by chromatin domain location. g., Comparison of the proportions of genes with positively and negatively correlated changes in 22G-RNAs and mRNAs in active and regulated chromatin domains.

Extended Data Fig. 8 Further analysis of variability in small RNA pathways and their target genes.

a, b, Variability analysis of piRNAs (A) and miRNAs (B). c, Enrichment of genes with hypervariable 22G-RNAs (HV22Gs) in groups of genes defined according to their evolutionary conservation. d, Epimutations unique to MA100 lines are hypervariable in the MA25 dataset. e, Epimutations unique to MA25 lines are hypervariable in the MA100 dataset. f, Epimutations from MA25 lines do not show increased variation at the mRNA level. g, Epimutations from MA100 lines do not show increased variation at the mRNA level. h, Comparison of mRNA abundance of HV22Gs and non-HV22Gs for all genes, and within small RNA pathway gene targets. Genes with >20 normalised 22G counts were considered for this analysis. Box plot shows interquartile range with a line at median, and whiskers extend to the greatest point no more than 1.5 times the interquartile range. i, j, Correlation analysis between mRNA abundance and variability in 22G-RNAs, for genes with >20 22G normalised counts, separating piRNA, HRDE-1 and WAGO-1 targets (F) and CSR-1 targets (G).

Extended Data Fig. 9 Analysis of epimutation clustering.

a, Number of neighbouring genes observed in the set of epimutable genes, compared to a null distribution derived from randomly sampled sets of genes of the same size. b, Number of groups of 2,3 and 4 consecutive epimutable genes identified. c, Examples of highly correlated (r>0.75) pairs of epimutable genes. 22G-RNA counts in each line for each gene in the pair are shown. d, Comparison of the odds ratio of enrichment in chromatin domains and small RNA regulatory pathways of the set of clustered epimutable genes (29) and the full set of epimutable genes (442). The red dashed line (slope=1, intercept=0) corresponds to identical enrichment in both sets. Deviation towards the top of the line shows stronger enrichment in the clustered set. e, f, Enrichment of clustered epimutable genes in H3K9me3 (e) and H3K27me3 (f) chromatin, compared to a null distribution derived from randomly sampled sets of epimutable genes of the same size.

Extended Data Fig. 10 Epimutations at transposable elements and other repeats.

a, Summary of epimutations at transposable elements (TEs) and other repeats. TEs are only moderately enriched in the epimutation sets from the MA25 and MA100 lines, and are not enriched in the set of epimutations observed in the consecutive generation experiment. b, Repeat class annotations of epimutated TEs and other repeats. c, Small RNA level states for epimutated TEs and other repeats in MA25 and MA100 lines, showing little correspondence between states. d, Distribution of variance test p-values for TEs and other repeats compared to the rest of genes. No TEs or other repeats had significantly reduced variance within lineages after multiple testing correction. e, f, Variability analysis of 22G-RNAs mapping to TEs and other repeats in MA25 (e) and MA100 (f) lines, showing increased variability in comparison to CSR-1 targets.

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Beltran, T., Shahrezaei, V., Katju, V. et al. Epimutations driven by small RNAs arise frequently but most have limited duration in Caenorhabditis elegans. Nat Ecol Evol 4, 1539–1548 (2020). https://doi.org/10.1038/s41559-020-01293-z

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