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Mutation load at a mimicry supergene sheds new light on the evolution of inversion polymorphisms

A Publisher Correction to this article was published on 05 February 2021

This article has been updated

Abstract

Chromosomal inversions are ubiquitous in genomes and often coordinate complex phenotypes, such as the covariation of behavior and morphology in many birds, fishes, insects or mammals1,2,3,4,5,6,7,8,9,10,11. However, why and how inversions become associated with polymorphic traits remains obscure. Here we show that despite a strong selective advantage when they form, inversions accumulate recessive deleterious mutations that generate frequency-dependent selection and promote their maintenance at intermediate frequency. Combining genomics and in vivo fitness analyses in a model butterfly for wing-pattern polymorphism, Heliconius numata, we reveal that three ecologically advantageous inversions have built up a heavy mutational load from the sequential accumulation of deleterious mutations and transposable elements. Inversions associate with sharply reduced viability when homozygous, which prevents them from replacing ancestral chromosome arrangements. Our results suggest that other complex polymorphisms, rather than representing adaptations to competing ecological optima, could evolve because chromosomal rearrangements are intrinsically prone to carrying recessive harmful mutations.

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Fig. 1: Genomic architecture of the H. numata wing-pattern polymorphism.
Fig. 2: Variation in inversion size due to accumulation of TEs.
Fig. 3: Accumulation of deleterious variants in inversions.
Fig. 4: Fitness variation associated with chromosomal inversions at the supergene in H. numata.

Data availability

The raw sequence data were deposited in the NCBI SRA and accession numbers are indicated in Supplementary Table 2. The whole-genome VCF file is available upon request. Whole-genome assemblies were deposited in the NCBI under accession number PRJNA676017. All data underlying the fitness assays are available in Supplementary Table 4.

Code availability

RepeatMasker results were parsed with scripts from https://github.com/4ureliek/Parsing-RepeatMasker-Outputs.git. Scripts used to compute the main analyses of this study are available at https://github.com/PaulYannJay/Mutation-load-analysis.

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Acknowledgements

We thank E. d’Alençon and M.-P. Dubois for their help in the laboratory; T. Aubier for DNA extraction; M. McClure, M. Tuatama and R. Mori-Pezo for their help during field work; P. David, P. Nosil and R. Villoutreix for their careful and critical reading of the manuscript; and K. Lhose, D. Laetsch, B. Nabholz, P.A. Gagnaire, M. Gautier, C. Lemaitre, F. Legeai, M. Elias and A.-S. Fiston-Lavier for insightful discussions. We thank the Peruvian government for providing the necessary research permits (236-2012-AG-DGFFS-DGEFFS, 201-2013-MINAGRI-DGFFS/DGEFFS and 002-2015-SERFOR-DGGSPFFS). This research was supported by Agence Nationale de la Recherche (ANR) grants no. ANR-12-JSV7-0005 and no. ANR-18-CE02-0019-01 and European Research Council grant no. ERC-StG-243179 to M.J., and by fellowships from the Natural Sciences and Engineering Research Council of Canada, a Marie Sklodowska-Curie fellowship (FITINV, N 655857) and an ‘Investissement d’Avenir’ grant managed by Agence Nationale de la Recherche (CEBA, ref. ANR-10-LABX-25-01) to M.C. V.L. was supported by the ANR grant DOMEVOL (no. ANR-JCJC-SVSE7-2013), and H.B. by the Emergence program from Paris City Council. This project benefited from the Montpellier Bioinformatics Biodiversity platform supported by the LabEx CeMEB, ANR ‘Investissements d’Avenir’ program ANR-10-LABX-04-01. MGX (H.P.) acknowledges financial support from the France Génomique National infrastructure, funded as part of ANR ‘Investissement d’Avenir’ program ANR-10-INBS-09.

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Contributions

P.J., M.C. and M.J. designed the study. P.J., M.C., A.W. and M.J. wrote the paper. P.J., A.W. and M.J. generated the genomic data. M.C., H.B. and V.L. generated the RNA-seq data. P.J. performed the genomic analyses with input from A.W. M.C. managed butterfly rearing and performed fitness assays. P.J. performed selection coefficient analyses. H.P. performed whole-genome sequencing. All authors contributed to editing the manuscript.

Corresponding authors

Correspondence to Paul Jay or Mathieu Chouteau or Mathieu Joron.

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The authors declare no competing interests.

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Peer review information Nature Genetics thanks Megan Dennis, Laurent Keller, Clemens Küpper and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Alignment of genome assemblies focused on the region of the supergene on chromosome 15.

a, Alignment of genome assemblies of H. numata silvana (Hn0, genome 38) and the H. melpomene Hmel2.5 genome focused on the region of the supergene on chromosome 15. No major chromosomal rearrangement is observed between Hn0 and Heliconius melpomene on chromosome 15. b, Alignment of the supergene region of genome 38 (H. n. silvana) against other H. numata genome assemblies.

Extended Data Fig. 2 PCA computed along the genome for H. numata specimens.

a, PCA computed on SNPs on the chromosome 15 but not within the supergene region. Each dot represents the position of a specimen on the PCA two first axis. The PCA reflect the geographic structure of the dataset. b, PCA computed on SNPs on P1 segment. The first axis of the PCA reflects individual genotypes for the inversion: homozygote for the ancestral gene order (P0/P0), homozygote for the inversion (P1/P1), or heterozygote (P0/P1). The second axis of the PCA reflects the geographic structure of the dataset (samples from French Guiana are highlighted with a black dotted edge). c, PCA computed on SNPs on P2+P3 segment. The first axis of the PCA reflects individual genotypes for the two inversions: homozygote for the ancestral gene order (P0/P0), homozygote for the two inversions (P23/P23), or heterozygote (P0/P23); the second axis of the PCA reflects the geographic structure of the dataset. Proportion of variance explained by axes are indicated in brackets. d, PCA computed on SNPs from the P1+P2+P3 segments. The two axes reflect individual genotypes at the supergene alongside the geographic structure of the dataset. Genotypes at the supergene (Supplementary Table 2) are inferred based on these analyses. To handle overplotting and allow a better visualization, dots were randomly moved from their original position using the geom_jitter fonction from ggplot2 R package, allowing 0.02 amount of height and width variation.

Extended Data Fig. 3 Differential gene expression across the chromosome 15.

Expression difference in early pupal (24h) wing discs between Hn0 and Hn123. Expression differences between groups was assessed with Genewise Negative Binomial Generalized Linear Models with Quasi-likelihood tests (EdgeR packageREF) and Benjamini & Hochberg (‘BH’/’fdr’) multiple test corrections. The -log10 of the false discovery rate (FDR) is plotted along the chromosome 15, with each dot representing a different transcript. Many genes within the inversion segments are differentially expressed between Hn0 and Hn123 and overall, the supergene is significantly enriched (p = 0.01, one-sided block Jackknife resampling test with 100 blocks of 400 transcripts) in differentially expressed genes. No gene ontology terms were over-represented among differently expressed genes. REF : Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

Extended Data Fig. 4 Phylogenies of the silvaniform clade with H. cydno and H. melpomene as outgroups, using the genomic segments orthologous to P1, P2 and P3 in H. numata.

Phylogenies of the silvaniform clade with H. cydno and H. melpomene as outgroups, using the genomic segments orthologous to P1, P2 and P3 in H. numata. Phylogenies computed with RAxMLREF1 using the GTRCAT model and only individuals homozygous for the inversions or the standard arrangement. a, Phylogeny of segments orthologous to P2 and P3. This shows the unique origin of the P2 and P3 inversions within H. numata. b, Phylogeny of segments orthologous to P1. This show the introgression of P1 from H. pardalinus into H. numata. Incongruent position of H. elevatus, H. hecale and H. ethilla result from incomplete lineage sorting at the clade level around the gene cortex and to gene flow among species of the clade (especially an introgression between H. elevatus and H. melpomene)REF2. REF1 : A. Stamatakis, RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics. 30, 1312–1313 (2014). REF2: Nadeau, N. J. et al. The gene cortex controls mimicry and crypsis in butterflies and moths. Nature 534, 106–110 (2016).

Extended Data Fig. 5 Analysis of divergence times between Hn123 and Hn0 and Fst analysis along the chromosome 15.

a, Divergence time estimates computed with Phylobayes61 on 5kb sliding windows. Bold red and blue lines represent the LOESS smoothing (span = 0.05) of the raw data (thin lines) and give the upper and lower bound of the times inversions P2 and P3 occurred. This supports the formation of P supergene by the stepwise accretion of P1, P2 and P3. b, Fst analysis between the three main supergene alleles : without inversion (Hn0), with P1 inversion (Hn1) and with all three inversion P1, P2 and P3 (Hn123). A ‘suspension bridge’ pattern of differentiation can be observed at P2-P3 by comparing Hn123 to Hn0 and Hn1 haplotypes, suggesting the rare occurrence of recombination around the center of the inversion, as predicted by Ref. 27. A peak of differentiation can be seen between Hn1 and Hn123 around the gene cortex, which controls melanic variations of the wing pattern in Heliconius butterflies25. This peak was unexpected since these two classes of haplotypes have the same genomic orientation (P1 inversion) in this region. Moreover, this region also show the highest differential gene expression when comparing Hn1 to Hn123 (Extended data Fig. 3). Analyses of assemblies as well as of read coverage (data not shown) do not support the presence of major rearrangements between Hn1 and Hn123 at this position, suggesting that this peak of differentiation on cortex is caused by selection on wing pattern divergence rather than recombination suppression via structural variation.

Extended Data Fig. 6 Proportion of TE classes and timing of TE insertion in distinct genomic regions.

a, Proportion of TE classes in whole genome, inversions, and insertions in inversions. b, Time of activity for the distinct classes of transposable elements found in inversions P1, P2, or P3 separately.

Extended Data Fig. 7 Proportion of transposable elements in the whole genome and in distinct genomic regions in H. numata and in H. erato and H. melpomene.

a, Proportion of transposable elements in the whole genome and for the region of the 3 inversions for inverted (nP1 = 7, nP2 = 6, nP3 = 6) and non-inverted (ancestral, nP1 = 3, nP2 = 4, nP3 = 4) segments in H. numata. Boxplot elements: central line: median, box limits: 25th and 75th percentiles, whiskers: 1.5x interquartile range. b, Proportion of transposable elements in the whole genome and in 4 regions associated with wing-pattern variation in H. erato and H. melpomene. Only one genome for each species was used : H. melpomene melpomene reference genome V2.5 and H. erato demophon reference genome V1 (http://ensembl.lepbase.org). Genomic positions of the wing pattern associated regions were defined according to REF1-4. REF1: Nadeau, N. J. et al. The gene cortex controls mimicry and crypsis in butterflies and moths. Nature 534, 106–110 (2016). REF2 : S. M. Van Belleghem, P. Rastas, A. Papanicolaou, S. H. Martin, C. F. Arias, M. A. Supple, J. J. Hanly, J. Mallet, J. J. Lewis, H. M. Hines, M. Ruiz, C. Salazar, M. Linares, G. R. P. Moreira, C. D. Jiggins, B. A. Counterman, W. O. McMillan, R. Papa, Complex modular architecture around a simple toolkit of wing pattern genes. Nature Ecology & Evolution.1, 1–12 (2017). REF3 : J. J. Lewis, R. C. Geltman, P. C. Pollak, K. E. Rondem, S. M. V. Belleghem, M. J. Hubisz, P. R. Munn, L. Zhang, C. Benson, A. Mazo-Vargas, C. G. Danko, B. A. Counterman, R. Papa, R. D. Reed, Parallel evolution of ancient, pleiotropic enhancers underlies butterfly wing pattern mimicry. PNAS. 116, 24174–24183 (2019). REF4 : R. W. R. Wallbank, S. W. Baxter, C. Pardo-Diaz, J. J. Hanly, S. H. Martin, J. Mallet, K. K. Dasmahapatra, C. Salazar, M. Joron, N. Nadeau, W. O. McMillan, C. D. Jiggins, Evolutionary Novelty in a Butterfly Wing Pattern through Enhancer Shuffling. PLOS Biology. 14, e1002353 (2016).

Extended Data Fig. 8 Mutation accumulation analysis in H. pardalinus.

Density curve representing the whole genome distribution computed in 500kb windows across 12 H. pardalinus specimens. Shades of blue are used to display 0.95 and 0.975 quantiles. P1 shows an excess of non-synonymous polymorphisms and substitutions compared to whole genome.

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Jay, P., Chouteau, M., Whibley, A. et al. Mutation load at a mimicry supergene sheds new light on the evolution of inversion polymorphisms. Nat Genet 53, 288–293 (2021). https://doi.org/10.1038/s41588-020-00771-1

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