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Dosage sensitivity and exon shuffling shape the landscape of polymorphic duplicates in Drosophila and humans

Nature Ecology & Evolution volume 6, pages 273–287 (2022)Cite this article

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Abstract

Despite polymorphic duplicate genes' importance for the early stages of duplicate gene evolution, they are less studied than old gene duplicates. Two essential questions thus remain poorly addressed: how does dosage sensitivity, imposed by stoichiometry in protein complexes or by X chromosome dosage compensation, affect the emergence of complete duplicate genes? Do introns facilitate intergenic and intragenic chimaerism as predicted by the theory of exon shuffling? Here, we analysed new data for Drosophila and public data for humans, to characterize polymorphic duplicate genes with respect to dosage, exon–intron structures and allele frequencies. We found that complete duplicate genes are under dosage constraint induced by protein stoichiometry but potentially tolerated by X chromosome dosage compensation. We also found that in the intron-rich human genome, gene fusions and intragenic duplications extensively use intronic breakpoints generating in-frame proteins, in accordance with the theory of exon shuffling. Finally, we found that only a small proportion of complete or partial duplicates are at high frequencies, indicating the deleterious nature of dosage or gene structural changes. Altogether, we demonstrate how mechanistic factors including dosage sensitivity and exon–intron structure shape the short-term functional consequences of gene duplication.

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Fig. 1: Project design and evaluation of breakpoint inference.
Fig. 2: Type and distribution of duplicate genes.
Fig. 3: Expression changes and dosage constraints of duplicate genes.
Fig. 4: Pervasive chimaerism due to incomplete duplications.
Fig. 5: Internal duplications often lead to alternative splicing.
Fig. 6: Distribution of duplicate genes with respect to allele frequencies.

Data availability

We released the raw and processed data. First, the resequencing data, RNA-seq data and Iso-seq data generated in this study were concurrently submitted to the NCBI BioProject database under accession numbers PRJNA681089, PRJNA681417 and PRJNA693662 and the Genome Sequence Archive in National Genomics Data Center (NGDC, part of the China National Center for Bioinformation) under accession numbers PRJCA004186, PRJCA001789 and PRJCA004319. Second, the processed fly data (duplication loci, DNA read alignment depth, RNA-seq alignments, assembled chimaeras, PacBio CCS reads and RT–PCR Sanger sequencing data) and human data (duplication loci and assembled chimaeras) were uploaded to the UCSC genome browser as public sessions ‘http://genome.ucsc.edu/cgi-bin/hgPublicSessions’ with names as ‘Drosophila Duplication’ and ‘Human Duplication’, respectively. We tried to make these tracks as useful as possible: (1) duplication ID refers to Supplementary Table 2; (2) when users click on each duplication ID, the corresponding lines or individuals harbouring this duplication are shown; and (3) assembled chimaeras were split based on breakpoints and separately aligned to the reference genome as Fig. 4c. Finally, the assembled contigs and the RT–PCR Sanger sequencing files were co-submitted to https://github.com/Zhanglab-IOZ/Polymorphic-Duplication and https://sandbox.zenodo.org/record/946570.

Code availability

The code for split-read-based duplication calling was submitted to: https://github.com/Zhanglab-IOZ/Polymorphic-Duplication. It is also archived at https://sandbox.zenodo.org/record/946570.

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Acknowledgements

We thank the DGRP team and the GTEx team for generating and releasing the data. We thank M. Cardoso-Moreira for improving the writing. We thank M. Long, B. He, X. Lan, W. Qian, B. Guo and Zhang Laboratory (Institute of Zoology (IOZ), Chinese Academy of Sciences) members for the helpful discussions about the project. We thank Y. Fu, Y. Ding, G. Gao and Z. Wang for the computational help. We thank J. Wang and X. Huang for experimental help. This research was supported by the National Key R&D Program of China (2018YFC1406902 and 2019YFA0802600), the Chinese Academy of Sciences (ZDBS-LY-SM005, XBZG-ZDSYS-201913 and XDPB17), the National Natural Science Foundation of China (31771410 and 31970565) and the Open Research Program of the Chinese Institute for Brain Research; all were granted to Y.E.Z. The computing was jointly supported by the HPC Platform of BIG and that of the Scientific Information Centre of IOZ.

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Author notes

  1. Chunyan Chen

    Present address: School of Ecology and Environment, Northwestern Polytechnical University, Xi’an, China

  2. These authors contributed equally: Dan Zhang, Liang Leng.

Authors and Affiliations

  1. Key Laboratory of Zoological Systematics and Evolution & State Key Laboratory of Integrated Management of Pest Insects and Rodents, Institute of Zoology, Chinese Academy of Sciences, Beijing, China

    Dan Zhang, Chunyan Chen, Jiawei Huang, Yaqiong Zhang, Hao Yuan, Chenyu Ma & Yong E. Zhang

  2. University of Chinese Academy of Sciences, Beijing, China

    Dan Zhang, Chunyan Chen, Jiawei Huang, Hao Yuan, Chenyu Ma, Hua Chen & Yong E. Zhang

  3. Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, China

    Liang Leng

  4. CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China

    Hua Chen

  5. CAS Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China

    Hua Chen & Yong E. Zhang

  6. China National Center for Bioinformation, Beijing, China

    Hua Chen

  7. Chinese Institute for Brain Research, Beijing, China

    Yong E. Zhang

Authors
  1. Dan Zhang
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  2. Liang Leng
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  3. Chunyan Chen
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  4. Jiawei Huang
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  5. Yaqiong Zhang
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  6. Hao Yuan
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  7. Chenyu Ma
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  8. Hua Chen
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  9. Yong E. Zhang
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Contributions

Y.E.Z. conceived and designed the study. D.Z. performed the experimental analyses with the help of J.W.H. and Y.Q.Z. D.Z. and L.L. performed the computational analyses with the help of C.Y.C., H.Y., C.Y.M. and H.C. Y.E.Z., D.Z. and L.L. wrote the paper.

Corresponding author

Correspondence to Yong E. Zhang.

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Competing interests

The authors declare no competing interests.

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Nature Ecology & Evolution thanks Ben-Yang Liao and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Phylogeny and features of 31 DGRP core lines.

We ordered 40 DGRP core lines from the Bloomington Stock Center but could only maintain 31 of these lines at the moment of sequencing. Lines infected with Wolbachia, lines harbouring inversions and lines with a fraction of segregating variants in at least one chromosome arm higher than 2% are marked in green, pink and blue, respectively. Among the remaining lines, six less related lines (marked in bold black) were subsequently used.

Extended Data Fig. 2 Heatmap of 108 tissue-specific genes.

The pheatmap package was used to generate this figure, in which expression was rescaled for each gene to highlight the tissue-specificity.

Extended Data Fig. 3 Size distribution of different types of duplicates and protein-coding genes.

a) All (top) duplications and the duplications spanning a single gene (bottom). b) Length distribution of all duplications relative to the length of all protein-coding genes. The median values are shown with each group. P values were calculated with Wilcoxon signed rank tests.

Extended Data Fig. 4 Proportion distribution in the shuffling dataset.

a) Proportion of complete/partial/intronic duplicates. b) Proportion of six arrangements of partial duplicates. C) Proportion of intronic breakpoints. We performed 100 simulations. For each simulation, we calculated the proportion of complete, partial and intronic duplicates. For partial duplicates, we further calculated the proportion of six subtypes. For each subtype, we also calculated the proportion of intronic breakpoints. We summarized 100 values as a boxplot. The median simulated values and observed values are shown along each group. For each plot, we marked the observed value as a dark black line and showed the empirical P value as the times out of 100 replicates where the observed value is more extreme. For example, in the case of partial duplicates, the observed proportion is 58.9%, which is only lower than that in 12 out of 100 random replicates (top left panel). The corresponding P value would be 0.12.

Extended Data Fig. 5 Tissue-level transcriptional fold-change distribution for complete duplicates.

The convention of this figure follows Fig. 3a. For each gene in each tissue, we calculated the fold-change with the median expression in duplication-present and duplication-absent lines or individuals. In flies, each tissue at least covers 19 transcribed (TPM > 1) genes. In humans, only 11 tissues or cell lines covering at least 15 transcribed genes were shown. Wilcoxon signed rank test was used to estimate the statistical significance relative to the expected 100% upregulation (top panel) and 50% upregulation (bottom panel). Despite some moderate fluctuation, the extent of upregulation generally does not deviate from the expectation (for 6 out of 7 tissues in flies, 11 out of 11 in humans). FFB, MFB and AG refer to female fat bodies, male fat bodies and accessory glands. Note that for AG, the deviation is significantly different from 100% simply because of the distribution of data most of which are smaller than 100% (purple curve). By contrast, FFB is not significant despite the smaller median (48% versus 54%).

Extended Data Fig. 6 The distribution of duplicates across different chromosomes and distribution of distances to HASs.

a) Chromosomal distribution of duplicates. We only included the major autosomes (Drosophila: 2 L, 2 R, 3 L, 3 R; Human: 1-22) and the X chromosome in the calculation. The proportion of annotated coding genes on each chromosome in the reference genome is used as the control (BG or background). The P was calculated with the one-sided proportion test. b) distances to HASs. The median distance (28,687 bp) of 8 complete duplicates relative to the closest HAS was highlighted with a green line. We generated 10,000 random samples and calculated the empirical P as the percentage of how many times random samples showed a shorter median distance. The blue line indicates the median value of random samples. The two median values are also shown.

Extended Data Fig. 7 Five fusion transcripts supported by PacBio reads and example of frameshift in KANSL1-ARL17A.

a) CG2818-CG31955 fusion. b) Rpll215-CG11697-Kmn1 fusion. c) 5’-Intergenic duplication of PpN58A. d) CG4069-Pmm2-sowah fusion. e) Subreads supporting three new introns of Sherpa-CG2469. F) KANSL1-ARL17A fusion. For Panels A to E, five out of 11 chimeric genes assembled in the testis samples of RAL-379 were confirmed with PacBio data. For A) to D), only the longest assembled contig was shown for simplicity. In A) to E), the conventions follow those in Fig. 4c. Similar to Fig. 4c, CCS reads sometimes encode novel exon/intron structures. Only the intron in the CG2818-CG31955 fusion transcript harbours the standard splicing signal (GT-AG, Panel A). Six subreads in Panel E consistently exhibit three novel introns, as shown in Fig. 4c. Note that different CCS reads have different sequence qualities, as determined by the number of low-quality supporting subreads (for example, Panel E). For example, CCS-2 has a higher quality than CCS-1 in Panel B. In Panel F, the duplicated region is framed with dashed lines. The fusion transcript involves a 2-bp intronic sequence (the splicing site, ‘gt’) causing the frameshift. Two neighbouring codons are also shown.

Extended Data Fig. 8 Sequence features of 5’-3’ fusion genes.

CDS refers to coding sequence. Whether a frameshift occurs was inferred based on the codons adjacent to the breakpoints. Domains are shown by blue (the leading gene) or red (the lagging gene) rectangles, and zigzags show mid-domain breaks. Note: the fused region of the leading gene for CG17387-ctrip, that of the lagging gene for Sherpa-CG2469 and both regions for TFDP2-XRN1 do not harbour annotated domains. The lengths of the rectangles are roughly proportional to the relative domain size within each gene. ‘IntronU’ and ‘intron’ refer to introns located within UTRs and introns within coding regions, respectively. For Prosbeta5R2-CG5681, Prosbeta5R2 was completely duplicated, whereas the 3’ part of CG5681 was duplicated. After fusion, Prosbeta5R2 harbours a longer 3’ UTR by incorporating a CG5681-derived sequence.

Extended Data Fig. 9 Sequence and transcription features of internally duplicated genes.

a) Partial duplication of the 5’ UTR in CG3409. CG3409 encodes different 5’ UTRs, one of which is duplicated and a longer 5’ UTR was generated. Because this 5’ exon is alternative the original coding sequence is presumably not affected. b) Post-duplication intron retention of TRAF3IP3. According to the duplication boundaries (highlighted in light blue), two contigs indicate inclusive transcripts where exons and flanking introns are simultaneously transcribed. c) Out-of-frame transcript of DENND5A. The duplicated region is framed with dashed line boxes. Codons are separated with commas, and amino acids are shown accordingly. The incompatibility of codon phases causes the frameshift. d) Read depth-based quantification of isoforms (see also Methods). Two examples (CG9663, SPG11) are shown. Given only 10 data points for the duplication-present lines of SPG11, we showed all specific values as grey dots. For each gene, we calculated the ratio of the read depth (duplicated region versus flanking region) in lines with or without duplication. For these two examples, the ratio found for lines with duplications was significantly (one-sided Wilcoxon signed rank test P ≤ 0.05) higher than that in lines without duplications, supporting the presence of inclusive isoforms. According to the median ratios, we further calculated the relative expression ratio of inclusive and exclusive transcripts (Fig. 5f). E) qRT–PCR-based quantification of isoforms in flies. Whole-body samples of duplication-absent lines were used as controls. Primers targeting inclusive transcripts did not show signals in the duplication-absent lines, and primers targeting both inclusive and exclusive transcripts generally generated weaker signals than their counterparts in the duplication-present lines. The error bar represents the standard deviation based on three technical replicates.

Extended Data Fig. 10 The frequency distribution of duplicates classified by the median size and proportions of duplicates across different allele frequency groups.

a) The upper and lower panels show all duplications and the duplications spanning a single gene, respectively. For each type of duplicates, we divided them into large (L) and small (S) groups according to the median size. The Wilcoxon signed rank test was used to compare the median frequencies in these two groups. Small duplicates generally show higher frequencies than large duplicates in humans. Moreover, despite the small sample size of fly dataset, larger duplications also seem to show lower allele frequencies. b) Allele frequency spectra of complete, partial and intronic duplicates in addition to PTCs and synonymous substitutions in Drosophila (the left panel) and humans (the right panel). Only duplications spanning a single gene were plotted. c) Proportion of complete, partial and intronic duplicates within different frequency groups. In the left panel, all duplicate loci were plotted with six-line allele frequency data. In the middle and right panels, only duplications spanning a single gene were plotted. The figure conventions of Panel B and C follow those of Fig. 6a and b, respectively.

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Zhang, D., Leng, L., Chen, C. et al. Dosage sensitivity and exon shuffling shape the landscape of polymorphic duplicates in Drosophila and humans. Nat Ecol Evol 6, 273–287 (2022). https://doi.org/10.1038/s41559-021-01614-w

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