CoChat
Sign Up
Cited by 18

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Only a Single Taxonomically Restricted Gene Family in the Drosophila melanogaster Subgroup Can Be Identified with High Confidence

Zile, Karina, Dessimoz, Christophe, Wurm, Yannick, Masel, Joanna·2020·Genome Biology and Evolution
DOI 10.1093/gbe/evaa127PMC PMC8059200
Open this paper in CoChat — free account

Abstract

Taxonomically restricted genes (TRGs) are genes that are present only in one clade. Protein-coding TRGs may evolve de novo from previously noncoding sequences: functional ncRNA, introns, or alternative reading frames of older protein-coding genes, or intergenic sequences. A major challenge in studying de novo genes is the need to avoid both false-positives (nonfunctional open reading frames and/or functional genes that did not arise de novo) and false-negatives. Here, we search conservatively for high-confidence TRGs as the most promising candidates for experimental studies, ensuring functionality through conservation across at least two species, and ensuring de novo status through examination of homologous noncoding sequences. Our pipeline also avoids ascertainment biases associated with preconceptions of how de novo genes are born. We identify one TRG family that evolved de novo in the Drosophila melanogaster subgroup. This TRG family contains single-copy genes in Drosophila simulans and Drosophila sechellia. It originated in an intron of a well-established gene, sharing that intron with another well-established gene upstream. These TRGs contain an intron that predates their open reading frame. These genes have not been previously reported as de novo originated, and to our knowledge, they are the best Drosophila candidates identified so far for experimental studies aimed at elucidating the properties of de novo genes.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1093/gbe/evaa127

Cites

2 of 2 references verified0 citation errors · 0 not checkable

Statistics

Figures

Fig. 1. Species tree of the Drosophila melanogaster subgroup. Branch lengths correspond to divergence time estimates by Obbard et al. (2012). We looked for TRGFs that emerged during the evolutionary time marked in red, that is, between ∼0.5 and ∼3.3 Ma. We ultimately confirm one TRGF shared only by D. simulans and D. sechellia, that is, that originated between ∼0.5 and ∼1.4 Ma.
Fig. 1. Species tree of the Drosophila melanogaster subgroup. Branch lengths correspond to divergence time estimates by Obbard et al. (2012). We looked for TRGFs that emerged during the evolutionary time marked in red, that is, between ∼0.5 and ∼3.3 Ma. We ultimately confirm one TRGF shared only by D. simulans and D. sechellia, that is, that originated between ∼0.5 and ∼1.4 Ma.CC BY · PubMed Central
Fig. 2. Protein length distributions in five Drosophila melanogaster subgroup species.
Fig. 2. Protein length distributions in five Drosophila melanogaster subgroup species.CC BY · PubMed Central
Fig. 3. The elimination of TRGFs with either evidence of being false-positive, or with insufficient evidence available.
Fig. 3. The elimination of TRGFs with either evidence of being false-positive, or with insufficient evidence available.CC BY · PubMed Central
Fig. 4. DNA regions homologous to the TRGF containing Dsim_GD19764 and Dsec_GM10790. Homologous protein-coding genes are the same color (each element corresponds to an exon), small nuclear RNA (snRNA) genes are white. The direction of the arrow shows which strand the gene is located on. Features with dashed outlines are not annotated. The diagram is not to scale. In the order from top to bottom, the orange genes are Dsim_GD29138, Dsec_GM10660, Dmel_CG12589, Dyak_GE25310, Dere_GG11200 (with a syntenic homolog Dana_GF16073 in Drosophila ananassae); the yellow genes are Dsim_GD19763, Dsec_GM10789, Dmel_CG12590, Dyak_GE25451, Dere_GG12627 (with syntenic homologs Dana_GF18925 and Dpse_GA11706 in D. ananassae and D. pseudoobscura, respectively); the blue genes are Dsim_GD19765, Dsec_GM10791, Dmel_CG12591, Dyak_GE25452, Dere_GG12638 (with syntenic homologs Dana_GF18926 and Dpse_GA11707 in D. ananassae and D. pseudoobscura, respectively); the purple genes are Dsim_GD19639, Dsec_GM10658, Dmel_CG12161, Dyak_GE25306, Dere_GG11178.
Fig. 4. DNA regions homologous to the TRGF containing Dsim_GD19764 and Dsec_GM10790. Homologous protein-coding genes are the same color (each element corresponds to an exon), small nuclear RNA (snRNA) genes are white. The direction of the arrow shows which strand the gene is located on. Features with dashed outlines are not annotated. The diagram is not to scale. In the order from top to bottom, the orange genes are Dsim_GD29138, Dsec_GM10660, Dmel_CG12589, Dyak_GE25310, Dere_GG11200 (with a syntenic homolog Dana_GF16073 in Drosophila ananassae); the yellow genes are Dsim_GD19763, Dsec_GM10789, Dmel_CG12590, Dyak_GE25451, Dere_GG12627 (with syntenic homologs Dana_GF18925 and Dpse_GA11706 in D. ananassae and D. pseudoobscura, respectively); the blue genes are Dsim_GD19765, Dsec_GM10791, Dmel_CG12591, Dyak_GE25452, Dere_GG12638 (with syntenic homologs Dana_GF18926 and Dpse_GA11707 in D. ananassae and D. pseudoobscura, respectively); the purple genes are Dsim_GD19639, Dsec_GM10658, Dmel_CG12161, Dyak_GE25306, Dere_GG11178.CC BY · PubMed Central
Fig. 5. DNA regions homologous to the gene family containing Dsim_GD20667 and Dsec_GM19408. Protein-coding genes are shown in color, pseudogenes in gray and ncRNA genes in white. Homologous protein-coding genes are marked by the same color, each element corresponds to an exon. Only the first of the seven exons of the dark blue gene is shown. Genes shown directly above/below each other share sequence similarity, but we did not infer homology of all pseudogenes and ncRNA in a rigorous way. The direction of the arrow shows which strand the gene is located on. Features with dashed outlines are not annotated. The diagram is not to scale.
Fig. 5. DNA regions homologous to the gene family containing Dsim_GD20667 and Dsec_GM19408. Protein-coding genes are shown in color, pseudogenes in gray and ncRNA genes in white. Homologous protein-coding genes are marked by the same color, each element corresponds to an exon. Only the first of the seven exons of the dark blue gene is shown. Genes shown directly above/below each other share sequence similarity, but we did not infer homology of all pseudogenes and ncRNA in a rigorous way. The direction of the arrow shows which strand the gene is located on. Features with dashed outlines are not annotated. The diagram is not to scale.CC BY · PubMed Central
Fig. 6. Sequence features of the ancestral ORF, which is annotated as a pseudogene in Drosophila yakuba. Start codons of the annotated pseudogene and of the shorter putative TRG in the simulans–sechellia–melanogaster clade are in green, well-conserved regions in yellow, frame-shift causing indels in blue, repetitive DNA in orange, and stop codons in black. We use the following frame numbering convention: the start codon of the putative pseudogene is denoted the +1 frame, the other two frames on the same strand are denoted +2 and +3 frames. The numbers in parentheses indicate how many more nucleotides (modulo 3) the species it is marked in has. The frames of the stop codons are not marked due to uncertainty about frame created by the repeat region. The two stop codons shown are located in the same frame.
Fig. 6. Sequence features of the ancestral ORF, which is annotated as a pseudogene in Drosophila yakuba. Start codons of the annotated pseudogene and of the shorter putative TRG in the simulans–sechellia–melanogaster clade are in green, well-conserved regions in yellow, frame-shift causing indels in blue, repetitive DNA in orange, and stop codons in black. We use the following frame numbering convention: the start codon of the putative pseudogene is denoted the +1 frame, the other two frames on the same strand are denoted +2 and +3 frames. The numbers in parentheses indicate how many more nucleotides (modulo 3) the species it is marked in has. The frames of the stop codons are not marked due to uncertainty about frame created by the repeat region. The two stop codons shown are located in the same frame.CC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 2 references

Research works printed but not linked: 69

  1. Abascal, 2018, Loose ends: almost one in five human genes still have unresolved coding status, Nucleic Acids Res, 46, 7070, 10.1093/nar/gky587 · DOI
  2. Albertin, 2015, The octopus genome and the evolution of cephalopod neural and morphological novelties, Nature, 524, 220, 10.1038/nature14668 · DOI
  3. Altenhoff, 2018, The OMA orthology database in 2018: retrieving evolutionary relationships among all domains of life through richer web and programmatic interfaces, Nucleic Acids Res, 46, D477, 10.1093/nar/gkx1019 · DOI · Paper page
  4. Ángyán, 2012, Estimating intrinsic structural preferences of de novo emerging random-sequence proteins: is aggregation the main bottleneck?, FEBS Lett, 586, 2468, 10.1016/j.febslet.2012.06.007 · DOI
  5. Arendsee, 2019, fagin: synteny-based phylostratigraphy and finer classification of young genes, BMC Bioinformatics, 20, 440, 10.1186/s12859-019-3023-y · DOI
  6. Baalsrud, 2018, De novo gene evolution of antifreeze glycoproteins in codfishes revealed by whole genome sequence data, Mol Biol Evol, 35, 593, 10.1093/molbev/msx311 · DOI · Paper page
  7. Balakirev, 2003, Pseudogenes: are they “junk” or functional DNA?, Annu Rev Genet, 37, 123, 10.1146/annurev.genet.37.040103.103949 · DOI
  8. Blevins, 2017, Comparative transcriptomics and ribo-seq: looking at de novo gene emergence in Saccharomycotina, PeerJ, 3030
  9. Brosius, 2005, Waste not, want not – transcript excess in multicellular eukaryotes, Trends Genet, 21, 287, 10.1016/j.tig.2005.02.014 · DOI
  10. Bungard, 2017, Foldability of a natural de novo evolved protein, Structure, 25, 1687, 10.1016/j.str.2017.09.006 · DOI
  11. Cai, 2008, De novo origination of a new protein-coding gene in Saccharomyces cerevisiae, Genetics, 179, 487, 10.1534/genetics.107.084491 · DOI
  12. Camacho, 2009, BLAST+: architecture and applications, BMC Bioinformatics, 10, 421, 10.1186/1471-2105-10-421 · DOI · Paper page
  13. Casola, 2018, From de novo to ‘de nono’: The majority of novel protein coding genes identified with phylostratigraphy are old genes or recent duplicates, Genome Biol Evol, 10, 2906
  14. Chen, 2010, New genes in Drosophila quickly become essential, Science, 330, 1682, 10.1126/science.1196380 · DOI
  15. Chen, 2015, Combinatorial DNA rearrangement facilitates the origin of new genes in ciliates, Genome Biol Evol, 7, 2859
  16. Cock, 2009, Biopython: freely available python tools for computational molecular biology and bioinformatics, Bioinformatics, 25, 1422, 10.1093/bioinformatics/btp163 · DOI · Paper page
  17. Domazet-Loso, 2003, An evolutionary analysis of orphan genes in Drosophila, Genome Res, 13, 2213, 10.1101/gr.1311003 · DOI
  18. Donoghue, 2011, Evolutionary origins of Brassicaceae specific genes in Arabidopsis thaliana, BMC Evol Biol, 11, 10.1186/1471-2148-11-47 · DOI
  19. 2007, Evolution of genes and genomes on the Drosophila phylogeny, Nature, 450, 203, 10.1038/nature06341 · DOI · Paper page
  20. Eddy, 2011, Accelerated profile HMM searches, PLoS Comput Biol, 7, e1002195, 10.1371/journal.pcbi.1002195 · DOI · Paper page
  21. Fernandez-Escamilla, 2004, Prediction of sequence-dependent and mutational effects on the aggregation of peptides and proteins, Nat Biotechnol, 22, 1302, 10.1038/nbt1012 · DOI
  22. Finn, 2016, The pfam protein families database: towards a more sustainable future, Nucleic Acids Res, 44, D279, 10.1093/nar/gkv1344 · DOI · Paper page
  23. Frigola, 2017, Reduced mutation rate in exons due to differential mismatch repair, Nat Genet, 49, 1684, 10.1038/ng.3991 · DOI
  24. Ghatak, 2019, The y-ome defines the 35% of Escherichia coli genes that lack experimental evidence of function, Nucleic Acids Res, 47, 2446, 10.1093/nar/gkz030 · DOI
  25. Graur, 2013, On the immortality of television sets: “function” in the human genome according to the evolution-free gospel of ENCODE, Genome Biol Evol, 5, 578, 10.1093/gbe/evt028 · DOI
  26. Guan, 2018, Gene refashioning through innovative shifting of reading frames in mosses, Nat Commun, 9, 10.1038/s41467-018-04025-x · DOI
  27. Guerzoni, 2016, De novo genes arise at a slow but steady rate along the primate lineage and have been subject to incomplete lineage sorting, Genome Biol Evol, 8, 1222, 10.1093/gbe/evw074 · DOI
  28. Haft, 2018, RefSeq: an update on prokaryotic genome annotation and curation, Nucleic Acids Res, 46, D851, 10.1093/nar/gkx1068 · DOI
  29. Heames, 2020, A Continuum of Evolving De Novo Genes Drives Protein-Coding Novelty in Drosophila, J Mol Evol, 88, 382, 10.1007/s00239-020-09939-z · DOI
  30. Hild, 2003, An integrated gene annotation and transcriptional profiling approach towards the full gene content of the Drosophila genome, Genome Biol, 5, R3, 10.1186/gb-2003-5-1-r3 · DOI
  31. Jain, 2019, The evolutionary traceability of a protein, Genome Biol Evol, 11, 531, 10.1093/gbe/evz008 · DOI
  32. Katoh, 2013, MAFFT multiple sequence alignment software version 7: improvements in performance and usability, Mol Biol Evol, 30, 772, 10.1093/molbev/mst010 · DOI · Paper page
  33. Knopp, 2019, De Novo Emergence of Peptides That Confer Antibiotic Resistance, mBio, 10
  34. Knopp, 2018, No beneficial fitness effects of random peptides, Nat Ecol Evol, 2, 1046, 10.1038/s41559-018-0585-4 · DOI
  35. Knowles, 2009, Recent de novo origin of human protein-coding genes, Genome Res, 19, 1752, 10.1101/gr.095026.109 · DOI · Paper page
  36. Levine, 2006, Novel genes derived from noncoding DNA in Drosophila melanogaster are frequently x-linked and exhibit testis-biased expression, Proc Natl Acad Sci U S A, 103, 9935, 10.1073/pnas.0509809103 · DOI
  37. Linquist, 2020, Getting clear about the F-word in genomics, PLoS Genet, 16, e1008702, 10.1371/journal.pgen.1008702 · DOI
  38. McLysaght, 2015, New genes from non-coding sequence: the role of de novo protein-coding genes in eukaryotic evolutionary innovation, Philos Trans R Soc B, 370, 20140332, 10.1098/rstb.2014.0332 · DOI
  39. McLysaght, 2016, Open questions in the study of de novo genes: what, how and why, Nat Rev Genet, 17, 567, 10.1038/nrg.2016.78 · DOI
  40. Neme, 2017, Random sequences are an abundant source of bioactive RNAs or peptides, Nat Ecol Evol, 1, 0127, 10.1038/s41559-017-0127 · DOI
  41. Neme, 2016, Fast turnover of genome transcription across evolutionary time exposes entire non-coding DNA to de novo gene emergence, eLife, 5, 09977, 10.7554/eLife.09977 · DOI
  42. Obbard, 2012, Estimating divergence dates and substitution rates in the Drosophila phylogeny, Mol Biol Evol, 29, 3459, 10.1093/molbev/mss150 · DOI
  43. O’Leary, 2016, Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation, Nucleic Acids Res, 44, D733, 10.1093/nar/gkv1189 · DOI · Paper page
  44. Pearson, 2000, Flexible sequence similarity searching with the FASTA3 program package, Methods Mol Biol, 132, 185
  45. Reinhardt, 2013, De novo ORFs in Drosophila are important to organismal fitness and evolved rapidly from previously non-coding sequences, PLoS Genet, 9, e1003860, 10.1371/journal.pgen.1003860 · DOI
  46. Remmert, 2012, HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment, Nat Methods, 9, 173, 10.1038/nmeth.1818 · DOI · Paper page
  47. Ruiz-Orera, 2018, Translation of neutrally evolving peptides provides a basis for de novo gene evolution, Nat Ecol Evol, 2, 890, 10.1038/s41559-018-0506-6 · DOI
  48. Savojardo, 2018, BUSCA: an integrative web server to predict subcellular localization of proteins, Nucleic Acids Res, 46, W459, 10.1093/nar/gky320 · DOI
  49. Schaffer, 2001, Improving the accuracy of PSI-BLAST protein database searches with composition-based statistics and other refinements, Nucleic Acids Res, 29, 2994, 10.1093/nar/29.14.2994 · DOI
  50. Schlötterer, 2015, Genes from scratch – the evolutionary fate of de novo genes, Trends Genet, 31, 215, 10.1016/j.tig.2015.02.007 · DOI
  51. Stewart, 2019, Chromosomal rearrangements as a source of new gene formation in Drosophila yakuba, PLoS Genet, 15, e1008314, 10.1371/journal.pgen.1008314 · DOI
  52. Sun, 2015, Identification and evolution of the orphan genes in the domestic silkworm, Bombyx mori, FEBS Lett, 589, 2731, 10.1016/j.febslet.2015.08.008 · DOI
  53. Toll-Riera, 2008, Origin of primate orphan genes: a comparative genomics approach, Mol Biol Evol, 26, 603, 10.1093/molbev/msn281 · DOI · Paper page
  54. Tretyachenko, 2017, Random protein sequences can form defined secondary structures and are well-tolerated in vivo, Sci Rep, 7, 15449, 10.1038/s41598-017-15635-8 · DOI
  55. Vakirlis, 2018, A molecular portrait of de novo genes in yeasts, Mol Biol Evol, 35, 631, 10.1093/molbev/msx315 · DOI
  56. Vakirlis, 2018, Methods in molecular biology, 63
  57. Veidenberg, 2016, Wasabi: an integrated platform for evolutionary sequence analysis and data visualization, Mol Biol Evol, 33, 1126, 10.1093/molbev/msv333 · DOI
  58. Waterhouse, 2018, BUSCO applications from quality assessments to gene prediction and phylogenomics, Mol Biol Evol, 35, 543, 10.1093/molbev/msx319 · DOI · Paper page
  59. Weisman, 2017, Gene evolution: getting something from nothing, Curr Biol, 27, R661, 10.1016/j.cub.2017.05.056 · DOI
  60. Werren, 2010, Functional and evolutionary insights from the genomes of three parasitoid Nasonia species, Science, 327, 343, 10.1126/science.1178028 · DOI
  61. Willis, 2018, Gene birth contributes to structural disorder encoded by overlapping genes, Genetics, 210, 303, 10.1534/genetics.118.301249 · DOI
  62. Wilson, 2017, Young genes are highly disordered as predicted by the preadaptation hypothesis of de novo gene birth, Nat Ecol Evol, 1, 0146, 10.1038/s41559-017-0146 · DOI
  63. Wilson, 2011, Putatively noncoding transcripts show extensive association with ribosomes, Genome Biol Evol, 3, 1245, 10.1093/gbe/evr099 · DOI
  64. Wissler, 2013, Mechanisms and dynamics of orphan gene emergence in insect genomes, Genome Biol Evol, 5, 439, 10.1093/gbe/evt009 · DOI
  65. Wu, 2018, Tracing the de novo origin of protein-coding genes in yeast, mBio, 9, 0102418, 10.1128/mBio.01024-18 · DOI
  66. Yang, 2011, De novo origin of new genes with introns in Plasmodium vivax, FEBS Lett, 585, 641, 10.1016/j.febslet.2011.01.017 · DOI
  67. Zhang, 2019, Rapid evolution of protein diversity by de novo origination in Oryza, Nat Ecol Evol, 3, 679, 10.1038/s41559-019-0822-5 · DOI · Paper page
  68. Zhao, 2014, Origin and spread of de novo genes in Drosophila melanogaster populations, Science, 343, 769, 10.1126/science.1248286 · DOI
  69. Zhou, 2008, On the origin of new genes in Drosophila, Genome Res, 18, 1446, 10.1101/gr.076588.108 · DOI

Cited by

Sorted by citations

Metadata

Authors
Zile, K., Dessimoz, C., Wurm, Y., & Masel, J.
Affiliations
  • Division of Biosciences, University College London, United Kingdom
  • Swiss Institute of Bioinformatics, Lausanne, Switzerland
  • School of Biological and Chemical Sciences, Queen Mary University of London, United Kingdom
  • Department of Ecology and Evolutionary Biology, University of Arizona
ORCID
Venue
Genome Biology and Evolution
Publisher
Oxford University Press (OUP)
Published
2020-06-26
Volume
12
Issue
8
Pages
1355-1366
ISSN
1759-6653
DOI
10.1093/gbe/evaa127
PMC
PMC8059200
Cited by
18
References
2
Language
en
Licence
creativecommons.org/licenses/by/4.0/ (vor)
Funders
  • Biotechnology and Biological Sciences Research Council (BB/M009513/1)
  • National Institutes of Health (GM104040)
  • Swiss National Science Foundation (183723)
  • Natural Environment Research Council (NE/L00626X/1)
  • Department of Computer Science cluster at University College London

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review