CoChat
Sign Up
Cited by 1,028RNA Research and SplicingEvolution and Genetic DynamicsGenomics and Rare Diseases

Genetic compensation triggered by mutant mRNA degradation

Open access
El-Brolosy, Mohamed A., Kontarakis, Zacharias, Rossi, Andrea, Kuenne, Carsten, Günther, Stefan, Fukuda, Nana, Kikhi, Khrievono, Boezio, Giulia L. M. +6 more·2019·Nature
DOI 10.1038/s41586-019-1064-zPMC PMC6707827

Published version of a preprint

Open this paper in CoChat — free accountOpen PDF

Abstract

Genetic robustness, or the ability of an organism to maintain fitness in the presence of harmful mutations, can be achieved via protein feedback loops. Previous work has suggested that organisms may also respond to mutations by transcriptional adaptation, a process by which related gene(s) are upregulated independently of protein feedback loops. However, the prevalence of transcriptional adaptation and its underlying molecular mechanisms are unknown. Here, by analysing several models of transcriptional adaptation in zebrafish and mouse, we uncover a requirement for mutant mRNA degradation. Alleles that fail to transcribe the mutated gene do not exhibit transcriptional adaptation, and these alleles give rise to more severe phenotypes than alleles displaying mutant mRNA decay. Transcriptome analysis in alleles displaying mutant mRNA decay reveals the upregulation of a substantial proportion of the genes that exhibit sequence similarity with the mutated gene's mRNA, suggesting a sequence-dependent mechanism. These findings have implications for our understanding of disease-causing mutations, and will help in the design of mutant alleles with minimal transcriptional adaptation-derived compensation.

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 · 10.1038/s41586-019-1064-z

Cites

10 of 10 references verified0 citation errors · 0 not checkable

Statistics

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?

  • Genetic compensation may influence expression of Ahr null alleles and thereby affect endpoint manifestation.

    “The recently described genetic compensation response may also influence expression of the various Ahr null alleles and therefore affect endpoint manifestation (El-Brolosy et al., 2019; Z. Ma et al., 2019).”

    From Rodent Genetic Models of Ah Receptor Signaling (2021) · cites this paper as El-Brolosy et al., 2019

    The ‘RNA decay triggers transcriptional adaptation’ section reports that different mutant alleles can differ in compensatory gene expression and phenotype. This supports the proposed mechanism, but the paper does not examine Ahr alleles or establish that compensation affects their endpoints; that application remains speculative. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

Self-check

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

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

Cites

Flagged first, then by citations

Research works printed but not linked: 52

  1. [1]Tautz D Redundancies, development and the flow of information BioEssays: news and reviews in molecular, cellular and developmental biology 1992 14 263 266 10.1002/bies.950140410 1596275 · DOI
  2. [3]Teng X Genome-wide consequences of deleting any single gene Molecular cell 2013 52 485 494 10.1016/j.molcel.2013.09.026 24211263 PMC3975072 · DOI
  3. [4]Rossi A Genetic compensation induced by deleterious mutations but not gene knockdowns Nature 2015 524 230 233 10.1038/nature14580 26168398 · DOI
  4. [6]Isken O Maquat LE Quality control of eukaryotic mRNA: safeguarding cells from abnormal mRNA function Genes & development 2007 21 1833 1856 10.1101/gad.1566807 17671086 · DOI
  5. [7]Mukherjee C Identification of cytoplasmic capping targets reveals a role for cap homeostasis in translation and mRNA stability Cell reports 2012 2 674 684 10.1016/j.celrep.2012.07.011 22921400 PMC3462258 · DOI
  6. [8]Boehm V Gerbracht JV Marx MC Gehring NH Interrogating the degradation pathways of unstable mRNAs with XRN1-resistant sequences Nature communications 2016 7 10.1038/ncomms13691 13691 PMC5150221 27917860 · DOI
  7. [9]Doi TS Absence of tumor necrosis factor rescues RelA-deficient mice from embryonic lethality Proceedings of the National Academy of Sciences of the United States of America 1999 96 2994 2999 10077625 PMC15883
  8. [10]Hao S Baltimore D The stability of mRNA influences the temporal order of the induction of genes encoding inflammatory molecules Nat Immunol 2009 10 281 288 10.1038/ni.1699 19198593 PMC2775040 · DOI · Paper page
  9. [11]Elkon R Zlotorynski E Zeller KI Agami R Major role for mRNA stability in shaping the kinetics of gene induction BMC genomics 2010 11 259 10.1186/1471-2164-11-259 20409322 PMC2864252 · DOI
  10. [12]Rabani M Metabolic labeling of RNA uncovers principles of RNA production and degradation dynamics in mammalian cells Nat Biotech 2011 29 436 442 10.1038/nbt.1861 PMC3114636 21516085 · DOI · Paper page
  11. [13]Sun M Comparative dynamic transcriptome analysis (cDTA) reveals mutual feedback between mRNA synthesis and degradation Genome research 2012 22 1350 1359 10.1101/gr.130161.111 22466169 PMC3396375 · DOI
  12. [14]Collins SR Functional dissection of protein complexes involved in yeast chromosome biology using a genetic interaction map Nature 2007 446 806 810 10.1038/nature05649 17314980 · DOI · Paper page
  13. [15]Berretta J Pinskaya M Morillon A A cryptic unstable transcript mediates transcriptional trans-silencing of the Ty1 retrotransposon in S. cerevisiae Genes & development 2008 22 615 626 10.1101/gad.458008 18316478 PMC2259031 · DOI
  14. [16]Pinskaya M Gourvennec S Morillon A H3 lysine 4 di- and tri-methylation deposited by cryptic transcription attenuates promoter activation The EMBO journal 2009 28 1697 1707 10.1038/emboj.2009.108 19407817 PMC2699354 · DOI
  15. [17]Haimovich G Gene expression is circular: factors for mRNA degradation also foster mRNA synthesis Cell 2013 153 1000 1011 10.1016/j.cell.2013.05.012 23706738 · DOI
  16. [18]Ghanbarian H Small RNA-directed epigenetic programming of embryonic stem cell cardiac differentiation Scientific reports 2017 7 10.1038/srep41799 41799 PMC5292948 28165496 · DOI
  17. [19]Modarresi F Inhibition of natural antisense transcripts in vivo results in gene-specific transcriptional upregulation Nature biotechnology 2012 30 453 459 10.1038/nbt.2158 PMC4144683 22446693 · DOI
  18. [20]Eisensmith RC Woo SL Molecular basis of phenylketonuria and related hyperphenylalaninemias: mutations and polymorphisms in the human phenylalanine hydroxylase gene Human mutation 1992 1 13 23 10.1002/humu.1380010104 1301187 · DOI
  19. [21]Myerowitz R Tay-Sachs disease-causing mutations and neutral polymorphisms in the Hex A gene Human mutation 1997 9 195 208 10.1002/(sici)1098-1004(1997)9:3<195::aid-humu1>3.0.co;2-7 9090523 · DOI
  20. [22]Genschel J Schmidt HH Mutations in the LMNA gene encoding lamin A/C Human mutation 2000 16 451 459 10.1002/1098-1004(200012)16:6<451::aid-humu1>3.0.co;2-9 11102973 · DOI
  21. [23]Chuzhanova NA Anassis EJ Ball EV Krawczak M Cooper DN Meta-analysis of indels causing human genetic disease: mechanisms of mutagenesis and the role of local DNA sequence complexity Human mutation 2003 21 28 44 10.1002/humu.10146 12497629 · DOI
  22. [24]Ferec C Cutting GR Assessing the Disease-Liability of Mutations in CFTR Cold Spring Harbor Perspectives in Medicine 2012 2 10.1101/cshperspect.a009480 a009480 PMC3543074 23209179 · DOI
  23. [25]Zhou Q Early-onset stroke and vasculopathy associated with mutations in ADA2 The New England journal of medicine 2014 370 911 920 10.1056/NEJMoa1307361 24552284 PMC4193683 · DOI
  24. [26]Dietz HC Four novel FBN1 mutations: significance for mutant transcript level and EGF-like domain calcium binding in the pathogenesis of Marfan syndrome Genomics 1993 17 468 475 10.1006/geno.1993.1349 8406497 · DOI
  25. [27]Hall GW Thein S Nonsense codon mutations in the terminal exon of the beta-globin gene are not associated with a reduction in beta-mRNA accumulation: a mechanism for the phenotype of dominant beta-thalassemia Blood 1994 83 2031 2037 8161774
  26. [29]Lek M Analysis of protein-coding genetic variation in 60,706 humans Nature 2016 536 285 291 10.1038/nature19057 27535533 PMC5018207 · DOI · Paper page
  27. [30]Gerri C Hif-1alpha regulates macrophage-endothelial interactions during blood vessel development in zebrafish Nature communications 2017 8 10.1038/ncomms15492 15492 PMC5493593 28524872 · DOI
  28. [31]Rossi A Regulation of Vegf signaling by natural and synthetic ligands Blood 2016 128 2359 2366 10.1182/blood-2016-04-711192 27557946 PMC5766839 · DOI
  29. [32]Lawson ND Weinstein BM In vivo imaging of embryonic vascular development using transgenic zebrafish Developmental biology 2002 248 307 318 12167406
  30. [33]Proulx K Lu A Sumanas S Cranial vasculature in zebrafish forms by angioblast cluster-derived angiogenesis Developmental biology 2010 348 34 46 10.1016/j.ydbio.2010.08.036 20832394 · DOI
  31. [34]Labun K Montague TG Gagnon JA Thyme SB Valen E CHOPCHOP v2: a web tool for the next generation of CRISPR genome engineering Nucleic acids research 2016 44 W272 276 10.1093/nar/gkw398 27185894 PMC4987937 · DOI
  32. [35]Cermak T Efficient design and assembly of custom TALEN and other TAL effector-based constructs for DNA targeting Nucleic acids research 2011 39 e82 10.1093/nar/gkr218 21493687 PMC3130291 · DOI
  33. [36]Doyle EL TAL effector specificity for base 0 of the DNA target is altered in a complex, effector- and assay-dependent manner by substitutions for the tryptophan in cryptic repeat -1 PloS one 2013 8 e82120 10.1371/journal.pone.0082120 24312634 PMC3849474 · DOI
  34. [37]Gagnon JA Efficient mutagenesis by Cas9 protein-mediated oligonucleotide insertion and large-scale assessment of single-guide RNAs PloS one 2014 9 e98186 10.1371/journal.pone.0098186 24873830 PMC4038517 · DOI
  35. [38]Vejnar CE Moreno-Mateos MA Cifuentes D Bazzini AA Giraldez AJ Optimized CRISPR-Cas9 System for Genome Editing in Zebrafish Cold Spring Harbor protocols 2016 2016 10.1101/pdb.prot086850 pdb.prot086850 27698232 · DOI
  36. [39]Theodosiou M Fermt2 cooperates with talin to activate integrins and induces cell spreading by directly binding paxillin eLife 2016 5 e10130 10.7554/eLife.10130 26821125 PMC4749545 · DOI · Paper page
  37. [40]Gapuzan ME Yufit PV Gilmore TD Immortalized embryonic mouse fibroblasts lacking the RelA subunit of transcription factor NF-kappaB have a malignantly transformed phenotype Oncogene 2002 21 2484 2492 10.1038/sj.onc.1205333 11971183 · DOI
  38. [43]Martin L Identification and characterization of small molecules that inhibit nonsense-mediated RNA decay and suppress nonsense p53 mutations Cancer Res 2014 74 3104 3113 10.1158/0008-5472.can-13-2235 24662918 PMC4040335 · DOI
  39. [44]Radle B Metabolic labeling of newly transcribed RNA for high resolution gene expression profiling of RNA synthesis, processing and decay in cell culture Journal of visualized experiments: JoVE 2013 10.3791/50195 PMC3854562 23963265 · DOI
  40. [45]Sun W Chen W Metabolic Labeling of Newly Synthesized RNA with 4sU to in Parallel Assess RNA Transcription and Decay Methods in molecular biology (Clifton, N.J.) 2018 1720 25 34 10.1007/978-1-4939-7540-2_3 29236249 · DOI
  41. [46]Vanhollebeke B Tip cell-specific requirement for an atypical Gpr124- and Reck-dependent Wnt/beta-catenin pathway during brain angiogenesis eLife 2015 4 10.7554/eLife.06489 PMC4456509 26051822 · DOI
  42. [47]Kwon HB In vivo modulation of endothelial polarization by Apelin receptor signalling Nature communications 2016 7 10.1038/ncomms11805 11805 PMC4895482 27248505 · DOI
  43. [48]Blecher-Gonen R High-throughput chromatin immunoprecipitation for genome-wide mapping of in vivo protein-DNA interactions and epigenomic states Nature protocols 2013 8 539 554 10.1038/nprot.2013.023 23429716 · DOI
  44. [49]Buenrostro JD Giresi PG Zaba LC Chang HY Greenleaf WJ Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position Nature methods 2013 10 1213 1218 10.1038/nmeth.2688 24097267 PMC3959825 · DOI · Paper page
  45. [52]Robinson JT Integrative Genomics Viewer Nature biotechnology 2011 29 24 26 10.1038/nbt.1754 PMC3346182 21221095 · DOI · Paper page
  46. [53]Ramirez F Dundar F Diehl S Gruning BA Manke T deepTools: a flexible platform for exploring deep-sequencing data Nucleic acids research 2014 42 W187 191 10.1093/nar/gku365 24799436 PMC4086134 · DOI · Paper page
  47. [54]Anders S Huber W Differential expression analysis for sequence count data Genome Biol 2010 11 R106 10.1186/gb-2010-11-10-r106 20979621 PMC3218662 · DOI · Paper page
  48. [56]Davis MP van Dongen S Abreu-Goodger C Bartonicek N Enright AJ Kraken: a set of tools for quality control and analysis of high-throughput sequence data Methods (San Diego, Calif.) 2013 63 41 49 10.1016/j.ymeth.2013.06.027 PMC3991327 23816787 · DOI
  49. [57]Liao Y Smyth GK Shi W featureCounts: an efficient general purpose program for assigning sequence reads to genomic features Bioinformatics 2014 30 923 930 10.1093/bioinformatics/btt656 24227677 · DOI · Paper page
  50. [60]Wintersinger JA Wasmuth JD Kablammo: an interactive, web-based BLAST results visualizer Bioinformatics 2015 31 1305 1306 10.1093/bioinformatics/btu808 25481007 · DOI
  51. [61]Edgar RC MUSCLE: a multiple sequence alignment method with reduced time and space complexity BMC bioinformatics 2004 5 113 10.1186/1471-2105-5-113 15318951 PMC517706 · DOI
  52. [62]Xie C KOBAS 2.0: a web server for annotation and identification of enriched pathways and diseases Nucleic acids research 2011 39 W316 322 10.1093/nar/gkr483 21715386 PMC3125809 · DOI

Cited by

Sorted by citations

Authors
El-Brolosy, M. A., Kontarakis, Z., Rossi, A., Kuenne, C., Günther, S., Fukuda, N., Kikhi, K., Boezio, G. L. M., Takacs, C. M., Lai, S.-L., Fukuda, R., Gerri, C., Giraldez, A. J., & Stainier, D. Y. R.
Affiliations
  • Max Planck Institute for Heart and Lung Research (DE)
  • Leibniz Institute of Environmental Medicine (DE)
  • Yale University (US)
  • Institute of Biomedical Sciences, Academia Sinica (TW)
  • The Francis Crick Institute (GB)
Corresponding author
Stainier, Didier Y. R.
ORCID
Venue
Nature
Publisher
Springer Science and Business Media LLC
Published
2019-04-03
Volume
568
Issue
7751
Pages
193-197
ISSN
  • 0028-0836 (ISSN-L)
  • 1476-4687
DOI
10.1038/s41586-019-1064-z
PMC
PMC6707827
OpenAlex
W2803271184
Cited by
1,028
References
10
Citations by year
2026: 97 · 2025: 119 · 2024: 119 · 2023: 100 · 2022: 144 · 2021: 155 · 2020: 189 · 2019: 102 · 2018: 2
FWCI
48.95
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature (publishedVersion)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • MPG.PuRe (Max Planck Society) (submittedVersion)
Funders
  • Boehringer Ingelheim Fonds
  • Deutsche Forschungsgemeinschaft
  • Fondation Leducq
  • Max-Planck-Gesellschaft
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Research and Splicing
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Evolution and Genetic Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
Keywords
genetic compensation, transcriptional adaptation, genetic robustness, pathogenic variants, mutant allele, zebrafish, mice
MeSH terms
Adaptation, Physiological, Alleles, Animals, Histones, Mutation, RNA, Messenger, Transcription, Genetic, Zebrafish, Up-Regulation, RNA Stability, Epigenesis, Genetic, Mice

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