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.
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We thank V. Serobyan, F. Mueller, Z. Jiang, A. Beisaw and F. Gunawan for discussion and comments on the manuscript; J. Pestel for the alcama mutant; A. Atzberger for support with cell sorting; and N. Gehring and V. Böhm for providing the XRN1-resistant sequence plasmid. M.A.E.-B. was supported by a Boehringer Ingelheim Fonds PhD fellowship. Research in the D.Y.R.S. laboratory is supported by the Max Planck Society, the EU, the DFG and the Leducq Foundation.
Nature thanks Miles Wilkinson and the other anonymous reviewer(s) for their contribution to the peer review of this work.
The authors declare no competing interests.
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Extended data figures and tables
Partial DNA sequences of the different mutant alleles generated for this study, and images of gels providing evidence for deletions in RNA-less alleles. Red indicates mutation (asterisks, deletion; upper-case letters, substitution; lower-case letters, insertion); green indicates stop codon in alleles with a PTC; arrows indicate genotyping primers.
a, qPCR analysis of epas1a, epas1b, vegfab, emilin3a and alcamb mRNA expression levels in wild-type and hif1ab, vegfaa, egfl7 and alcama mutant embryos injected (inj.) with eGFP mRNA (control; ctrl) or wild-type hif1ab, vegfaa, egfl7 or alcama mRNA. b, qPCR analysis of vclb, epas1a, epas1b and emilin3a mRNA expression levels in vcla, hif1ab and egfl7 wild-type, heterozygous and mutant zebrafish. c, qPCR analysis of hbegfa, hif1ab, vegfaa and alcama mRNA expression levels in hbegfa, hif1ab, vegfaa and alcama wild-type and heterozygous zebrafish, using primers specific for the wild-type allele. d, qPCR analysis of Fermt1 and Rel mRNA expression levels in wild-type and Fermt2 and Rela knockout cells transfected with empty vectors (control) or plasmids encoding wild-type FERMT2 or RELA. e, Western blot analysis of FERMT2 and ACTB levels in Fermt2 knockout cells transfected with empty vectors (control) or plasmids encoding wild-type FERMT2. f, Western blot analysis of RELA and ACTB levels in Rela knockout cells transfected with empty vectors (control) or plasmids encoding wild-type RELA. g, qPCR analysis of Actg1 mRNA expression levels in wild-type and heterozygous Actb mESCs. n = 3 biologically independent samples. Wild-type or control expression levels were set at 1 for each assay. Data are mean ± s.d., and a two-tailed Student’s t-test was used to calculate P values (a–d, g). The experiments in e, f were performed only once. For the source data for western blots, see Supplementary Fig. 1.
Extended Data Fig. 3 Transcriptional adaptation involves enhanced transcription and is independent of the DNA lesion itself.
a, qPCR analysis of hbegfb and emilin3a mRNA and pre-mRNA expression levels in hbegfa and egfl7 wild-type and mutant zebrafish. b, qPCR analysis of Fermt1 and Rel mRNA and pre-mRNA expression levels in Fermt2 and Rela wild-type and knockout cells. c, IGV tracks of the Fermt1 locus showing ATAC-seq signals in wild-type and Fermt2 knockout cells. d, qPCR analysis of hbegfa, hbegfb, egfl7 and emilin3a mRNA expression levels in hbegfa and egfl7 wild-type and Δ3 mutant zebrafish. e, qPCR analysis of vegfaa, vegfab, egfl7 and emilin3a mRNA expression levels in vegfaa and egfl7 wild-type and 5′UTR mutant zebrafish. f, qPCR analysis of vcla and vclb mRNA expression levels in vcla wild-type and last exon (exon 22) mutant zebrafish. n = 3 biologically independent samples. Wild-type expression levels were set at 1 for each assay. Data are mean ± s.d., and a two-tailed Student’s t-test was used to calculate P values.
a, qPCR analysis of hbegfa, egfl7 and alcama mRNA and pre-mRNA expression levels in hbegfa, egfl7 and alcama wild-type and mutant zebrafish. b, qPCR analysis of Fermt2 and Rela mRNA and pre-mRNA expression levels in Fermt2 and Rela wild-type and knockout cells. c, qPCR analysis of 4sU-labelled Fermt2, Rela and Actg1 mRNA and pre-mRNA expression levels in Fermt2, Rela and Actg1 wild-type and knockout cells. d, Fitted exponential decay curves of Fermt2 mRNA expression levels in wild-type and Fermt2 knockout cells. t1/2, half-life. e, Fitted exponential decay curves of Rela mRNA expression levels in wild-type and Rela knockout cells. f, Fitted exponential decay curves of Actg1 mRNA expression levels in wild-type and Actg1 knockout cells. n = 3 (a, b, d–f) or n = 2 (c) biologically independent samples. Wild-type expression levels were set at 1 for each assay (a–c). Data are mean ± s.d., and a two-tailed Student’s t-test was used to calculate P values.
a, qPCR analysis of hbegfa, vegfaa and vcla mRNA expression levels in upf1;hbegfa, upf1;vegfaa and upf1;vcla double mutant zebrafish. b, qPCR analysis of Rela mRNA expression levels after siRNA-mediated knockdown of the indicated proteins in Rela knockout cells. c, qPCR analysis of Actb mRNA expression levels after siRNA-mediated knockdown of the indicated proteins in Actb knockout cells. d, qPCR analysis of hbegfa mRNA expression levels in 6 dpf hbegfa mutants treated with NMD inhibitor (NMDi). e, qPCR analysis of hbegfb mRNA expression levels in 6 dpf hbegfa mutants treated with NMDi. f, qPCR analysis of Rela mRNA expression levels in Rela knockout cells treated with cycloheximide (CHX). g, qPCR analysis of Rel mRNA expression levels in Rela knockout cells treated with CHX. H, qPCR analysis of endogenous hif1ab and vegfaa mRNA expression levels in 6 hpf wild-type embryos injected with uncapped hif1ab or vegfaa RNA. I, qPCR analysis of Actg1 mRNA expression levels in mESCs transfected with uncapped Actb RNA at different times after transfection. j, qPCR analysis of injected hif1ab, epas1a, injected vegfaa and vegfab RNA expression levels in 6 hpf wild-type embryos injected with uncapped hif1ab or vegfaa transcripts with or without a 5′ XRN1-resistant (xr) sequence. k, qPCR analysis of epas1a and vegfab mRNA expression levels in 6 hpf wild-type zebrafish embryos injected with uncapped sense or antisense (rev) hif1ab or vegfaa RNA; the same eGFP uncap. control samples were used for the epas1a experiments. Wild-type or control expression levels were set at 1 for each assay (a–d, f, h–k). n = 3 biologically independent samples. Data are mean ± s.d., and a two-tailed Student’s t-test was used to calculate P values.
a, qPCR analysis of Fermt2 and Fermt1 mRNA expression levels following CRISPR interference-mediated knockdown of Fermt2 transcription in Fermt2 knockout cells. b, qPCR analysis of emilin3a, emilin3b and emilin2a mRNA expression levels in 20 hpf wild types, egfl7Δ4 mutants and egfl7full locus del. mutants. c, Number of central arteries (CtAs) connecting to the basilar artery (BA) in 58 hpf vegfaaΔ10 and vegfaapromoter-less mutants. d, Blood-flow velocity in 78 hpf wild types, hbegfaΔ7 mutants and hbegfafull locus del. mutants. e, Quantification of the cardiac ventricle length in 100 hpf wild types, alcama Δ8 mutants and alcamapromoter-less mutants. Wild-type or control expression levels were set at 1 for each assay (a, b). n = 3 (a, b); n = 13 (vegfaaΔ10−/−) and 19 (vegfaapromoter-less−/−) (c); n = 25 (d); and n = 18 (wild-type siblings of alcamaΔ8−/−), 7 (alcamaΔ8−/−), 22 (wild-type siblings of alcamapromoter-less−/−) and 15 (alcamapromoter-less−/−) (e) animals. Data are mean ± s.d., and a two-tailed Student’s t-test was used to calculate P values.
Extended Data Fig. 7 Analysis of sequence similarity parameters in models of transcriptional adaptation.
a, Numbers of differentially expressed genes in the different knockout cell line models; P ≤ 0.05; these genes are distributed throughout the genome (data not shown). b, Venn diagram of genes upregulated in the three different cell line models with L2F knockout > wild-type and P ≤ 0.05. c, KEGG pathway enrichment analysis for genes commonly upregulated in Fermt2, Actg1 and Actb knockout compared to wild-type cells. The top ten pathways based on P value are displayed. The dashed line marks a P value of 0.05. Circle sizes provide an estimation of scale; outer grey circles represent the total number of genes in the pathway; and centred coloured circles represent the number of genes in the pathway that are commonly upregulated. d, Impact of various values of three different BLASTn alignment-quality parameters (alignment length, bit score and E value) on the significance of the observed correlation between upregulation and sequence similarity, and therefore the identification or prediction of putative adapting genes. The E value describes the probability of the match resulting from chance (a lower value corresponds to a lower probability), and the bit score evaluates the combination of alignment quality and length (a higher value corresponds to a better alignment).The y axis of each diagram shows the negative log10 of the P value and the x axis shows the respective parameter value. A P value of 0.05 is marked with a black horizontal line. The E value thresholds used in our analyses are highlighted with a circle. Lines ending preliminarily indicate a lack of any remaining alignments after that point. The first row of diagrams explores large variations of thresholds, in an attempt to identify the total range, whereas the second row focuses on the most relevant window for the three genes investigated. The optimal thresholds differ considerably depending on the gene analysed. n = 2 biologically independent samples. P value was computed by bootstrapping random subsamples (see the ‘Sequence similarity and subsampling analyses’ section of the Methods). P values were not corrected for multiple testing.
Extended Data Fig. 8 Expression level of genes exhibiting sequence similarity in the different mouse cell line models.
a–c, RNA-seq analysis of genes exhibiting sequence similarity with Fermt2 (a), Actg1 (b) or Actb (c) in knockout compared to wild-type cells. Bold, significantly upregulated in knockout relative to wild-type cells; red, L2F>0, blue, L2F<0; green, P value or adjusted P value ≤ 0.05; purple; genes exhibiting sequence similarity with the mutated gene's mRNA in their promoter region; yellow, genes exhibiting sequence similarity with the mutated gene's mRNA in their 3′UTR region. Other non-coloured genes exhibit sequence similarity with the mutated gene's mRNA in their exons or introns. Boxed, upregulated in knockout but not RNA-less cells; no Fermt2 RNA-less allele was analysed. d, qPCR analysis of Ubapl, Fmnl2, Cdk12 and Actr1a pre-mRNA expression levels in Actg1 knockout relative to wild-type cells. e, qPCR analysis of actb1 mRNA expression levels in 6 hpf wild-type zebrafish injected with uncapped mouse Actb RNA. f, Schematic representation of regions of sequence similarity between hif1ab mRNA and the epas1a locus. Grey shaded triangles represent the alignments; intensity represents the alignment quality; and width at the base represents the length of the similarity region. g, qPCR analysis of epas1a mRNA expression levels in 6 hpf wild-type zebrafish embryos injected with uncapped RNA composed solely of the hif1ab sequences similar to the epas1a promoter, exons, introns or 3′UTR; the same eGFP uncap. control samples were used for all comparisons. n = 2 (a–c) or n = 3 (d, e, g) biologically independent samples. DESeq2 tests were used to test for significance of coefficients in a negative binomial generalized linear model with the Wald test (a–c). P values were not corrected for multiple testing. Wild-type or control expression levels were set at 1 for each assay (d, e, g). Data are mean ± s.d., and a two-tailed Student’s t-test was used to calculate P values.
Extended Data Fig. 9 Transcriptional adaptation involves chromatin remodelling that is dependent on the activity of decay factors.
a, qPCR analysis of Rel mRNA expression levels after siRNA-mediated knockdown of the indicated proteins in Rela knockout cells. b, ChIP–qPCR analysis of H3K4me3 occupancy at non-promoter regions (as a control) of Fermt1, Rel and Actg2 in Fermt2, Rela and Actg1 knockout cells, respectively, compared to wild-type cells. c, ChIP–qPCR analysis of H3K4me3 occupancy near the Rel TSS and a non-promoter region (as a control) after siRNA-mediated knockdown of the indicated proteins in Rela knockout cells. d, Current expanded model of transcriptional adaptation to mutations. RNA decay fragments may act as intermediates to bring decay factors and chromatin remodellers to adapting gene loci, thereby triggering increased gene expression. Alternatively, RNA decay fragments may function by repressing antisense RNAs at the adapting gene loci, thus allowing for increased sense mRNA expression. It is, however, likely that additional mechanisms are involved in transcriptional adaptation, and possibly in a gene-dependent manner. n = 3 (a) or n = 2 (b, c) biologically independent samples. Data are mean ± s.d., and a two-tailed Student’s t-test was used to calculate P values.
Extended Data Fig. 10 The potential role of antisense transcripts in the transcriptional adaptation response.
a, qPCR analysis of Cdk9 and Sox9 mRNA expression levels in cells transfected with uncapped Cdk9 or Sox9 RNA. b, qPCR analysis of BDNF and BDNF-AS mRNA expression levels in HEK293T cells transfected with uncapped BDNF RNA. c, Integrated genome viewer tracks of vclb and hbegfb loci, showing the location of the annotated antisense transcripts. Two alignments of 105 and 147 bp were observed between vcla mRNA and vclb antisense RNAs, and an alignment of 39 bp was observed between hbegfa mRNA and hbegfb antisense RNA. Antisense transcripts shown were acquired from the datasets in GSE32898. d, qPCR analysis of vclb and hbegfb antisense (AS) RNA expression levels in vcla and hbegfa wild-type and mutant zebrafish at 24 and 72 hpf, respectively. Control expression levels were set at 1 for each assay. n = 3 biologically independent samples. Data are mean ± s.d., and a two-tailed Student’s t-test was used to calculate P values.
This file contains a Supplementary Discussion; Supplementary Data and Supplementary Figures. The merged supplementary information PDF includes: 1. Supplementary Discussion, 2. Supplementary Data: Includes (1) MUSCLE alignment of zebrafish actb1 and mouse Actb coding sequences, (2) MUSCLE alignment of the synthetic hif1ab transcript which consists of sequences similar to epas1a, and the full hif1ab transcript, (3) BLASTn alignment of hif1ab transcript and epas1a and (4) sequence of the synthetic transcript composed of sequences of hif1ab mRNA not exhibiting similarity with epas1a genomic locus, 3. Supplementary Figure 1: uncropped images of western blots.
This file contains Supplementary Table 1: Commonly upregulated genes in Fermt2, Actg1 and Actb K.O. cells.
This file contains Supplementary Table 2: siRNAs used in the study.
This file contains Supplementary Table 3: Genotyping primers.
This file contains Supplementary Table 4: qPCR primers.
This file contains Supplementary Table 5: gRNAs used in the study.
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El-Brolosy, M.A., Kontarakis, Z., Rossi, A. et al. Genetic compensation triggered by mutant mRNA degradation. Nature 568, 193–197 (2019). https://doi.org/10.1038/s41586-019-1064-z
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