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Mitochondrial protein-induced stress triggers a global adaptive transcriptional programme

Nature Cell Biology volume 21, pages 442–451 (2019)Cite this article

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A Publisher Correction to this article was published on 29 April 2019

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Abstract

The cytosolic accumulation of mitochondrial precursors is hazardous to cellular fitness and is associated with a number of diseases. However, it is not observed under physiological conditions. Individual mechanisms that allow cells to avoid cytosolic accumulation of mitochondrial precursors have recently been discovered, but their interplay and regulation remain elusive. Here, we show that cells rapidly launch a global transcriptional programme to restore cellular proteostasis after induction of a ‘clogger’ protein that reduces the number of available mitochondrial import sites. Cells upregulate the protein folding and proteolytic systems in the cytosol and downregulate both the cytosolic translation machinery and many mitochondrial metabolic enzymes, presumably to relieve the workload of the overstrained mitochondrial import system. We show that this transcriptional remodelling is a combination of a ‘wideband’ core response regulated by the transcription factors Hsf1 and Rpn4 and a unique mitoprotein-induced downregulation of the oxidative phosphorylation components, mediated by an inactivation of the HAP complex.

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Fig. 1: Clogger protein expression triggers a mitoprotein-induced stress response.
Fig. 2: Mitoprotein-induced stress triggers consistent and characteristic remodelling of gene expression.
Fig. 3: Clogger-mediated Rpn4 induction upregulates the ubiquitin–proteasome system.
Fig. 4: Mitoprotein-induced stress induces a heat stress response that upregulates Rpn4.
Fig. 5: OXPHOS gene repression via HAP complex inactivation augments the cytosolic proteostasis cascade to combat mitochondrial import stress.
Fig. 6: The clogger-induced transcriptional footprint translates into changes on the protein level.
Fig. 7: The mitoprotein-induced stress response is at the heart of a cellular response network.

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Code availability

Custom code used for data analysis is available from the authors upon reasonable request.

Data availability

The data generated or analysed for the current study are included in this published article and its Supplementary Information. All numerical source data used for statistical analyses and graphical representations for Figs. 1–7 and Supplementary Figs. 1–7 are provided in Supplementary Table 9. The deep-sequencing (RNA-seq) raw data have been deposited into GEO with the accession number GSE116749. The MS proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE58 partner repository with the dataset identifier PXD011789. Visualizations of all RNA-seq and proteomics data are deposited into Figshare at https://doi.org/10.6084/m9.figshare.7611107. Previously published gene expression datasets59,60,61,62,63,64,65,66,67,68,69,70 (PMID numbers 11830665, 23643537, 18604275,18212068, 12740579, 26391581, 23222101, 16209719, 17327914, 11102521, 24952590, 12820961, 18753408) were downloaded from the SPELL database and are available at https://downloads.yeastgenome.org/expression/microarray/.

Change history

  • 29 April 2019

    In the version of this article originally published, parts of Figure 5 were misaligned because of a shift during production. In a, one data point was outside of the graph border. In b, axes lines were not connected, and graph lines did not reach the data points. In c and d, the axes lines were not connected. In e and g, the axes lines were not connected, and error bars and columns were not aligned. Shown below are the original and corrected versions of Figure 5. The errors have been corrected in the PDF and HTML versions of the paper.

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Acknowledgements

The authors thank the following individuals: K. Hansen, S. Backes, J. Laborenz, K. Knoeringer, S. Knaus, A. Trinkaus, J. Pistolic and J. Provaznik for help with the experiments; M. Schuldiner, D. Pincus, S. Schuck, D. Wolf, G. Kramer, K. Morano, D. Thiele, M. Bolotin-Fukuhara and T. Becker for reagents and strains; and B. Morgan, S. Schuck and M. Schuldiner for comments on the manuscript. The authors acknowledge the Deutsche Forschungsgemeinschaft (DIP MitoBalance, IRTG1830 and HE2803/8-2 to J.M.H.; SFB 894 and IRTG 1830 to M.v.d.L.), the Joachim Herz Stiftung (to F.B.), the Forschungsinitiative Rheinland Pfalz BioComp (to J.M.H.) and the Boehringer Ingelheim Fonds (predoctoral fellowship to F.W.) for funding.

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Authors and Affiliations

  1. Cell Biology, University of Kaiserslautern, Kaiserslautern, Germany

    Felix Boos, Lena Krämer, Carina Groh, Adrian Gackstatter, Eva Zöller & Johannes M. Herrmann

  2. Genomics Core Facility, EMBL Heidelberg, Heidelberg, Germany

    Ferris Jung & Vladimir Benes

  3. Proteomics Core Facility, EMBL Heidelberg, Heidelberg, Germany

    Per Haberkant, Frank Stein & Mikhail M. Savitski

  4. Medical Biochemistry and Molecular Biology, Center for Molecular Signaling, PZMS, Saarland University, Homburg, Germany

    Florian Wollweber & Martin van der Laan

  5. Genome Biology Unit, EMBL Heidelberg, Heidelberg, Germany

    Mikhail M. Savitski

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Contributions

F.B. and J.M.H. conceived the project. F.B., L.K., C.G. and A.G. designed, cloned and verified the constructs and strains, and F.B., C.G., F.J. and V.B. produced the cDNA and carried out the RNA-seq experiments. F.B., P.H. and M.M.S. carried out the MS experiments. F.B., E.Z., L.K. and C.G. measured the transcript and protein levels in cell extracts, and F.B., F.W. and M.v.d.L. performed the blue native–PAGE assays. F.B. performed the bioinformatics analyses of the RNA-seq data, and F.B. and F.S. performed the bioinformatics analyses of the proteomics data. F.B., L.K. and C.G. designed and set up the gene expression reporter assays. F.B., L.K., C.G., A.G. and J.M.H. analysed the data, and F.B. and J.M.H. wrote the manuscript.

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Correspondence to Johannes M. Herrmann.

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Integrated supplementary information

Supplementary Figure 1 Clogger expression leads to characteristic changes of the transcriptome and prevents cell growth, but does not cause cell death or respiration deficiencies.

a, Clogger-expressing wild type cells and controls were grown to log phase on synthetic lactate medium. Tenfold serial dilutions were dropped onto lactate plates lacking (left) or containing 0.5% galactose (middle). For the plate shown on the right, samples were pre-grown for 4.5 h on galactose-containing medium to induce the expression of the DHFR fusion proteins. Cells were washed and spotted on galactose-free plates. Despite the strong growth arrest in the presence of galactose, clogger-expressing strains were fully viable as soon as clogger expression was repressed. b, Cell viability was measured by a CASY cell counter upon several hours of clogger induction. A wildtype strain was treated with CASYblue for 5 min according to the manufacturers instructions as reference for dying cells. c, Cells were grown in galactose-containing medium for 4.5 h, harvest, resuspended in 1 ml of lactate medium and their oxygen consumption rate was measured. Clogger induction for 4.5 h did not affect respiration rates. Experiments were repeated independently n = 4 (a) or n = 2 (b, c) times with similar results. d, Volcano Plots showing the relative log2 fold changes (mean of n = 4 independent biological samples) of cells expressing cytosolic DHFR, b2-DHFR or b2Δ-DHFR relative to strain containing just an empty vector. While cytosolic DHFR does not alter gene expression, the expression of four characteristic groups of genes is consistently changed upon induction of the cloggers: Several members of the chaperone system / protein folding (GO:0006457) are induced within 30 min. The proteasome complex (GO:0000502) is induced between 30 and 90 min after clogger induction. The cytosolic ribosome (GO:0022626) and components of the oxidative phosphorylation machinery (GO:0006119) are repressed between 30 and 90 min after clogger induction. P values were calculated with the likelihood ratio test and corrected for multiple hypothesis testing with the Benjamini-Hochberg procedure within the DESeq2 package.

Supplementary Figure 2 The transcription factor Rpn4 regulates the clogger-mediated expression of proteasome subunits via PACE promoter elements.

a Expression profiles of proteasome core subunits and other factors of the ubiquitin-proteasome system in clogger expressing and control cells. The read numbers were only corrected for the size differences of the four cDNA libraries and are directly shown. Please note the surprisingly homogeneous upregulation of all core subunits of the proteasome and other proteins of the ubiquitin-proteasome system between the 30 and 90 min time point after clogger induction. b, Logo plot presentation of PACE elements which is similar but different to the binding site of Met31/32. c, Overview of the synthetic Rpn4 activity reporter consisting of four PACE elements upstream of a minimal promoter and a YFP-coding reading frame. d, Schematic representation of the fluorescence reporter for the detection and quantification of Rpn4-mediated gene expression. e, An increase in temperature induces an Rpn4-driven YFP expression. f, In the absence of Rpn4, YFP expression is very low and does not increase upon clogger induction. This shows that the reporter gene specifically responds to Rpn4 activation. g, Transcript levels of RPN8 were measured upon clogger induction of 4.5 h in wild type or Δrpn4 cells. h, The levels of proteasome subunits in cell extracts of the indicated strains were analyzed by Western blotting. Bar graphs show mean values and standard deviations. Experiments were repeated independently n = 4 (a), n = 1 (e) or n = 3 (f, g, h) times; significance was assessed with a one-sided, paired Student’s t-test. Unprocessed blots are shown in Supplementary Fig. 8.

Supplementary Figure 3 On basis of their response to clogger induction, chaperones can be sorted into different categories.

a, Based on the profiles of transcript (left, n = 4 independent biological samples) or protein (right, n = 3 independent biological samples) levels relative to control strains, genes from the protein folding GO term (GO:0006457) were clustered according to correlation of their profiles over time. Both datasets gave rise to similar groups, which also matched with reported functions of the comprised genes: Components of the general posttranslational folding machinery (such as Ssa1-Ssa4, Hsp82 or Ydj1) are strongly and rapidly induced by clogger induction. Several cochaperones and, notably, all subunits of the cytosolic chaperonin show a moderate and late upregulation and drop again at late time points on the protein level. Cotranslationally acting chaperones (such as Ssb1/Ssb2, Ssz1, zuotin) are slightly downregulated upon clogger induction. Components of the general posttranslational folding machinery (such as Ssa1-Ssa4, Ssc1 or Ydj1) are strongly and rapidly induced by clogger induction. b, Overexpression of Hsp70 (Ssa2) and Hsp40 (Ydj1) from strong constitutive promoters partially restored growth upon clogger induction. The experiment was repeated independently n = 2 times with similar results.

Supplementary Figure 4 Mitoprotein-induced stress inhibits both cytosolic and mitochondrial protein synthesis.

a, Radiolabelling of newly synthesized proteins showed a strong reduction of translation rates 3 h after clogger induction. b, Protein components of the cytosolic ribosome slightly decreased in abundance upon clogger induction as measured by mass spectrometry. c, mRNA levels of the mitochondrially encoded gene ATP6 showed no decrease upon clogger induction. Means and standard deviations are shown. d, Protein levels of Cox1 and Cox2 moderately reduced over time, as measured by mass spectrometry. e, Synthesis of mitochondrial translation products upon clogger induction was analyzed by radioactive labeling in the presence of cycloheximide. Blocking mitochondrial protein import strongly decreased mitochondrial translation. f, g, Reduced levels of mitochondrial ribosomal proteins were observed in the proteomics analysis, including the mitochondrially encoded subunit Var1. This effect was not seen on the transcript level. Experiments were repeated independently n = 1 (a, e) or n = 3 (b, c, d, f, g) times. p values were calculated according to a two-sided Student’s t-test, adjusted for multiple comparisons using the Benjamini-Hochberg procedure (see Methods for details). Unprocessed blots are shown in Supplementary Fig. 8.

Supplementary Figure 5 Mitoprotein-induced stress induces downregulation of OXPHOS genes.

a, The repression of OXPHOS genes such as FUM1 is independent of Rpn4. b, Clogger induction affects genes that contain a HAP complex-binding CCAAT element in a similar manner than the deletion of Hap2. c, Hap4 levels are reduced upon induction of clogger proteins. d, Predominantly the highly abundant mitochondrial proteins are downregulated upon clogger induction. e, Overexpression of HAP4 from a GAL promoter did not affect clogger expression nor altered induction of chaperones. Means and standard deviations are shown. Experiments were repeated independently n = 3 (a), n = 1 (c) or n = 4 (e) times. Unprocessed blots are shown in Supplementary Fig. 8.

Supplementary Figure 6 Deep proteomic profiling of clogger-induced changes in protein abundance.

a, Samples were collected from clogger expressing and control cells after indicated times of induction. Protein extracts were digested with trypsin and labelled with TMT10 reagent. Samples were mixed and subjected to mass spectrometry. Reporter ions were quantified in the MS2 spectra. b, 3,511 proteins were quantified in at least two out of three replicates (independent biological samples). c, Mitoprotein-induced stress alters the cellular proteome in a consistent manner which is clearly distinct from the changes induced by the addition of galactose. d, Overall fold changes of clogger expressing versus control cells increased over time. Genes with the strongest up- or down-regulation at each time point are labelled. e, Changes in protein abundance of clogger-expressing cells relative to control cells over time were measured by mass spectrometry. Shown here are mean log2 fold changes from n = 3 independent biological samples. Consistent with the transcriptional responses, a consistent induction of the proteasome as well as of many chaperones is observed, as well as prominent decrease in OXPHOS components and reduced levels of cytosolic ribosomes and its associated chaperones. Some components of the mitochondrial protein import machinery decrease in abundance, while they were not affected on the transcript level. Experiments were repeated independently n = 3 (b-e) times. Significance was assessed with a two-sided Student’s t-test, adjusted for multiple comparisons using the Benjamini-Hochberg procedure (see Methods for details).

Supplementary Figure 7 The mitoprotein-induced stress response is similar to, but distinct from a heat stress response and comprises the Pdr3-mediated mitoCPR response as a late sub-routine.

a, The clogger-induced expression changes were compared to the patterns of the yeast transcription SPELL database. Shown are percentages of transcription datasets that are similar to that of clogger-induced expression changes. b, For all heat stress transcriptome analyses deposited in SPELL, the mean changes in gene expression for the four GO terms shown were calculated and compared to that of the clogger-mediated response (after 90 min). Note that in none of the studies except the present one on mitoprotein-induced stress, a combination of OXPHOS downregulation with the classical heat stress responses of chaperones, proteasome and 80S ribosome was observed59,60,61,62,63,64,65,66,67,68,69,70. c, Expression profile of PDR3 upon clogger induction. d, GO enrichment analysis of genes with expression profiles that are similar to that of PDR3. e, The promoter of PDR3 contains two PACE elements and two PDRE elements. f, Individual profiles of genes that are transcriptionally controlled by Pdr3. Experiments were repeated independently n = 4 times (a–f). P values for enrichment (a, d) were calculated assuming a hypergeometric distribution (one-sided Fisher’s exact test) and corrected for multiple hypothesis testing using the Benjamini-Hochberg procedure.

Supplementary Figure 8

Unprocessed images of all gels and blots.

Supplementary information

Supplementary Information

Supplementary Figures 1–8 and legends for Supplementary Tables 1–9.

Reporting Summary

Supplementary Table 1

Yeast strains used in this study.

Supplementary Table 2

Plasmids used in this study.

Supplementary Table 3

qPCR primers used in this study.

Supplementary Table 4

RNAseq data. Read counts, normalized with respect to library size.

Supplementary Table 5

RNAseq data. log2 fold changes normalized to the empty vector control.

Supplementary Table 6

Pairwise similarity scores for the transcript profiles of all genes measured in the RNA-seq experiment.

Supplementary Table 7

Experimentally verified target genes of the transcription factors Hsf1, Yap1 and Pdr1/Pdr3.

Supplementary Table 8

Proteomics data. log2 fold changes of b2-DHFR normalized to cyt DHFR.

Supplementary Table 9

Statistics source data.

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Boos, F., Krämer, L., Groh, C. et al. Mitochondrial protein-induced stress triggers a global adaptive transcriptional programme. Nat Cell Biol 21, 442–451 (2019). https://doi.org/10.1038/s41556-019-0294-5

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