Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2017 Jul 3;216(7):2027-2045.
doi: 10.1083/jcb.201702058. Epub 2017 May 31.

Multi-omics analysis identifies ATF4 as a key regulator of the mitochondrial stress response in mammals

Pedro M Quirós  1 , Miguel A Prado  2 , Nicola Zamboni  3 , Davide D'Amico  1 , Robert W Williams  4 , Daniel Finley  2 , Steven P Gygi  2 , Johan Auwerx  5
Affiliations

Multi-omics analysis identifies ATF4 as a key regulator of the mitochondrial stress response in mammals

Pedro M Quirós et al. J Cell Biol. .

Abstract

Mitochondrial stress activates a mitonuclear response to safeguard and repair mitochondrial function and to adapt cellular metabolism to stress. Using a multiomics approach in mammalian cells treated with four types of mitochondrial stressors, we identify activating transcription factor 4 (ATF4) as the main regulator of the stress response. Surprisingly, canonical mitochondrial unfolded protein response genes mediated by ATF5 are not activated. Instead, ATF4 activates the expression of cytoprotective genes, which reprogram cellular metabolism through activation of the integrated stress response (ISR). Mitochondrial stress promotes a local proteostatic response by reducing mitochondrial ribosomal proteins, inhibiting mitochondrial translation, and coupling the activation of the ISR with the attenuation of mitochondrial function. Through a trans-expression quantitative trait locus analysis, we provide genetic evidence supporting a role for Fh1 in the control of Atf4 expression in mammals. Using gene expression data from mice and humans with mitochondrial diseases, we show that the ATF4 pathway is activated in vivo upon mitochondrial stress. Our data illustrate the value of a multiomics approach to characterize complex cellular networks and provide a versatile resource to identify new regulators of mitochondrial-related diseases.

PubMed Disclaimer

Figures

Figure 1.
Figure 1.
Mitochondrial stress alters mitochondrial function. (A) Schematic representation of the mechanism of action of the compounds selected for the analysis. Acti, actinonin; Dox, doxycycline; MB, MitoBloCK-6. (B) Mitochondrial membrane potential after 24 h of treatment with the selected chemicals. Tetramethylrhodamine, methyl ester (TMRM) was used to determine mitochondrial membrane potential (n = 4 independent experiments; mean values ± SEM). (C) Mitochondrial and (D) total ROS levels after 24 h of treatment with the selected chemicals. Dichlorofluorescin diacetate (DCF-DA) reflects total cellular ROS levels, whereas MitoSox measure mitochondrial superoxide level. RFU, relative fluorescence units (n = 4 independent experiments; mean values ± SEM). (E) Oxygen consumption rate (OCR) of cells treated with the different compounds. Dashed vertical lines indicate the subsequent addition of the ATPase inhibitor oligomycin (Olig.), the uncoupling reagent FCCP and the inhibitors of the electron transport chain rotenone/antimycin A (Rot/Ant). (F and G) Boxplots representing OCR (F) in basal conditions and (G) after treatment with the uncoupler FCCP (maximal respiration). (H) Boxplot representing the ATP-dependent respiration (oligomycin-sensitive respiration) calculated as the difference in OCR before and after the addition of oligomycin. (I) Ratio of OCR and extracellular acidification rate (ECAR) as an indicator of the relation between mitochondrial respiration and glycolysis. (J) ECAR in basal conditions as indication of glycolytic rate. For E–J, n = 2 independent experiments, using 10 replicates per experiment; mean values ± SEM of a representative experiment. (K) Inmunoblot analysis showing the effects of the compounds on different mitochondrial OXPHOS subunits (ATPA5, complex V; UCQRC2, complex III; MTCO1, complex IV; SDHB, complex II; and NDUFB8, complex I). HSP90 was used as loading control. *, P < 0.05; **, P < 0.01; ***, P < 0.001.
Figure 2.
Figure 2.
RNA sequencing and proteomic analysis upon mitochondrial stress. (A) Schematic representation of the omics experiments. HeLa cells were treated with compounds for 24 h, and the transcriptome, proteome, and metabolome were analyzed. Control cells were treated with DMSO. (B) Pie charts representing the total number of genes and proteins identified in all conditions by RNA sequencing and TMT quantitative proteomic analysis. Number of mitochondrial genes and proteins are represented in both charts, as well as the percentage relative to the total. (C) Heatmap analysis of the transcriptome (top) and proteome (bottom). Hierarchical clustering of samples (columns) and genes/proteins (rows) was based on Pearson’s correlation coefficient to measure the distance and the mean to cluster the samples. (D) Multidimensional scaling (MDS) using the distances between transcript expression profiles (top) and principal-component analysis (PCA) of the proteomic profiles (bottom) show a similar clustering between the duplicates and treatments. BCV, biological coefficient of variation; Comp, principal component. (E) Bar plots representing the number of differentially expressed (DE) genes (top) and proteins (bottom) in each condition (FDR 5%). Percentage of DE mitochondrial genes and proteins is shown, observing a greater impact on the mitochondrial proteome than on the mitochondrial transcriptome. Acti, actinonin; Dox, doxycycline; MB, MitoBloCK-6.
Figure 3.
Figure 3.
Mitochondrial stress inhibits mitochondrial translation and decreases mitochondrial ribosomal proteins and OXPHOS complexes. (A) Venn diagrams showing commonly down-regulated genes (top) and proteins (bottom) with an FDR < 0.05. (B) Enrichment analysis of 101 proteins down-regulated was performed using Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and represented as negative of log10 of p-value after Bonferroni correction. (C) A STRING network showing the enrichment in mitochondrial ribosomal proteins (MRPs; yellow) and OXPHOS proteins (blue). Line thickness indicates the strength of the association. (D and E) Correlation of transcript and protein data for the (D) MRPs and (E) OXPHOS components with each stressor. Values are represented as log2-fold changes (FC) relative to control. Dashes lines depict the 0 values to indicate the up- or down-regulation in each analysis. (F) Western blot analysis of OPA1 upon treatment for 24 h with each stressor. Long OPA1 isoforms (L-OPA1) and short OPA1 isoforms (S-OPA1) are indicated. Acti, actinonin; Dox, doxycycline; MB, MitoBloCK-6; NAFLD, non-alcoholic fatty liver disease.
Figure 4.
Figure 4.
Mitochondrial stress increases biosynthesis of amino acids. (A) Venn diagrams showing commonly up-regulated transcripts (top) and proteins (bottom) with an FDR < 0.05. (B) Enrichment analysis of 59 transcripts (top) and 17 proteins (bottom) up-regulated were performed using KEGG pathways and are represented as negative of log10 of p-value after Bonferroni correction. Common enriched pathways in RNA sequencing and proteomics are represented in bold. (C) Graphical representation of enzymes and metabolites of de novo serine biosynthesis and their integration in cellular metabolism. Enzymes highlighted in red are up-regulated in RNA sequencing and in red bold in both RNA sequencing and proteomics. (D) Correlation of cytosolic and mitochondrial amino acid-tRNA synthetase (aaRS) genes and proteins in each stress condition. Values are represented as log2-fold changes (FC) relative to control. Dashes lines depict the 0 values to indicate the up- or down-regulation in each analysis. (E) Enrichment score plots from GSEA using a combine mitochondrial stress expression extracted from the RNA seq. 3PG, 3-phosphoglycerate; Acti, actinonin; Dox, doxycycline; FDR, false discovery rate; Glc, glucose; MB, MitoBloCK-6; NES, normalized enrichment score; PEP, phosphoenolpyruvate; PHGDH, phosphoglycerate dehydrogenase; PSAT1, phosphoserine aminotransferase 1; PSPH, phosphoserine phosphatase; Pyr, pyruvate; SDSL, serine dehydratase; Ser, serine; TCA, tricarboxylic acid.
Figure 5.
Figure 5.
Metabolomic analysis of mitochondrial stress. (A) Multidimensional scaling (MDS) using the unique metabolites identified. Metabolome pathway enrichment of (B) up-regulated and (C) down-regulated metabolites upon mitochondrial stress. Scatterplots represents p-values from integrated enrichment analysis and impact values from pathway topology analysis. The node color is based on the p-values and the node radius represents the pathway impact values. (D) Dot plot representing the fold change of the top changed metabolites in each mitochondrial stress condition. Fold change is represented in log scale and all metabolites show significant differences with an FDR <0.05. (E) Box-whisker plots representing the ratio of serine and phosphoserine levels in each mitochondrial stress condition. (F) Graphical representation of enzymes and metabolites of the glutathione (GSH) cycle. Enzymes and metabolites highlighted in red are up-regulated upon mitochondrial stress, whereas those in blue are down-regulated. (G and H) Box-whisker plots representing the levels of (G) oxidized glutathione (GSSG) and the ratios of GSH to GSSG (GSH/GSSG) and (H) GSH to cysteine glutathione disulfide (GSH/CYSSG). **, P < 0.01; ***, P < 0.001. Acti, actinonin; CE, cholesterol ester; DG, diacylglycerol; Dox, doxycycline; MB, MitoBloCK-6; MG, monoacylglycerol; PC, phosphatidylcholine; PE, phosphatidylethanolamine; PG, phosphatidylglycerol; PS, phosphatidylserine.
Figure 6.
Figure 6.
ATF4 mediates the mitochondrial stress response. (A) Consensus de novo motif identified using HOMER software by selecting the common up-regulated genes in all stress conditions. (B) Representation of the common transcription factors that match with the identified motif. (C and D) Venn diagram showing the similarity between the ATF4 targets and the common induced (C) transcripts and (D) proteins reflecting mitochondrial stress in RNA sequencing and proteomics analysis, respectively. ATF4 chromatin immunoprecipitation and sequence: GSE35681. (E) Relative mRNA, represented as normalized counts, and relative protein levels of ATF4. Data were extracted from RNA sequencing and proteomics, respectively. †, P < 0.1; **, P < 0.01; ***, P < 0.001. (F) Transcript levels of ATF4, CHOP (DDIT3), and ASNS upon 6 and 24 h of exposure to the different mitochondrial stressors. Expression of ATF4 and its targets genes showed time-dependence activation. **, P < 0.01; ***, P < 0.001 relative to control at 6 h; $, P < 0.001 relative to control at 24 h. (G) mRNA expression analysis of ATF4 targets, ASNS, CHAC1, PCK2, PSPH, and the ER stress marker BIP upon 6 h of treatment with the different mitochondrial stressors and the ER stressor tunicamycin (Tn at 2.5 µg/ml) in WT and ATF4 LOF HeLa cells. (H) Boxplots showing the basal and ATP-dependent respiration of ATF4 LOF HeLa cells. OCR, oxygen consumption rate. (I) Doubling time in hours of WT and ATF4 LOF HeLa cells. (J) Relative mtDNA levels of WT and ATF4 LOF HeLa cells after 6 d of 50 ng/ml EtBr treatment and 4 d of recovery (10 d). (K) Cumulative cell number of WT and ATF4 LOF HeLa cells treated or untreated with EtBr for 10 d (6 d with EtBr and 4 d of recovery). All experiments were independently performed at least two times, using triplicates for each condition; data are presented as mean ± SEM of a representative experiment, *, P < 0.05; **, P < 0.01; ***, P < 0.001. Acti, actinonin; Dox, doxycycline; MB, MitoBloCK-6.
Figure 7.
Figure 7.
ATF4 is activated through the ISR. (A) Western blot analysis showing the increased phosphorylation of eIF2α (Ser51) upon 6 h of treatment with the different mitochondrial stressors. Bottom, ratio between P-eIF2α and eIF2α total levels. (B and C) mRNA expression analysis of ATF4 and its target genes, CHOP (DDIT3), ASNS, CHAC1, PCK2, and the ER stress marker BIP, upon 6 h of treatment with the different mitochondrial stressors and the ER stressor tunicamycin (Tn at 2.5 µg/ml) in HeLa cells, together with the inhibitor of the integrated stress response (ISRIB at 500 nM). (D) Boxplots showing an increase in basal and ATP-dependent respiration of HeLa cells treated with 500 nM of ISRIB for 24 h. OCR: oxygen consumption rate. (E) mRNA expression analysis of eIF2α kinases upon knock down with specific shRNAs. Data are presented as mean ± SEM of two independent shRNAs for each gene. Statistical differences were calculated compared with pLKO1. (F) mRNA expression analysis of ATF4 and some of its target genes upon knock down of the eIF2α kinases and 6 h of treatment with FCCP. Data are presented as mean ± SEM of two independent shRNAs for each gene. No statistical differences were found between the FCCP treated conditions. All experiments were independently performed at least two times, using triplicates for each condition; data are presented as mean ± SEM of a representative experiment; *, P < 0.05; **, P < 0.01; ***, P < 0.001. Acti, actinonin; Dox, doxycycline; MB, MitoBloCK-6.
Figure 8.
Figure 8.
Mitochondrial stress activates the ATF4 pathway in vitro and in vivo independent of the UPRmt. (A) mRNA expression analysis of the ATF4 pathway and UPRmt genes in HeLa cells 6 and 24 h after exposure to paraquat (PQ) at a final concentration of 400 µM. (B and C) Enrichment score plots from GSEA using RNA sequencing data of cells treated with (B) the TRAP1 inhibitor GTPP and (C) the LONP1 inhibitor CDDO (GSE75247). List of mitochondrial stress genes (mt-stress genes; Table S5) was used as the gene set of interest. FDR q-val, false discovery rate adjusted p-value; NES, normalized enrichment score. (D and E) mRNA expression analysis of the ATF4 pathway and UPRmt genes in HeLa cells after (D) knockdown of LONP1 using two different shRNAs and (E) 6 and 24 h of exposure to mdivi-1 at a final concentration of 50 µM. (F and G) Enrichment score plots from GSEA using microarray data (F) from hearts of mice deficient in ClpP (GSE40207) and (G) from brains of mice deficient in Htra2 (GSE13035). NOM p-val, nominal p-value. (H and I) Barcode plot representing the enrichment in the core mt-stress genes in (H) mitochondrial myopathies versus nonmitochondrial myopathies (GSE43698) and (I) myopathies caused by mtDNA deletions (GSE1462). Enrichment was analyzed using the fold-change relative to control. p-value was calculated using the ROAST test, which confirmed that gene expression changes induced by mitochondrial stress significantly correlate with gene expression changes caused by mitochondrial myopathies. Black vertical lines represent the genes, and the red and blue rectangles the cutoff for the up-regulated and down-regulated genes, respectively. The black horizontal line indicates neutral enrichment, whereas the red line shows the enrichment of up-regulated genes. All experiments were independently performed at least two times, using triplicates for each condition; data are presented as mean ± SEM of a representative experiment; *, P < 0.05; **, P < 0.01; ***, P < 0.001.
Figure 9.
Figure 9.
Genetic link of ATF4 and mitochondrial stress in human and mouse populations. (A and B) Multitissue correlation network analysis of transcript levels across (A) 49 human tissues in GTEx and (B) 16 mouse tissues in the BXD genetic reference population. Nodes represent the transcripts and the width of the ties among nodes indicate the probability to show a significant positive correlation in all tissues analyzed. The human network shows a tighter clustering likely caused by the higher number of tissues and samples per tissues. (C) Heatmap representing the KEGG pathway analysis of the top negative-correlated genes across 49 tissues using GTEx data for ATF4, CEBPB, DDIT3/CHOP, and NCOR1 (used as positive control). Color key represents the negative logarithm base 10 of the p-value of each pathway obtained in the analysis. (D) Expression quantitative trait locus (eQTL) analysis of Atf4 transcript level in the prefrontal cortex. The yellow mark represents the Atf4 gene locus on chromosome 15, whereas the red mark represent the position of a trans-eQTL for Atf4 expression levels on chromosome 1 (Chr1). (E) eQTL mapping of Atf4 transcript levels across several tissues identifies a common and strong trans-eQTL on chromosome 1 (170–180 Mb). (F) Representation of the chromosome 1 locus (170–180 Mb) containing 142 genes, 6 of which have nonsynonymous substitutions, and only one gene, Fh1, encodes a mitochondrial protein. Fh1 contains a nonsynonymous sequence variant (A296T; rs32536342) that segregates in the BXDs. This sequence variant regulates the expression levels of Fh1, which in turn regulates Atf4 expression. (G) mRNA expression analysis of HeLa cells after knockdown of fumarate hydratase (shFH) and treatment with monomethyl fumarate at 2.5 mM for 24 h. Data are presented as mean ± SEM; *, P < 0.05; **, P < 0.01; ***, P < 0.001. (H) Enrichment score plot from GSEA using microarray data from renal cysts of mice with renal tubule specific inactivation of Fh1 (Fh1−/−; GSE29988). The list of mitochondrial stress genes (mt-stress genes; Table S5) was used as the gene set of interest. FDR q-val: false discovery rate adjusted p-value; NES, normalized enrichment score; NOM p-val, nominal p-value. (I) Scheme summarizing our working hypothesis. Mitochondrial stress stimulates the phosphorylation of the eIF2α, which inhibits cytosolic translation and activates the ATF4 pathway. At the same time, mitochondrial stress also reduces the expression of MRPs to inhibit mitochondrial translation and protect mitochondrial function.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Adam J., Hatipoglu E., O’Flaherty L., Ternette N., Sahgal N., Lockstone H., Baban D., Nye E., Stamp G.W., Wolhuter K., et al. . 2011. Renal cyst formation in Fh1-deficient mice is independent of the Hif/Phd pathway: Roles for fumarate in KEAP1 succination and Nrf2 signaling. Cancer Cell. 20:524–537. 10.1016/j.ccr.2011.09.006 - DOI - PMC - PubMed
    1. Anders S., Pyl P.T., and Huber W.. 2015. HTSeq: A Python framework to work with high-throughput sequencing data. Bioinformatics. 31:166–169. 10.1093/bioinformatics/btu638 - DOI - PMC - PubMed
    1. Andreux P.A., Williams E.G., Koutnikova H., Houtkooper R.H., Champy M.F., Henry H., Schoonjans K., Williams R.W., and Auwerx J.. 2012. Systems genetics of metabolism: the use of the BXD murine reference panel for multiscalar integration of traits. Cell. 150:1287–1299. 10.1016/j.cell.2012.08.012 - DOI - PMC - PubMed
    1. Andreux P.A., Houtkooper R.H., and Auwerx J.. 2013. Pharmacological approaches to restore mitochondrial function. Nat. Rev. Drug Discov. 12:465–483. 10.1038/nrd4023 - DOI - PMC - PubMed
    1. Andreux P.A., Mouchiroud L., Wang X., Jovaisaite V., Mottis A., Bichet S., Moullan N., Houtkooper R.H., and Auwerx J.. 2014. A method to identify and validate mitochondrial modulators using mammalian cells and the worm C. elegans. Sci. Rep. 4:5285 10.1038/srep05285 - DOI - PMC - PubMed

Publication types

MeSH terms