Mitochondria are epicentres of eukaryotic metabolism and bioenergetics. Pioneering efforts in recent decades have established the core protein componentry of these organelles1 and have linked their dysfunction to more than 150 distinct disorders2,3. Still, hundreds of mitochondrial proteins lack clear functions4, and the underlying genetic basis for approximately 40% of mitochondrial disorders remains unresolved5. Here, to establish a more complete functional compendium of human mitochondrial proteins, we profiled more than 200 CRISPR-mediated HAP1 cell knockout lines using mass spectrometry-based multiomics analyses. This effort generated approximately 8.3 million distinct biomolecule measurements, providing a deep survey of the cellular responses to mitochondrial perturbations and laying a foundation for mechanistic investigations into protein function. Guided by these data, we discovered that PIGY upstream open reading frame (PYURF) is an S-adenosylmethionine-dependent methyltransferase chaperone that supports both complex I assembly and coenzyme Q biosynthesis and is disrupted in a previously unresolved multisystemic mitochondrial disorder. We further linked the putative zinc transporter SLC30A9 to mitochondrial ribosomes and OxPhos integrity and established RAB5IF as the second gene harbouring pathogenic variants that cause cerebrofaciothoracic dysplasia. Our data, which can be explored through the interactive online MITOMICS.app resource, suggest biological roles for many other orphan mitochondrial proteins that still lack robust functional characterization and define a rich cell signature of mitochondrial dysfunction that can support the genetic diagnosis of mitochondrial diseases.
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All associated mass spectrometry RAW files and search results were deposited into the MassIVE data repository under accession #MSV000086685. All associated code for data processing, analysis, and the companion webtool can be found in the GitHub repository https://github.com/coongroup/MITOMICS. Other relevant data are available from the corresponding authors upon reasonable request. HAP1 KO cell lines are available from Horizon Discovery.
Calvo, S. E. & Mootha, V. K. The mitochondrial proteome and human disease. Annu. Rev. Genomics Hum. Genet. 11, 25–44 (2010).
Vafai, S. B. & Mootha, V. K. Mitochondrial disorders as windows into an ancient organelle. Nature 491, 374–383 (2012).
Nunnari, J. & Suomalainen, A. Mitochondria: in sickness and in health. Cell 148, 1145–1159 (2012).
Pagliarini, D. et al. A mitochondrial protein compendium elucidates complex I disease biology. Cell 134, 112–123 (2008).
Frazier, A. E., Thorburn, D. R. & Compton, A. G. Mitochondrial energy generation disorders: genes, mechanisms, and clues to pathology. J. Biol. Chem. 294, 5386–5395 (2019).
Lane, N. & Martin, W. The energetics of genome complexity. Nature 467, 929–934 (2010).
Weissig, V. Drug development for the therapy of mitochondrial diseases. Trends Mol. Med. 26, 40–57 (2020).
Sung, A. Y., Floyd, B. J. & Pagliarini, D. J. Systems biochemistry approaches to defining mitochondrial protein function. Cell Metab. 31, 669–678 (2020).
Hebert, A. S. et al. The one hour yeast proteome. Mol. Cell. Proteomics 13, 339–347 (2014).
Hutchins, P. D., Russell, J. D. & Coon, J. J. LipiDex: an integrated software package for high-confidence lipid identification. Cell Syst. 6, 621–625 (2018).
Peterson, A. C. et al. Development of a GC/quadrupole-Orbitrap mass spectrometer, part I: design and characterization. Anal. Chem. 86, 10036–10043 (2014).
Stefely, J. A. et al. Mitochondrial protein functions elucidated by multi-omic mass spectrometry profiling. Nat. Biotechnol. 34, 1191–1197 (2016).
Phang, J. M., Liu, W., Hancock, C. & Christian, K. J. The proline regulatory axis and cancer. Front. Oncol. 2, 60 (2012).
Tran, D. H. et al. Mitochondrial NADP+ is essential for proline biosynthesis during cell growth. Nat. Metab. 3, 571–585 (2021).
Zhu, J. et al. Mitochondrial NADP(H) generation is essential for proline biosynthesis. Science 372, 968–972 (2021).
Xu, Y., Malhotra, A., Ren, M. & Schlame, M. The enzymatic function of tafazzin. J. Biol. Chem. 281, 39217–39224 (2006).
Kerner, J. & Hoppel, C. Fatty acid import into mitochondria. Biochim. Biophys. Acta 1486, 1–17 (2000).
Santel, A. & Fuller, M. T. Control of mitochondrial morphology by a human mitofusin. J. Cell Sci. 114, 867–874 (2001).
Eramo, M. J., Lisnyak, V., Formosa, L. E. & Ryan, M. T. The ‘mitochondrial contact site and cristae organising system’ (MICOS) in health and human disease. J. Biochem. 167, 243–255 (2020).
Niemi, N. M. et al. Pptc7 is an essential phosphatase for promoting mammalian mitochondrial metabolism and biogenesis. Nat. Commun. 10, 3197 (2019).
Calvo, S. E., Clauser, K. R. & Mootha, V. K. MitoCarta2.0: an updated inventory of mammalian mitochondrial proteins. Nucleic Acids Res. 44, D1251–D1257 (2016).
Rhee, H. W. et al. Proteomic mapping of mitochondria in living cells via spatially restricted enzymatic tagging. Science 339, 1328–1331 (2013).
Greber, B. J. & Ban, N. Structure and function of the mitochondrial ribosome. Annu. Rev. Biochem. 85, 103–132 (2016).
Perez, Y. et al. SLC30A9 mutation affecting intracellular zinc homeostasis causes a novel cerebro-renal syndrome. Brain 140, 928–939 (2017).
Ghandi, M. et al. Next-generation characterization of the Cancer Cell Line Encyclopedia. Nature 569, 503–508 (2019).
Floyd, B. J. et al. Mitochondrial protein interaction mapping identifies regulators of respiratory chain function. Mol. Cell 63, 621–632 (2016).
Gopalakrishna, S. et al. C6orf203 is an RNA-binding protein involved in mitochondrial protein synthesis. Nucleic Acids Res. 47, 9386–9399 (2019).
Stefely, J. A. & Pagliarini, D. J. Biochemistry of mitochondrial coenzyme Q biosynthesis. Trends Biochem. Sci. 42, 824–843 (2017).
Thompson, K. et al. Recent advances in understanding the molecular genetic basis of mitochondrial disease. J. Inherit. Metab. Dis. 43, 36–50 (2020).
Arroyo, J. D. et al. A genome-wide CRISPR death screen identifies genes essential for oxidative phosphorylation. Cell Metab. 24, 875–885 (2016).
Mimaki, M., Wang, X., McKenzie, M., Thorburn, D. R. & Ryan, M. T. Understanding mitochondrial complex I assembly in health and disease. Biochim. Biophys. Acta 1817, 851–862 (2012).
Murakami, Y. et al. The initial enzyme for glycosylphosphatidylinositol biosynthesis requires PIG-Y, a seventh component. Mol. Biol. Cell 16, 5236–5246 (2005).
Li, W., Shi, Y., Zhang, T., Ye, J. & Ding, J. Structural insight into human N6AMT1–Trm112 complex functioning as a protein methyltransferase. Cell Discov. 5, 51 (2019).
Lapointe, C. P. et al. Multi-omics reveal specific targets of the RNA-binding protein Puf3p and its orchestration of mitochondrial biogenesis. Cell Syst. 6, 125–135 (2017).
Veling, M. T. et al. Multi-omic mitoprotease profiling defines a role for Oct1p in coenzyme Q production. Mol. Cell 68, 970–977 (2017).
Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (2016).
1000 Genomes Project Consortium. A global reference for human genetic variation. Nature 526, 68–74 (2015).
Karczewski, K. J. et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature 581, 434–443 (2020).
Adzhubei, I. A. et al. A method and server for predicting damaging missense mutations. Nat. Methods 7, 248–249 (2010).
Kircher, M. et al. A general framework for estimating the relative pathogenicity of human genetic variants. Nat. Genet. 46, 310–315 (2014).
Ng, P. C. & Henikoff, S. SIFT: predicting amino acid changes that affect protein function. Nucleic Acids Res. 31, 3812–3814 (2003).
Campello, R. J. G. B., Moulavi, D. & Sander, J. in Advances in Knowledge Discovery and Data Mining. PAKDD 2013. Lecture Notes in Computer Science vol. 7819 (eds. Pei, J. et al.) 160–172 (Springer, 2013).
Zhang, Z. et al. Molecular cloning, expression patterns and subcellular localization of porcine TMCO1 gene. Mol. Biol. Rep. 37, 1611–1618 (2010).
McGilvray, P. T. et al. An ER translocon for multi-pass membrane protein biogenesis. eLife 9, e56889 (2020).
Wang, Q. C. et al. TMCO1 is an ER Ca2+ load-activated Ca2+ channel. Cell 165, 1454–1466 (2016).
Moutaoufik, M. T. et al. Rewiring of the human mitochondrial interactome during neuronal reprogramming reveals regulators of the respirasome and neurogenesis. iScience 19, 1114–1132 (2019).
Alanay, Y. et al. TMCO1 deficiency causes autosomal recessive cerebrofaciothoracic dysplasia. Am. J. Med. Genet. A 164A, 291–304 (2014).
Schlieben, L. D. & Prokisch, H. The dimensions of primary mitochondrial disorders. Front. Cell Dev. Biol. 8, 600079 (2020).
We thank J. Stefely for suggestions on experimental design, B. Floyd and N. Niemi for advice on knockout target selection, M. McDevitt and J. Stefely for guidance on lipid extraction, Z. Baker for data observations and A. Bartlett, M. Stefely, A. Sung and S. Hwang for graphic contributions. We thank Y. Murakami and T. Kinoshita (Research Institute for Microbial Diseases, Osaka University, Osaka, Japan) for providing the pRL-CMV-PreYF-PIG-YF expression constructs, J. Fan for advice on metabolite extraction and Y. Sancak for advice on cellular calcium analysis. This work was supported by NIH awards R35 GM131795 (D.J.P.), P41 GM108538 (J.J.C. and D.J.P.) and U54 AI117924 (Y.S. and M.C.); a UW2020 award (D.J.P. and J.J.C.); funds from the BJC Investigator Program (D.J.P.); and a grant from the Scientific and Technological Research Council of Turkey, 108S420 (N.A.A.) under the framework of ERA-NET for Research on Rare Disease, CRANIRARE Consortium (R07197KS). R.W.T. was supported by the Wellcome Centre for Mitochondrial Research (203105/Z/16/Z), the Medical Research Council International Centre for Genomic Medicine in Neuromuscular Disease (MR/S005021/1), the UK NIHR Biomedical Research Centre for Ageing and Age-related Disease award to the Newcastle upon Tyne Foundation Hospitals NHS Trust, the Mitochondrial Disease Patient Cohort (UK) (G0800674), the Lily Foundation, the Pathological Society and the NHS Highly Specialised Service for Rare Mitochondrial Disorders.
J.J.C. is a consultant for Thermo Fisher Scientific.
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Extended data figures and tables
a, Criteria and filtering approach for knockout (KO) target selection. b, Features of each gene target and their representation in other select large-scale analyses at the time of selection. Metrics were taken from OMIM (omim.org), NCBI HomoloGene (ncbi.nlm.nih.gov/homologene), TMHMM (PMID: 11152613), The BioPlex Interactome (PMID: 28514442), The Y3K Project (PMID: 27669165), and Floyd et al., 2016 (PMID: 27499296). c, PubMed citations versus NCBI GeneRIFs (References Into Function) for each gene target at the time of selection. d, Cell density of wild-type (WT) reference cells across each analysis batch that were used to normalize cell growth measurements (mean ± s.d., n = 3-4). e, Relative cell density of each KO cell line compared to WT cells versus statistical significance (mean, n = 3-4, two-sided Welch’s t-test). f, Distribution of % relative standard deviation (% RSD) of molecular abundance measurements made in 3-4 replicates of the KO cell lines. g, Distribution of log2 range in measured molecular abundances of all analytes calculated by subtracting the minimum observed intensity from the maximum observed intensity of each molecule across all cell lines. h, Histogram illustrating the count of quantitative measurements made per protein group across all analyzed samples.
a-e, Relative molecule abundance (protein, lipid, or metabolite) in the indicated KO compared to WT versus statistical significance, relative molecule abundance in KO versus KO compared to WT, or relative abundance of an individual molecule versus statistical significance across all KO lines with an accompanying summary of our observations. Data displayed as mean, n = 3-4, and two-sided Welch’s t-test for all panels.
a, Relative protein abundance in HAP1 MRPS22KO cells versus SLC30A9KO cells compared to WT cells with mitoribosome, OxPhos, and mtDNA-encoded proteins highlighted. Data displayed as mean, n = 3-4, and two-sided Welch’s t-test. b, Level of mtDNA-encoded MT-CO2 and mitoribosome proteins in the indicated KO cell lines as assessed by immunoblotting. c, Level of the indicated proteins in HAP1 WT and SLC30A9 c.1047_1049delGCA knock-in cells (two clones) as assessed by immunoblotting. d, e, Gene correlations with SLC30A9 in DepMap project RNAi (d) and CRISPR (e) datasets with genes encoding mitochondrial and mitoribosome proteins highlighted and the top three GO annotations (most specific subclass term within a functional class) in each category for the 100 highest-ranking genes. f, Meta-analysis of protein-protein interaction data from Floyd et al., 2016 (PMID: 27499296) 26 displaying the two bait proteins (out of 78) that interacted with SLC30A9 and the top 2% of their interactors with mitoribosome core subunits, mitoribosome assembly factors, and zinc cofactor binding proteins (based on UniProt annotations) highlighted. For western source data, see Supplementary Figure 1.
a-e, Relative protein abundance in the indicated KO compared to WT versus statistical significance, relative protein abundance in KO versus KO compared to WT, or relative abundance of an individual protein versus statistical significance across all KO lines with an accompanying summary of our observations. Data displayed as mean, n = 3-4, and two-sided Welch’s t-test for all panels.
a, b, Relative abundance of CoQ10 (a) and NDUFS3 (b) versus statistical significance across all KO lines. c, Relative abundance of dihydroorotate (DHO) and CoQ10 in the indicated KO cell lines compared to WT cells. (a-c) Data displayed as mean, n = 3-4, and two-sided Welch’s t-test. d, Level of complex I (CI), CI-assembly factor, and CoQ biosynthetic proteins in the indicated KO cell lines as assessed by immunoblotting. e, Relative abundance of CoQ10 and biosynthetic pathway intermediates analyzed via targeted LC-MS (mean, n = 3-4, two-sided Welch’s t-test). PPHB, polyprenyl-hydroxybenzoate; DMQ, demethoxy-coenzyme Q; DMeQ, demethyl-coenzyme Q. f, CoQ biosynthesis pathway following polyisoprenoid tail attachment. Molecules quantified in (e) are indicated in red. 4-HB, 4-hydroxybenzoate; PPDHB, polyprenyl-dihydroxybenzoate; PPVA, polyprenyl-vanillic acid; DDMQ, demethoxy-demethyl-coenzyme Q. Supportive role for reactions is indicated by ‘+’ symbol next to arrows. g, Level of the indicated transcripts in 293 cells treated with siRNA for five days as assessed by qPCR (mean ± s.d., n = 3). h, Level of COQ5 and NDUFAF5 in mouse C2C12 cells treated with the indicated siRNAs for five days as assessed by immunoblotting. i, Relative abundance of protein interactors for WT PYURF compared to maltose-binding protein (MBP) captured from a HAP1 mitochondrial lysate detected via immunoprecipitation (IP)-LC-MS/MS analysis (mean, n = 3, two-sided Student’s t-test). j, Purity of NDUFAF5, WT PYURF, c.289_290dup patient variant, and point mutants analyzed via SDS-PAGE and Coomassie stain. k, Melting temperature of NDUFAF5 with combinations of WT PYURF or c.289_290dup mutant PYURF, peptide, and S-adenosylmethionine (SAM) compared to NDUFAF5 only as measured by differential scanning fluorimetry (mean ± s.d., n = 3). For western and gel source data, see Supplementary Figure 1.
a, First-derivative plots of the differential scanning fluorimetry analysis in Fig. 3h (n = 3).
Extended Data Fig. 7 PYURF (NDUFAFQ) is important for mitochondrial function and disrupted in human disease.
a, b, Level of the indicated proteins in 293 cells during a cyclohexamide chase experiment following PYURF knockdown (a), and quantification of the immunoblot data (b). c, Level of assembled complex I in HAP1 WT and PYURFKO cells as assessed by BN-PAGE and immunoblotting. d, Parameters of mitochondrial function for WT and PYURFKO cells calculated from the mitochondrial stress test assay in Fig. 3j (mean ± s.d., n = 10-14, two-sided Student’s t-test). e, Brian MRI of the PYURF case demonstrating increased extra axial CSF spaces, cystic high signal cerebellar white-matter, cerebellar atrophy, and decreased myelination in the internal capsule. f, Whole exome sequencing analysis and filtering for rare, autosomal recessive variants in nuclear genes encoding mitochondrial proteins. MAF, minor allele frequency. g, Level of the indicated proteins in HAP1 unedited PYURF WT cells and PYURF c.289_290dup knock-in cells (two clones each) as assessed by immunoblotting. For western source data, see Supplementary Figure 1.
a-c, t-SNE analysis of the MITOMICS data (mean log2 fold-changes and associated multi-ome q-values from 191 conditions) displaying all molecules (a), core clusters (b), and extended clusters (c). d-k, Analysis of the MXP KO targets in the t-SNE plot to identify proteins that fall within their close proximity (one unit radius) with accompanying summaries of our observations.
a, Relative protein abundance in RAB5IFKO1 cells versus RAB5IFKO2 cells compared to WT. b, Relative abundance of indicated proteins across all KO lines. (a, b) Data displayed as mean, n = 3-4, and two-sided Welch’s t-test. c, Protein correlations with RAB5IF versus protein correlations with TMCO1 in the DepMap proteomics dataset. d, Level of the indicated proteins in HAP1 WT and RAB5IF KO cells assessed by immunoblotting. e, f, Fura-2 fluorescence (mean, n = 3) following thapsigargin (TG) treatment in WT and RAB5IF KO cells (e), and HeLa cells treated with indicated siRNAs for three days (f) with area under the curve (AUC) measurements. g, RAB5IF and TMCO1 levels in HeLa cells treated with indicated siRNAs for three days assessed by immunoblotting. h, Homozygosity mapping of a 24.7 Mbp candidate region in chromosome 20p11.23-q13.12. Homozygous genotypes in the index (III:8) shown in blue. In other individuals, identical homozygous genotypes are also in blue, whereas contrasting homozygous genotypes are in white. Heterozygous genotypes are orange, while non-informative genotypes resulting from heterozygous SNPs in parent-child trios are yellow. Note that the index is homozygous for the candidate region, while the cousin (III:1) is heterozygous for the entire region. i, Sanger sequencing showing the c.75G > A (p.Trp25*) mutation as homozygous in the index (III:8) and heterozygous in his father (II:7). j, Pedigree of affected family with genotypes and associated phenotypes. Note that individuals II:7 and III:1 only have cleft lip and/or palate without other clinical features of CFSMR and are heterozygous for the RAB5IF variant. k, l, Indicated protein levels in HAP1 WT and RAB5IF c.75G > A knock-in cells (2 clones) (k), and normal adult human primary dermal fibroblasts (HDFa) and primary patient fibroblasts with the RAB5IF c.75G > A mutation (l) assessed by immunoblotting. For western source data, see Supplementary Figure 1.
a supplementary table guide and Supplementary Fig. 1, which shows uncropped versions of the cropped blots and gels in this study.
KO targets, cell density measurements, KO cell class categories and level of KO protein target in respective KO cells.
MITOMICS quantitative dataset (log2 biomolecule intensities).
MITOMICS quantitative dataset (log2 fold changes, s.d. and P values).
IP–MS PYURF interactome.
KO-specific phenotype (outlier) analysis results.
KO cell line and reagent lists.
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Rensvold, J.W., Shishkova, E., Sverchkov, Y. et al. Defining mitochondrial protein functions through deep multiomic profiling. Nature 606, 382–388 (2022). https://doi.org/10.1038/s41586-022-04765-3
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