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Quantitative subcellular reconstruction reveals a lipid mediated inter-organelle biogenesis network

Nature Cell Biology volume 26, pages 57–71 (2024)Cite this article

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Abstract

The structures and functions of organelles in cells depend on each other but have not been systematically explored. We established stable knockout cell lines of peroxisomal, Golgi and endoplasmic reticulum genes identified in a whole-genome CRISPR knockout screen for inducers of mitochondrial biogenesis stress, showing that defects in peroxisome, Golgi and endoplasmic reticulum metabolism disrupt mitochondrial structure and function. Our quantitative total-organelle profiling approach for focussed ion beam scanning electron microscopy revealed in unprecedented detail that specific organelle dysfunctions precipitate multi-organelle biogenesis defects, impair mitochondrial morphology and reduce respiration. Multi-omics profiling showed a unified proteome response and global shifts in lipid and glycoprotein homeostasis that are elicited when organelle biogenesis is compromised, and that the resulting mitochondrial dysfunction can be rescued with precursors for ether-glycerophospholipid metabolic pathways. This work defines metabolic and morphological interactions between organelles and how their perturbation can cause disease.

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Fig. 1: Whole-genome screening for biogenesis stress responses.
Fig. 2: Organelle defects cause cell-wide morphology changes.
Fig. 3: Morphometric profiling reveals unified mitochondrial morphology defects across peroxisome, Golgi and ER dysfunction.
Fig. 4: Organelle defects alter cell-wide organelle interactions and communication.
Fig. 5: Organelle defects trigger an inter-organelle biogenesis response in the proteome.
Fig. 6: Defects in ether-lipid metabolism underlie the global morphological and functional changes in mitochondria, peroxisomes, ER and Golgi.
Fig. 7: Ether-lipid supplementation rescues fragmented mitochondrial morphology in cells with defects in peroxisome, Golgi and ER biogenesis.

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Data availability

Genomic data were deposited to the Gene Expression Omnibus under the accession code GSE201573 and the mass spectrometry proteomics data were deposited to ProteomeXchange (http://proteomecentral.proteomexchange.org) via the PRIDE partner repository project accession code PXD033871. Lipidomic datasets were uploaded to Metabolomics Workbench and can be accessed directly via its project: https://doi.org/10.21228/M8143Q. N- and O-glycome raw data files are available via GlycoPost92 under the identifier GPST000271. Data related to FIB-SEM imaging and AIVE analysis are available via EMPIAR93 with the public accession code EMPIAR-11695. Source data are provided with this paper. All other data supporting the findings of this study are available from the corresponding author on reasonable request and material requests are subject to a material transfer agreement mandated by our institutions.

References

  1. Rackham, O. & Filipovska, A. Organization and expression of the mammalian mitochondrial genome. Nat. Rev. Genet. 23, 606–623 (2022).

    Article  CAS  PubMed  Google Scholar 

  2. Morré, D. J., Merritt, W. D. & Lembi, C. A. Connections between mitochondria and endoplasmic reticulum in rat liver and onion stem. Protoplasma 73, 43–49 (1971).

    Article  PubMed  Google Scholar 

  3. Mattie, S., Krols, M. & McBride, H. M. The enigma of an interconnected mitochondrial reticulum: new insights into mitochondrial fusion. Curr. Opin. Cell Biol. 59, 159–166 (2019).

    Article  CAS  PubMed  Google Scholar 

  4. Murley, A. & Nunnari, J. The emerging network of mitochondria–organelle contacts. Mol. Cell 61, 648–653 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Rowland, A. A. & Voeltz, G. K. Endoplasmic reticulum–mitochondria contacts: function of the junction. Nat. Rev. Mol. Cell Biol. 13, 607–625 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Schwarz, D. S. & Blower, M. D. The endoplasmic reticulum: structure, function and response to cellular signaling. Cell. Mol. Life Sci. 73, 79–94 (2016).

    Article  CAS  PubMed  Google Scholar 

  7. Scharwey, M., Tatsuta, T. & Langer, T. Mitochondrial lipid transport at a glance. J. Cell Sci. 126, 5317–5323 (2013).

    CAS  PubMed  Google Scholar 

  8. Tatsuta, T., Scharwey, M. & Langer, T. Mitochondrial lipid trafficking. Trends Cell Biol. 24, 44–52 (2014).

    Article  CAS  PubMed  Google Scholar 

  9. Dimitrov, L., Lam, S. K. & Schekman, R. The role of the endoplasmic reticulum in peroxisome biogenesis. Cold Spring Harb. Perspect. Biol. 5, a013243 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Jan, C. H., Williams, C. C. & Weissman, J. S. Principles of ER cotranslational translocation revealed by proximity-specific ribosome profiling. Science 346, 1257521 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  11. Sugiura, A., Mattie, S., Prudent, J. & McBride, H. M. Newly born peroxisomes are a hybrid of mitochondrial and ER-derived pre-peroxisomes. Nature 542, 251–254 (2017).

    Article  CAS  PubMed  Google Scholar 

  12. Fagone, P. & Jackowski, S. Membrane phospholipid synthesis and endoplasmic reticulum function. J. Lipid Res. 50, S311–S316 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Jiang, S. et al. TEFM regulates both transcription elongation and RNA processing in mitochondria. EMBO Rep. 20, e48101 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Kühl, I. et al. Transcriptomic and proteomic landscape of mitochondrial dysfunction reveals secondary coenzyme Q deficiency in mammals. eLife 6, e30952 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Perks, K. L. et al. PTCD1 is required for 16S rRNA maturation complex stability and mitochondrial ribosome assembly. Cell Rep. 23, 127–142 (2018).

    Article  CAS  PubMed  Google Scholar 

  16. Rackham, O. et al. Hierarchical RNA processing is required for mitochondrial ribosome assembly. Cell Rep. 16, 1874–1890 (2016).

    Article  CAS  PubMed  Google Scholar 

  17. Rudler, D. L. et al. Fidelity of translation initiation is required for coordinated respiratory complex assembly. Sci. Adv. 5, eaay2118 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Siira, S. J. et al. Concerted regulation of mitochondrial and nuclear non‐coding RNAs by a dual‐targeted RNase Z. EMBO Rep. 19, e46198 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Shalem, O. et al. Genome-scale CRISPR–Cas9 knockout screening in human cells. Science 343, 84–87 (2014).

    Article  CAS  PubMed  Google Scholar 

  20. Rath, S. et al. MitoCarta3.0: an updated mitochondrial proteome now with sub-organelle localization and pathway annotations. Nucleic Acids Res. 49, D1541–D1547 (2021).

    Article  CAS  PubMed  Google Scholar 

  21. Thul, P. J. & Lindskog, C. The human protein atlas: a spatial map of the human proteome. Protein Sci. 27, 233–244 (2018).

    Article  CAS  PubMed  Google Scholar 

  22. Schlüter, A., Real-Chicharro, A., Gabaldón, T., Sánchez-Jiménez, F. & Pujol, A. PeroxisomeDB 2.0: an integrative view of the global peroxisomal metabolome. Nucleic Acids Res. 38, D800–D805 (2010).

    Article  PubMed  Google Scholar 

  23. Li, W. et al. MAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens. Genome Biol. 15, 554 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Matsumoto, N., Tamura, S. & Fujiki, Y. The pathogenic peroxin Pex26p recruits the Pex1p–Pex6p AAA ATPase complexes to peroxisomes. Nat. Cell Biol. 5, 454–460 (2003).

    Article  CAS  PubMed  Google Scholar 

  25. Diao, A., Rahman, D., Pappin, D. J. C., Lucocq, J. & Lowe, M. The coiled-coil membrane protein golgin-84 is a novel rab effector required for Golgi ribbon formation. J. Cell Biol. 160, 201–212 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Gaudet, P., Livstone, M. S., Lewis, S. E. & Thomas, P. D. Phylogenetic-based propagation of functional annotations within the Gene Ontology consortium. Brief. Bioinform. 12, 449–462 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  27. Lang, S. et al. Different effects of Sec61α, Sec62 and Sec63 depletion on transport of polypeptides into the endoplasmic reticulum of mammalian cells. J. Cell Sci. 125, 1958–1969 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Geisbrecht, B. V., Collins, C. S., Reuber, B. E. & Gould, S. J. Disruption of a PEX1–PEX6 interaction is the most common cause of the neurologic disorders Zellweger syndrome, neonatal adrenoleukodystrophy, and infantile Refsum disease. Proc. Natl Acad. Sci. USA 95, 8630–8635 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Nguyen, T. N. et al. ATG4 family proteins drive phagophore growth independently of the LC3/GABARAP lipidation system. Mol. Cell 81, 2013–2030 (2021).

    Article  CAS  PubMed  Google Scholar 

  30. Heinrich, L. et al. Whole-cell organelle segmentation in volume electron microscopy. Nature 599, 141–146 (2021).

    Article  CAS  PubMed  Google Scholar 

  31. Parlakgül, G. et al. Regulation of liver subcellular architecture controls metabolic homeostasis. Nature 603, 736–742 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Satopaa, V., Albrecht, J., Irwin, D. & Raghavan, B. Finding a ‘kneedle’ in a haystack: detecting knee points in system behavior. In 2011 31st International Conference on Distributed Computing Systems Workshops 166–171 (IEEE, 2011); https://doi.org/10.1109/ICDCSW.2011.20

  33. Jing, J., Liu, G., Huang, Y. & Zhou, Y. A molecular toolbox for interrogation of membrane contact sites. J. Physiol. 598, 1725–1739 (2020).

    Article  CAS  PubMed  Google Scholar 

  34. Cieri, D. et al. SPLICS: a split green fluorescent protein-based contact site sensor for narrow and wide heterotypic organelle juxtaposition. Cell Death Differ. 25, 1131–1145 (2018).

    Article  CAS  PubMed  Google Scholar 

  35. Braschi, E. et al. Vps35 mediates vesicle transport between the mitochondria and peroxisomes. Curr. Biol. 20, 1310–1315 (2010).

    Article  CAS  PubMed  Google Scholar 

  36. Ferreira, N. et al. Stress signaling and cellular proliferation reverse the effects of mitochondrial mistranslation. EMBO J. 38, e102155 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Richman, T. R. et al. Mitochondrial mistranslation modulated by metabolic stress causes cardiovascular disease and reduced lifespan. Aging Cell 20, e13408 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Shum, E. Y. et al. The antagonistic gene paralogs Upf3a and Upf3b govern nonsense-mediated RNA decay. Cell 165, 382–395 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Kooijman, E. E., Chupin, V., de Kruijff, B. & Burger, K. N. J. Modulation of membrane curvature by phosphatidic acid and lysophosphatidic acid. Traffic 4, 162–174 (2003).

    Article  CAS  PubMed  Google Scholar 

  40. Hayashi, H. & Oohashi, M. Incorporation of acetyl-CoA generated from peroxisomal β-oxidation into ethanolamine plasmalogen of rat liver. Biochim. Biophys. Acta 1254, 319–325 (1995).

    Article  PubMed  Google Scholar 

  41. Lodhi, I. J. & Semenkovich, C. F. Peroxisomes: a nexus for lipid metabolism and cellular signaling. Cell Metab. 19, 380–392 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Jiménez-Rojo, N. & Riezman, H. On the road to unraveling the molecular functions of ether lipids. FEBS Lett. 593, 2378–2389 (2019).

    Article  PubMed  Google Scholar 

  43. Sano, R. et al. GM1-ganglioside accumulation at the mitochondria-associated ER membranes links ER stress to Ca2+-dependent mitochondrial apoptosis. Mol. Cell 36, 500–511 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Abrahams, J. L., Campbell, M. P. & Packer, N. H. Building a PGC-LC-MS N-glycan retention library and elution mapping resource. Glycoconj. J. 35, 15–29 (2018).

    Article  CAS  PubMed  Google Scholar 

  45. Balgoma, D. & Hedeland, M. Etherglycerophospholipids and ferroptosis: structure, regulation, and location. Trends Endocrinol. Metab. 32, 960–962 (2021).

    Article  CAS  PubMed  Google Scholar 

  46. Dean, J. M. & Lodhi, I. J. Structural and functional roles of ether lipids. Protein Cell 9, 196–206 (2018).

    Article  CAS  PubMed  Google Scholar 

  47. Eiyama, A., Aaltonen, M. J., Nolte, H., Tatsuta, T. & Langer, T. Disturbed intramitochondrial phosphatidic acid transport impairs cellular stress signaling. J. Biol. Chem. 296, 100335 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Paradies, G., Paradies, V., Ruggiero, F. M. & Petrosillo, G. Role of cardiolipin in mitochondrial function and dynamics in health and disease: molecular and pharmacological aspects. Cells 8, 728 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Vance, J. E. Phospholipid synthesis and transport in mammalian cells. Traffic 16, 1–18 (2015).

    Article  CAS  PubMed  Google Scholar 

  50. MacVicar, T. et al. Lipid signalling drives proteolytic rewiring of mitochondria by YME1L. Nature 575, 361–365 (2019).

    Article  CAS  PubMed  Google Scholar 

  51. Rahim, R. S., Chen, M., Nourse, C. C., Meedeniya, A. C. B. & Crane, D. I. Mitochondrial changes and oxidative stress in a mouse model of Zellweger syndrome neuropathogenesis. Neuroscience 334, 201–213 (2016).

    Article  CAS  PubMed  Google Scholar 

  52. Nuebel, E. et al. The biochemical basis of mitochondrial dysfunction in Zellweger spectrum disorder. EMBO Rep. 22, e51991 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Vincent, A. E. et al. Quantitative 3D mapping of the human skeletal muscle mitochondrial network. Cell Rep. 26, 996–1009 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Neelamegham, S. et al. Updates to the Symbol Nomenclature for Glycans guidelines. Glycobiology 29, 620–624 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10–12 (2011).

    Article  Google Scholar 

  56. Mootha, V. K. et al. PGC-1α-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat. Genet. 34, 267–273 (2003).

    Article  CAS  PubMed  Google Scholar 

  57. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Eden, E., Navon, R., Steinfeld, I., Lipson, D. & Yakhini, Z. GOrilla: a tool for discovery and visualization of enriched GO terms in ranked gene lists. BMC Bioinform. 10, 48 (2009).

    Article  Google Scholar 

  59. Supek, F., Bošnjak, M., Škunca, N. & Šmuc, T. REVIGO summarizes and visualizes long lists of gene ontology terms. PLoS ONE 6, e21800 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Kuznetsova, I., Lugmayr, A., Siira, S. J., Rackham, O. & Filipovska, A. CirGO: an alternative circular way of visualising gene ontology terms. BMC Bioinform. 20, 84 (2019).

    Article  Google Scholar 

  61. Davies, S. M. K. et al. Pentatricopeptide repeat domain protein 3 associates with the mitochondrial small ribosomal subunit and regulates translation. FEBS Lett. 583, 1853–1858 (2009).

    Article  CAS  PubMed  Google Scholar 

  62. Vallese, F. et al. An expanded palette of improved SPLICS reporters detects multiple organelle contacts in vitro and in vivo. Nat. Commun. 11, 6069 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Lee, R. G. et al. Cardiolipin is required for membrane docking of mitochondrial ribosomes and protein synthesis. J. Cell Sci. 133, jcs240374 (2020).

    Article  CAS  PubMed  Google Scholar 

  64. Rackham, O. et al. Pentatricopeptide repeat domain protein 1 lowers the levels of mitochondrial leucine tRNAs in cells. Nucleic Acids Res. 37, 5859–5867 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Legland, D., Arganda-Carreras, I. & Andrey, P. MorphoLibJ: integrated library and plugins for mathematical morphology with ImageJ. Bioinformatics 32, 3532–3534 (2016).

    Article  CAS  PubMed  Google Scholar 

  66. Lehmann, G. & Legland, D. Efficient N-dimensional surface estimation using Crofton formula and run-length encoding. Insight J. https://doi.org/10.54294/wdu86d (2012).

    Article  Google Scholar 

  67. Community, B. O. Blender—a 3D Modelling and Rendering Package (Blender Foundation, 2018).

  68. Krueger, F., James, F., Ewels, P., Afyounian, E. & Schuster-Boeckler, B. FelixKrueger/TrimGalore: v0.6.7—DOI via Zenodo (2021); https://doi.org/10.5281/zenodo.5127899

  69. Andrews, S. FASTQC. A Quality Control Tool for High Throughput Sequence Data (2010).

  70. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    Article  CAS  PubMed  Google Scholar 

  71. Patro, R., Duggal, G., Love, M. I., Irizarry, R. A. & Kingsford, C. Salmon provides fast and bias-aware quantification of transcript expression. Nat. Methods 14, 417–419 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Soneson, C., Love, M. I. & Robinson, M. D. Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Res. 4, 1521 (2015).

    Article  PubMed  Google Scholar 

  73. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  74. Zhu, A., Ibrahim, J. G. & Love, M. I. Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences. Bioinformatics 35, 2084–2092 (2019).

    Article  CAS  PubMed  Google Scholar 

  75. Huang, D. W., Sherman, B. T. & Lempicki, R. A. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 37, 1–13 (2009).

    Article  PubMed  Google Scholar 

  76. Huang, D. W., Sherman, B. T. & Lempicki, R. A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 4, 44–57 (2009).

    Article  CAS  PubMed  Google Scholar 

  77. R: A Language and Environment for Statistical Computing (R Core Team, 2013).

  78. Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).

  79. Vries, A. D. & Ripley, B. Create Dendrograms and Tree Diagrams Using ‘ggplot2’. R package ggdendro version 0.1.22 (2020).

  80. Bruderer, R. et al. Extending the limits of quantitative proteome profiling with data-independent acquisition and application to acetaminophen-treated three-dimensional liver microtissues. Mol. Cell Proteom. 14, 1400–1410 (2015).

    Article  CAS  Google Scholar 

  81. Kuznetsova, I., Lugmayr, A., Rackham, O. & Filipovska, A. OmicsVolcano: software for intuitive visualization and interactive exploration of high-throughput biological data. STAR Protoc. 2, 100279 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Lydic, T. A., Busik, J. V. & Reid, G. E. A monophasic extraction strategy for the simultaneous lipidome analysis of polar and nonpolar retina lipids. J. Lipid Res. 55, 1797–1809 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Hofferek, V., Su, H. & Reid, G. E. in Mass Spectrometry-Based Lipidomics: Methods and Protocols (ed. Hsu, F. -F.) 61–75 (Springer US, 2021); https://doi.org/10.1007/978-1-0716-1410-5_5

  84. Fahy, E. et al. A comprehensive classification system for lipids. J. Lipid Res. 46, 839–862 (2005).

    Article  CAS  PubMed  Google Scholar 

  85. Fahy, E. et al. Update of the LIPID MAPS comprehensive classification system for lipids. J. Lipid Res. 50, S9–S14 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  86. Rustam, Y. H. & Reid, G. E. Analytical challenges and recent advances in mass spectrometry based lipidomics. Anal. Chem. 90, 374–397 (2018).

    Article  CAS  PubMed  Google Scholar 

  87. Jensen, P. H., Karlsson, N. G., Kolarich, D. & Packer, N. H. Structural analysis of N- and O-glycans released from glycoproteins. Nat. Protoc. 7, 1299–1310 (2012).

    Article  CAS  PubMed  Google Scholar 

  88. Moh, E. S. X. et al. Long-term intrathecal administration of morphine vs. baclofen: differences in CSF glycoconjugate profiles using multiglycomics. Glycobiology 32, 50–59 (2022).

    Article  CAS  PubMed  Google Scholar 

  89. Packer, N. H., Lawson, M. A., Jardine, D. R. & Redmond, J. W. A general approach to desalting oligosaccharides released from glycoproteins. Glycoconj. J. 15, 737–747 (1998).

    Article  CAS  PubMed  Google Scholar 

  90. Ashwood, C., Lin, C. -H., Thaysen-Andersen, M. & Packer, N. H. Discrimination of isomers of released N- and O-glycans using diagnostic product ions in negative ion PGC-LC-ESI-MS/MS. J. Am. Soc. Mass. Spectrom. 29, 1194–1209 (2018).

    Article  CAS  PubMed  Google Scholar 

  91. Sanchez, M. I. G. L. et al. RNA processing in human mitochondria. Cell Cycle 10, 2904–2916 (2011).

    Article  CAS  PubMed  Google Scholar 

  92. Watanabe, Y., Aoki-Kinoshita, K. F., Ishihama, Y. & Okuda, S. GlycoPOST realizes FAIR principles for glycomics mass spectrometry data. Nucleic Acids Res. 49, D1523–D1528 (2021).

    Article  PubMed  Google Scholar 

  93. Iudin, A. et al. EMPIAR: the Electron Microscopy Public Image Archive. Nucleic Acids Res. 51, D1503–D1511 (2023).

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

We thank A.-M. Shearwood for genotyping, J. Ermer and J. Matthews for assistance with sample handling during the whole-genome screens, T. Gulati and I. Nikolic (Victorian Centre for Functional Genomics) for advice on CRISPR screening, and S. Paul, P. Meikle and T. Mercer for their valuable advice. The CAL51 Cas9–mCherry stable cell line was a kind gift from V. Wickramasinghe. We thank Microscopy Australia at the Centre for Microscopy, Characterisation and Analysis (The University of Western Australia) for scientific and technical assistance, and Thermo Fisher Scientific for providing the FAIMS Pro interface as part of a collaborative research agreement with G.E.R. This project was supported by fellowships and project grants from the NHMRC, ARC, WACRF and Telethon Trust (to O.R. and A.F.), and a project grant from the McCusker Foundation (to A.F.). R.G.L. was supported by the Mito Foundation and WACRF. M.S. and D.L.R. were supported by UWA Postgraduate Scholarships. N.D. was supported by a MQ Research Excellence Scholarship and CSIRO FSP Synbio Top-up Scholarship. The Queensland Metabolomics and Proteomics node is supported by the Australian Government Department of Education through the National Collaborative Research Infrastructure Strategy, the Super Science Initiative, and the Collaborative Research Infrastructure Scheme and the Queensland Government. The Victorian Centre for Functional Genomics (K.J.S.) is funded by the Australian Cancer Research Foundation, Phenomics Australia through funding from the Australian Government’s National Collaborative Research Infrastructure Strategy programme, the Peter MacCallum Cancer Centre Foundation and the University of Melbourne Research Collaborative Infrastructure Program. Metabolomics Workbench, where the lipidomics datasets are deposited, is supported by NIH grant numbers U2C-DK119886 and OT2-OD030544.

Author information

Author notes

  1. Aleksandra Filipovska

    Present address: Telethon Kids Institute, Northern Entrance, Perth Children′s Hospital, Nedlands, Western Australia, Australia

Authors and Affiliations

  1. Harry Perkins Institute of Medical Research, QEII Medical Centre, Nedlands, Western Australia, Australia

    Richard G. Lee, Danielle L. Rudler, Samuel A. Raven, Anaëlle Chopin, Stefan J. Siira, Samuel V. Fagan, Maike Stentenbach, Jasmin Browne, Filip F. Rackham & Oliver Rackham

  2. ARC Centre of Excellence in Synthetic Biology, QEII Medical Centre, Nedlands, Western Australia, Australia

    Richard G. Lee, Danielle L. Rudler, Samuel A. Raven, Anaëlle Chopin, Stefan J. Siira, Samuel V. Fagan, Maike Stentenbach, Jasmin Browne, Oliver Rackham & Aleksandra Filipovska

  3. Centre for Medical Research, The University of Western Australia, QEII Medical Centre, Nedlands, Western Australia, Australia

    Richard G. Lee, Danielle L. Rudler, Anaëlle Chopin, Stefan J. Siira, Samuel V. Fagan, Maike Stentenbach, Jasmin Browne, Ji Li & Oliver Rackham

  4. Telethon Kids Institute, Northern Entrance, Perth Children′s Hospital, Nedlands, Western Australia, Australia

    Richard G. Lee, Anaëlle Chopin, Stefan J. Siira, Samuel V. Fagan, Maike Stentenbach, Jasmin Browne & Benjamin S. Padman

  5. Curtin Medical School, Curtin University, Bentley, Western Australia, Australia

    Samuel A. Raven & Oliver Rackham

  6. Curtin Health Innovation Research Institute, Curtin University, Bentley, Western Australia, Australia

    Samuel A. Raven & Oliver Rackham

  7. School of Chemistry, The University of Melbourne, Parkville, Victoria, Australia

    Liuyu Peng & Gavin E. Reid

  8. ARC Centre of Excellence in Synthetic Biology, Macquarie University, Sydney, New South Wales, Australia

    Edward S. X. Moh, Nicholas J. DeBono & Nicolle H. Packer

  9. School of Natural Sciences, Macquarie University, Sydney, New South Wales, Australia

    Edward S. X. Moh, Nicholas J. DeBono & Nicolle H. Packer

  10. Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Queensland, Australia

    Tim McCubbin & Esteban Marcellin

  11. ARC Centre of Excellence in Synthetic Biology, The University of Queensland, Queensland, Australia

    Tim McCubbin & Esteban Marcellin

  12. Sir Peter MacCallum Department of Oncology, University of Melbourne, Parkville, Victoria, Australia

    Kaylene J. Simpson

  13. Victorian Centre for Functional Genomics, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia

    Kaylene J. Simpson

  14. Department of Biochemistry and Pharmacology, The University of Melbourne, Parkville, Victoria, Australia

    Kaylene J. Simpson & Gavin E. Reid

  15. Bio21 Molecular Science and Biotechnology Institute, The University of Melbourne, Parkville, Victoria, Australia

    Gavin E. Reid

  16. Centre for Microscopy, Characterisation and Analysis, The University of Western Australia, Perth, Western Australia, Australia

    Benjamin S. Padman

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Contributions

Conceptualization: O.R. and A.F. Investigation: R.G.L., D.L.R., S.J.S., S.A.R., B.S.P., L.P., E.S.X.M., T.M., N.J.D., M.S., J.B., F.F.R., B.S.P., A.C., S.V.F., K.J.S., J.L., E.M., G.E.R., O.R. and A.F. Visualization: R.G.L., D.L.R., S.J.S., S.A.R., B.S.P., O.R. and A.F. Funding acquisition: O.R., A.F., G.R., E.M., N.H.P. and K.J.S. Project administration: A.F. Writing (original draft): R.G.L. and A.F. Writing (review and editing): all authors.

Corresponding author

Correspondence to Aleksandra Filipovska.

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Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Cell Biology thanks Antentor Hinton Jr, Irfan Lodhi and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Establishing the CAL51MG cell line.

a, Schematic of the strategy used to incorporate the GFP reporter into the MRPL12 gene of CAL51 cells. b, Mitochondria from 143B cells transfected with MRPL12-GFP were separated on a 10-30% sucrose gradient. Migration of MRPL12-GFP was examined relative to endogenous MRPL12 (large subunit) and MRPS16 (small subunit) by immunoblotting. c, 50 µg of cell lysate from wild-type CAL51 and CAL51MG cells was resolved by SDS-PAGE and immunoblotted for MRPL12 or GFP. GAPDH was used as a loading control. d, Fluorescence activated cell sorting analysis of GFP intensity in wild-type CAL51, CAL51MG, and CAL51MG cells transfected with a gRNA targeting MRPP3. e, Biological process gene ontologies of enriched genes localizing to the nucleus, peroxisomes, Golgi, and ER in the 3-day populations. f,g, Significantly enriched genes in f, 3-day and g, 7-day populations determined by MaGeCK MLE analysis, were used to examine KEGG pathways and biological process gene ontologies of hits from both populations. h, Significantly enriched KEGG pathways relating to organelle and gene expression machinery function in the 3- and 7-day populations. Source numerical data and unprocessed blots are available in source data and Supplementary Table 1.

Source data

Extended Data Fig. 2 Establishing organelle knockout cell lines.

a, Sanger sequencing of five knockout (KO) cell lines, illustrating CRISPR-induced indels in CAL51 cells. b, Sanger sequencing of PEX26 knockout alleles in HeLa cells, illustrating CRISPR-induced deletions. c, Immunoblots showing loss of PEX26, GOLGA5 and SEC62 in CAL51 cells, relative to β-actin that was used as a loading control. d, Immunoblots showing loss of PEX26 in HeLa cells, relative to β-actin that was used as a loading control. Unprocessed blots are available in source data.

Source data

Extended Data Fig. 3 Organelle knockout cell lines differentially affect mitochondrial function extended data.

a, Levels of de novo mitochondrial translation were examined in cell lines using 35S-methionine/cystine labelling. 20 µg of cell lysates were resolved on a 4–12% Bis-Tris gel and visualized by autoradiography. Equal loading was confirmed by Coomassie staining. b, Cellular ATP levels were measured using firefly luciferase bioluminescence assays (n = 10 samples). All data are presented as mean ± SEM, Student’s two-tailed t test p < 0.001, ***. Glucose: PEX26KO p = 0.000672, GOLGA5KO p = 4.21 x 10-7, GOLGA8MKO p = 5.87 x 10-6, SEC61A2KO, p = 1.69 x 10-5 and SEC62KO p = 3 x 10-7. Galactose: GOLGA5KO p = 7.24 x 10-8, GOLGA8MKO p = 3.51 x 10-6, SEC61A2KO, p = 4.35 x 10-5 and SEC62KO p = 1.16 x 10-9. c, Mitochondrial DNA copy number was measured by qPCR in all cells and normalized to levels of the β-globin gene. Data are presented as mean ± SD (n = 3 samples per genotype). Student’s two-tailed t test p < 0.05, *; p < 0.001, ***. PEX26KO p = 0.019, GOLGA5KO p = 5.8 x 10-7, SEC61A2KO, p = 0.0272 and SEC62KO p = 0.0254. d, Mitochondrial morphology was examined using MitoTracker Orange staining in HeLa control and PEX26KO cell lines. Scale bars represent 5 µm and the inset is 0.5 µm. e, Qualitative scoring of mitochondrial network morphology (n = 50 cells per genotype) for HeLa control and PEX26KO cells. Data are shown as mean ± SD. Student’s two-tailed t test p < 0.05, *PEX26KO p = 0.0117. f, Co-localization of PEX3-mCherry (peroxisome membrane) and peroxisome targeting sequence 1 (PTS1)-GFP in CAL51 control but not PEX26KO cells. Scale bars represent 5 µm and the inset is 0.5 µm. g, Mitochondrial morphology was examined using MRPL44 tagged with mCherry at the C terminus, in all cells rescued with plasmids expressing wild-type versions of their depleted proteins. Control cells were transfected with an empty vector. Scale bars represent 5 µm and the inset is 0.5 µm. h, Qualitative scoring of mitochondrial network morphology (n = 50 cells per genotype) for all cell lines. Data are shown as the mean ± SD. Source numerical data and unprocessed blots are available in source data.

Source data

Extended Data Fig. 4 Visualization of AIVE-processed voxels used for image segmentation.

(a-f) Examples of AIVE-processed data for each organelle category and cell line used in this study, with direct 2D comparisons between the FIB-SEM image data and AIVE-processed voxels for (a) peroxisomes in WT cells, (b) endosomes in PEX26 KO cells, (c) mitochondria in GOLGA5 KO cells, (d) ER in GOLGA8 KO cells, (e) Golgi in SEC61A KO cells, and (f) vesicles in SEC62 KO cells. Data are shown as full frame overview images and 2D slices (20 nm sum-projected) of an inset region spanning 200 nm of the data. (g) 3D renderings of AIVE-processed data from the inset regions depicted in a, b, c, d, e and f, respectively. Scale bars represent 1 µm. Source numerical data are available in Supplementary Table 2.

Extended Data Fig. 5 Organelle defects cause cell-wide morphology defects extended data.

a, Schematic illustrating the clustering model used to quantitate membrane volume, sphericity and Euler characteristic of organelles. b, Three-tier k-means hierarchical clustering of membrane morphometric characteristics for peroxisomes in the control and five knockout cell lines. The coloured circles represent mitochondria from the specified cell line and the grey circles are mitochondria from all cell lines as a reference; the grey dashed lines mark out the zero values of the relative sphericity and Euler characteristic. c, Proportion of peroxisomes from the seven volume classes identified by hierarchical clustering across all cell lines. d, 3D reconstructions of representative peroxisomes from control and five knockout cell lines. Scale bars represent 0.5 µm. Most prominent volumetric clusters identified by hierarchical clustering across (e) mitochondria and (f) peroxisomes from the control and five knockout cell lines. Quantitation of (g) mitochondria-ER interactions in control and SEC62KO cell lines and (h) peroxisome-ER interactions in control and GOLGA8MKO cell lines using SPLICS reporters. Data are presented as the mean ± SD of three biologically independent experiments (each performed with 4 replicates per genotype per experiment). Student’s two-tailed t test p < 0.01, **; p < 0.001, ***. SPLICS mito ER p = 0.0036 and SPLICS peroxi-ER p = 0.0001. Lines indicate the mean within each box and whisker plot, boxes indicate quartiles 2 and 3, with whiskers indicating the range from 5% to 95%. Source numerical data are available in Supplementary Table 2.

Source data

Extended Data Fig. 6 Organelle defects alter cell-wide organelle interaction and communication extended data.

Boxplots showing the standardized interacting membrane volumes between (a) mitochondria and (b) peroxisomes and other target organelles at 10, 20, and 40 nm distances, where all mitochondria or peroxisome volumes were standardized to 1 µm3 and interacting organelle volumes were scaled accordingly to the respective base organelle. Data are presented as the mean ± SD. Welch’s two tailed t test *, p < 0.05; **, p < 0.01; ***, p < 0.001. Radar plots showing interactions between mitochondria and peroxisomes with ER and Golgi at (c) 10 nm and (d) 20 nm distances. Lines indicate the mean within each box and whisker plot, boxes indicate quartiles 2 and 3, with whiskers indicating the range from 5% to 95%. Source numerical data are available in Supplementary Table 2.

Extended Data Fig. 7 Organelle defects elicit targeted transcriptomic stress responses.

a, Enrichment of biological processes gene ontologies was determined by differential expression analysis of control and five knockout cell lines. Significant enrichment was determined at s < 0.05 for differentially expressed transcripts and FDR < 0.05 for gene ontology enrichment analysis. b, Matrix of similarly changing proteins relative to control for all five knockout cell lines. c, Proportion of significantly changing RNAs in all five cell lines (red) vs RNAs that are selectively changing in only a subset of cell lines (grey). d, Hierarchical clustering of differentially expressed RNAs significantly changing (s < 0.05) commonly in all five cell lines relative to control. Gene clusters similarly enriched in all five knockout cell lines are highlighted in orange. Source numerical data are available in Supplementary Tables 3 and 4.

Extended Data Fig. 8 Organelle defects trigger inter-organelle biogenesis responses extended data.

a, Bubble plots summarized and generated by REVIGO for biological process gene ontologies determined by quantitative whole-cell proteomics from control and five knockout cell lines. b, Standard deviation of significantly changing proteins from biological processes between the five knockout cell lines. Standard deviations <0.4 are highlighted in grey and outliers are indicated with arrowheads. c, Hierarchical clustering of individual protein changes with a standard deviation cut-off of ≤0.4 in organelle-related biological process GOs in all five knockout cell lines compared to control, described in Fig. 4a. Source numerical data are available in Supplementary Table 4.

Extended Data Fig. 9 Organelle defects trigger inter-organelle biogenesis responses extended data.

a, Volcano plots of quantitative whole-cell proteomic analyses, showing changes of select mitochondrial processes in all five organelle knockout cell lines relative to control. Values that fall outside the limits of the axis are indicated with arrowheads at the ends of the x-axes and are listed in Supplementary Table 3. b, Boxplots showing changes in peroxisome, Golgi, and ER proteins in the five knockout cell lines relative to controls. Lines indicate the mean within each box and whisker plot, boxes indicate quartiles 2 and 3, with whiskers indicating the range from 5% to 95%. Source numerical data are available in Supplementary Table 4.

Extended Data Fig. 10 Lipidomic analyses extended data and predictions of pathogenic variants in genes implicated in inter-organelle biogenesis.

a, Lipid levels in control and the five knockout cell lines determined by mass spectrometry based lipidome analysis in n = 5 (WT, PEX26KO and SEC62KO) or n = 4 (GOLGA5KO, GOLGA8MKO and SEC61A2KO) independent biological samples. The abundances of phosphatidylserine (PS), ceramide (Cer) and hexocylceramide (that is, glucosyl- and galactosyl-ceramide) (HexCer) are calculated as mol percent of total detected lipids and shown as the mean ± SD. FDR < 0.05, *; FDR < 0.01, ** FDR < 0.001, ***. b, Hierarchical clustering of the expression and stability of specific enzymes involved in GP, GL, SP and ST metabolism, significantly altered in the transcriptomes and proteomes of the five knockout cell lines compared to control cells. c, Computational prediction of deleterious protein consequences of coding region variants (PHRED scores) identified in gnomAD for PEX26, GOLGA5, GOLGA8M, SEC61A2, and SEC62. X-axis annotation describes the domain architecture of each encoded protein based on conserved domain analysis and literature descriptions. d, Proportions of variants identified for PEX26, GOLGA5, GOLGA8M, SEC61A2, and SEC62 with a PHRED score ≥ known PEX26 variants implicated in Zellweger syndrome pathogenesis ( ≥ 24.5). Source numerical data are available in Supplementary Table 5.

Supplementary information

Reporting Summary

Peer Review File

Supplementary Table 1

1. Enrichment of sgRNAs by FACS at 3 and 7 d post transduction compared with unsorted cells. 2. Mito pathway annotations for enriched genes at 3 and 7 d post transduction compared with unsorted cells. 3. Enriched biological process GOs for enriched genes at 3 and 7 d post transduction compared with unsorted cells. 4. MAGeCK MLE analysis of sgRNA enrichment at 3 and 7 d post transduction. 5. Enrichment of biological process GOs in enriched genes in the MAGeCK MLE analysis. 6. KEGG pathway annotation of significantly enriched genes in the MAGeCK analysis

Supplementary Table 2

1. Morphometric data for organelles detected in control cell FIB-SEM. 2. Morphometric data for organelles detected in PEX26KO cell FIB-SEM. 3. Morphometric data for organelles detected in GOLGA5KO cell FIB-SEM. 4. Morphometric data for organelles detected in GOLGA8MKO cell FIB-SEM. 5. Morphometric data for organelles detected in SEC61A2KO cell FIB-SEM. 6. Morphometric data for organelles detected in SEC62KO cell FIB-SEM. 7. Three-tiered k-means clustering analysis for mitochondria from control, PEX26KO, GOLGA5KO, GOLGA8MKO, SEC61A2KO and SEC62KO cell lines. 8. Three-tiered k-means clustering analysis for peroxisomes from control, PEX26KO, GOLGA5KO, GOLGA8MKO, SEC61A2KO and SEC62KO cell lines. 9. Organelle contact analysis for mitochondria and peroxisomes in control, PEX26KO, GOLGA5KO, GOLGA8MKO, SEC61A2KO and SEC62KO cell lines.

Supplementary Table 3

1. Differential expression analysis for transcripts from PEX26KO, GOLGA5KO, GOLGA8MKO, SEC61A2KO and SEC62KO cell lines compared with controls. 2. Significantly enriched reactome pathways of significantly changing genes in PEX26KO, GOLGA5KO, GOLGA8MKO, SEC61A2KO and SEC62KO cell lines.

Supplementary Table 4

1. Log2-transformed fold change of proteins in PEX26KO, GOLGA5KO, GOLGA8MKO, SEC61A2KO and SEC62KO cells compared with controls. 2. Significantly enriched (FDR < 0.05) biological process GOs for significantly changed genes in PEX26KO, GOLGA5KO, GOLGA8MKO, SEC61A2KO and SEC62KO cell lines. 3. Common significantly changed genes across PEX26KO, GOLGA5KO, GOLGA8MKO, SEC61A2KO and SEC62KO cell lines. 4. Significantly enriched (FDR < 0.05) biological process GOs for common significantly changed genes across PEX26KO, GOLGA5KO, GOLGA8MKO, SEC61A2KO and SEC62KO cell lines. 5. Standard deviation of the log2-transformed fold change of genes that are part of the common significantly enriched GOs from tab 4. 6. Significantly changed proteins in mitochondrial pathways in PEX26KO, GOLGA5KO, GOLGA8MKO, SEC61A2KO and SEC62KO cell lines. 7. Significantly changed peroxisome, Golgi and ER proteins in PEX26KO, GOLGA5KO, GOLGA8MKO, SEC61A2KO and SEC62KO cell lines.

Supplementary Table 5

1. Lipids detected in whole-cell fractions from control, PEX26KO, GOLGA5KO, GOLGA8MKO, SEC61A2KO and SEC62KO cell lines. 2. Lipids detected in isolated mitochondrial fractions from control, PEX26KO, GOLGA5KO, GOLGA8MKO, SEC61A2KO and SEC62KO cell lines. 3. Lipids detected in whole-cell fractions from control, PEX26KO, GOLGA5KO, GOLGA8MKO, SEC61A2KO and SEC62KO cell lines treated with ethanol (vehicle control), 16:O-AG or 18:O-AG. 4. Lipids detected in isolated mitochondrial fractions from control, PEX26KO, GOLGA5KO, GOLGA8MKO, SEC61A2KO and SEC62KO cell lines treated with either ethanol (vehicle control) or 16:O-AG.

Supplementary Table 6

1. Total glycosaminoglycan abundances in control, GOLGA5KO and GOLGA8MKO cell lines. 2. N-glycan abundances in control, GOLGA5KO and GOLGA8MKO cell lines. 3. O-glycan abundances in control, GOLGA5KO and GOLGA8MKO cell lines.

Supplementary Table 7

Oligonucleotides used in this study.

Source data

Source Data Fig. 1

Cell morphology data.

Source Data Fig. 7

Respiration data.

Source Data Extended Data Fig. 1

Western blots.

Source Data Extended Data Fig. 2

Western blots.

Source Data Extended Data Fig. 3

SDS–PAGE gels.

Source Data Extended Data Fig. 3

Raw data for ATPlite mitochondrial DNA copy number quantitation and morphology.

Source Data Extended Data Fig. 5

FACS gating.

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Lee, R.G., Rudler, D.L., Raven, S.A. et al. Quantitative subcellular reconstruction reveals a lipid mediated inter-organelle biogenesis network. Nat Cell Biol 26, 57–71 (2024). https://doi.org/10.1038/s41556-023-01297-4

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