Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Letter
  • Published:

Metabolic coessentiality mapping identifies C12orf49 as a regulator of SREBP processing and cholesterol metabolism

Nature Metabolism volume 2, pages 487–498 (2020)Cite this article

Subjects

Abstract

Coessentiality mapping has been useful to systematically cluster genes into biological pathways and identify gene functions1,2,3. Here, using the debiased sparse partial correlation (DSPC) method3, we construct a functional coessentiality map for cellular metabolic processes across human cancer cell lines. This analysis reveals 35 modules associated with known metabolic pathways and further assigns metabolic functions to unknown genes. In particular, we identify C12orf49 as an essential regulator of cholesterol and fatty acid metabolism in mammalian cells. Mechanistically, C12orf49 localizes to the Golgi, binds membrane-bound transcription factor peptidase, site 1 (MBTPS1, site 1 protease) and is necessary for the cleavage of its substrates, including sterol regulatory element binding protein (SREBP) transcription factors. This function depends on the evolutionarily conserved uncharacterized domain (DUF2054) and promotes cell proliferation under cholesterol depletion. Notably, c12orf49 depletion in zebrafish blocks dietary lipid clearance in vivo, mimicking the phenotype of mbtps1 mutants. Finally, in an electronic health record (EHR)-linked DNA biobank, C12orf49 is associated with hyperlipidaemia through phenome analysis. Altogether, our findings reveal a conserved role for C12orf49 in cholesterol and lipid homeostasis and provide a platform to identify unknown components of other metabolic pathways.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Genetic coessentiality analysis assigns metabolic functions to uncharacterized genes.
Fig. 2: C12orf49 is necessary for cholesterol synthesis and SREBP-induced gene expression in human cells.
Fig. 3: C12orf49 is a Golgi-localized protein and binds MBTPS1 to regulate cholesterol metabolism.
Fig. 4: C12orf49 function is conserved and essential for organismal lipid homeostasis.

Similar content being viewed by others

Data availability

The data supporting the findings of this study are available from the corresponding author upon reasonable request. Source data for Figs. 1–4 and Extended Data Figs. 1,3,5,7 are presented with the paper. The mass spectrometry proteomics data have been deposited in the ProteomeXchange Consortium via the PRIDE partner repository under dataset identifier PXD018368.

Code availability

The code for the computational analysis that was used in this study is available from the corresponding author upon reasonable request.

References

  1. Wang, T. et al. Gene essentiality profiling reveals gene networks and synthetic lethal interactions with oncogenic Ras. Cell 168, 890–903 e815 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Pan, J. et al. Interrogation of mammalian protein complex structure, function, and membership using genome-scale fitness screens. Cell Syst. 6, 555–568.e7 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Wainberg, M. et al. A genome-wide almanac of co-essential modules assigns function to uncharacterized genes. Preprint at bioRxiv https://doi.org/10.1101/827071 (2019).

  4. Kanehisa, M., Furumichi, M., Tanabe, M., Sato, Y. & Morishima, K. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 45, D353–D361 (2017).

    Article  CAS  PubMed  Google Scholar 

  5. Rozman, J. et al. Identification of genetic elements in metabolism by high-throughput mouse phenotyping. Nat. Commun. 9, 288 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  6. Schnoes, A. M., Brown, S. D., Dodevski, I. & Babbitt, P. C. Annotation error in public databases: misannotation of molecular function in enzyme superfamilies. PLoS Comput. Biol. 5, e1000605 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  7. Pandey, A. K., Lu, L., Wang, X., Homayouni, R. & Williams, R. W. Functionally enigmatic genes: a case study of the brain ignorome. PLoS One 9, e88889 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  8. Hadadi, N., MohammadiPeyhani, H., Miskovic, L., Seijo, M. & Hatzimanikatis, V. Enzyme annotation for orphan and novel reactions using knowledge of substrate reactive sites. Proc. Natl Acad. Sci. USA 116, 7298–7307 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Meyers, R. M. et al. Computational correction of copy number effect improves specificity of CRISPR–Cas9 essentiality screens in cancer cells. Nat. Genet. 49, 1779–1784 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Tsherniak, A. et al. Defining a cancer dependency map. Cell 170, 564–576.e16 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Kim, E et al. A network of human functional gene interactions from knockout fitness screens in cancer cells. Life Sci. Alliance 2, e201800278 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Krumsiek, J., Suhre, K., Illig, T., Adamski, J. & Theis, F. J. Gaussian graphical modeling reconstructs pathway reactions from high-throughput metabolomics data. BMC Syst. Biol. 5, 21 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Basu, S. et al. Sparse network modeling and metscape-based visualization methods for the analysis of large-scale metabolomics data. Bioinformatics 33, 1545–1553 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Schumacher, M. M., Elsabrouty, R., Seemann, J., Jo, Y. & DeBose-Boyd, R. A. The prenyltransferase UBIAD1 is the target of geranylgeraniol in degradation of HMG CoA reductase. eLife 4, e05560 (2015).

    Article  PubMed Central  CAS  Google Scholar 

  15. Zhu, X. G. et al. CHP1 regulates compartmentalized glycerolipid synthesis by activating GPAT4. Mol. Cell 74, 45–58.e7 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Gallego-Garcia, A. et al. A bacterial light response reveals an orphan desaturase for human plasmalogen synthesis. Science 366, 128–132 (2019).

    Article  CAS  PubMed  Google Scholar 

  17. Garcia-Bermudez, J. et al. Squalene accumulation in cholesterol auxotrophic lymphomas prevents oxidative cell death. Nature 567, 118–122 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Horton, J. D., Goldstein, J. L. & Brown, M. S. SREBPs: activators of the complete program of cholesterol and fatty acid synthesis in the liver. J. Clin. Invest. 109, 1125–1131 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Wang, X., Sato, R., Brown, M. S., Hua, X. & Goldstein, J. L. SREBP-1, a membrane-bound transcription factor released by sterol-regulated proteolysis. Cell 77, 53–62 (1994).

    Article  CAS  PubMed  Google Scholar 

  20. Brown, M. S. & Goldstein, J. L. The SREBP pathway: regulation of cholesterol metabolism by proteolysis of a membrane-bound transcription factor. Cell 89, 331–340 (1997).

    Article  CAS  PubMed  Google Scholar 

  21. Sakai, J. et al. Sterol-regulated release of SREBP-2 from cell membranes requires two sequential cleavages, one within a transmembrane segment. Cell 85, 1037–1046 (1996).

    Article  CAS  PubMed  Google Scholar 

  22. Sakakura, Y. et al. Sterol regulatory element-binding proteins induce an entire pathway of cholesterol synthesis. Biochem. Biophys. Res. Commun. 286, 176–183 (2001).

    Article  CAS  PubMed  Google Scholar 

  23. Matsuda, M. et al. SREBP cleavage-activating protein (SCAP) is required for increased lipid synthesis in liver induced by cholesterol deprivation and insulin elevation. Genes Dev. 15, 1206–1216 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Yang, J. et al. Decreased lipid synthesis in livers of mice with disrupted Site-1 protease gene. Proc. Natl Acad. Sci. USA 98, 13607–13612 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Hua, X., Nohturfft, A., Goldstein, J. L. & Brown, M. S. Sterol resistance in CHO cells traced to point mutation in SREBP cleavage-activating protein. Cell 87, 415–426 (1996).

    Article  CAS  PubMed  Google Scholar 

  26. Kleinfelter, L. M. et al. Haploid genetic screen reveals a profound and direct dependence on cholesterol for hantavirus membrane fusion. mBio 6, e00801 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Osuna-Ramos, J. F., Reyes-Ruiz, J. M. & Del Ángel, R. M. The role of host cholesterol during Flavivirus infection. Front. Cell Infect. Microbiol. 8, 388 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Pombo, J. P. & Sanyal, S. Perturbation of intracellular cholesterol and fatty acid homeostasis during Flavivirus infections. Front. Immunol. 9, 1276 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  29. Ericsson, J., Jackson, S. M. & Edwards, P. A. Synergistic binding of sterol regulatory element-binding protein and NF-Y to the farnesyl diphosphate synthase promoter is critical for sterol-regulated expression of the gene. J. Biol. Chem. 271, 24359–24364 (1996).

    Article  CAS  PubMed  Google Scholar 

  30. Vallett, S. M., Sanchez, H. B., Rosenfeld, J. M. & Osborne, T. F. A direct role for sterol regulatory element binding protein in activation of 3-hydroxy-3-methylglutaryl coenzyme A reductase gene. J. Biol. Chem. 271, 12247–12253 (1996).

    Article  CAS  PubMed  Google Scholar 

  31. Guan, G., Dai, P. H., Osborne, T. F., Kim, J. B. & Shechter, I. Multiple sequence elements are involved in the transcriptional regulation of the human squalene synthase gene. J. Biol. Chem. 272, 10295–10302 (1997).

    Article  CAS  PubMed  Google Scholar 

  32. Edwards, P. A., Tabor, D., Kast, H. R. & Venkateswaran, A. Regulation of gene expression by SREBP and SCAP. Biochim. Biophys. Acta 1529, 103–113 (2000).

    Article  CAS  PubMed  Google Scholar 

  33. DeBose-Boyd, R. A. et al. Transport-dependent proteolysis of SREBP: relocation of site-1 protease from Golgi to ER obviates the need for SREBP transport to Golgi. Cell 99, 703–712 (1999).

    Article  CAS  PubMed  Google Scholar 

  34. Lippincott-Schwartz, J., Yuan, L. C., Bonifacino, J. S. & Klausner, R. D. Rapid redistribution of Golgi proteins into the ER in cells treated with brefeldin A: evidence for membrane cycling from Golgi to ER. Cell 56, 801–813 (1989).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Espenshade, P. J., Cheng, D., Goldstein, J. L. & Brown, M. S. Autocatalytic processing of site-1 protease removes propeptide and permits cleavage of sterol regulatory element-binding proteins. J. Biol. Chem. 274, 22795–22804 (1999).

    Article  CAS  PubMed  Google Scholar 

  36. Cheng, D. et al. Secreted site-1 protease cleaves peptides corresponding to luminal loop of sterol regulatory element-binding proteins. J. Biol. Chem. 274, 22805–22812 (1999).

    Article  CAS  PubMed  Google Scholar 

  37. Velho, R. V. et al. Site-1 protease and lysosomal homeostasis. Biochim. Biophys. Acta Mol. Cell Res. 1864, 2162–2168 (2017).

    Article  CAS  PubMed  Google Scholar 

  38. Asada, R., Kanemoto, S., Kondo, S., Saito, A. & Imaizumi, K. The signalling from endoplasmic reticulum-resident bZIP transcription factors involved in diverse cellular physiology. J. Biochem. 149, 507–518 (2011).

    Article  CAS  PubMed  Google Scholar 

  39. Shao, W. & Espenshade, P. J. Sterol regulatory element-binding protein (SREBP) cleavage regulates Golgi-to-endoplasmic reticulum recycling of SREBP cleavage-activating protein (SCAP). J. Biol. Chem. 289, 7547–7557 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Passeri, M. J., Cinaroglu, A., Gao, C. & Sadler, K. C. Hepatic steatosis in response to acute alcohol exposure in zebrafish requires sterol regulatory element binding protein activation. Hepatology 49, 443–452 (2009).

    Article  CAS  PubMed  Google Scholar 

  41. Unlu, G. et al. GRIK5 genetically regulated expression associated with eye and vascular phenomes: discovery through iteration among biobanks, electronic health records, and zebrafish. Am. J. Hum. Genet. 104, 503–519 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Roden, D. M. et al. Development of a large-scale de-identified DNA biobank to enable personalized medicine. Clin. Pharmacol. Ther. 84, 362–369 (2008).

    Article  CAS  PubMed  Google Scholar 

  43. Gamazon, E. R. et al. A gene-based association method for mapping traits using reference transcriptome data. Nat. Genet. 47, 1091–1098 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Mazumder, R. & Hastie, T. Exact covariance thresholding into connected components for large-scale graphical lasso. J. Mach. Learn. Res. 13, 781–794 (2012).

    PubMed  PubMed Central  Google Scholar 

  45. Possemato, R. et al. Functional genomics reveal that the serine synthesis pathway is essential in breast cancer. Nature 476, 346–350 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Weber, R. A. et al. Maintaining iron homeostasis Is the key role of lysosomal acidity for cell proliferation. Mol. Cell 77, 645–655.e7 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Jankova, J.& van de Geer, S. Confidence intervals for high-dimensional inverse covariance estimation. Electron J. Statist. 9, 1205–1229 (2015).

    Article  Google Scholar 

  48. Cullot, G. et al. CRISPR–Cas9 genome editing induces megabase-scale chromosomal truncations. Nat. Commun. 10, 1136 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  49. Adikusuma, F. et al. Large deletions induced by Cas9 cleavage. Nature 560, E8–E9 (2018).

    Article  CAS  PubMed  Google Scholar 

  50. Harsha, H. C. et al. Activated epidermal growth factor receptor as a novel target in pancreatic cancer therapy. J Proteome Res. 7, 4651–4658 (2008).

    Article  CAS  PubMed  Google Scholar 

  51. Schilling, B. et al. Platform-independent and label-free quantitation of proteomic data using MS1 extracted ion chromatograms in skyline: application to protein acetylation and phosphorylation. Mol. Cell Proteomics 11, 202–214 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Rappsilber, J., Mann, M. & Ishihama, Y. Protocol for micro-purification, enrichment, pre-fractionation and storage of peptides for proteomics using StageTips. Nat. Protoc. 2, 1896–1906 (2007).

    Article  CAS  PubMed  Google Scholar 

  53. Cox, J. & Mann, M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 26, 1367–1372 (2008).

    Article  CAS  PubMed  Google Scholar 

  54. Schwanhausser, B. et al. Global quantification of mammalian gene expression control. Nature 473, 337–342 (2011).

    Article  PubMed  CAS  Google Scholar 

  55. Tyanova, S. et al. The Perseus computational platform for comprehensive analysis of (prote)omics data. Nat. Methods 13, 731–740 (2016).

    Article  CAS  PubMed  Google Scholar 

  56. Perez-Riverol, Y. et al. The PRIDE database and related tools and resources in 2019: improving support for quantification data. Nucleic Acids Res. 47, D442–D450 (2019).

    Article  CAS  PubMed  Google Scholar 

  57. Montague, T. G., Cruz, J. M., Gagnon, J. A., Church, G. M. & Valen, E. CHOPCHOP: a CRISPR/Cas9 and TALEN web tool for genome editing. Nucleic Acids Res. 42, W401–W407 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Varshney, G. K et al. High-throughput gene targeting and phenotyping in zebrafish using CRISPR/Cas9. Genome Res. 25, 1030–1042 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Yin, L. et al. Multiplex conditional mutagenesis using transgenic expression of Cas9 and sgRNAs. Genetics 200, 431–441 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Levic, D. S. et al. Animal model of Sar1b deficiency presents lipid absorption deficits similar to Anderson disease. J. Mol. Med. 93, 165–176 (2015).

    Article  CAS  PubMed  Google Scholar 

  61. Unlu, G. et al. Phenome-based approach identifies RIC1-linked Mendelian syndrome through zebrafish models, biobank associations and clinical studies. Nat. Med. 26, 98–109 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Denny, J. C. et al. Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data. Nat. Biotechnol. 31, 1102–1110 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. The GTEx Consortium The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, 648–660 (2015).

    Article  CAS  Google Scholar 

  64. The GTEx Consortium Genetic effects on gene expression across human tissues. Nature 550, 204–213 (2017).

    Article  Google Scholar 

  65. Storey, J. D. & Tibshirani, R. Statistical significance for genomewide studies. Proc. Natl Acad. Sci. USA 100, 9440–9445 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Schaffter, T., Marbach, D. & Floreano, D. GeneNetWeaver: in silico benchmark generation and performance profiling of network inference methods. Bioinformatics 27, 2263–2270 (2011).

    Article  CAS  PubMed  Google Scholar 

  67. Shi, X., van Mierlo, J. T., French, A. & Elliott, R. M. Visualizing the replication cycle of Bunyamwera orthobunyavirus expressing fluorescent protein-tagged Gc glycoprotein. J. Virol. 84, 8460–8469 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. del Solar, V. et al. Differential regulation of specific sphingolipids in colon cancer cells during staurosporine-induced apoptosis. Chem. Biol. 22, 1662–1670 (2015).

    Article  PubMed  CAS  Google Scholar 

Download references

Acknowledgements

We thank all members of the Birsoy lab for helpful suggestions. This research is supported by funds from a Merck Postdoctoral Fellowship (E.C.B.) at Rockefeller University. The project described was cosponsored by the Center for Basic and Translational Research on Disorders of the Digestive System through the generosity of the Leona M. and Harry B. Helmsley Charitable Trust. Research is supported by NIDDK (R01 DK123323-01 to K.B.), the Irma–Hirschl Trust (K.B.), NSF DMS-1812128 (S.B.), R01 MH113362-02 (E.W.K.), R01 GM117473-02 (E.W.K.), R35 HG010718 (E.R.G) and 1R01GM135926-01 (S.B.). K.B. is a Searle Scholar, Pew-Stewart Scholar and Basil O’Connor Scholar of the March of Dimes.

Author information

Authors and Affiliations

  1. Laboratory of Metabolic Regulation and Genetics, The Rockefeller University, New York, NY, USA

    Erol C. Bayraktar, Konnor La, Gokhan Unlu, Ceren Ozerdem & Kıvanç Birsoy

  2. Center for Applied Mathematics, Cornell University, Ithaca, NY, USA

    Kara Karpman

  3. Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA

    Gokhan Unlu, Dylan J. Ritter, Eric R. Gamazon, Amy R. Rushing & Ela W. Knapik

  4. Department of Cell and Developmental Biology, Vanderbilt University, Nashville, TN, USA

    Gokhan Unlu, Dylan J. Ritter & Ela W. Knapik

  5. Proteomics Resource Center, The Rockefeller University, New York, NY, USA

    Hanan Alwaseem & Henrik Molina

  6. Laboratory of Virology and Infectious Disease, The Rockefeller University, New York, NY, USA

    Hans-Heinrich Hoffmann

  7. Department of Chemistry, University at Buffalo, The State University of New York (SUNY), Buffalo, NY, USA

    Alec Millner & G. Ekin Atilla-Gokcumen

  8. Department of Statistics and Data Science, Cornell University, Ithaca, NY, USA

    Sumanta Basu

Authors
  1. Erol C. Bayraktar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Konnor La
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kara Karpman
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Gokhan Unlu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ceren Ozerdem
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Dylan J. Ritter
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Hanan Alwaseem
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Henrik Molina
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Hans-Heinrich Hoffmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Alec Millner
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. G. Ekin Atilla-Gokcumen
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Eric R. Gamazon
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Amy R. Rushing
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Ela W. Knapik
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Sumanta Basu
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Kıvanç Birsoy
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

K.B., and E.C.B. conceived the project and designed the experiments. K.L., K.K. and S.B. performed computational analysis and constructed the coessentiality network. E.C.B., K.L. and C.O. performed follow-up validation experiments. H.-H.H. performed viral infection experiments. G.U., E.W.K., D.J.R. and A.R.R. performed zebrafish experiments. H.A. and H.M. performed metabolomics and proteomics experiments. A.M. conducted the fatty acid lipidomics analysis, G.E.A.-G. supervised the analysis. E.R.G. assisted with the GWAS and human genetics analysis. K.B., and E.C.B. wrote and edited the manuscript. All the authors read and approved the manuscript.

Corresponding author

Correspondence to Kıvanç Birsoy.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Primary Handling Editor: Pooja Jha.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Comparative Simulation between partial and Pearson correlation.

a, Simulation experiment of a subnetwork from an E. coli network demonstrating the advantage of using partial correlation over Pearson correlation. b, Receiver operating characteristic (ROC) curve based on the simulated data. (n= 500 independent samples).

Source data

Extended Data Fig. 2 Metabolic coessentiality modules.

35 Metabolic coessentiality modules. Blue line indicates a previously known interaction between the genes. Poorly characterized genes are highlighted as orange.

Extended Data Fig. 3 C12orf49 is necessary for cell growth under sterol depletion.

a, Pearson correlation values of the essentiality scores of the indicated genes across different cancer cell lines (n=558). b, Differential sgRNA score for C12orf49 gene of Jurkat cell line in the presence or absence of sterols. c, Fold change in cell number (log2) of U-87 MG or MDA-MB-435 c12orf49_KO cell line following a 6-day growth under lipoprotein depletion in the absence or presence of sterols. (mean ± SD, n=3 biologically independent samples). Statistical significance was determined by two-tailed unpaired t-test. d, Immunoblots of c12orf49 in the indicated knockout cells of HEK293T. Actin was used as the loading control. The experiment was repeated independently twice with similar results. e, (left) Immunoblots of c12orf49 knockout and addback cells in Jurkat cells. Actin was used as the loading control. The experiment was repeated independently twice with similar results. (right) Fold change in cell number (log2) of indicated knockout and rescued addback Jurkat cells following a 6-day growth under lipoprotein depletion in the absence or presence of sterols. (mean ± SD, n=3 biologically independent samples). Statistical significance was determined by two-tailed unpaired t-test. f, Fold change in cell number (log2) of indicated knockout and rescued addback HEK293T cells following a 6-day growth under lipoprotein depletion in the absence or presence of sterols. (mean ± SD, n=3 biologically independent samples). Statistical significance was determined by two-tailed unpaired t-test.

Source data

Extended Data Fig. 4 TMEM41A is involved in lipid metabolism.

a, Pearson correlation values of the essentiality scores of the indicated genes across different cancer cell lines (n=558). b, Localization of TMEM41A to ER. Wild type HEK293T cells expressing FLAG-TMEM41A cDNA were processed for immunofluorescence analysis using antibodies against FLAG and PDI (ER). White color indicates overlap (Scale bar, 8 µm). The experiment was repeated independently twice with similar results. c, Heatmap showing the relative abundance of indicated lipid species in TMEM41-null Jurkat cells and those expressing sgRNA resistant TMEM41A cDNA. d, Immunoblot of TMEM41A in Jurkat wild type cell line, TMEM41A nulls and those expressing TMEM41A cDNA. Actin was used as the loading control. The experiment was repeated independently twice with similar results. e, Fold change in cell number (log2) of Jurkat wild type cell line, TMEM41A-null cells and those expressing TMEM41A cDNA after a 7-day growth upon treatment of indicated palmitate concentrations (0–80 uM). (mean ± SD, n=3 biologically independent samples). Statistical significance was determined by two-tailed unpaired t-test.

Source data

Extended Data Fig. 5 Role of C12orf49 in sterol synthesis and SREBP-mediated transcription.

a, (top left) Percentage of Bunyamwera virus-positive cells at 72 h post-infection (MOI=0.1IU/Ml) in indicated knockout and addback HEK293T cells (mean ± SD, n=3 biologically independent samples). Statistical significance was determined by two-tailed unpaired t-test. (top right) Viral titer measured by TCID50 assays on BHK-21 cells with the harvested supernatant from the Bunyamwera virus infected HEK293T cells of C12orf49 knockouts and addbacks. (mean ± SD, n=3 biologically independent samples) Statistical significance was determined by two-tailed unpaired t-test. (bottom) Growth of the viral titers at different time points in the knockout and addback cells. b, Fold change in mRNA levels (log2) of SREBP target genes in indicated Jurkat cell lines following 8h growth under lipoprotein depletion in the presence and absence of sterols (mean ± SD, n=3). c, Relative luminescence activity (Luciferase/Renilla) in the indicated HEK293 cell lines following transfection with firefly luciferase under SRE promoter and Renilla luciferase for normalization of transfection following 24h growth under lipoprotein depletion in the presence and absence of sterols (mean ± SD, n=3 biologically independent samples). Statistical significance was determined by two-tailed unpaired t-test.

Source data

Extended Data Fig. 6 C12orf49 gene expression in various tissues.

a, Gene expression analysis across different tissues for C12orf49. Box plots are shown as median and 25th and 75th percentiles; points are displayed as outliers if they are above or below 1.5 times the interquartile range (Source: GTEx Portal). b, DUF2054 profile hidden Markov Model (HMM) logo from Pfam shows 14 conserved cysteines, 3 of which are CC-dimers. c, Different architectures of DUF2054 in different species. (Source: Pfam) d, Occurrence of DUF2054 domain across different species. e, Predicted N-glycosylation site (UniProtKB) and transmembrane domains (predicted with TMHMM v.2.0) for C12orf49. f, Scheme for different functional domains of C12orf49.

Extended Data Fig. 7 The impact of C12orf49 loss on the cleavage of MBTPS1 targets.

a, Immunoblot analysis of OS9 in the C12orf49 immunoprecipitates of the HEK293T cell line expressing the indicated cDNAs. The experiment was repeated independently twice with similar results. b, Immunoblot analysis of cleavage of other site-1 protease targets, GNPTAB, CREB3L2 and CREB4 at 24 h following transfection in the C12orf49-knockout and addback HEK293T cells. Actin was used as loading control. The experiment was repeated independently twice with similar results. c, Localization of SCAP-GFP in c12orf49 null HEK293T cells expressing control or C12orf49 cDNA under lipoprotein depletion in the presence or absence of sterols (Scale bar, 8 µm). The experiment was repeated independently twice with similar results.

Source data

Extended Data Fig. 8 Conservation of C12orf49 function in metazoa and zebrafish.

a, Phylogenetic tree of the C12orf49 genes across species (Source: TreeFam). b, DNA gel showing the cutting efficiencies of c12orf49 sgRNAs used in the zebrafish experiments. Upper bands (smears) represent DNA heteroduplexes caused by CRISPR-Cas9 mutations; lower band is unedited DNA. This assay was repeated twice with similar results. c, Strategy to evaluate the effect of CRISPR-Cas9-generated c12orf49 mutations at transcript level. c12orf49-g2 founder F0 fish were crossed and F1 progeny was individually analysed. Briefly, RNA was isolated from individual larvae, then cDNA was synthesized. Using exon-specific primers g2 target site was PCR amplified and sequenced. Various mutations detected from transcripts are shown.

Extended Data Fig. 9 GReX analysis identifies C12orf49 association with mixed hyperlipidemia.

Disease traits associated with reduced c12orf49 GReX in BioVU biobank. Phecodes are indicated in parentheses. Traits are categorized into systems (y-axis), and significance is displayed on x-axis. Significance is tested by logistic regression analysis (two-sided), n = 25,000. Multiple testing adjustment is done using Bonferroni correction.

Supplementary information

Supplementary Information

Supplementary Note

Reporting Summary

Supplementary Tables

Supplementary Table 1: List of sgRNA sequences and guide scores under lipoprotein depletion with or without sterols. Supplementary Table 2: Number of the false positives and true positives for Pearson correlation and the two types of partial correlation methods (pcor and DGLASSO). Standard errors calculated over the n = 20 replicates are shown in parenthesis

Source data

Source Data Fig. 1

Statistical source data

Source Data Fig. 2

Statistical source data

Source Data Fig. 2

Unprocessed Western Blots

Source Data Fig. 3

Statistical source data

Source Data Fig. 3

Unprocessed Western Blots

Source Data Fig. 4

Statistical source data

Source Data Fig. 4

Unprocessed Western Blots

Source Data Extended Data Fig. 1

Statistical source data

Source Data Extended Data Fig. 3

Statistical source data

Source Data Extended Data Fig. 3

Unprocessed Western Blots

Source Data Extended Data Fig. 4

Statistical source data

Source Data Extended Data Fig. 4

Unprocessed Western Blots

Source Data Extended Data Fig. 5

Statistical source data

Source Data Extended Data Fig. 7

Unprocessed Western Blots

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bayraktar, E.C., La, K., Karpman, K. et al. Metabolic coessentiality mapping identifies C12orf49 as a regulator of SREBP processing and cholesterol metabolism. Nat Metab 2, 487–498 (2020). https://doi.org/10.1038/s42255-020-0206-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s42255-020-0206-9

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research