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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

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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.

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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.

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.

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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.

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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

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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.

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The authors declare no competing interests.

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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.

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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.

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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

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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

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