Systematic mapping of genetic interactions for de novo fatty acid synthesis identifies C12orf49 as a regulator of lipid metabolism

Abstract

The de novo synthesis of fatty acids has emerged as a therapeutic target for various diseases, including cancer. Because cancer cells are intrinsically buffered to combat metabolic stress, it is important to understand how cells may adapt to the loss of de novo fatty acid biosynthesis. Here, we use pooled genome-wide CRISPR screens to systematically map genetic interactions (GIs) in human HAP1 cells carrying a loss-of-function mutation in fatty acid synthase (FASN), whose product catalyses the formation of long-chain fatty acids. FASN-mutant cells show a strong dependence on lipid uptake that is reflected in negative GIs with genes involved in the LDL receptor pathway, vesicle trafficking and protein glycosylation. Further support for these functional relationships is derived from additional GI screens in query cell lines deficient in other genes involved in lipid metabolism, including LDLR, SREBF1, SREBF2 and ACACA. Our GI profiles also identify a potential role for the previously uncharacterized gene C12orf49 (which we call LUR1) in regulation of exogenous lipid uptake through modulation of SREBF2 signalling in response to lipid starvation. Overall, our data highlight the genetic determinants underlying the cellular adaptation associated with loss of de novo fatty acid synthesis and demonstrate the power of systematic GI mapping for uncovering metabolic buffering mechanisms in human cells.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Genome-scale identification of digenic interactions with FASN.
Fig. 2: Querying five additional lipid metabolism genes for digenic interactions.
Fig. 3: GIs reveal multiple levels of functional enrichment.
Fig. 4: C12orf49's GI profile suggests it has a functional role in lipid metabolism.
Fig. 5: C12orf49 shuttles between the ER and Golgi, and regulates lipid uptake through modulation of SREBP2 processing.
Fig. 6: LUR1 shuttles between the ER and Golgi and regulates SREBF2 activation and lipid uptake.

Data availability

The datasets generated and analysed in this study are included in the manuscript. The raw FASTQ files for the sequencing data are available upon request and have also been deposited to the Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/): RNA-sequencing data, GSE147770; CRISPR screen sequencing data, GSE148627. All mass spectrometry data have been deposited to the MassIVE repository (https://massive.ucsd.edu/ProteoSAFe/static/massive.jsp) and assigned the accession number MSV000085005. The ProteomeXchange accession is PXD017719. Descriptions of the analyses, tools and algorithms are provided in the Methods and the Reporting Summary. Custom code for generating gRNA counts from FASTQ files and code for generating qGI scores is available on GitHub (https://github.com/csbio/metabolicGIN). Source data for Fig. 5 and Extended Data Figs. 1 and 6 are presented with the paper.

References

  1. 1.

    Chen, M. & Huang, J. The expanded role of fatty acid metabolism in cancer: new aspects and targets. Precis. Clin. Med 2, 183–191 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  2. 2.

    Chen, R. R. et al. Targeting of lipid metabolism with a metabolic inhibitor cocktail eradicates peritoneal metastases in ovarian cancer cells. Commun. Biol 2, 281 (2019).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  3. 3.

    Imoto, M. Chemistry and biology for the small molecules targeting characteristics of cancer cells. Biosci. Biotechnol. Biochem. 83, 1–10 (2018).

    Google Scholar 

  4. 4.

    Menendez, J. A. & Lupu, R. Fatty acid synthase (FASN) as a therapeutic target in breast cancer. Expert Opin. Ther. Targets 21, 1001–1016 (2017).

    CAS  PubMed  Article  Google Scholar 

  5. 5.

    Garber, K. Cancer anabolic metabolism inhibitors move into clinic. Nat. Biotechnol. 34, 794–795 (2016).

    CAS  PubMed  Article  Google Scholar 

  6. 6.

    Röhrig, F. & Schulze, A. The multifaceted roles of fatty acid synthesis in cancer. Nat. Rev. Cancer 16, 732–749 (2016).

    PubMed  Article  CAS  Google Scholar 

  7. 7.

    Jones, S. F. & Infante, J. R. Molecular pathways: fatty acid synthase. Clin. Cancer Res. 21, 5434–5438 (2015).

    CAS  PubMed  Article  Google Scholar 

  8. 8.

    Benjamin, D. I. et al. Diacylglycerol metabolism and signaling is a driving force underlying FASN inhibitor sensitivity in cancer cells. ACS Chem. Biol. 10, 1616–1623 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  9. 9.

    Birsoy, K. et al. Metabolic determinants of cancer cell sensitivity to glucose limitation and biguanides. Nature 508, 108–112 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  10. 10.

    Birsoy, K. et al. An essential role of the mitochondrial electron transport chain in cell proliferation is to enable aspartate synthesis. Cell 162, 540–551 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  11. 11.

    Costanzo, M. et al. A global genetic interaction network maps a wiring diagram of cellular function. Science 353, aaf1420 (2016).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  12. 12.

    Fischer, B. et al. A map of directional genetic interactions in a metazoan cell. eLife 4, e05464 (2015).

    PubMed Central  Article  CAS  Google Scholar 

  13. 13.

    Harrison, R., Papp, B., Pál, C., Oliver, S. G. & Delneri, D. Plasticity of genetic interactions in metabolic networks of yeast. Proc. Natl Acad. Sci. USA 104, 2307–2312 (2007).

    CAS  PubMed  Article  Google Scholar 

  14. 14.

    Szappanos, B. et al. An integrated approach to characterize genetic interaction networks in yeast metabolism. Nat. Genet. 43, 656–662 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  15. 15.

    Wright, A. V., Nuñez, J. K. & Doudna, J. A. Biology and applications of CRISPR systems: harnessing nature’s toolbox for genome engineering. Cell 164, 29–44 (2016).

    CAS  PubMed  Article  Google Scholar 

  16. 16.

    Doench, J. G. Am I ready for CRISPR? A user’s guide to genetic screens. Nat. Rev. Genet. 19, 67–80 (2018).

    CAS  PubMed  Article  Google Scholar 

  17. 17.

    Currie, E., Schulze, A., Zechner, R., Walther, T. C. & Farese, R. V. Cellular fatty acid metabolism and cancer. Cell Metab. 18, 153–161 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  18. 18.

    Carette, J. E. et al. Ebola virus entry requires the cholesterol transporter Niemann-Pick C1. Nature 477, 340–343 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  19. 19.

    Hart, T. et al. Evaluation and design of genome-wide CRISPR/SpCas9 knockout screens. G3 (Bethesda) 7, 2719–2727 (2017).

    CAS  Article  Google Scholar 

  20. 20.

    Billmann, M., Chaudhary, V., ElMaghraby, M. F., Fischer, B. & Boutros, M. Widespread rewiring of genetic networks upon cancer signaling pathway activation. Cell Syst. 6, 52–64(2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  21. 21.

    Costanzo, M. et al. The genetic landscape of a cell. Science 327, 425–431 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  22. 22.

    Ryczko, M. C. et al. Metabolic reprogramming by hexosamine biosynthetic and Golgi N-glycan branching pathways. Sci. Rep. 6, 23043 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  23. 23.

    Cheng, C. et al. Glucose-mediated N-glycosylation of SCAP is essential for SREBP-1 activation and tumor growth. Cancer Cell 28, 569–581 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  24. 24.

    Wang, S. et al. Site-specific O-glycosylation of members of the low-density lipoprotein receptor superfamily enhances ligand interactions. J. Biol. Chem. 293, 7408–7422 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  25. 25.

    Obaidat, A., Roth, M. & Hagenbuch, B. The expression and function of organic anion transporting polypeptides in normal tissues and in cancer. Annu. Rev. Pharmacol. Toxicol. 52, 135–151 (2012).

    CAS  PubMed  Article  Google Scholar 

  26. 26.

    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 (2008).

    Article  Google Scholar 

  27. 27.

    Romero, P. et al. Computational prediction of human metabolic pathways from the complete human genome. Genome Biol. 6, R2 (2005).

    PubMed  Article  Google Scholar 

  28. 28.

    Scott, C. C., Vossio, S., Rougemont, J. & Gruenberg, J. TFAP2 transcription factors are regulators of lipid droplet biogenesis. eLife 7, e36330 (2018).

    PubMed  PubMed Central  Article  Google Scholar 

  29. 29.

    Shimano, H. & Sato, R. SREBP-regulated lipid metabolism: convergent physiology — divergent pathophysiology. Nat. Rev. Endocrinol. 13, 710–730 (2017).

    CAS  PubMed  Article  Google Scholar 

  30. 30.

    Zhou, Z., Zhou, J., Su, Z. & Gu, X. Asymmetric evolution of human transcription factor regulatory networks. Mol. Biol. Evol 31, 2149–2155 (2014).

    CAS  PubMed  Article  Google Scholar 

  31. 31.

    VanderSluis, B. et al. Genetic interactions reveal the evolutionary trajectories of duplicate genes. Mol. Syst. Biol. 6, 429 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  32. 32.

    Ascencio, D., Ochoa, S., Delaye, L. & DeLuna, A. Increased rates of protein evolution and asymmetric deceleration after the whole-genome duplication in yeasts. BMC Evol. Biol. 17, 40 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  33. 33.

    The UniProt Consortium. UniProt: a worldwide hub of protein knowledge. Nucleic Acids Res. 47, D506–D515 (2019).

    Article  CAS  Google Scholar 

  34. 34.

    Mitchell, A. L. et al. InterPro in 2019: improving coverage, classification and access to protein sequence annotations. Nucleic Acids Res. 47, D351–D360 (2019).

    CAS  PubMed  Article  Google Scholar 

  35. 35.

    Uhlen, M. et al. Tissue-based map of the human proteome. Science 347, 1260419–1260419 (2015).

    PubMed  Article  CAS  Google Scholar 

  36. 36.

    Nagy, Á., Lánczky, A., Menyhárt, O. & Győrffy, B. Validation of miRNA prognostic power in hepatocellular carcinoma using expression data of independent datasets. Sci. Rep. 8, 9227 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  37. 37.

    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).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  38. 38.

    Behan, F. M. et al. Prioritization of cancer therapeutic targets using CRISPR–Cas9 screens. Nature 568, 511–516 (2019).

    CAS  PubMed  Article  Google Scholar 

  39. 39.

    Klarin, D. et al. Genetics of blood lipids among ~300,000 multi-ethnic participants of the million veteran program. Nat. Genet. 50, 1514–1523 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  40. 40.

    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).

    CAS  PubMed  Article  Google Scholar 

  41. 41.

    Go, C. D. et al. A proximity biotinylation map of a human cell. Preprint at bioRxiv https://doi.org/10.1101/796391(2019).

  42. 42.

    Costanzo, M. et al. Global genetic networks and the genotype-to-phenotype relationship. Cell 177, 85–100 (2019).

    CAS  Article  PubMed  Google Scholar 

  43. 43.

    Horlbeck, M. A. et al. Mapping the genetic landscape of human cells. Cell 174, 953–967.e22 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  44. 44.

    Najm, F. J. et al. Orthologous CRISPR–Cas9 enzymes for combinatorial genetic screens. Nat. Biotechnol. 36, 179–189 (2018).

    CAS  PubMed  Article  Google Scholar 

  45. 45.

    Han, K. et al. Synergistic drug combinations for cancer identified in a CRISPR screen for pairwise genetic interactions. Nat. Biotechnol. 35, 463–474 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  46. 46.

    Norman, T. M. et al. Exploring genetic interaction manifolds constructed from rich single-cell phenotypes. Science 365, 786–793 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  47. 47.

    Shen, J. P. et al. Combinatorial CRISPR–Cas9 screens for de novo mapping of genetic interactions. Nat. Methods 14, 573–576 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  48. 48.

    Gonatopoulos-Pournatzis, T. et al. Genetic interaction mapping and exon-resolution functional genomics with a hybrid Cas9–Cas12a platform. Nat. Biotechnol. 38, 638–648 (2020).

    CAS  PubMed  Article  Google Scholar 

  49. 49.

    Brenner, A. et al. Heavily pre-treated breast cancer patients show promising responses in the first in human study of the first-In-class fatty acid synthase (FASN) inhibitor, TVB-2640 in combination with paclitaxel. in Cancer Res. 77, abstr. P6-11-09 (2017).

  50. 50.

    Gimeno, R. E. Fatty acid transport proteins. Curr. Opin. Lipidol. 18, 271–276 (2007).

    CAS  PubMed  Article  Google Scholar 

  51. 51.

    Boyle, E. A., Pritchard, J. K. & Greenleaf, W. J. High-resolution mapping of cancer cell networks using co-functional interactions. Mol. Syst. Biol. 14, e8594 (2018).

    PubMed  PubMed Central  Article  Google Scholar 

  52. 52.

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

    PubMed  PubMed Central  Article  Google Scholar 

  53. 53.

    Luciano, M. et al. Association analysis in over 329,000 individuals identifies 116 independent variants influencing neuroticism. Nat. Genet. 50, 6–11 (2018).

    CAS  PubMed  Article  Google Scholar 

  54. 54.

    Kichaev, G. et al. Leveraging polygenic functional enrichment to improve GWAS power. Am. J. Hum. Genet. 104, 65–75 (2019).

    CAS  PubMed  Article  Google Scholar 

  55. 55.

    Nagel, M., Watanabe, K., Stringer, S., Posthuma, D. & van der Sluis, S. Item-level analyses reveal genetic heterogeneity in neuroticism. Nat. Commun. 9, 905 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  56. 56.

    Aregger, M., Chandrashekhar, M., Tong, A. H. Y., Chan, K. & Moffat, J. Pooled lentiviral CRISPR–Cas9 screens for functional genomics in mammalian cells. Methods Mol. Biol. 1869, 169–188 (2019).

    CAS  PubMed  Article  Google Scholar 

  57. 57.

    Hesketh, G. G., Youn, J. Y., Samavarchi-Tehrani, P., Raught, B. & Gingras, A. C. Parallel exploration of interaction space by BioID and affinity purification coupled to mass spectrometry. Methods Mol. Biol 1550, 115–136 (2017).

    CAS  PubMed  Article  Google Scholar 

  58. 58.

    Branon, T. C. et al. Efficient proximity labeling in living cells and organisms with TurboID. Nat. Biotechnol. 36, 880–887 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  59. 59.

    Knight, J. D. R. et al. ProHits-viz: a suite of web tools for visualizing interaction proteomics data. Nat. Methods 14, 645–646 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  60. 60.

    Liu, G. et al. Data independent acquisition analysis in ProHits 4.0. J. Proteomics 149, 64–68 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  61. 61.

    Deutsch, E. W. et al. Trans-Proteomic pipeline, a standardized data processing pipeline for large-scale reproducible proteomics informatics. Proteomics. Clin. Appl. 9, 745–754 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  62. 62.

    Teo, G. et al. SAINTexpress: improvements and additional features in significance analysis of INTeractome software. J. Proteomics 100, 37–43 (2014).

    CAS  PubMed  Article  Google Scholar 

  63. 63.

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

    CAS  Article  Google Scholar 

  64. 64.

    Abdel Rahman, A. M., Ryczko, M., Pawling, J. & Dennis, J. W. Probing the hexosamine biosynthetic pathway in human tumor cells by multitargeted tandem mass spectrometry. ACS Chem. Biol. 8, 2053–2062 (2013).

    CAS  PubMed  Article  Google Scholar 

  65. 65.

    Hart, T. et al. High-resolution CRISPR screens reveal fitness genes and genotype-specific cancer liabilities. Cell 163, 1515–1526 (2015).

    CAS  PubMed  Article  Google Scholar 

  66. 66.

    Greene, C. S. et al. Understanding multicellular function and disease with human tissue-specific networks. Nat. Genet. 47, 569–576 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  67. 67.

    Ashburner, M. et al. Gene Ontology: tool for the unification of biology. Nat. Genet. 25, 25–29 (2000).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  68. 68.

    Liberzon, A. et al. Molecular signatures database (MSigDB) 3.0. Bioinformatics 27, 1739–1740 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  69. 69.

    Raudvere, U. et al. g:Profiler: a web server for functional enrichment analysis and conversions of gene lists (2019 update). Nucleic Acids Res. 47, W191–W198 (2019).

Download references

Acknowledgements

We thank members of the Moffat lab for helpful discussions. Q. Huang, M. Olivieri, C. Sheene, R. Akthar and S. Sidhu are gratefully acknowledged for assistance with molecular biology experiments. Next-generation sequencing was performed at the Donnelly Sequencing Centre at the University of Toronto. Proteomics work was performed at the Network Biology Collaborative Centre at the Lunenfeld-Tanenbaum Research Institute, a facility supported by Canada Foundation for Innovation funding, by the Ontarian Government and by Genome Canada and Ontario Genomics (OGI-097, OGI-139). M.A. was supported by a Swiss National Science Foundation Postdoctoral Fellowship; K.A.L. was supported by a Vanier Canada Graduate Scholarship and Studentship award from the Kidney Cancer Research Network of Canada. M.B. was supported by a DFG Fellowship (Bi 2086/1-1). This research was funded by grants from the Canadian Institutes for Health Research (J.M., C.B, B.J.A. and A.-C.G.), Ontario Research Fund (B.J.A., C.B. and J.M) and Canada Research Chairs Program (J.M., C.B. and A.-C.G.). C.L.M., M.B. and M.R. are partially supported by grants from the National Science Foundation (MCB 1818293) and the National Institutes of Health (R01HG005084, R01HG005853).

Author information

Affiliations

Authors

Contributions

Conceptualization and design of the study: M.A., K.A.L. and J.M.; Experimental investigation: M.A., K.A.L., A.H.Y.T., K.C., L.N., O.S., A.H., J.P., Z.-Y.L., H.A., C.J.W. and A.W. Data analysis: M.A., K.L., M.B., M.C., M.R., K.R.B., C.R., M.U., P.M., J.W.D., A.C.G, J.L.M. and J.M.; Writing and editing: M.A., K.A.L., M.B., M.C., B.J.A., C.B., C.L.M. and J.M. with input from other authors; Supervision: J.W.D., A.C.G., C.L.M., B.J.A., C.B. and J.M.; Funding acquisition: C.L.M., B.J.A., C.B. and J.M.

Corresponding authors

Correspondence to Chad L. Myers or Brenda J. Andrews or Charles Boone or Jason Moffat.

Ethics declarations

Competing interests

J.M., B.J.A. and C.B are shareholders in Northern Biologics. J.M. is a shareholder in Pionyr Immunotherapeutics, is acting chief scientific officer and shareholder in Empirica Therapeutics and is an SAB member and shareholder of Aelian Biotechnology. C.B. is an SAB member of Yumanity Therapeutics.

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 Validation of FASN-KO cells and genetic interactions screens.

a, Western blot depicting FASN and β-Actin levels in HAP1 parental wildtype (WT) and FASN-KO cells. Representative data from three biologically independent experiments. b, Bar plot depicting malonyl-CoA levels in HAP1 WT and FASN-KO cells as detected by mass spectrometry-based metabolite profiling, normalized to parent HAP1 WT cells. Data are represented as means ± standard deviation; n = four biologically independent experiments; two-tailed Mann Whitney U test. c, Growth curves of HAP1 WT cells depicting relative cell numbers over 3 days, plotted as a function of glucose concentration in mM, in either 0.5 mM (blue), 1 mM (red), 1.5 mM (yellow), or 2 mM (black) glutamine. d, Histogram showing a frequency distribution of all pairwise Pearson correlation coefficients for LFC values (T0/T18) of the 21 WT HAP1 screens. e, Precision–recall curves for the three CRISPR replicate screens in HAP1 FASN-KO cells using the reference core essential gene set (CEG2) defined in Hart et al., 201719. f, Fitness effect (log2 fold-change, LFC) distributions for reference core essential (CEG2) and non-essential gene sets defined in Hart et al., 201719 across the three FASN-KO query screens. g, Agreement of gRNA-level genetic interaction scores with FASN. Scatter plots show all possible pairwise combinations of three biological replicate screens. The Pearson correlation coefficient (r), based on comparison values for 70,152 gRNAs. h, Agreement of FASN quantitative genetic interactions (qGIs). Scatter plots show gene-level FASN genetic interactions (qGI scores) derived from all possible pairwise combinations of three biological replicate screens. The Pearson correlation coefficient (r), based on comparison of all qGI scores (r shown in grey, calculated on all the grey and purple data points in the scatter plots), or only genetic interactions that exceed a given significance threshold (|qGI| > 0.5, FDR < 0.5) in either one screen (logic OR; purple). i, Scatter plot showing reproducibility scores as a function of qGI scores for a single FASN-KO screen (replicate A). Pairwise reproducibility of a qGI score was calculated by computing the contribution of each of the 17,804 genes to the covariance between a pair of screens divided by the sum of standard deviations. The reproducibility score represents the sum of those values across the three pairwise comparisons. Five genes with highest reproducibility scores and the most negative qGI scores with the FASN-KO screen (replicate A) are labelled. j, Establishing the AAVS1 target locus as a good negative control site in HAP1 WT and FASN-KO cells. Schematic depicting co-culture validation assays (upper panel). Parental WT and FASN-KO cells were stably transduced with color-coded gRNA expression vectors carrying an intergenic control or screen hit-targeting gRNA. Color-coded cells are mixed at an equal ratio, cultured over two weeks and the relative proportion of green and red cells was quantified by flow cytometry. Control co-culture experiments performed in parallel to the validation of hit genes depicted in Fig. 1e as indicated above each barplot (lower panel). Bar plots are depicting the color ratio of cells carrying two colour-coded gRNAs targeting AAVS1 (intergenic control) across WT and two FASN-KO clones as indicated. Experiments were performed with three independent gRNA targeting AAVS1 and using both color orientations. All data are represented as means ± standard deviation; n = three (LDLR) or four (SLCO4A1, C12orf49) biologically independent experiments. k, Scatter plots reproducibility scores as a function of qGI scores for the negative genetic interaction hits depicted in Fig. 1h functioning in lipid uptake and homeostasis (red), vesicle transport genes (black) and glycosylation (blue). l, Precision and recall values for GIs with FASN measured at the standard (|qGI| > 0.5, FDR < 0.5) and stringent (|qGI| > 0.7, FDR < 0.2) thresholds. Precision and recall values were computed using an MCMC-based approach (see Methods). Source data

Extended Data Fig. 2 Quality control of genetic interaction screens for fatty acid synthesis-related query genes.

a, Precision-recall curves distinguishing the reference core essential gene set (CEG2) defined in Hart et al., 201719 and a non-essential gene set in CRISPR screens in five HAP1 knockout query cell lines (LDLR, C12orf49, SREBF2, ACACA, SREBF1-KO). b, Fitness effect (LFC) distributions for reference core essential (CEG2) and non-essential gene sets defined in Hart et al., 201719 across CRISPR screens in five HAP1 KO cell lines (LDLR, C12orf49, SREBF2, ACACA, SREBF1). c, Bar plot of enrichment of co-annotation as defined by the Human Functional Network, Gene Ontology Bioprocesses (BP), HumanCyc or and aggregation of pathway standards (including REACTOME, KEGG or BIOCARTA) for genetic interactions identified across all six query genome-wide screens (FASN, LDLR, C12orf49, SREBF2, ACACA, SREBF1). Enrichment was tested using a hypergeometric test. See methods for details of analysis.

Extended Data Fig. 3 Pathway enrichment analysis of genetic interactions for fatty acid synthesis-related query genes in additional HumanCyc sub-categories.

a, Dot plot of normalized pathway enrichment values for aggregate GIs across the six query genes (FASN, C12orf49, LDLR, SREBF2, ACACA, SREBF1) with sub-categories from HumanCyc are indicated. A GI is identified for a query-library pair if the |qGI| > 0.5 and FDR < 0.5. Enrichment for positive (yellow) and negative (blue) GI is tested inside Glycan Pathways and Generation of precursor metabolite and energy HumanCyc branches using a hypergeometric test. Enrichment with p-value < 0.05 are blue (negative GI) and yellow (positive GI). Dot size is proportional to the fold-enrichment in the indicated categories and specified in the legend.

Extended Data Fig. 4 Overview of C12orf49, cancer associations, and functional correlates.

a, Cartoon of C12orf49 protein sequence features and domains. b–e, Kaplan Meier survival plots displaying univariate analysis of TCGA data across multiple tumor types including kidney, breast, liver and sarcoma for C12orf49 high vs. low expressing tumor tissue (www.kmplot.com)36. Patient numbers at risk (n) are indicated below each plot; two-sided logrank test. f-h, GI overlap between the 17,804 C12orf49 and SREBF2, SREBF1 and ACACA qGI scores shown as pairwise scatter plots with C12orf49 as function of SREBF2 (f), SREBF1 (g) and ACACA (h). A common negative GI is called if it is significant (qGI < -0.5, FDR < 0.5) in both screens (indicated in blue). The top 10 strongest common GIs and lipid metabolism genes are labelled. i, Profile similarity of C12orf49 across genome-wide DepMap CRISPR/Cas9 screens. Similarity was quantified by taking all pairwise gene-gene Pearson correlation coefficients of CERES score profiles across 563 screens (19Q2 DepMap data release). The distribution of 17,633 CERES profile similarity is plotted as a quantile-quantile plot, and the top 18 most similar out of 17,633 genes are labelled. Genes associated with lipid metabolism are indicated in red. j, Pathway analysis of C12orf49 profile similarity. C12orf49 profile similarity scores for all 17,634 genes represented in the DepMap were mean-summarized by pathway as defined in the HumanCyc standard27. Tendencies towards pathway-level similarity (co-essentiality) and dissimilarity (exclusivity) with C12orf49 were tested using a two-sided Wilcoxon rank-sum test with multiple-hypothesis correction using the Benjamini and Hochberg procedure.

Extended Data Fig. 5 Regulation of LDL uptake and LDLR expression by C12orf49.

a, Bar plots showing the results of a low density lipoprotein (LDL) uptake assay across the indicated HAP1 cell lines using pHrodo-LDL probe. All data are represented as means ± standard deviation; n = two (SREBF1, SREBF2) or three (WT, FASN, LDLR, C12orf49, WT + C12orf49-V5, C12orf49 + C12orf49-V5) biologically independent experiments; one-way ANOVA. b, Bar plots showing the results of a transferin uptake assay across the indicated HAP1 cell lines using pHrodo-Transferin probe. All data are represented as means ± standard deviation; n = two (SREBF1, SREBF2) or three (WT, FASN, LDLR, C12orf49, WT + C12orf49-V5, C12orf49 + C12orf49-V5) biologically independent experiments; one-way ANOVA. c, Bar plots showing the results of a low density lipoprotein (LDL) uptake assay across the indicated in lipoprotein-deprived HAP1 cell lines using Dil-LDL probe. All data are represented as means ± standard deviation; n = two biologically independent experiments. d, Pathway enrichment analysis of BioID preys captured with N-terminal (top panel) or C-terminal (bottom panel) miniTurbo-tagged C12orf49 under normal growth conditions using the GO molecular function, biological process, cellular compartments, KEGG and Reactome standards. Terms for significantly enriched gene sets (p < 0.05, maximum term size 105) are indicated and bars depict mean percentage overlap with the indicated term. Numbers next to each bar indicate the mean overlap and term sizes, respectively. The greyscale color legend for p-values is indicated on the right. P-values were calculated using gProfileR69. e, Barplots depicting the number of proteins localizing to indicated cellular compartments for preys captured with N-terminal (grey) or C-terminal (black) miniTurbo-tagged C12orf49 in 293 cells under serum-starvation.

Extended Data Fig. 6 RNAseq and western blot analysis in response to serum or lipoprotein starvation and upon inhibition of the proteasome.

a, Gene ontology enrichment analysis of genes upregulated under serum starvation in HAP1 wildtype (WT), C12orf49 or SREBF2-KO cells using GO molecular functions, GO bioprocesses and Reactome standards. Gene sets with overlapping members have been merged and bars depict mean percentage overlap with the indicated term. Numbers next to each bar indicate the mean overlap and term sizes, respectively. The greyscale color legend for p-values is indicated on the right; p-values were calculated using gProfileR69. b, Boxplots depicting mean expression and induction of genes assigned with the indicated term across HAP1 WT, C12orf49 and SREBF2-KO cells under normal (+FBS) and serum-starved (-FBS) conditions; n = three biologically independent experiments, two-tailed student’s t test. Boxes show IQR, 25th to 75th percentile, with the median indicated by a horizontal line. Whiskers extend to the quartile ± 1.5 x IQR. c, Western blotting analysis of SREBP2, LDLR and β-Actin levels across the indicated HAP1 co-isogenic knockout cell lines in response to overnight lipoprotein withdrawal (-) and a short refeeding period (-/+) in presence or absence of the proteasomal inhibitor MG132 (10µM MG132 for 5 hours) as indicated. Unprocessed full length and processed C-terminal SREBP2 products are indicated. Representative data from two biologically independent experiments. Source data

Extended Data Fig. 7 Gating strategy for flow cytometry experiments.

Gating strategies for flow cytometry experiments for a, co-culture assays and b, LDL/Transferrin uptake assays. In all cases the following steps were taken: 1. Forward scatter area (FSC-A) vs. side scatter area (SSC-A) were used to separate cell events from debris. 2. Forward scatter height (FSC-H) vs. forward scatter width (FSC-W) was used separate single cells from aggregates. 3. Forward scatter area (FSC-A) vs. viability stain (7AAD/B695-40 or Zombie NIR/R780-60) was utilized to gate live cells. For co-culture assays, gating scheme for separating GFP/B530-30 vs RFP/YG610-20 positive cells including steps 1-3 are shown in panel (a). For Dil-LDL/YG585-15 quantification, marker-positive live cells were quantified relative to unstained controls following steps 1-3 as displayed panel (b).

Supplementary information

Reporting Summary

Supplementary Table 1

FASN qGI reproducibility analysis across FASN replicate screens

Supplementary Table 2

qGI genetic interaction scores

Supplementary Table 3

Pathway enrichment negative genetic interactions FASN and C12orf49

Supplementary Table 4

Pathway enrichment GI z-scores

Supplementary Table 5

BioID C12orf49

Supplementary Table 6

Pathway enrichment BioID C12orf49

Supplementary Tables 7 and 8

RNAseq HAP1 WT, C12orf49-KO, SREBF2-KO plus-minus FBS Primer and oligo list

Source data

Source Data Fig. 5

Uncropped western blots

Source Data Extended Data Fig. 1

Uncropped western blots

Source Data Extended Data Fig. 6

Uncropped western blots

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Aregger, M., Lawson, K.A., Billmann, M. et al. Systematic mapping of genetic interactions for de novo fatty acid synthesis identifies C12orf49 as a regulator of lipid metabolism. Nat Metab 2, 499–513 (2020). https://doi.org/10.1038/s42255-020-0211-z

Download citation

Further reading