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.
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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.
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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).
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.
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.
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.
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.
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
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).
FASN qGI reproducibility analysis across FASN replicate screens
qGI genetic interaction scores
Pathway enrichment negative genetic interactions FASN and C12orf49
Pathway enrichment GI z-scores
Pathway enrichment BioID C12orf49
RNAseq HAP1 WT, C12orf49-KO, SREBF2-KO plus-minus FBS Primer and oligo list
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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
Nature Metabolism (2020)