The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Systematic mapping of genetic interactions for de novo fatty acid synthesis identifies C12orf49 as a regulator of lipid metabolism
Published version of a preprint
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“We and others have proposed that, in cultured cells, SPRING is implicated in two important regulatory steps in the SREBP pathway, i.e. the retrograde transport of SCAP and in regulating the activity of S1P10–13.”
From Hepatic SREBP signaling requires SPRING to govern systemic lipid metabolism in mice and humans (2023) · cites this paper as [11]
Experimental — accuracy not yet measuredIn the citing paper
“We and others have proposed that, in cultured cells, SPRING is implicated in two important regulatory steps in the SREBP pathway, i.e. the retrograde transport of SCAP and in regulating the activity of S1P10–13.”
From Hepatic SREBP signaling requires SPRING to govern systemic lipid metabolism in mice and humans (2023) · cites this paper as [11]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Molecular signatures database (MSigDB) 3.0
Liberzon, A., Subramanian, A., Pinchback, R., Thorvaldsdóttir, H., Tamayo, P., & Mesirov, J. P. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr260 · Cited by 7,111
SREBPs: activators of the complete program of cholesterol and fatty acid synthesis in the liver
Horton, J. D., Goldstein, J. L., & Brown, M. S. (2002) · Journal of Clinical Investigation · doi:10.1172/jci0215593 · Cited by 4,092
The Genetic Landscape of a Cell
Costanzo, M., Baryshnikova, A., Bellay, J., Kim, Y., Spear, E. D., Sevier, C. S., Ding, H., Koh, J. L. Y., Toufighi, K., Mostafavi, S., et al. (2010) · Science · doi:10.1126/science.1180823 · Cited by 2,162
Self-citationCellular Fatty Acid Metabolism and Cancer
Currie, E., Schulze, A., Zechner, R., Walther, T. C., & Farese, R. V. (2013) · Cell Metabolism · doi:10.1016/j.cmet.2013.05.017 · Cited by 2,123
Prioritization of cancer therapeutic targets using CRISPR–Cas9 screens
Behan, F. M., Iorio, F., Picco, G., Gonçalves, E., Beaver, C. M., Migliardi, G., Santos, R., Rao, Y., Sassi, F., Pinnelli, M., et al. (2019) · Nature · doi:10.1038/s41586-019-1103-9 · Cited by 1,338
A global genetic interaction network maps a wiring diagram of cellular function
Costanzo, M., VanderSluis, B., Koch, E. N., Baryshnikova, A., Pons, C., Tan, G., Wang, W., Usaj, M., Hanchard, J., Lee, S. D., et al. (2016) · Science · doi:10.1126/science.aaf1420 · Cited by 1,240
Self-citationMetabolic determinants of cancer cell sensitivity to glucose limitation and biguanides
Birsoy, K., Possemato, R., Lorbeer, F. K., Bayraktar, E. C., Thiru, P., Yucel, B., Wang, T., Chen, W. W., Clish, C. B., & Sabatini, D. M. (2014) · Nature · doi:10.1038/nature13110 · Cited by 710
Evaluation and Design of Genome-Wide CRISPR/SpCas9 Knockout Screens
Hart, T., Tong, A. H. Y., Chan, K., Van Leeuwen, J., Seetharaman, A., Aregger, M., Chandrashekhar, M., Hustedt, N., Seth, S., Noonan, A., et al. (2017) · G3 Genes|Genomes|Genetics · doi:10.1534/g3.117.041277 · Cited by 599
Self-citationExploring genetic interaction manifolds constructed from rich single-cell phenotypes
Norman, T. M., Horlbeck, M. A., Replogle, J. M., Ge, A. Y., Xu, A., Jost, M., Gilbert, L. A., & Weissman, J. S. (2019) · Science · doi:10.1126/science.aax4438 · Cited by 392
Combinatorial CRISPR–Cas9 screens for de novo mapping of genetic interactions
Shen, J. P., Zhao, D., Sasik, R., Luebeck, J., Birmingham, A., Bojorquez-Gomez, A., Licon, K., Klepper, K., Pekin, D., Beckett, A. N., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4225 · Cited by 359
Research works printed but not linked: 56
- Chen, M. & Huang, J. The expanded role of fatty acid metabolism in cancer: new aspects and targets. Precis. Clin. Med 2, 183–191 (2019). · DOI
- 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). · DOI
- Imoto, M. Chemistry and biology for the small molecules targeting characteristics of cancer cells. Biosci. Biotechnol. Biochem. 83, 1–10 (2018).
- Menendez, J. A. & Lupu, R. Fatty acid synthase (FASN) as a therapeutic target in breast cancer. Expert Opin. Ther. Targets 21, 1001–1016 (2017). · DOI
- Garber, K. Cancer anabolic metabolism inhibitors move into clinic. Nat. Biotechnol. 34, 794–795 (2016). · DOI
- Röhrig, F. & Schulze, A. The multifaceted roles of fatty acid synthesis in cancer. Nat. Rev. Cancer 16, 732–749 (2016). · DOI · Paper page
- Jones, S. F. & Infante, J. R. Molecular pathways: fatty acid synthase. Clin. Cancer Res. 21, 5434–5438 (2015). · DOI
- 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). · DOI
- 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). · DOI · Paper page
- Fischer, B. et al. A map of directional genetic interactions in a metazoan cell. eLife 4, e05464 (2015). · DOI
- 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). · DOI
- Szappanos, B. et al. An integrated approach to characterize genetic interaction networks in yeast metabolism. Nat. Genet. 43, 656–662 (2011). · DOI
- 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). · DOI · Paper page
- Doench, J. G. Am I ready for CRISPR? A user’s guide to genetic screens. Nat. Rev. Genet. 19, 67–80 (2018). · DOI · Paper page
- Carette, J. E. et al. Ebola virus entry requires the cholesterol transporter Niemann-Pick C1. Nature 477, 340–343 (2011). · DOI
- 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). · DOI
- Ryczko, M. C. et al. Metabolic reprogramming by hexosamine biosynthetic and Golgi N-glycan branching pathways. Sci. Rep. 6, 23043 (2016). · DOI
- 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). · DOI
- 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). · DOI
- 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). · DOI
- Romero, P. et al. Computational prediction of human metabolic pathways from the complete human genome. Genome Biol. 6, R2 (2005). · DOI
- Scott, C. C., Vossio, S., Rougemont, J. & Gruenberg, J. TFAP2 transcription factors are regulators of lipid droplet biogenesis. eLife 7, e36330 (2018). · DOI
- Shimano, H. & Sato, R. SREBP-regulated lipid metabolism: convergent physiology — divergent pathophysiology. Nat. Rev. Endocrinol. 13, 710–730 (2017). · DOI · Paper page
- Zhou, Z., Zhou, J., Su, Z. & Gu, X. Asymmetric evolution of human transcription factor regulatory networks. Mol. Biol. Evol 31, 2149–2155 (2014). · DOI
- 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). · DOI
- The UniProt Consortium. UniProt: a worldwide hub of protein knowledge. Nucleic Acids Res. 47, D506–D515 (2019). · DOI · Paper page
- Mitchell, A. L. et al. InterPro in 2019: improving coverage, classification and access to protein sequence annotations. Nucleic Acids Res. 47, D351–D360 (2019). · DOI · Paper page
- Uhlen, M. et al. Tissue-based map of the human proteome. Science 347, 1260419–1260419 (2015). · DOI · Paper page
- 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). · DOI
- 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). · DOI · Paper page
- 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). · DOI · Paper page
- 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). · DOI · Paper page
- Go, C. D. et al. A proximity biotinylation map of a human cell. Preprint at bioRxiv https://doi.org/10.1101/796391(2019). · DOI
- Costanzo, M. et al. Global genetic networks and the genotype-to-phenotype relationship. Cell 177, 85–100 (2019). · DOI · Paper page
- Han, K. et al. Synergistic drug combinations for cancer identified in a CRISPR screen for pairwise genetic interactions. Nat. Biotechnol. 35, 463–474 (2017). · DOI
- 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). · DOI · Paper page
- 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). · DOI · Paper page
- Gimeno, R. E. Fatty acid transport proteins. Curr. Opin. Lipidol. 18, 271–276 (2007). · DOI
- 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). · DOI
- Kim, E. et al. A network of human functional gene interactions from knockout fitness screens in cancer cells. Life Sci. Alliance 2, e201800278 (2019). · DOI
- Luciano, M. et al. Association analysis in over 329,000 individuals identifies 116 independent variants influencing neuroticism. Nat. Genet. 50, 6–11 (2018). · DOI · Paper page
- Kichaev, G. et al. Leveraging polygenic functional enrichment to improve GWAS power. Am. J. Hum. Genet. 104, 65–75 (2019). · DOI · Paper page
- 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). · DOI
- 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). · DOI
- 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). · DOI
- Branon, T. C. et al. Efficient proximity labeling in living cells and organisms with TurboID. Nat. Biotechnol. 36, 880–887 (2018). · DOI · Paper page
- Knight, J. D. R. et al. ProHits-viz: a suite of web tools for visualizing interaction proteomics data. Nat. Methods 14, 645–646 (2017). · DOI
- Liu, G. et al. Data independent acquisition analysis in ProHits 4.0. J. Proteomics 149, 64–68 (2016). · DOI
- 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). · DOI · Paper page
- Teo, G. et al. SAINTexpress: improvements and additional features in significance analysis of INTeractome software. J. Proteomics 100, 37–43 (2014). · DOI
- Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013). · DOI · Paper page
- 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). · DOI
- Hart, T. et al. High-resolution CRISPR screens reveal fitness genes and genotype-specific cancer liabilities. Cell 163, 1515–1526 (2015). · DOI · Paper page
- Greene, C. S. et al. Understanding multicellular function and disease with human tissue-specific networks. Nat. Genet. 47, 569–576 (2015). · DOI · Paper page
- Ashburner, M. et al. Gene Ontology: tool for the unification of biology. Nat. Genet. 25, 25–29 (2000). · DOI · Paper page
- 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). · DOI · Paper page
Cited by
Sorted by citations
A new era in functional genomics screens
Przybyla, L., & Gilbert, L. A. (2022) · Nature Reviews Genetics · doi:10.1038/s41576-021-00409-w · Cited by 281
VRK1 as a synthetic lethal target in VRK2 promoter-methylated cancers of the nervous system
So, J., Mabe, N. W., Englinger, B., Chow, K.-H., Moyer, S. M., Yerrum, S., Trissal, M. C., Marques, J. G., Kwon, J. J., Shim, B., et al. (2022) · JCI Insight · doi:10.1172/jci.insight.158755 · Cited by 45
POST1/C12ORF49 regulates the SREBP pathway by promoting site-1 protease maturation
Xiao, J., Xiong, Y., Yang, L.-T., Wang, J.-Q., Zhou, Z.-M., Dong, L.-W., Shi, X.-J., Zhao, X., Luo, J., & Song, B.-L. (2021) · Protein & Cell · doi:10.1007/s13238-020-00753-3 · Cited by 49
Sparse dictionary learning recovers pleiotropy from human cell fitness screens
Pan, J., Kwon, J. J., Talamas, J. A., Borah, A. A., Vazquez, F., Boehm, J. S., Tsherniak, A., Zitnik, M., McFarland, J. M., & Hahn, W. C. (2022) · Cell Systems · doi:10.1016/j.cels.2021.12.005 · Cited by 37
Hepatic SREBP signaling requires SPRING to govern systemic lipid metabolism in mice and humans
Hendrix, S., Kingma, J., Ottenhoff, R., Valiloo, M., Svecla, M., Zijlstra, L. F., Sachdev, V., Kovac, K., Levels, J. H. M., Jongejan, A., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-40943-1 · Cited by 34
SPRING is a Dedicated Licensing Factor for SREBP-Specific Activation by S1P
Hendrix, S., Tan, J. M. E., Ndoj, K., Kingma, J., Valiloo, M., Zijlstra, L. F., Ottenhoff, R., Seidah, N. G., Loregger, A., Kober, D. L., et al. (2024) · Molecular and Cellular Biology · doi:10.1080/10985549.2024.2348711 · Cited by 9
Metadata
- Authors
- Aregger, M., Lawson, K. A., Billmann, M., Costanzo, M., Tong, A. H. Y., Chan, K., Rahman, M., Brown, K. R., Ross, C., Usaj, M., Nedyalkova, L., Sizova, O., Habsid, A., Pawling, J., Lin, Z.-Y., Abdouni, H., Wong, C. J., Weiss, A., Mero, P., Dennis, J. W., Gingras, A.-C., Myers, C. L., Andrews, B. J., Boone, C., & Moffat, J.
- ORCID
- Venue
- Nature Metabolism
- Published
- 2020-06-01
- Volume
- 2
- Issue
- 6
- Pages
- 499-513
- ISSN
- 2522-5812
- PMC
- PMC7566881
- Cited by
- 115
- References
- 13
- Language
- en
- Licence
- Funders
- Swiss National Science Foundation | nccr – on the move
- Gouvernement du Canada | Instituts de Recherche en Santé du Canada | CIHR Skin Research Training Centre (Vanier)
- Kidney Cancer Association
- Kidney Cancer Research Network of Canada
- Ontario Research Foundation
- U.S. Department of Health & Human Services | National Institutes of Health (R01HG005084)
- National Science Foundation (R01HG005084)
- Gouvernement du Canada | Canadian Institutes of Health Research (MOP-142375)
- Canada Research Chairs
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review