IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v12y2021i1d10.1038_s41467-021-26867-8.html
   My bibliography  Save this article

Discovery of putative tumor suppressors from CRISPR screens reveals rewired lipid metabolism in acute myeloid leukemia cells

Author

Listed:
  • W. Frank Lenoir

    (The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences; The University of Texas MD Anderson Cancer Center
    The University of Texas MD Anderson Cancer Center)

  • Micaela Morgado

    (The University of Texas MD Anderson Cancer Center)

  • Peter C. DeWeirdt

    (Broad Institute of MIT and Harvard)

  • Megan McLaughlin

    (The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences; The University of Texas MD Anderson Cancer Center
    The University of Texas MD Anderson Cancer Center)

  • Audrey L. Griffith

    (Broad Institute of MIT and Harvard)

  • Annabel K. Sangree

    (Broad Institute of MIT and Harvard)

  • Marissa N. Feeley

    (Broad Institute of MIT and Harvard)

  • Nazanin Esmaeili Anvar

    (The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences; The University of Texas MD Anderson Cancer Center
    The University of Texas MD Anderson Cancer Center)

  • Eiru Kim

    (The University of Texas MD Anderson Cancer Center)

  • Lori L. Bertolet

    (The University of Texas MD Anderson Cancer Center)

  • Medina Colic

    (The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences; The University of Texas MD Anderson Cancer Center
    The University of Texas MD Anderson Cancer Center)

  • Merve Dede

    (The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences; The University of Texas MD Anderson Cancer Center
    The University of Texas MD Anderson Cancer Center)

  • John G. Doench

    (Broad Institute of MIT and Harvard)

  • Traver Hart

    (The University of Texas MD Anderson Cancer Center
    The University of Texas MD Anderson Cancer Center)

Abstract

CRISPR knockout fitness screens in cancer cell lines reveal many genes whose loss of function causes cell death or loss of fitness or, more rarely, the opposite phenotype of faster proliferation. Here we demonstrate a systematic approach to identify these proliferation suppressors, which are highly enriched for tumor suppressor genes, and define a network of 145 such genes in 22 modules. One module contains several elements of the glycerolipid biosynthesis pathway and operates exclusively in a subset of acute myeloid leukemia cell lines. The proliferation suppressor activity of genes involved in the synthesis of saturated fatty acids, coupled with a more severe loss of fitness phenotype for genes in the desaturation pathway, suggests that these cells operate at the limit of their carrying capacity for saturated fatty acids, which we confirm biochemically. Overexpression of this module is associated with a survival advantage in juvenile leukemias, suggesting a clinically relevant subtype.

Suggested Citation

  • W. Frank Lenoir & Micaela Morgado & Peter C. DeWeirdt & Megan McLaughlin & Audrey L. Griffith & Annabel K. Sangree & Marissa N. Feeley & Nazanin Esmaeili Anvar & Eiru Kim & Lori L. Bertolet & Medina C, 2021. "Discovery of putative tumor suppressors from CRISPR screens reveals rewired lipid metabolism in acute myeloid leukemia cells," Nature Communications, Nature, vol. 12(1), pages 1-15, December.
  • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-26867-8
    DOI: 10.1038/s41467-021-26867-8
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-021-26867-8
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-021-26867-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Feng Gao & Xiaomin He, 2020. "Survival Analysis: Theory and Application in Finance," World Scientific Book Chapters, in: Cheng Few Lee & John C Lee (ed.), HANDBOOK OF FINANCIAL ECONOMETRICS, MATHEMATICS, STATISTICS, AND MACHINE LEARNING, chapter 120, pages 4087-4118, World Scientific Publishing Co. Pte. Ltd..
    2. Benaglia, Tatiana & Chauveau, Didier & Hunter, David R. & Young, Derek S., 2009. "mixtools: An R Package for Analyzing Mixture Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 32(i06).
    3. Jens Keilwagen & Ivo Grosse & Jan Grau, 2014. "Area under Precision-Recall Curves for Weighted and Unweighted Data," PLOS ONE, Public Library of Science, vol. 9(3), pages 1-13, March.
    4. Fiona M. Behan & Francesco Iorio & Gabriele Picco & Emanuel Gonçalves & Charlotte M. Beaver & Giorgia Migliardi & Rita Santos & Yanhua Rao & Francesco Sassi & Marika Pinnelli & Rizwan Ansari & Sarah H, 2019. "Prioritization of cancer therapeutic targets using CRISPR–Cas9 screens," Nature, Nature, vol. 568(7753), pages 511-516, April.
    5. Gerard I. Evan & Karen H. Vousden, 2001. "Proliferation, cell cycle and apoptosis in cancer," Nature, Nature, vol. 411(6835), pages 342-348, May.
    6. Charles R. Harris & K. Jarrod Millman & Stéfan J. Walt & Ralf Gommers & Pauli Virtanen & David Cournapeau & Eric Wieser & Julian Taylor & Sebastian Berg & Nathaniel J. Smith & Robert Kern & Matti Picu, 2020. "Array programming with NumPy," Nature, Nature, vol. 585(7825), pages 357-362, September.
    7. Jeffrey W. Tyner & Cristina E. Tognon & Daniel Bottomly & Beth Wilmot & Stephen E. Kurtz & Samantha L. Savage & Nicola Long & Anna Reister Schultz & Elie Traer & Melissa Abel & Anupriya Agarwal & Auro, 2018. "Functional genomic landscape of acute myeloid leukaemia," Nature, Nature, vol. 562(7728), pages 526-531, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Miquel Anglada-Girotto & Ludovica Ciampi & Sophie Bonnal & Sarah A. Head & Samuel Miravet-Verde & Luis Serrano, 2024. "In silico RNA isoform screening to identify potential cancer driver exons with therapeutic applications," Nature Communications, Nature, vol. 15(1), pages 1-19, December.
    2. Fadi J. Najm & Peter DeWeirdt & Molly M. Moore & Samantha M. Bevill & Chadi A. El Farran & Kevin A. Macias & Mudra Hegde & Amanda L. Waterbury & Brian B. Liau & Peter Galen & John G. Doench & Bradley , 2023. "Chromatin complex dependencies reveal targeting opportunities in leukemia," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    3. Nazanin Esmaeili Anvar & Chenchu Lin & Xingdi Ma & Lori L. Wilson & Ryan Steger & Annabel K. Sangree & Medina Colic & Sidney H. Wang & John G. Doench & Traver Hart, 2024. "Efficient gene knockout and genetic interaction screening using the in4mer CRISPR/Cas12a multiplex knockout platform," Nature Communications, Nature, vol. 15(1), pages 1-14, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Geeraert, Joke & Rocha, Luis E.C. & Vandeviver, Christophe, 2024. "The impact of violent behavior on co-offender selection: Evidence of behavioral homophily," Journal of Criminal Justice, Elsevier, vol. 94(C).
    2. Furqan Dar & Samuel R. Cohen & Diana M. Mitrea & Aaron H. Phillips & Gergely Nagy & Wellington C. Leite & Christopher B. Stanley & Jeong-Mo Choi & Richard W. Kriwacki & Rohit V. Pappu, 2024. "Biomolecular condensates form spatially inhomogeneous network fluids," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    3. López Pérez, Mario & Mansilla Corona, Ricardo, 2022. "Ordinal synchronization and typical states in high-frequency digital markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 598(C).
    4. Menchón, S.A. & Condat, C.A., 2011. "Quiescent cells: A natural way to resist chemotherapy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(20), pages 3354-3361.
    5. Jessica M. Vanslambrouck & Sean B. Wilson & Ker Sin Tan & Ella Groenewegen & Rajeev Rudraraju & Jessica Neil & Kynan T. Lawlor & Sophia Mah & Michelle Scurr & Sara E. Howden & Kanta Subbarao & Melissa, 2022. "Enhanced metanephric specification to functional proximal tubule enables toxicity screening and infectious disease modelling in kidney organoids," Nature Communications, Nature, vol. 13(1), pages 1-23, December.
    6. Dennis Bontempi & Leonard Nuernberg & Suraj Pai & Deepa Krishnaswamy & Vamsi Thiriveedhi & Ahmed Hosny & Raymond H. Mak & Keyvan Farahani & Ron Kikinis & Andrey Fedorov & Hugo J. W. L. Aerts, 2024. "End-to-end reproducible AI pipelines in radiology using the cloud," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    7. Pablo García-Risueño, 2025. "Historical Simulation Systematically Underestimates the Expected Shortfall," JRFM, MDPI, vol. 18(1), pages 1-12, January.
    8. Lauren L. Porter & Allen K. Kim & Swechha Rimal & Loren L. Looger & Ananya Majumdar & Brett D. Mensh & Mary R. Starich & Marie-Paule Strub, 2022. "Many dissimilar NusG protein domains switch between α-helix and β-sheet folds," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    9. Crucinio, Francesca R. & De Bortoli, Valentin & Doucet, Arnaud & Johansen, Adam M., 2024. "Solving a class of Fredholm integral equations of the first kind via Wasserstein gradient flows," Stochastic Processes and their Applications, Elsevier, vol. 173(C).
    10. J. McClatchy & R. Strogantsev & E. Wolfe & H. Y. Lin & M. Mohammadhosseini & B. A. Davis & C. Eden & D. Goldman & W. H. Fleming & P. Conley & G. Wu & L. Cimmino & H. Mohammed & A. Agarwal, 2023. "Clonal hematopoiesis related TET2 loss-of-function impedes IL1β-mediated epigenetic reprogramming in hematopoietic stem and progenitor cells," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    11. Oren Amsalem & Hidehiko Inagaki & Jianing Yu & Karel Svoboda & Ran Darshan, 2024. "Sub-threshold neuronal activity and the dynamical regime of cerebral cortex," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    12. Matthew Rosenblatt & Link Tejavibulya & Rongtao Jiang & Stephanie Noble & Dustin Scheinost, 2024. "Data leakage inflates prediction performance in connectome-based machine learning models," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    13. Ozonder, Gozde & Miller, Eric J., 2021. "Longitudinal investigation of skeletal activity episode timing decisions – A copula approach," Journal of choice modelling, Elsevier, vol. 40(C).
    14. Alexendar R. Perez & Laura Sala & Richard K. Perez & Joana A. Vidigal, 2021. "CSC software corrects off-target mediated gRNA depletion in CRISPR-Cas9 essentiality screens," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
    15. Sayedali Shetab Boushehri & Katharina Essig & Nikolaos-Kosmas Chlis & Sylvia Herter & Marina Bacac & Fabian J. Theis & Elke Glasmacher & Carsten Marr & Fabian Schmich, 2023. "Explainable machine learning for profiling the immunological synapse and functional characterization of therapeutic antibodies," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    16. Xuefeng Wang & Shuo Zhang & Yuqin liu, 2022. "ITGInsight–discovering and visualizing research fronts in the scientific literature," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6509-6531, November.
    17. Khaled Akkad & David He, 2023. "A dynamic mode decomposition based deep learning technique for prognostics," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2207-2224, June.
    18. Minjung Kyung & Ju-Hyun Park & Ji Yeh Choi, 2022. "Bayesian Mixture Model of Extended Redundancy Analysis," Psychometrika, Springer;The Psychometric Society, vol. 87(3), pages 946-966, September.
    19. Romain Fournier & Zoi Tsangalidou & David Reich & Pier Francesco Palamara, 2023. "Haplotype-based inference of recent effective population size in modern and ancient DNA samples," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    20. Xue, Jiacheng & Yao, Weixin, 2022. "Machine Learning Embedded Semiparametric Mixtures of Regressions with Covariate-Varying Mixing Proportions," Econometrics and Statistics, Elsevier, vol. 22(C), pages 159-171.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-26867-8. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.