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    Automatic identification of small molecules that promote cell conversion and reprogramming

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    Type
    Preprint
    Authors
    Napolitano, Francesco
    Rapakoulia, Trisevgeni cc
    Annunziata, Patrizia
    Hasegawa, Akira
    Cardon, Melissa
    Napolitano, Sara
    Vaccaro, Lorenzo
    Iuliano, Antonella
    Wanderlingh, Luca Giorgio
    Kasukawa, Takeya
    Medina, Diego L.
    Cacchiarelli, Davide
    Gao, Xin cc
    di Bernardo, Diego
    Arner, Erik
    KAUST Department
    Computational Bioscience Research Center (CBRC)
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Structural and Functional Bioinformatics Group
    KAUST Grant Number
    FCC/1/1976-18-01
    FCC/1/1976-23-01
    FCC/1/1976-25-01
    FCC/1/1976-26-01
    Date
    2020-04-02
    Permanent link to this record
    http://hdl.handle.net/10754/662486
    
    Metadata
    Show full item record
    Abstract
    Abstract: Controlling cell fate has great potential for regenerative medicine, drug discovery, and basic research. Although numerous transcription factors have been discovered that are able to promote cell reprogramming and trans-differentiation, methods based on their up-regulation tend to show low efficiency. The identification of small molecules that can facilitate conversion between cell types can ameliorate this problem working through safe, rapid, and reversible mechanisms. Here we present DECCODE, an unbiased computational method for the identification of such molecules solely based on transcriptional data. DECCODE matches the largest available collection of drug-induced profiles (the LINCS database) for drug treatments against the largest publicly available dataset of primary cell transcriptional profiles (FANTOM5), to identify drugs that either alone or in combination enhance cell reprogramming and cell conversion. Extensive <jats:italic>in silico</jats:italic> and <jats:italic>in vitro</jats:italic> validation of DECCODE in the context of human induced pluripotent stem cells (hIPSCs) generation shows that the method is able to prioritize drugs enhancing cell reprogramming. We also generated predictions for cell conversion with single drugs and drug combinations for 145 different cell types and made them available for further studies.
    Citation
    Napolitano, F., Rapakoulia, T., Annunziata, P., Hasegawa, A., Cardon, M., Napolitano, S., … Arner, E. (2020). Automatic identification of small molecules that promote cell conversion and reprogramming. doi:10.1101/2020.04.01.021089
    Sponsors
    EA was supported by a Research Grant from MEXT to the RIKEN Center for Integrative Medical Sciences. XG was supported by funding from King Abdullah University of Science and Technology (KAUST), under award number FCC/1/1976-18-01, FCC/1/1976-23-01, FCC/1/1976-25-01, FCC/1/1976-26-01, and FCS/1/4102-02-01. DLM was supported by the Italian Telethon Foundation under the project number TMDMCBX16TT. DC was supported by Fondazione Telethon Core Grant, Armenise-Harvard Foundation Career Development Award, European Research Council (grant agreement 759154, CellKarma), and the Rita-Levi Montalcini program from MIUR.
    Publisher
    Cold Spring Harbor Laboratory
    DOI
    10.1101/2020.04.01.021089
    Additional Links
    http://biorxiv.org/lookup/doi/10.1101/2020.04.01.021089
    https://www.biorxiv.org/content/biorxiv/early/2020/04/02/2020.04.01.021089.full.pdf
    ae974a485f413a2113503eed53cd6c53
    10.1101/2020.04.01.021089
    Scopus Count
    Collections
    Preprints; Structural and Functional Bioinformatics Group; Computer Science Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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