Literature DB >> 34761218

Taiji-reprogram: a framework to uncover cell-type specific regulators and predict cellular reprogramming cocktails.

Jun Wang1, Cong Liu1, Yue Chen1, Wei Wang1.   

Abstract

Cellular reprogramming is a promising technology to develop disease models and cell-based therapies. Identification of the key regulators defining the cell type specificity is pivotal to devising reprogramming cocktails for successful cell conversion but remains a great challenge. Here, we present a systems biology approach called Taiji-reprogram to efficiently uncover transcription factor (TF) combinations for conversion between 154 diverse cell types or tissues. This method integrates the transcriptomic and epigenomic data to construct cell-type specific genetic networks and assess the global importance of TFs in the network. Comparative analysis across cell types revealed TFs that are specifically important in a particular cell type and often tightly associated with cell-type specific functions. A systematic search of TFs with differential importance in the source and target cell types uncovered TF combinations for desired cell conversion. We have shown that Taiji-reprogram outperformed the existing methods to better recover the TFs in the experimentally validated reprogramming cocktails. This work not only provides a comprehensive catalog of TFs defining cell specialization but also suggests TF combinations for direct cell conversion.
© The Author(s) 2021. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics.

Entities:  

Year:  2021        PMID: 34761218      PMCID: PMC8573821          DOI: 10.1093/nargab/lqab100

Source DB:  PubMed          Journal:  NAR Genom Bioinform        ISSN: 2631-9268


  66 in total

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Journal:  Nat Immunol       Date:  2017-05-18       Impact factor: 25.606

2.  ZSCAN10 expression corrects the genomic instability of iPSCs from aged donors.

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Journal:  Nat Cell Biol       Date:  2017-08-28       Impact factor: 28.824

3.  Mesodermal Nkx2.5 is necessary and sufficient for early second heart field development.

Authors:  Lu Zhang; Aya Nomura-Kitabayashi; Nishat Sultana; Weibin Cai; Xiaoqiang Cai; Anne M Moon; Chen-Leng Cai
Journal:  Dev Biol       Date:  2014-03-05       Impact factor: 3.582

4.  Transcription factor MITF regulates cardiac growth and hypertrophy.

Authors:  Sagi Tshori; Dan Gilon; Ronen Beeri; Hovav Nechushtan; Dmitry Kaluzhny; Eli Pikarsky; Ehud Razin
Journal:  J Clin Invest       Date:  2006-09-21       Impact factor: 14.808

5.  Functional analysis of Rfx6 and mutant variants associated with neonatal diabetes.

Authors:  Esther J Pearl; Zeina Jarikji; Marko E Horb
Journal:  Dev Biol       Date:  2011-01-04       Impact factor: 3.582

Review 6.  Calcium Channel Mutations in Cardiac Arrhythmia Syndromes.

Authors:  Matthew J Betzenhauser; Geoffrey S Pitt; Charles Antzelevitch
Journal:  Curr Mol Pharmacol       Date:  2015       Impact factor: 3.339

Review 7.  Adult cell plasticity in vivo: de-differentiation and transdifferentiation are back in style.

Authors:  Allyson J Merrell; Ben Z Stanger
Journal:  Nat Rev Mol Cell Biol       Date:  2016-03-16       Impact factor: 94.444

8.  A predictive computational framework for direct reprogramming between human cell types.

Authors:  Owen J L Rackham; Jaber Firas; Hai Fang; Matt E Oates; Melissa L Holmes; Anja S Knaupp; Harukazu Suzuki; Christian M Nefzger; Carsten O Daub; Jay W Shin; Enrico Petretto; Alistair R R Forrest; Yoshihide Hayashizaki; Jose M Polo; Julian Gough
Journal:  Nat Genet       Date:  2016-01-18       Impact factor: 38.330

9.  Comparative and evolutionary analysis of the HES/HEY gene family reveal exon/intron loss and teleost specific duplication events.

Authors:  Mi Zhou; Jun Yan; Zhaowu Ma; Yang Zhou; Nibras Najm Abbood; Jianfeng Liu; Li Su; Haibo Jia; An-Yuan Guo
Journal:  PLoS One       Date:  2012-07-13       Impact factor: 3.240

10.  Estimating the activity of transcription factors by the effect on their target genes.

Authors:  Theresa Schacht; Marcus Oswald; Roland Eils; Stefan B Eichmüller; Rainer König
Journal:  Bioinformatics       Date:  2014-09-01       Impact factor: 6.937

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