Better explicating the strengths and shortcomings of these models will help refine projections and improve transparency in the years ahead.
Image credit: Witsawat.S.
Edited by Michael R. Green, University of Massachusetts Medical School, Worcester, MA, and approved November 14, 2016 (received for review July 7, 2016)
Significance
The ability to convert cells into desired cell types enables tissue engineering, disease modeling, and regenerative medicine; however, methods to generate desired cell types remain difficult, uncertain, and laborious. We developed a strategy to screen gene regulatory elements on a genome scale to discover paths that trigger cell fate changes. The proteins used in this study cooperatively bind DNA and activate genes in a synergistic manner. Subsequent identification of transcriptional networks does not depend on prior knowledge of specific regulators important in the biological system being tested. This powerful forward genetic approach enables direct cell state conversions as well as other challenging manipulations of cell fate.
Abstract
Artificial transcription factors (ATFs) are precision-tailored molecules designed to bind DNA and regulate transcription in a preprogrammed manner. Libraries of ATFs enable the high-throughput screening of gene networks that trigger cell fate decisions or phenotypic changes. We developed a genome-scale library of ATFs that display an engineered interaction domain (ID) to enable cooperative assembly and synergistic gene expression at targeted sites. We used this ATF library to screen for key regulators of the pluripotency network and discovered three combinations of ATFs capable of inducing pluripotency without exogenous expression of Oct4 (POU domain, class 5, TF 1). Cognate site identification, global transcriptional profiling, and identification of ATF binding sites reveal that the ATFs do not directly target Oct4; instead, they target distinct nodes that converge to stimulate the endogenous pluripotency network. This forward genetic approach enables cell type conversions without a priori knowledge of potential key regulators and reveals unanticipated gene network dynamics that drive cell fate choices.
Footnotes
- ↵1To whom correspondence should be addressed. Email: azansari{at}wisc.edu.
Author contributions: A.E. and A.Z.A. designed research; A.E., M.J.W., M.C.S., E.A.H., and C.K.V. performed research; P.R., T.J.K., I.S., J.A.T., and J.R.D. contributed new reagents/analytic tools; A.E., A.S.K., D.B., S.A.S., and R.S. analyzed data; and A.E., J.R.D., and A.Z.A. wrote the paper.
Conflict of interest statement: A.Z.A. is the sole member of VistaMotif, LLC, and founder of the nonprofit WINStep Forward.
This article is a PNAS Direct Submission.
Data deposition: The RNA-seq and ChIP-seq data reported in this paper have been deposited in the Gene Expression Omnibus (GEO) database, www.ncbi.nlm.nih.gov/geo (accession no. GSE89221).
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1611142114/-/DCSupplemental.
Artificial transcription factor reprogramming
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- Cell Biology
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