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DECCODE-ing Cell Reprogramming with Small Molecules

Review of “Automatic identification of small molecules that promote cell conversion and reprogramming” from Stem Cell Reports by Stuart P. Atkinson

Efforts to improve the generally low efficiency of cellular reprogramming strategies have prompted the development of computational approaches to identify novel transcription factor combinations [1, 2]; however, similar tools for the identification of synergistic small molecule combinations that can induce cell fate changes remain undescribed. To this end, researchers led by Davide Cacchiarelli, Diego di Bernardo (University of Naples Federico II, Naples, Italy), Xin Gao (KAUST, Thuwal, Saudi Arabia), and Erik Arner (RIKEN, Yokohama, Kanagawa, Japan) recently combined gene expression and drug transcriptional response data to create a tool called DECCODE (Drug Enhanced Cell COnversion using Differential Expression) that automatically identifies small molecules that enhance cell reprogramming [3].

Napolitano and Rapakoulia et al. analyzed nearly 450 genome-wide expression profiles of untreated primary cells (the FANTOM5 project [4]) alongside over 100,000 transcriptional responses to small-molecule treatment (the LINCS project [5]) to allow for the unbiased identification of small molecule drugs that may drive the optimized reprogramming of one cell type, such as a fibroblast, into another cell type, such as induced pluripotent stem cells (iPSCs). The DECCODE tool uses these data to return the top compounds predicted to enhance reprogramming when queried.

In their new study, the authors applied DECCODE in single-drug mode to identify small molecules that enhance the generation of iPSCs. Interestingly, the top twenty-five drugs provided by DECCODE modulated pathways known to be associated with pluripotency. In drug-combination mode, the top thirty small molecule pairs suggested by DECCODE tended to display transcriptional differences but offered increased reprogramming efficiency compared with single-drug administration.

Experimental validation of the top-ranked twenty-five single drugs predicted by DECCODE to enhance the reprogramming of somatic cells into iPSCs employed the exposure of the identified small molecules during the reprogramming of human secondary fibroblasts harboring a doxycycline-inducible OCT4, SOX2, KLF4, C-MYC gene cassette. Overall, drugs ranked higher by DECCODE provided for more efficient reprogramming than lower-ranked drugs; interestingly, the beta-lactamase inhibitor class antibiotic tazobactam represented one of the top-performing drugs and had yet to be described in the context of cell reprogramming. In addition, experimental validation of the best predicted small molecule drug combinations by DECCODE provided evidence of an overall improvement in reprogramming compared to single drug exposure; of note, the top-performing combination (tazobactam and motesanib, an angiokinase inhibitor) induced a four-fold improvement over controls.

Overall, the authors present DECCODE as an exciting new tool to increase reprogramming efficiency by identifying small molecules specific to the cell types involved. The authors also note that optimized small molecule cocktails have replaced transcription factors in some reprogramming processes (such as iPSC generation [6]); therefore, DECCODE could significantly expand this approach.

For more on DECCODE and small molecule-mediated reprogramming efforts, stay tuned to the Stem Cells Portal!

References

  1. Cahan P, Li H, Morris Samantha A, et al., CellNet: Network Biology Applied to Stem Cell Engineering. Cell 2014;158:903-915.
  2. Rackham OJL, Firas J, Fang H, et al., A predictive computational framework for direct reprogramming between human cell types. Nature Genetics 2016;48:331-335.
  3. Napolitano F, Rapakoulia T, Annunziata P, et al., Automatic identification of small molecules that promote cell conversion and reprogramming. Stem Cell Reports 2021;16:1381-1390.
  4. Forrest ARR, Kawaji H, Rehli M, et al., A promoter-level mammalian expression atlas. Nature 2014;507:462-470.
  5. Keenan AB, Jenkins SL, Jagodnik KM, et al., The Library of Integrated Network-Based Cellular Signatures NIH Program: System-Level Cataloging of Human Cells Response to Perturbations. Cell Systems 2018;6:13-24.
  6. Hou P, Li Y, Zhang X, et al., Pluripotent Stem Cells Induced from Mouse Somatic Cells by Small-Molecule Compounds. Science 2013;341:651.