Advances in computational and experimental approaches for deciphering transcriptional regulatory networks: Understanding the roles of cis-regulatory elements is essential, and recent research utilizing MPRAs, STARR-seq, CRISPR-Cas9, and machine learning has yielded valuable insights.

Autor: Moeckel C; Department of Biochemistry and Molecular Biology, Institute for Personalized Medicine, The Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA., Mouratidis I; Department of Biochemistry and Molecular Biology, Institute for Personalized Medicine, The Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA.; Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, Pennsylvania, USA., Chantzi N; Department of Biochemistry and Molecular Biology, Institute for Personalized Medicine, The Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA., Uzun Y; Department of Biochemistry and Molecular Biology, Institute for Personalized Medicine, The Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA.; Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, Pennsylvania, USA.; Department of Pediatrics, The Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA., Georgakopoulos-Soares I; Department of Biochemistry and Molecular Biology, Institute for Personalized Medicine, The Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA.; Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, Pennsylvania, USA.
Jazyk: angličtina
Zdroj: BioEssays : news and reviews in molecular, cellular and developmental biology [Bioessays] 2024 Jul; Vol. 46 (7), pp. e2300210. Date of Electronic Publication: 2024 May 08.
DOI: 10.1002/bies.202300210
Abstrakt: Understanding the influence of cis-regulatory elements on gene regulation poses numerous challenges given complexities stemming from variations in transcription factor (TF) binding, chromatin accessibility, structural constraints, and cell-type differences. This review discusses the role of gene regulatory networks in enhancing understanding of transcriptional regulation and covers construction methods ranging from expression-based approaches to supervised machine learning. Additionally, key experimental methods, including MPRAs and CRISPR-Cas9-based screening, which have significantly contributed to understanding TF binding preferences and cis-regulatory element functions, are explored. Lastly, the potential of machine learning and artificial intelligence to unravel cis-regulatory logic is analyzed. These computational advances have far-reaching implications for precision medicine, therapeutic target discovery, and the study of genetic variations in health and disease.
(© 2024 The Authors. BioEssays published by Wiley Periodicals LLC.)
Databáze: MEDLINE
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