RESUMO
Functional specialization of cells and tissues in metazoans require specific gene expression patterns. Biological processes, thus, need precise temporal and spatial coordination of gene activity. Regulation of the fate of messenger RNA plays a crucial role in this context. In the present review, the current knowledge related to the role of RNA-binding proteins in the whole mRNA life-cycle is summarized. This field opens up a new angle for understanding the importance of the post-transcriptional control of gene expression in cancer cells. The emerging role of non-classic RNA-binding proteins is highlighted. The goal of this review is to encourage readers to view, through the mRNA life-cycle, novel aspects of the molecular basis of cancer and the potential to develop RNA-based therapies.
Assuntos
Regulação Neoplásica da Expressão Gênica , Neoplasias/genética , Estabilidade de RNA , RNA Mensageiro/genética , Animais , Humanos , Terapia de Alvo Molecular , Neoplasias/metabolismo , Neoplasias/terapia , Ligação Proteica , Domínios e Motivos de Interação entre Proteínas , Processamento Pós-Transcricional do RNA , Splicing de RNA , Transporte de RNA , RNA Mensageiro/metabolismo , Proteínas de Ligação a RNA/antagonistas & inibidores , Proteínas de Ligação a RNA/química , Proteínas de Ligação a RNA/genética , Proteínas de Ligação a RNA/metabolismo , Transcrição GênicaRESUMO
Proteins interact with nucleic acids to regulate the life activities of organisms. Therefore, how to accurately and efficiently identify nucleic acid-binding proteins (NABPs) is particularly significant. Some sequence-based computational methods have been proposed to identify DNA- and RNA-binding proteins in previous studies. However, the benchmark datasets used by these methods ignore the proportion of NABPs in the real world, and some integration methods only integrate traditional machine learning algorithms, resulting in limited prediction performance. In this study, we proposed a sequence-based method called iDRBP-ECHF to predict the DNA-binding proteins (DBPs) and RNA-binding proteins (RBPs). We constructed a benchmark dataset by considering the proportion of positive and negative samples in the real world, and used down-sampling to generate three relatively balanced datasets to train the iDRBP-ECHF. In addition, we incorporated the deep learning algorithms into the framework to obtain a more compact high-level feature representation of the input data. The results on two independent datasets show that it achieves the most advanced performance and is superior to the other existing sequence-based DBP and RBP prediction methods. In addition, we set up a webserver iDRBP-ECHF, which can be accessed at http://bliulab.net/iDRBP-ECHF.
Assuntos
Aprendizado de Máquina , Proteínas de Ligação a RNA , Algoritmos , Sítios de Ligação , Biologia Computacional/métodos , DNA/genética , Proteínas de Ligação a DNA/genética , Proteínas de Ligação a DNA/metabolismo , Proteínas de Ligação a RNA/química , Proteínas de Ligação a RNA/genética , Proteínas de Ligação a RNA/metabolismoRESUMO
Embryonic stem cell (ESC) self-renewal and cell fate decisions are driven by a broad array of molecular signals. While transcriptional regulators have been extensively studied in human ESCs (hESCs), the extent to which RNA-binding proteins (RBPs) contribute to human pluripotency remains unclear. Here, we carry out a proteome-wide screen and identify 810 proteins that bind RNA in hESCs. We reveal that RBPs are preferentially expressed in hESCs and dynamically regulated during early stem cell differentiation. Notably, many RBPs are affected by knockdown of OCT4, a master regulator of pluripotency, several dozen of which are directly targeted by this factor. Using cross-linking and immunoprecipitation (CLIP-seq), we find that the pluripotency-associated STAT3 and OCT4 transcription factors interact with RNA in hESCs and confirm the binding of STAT3 to the conserved NORAD long-noncoding RNA. Our findings indicate that RBPs have a more widespread role in human pluripotency than previously appreciated.