The Nuclear RNase III Drosha Initiates Microrna Processing

Yoontae Lee,Chiyoung Ahn,Jinju Han,Hyounjeong Choi,Jaekwang Kim,Jeongbin Yim,Junho Lee,Patrick Provost,Olof Rådmark,Sunyoung Kim,V. Narry Kim

Nature（2003）SCI 1区

Institute of Molecular Biology and Genetics and School of Biological Sciences | Department of Biology | Centre de Recherche en Rhumatologie et Immunologie | Department of Medical Biochemistry and Biophysics

Cited 7573|Views43

Abstract

Hundreds of small RNAs of ∼22 nucleotides, collectively named microRNAs (miRNAs), have been discovered recently in animals and plants 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 . Although their functions are being unravelled 1 , 2 , 11 , 12 , 13 , their mechanism of biogenesis remains poorly understood. miRNAs are transcribed as long primary transcripts (pri-miRNAs) whose maturation occurs through sequential processing events: the nuclear processing of the pri-miRNAs into stem-loop precursors of ∼70 nucleotides (pre-miRNAs), and the cytoplasmic processing of pre-miRNAs into mature miRNAs 14 . Dicer, a member of the RNase III superfamily of bidentate nucleases, mediates the latter step 15 , 16 , 17 , 18 , 19 , whereas the processing enzyme for the former step is unknown. Here we identify another RNase III, human Drosha, as the core nuclease that executes the initiation step of miRNA processing in the nucleus. Immunopurified Drosha cleaved pri-miRNA to release pre-miRNA in vitro . Furthermore, RNA interference of Drosha resulted in the strong accumulation of pri-miRNA and the reduction of pre-miRNA and mature miRNA in vivo . Thus, the two RNase III proteins, Drosha and Dicer, may collaborate in the stepwise processing of miRNAs, and have key roles in miRNA-mediated gene regulation in processes such as development and differentiation.

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Science,Humanities and Social Sciences,multidisciplinary

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Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

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