What Genetics Has Told Us and How It Can Inform Future Experiments for Spinal Muscular Atrophy, a Perspective.
International Journal of Molecular Sciences（2021）
Ohio State Univ
Abstract
Proximal spinal muscular atrophy (SMA) is an autosomal recessive neurodegenerative disorder characterized by motor neuron loss and subsequent atrophy of skeletal muscle. SMA is caused by deficiency of the essential survival motor neuron (SMN) protein, canonically responsible for the assembly of the spliceosomal small nuclear ribonucleoproteins (snRNPs). Therapeutics aimed at increasing SMN protein levels are efficacious in treating SMA. However, it remains unknown how deficiency of SMN results in motor neuron loss, resulting in many reported cellular functions of SMN and pathways affected in SMA. Herein is a perspective detailing what genetics and biochemistry have told us about SMA and SMN, from identifying the SMA determinant region of the genome, to the development of therapeutics. Furthermore, we will discuss how genetics and biochemistry have been used to understand SMN function and how we can determine which of these are critical to SMA moving forward.
MoreTranslated text
Key words
SMA,spinal muscular atrophy,genetics,biochemistry,SMN function,SMN missense mutants,survival motor neuron,suppressor screen,motor neuron
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- 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
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined