基因集富集分析表明,mTOR信号通路在综合征型和非综合征型自闭症之间具有趋同性

Victor Gustavo Oliveira Evangelho , Murilo Lamim Bello , Helena Carla Castro , Marcia Rodrigues Amorim
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引用次数: 0

摘要

自闭症是一种发育障碍,影响着全球约6210万人。对其流行程度的估计一直在上升。最近的研究表明,到2025年,仅在美国,照顾自闭症患者的费用就可能达到4610亿美元,其中包括医疗费用。自闭症是遗传和环境因素共同作用的结果,分子诊断往往具有挑战性。因此,需要更可靠的生物标志物来辅助临床评估。在这里,我们采用了一种生物信息学技术,基因集富集分析(GSEA),该技术允许使用从遗传生物银行提取的数据来评估与自闭症相关的特定基因是否与常见的生物学途径和特定的分子过程相关。因此,通过GSEA可以验证910个与自闭症相关的基因。生成的数据表明遗传趋同存在于一条分子通路中,提示RAS-MAPK和PI3K-AKT信号级联的无序激活在mTOR通路中趋同。细胞分型显示纹状神经元D1型(p=5,947e-04)和D2型(p=1,292e-05)高表达。总之,我们的分子通路数据可以通过计算机建模来评估治疗自闭症的新候选药物是否与mTOR通路相关的蛋白质相互作用,从而优化新药的筛选。此外,随着这些生物标志物的证据和易于获得的实验室测试的发展,未来自闭症的早期临床诊断可能会得到显著改善。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Gene set enrichment analysis indicates convergence in the mTOR signalling pathway between syndromic and non-syndromic autism

Gene set enrichment analysis indicates convergence in the mTOR signalling pathway between syndromic and non-syndromic autism

Autism is a developmental disorder that affects around 62.1 million people globally. Estimates of its prevalence have been on the rise. Recent research suggests that in the United States alone, the cost of caring for individuals with autism could reach $461 billion by 2025, including medical expenses. Autism results from a combination of genetic and environmental factors, and molecular diagnosis can often be challenging. Therefore, there is a need for more reliable biomarkers to assist in clinical evaluation. Here, we employed a bioinformatics technique, Gene Set Enrichment Analysis (GSEA), that allows for the evaluation of whether specific genes associated with autism are related to common biological pathways and particular molecular processes using data extracted from genetic biobanks. Thus, it was possible to validate 910 genes related to autism by means of GSEA. The generated data indicated genetic convergence in a molecular pathway, suggesting that the disordered activation of the RAS-MAPK and PI3K-AKT signaling cascades converges in the mTOR pathway. Cell typification in silico indicated high expression in striated neurons, type D1 (p=5,947e-04) and type D2 (p=1,292e-05). In conclusion, our molecular pathway data can be used to assess, using computer modeling, whether new drug candidates for treating autism interact with proteins involved in the mTOR pathway, thus optimizing the screening of new drugs. In addition, with the evidence of such biomarkers and the development of easily accessible laboratory tests, in the future, the early clinical diagnosis of autism could be significantly improved.

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来源期刊
Neuroscience informatics
Neuroscience informatics Surgery, Radiology and Imaging, Information Systems, Neurology, Artificial Intelligence, Computer Science Applications, Signal Processing, Critical Care and Intensive Care Medicine, Health Informatics, Clinical Neurology, Pathology and Medical Technology
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