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Robust Sampling of Defective Pathways in Alzheimer’s Disease. Implications in Drug Repositioning

机译:阿尔茨海默病缺陷途径的鲁棒采样。药物重新定位的影响

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We present the analysis of the defective genetic pathways of the Late-Onset Alzheimer’s Disease (LOAD) compared to the Mild Cognitive Impairment (MCI) and Healthy Controls (HC) using different sampling methodologies. These algorithms sample the uncertainty space that is intrinsic to any kind of highly underdetermined phenotype prediction problem, by looking for the minimum-scale signatures (header genes) corresponding to different random holdouts. The biological pathways can be identified performing posterior analysis of these signatures established via cross-validation holdouts and plugging the set of most frequently sampled genes into different ontological platforms. That way, the effect of helper genes, whose presence might be due to the high degree of under determinacy of these experiments and data noise, is reduced. Our results suggest that common pathways for Alzheimer’s disease and MCI are mainly related to viral mRNA translation, influenza viral RNA transcription and replication, gene expression, mitochondrial translation, and metabolism, with these results being highly consistent regardless of the comparative methods. The cross-validated predictive accuracies achieved for the LOAD and MCI discriminations were 84% and 81.5%, respectively. The difference between LOAD and MCI could not be clearly established (74% accuracy). The most discriminatory genes of the LOAD-MCI discrimination are associated with proteasome mediated degradation and G-protein signaling. Based on these findings we have also performed drug repositioning using Dr. Insight package, proposing the following different typologies of drugs: isoquinoline alkaloids, antitumor antibiotics, phosphoinositide 3-kinase PI3K, autophagy inhibitors, antagonists of the muscarinic acetylcholine receptor and histone deacetylase inhibitors. We believe that the potential clinical relevance of these findings should be further investigated and confirmed with other independent studies.
机译:我们目前相比,采用抽样的不同方法的轻度认知障碍(MCI)和健康对照(HC)晚发型阿尔茨海默病(LOAD)的有缺陷的遗传途径的分析。这些算法取样不确定性空间是固有的任何种类的高度欠定的表型预测的问题,通过寻找对应于不同的随机残余分子的最小规模的签名(标题基因)。的生物学途径可识别执行经由交叉验证不让步建立这些签名的后分析和堵塞该组的最频繁采样基因导入不同的本体的平台。这样,辅助基因的作用,其存在可能的高度在这些实验和数据噪音的确定性是由于,减少。我们的研究结果表明,对于阿尔茨海默氏病和MCI共同途径主要涉及到病毒mRNA的翻译,流感病毒RNA转录和复制,基因表达,线粒体蛋白翻译和代谢,这些结果是无论比较的方法高度一致。对LOAD和MCI歧视取得的交叉验证的预测精度分别为84%和81.5%。 LOAD和MCI之间的差不能被清楚地确定(74%的准确度)。负载MCI歧视最歧视性基因与蛋白酶体介导的降解和G蛋白信号传导相关。基于这些发现,我们使用Insight博士包,提出的药物以下不同类型学还进行药物重新定位:异喹啉生物碱,抗肿瘤抗生素,磷酸肌醇3-激酶PI3K,自噬抑制剂,毒蕈碱乙酰胆碱受体和组蛋白脱乙酰酶抑制剂的拮抗剂。我们认为,这些发现的潜在临床意义,应进一步调查,并与其他独立研究的证实。

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