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    • 2. 发明申请
    • SYSTEMS, APPARATUS, AND METHODS FOR ANALYZING AND PREDICTING CELLULAR PATHWAYS
    • 用于分析和预测细胞途径的系统,装置和方法
    • WO2017027559A1
    • 2017-02-16
    • PCT/US2016/046289
    • 2016-08-10
    • MASSACHUSETTS INSTITUTE OF TECHNOLOGYPIRHAJI, LeilaFRAENKEL, Ernest
    • PIRHAJI, LeilaFRAENKEL, Ernest
    • G06F19/10G06F19/12G06F19/18G06F19/24G06F19/26G06F19/28
    • G06F19/12G01N33/5038G01N33/6848G01N33/6896G01N2800/2835G06F19/18
    • Integrative analysis of metabolites is essential to obtain a comprehensive view of dysregulated biological pathways leading to a disease. Despite the great potential of metabolites their system level analysis has been limited. Global measurements of the metabolites by liquid chromatography-mass spectrometry (MS) detects metabolites features changing in a disease. However, identification of each feature is a bottleneck in metabolomics, in which a fraction of them are identified via tandem MS. Consequently, the scarcity of these data add additional barriers to decipher their biological meaning, especially in relation to other 'omic data such as proteomics. To address these challenges, a novel network-based approach called PIUMet is described. PIUMet infers dysregulated pathways and components from the differential metabolite features between control and disease systems without the need for the prior identification. The application of PIUMet is demonstrated by integrative analysis of untargeted lipid profiling data of a cell line model of Huntington's disease. The results show that PIUMet inferred dysregulation of sphingolipid metabolism in the disease cells. Additionally, PIUMet identified disease-modifying metabolite in the pathway that remained undetected experimentally. Furthermore, the lipidomic data of these cell lines was integrated with global phospho-proteomic ones. Integrative analysis of these data using PIUMet was shown to systematically lead to identifying dysregulated proteins in the disease cells that cannot be distinguished with individual analysis of each dataset.
    • 代谢物的综合分析对于获得导致疾病的失调生物学途径的综合观点至关重要。 尽管代谢物的潜力很大,但其系统水平分析却受到限制。 通过液相色谱 - 质谱法(MS)对代谢物进行全球测量可以检测出疾病发生变化的代谢物特征。 然而,每个特征的识别是代谢组学的瓶颈,其中一部分通过串联MS鉴定。 因此,这些数据的稀缺性增加了破坏其生物学意义的更多障碍,特别是与其他“蛋白质组学”等数据相关。 为了解决这些挑战,描述了一种称为PIUMet的新颖的基于网络的方法。 PIUMet推测控制和疾病系统之间差异代谢特征的失调途径和成分,而不需要先前的鉴定。 通过对亨廷顿舞蹈病细胞系模型的非靶向脂质分析数据的综合分析证明了PIUMet的应用。 结果表明,PIUMet推测疾病细胞中鞘脂代谢紊乱。 此外,PIUMet确定了通路中的疾病修复代谢物,在实验中未被发现。 此外,这些细胞系的脂质组学数据与全球磷蛋白质组合。 显示了使用PIUMet的这些数据的综合分析,以系统地导致识别疾病细胞中的失调蛋白质,这些蛋白质不能用每个数据集的个体分析来区分。