High-Throughput Functional Analysis Distinguishes Pathogenic, Nonpathogenic, and Compensatory Transcriptional Changes in Neurodegeneration.
High-Throughput Functional Analysis Distinguishes Pathogenic, Nonpathogenic, and Compensatory Transcriptional Changes in Neurodegeneration.
复制标题
DOI：
10.1016/j.cels.2018.05.010
复制
发表时间：
2018-07-25
期刊：
影响因子：
9.3
通讯作者：
Botas J
中科院分区：
文献类型：
--
作者：
Al-Ramahi I;Lu B;Di Paola S;Pang K;de Haro M;Peluso I;Gallego-Flores T;Malik NT;Erikson K;Bleiberg BA;Avalos M;Fan G;Rivers LE;Laitman AM;Diaz-García JR;Hild M;Palacino J;Liu Z;Medina DL;Botas J
区分驱动疾病发病机制的转录变化与非致病性和代偿性反应是一项艰巨的挑战。对于神经退行性疾病尤其如此，这些疾病在不同的疾病阶段影响不同大脑区域中数千个基因的表达。在这里，我们整合功能测试和网络方法来分析先前报道的亨廷顿病（HD）患者大脑中的转录改变。我们选择了312个基因，其表达在HD患者和HD小鼠中均失调，然后在果蝇HD模型中复制和/或拮抗每个改变。在该模型和对照中的高通量行为测试显示，突触生物学和钙信号传导中的转录变化是补偿性的，而涉及肌动蛋白细胞骨架和炎症的改变驱动疾病。在HD患者来源的细胞中敲除疾病驱动基因降低了突变亨廷顿蛋白水平并激活了巨噬细胞，这表明了减轻发病机制的机制。因此，我们的多层方法可以解开转录组学产生的丰富信息，并确定早期治疗干预点。各种“组学”方法产生了大量的数据，但区分驱动疾病的变化与代表代偿机制或继发反应的变化是非常困难、耗时且昂贵的。功能和网络方法的结合可以大规模地应对这一挑战，如这里使用亨廷顿病转录组学作为测试案例所示。
Discriminating transcriptional changes that drive disease pathogenesis from nonpathogenic and compensatory responses is a daunting challenge. This is particularly true for neurodegenerative diseases, which affect the expression of thousands of genes in different brain regions at different disease stages. Here we integrate functional testing and network approaches to analyze previously reported transcriptional alterations in the brains of Huntington’s Disease (HD) patients. We selected 312 genes whose expression is dysregulated both in HD patients and in HD mice, and then replicated and/ or antagonized each alteration in a Drosophila HD model. High-throughput behavioral testing in this model and controls revealed that transcriptional changes in synaptic biology and calcium signaling are compensatory, whereas alterations involving the actin cytoskeleton and inflammation drive disease. Knockdown of disease-driving genes in HD patient-derived cells lowered mutant Huntingtin levels and activated macroauthophagy, suggesting a mechanism for mitigating pathogenesis. Our multilayered approach can thus untangle the wealth of information generated by transcriptomics and identify early therapeutic intervention points. Various ‘omic’ methodologies produce voluminous amounts of data, but distinguishing the changes that drive a disease from those that represent compensatory mechanisms or secondary responses is exceedingly difficult, time-consuming, and costly. A combination of functional and network approaches can tackle the challenge on a large scale, as shown here using Huntington’s disease transcriptomics as a test case.
登录
查看更多内容
影响因子：
16.2
作者：
Ingram M;Wozniak EAL;Duvick L;Yang R;Bergmann P;Carson R;O'Callaghan B;Zoghbi HY;Henzler C;Orr HT
通讯作者：
Orr HT
影响因子：
--
作者：
Choi AJ;Ryter SW
通讯作者：
Ryter SW
影响因子：
16.8
作者：
Crotti A;Glass CK
通讯作者：
Glass CK
影响因子：
25
作者：
Langfelder P;Cantle JP;Chatzopoulou D;Wang N;Gao F;Al-Ramahi I;Lu XH;Ramos EM;El-Zein K;Zhao Y;Deverasetty S;Tebbe A;Schaab C;Lavery DJ;Howland D;Kwak S;Botas J;Aaronson JS;Rosinski J;Coppola G;Horvath S;Yang XW
通讯作者：
Yang XW
影响因子：
7.4
作者：
Cortes, Constanza J.;La Spada, Albert R.
通讯作者：
La Spada, Albert R.