High-Throughput Functional Analysis Distinguishes Pathogenic, Nonpathogenic, and Compensatory Transcriptional Changes in Neurodegeneration

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Abstract

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 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 macroautophagy, 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 disease transcriptomics as a test case.

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Al-Ramahi, I., Lu, B., Di Paola, S., Pang, K., de Haro, M., Peluso, I., … Botas, J. (2018). High-Throughput Functional Analysis Distinguishes Pathogenic, Nonpathogenic, and Compensatory Transcriptional Changes in Neurodegeneration. Cell Systems, 7(1), 28-40.e4. https://doi.org/10.1016/j.cels.2018.05.010

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