Arginine-rich dipeptide repeat proteins (R-DPRs), abnormal translational products of a GGGGCC hexanucleotide repeat expansion in C9ORF72 , play a critical role in C9ORF72 -related amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD), the most common genetic form of the disorders (c9ALS/FTD). R-DPRs form liquid condensates in vitro, induce stress granule formation in cultured cells, aggregate, and sometimes coaggregate with TDP-43 in postmortem tissue from patients with c9ALS/FTD. However, how these processes are regulated is unclear. Here, we show that loss of poly(ADP-ribose) (PAR) suppresses neurodegeneration in c9ALS/FTD fly models and neurons differentiated from patient-derived induced pluripotent stem cells. Mechanistically, PAR induces R-DPR condensation and promotes R-DPR–induced stress granule formation and TDP-43 aggregation. Moreover, PAR associates with insoluble R-DPR and TDP-43 in postmortem tissue from patients. These findings identified PAR as a promoter of R-DPR toxicity and thus a potential target for treating c9ALS/FTD.

Background: Context‐specific networks can capture functionally important genes that may not be differentially expressed. Leveraging seven different gene expression datasets of Alzheimer’s disease (AD) and healthy control brains, we constructed a novel pipeline that includes such network analysis, augmented by in vivo validations and in silico drug repurposing to facilitate prioritization of AD therapeutic targets.Method: We utilized publicly available gene expression data from 733 AD and 454 control brains across 7 datasets and the NetDecoder (PMID: 26975659) network biology platform to build context‐specific networks. Based on the known protein‐protein interaction and gene expression correlations, information flow from the top differentially expressed genes (DEG) to transcriptional regulator genes was calculated using a process‐guided flow algorithm. Genes with the greatest informational flow changes across AD cohorts were systematically prioritized. We next validated these genes in a medium‐throughput screen of a tau‐transgenic Drosophila model (PMID: 11408621). Further, we identified existing small‐molecule drugs to target the candidate genes utilizing curated databases.Results: A total of 14 context‐specific networks were constructed across 7 datasets. We found AD‐related genes, such as APP, to have high flow differences between AD and control networks and high connectivity. Notably, many such genes were not DEGs between AD and control, suggesting that the information flow captures novel aspects of AD pathophysiology. We nominated 224 unique genes with the greatest flow differences for functional validation. We screened 111 of the 224 candidate genes with readily available, high‐confidence orthologs in Drosophila. We identified genes that suppress (N = 68) or enhance (N = 27) tau‐related neurodegeneration in this model. We confirmed the effects of previously nominated genes, such as HDAC1, which enhances tau‐neurodegeneration if overexpressed and suppresses it if knocked down. We also identified and validated additional therapeutic targets. Lastly, we applied a network‐proximity‐based algorithm and identified small molecule drugs for the candidate genes.Conclusion: We present a novel pipeline that integrates analytic approaches, experimental validation, and drug repurposing to facilitate therapeutic discovery in AD. We applied this pipeline and nominated previously known and novel genes implicated in AD pathogenesis. Our pipeline demonstrates a cross‐species, systems‐level approach to therapeutic nomination in AD and related neurodegenerative diseases.