Na, Dokyun Lim, Do-Hwan Hong, Jae-Sang Lee, Hyang-Mi Cho, Daeahn Yu, Myeong-Sang Shaker, Bilal Ren, Jun Lee, Bomi Song, Jae Gwang Oh, Yuna Lee, Kyungeun Oh, Kwang-Seok Lee, Mi Young Choi, Min-Seok Choi, Han Saem Kim, Yang-Hee Bui, Jennifer M. Lee, Kangseok Kim, Hyung Wook Lee, Young Sik Gsponer, Jorg 2024-01-19T08:03:09Z 2024-01-19T08:03:09Z 2023-11-30 2023-12 1744-4292 https://pubs.kist.re.kr/handle/201004/113044 The accumulation of misfolded and aggregated proteins is a hallmark of neurodegenerative proteinopathies. Although multiple genetic loci have been associated with specific neurodegenerative diseases (NDs), molecular mechanisms that may have a broader relevance for most or all proteinopathies remain poorly resolved. In this study, we developed a multi-layered network expansion (MLnet) model to predict protein modifiers that are common to a group of diseases and, therefore, may have broader pathophysiological relevance for that group. When applied to the four NDs Alzheimer's disease (AD), Huntington's disease, and spinocerebellar ataxia types 1 and 3, we predicted multiple members of the insulin pathway, including PDK1, Akt1, InR, and sgg (GSK-3 beta), as common modifiers. We validated these modifiers with the help of four Drosophila ND models. Further evaluation of Akt1 in human cell-based ND models revealed that activation of Akt1 signaling by the small molecule SC79 increased cell viability in all models. Moreover, treatment of AD model mice with SC79 enhanced their long-term memory and ameliorated dysregulated anxiety levels, which are commonly affected in AD patients. These findings validate MLnet as a valuable tool to uncover molecular pathways and proteins involved in the pathophysiology of entire disease groups and identify potential therapeutic targets that have relevance across disease boundaries. MLnet can be used for any group of diseases and is available as a web tool at . imageMLnet is a multi-layered network expansion model that finds proteins with pathophysiological relevance for groups of diseases. Application to four neurodegenerative diseases predicts multiple members of the insulin pathway as common modifiers.MLnet uses data integration and a multi-layered network expansion model to identify and prioritize for experimental testing proteins that affect pathophysiology across multiple diseases.When applied to Alzheimer's disease, Huntington's disease, and spinocerebellar ataxia types 1 and 3, MLnet identifies multiple members of the insulin pathway, proteostasis machinery and microtubule apparatus as common modifiers.The impact of the identified genes on neurodegenerative disease phenotypes is tested in Drosophila, human cell lines and mouse disease models.MLnet is available at and can be used for any group of diseases. MLnet is a multi-layered network expansion model that finds proteins with pathophysiological relevance for groups of diseases. Application to four neurodegenerative diseases predicts multiple members of the insulin pathway as common modifiers.image English EMBO Press A multi-layered network model identifies Akt1 as a common modulator of neurodegeneration Article 10.15252/msb.202311801 1 Molecular Systems Biology, v.19, no.12 Molecular Systems Biology 19 12 Y scie scopus 001104384400001 2-s2.0-85177210445 Biochemistry & Molecular Biology Biochemistry & Molecular Biology Article CELL EXPRESSION MODIFIERS AUTOPHAGY HUNTINGTONS-DISEASE GENE PRIORITIZATION OXIDATIVE STRESS AMYLOID-BETA PROTEIN INSULIN common modifier insulin signaling pathway multi-layered network expansion neurodegenerative diseases proteostasis