Exploratory Analysis of iPSCS-Derived Neuronal Cells as Predictors of Diagnosis and Treatment of Alzheimer Disease

• Published in: Brain sciences Vol. 10; no. 3; p. 166
• Main Authors: Cavalli, Eugenio, Battaglia, Giuseppe, Basile, Maria Sofia, Bruno, Valeria, Petralia, Maria Cristina, Lombardo, Salvo Danilo, Pennisi, Manuela, Kalfin, Reni, Tancheva, Lyubka, Fagone, Paolo, Nicoletti, Ferdinando, Mangano, Katia
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 13.03.2020; MDPI
• Subjects: alzheimer disease; Alzheimer's disease; Biomarkers; Cognitive ability; Datasets; Diagnosis; Disease; DNA microarrays; Drug development; drug repurposing; Drugs; Fibroblasts; Gene expression; Genotype & phenotype; induced pluripotent stem cells-derived neuronal cells; Inhibitory postsynaptic potentials; Muscular dystrophy; Neurodegeneration; Neurodegenerative diseases; Pluripotency; Principal components analysis; Statistical analysis; Stem cells; Induced pluripotent stem cells-derived neuronal cells; drug repurposing; Alzheimer disease; biomarkers
• ISSN: 2076-3425; 2076-3425
• DOI: 10.3390/brainsci10030166

Alzheimer’s disease (AD) represents the most common neurodegenerative disorder, with 47 million affected people worldwide. Current treatment strategies are aimed at reducing the symptoms and do slow down the progression of the disease, but inevitably fail in the long-term. Induced pluripotent stem cells (iPSCs)-derived neuronal cells from AD patients have proven to be a reliable model for AD pathogenesis. Here, we have conducted an in silico analysis aimed at identifying pathogenic gene-expression profiles and novel drug candidates. The GSE117589 microarray dataset was used for the identification of Differentially Expressed Genes (DEGs) between iPSC-derived neuronal progenitor (NP) cells and neurons from AD patients and healthy donors. The Discriminant Analysis Module (DAM) algorithm was used for the identification of biomarkers of disease. Drugs with anti-signature gene perturbation profiles were identified using the L1000FWD software. DAM analysis was used to identify a list of potential biomarkers among the DEGs, able to discriminate AD patients from healthy people. Finally, anti-signature perturbation analysis identified potential anti-AD drugs. This study set the basis for the investigation of potential novel pharmacological strategies for AD. Furthermore, a subset of genes for the early diagnosis of AD is proposed.