Dynamic molecular network analysis of iPSC-Purkinje cells differentiation delineates roles of ISG15 in SCA1 at the earliest stage.
Hidenori HommaYuki YoshiokaKyota FujitaShinichi ShiraiYuka HamaHajime KomanoYuko SaitoIchiro YabeHideyuki OkanoHidenao SasakiHikari TanakaHitoshi OkazawaPublished in: Communications biology (2024)
Better understanding of the earliest molecular pathologies of all neurodegenerative diseases is expected to improve human therapeutics. We investigated the earliest molecular pathology of spinocerebellar ataxia type 1 (SCA1), a rare familial neurodegenerative disease that primarily induces death and dysfunction of cerebellum Purkinje cells. Extensive prior studies have identified involvement of transcription or RNA-splicing factors in the molecular pathology of SCA1. However, the regulatory network of SCA1 pathology, especially central regulators of the earliest developmental stages and inflammatory events, remains incompletely understood. Here, we elucidated the earliest developmental pathology of SCA1 using originally developed dynamic molecular network analyses of sequentially acquired RNA-seq data during differentiation of SCA1 patient-derived induced pluripotent stem cells (iPSCs) to Purkinje cells. Dynamic molecular network analysis implicated histone genes and cytokine-relevant immune response genes at the earliest stages of development, and revealed relevance of ISG15 to the following degradation and accumulation of mutant ataxin-1 in Purkinje cells of SCA1 model mice and human patients.
Keyphrases
- induced pluripotent stem cells
- induced apoptosis
- network analysis
- cell cycle arrest
- immune response
- rna seq
- oxidative stress
- single cell
- end stage renal disease
- chronic kidney disease
- transcription factor
- cell death
- newly diagnosed
- single molecule
- small molecule
- type diabetes
- genome wide
- inflammatory response
- machine learning
- metabolic syndrome
- ejection fraction
- electronic health record
- insulin resistance
- high resolution
- dendritic cells
- toll like receptor
- big data
- patient reported outcomes
- deep learning
- genome wide analysis
- genome wide identification