Bipolar DisordersNeurosciencePeer-reviewed journal article
The toll-like receptor signalling pathway is altered in iPSC-derived cortical networks from people with bipolar disorder.
Bruna PanizzuttiiD, Chiara C. BortolasciiD, Megan K. EllisiD, Briana SpoldingiD, Courtney Swinton, Trang T. T. TruongiD, Cassie FieldiD, Zoe Shih-Jung LiuiD, Damián HernándeziD, Greg RoebuckiD, Ajeet SinghiD, Bruno AgustiniiD, Robson ZazulaiD, Ana C. AndreazzaiD, Dana El Soufi El SabbaghiD, Hyunjin JeongiD, Olivia May DeaniD, Jee Hyun KimiD, Michael BerkiD, Ken R. WalderiD
Abstract
BackgroundInduced pluripotent stem cell (iPSC)-derived brain cells are widely utilized as in vitro models for several neuropsychiatric disorders, as they retain the donors genetic profile, offering a unique opportunity to study living human brain cells and perform controlled experimental manipulations. In this study, we conducted whole transcriptome sequencing of cortical networks (co-cultures of neurons and astrocytes) derived from 12 participants with bipolar disorder (BD) and 12 participants without a history of mental health disorders. We aimed to identify new molecular mechanisms underlying the pathophysiology of bipolar disorder. MethodsiPSCs were generated by reprogramming peripheral blood mononuclear cells using episomal vectors. They were then differentiated into neural progenitor cells and matured into cortical networks that express markers of neurons and astrocytes. Whole transcriptome data were obtained using the Illumina NovaSeq X sequencing platform. ResultsDifferential expression analysis was performed using DESeq2 in R, and the identified genes were used for gene set enrichment analysis, which identified 191 enriched pathways in BD. Of these, the toll-like signalling pathway, which is downregulated in BD, was further investigated. ConclusionOur results suggest a profound immune dysregulation in BD, particularly highlighting the immune systems role as a complex signalling network.
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 0
- FWCI
- 0.00
- Field percentile
- top 42%
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