Functional engineering of human iPSC-derived parasympathetic neurons enhances responsiveness to gastrointestinal hormones.
Yuka AkagiYuzo TakayamaYuma NihashiAzusa YamashitaRisa YoshidaYasuhisa MiyamotoYasuyuki S KidaPublished in: FEBS open bio (2023)
Food-derived biological signals are transmitted to the brain via peripheral nerves through the paracrine activity of gastrointestinal (GI) hormones. The signal transduction circuit of the brain-gut axis has been analyzed in animals; however, species-related differences and animal welfare concerns necessitate investigation using in vitro human experimental models. Here, we focused on the receptors of five GI hormones (CCK, GLP1, GLP2, PYY, and serotonin (5-HT)), and established human induced pluripotent stem cell (iPSC) lines that functionally expressed each receptor. Compared to the original iPSCs, iPSCs expressing one of the receptors did not show any differences in global mRNA expression, genomic stability, or differentiation capacities of the three germ layers. We induced parasympathetic neurons from these established iPSC lines to assess vagus nerve activity. We generated GI hormone receptor-expressing neurons (CCKAR, GLP1R, and NPY2R-neuron) and tested their responsiveness to each ligand using Ca 2+ imaging and microelectrode array (MEA) recording. GI hormone receptor-expressing neurons (GLP2R and HTR3A) were generated directly by gene induction into iPSC-derived peripheral nerve progenitors. These receptor-expressing neurons promise to contribute to a better understanding of how the body responds to GI hormones via the brain-gut axis, aid in drug development, and offer an alternative to animal studies.
Keyphrases
- induced pluripotent stem cells
- endothelial cells
- spinal cord
- peripheral nerve
- stem cells
- high glucose
- white matter
- high resolution
- diabetic rats
- pluripotent stem cells
- heart rate variability
- spinal cord injury
- mass spectrometry
- mesenchymal stem cells
- multiple sclerosis
- cerebral ischemia
- climate change
- bone marrow
- big data
- stress induced
- cell therapy
- high density
- machine learning
- transcription factor