The unique pancreatic stellate cell gene expression signatures are associated with the progression from acute to chronic pancreatitis.

Chronic pancreatitis (CP) is characterized by irreversible fibro-inflammatory changes induced by pancreatic stellate cell (PSC). Unresolved or recurrent injury causes dysregulation of biological process following AP, which would cause CP. Here, we systematically identify genes whose expressions are unique to PSC by comparing transcriptome profiles among total pancreas, pancreatic stellate, acinar, islet and immune cells. We then identified candidate genes and correlated them with the pancreatic disease continuum by performing intersection analysis among total PSC and activated PSC genes, and genes persistently differentially expressed during acute pancreatitis (AP) recovery. Last, we examined the association between candidate genes and AP, and substantiated their potential as biomarkers in experimental AP and recurrent AP (RAP) models. A total of 68 genes were identified as highly and uniquely expressed in PSC. The PSC signatures were highly enriched with extracellular matrix remodeling genes and were significantly enriched in AP pancreas compared to healthy control tissues. Among PSC signature genes that comprised a fibrotic phenotype, 10 were persistently differentially expressed during AP recovery. SPARC was determined as a candidate marker for the pancreatic disease continuum, which was not only persistently differentially expressed even five days after AP injury, but also highly expressed in two clinical datasets of CP. Sparc was also validated as highly elevated in RAP compared to AP mice. This work highlights the unique transcriptional profiles of PSC. These PSC signatures' expression may help to identify patients with high risk of AP progression to CP.
Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(© 2021 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.)