Chronic inflammation increases the risk of developing one of several types of cancer. Inflammatory responses are currently thought to be controlled by mechanisms that rely on transcriptional networks that are distinct from those involved in cell differentiation1,2,3. The orphan nuclear receptor NR5A2 participates in a wide variety of processes, including cholesterol and glucose metabolism in the liver, resolution of endoplasmic reticulum stress, intestinal glucocorticoid production, pancreatic development and acinar differentiation4,5,6,7,8. In genome-wide association studies9,10, single nucleotide polymorphisms in the vicinity of NR5A2 have previously been associated with the risk of pancreatic adenocarcinoma. In mice, Nr5a2 heterozygosity sensitizes the pancreas to damage, impairs regeneration and cooperates with mutant Kras in tumour progression11. Here, using a global transcriptomic analysis, we describe an epithelial-cell-autonomous basal pre-inflammatory state in the pancreas of Nr5a2+/− mice that is reminiscent of the early stages of pancreatitis-induced inflammation and is conserved in histologically normal human pancreases with reduced expression of NR5A2 mRNA. In Nr5a2+/−mice, NR5A2 undergoes a marked transcriptional switch, relocating from differentiation-specific to inflammatory genes and thereby promoting gene transcription that is dependent on the AP-1 transcription factor. Pancreatic deletion of Jun rescues the pre-inflammatory phenotype, as well as binding of NR5A2 to inflammatory gene promoters and the defective regenerative response to damage. These findings support the notion that, in the pancreas, the transcriptional networks involved in differentiation-specific functions also suppress inflammatory programmes. Under conditions of genetic or environmental constraint, these networks can be subverted to foster inflammation.
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Gene Expression Omnibus
We thank O. Domínguez, J. Herranz, T. Lobato, L. Martínez, and Y. Cecilia, as well as members of the CNIO core facilities, Epithelial Carcinogenesis Group, and Genes, Development and Disease Group; L. Montuenga, C. Rodríguez-Ortigosa, B. Bréant and cited investigators for providing antibodies; and E. Batlle and P. Muñoz-Cánoves for critical comments. This study used the high-performance computational capabilities of the Biowulf Linux cluster (https://hpc.nih.gov/). The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, US National Institutes of Health (NIH), nor does mention of trade names, commercial products or organizations imply endorsement by the US government. This work was supported in part by grants SAF2011-29530 and SAF2015-70553-R from the Ministerio de Economía y Competitividad (co-funded by the ERDF-EU), RTICC from the Instituto de Salud Carlos III (RD12/0036/0034, RD12/0036/0050) and grants 256974 and 289737 from the European Union Seventh Framework Program to F.X.R.; grants BFU 2012-40230 and SAF2015-70857 from the Ministerio de Economía y Competitividad (co-funded by the ERDF-EU) and Worldwide Cancer Research (13-0216) to E.F.W.; grants PI12/00815 and PI1501573 from the Fondo de Investigaciones Sanitarias, Instituto de Salud Carlos III, Spain and EUPancreas COST Action BM1204 to N.M.; grant P30CA008748 from the US NIH, National Cancer Institute to S.H.O.; the Intramural Research Program of the NIH, National Cancer Institute; and Mayo Clinic SPORE in Pancreatic Cancer funded by National Cancer Institute grant P50 CA102701. L.T. and T.B. were supported by the Department of Technology, Norwegian University of Science and Technology, the Central Norway Regional Health Authority and by the European Science Foundation. P.M. and I.C. are recipients of Juan de la Cierva and Beca de Formación del Personal Investigador, respectively, from Ministerio de Economía y Competitividad. I.F. is the recipient of a ‘Juegaterapia-Amigos del CNIO’ Postdoctoral Fellowship. F.X.R. acknowledges the support of Asociación Española Contra el Cáncer.
Extended data figures
This file contains Supplementary Table 2: Gene Set Enrichment Analysis for the pre-ranked list of differentially expressed genes (DEG) (Nr5a2+/- vs. Nr5a2+/+) in basal conditions, Supplementary Table 3: Gene Set Enrichment Analysis for the genes up-regulated in Nr5a2+/- mice in basal conditions. Top-20 most significant gene sets when computing with the Molecular Signature tool of GSEA, using the Biological Processes dataset, Supplementary Table 4: Primers used for RT-qPCR and Supplementary Table 5: Primers used for ChIP-qPCR.
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