Mucosal transcriptomics highlight lncRNAs implicated in ulcerative colitis, Crohn’s disease, and celiac disease
Tzipi Braun, … , Lee A. Denson, Yael Haberman
Tzipi Braun, … , Lee A. Denson, Yael Haberman
Published June 1, 2023
Citation Information: JCI Insight. 2023;8(14):e170181. https://doi.org/10.1172/jci.insight.170181.
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Research Article Gastroenterology

Mucosal transcriptomics highlight lncRNAs implicated in ulcerative colitis, Crohn’s disease, and celiac disease

Abstract

Ulcerative colitis (UC), Crohn’s disease (CD), and celiac disease are prevalent intestinal inflammatory disorders with nonsatisfactory therapeutic interventions. Analyzing patient data-driven cohorts can highlight disease pathways and new targets for interventions. Long noncoding RNAs (lncRNAs) are attractive candidates, since they are readily targetable by RNA therapeutics, show relative cell-specific expression, and play key cellular functions. Uniformly analyzing gut mucosal transcriptomics from 696 subjects, we have highlighted lncRNA expression along the gastrointestinal (GI) tract, demonstrating that, in control samples, lncRNAs have a more location-specific expression in comparison with protein-coding genes. We defined dysregulation of lncRNAs in treatment-naive UC, CD, and celiac diseases using independent test and validation cohorts. Using the Predicting Response to Standardized Pediatric Colitis Therapy (PROTECT) inception UC cohort, we defined and prioritized lncRNA linked with UC severity and prospective outcomes, and we highlighted lncRNAs linked with gut microbes previously implicated in mucosal homeostasis. HNF1A-AS1 lncRNA was reduced in all 3 conditions and was further reduced in more severe UC form. Similarly, the reduction of HNF1A-AS1 ortholog in mice gut epithelia showed higher sensitivity to dextran sodium sulfate–induced colitis, which was coupled with alteration in the gut microbial community. These analyses highlight prioritized dysregulated lncRNAs that can guide future preclinical studies for testing them as potential targets.

Authors

Tzipi Braun, Katya E. Sosnovski, Amnon Amir, Marina BenShoshan, Kelli L. VanDussen, Rebekah Karns, Nina Levhar, Haya Abbas-Egbariya, Rotem Hadar, Gilat Efroni, David Castel, Camila Avivi, Michael J. Rosen, Anne M. Grifiths, Thomas D. Walters, David R. Mack, Brendan M. Boyle, Syed Asad Ali, Sean R. Moore, Melanie Schirmer, Ramnik J. Xavier, Subra Kugathasan, Anil G. Jegga, Batya Weiss, Chen Mayer, Iris Barshack, Shomron Ben-Horin, Igor Ulitsky, Anthony Beucher, Jorge Ferrer, Jeffrey S. Hyams, Lee A. Denson, Yael Haberman

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Figure 5

lncRNA prioritization and inferred function from coexpression with protein-coding genes.

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lncRNA prioritization and inferred function from coexpression with prote...
(A) WGCNA coexpression modules heatmap that includes lncRNAs and protein-coding genes, generated using the PROTECT cohort (206 UC and 20 controls). Modules that were correlated with UC diagnosis (P < 0.001) and other clinical features are shown. Numbers represent the correlation coefficient and P value for each comparison. (B) For each WGCNA module associated with disease and for all modules combined, the fraction of lncRNAs and protein-coding genes are marked on the x axis, and the actual number of genes is written within the bar. (C) ToppGene/ToppCluster functional annotation enrichment of protein-coding genes within each module. FDR is shown as circle size; selected annotations origin database is marked on the y axis (full list in Supplemental Data 1). (D) Heatmap showing the overlap between UC lncRNA–only modules (numbered M1–M5), and UC lncRNA plus protein-coding gene modules (colored). For each lncRNA module, the number of lncRNAs shared between lncRNAs plus protein-coding gene modules is noted as well as the percentage of those lncRNAs of the lncRNAs-only WGCNA modules.