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 6

lncRNAs showing UC severity predict outcomes similar to protein-coding genes and are associated with the gut microbiome.

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lncRNAs showing UC severity predict outcomes similar to protein-coding g...
(A) Volcano plot of the 192 differentially expressed lncRNAs between 53 patients with mild UC and 68 patients with severe UC (FC ≥ 1.5, FDR ≤ 0.05). (B and C) Receiver operating characteristic (ROC) curves of random forest (RF) analysis using either the 960 protein-coding genes or the 192 lncRNA severity-associated genes, or both lncRNAs and protein-coding genes, for W4 early response and for W52SFR in the moderate-severe patients’ group (n = 153) that received standardized initial treatment with corticosteroids. The graph showed 1 representative iteration out of 100 RF performed iterations. The mean ROC AUC for W4, using the lncRNA was 0.68 (min, 0.65; max, 0.70), which was similar to those obtained using only the protein-coding genes (mean, 0.67; min, 0.65; max, 0.69), and those obtained using both lncRNAs and protein-coding genes (mean, 0.67; min, 0.65; max, 0.69). For W52SFR, the mean ROC AUC using the lncRNA was 0.63 (min, 0.60; max, 0.66), the mean ROC AUC using the protein-coding was 0.65 (min, 0.63; max, 0.67) and the mean ROC AUC using both lncRNAs and protein-coding genes was 0.65 (min, 0.62; max, 0.67). (D) Heatmap showing significant differential bacterial ASVs (47 ASVs more abundant in mild and 12 more abundant in severe cases) between 38 samples from patients with mild UC and 54 samples from patients with severe UC (rank-mean test with FDR < 0.1). Each row represents an ASV, and each column is a patient sample (38 mild, 64 moderate, 54 severe). (E) Heatmap summarizing the association between lncRNA expression and microbial ASV abundance using HAllA testing, with FDR < 0.1, using the 156 samples with matching microbial ASV and lncRNA expression data.