Abstract
Due to the lack of clinical, immunologic, genetic, and laboratory markers to predict remission in ulcerative colitis (UC) without relapse, there is no clear recommendation regarding withdrawal of therapy. Therefore, this study was to investigate if transcriptional analysis together with Cox survival analysis might be able to reveal molecular markers that are specific for remission duration and outcome. Mucosal biopsies from patients in remission with active treatment-naïve UC and healthy control subjects underwent whole-transcriptome RNA-seq. Principal component analysis (PCA) and Cox proportional hazards regression analysis were applied to the remission data concerning duration and status of patients. A randomly chosen remission sample set was used for validation of the applied methods and results. The analyses distinguished two different UC remission patient groups with respect to remission duration and outcome (relapse). Both groups showed that altered states of UC with quiescent microscopic disease activity are still present. The patient group with the longest remission duration and no relapse revealed specific and increased expression of antiapoptotic factors belonging to the MTRNR2-like gene family and non-coding RNAs. In summary, the expression of anti-apoptotic factors and non-coding RNAs may contribute to personalized medicine approaches in UC by improving patient stratification for different treatment regimens.
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Introduction
Ulcerative colitis (UC) is a chronic inflammatory disorder which requires long-term treatment in order to achieve remission (Ungaro et al. 2017). The inflammation status of UC patients is usually determined by endoscopic, histologic, and laboratory parameters (Peyrin-Biroulet et al. 2014; Rogler et al. 2013). Different guidelines for medical and surgical treatment of UC are available (Dassopoulos et al. 2015; Magro et al. 2017). In general, a step-up approach is recommended with the goal of obtaining clinical remission (Danese et al. 2014). Biological therapy is recommended for patients with moderate to severe disease refractory or patients dependent on steroid treatment. Side effects of both types of medication are common. The current management programs for UC aim for induction and maintenance of clinical remission to prevent treatment-induced and disease-related complications.
Today, different scoring systems for UC activity are in use to evaluate endoscopic disease activity and activity status, but none of the scoring systems have had all criteria fully determined (Travis et al. 2011; Rutter et al. 2004). There is no validated current definition of remission, and therefore still no consensus on how to define clinical remission (Magro et al. 2017). The guidelines from the European Colitis and Crohn’s organization (ECCO) for remission suggest the absence of visible mucosal lesions (Mayo endoscopic grade 0) in remission (Magro et al. 2013), whereas others allow Mayo ≤ 1 including endoscopic grade 1 in remission (Lamb et al. 2019; Rutgeerts et al. 2005; Schroeder et al. 1987). However, it is generally accepted that healed mucosa with the absence of mucosal lesions is a treatment goal. “Histological” healed mucosa is not included in clinical remission, but there is an increasing focus of including histological criteria in healed mucosa (Peyrin-Biroulet et al. 2014). It is well known that even in the absence of gastrointestinal symptoms as well as normal endoscopic and clinical findings, patients may have persisting microscopic inflammatory activity even in the absence of gastrointestinal symptoms (Korelitz 2010; Magro et al. 2018; DeRoche et al. 2014). This activity can result in progressive accumulation of bowel damage, such as fibrosis, dysmotility, and increased risk of colorectal neoplasm (Gupta et al. 2007).
It is self-evident that there is a need for standardization of both assessment and validation as well as prognostic values. There is still a need to characterize the complex pathogenic and healing mechanisms in UC. Due to the lack of clinical, immunologic, genetic, and laboratory markers to predict remission without relapse, there is no clear recommendation regarding withdrawal of therapy. Therefore, the current study aims to identify molecular signatures in a UC remission cohort obtained by whole-transcriptome RNA-Seq with the intent to provide a better understanding of the molecular mechanisms responsible for remission duration and disease outcome. Altogether, this knowledge might lead to novel personalized therapeutic approaches that will help patients to stay in remission.
Materials and methods
Patient material
A standardized sampling method was used to collect mucosal biopsies (n = 56) from patients in remission (RR; n = 26). For comparison purposes, normal patient biopsies (NN; n = 16) and biopsies from patients with active UC (UC; n = 14) were adapted from an earlier study (Fenton et al. 2021). The level of inflammation in UC patients was diagnosed based upon established clinical endoscopic and histological criteria as defined by the European Colitis and Crohn’s Organization (ECCO) guidelines (Magro et al. 2017). A total Geboes score was determined for the remission samples (Geboes et al. 2000). TNF mRNA levels in biopsies were estimated by qPCR (Olsen et al. 2007). TNF-α values of <7000 copies/ug RNA were considered non-inflamed tissues. Faecal calprotectin was measured with the Calprest ELISA kit (Eurospital). All patient characteristics are depicted in Table 1. All methods were performed in accordance with the Declaration of Helsinki. The study participants signed informed and written consent forms. Approvals were granted by the Regional Committee of Medical Ethics of Northern Norway, Ref no: 14/2004, 1349/2012 and 29895/2020. The samples were taken from an established biobank approved by the Norwegian Board of Health (952/2006).
RNA isolation
Total RNA was isolated using the Allprep DNA/RNA Mini Kit from Qiagen (catalogue number 80204) and the QIAcube instrument (Qiagen), according to the manufacturer’s protocol. Quantity and purity of the RNA were assessed by using the NanoDrop ND-1000 spectrophotometer (Thermo Fisher Scientific, Wilmington, DE). The Experion Automated Electrophoresis System (Bio-Rad, Hercules, CA) and the RNA StdSens Analysis Kit (Bio-Rad, catalogue # 700–7103) were used to evaluate RNA integrity. The RNA samples were kept at −70 °C until further use. All RNA samples used for analyses showed an RNA integrity number (RIN) value of between 8.0 and 10.0.
Library preparation and next-generation sequencing
Whole transcriptome libraries of UC remission samples were prepared with the TruSeq Stranded Total RNA LT Sample Prep Kit from Illumina (Catalogue number RS-122–2203). The amount of input material was 1 μg of total RNA. The Bioanalyzer 2100 (Agilent Technologies, Santa Clara, CA) and the Agilent DNA 1000 kit (Catalogue number 5067-1504) were used to assess RNA library quality, according to the instruction manual. The libraries were normalized to 10 nM and subsequently paired- end sequenced with the NextSeq 550 instrument (Illumina) according to the manufacturer’s instructions. The average number of uniquely mapped reads per sequencing run was 85 million reads per sample.
Data analysis
The entire design and workflow of the study is depicted in Fig. 1.
Data quality assessment and initial principal component analysis (PCA)
Quality scoring and base calling were performed on the Illumina NextSeq 550 sequencing instrument. The output FastQ file was aligned with reference GENCODE Human Release 33 (Human Genome Assembly GRCh38.p13) (https://www.ncbi.nlm.nih.gov/grc/human/data) by STAR (Version 2.7.3a) with 2-pass mapping and gene counts parameters in STAR (Dobin et al. 2013). After alignment, the read quality was controlled by multiQC (Ewels et al. 2016). The gene counts were analysed and log-normalized by DESeq2 (Love et al. 2014); genes with an average log2 expression less than 4 were filtered out prior to normalization. Seven remission samples were randomly chosen for verification. Initial principal component analysis (PCA) was performed based on the top 15,000 variable genes after normalization.
Processed RNA-Seq data have been deposited in NCBI’s Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/) and are accessible through GEO series accession numbers GSE128682 and GSE169360.
Cox survival analysis of remission samples
After PCA, the remission patient group was investigated with Cox survival analysis in R using plsRcox (Bastien et al. 2015). Using remission patient information (Table 1) indicating state (relapse or not) and duration (time to relapse), a Cox model was created. The Cox model was applied on the normalized gene count matrix from remission patients. The initial Cox model was significant with a p < 0.01 in the likelihood ratio test and p = 0.03 in the Wald test for all normalized genes in the remission group, thus, suggesting that there is enough information in the gene matrix to explain patient risk. To further identify which genes influence risk for relapse, the R package survival (coxph) was then applied on each individual gene (Therneau 2021). The second analysis revealed 287 genes that significantly contribute to risk with a p value< 0.01. Those 287 genes were used for PCA analysis and visualization. Hazard beta-coefficients were calculated for the 23 selected genes.
Annotations
Genes were manually annotated using GeneCards (https://www.genecards.org/). EnrichGO of the clusterProfiler R package (Yu et al. 2012) was applied to the protein-coding genes. Only biological process GO terms for comparisons of patient groups with padj < 0.05 were kept.
Validations
Prior to analysis, seven remission samples were excluded from the remission patient cohort for validation. Gene counts from the four MTRNR2-like family genes (MTRNR2L6, MTRNR2L3, MTRNR2L12, and MTRNR2L8) from the validation samples were tested using the plsRcox package (Ginestet 2011). The statistics of all the Cox models are shown in Table S2.
Data visualization
Heatmaps were generated by ComplexHeatmap (Gu et al. 2016). Among 287 genes, protein coding genes and non-protein coding genes were ranked by means of each gene in the remission samples divided by the sum of means of each gene in each group, respectively. The rows were clustered for better visualization.
Results
Transcriptomic analysis discriminates different states of UC
The whole transcriptome representing treatment-naïve active UC (UC; n = 14), UC in remission (RR; n = 19), and normal control samples (NN; n =16) was established by RNA-seq. Pre-processing of the sequencing data revealed a total of 18,783 expressed genes. The normalization of the expression of gene matrices for all groups showed no batch effects (Table S1). The initial principal component analysis (PCA) with 15,000 most variable genes resulted in a clear distinction between normal (NN), ulcerative colitis (UC), and remission (RR) samples, along the first principal component (PC1) with a 30.2% explained variance and an 11% explained variance along with the second component (PC2) (Fig. 2). Note, that prior to the initial PCA analysis, seven remission patient samples were randomly removed from the remission data set for validation of a Cox survival model (see below).
Cox survival analysis discriminates genes related to remission duration and state
PCA alone did not result in a separation of remission samples, although they differed in terms of remission duration and time of relapse. Therefore, an attempt was made to distinguish remission samples by using different Cox models. Using the remission patient characteristics depicted in Table 1, a Cox survival model based on partial least squares was established using remission duration as survival time and relapse as an event. The model returned a likelihood test and Wald test p < 0.05 (Table S2), thus suggesting that there is enough information in the gene matrix to explain patient risk.
Remission significant genes obtained by Cox analysis
The second Cox analysis for each individual gene of the remission gene matrix returned 287 significant genes p <0.01 related to risk (Tables S2 and S3). Of the 287 genes, 188 represented protein-coding genes, 28 small RNAs, 25 non-coding RNAs, 31 pseudogenes, and 15 miscellaneous RNAs, which are all listed in Table S3. Significant genes (n = 287) obtained by the remission Cox analysis were visualized by PCA with co-normalized normal control samples (NN) and UC samples (UC) included. The result of the PCA shows that the significant genes from of the Cox model can clearly separate the remission samples into two groups with 38.1% and 12.9% of explained variances for principal component 1 (PC1) and principal component 2 (PC2) (Fig. 3). Both remission groups showed clear differences with respect to endoscopic, histological, and laboratory parameters and were then denominated accordingly RM (remission without relapse) and RL (remission with relapse) (Fig. 3 and Table S4). The PCA biplot shows both PC scores of samples (dots) and loadings of variables (vectors). The further away these vectors are from a PC origin, the more influence they have on that PC. Notably, the RM samples grouped closer to the normal control samples, whereas RL samples clustered and in part overlapped with UC samples (Fig. 3).
Genes of the MTRNR2-like family separate remission duration
Two biplots were constructed on the PCA, one including all 188 protein-coding genes and one including 99 non-coding genes indicating the effect of each individual gene on the principal components (Figs. S1 and S2). For illustrative purposes, twenty-three relevant protein-coding genes with high influence were chosen for construction of a biplot (Fig. 3). In addition, the expressions of relevant 30 protein-coding genes and non-coding genes found for the different patient groups were visualized in a heatmap (Fig. 4).
Genes of the MTRNR2-like family influenced the remission samples separation. Solute carrier family 44 member 5 (SLC44A5), glucagon like peptide 2 receptor (GLP2R), prominin 1 (PROM1), NEDD4 like E3 ubiquitin protein ligase (NEDD4L), and methylmalonyl-CoA mutase (MMUT) are the main participants for components for separation towards the normal control group. Genes like interleukin enhancer binding factor 3 (ILF3), carbamoyl-phosphate synthetase 2, aspartate transcarbamylase, and dihydroorotase (CAD), Mal, T cell differentiation protein (MAL), and granzyme M (GZMM) are influencing the separation of UC samples and RL samples.
Hazard values confirm separation and specificity of expressed genes
To confirm this finding, a PCA using the 287 genes from the remission gene count matrix only was performed (Fig. 5). The result confirmed the separation and specificity of the expressed genes with 59.1% and 9.8% of explained variances for PC1 and PC2 in the remission matrix PCA. Beta-coefficients (hazard values) for selected 23 individual genes are shown in Fig. 6. Ten genes including ILF3, mitochondrial ATP-dependent protease Lon (LONP1), proteasome 20S subunit alpha 3 (PSMA3), and CAD were found to increase the chance of relapse which is reflected by negative coefficients. Thirteen genes including MTRNR2 like family, PROM1, and NEDD4L decrease the probability of relapse which is reflected by positive coefficients. A complete beta value list of all genes (n = 287) can be found in Table S5.
Annotation reveals involvement of apoptotic and RNA processing pathways
Among the 287 genes, 188 were protein-coding genes. GO enrichment of these 188 protein-coding genes is shown in Fig. 7. Significantly enriched gene sets revealed biological processes like negative regulation of the execution phase of apoptosis with genes of the MTRNR2-like family (MTRNR2L6, MTRNR2L8, MTRNR2L3, MTRNR2L12), ribosome biogenesis, rRNA processing, RNA splicing, signal transduction by p53 class mediator, and ribonucleoprotein complex biogenesis. The cellular component preribosome and molecular functions including single-stranded RNA binding and receptor antagonist activity were enriched. The full enrichment list is shown in Table S6.
MTRNR2-like genes are predictors for risk of relapse
Seven remission testing samples were used to validate the influence of the four MTRNR2-like genes (MTRNR2L6, MTRNR2L8, MTRNR2L3, and MTRNR2L12) using the Cox model. The correlation between predicted duration and actual duration R= 0.641 (Fig. 8). This indicates that the MTRNR2-like genes are good predictors for risk of relapse.
Discussion
Today, the recommendations regarding the withdrawal of therapy during UC are not clear. Therefore, the current study aimed to identify molecular signatures in a UC remission patient cohort with focus on remission duration after treatment and disease outcome. The analysis of transcriptional expression data of UC remission samples obtained by RNA-Seq, Cox survival analysis, and downstream PCA analysis of genes obtained by Cox survival analysis clearly revealed a relationship between remission event (duration of remission) and remission states (relapse or no relapse). Initial PCA analysis (Fig. 2) on the normalized expression matrix of all patient samples confirmed similar distribution patterns for remission samples found for two independent UC remission cohort studies studying the differential expression of genes, showing a clear distinction between UC in remission, normal controls, and active UC samples (Fenton et al. 2021; Planell et al. 2013). PCA analysis of the genes obtained by Cox analysis could clearly separate remission samples into two groups representing UC remission, one with relapse (RL) and one without relapse (RM) (Figs. 3 and 5). The Cox analysis, using the remission gene matrix only, showed that the model was independent of the other sample groups, UC and NN (Fig. 5). Therefore, it is surprising that a clear relationship between selected genes and the UC and NN background samples could be observed (Fig. 3).
The obtained molecular signatures did show different inflammatory states in the remission groups (Fig. 3, Tables S3 and S4). A quiescent inflammation is still present in remission which is reflected by the expression of interleukin enhancer binding factor 3 (ILF3) which is involved in innate immune responses and myeloid dendritic cell maturation in IBD (Aitchison et al. 2021). The influence shown in the biplot (Figs. 3 and S1) on the first principal component and a high beta coefficient found for ILF3 confirms inflammatory signals in RL samples (Fig. 6 and Table S5). Likewise, increased expression of other inflammatory genes like CAD which is involved in the inhibition of NOD2 antibacterial function in intestinal epithelial cells (Richmond et al. 2012) and PSMA3 which is involved in the proteasome-mediated NF-dB activation in UC (Goetzke et al. 2021) was observed. Recently, a relationship between UC and atherosclerosis has been implicated (Weissman et al. 2020; Roifman et al. 2009). The reported higher risk of cardiovascular events in UC patients may be pertinent in inflammation-mediated atherosclerosis (Rungoe et al. 2013; Kristensen et al. 2013). The mitochondrial matrix protein LONP1 has been shown to be involved in atherosclerosis mitochondrial protein quality control (Hansen et al. 2008; Onat et al. 2019) and is a strong risk factor of relapse (Fig. 6). All the above-mentioned genes are shown to have an influence pointing towards inflammation and increased risk of relapse especially for patients in the RL group (Figs. 3, 6, and S1).
It is well-known that mitochondrial function in the intestinal epithelium plays a critical role in maintaining intestinal health (Urbauer et al. 2021). A recent paediatric UC patient cohort study revealed suppressed expression of mitochondrial genes in active UC (Haberman et al. 2019). The here observed increased expression of MTRNR2-like genes might improve the remission state (Fig. 3, Table S3, Fig. S1). Mitochondrial dysfunction and dysbiosis of gut microbiota have been shown to be associated with IBD (Jackson and Theiss 2020). Therefore, a recovery of the gut-microbiota environment and restoring of rectal mitochondrial energy functions can be implied for remission without relapse (RM) where commensal bacterial-induced mitochondrial signalling potentiates epithelial homeostasis. The specific expression of MTRNR2-like genes in RM might represent these genes as potential molecular markers for disease outcome (Figs. 3 and 4, and Table S3). The GO annotations confirmed enrichment of genes involved in the regulation of execution phase of apoptosis (Fig. 7).
It is interesting to note that MTRNR2 treatment may exert beneficial effects in UC by decreasing inflammatory reactions and apoptosis (Gultekin et al. 2017). The mitochondrial metabolism in the intestinal stem cell niche plays also a pivotal role in regulating intestinal epithelial cell homeostasis, including self- renewal and differentiation (Urbauer et al. 2021). The observed expression of stem cell marker prominin 1 (PROM1) (Karim et al. 2014) and NEDD4 like E3 ubiquitin protein ligase (NEDD4L) points to a maintenance of proliferation and differentiation of the colonic epithelium in RM (Kimura et al. 2011) (Figs. 3 and 4). NEDDL4 strongly contributes to a lower risk of relapse (Fig. 6). In addition, increased expression of the vitamin B12 dependent, mitochondrial MMUT (Park et al. 2021) in RM points to a lower B12 deficiency reported for UC patients thereby lowering the risk of relapse (Fig. 6) (Battat et al. 2014; Mortimore and Florin 2010).
Top genes with great influences towards normal control samples in the PCA are HMGSCS2, BAGALNTT2, and GLP2R (Figs. 3 and S1). HMGCS2 encodes a mitochondrial protein that belongs to the HMG-CoA synthase family and catalyses the first reaction of ketogenesis. Elevated expression of HGMCS2 has been reported recently for long-duration ulcerative colitis (Low et al. 2019). Here, HMGCS2 showed increased expression in both remission groups when compared to UC and contributes to a lower risk of relapse (Table S3 and Fig. 6). However, a high expression of HMGCS2 has been associated with the development of colorectal cancer (CRC) which is contrary to these findings (Chen et al. 2017). Increased expression of glycosyltransferase B4GALNT2 in RM points to a maintenance of the intestinal mucus barrier function (Table S3) (Bergstrom et al. 2017). The increased expression of GLP2R involved in the stimulation of intestinal growth, increase of crypt cell proliferation and decrease of enterocyte apoptosis by glucagon-like peptides, prevents intestinal hypoplasia (Drucker 2003).
Interestingly, nearly all the non-coding genes shown in the biplot demonstrate an influence towards RM and normal controls in the PCA (Fig. S2). The expression of 20 small nucleolar RNAs (snoRNAs) (Fig. 4, Table S3, and Fig. S2) may be involved in the mediation of cell–cell communication and improvement of cell survival in the face of stress and/or infection (Rimer et al. 2018), and long non-coding RNAs (lncRNAs) have been shown to have relevance for ulcerative colitis pathogenesis (Ghafouri-Fard et al. 2020; Yarani et al. 2018; Ray et al. 2022). Functions of non-coding RNAs in ribosomal RNA (rRNA) regulation have been recently reported where especially snoRNAs and long non-coding RNAs play important roles in pre-rRNA transcription, processing, and maturation (Li et al. 2013). These pathways are shown to be enriched in RM (Fig. 7).
However, the relevance of specific expression of non-coding RNAs for UC remission duration and outcome needs further evaluation. In this context, it is interesting to note that synergistic gene regulation by pseudogenes and non-coding RNAs has been considered a novel regulatory mechanism which might have a role in UC pathogenesis (Li et al. 2013; Milligan et al. 2015).
This study is not without limitations and is limited by a restricted number of patient samples. Yet, a separation in the PCA after Cox analysis was clearly derived (Figs. 3 and 5). Although several studies present gene expression data of UC patients in remission, separate patient samples with indicated time of relapse were not available for validation of the Cox model. Knowing that the sample number was low, the Cox survival model was then validated with 7 randomly chosen remission patient samples and could confirm the model (Fig. 8) using four MTRNR2-like genes. In addition, a patient cohort with the possibility to investigate the remission state in the same patients consecutively was not available at the time of this study. Nevertheless, the different remission groups do not resemble a normal control phenotype. Patients in the RM group that have been previously treated with anti-TNF therapy (infliximab) until endoscopic remission and subsequently been treated with 5-aminosalicylic acid (5-ASA) only did not experience relapse (Johnsen et al. 2017). Patients in the RL remission group remained in remission for up to 8 months with additional immunosuppressive treatment but had a relapse at some point during the treatment period.
Conclusions
The data clearly demonstrate that remission is an altered state of UC with quiescent microscopic disease activity still present. This disease activity is independent of remission duration and outcome. Transcription expression analysis and Cox survival analysis revealed potential markers genes that could be useful to predict disease outcome. These markers include mitochondrial MTRNR2-like genes and non-coding RNAs. Especially, the expression of anti-apoptotic factors and snoRNAs may contribute to personalized medicine approaches in UC by improving patient stratification for different treatment regimens. The data presented might be of clinical utility in the future.
Data availability
The dataset generated and analysed during the current study is available at GEO accession number GSE169360: Go to https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE169360. Enter token ovgloyiyptofdud into the box. All other data generated or analysed during this study are included in this published article and supplementary files.
References
Aitchison D, Aderem A, Johnson JS, Rodolfo Nazitto AH, Amon LM, Mast FD (2021) Dendritic cell maturation of innate immune responses and myeloid ILF3 is a negative transcriptional regulator. https://doi.org/10.4049/jimmunol.2001235
Bastien P, Bertrand F, Meyer N, Maumy-Bertrand M (2015) Deviance residuals-based sparse PLS and sparse kernel PLS regression for censored data. Bioinformatics 31. https://doi.org/10.1093/bioinformatics/btu660
Battat R, Kopylov U, Szilagyi A, Saxena A, Rosenblatt DS, Warner M, Bessissow T, Seidman E, Bitton A (2014) Vitamin B 12 deficiency in inflammatory bowel disease: prevalence, risk factors, evaluation, and management. https://doi.org/10.1097/MIB.0000000000000024
Bergstrom K, Fu J, Johansson M, Liu X, Gao N, Wu Q, Song J, Mcdaniel JM, Mcgee S, Chen W et al (2017) Core 1- and 3-derived O-glycans collectively maintain the colonic mucus barrier and protect against spontaneous colitis in mice. https://doi.org/10.1038/mi.2016.45
Chen SW, Chou CT, Chang CC, Li YJ, Chen ST, Lin IC, Kok SH, Cheng SJ, Lee JJ, Wu TS et al (2017) HMGCS2 enhances invasion and metastasis via direct interaction with PPARα to activate Src signaling in colorectal cancer and oral cancer. Oncotarget 8:22460. https://doi.org/10.18632/ONCOTARGET.13006
Danese S, Siegel CA, Peyrin-Biroulet L (2014) Review article: integrating budesonide-MMX into treatment algorithms for mild-to-moderate ulcerative colitis. Aliment Pharmacol Ther 39:1095–1103
Dassopoulos T, Cohen RD, Scherl EJ, Schwartz RM, Kosinski L, Regueiro MD (2015) Ulcerative colitis care pathway. Gastroenterology 149:238–245. https://doi.org/10.1053/j.gastro.2015.05.036
DeRoche TC, Xiao SY, Liu X (2014) Histological evaluation in ulcerative colitis. Gastroenterol Rep (Oxf) 2:178–192. https://doi.org/10.1093/gastro/gou031
Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, Batut P, Chaisson M, Gingeras TR (2013) STAR: ultrafast universal RNA-Seq aligner. Bioinformatics 29:15–21. https://doi.org/10.1093/bioinformatics/bts635
Drucker DJ (2003) Glucagon-like peptides: regulators of cell proliferation, differentiation, and apoptosis. https://doi.org/10.1210/me.2002-0306
Ewels P, Magnusson M, Lundin S, Kaller M (2016) MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics 32:3047–3048. https://doi.org/10.1093/bioinformatics/btw354
Fenton CG, Taman H, Florholmen J, Sørbye SW, Paulssen RH (2021) Transcriptional signatures that define ulcerative colitis in remission. Inflamm Bowel Dis 27:94–105. https://doi.org/10.1093/ibd/izaa075
Geboes K, Riddell R, Ost A, Jensfelt B, Persson T, Lofberg R (2000) A reproducible grading scale for histological assessment of inflammation in ulcerative colitis. Gut 47:404–409
Ghafouri-Fard S, Eghtedarian R, Taheri M (2020) The crucial role of non-coding RNAs in the pathophysiology of inflammatory bowel disease. Biomed Pharmacother 129:110507. https://doi.org/10.1016/J.BIOPHA.2020.110507
Ginestet C (2011) Ggplot2: elegant graphics for data analysis. J R Stat Soc A Stat Soc 174. https://doi.org/10.1111/j.1467-985x.2010.00676_9.x
Goetzke CC, Ebstein F, Kallinich T, Feist E, Möller B (2021) Clinical medicine role of proteasomes in inflammation. Inflammation J Clin Med 10. https://doi.org/10.3390/jcm10081783
Gu Z, Eils R, Schlesner M (2016) Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics (Oxford, England) 32:2847–2849. https://doi.org/10.1093/BIOINFORMATICS/BTW313
Gultekin FA, Emre AU, Celik SK, Barut F, Tali U, Sumer D, Turkcu UO (2017) Effects of humanin on experimental colitis induced by 2,4,6-trinitrobenzene sulphonic acid in rats. Saudi J Gastroenterol: Official J Saudi Gastroenterol Assoc 23(2):105. https://doi.org/10.4103/sjg.SJG_318_16
Gupta RB, Harpaz N, Itzkowitz S, Hossain S, Matula S, Kornbluth A, Bodian C, Ullman T (2007) Histologic inflammation is a risk factor for progression to colorectal neoplasia in ulcerative colitis: a cohort study. Gastroenterology 133:1091–1099. https://doi.org/10.1053/j.gastro.2007.08.001
Haberman Y, Karns R, Dexheimer PJ, Schirmer M, Somekh J, Jurickova I, Braun T, Novak E, Bauman L, Collins MH et al (2019) Ulcerative colitis mucosal transcriptomes reveal mitochondriopathy and personalized mechanisms underlying disease severity and treatment response. Nat Commun 10:38. https://doi.org/10.1038/s41467-018-07841-3
Hansen J, Corydon TJ, Palmfeldt J, Dürr A, Fontaine B, Nielsen MN, Christensen JH, Gregersen N, Bross P (2008) Decreased expression of the mitochondrial matrix proteases Lon and ClpP in cells from a patient with hereditary spastic paraplegia (SPG13). Neuroscience 153:474–482. https://doi.org/10.1016/j.neuroscience.2008.01.070
Jackson DN, Theiss AL (2020) Gut bacteria signaling to mitochondria in intestinal inflammation and cancer. Gut Microbes 11:285–304
Johnsen K-M, Goll R, Hansen V, Olsen T, Rismo R, Heitmann R, Gundersen MD, Kvamme JM, Paulssen EJ, Kileng H et al (2017) Repeated intensified infliximab induction - results from an 11-year prospective study of ulcerative colitis using a novel treatment algorithm. Eur J Gastroenterol Hepatol 29:98–104. https://doi.org/10.1097/MEG.0000000000000753
Karim BO, Rhee KJ, Liu G, Yun K, Brant SR (2014) Prom1 function in development, intestinal inflammation, and intestinal tumorigenesis. Frontiers. Oncology 4. https://doi.org/10.3389/fonc.2014.00323
Kimura T, Kawabe H, Jiang C, Zhang W, Xiang YY, Lu C, Salter MW, Brose N, Lu WY, Rotin D (2011) Deletion of the ubiquitin ligase Nedd4L in lung epithelia causes cystic fibrosis-like disease. Proc Natl Acad Sci U S A 108:3216–3221. https://doi.org/10.1073/PNAS.1010334108/SUPPL_FILE/PNAS.1010334108_SI.PDF
Korelitz BI (2010) Mucosal healing as an index of colitis activity: back to histological healing for future indices. Inflamm Bowel Dis 16:1628–1630. https://doi.org/10.1002/ibd.21268
Kristensen SL, Ahlehoff O, Lindhardsen J, Erichsen R, Jensen GV, Torp-Pedersen C, Nielsen OH, Gislason GH, Hansen PR (2013) Disease activity in inflammatory bowel disease is associated with increased risk of myocardial infarction, stroke and cardiovascular death--a Danish nationwide cohort study. PLoS One 8. https://doi.org/10.1371/JOURNAL.PONE.0056944
Lamb CA, Kennedy NA, Raine T, Hendy PA, Smith PJ, Limdi JK, Hayee B, Lomer MCE, Parkes GC, Selinger C et al (2019) British Society of Gastroenterology consensus guidelines on the management of inflammatory bowel disease in adults. Gut 68:s1–s106
Li W, Yang W, Wang XJ (2013) Pseudogenes: pseudo or real functional elements? J Genet Genomics = Yi Chuan Xue Bao 40:171–177. https://doi.org/10.1016/J.JGG.2013.03.003
Love MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-Seq data with DESeq2. Genome Biol 15. https://doi.org/10.1186/s13059-014-0550-8
Low END, Mokhtar NM, Wong Z, Raja Ali RA (2019) Colonic mucosal transcriptomic changes in patients with long-duration ulcerative colitis revealed colitis-associated cancer pathways. J Crohns Colitis 13:755–763. https://doi.org/10.1093/ecco-jcc/jjz002
Magro F, Gionchetti P, Eliakim R, Ardizzone S, Armuzzi A, Barreiro-de Acosta M, Burisch J, Gecse KB, Hart AL, Hindryckx P et al (2017) Third European evidence-based consensus on diagnosis and management of ulcerative colitis. Part 1: definitions, diagnosis, extra-intestinal manifestations, pregnancy, cancer surveillance, surgery, and ileo-anal pouch disorders. J Crohns Colitis 11:649–670. https://doi.org/10.1093/ecco-jcc/jjx008
Magro F, Langner C, Driessen A, Ensari A, Geboes K, Mantzaris GJ, Villanacci V, Becheanu G, Borralho Nunes P, Cathomas G et al (2013) European consensus on the histopathology of inflammatory bowel disease. J Crohns Colitis 7:827–851. https://doi.org/10.1016/j.crohns.2013.06.001
Magro F, Lopes J, Borralho P, Lopes S, Coelho R, Cotter J, Castro FD, Sousa HT, Salgado M, Andrade P et al (2018) Comparison of different histological indexes in the assessment of UC activity and their accuracy regarding endoscopic outcomes and faecal calprotectin levels. Gut 68:594–603. https://doi.org/10.1136/gutjnl-2017-315545
Milligan MJ, Lipovich L, Rossbach M, Alvarez-Dominguez JR (2015) Pseudogene-derived LncRNAs: emerging regulators of gene expression. https://doi.org/10.3389/fgene.2014.00476
Mortimore M, Florin THJ (2010) A role for B 12 in inflammatory bowel disease patients with suppurative dermatoses? An experience with high dose vitamin B 12 therapy. https://doi.org/10.1016/j.crohns.2010.02.007
Olsen T, Goll R, Cui G, Husebekk A, Vonen B, Birketvedt GS, Florholmen J (2007) Tissue levels of tumor necrosis factor-alpha correlates with grade of inflammation in untreated ulcerative colitis. Scand J Gastroenterol 42:1312–1320. https://doi.org/10.1080/00365520701409035
Onat UI, Yildirim AD, Tufanli Ö, Çimen I, Kocatürk B, Veli Z, Hamid SM, Shimada K, Chen S, Sin J et al (2019) Intercepting the lipid-induced integrated stress response reduces atherosclerosis. J Am Coll Cardiol 73:1149–1169. https://doi.org/10.1016/J.JACC.2018.12.055
Park YE, Park SJ, Park JJ, Cheon JH, Kim T, Il; Kim, W.H. (2021) Incidence and risk factors of micronutrient deficiency in patients with IBD and intestinal Behçet’s disease: folate, vitamin B12, 25-OH-vitamin D, and ferritin. BMC Gastroenterol 21:1–9. https://doi.org/10.1186/S12876-021-01609-8/FIGURES/3
Peyrin-Biroulet L, Bressenot A, Kampman W (2014) Histologic remission: the ultimate therapeutic goal in ulcerative colitis? Clin Gastroenterol Hepatol 12:929–34 e2. https://doi.org/10.1016/j.cgh.2013.07.022
Planell N, Lozano JJ, Mora-Buch R, Masamunt MC, Jimeno M, Ordas I, Esteller M, Ricart E, Pique JM, Panes J et al (2013) Transcriptional analysis of the intestinal mucosa of patients with ulcerative colitis in remission reveals lasting epithelial cell alterations. Gut 62:967–976. https://doi.org/10.1136/gutjnl-2012-303333
Ray MK, Fenton CG, Paulssen RH (2022) Novel long non-coding RNAs of relevance for ulcerative colitis pathogenesis. Non-coding RNA Res 7:40–47. https://doi.org/10.1016/J.NCRNA.2022.02.001
Richmond AL, Kabi A, Homer CR, Marina-García N, Nickerson KP, Nesvizhskii AI, Sreekumar A, Chinnaiyan AM, Nuñez G, McDonald C (2012) The nucleotide synthesis enzyme CAD inhibits NOD2 antibacterial function in human intestinal epithelial cells. Gastroenterology p 142. https://doi.org/10.1053/J.GASTRO.2012.02.040
Rimer JM, Lee J, Holley CL, Crowder RJ, Chen DL, Hanson PI, Ory DS, Schaffer JE (2018) Long-range function of secreted small nucleolar RNAs that direct 2-O-methylation. J Biol Chem 293:13284–13296. https://doi.org/10.1074/jbc.RA118.003410
Rogler G, Vavricka S, Schoepfer A, Lakatos PL (2013) Mucosal healing and deep remission: what does it mean? World J Gastroenterol 19:7552–7560. https://doi.org/10.3748/wjg.v19.i43.7552
Roifman I, Sun YC, Fedwick JP, Panaccione R, Buret AG, Liu H, Rostom A, Anderson TJ, Beck PL (2009) Evidence of endothelial dysfunction in patients with inflammatory bowel disease. Clin Gastroenterol Hepatol 7:175–182. https://doi.org/10.1016/J.CGH.2008.10.021
Rungoe C, Basit S, Ranthe MF, Wohlfahrt J, Langholz E, Jess T (2013) Risk of ischaemic heart disease in patients with inflammatory bowel disease: a nationwide Danish cohort study. Gut 62:689–694. https://doi.org/10.1136/GUTJNL-2012-303285
Rutgeerts P, Sandborn WJ, Feagan BG, Reinisch W, Olson A, Johanns J, Travers S, Rachmilewitz D, Hanauer SB, Lichtenstein GR et al (2005) Infliximab for induction and maintenance therapy for ulcerative colitis. N Engl J Med 353:2462–2476. https://doi.org/10.1056/NEJMoa050516
Rutter M, Bernstein C, Matsumoto T, Kiesslich R, Neurath M (2004) Endoscopic appearance of dysplasia in ulcerative colitis and the role of staining. Endoscopy 36:1109–1114. https://doi.org/10.1055/s-2004-826049
Schroeder KW, Tremaine WJ, Ilstrup DM (1987) Coated oral 5-aminosalicylic acid therapy for mildly to moderately active ulcerative colitis. A randomized study. N Engl J Med 317:1625–1629. https://doi.org/10.1056/NEJM198712243172603
Therneau TM (2021) Survival: a package for survival analysis in R. R Package Version 2:38
Travis SP, Higgins PD, Orchard T, Van Der Woude CJ, Panaccione R, Bitton A, O’Morain C, Panes J, Sturm A, Reinisch W et al (2011) Review article: defining remission in ulcerative colitis. Aliment Pharmacol Ther 34:113–124. https://doi.org/10.1111/j.1365-2036.2011.04701.x
Ungaro R, Mehandru S, Allen PB, Peyrin-Biroulet L, Colombel JF (2017) Ulcerative colitis. Lancet 389:1756–1770
Urbauer E, Rath E, Haller D (2021) Mitochondrial metabolism in the intestinal stem cell niche—sensing and signaling in health and disease. Front Cell Dev Biol 8. https://doi.org/10.3389/fcell.2020.602814
Weissman S, Sinh P, Mehta TI, Thaker RK, Derman A, Heiberger C, Qureshi N, Amrutiya V, Atoot A, Dave M et al (2020) Atherosclerotic cardiovascular disease in inflammatory bowel disease: the role of chronic inflammation. World J Gastrointest Pathophysiol 11:104–113. https://doi.org/10.4291/WJGP.V11.I5.104
Yarani R, Mirza AH, Kaur S, Pociot F (2018) The emerging role of Lncrnas in inflammatory bowel disease. Exp Mol Med 50. https://doi.org/10.1038/S12276-018-0188-9
Yu G, Wang LG, Han Y, He QY (2012) ClusterProfiler: an R package for comparing biological themes among gene clusters. OMICS J Integr Biol 16:284–287. https://doi.org/10.1089/omi.2011.0118
Acknowledgements
The authors thank Ingrid Christiansen for the technical help performing the tumour necrosis factor-alpha level measurements, Lars Daniel Håland at the Genomics Support Centre Tromsø (GSCT; www.uit.no/helsefak/genomics) for sequencing, Sveinung W. Sørbye for histological evaluations, and Renate Meyer for administrating patient samples.
Funding
Open access funding provided by UiT The Arctic University of Norway (incl University Hospital of North Norway) This work was supported by institutional funding.
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W.M. performed statistical and functional analyses and wrote parts of the manuscript. K.M.J. was involved in evaluating and providing clinical samples from patients in long-term remission. C.G.F. was involved in data analysis. J.F. was involved in evaluating and providing clinical samples from patients and healthy controls. R.H.P was involved in project inception, project design, data analysis, supervision, and manuscript writing. All authors revised the manuscript and approved the final version of the manuscript.
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The study was conducted according to the guidelines of the Declaration of Helsinki. Approvals were granted by the Regional Committee of Medical Ethics of Northern Norway, Ref no: 14/2004, 1349/2012 and 29895/2020. The samples were taken from an established biobank approved by the Norwegian Board of Health (952/2006). Written informed consent has been obtained from the study participants to publish this paper.
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Figure S1: Biplots of 188 protein-coding genes. (PDF 505 kb)
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Figure S2: Biplot of 99 non-protein coding genes. (PDF 407 kb)
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Table S1: Normalization of the expression gene matrices of all patient groups. (PDF 31 kb)
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Table S2: Statistics of the used Cox models. (PDF 229 kb)
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Table S3: All 287 genes from the Cox model on gene expression level. (XLSX 42 kb)
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Table S4: Patients characteristics of two separate remission patient groups (DOCX 15 kb)
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Table S5: Beta values obtained for each gene. (XLSX 23 kb)
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Table S6: GO enrichment of 287 genes with adjusted p value< 0.05. (XLSX 11 kb)
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Meng, W., Johnsen, KM., Fenton, C.G. et al. Anti-apoptotic genes and non-coding RNAs are potential outcome predictors for ulcerative colitis. Funct Integr Genomics 23, 165 (2023). https://doi.org/10.1007/s10142-023-01099-9
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DOI: https://doi.org/10.1007/s10142-023-01099-9