Reduced SARS-CoV-2 infection levels and pathotype specific altered antiviral transcriptional response in IBD intestinal organoids

Reduced SARS-CoV-2 infection levels and pathotype specific altered antiviral transcriptional response in IBD intestinal organoids | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Reduced SARS-CoV-2 infection levels and pathotype specific altered antiviral transcriptional response in IBD intestinal organoids Barbara Jelusic, Stefan Boerno, Patrick Schimmel, Philipp Wurm, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8029502/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background IBD is characterized by altered immune reactions and infections are thought to trigger chronic inflammation in IBD. The gut represents a productive reservoir for SARS-CoV-2 and the aforementioned factors together with immunosuppression used to treat IBD are likely influencing the outcomes of IBD patients with COVID-19. Methods We used large and small intestinal organoids from ulcerative colitis and Crohn's disease patients and controls to comparatively assess infection levels and transcriptional response of the gut epithelium during SARS-CoV-2 infection. Results Our analysis showed that IBD epithelia exhibit reduced viral loads compared to controls associated with a reduced expression of SARS-CoV-2 entry factors including the host receptor ACE2. Moreover, several genes implicated in the epithelial response to viral infection are intrinsically altered in IBD potentially counteracting viral propagation. Notably, differences between IBD phenotypes exist wherein ulcerative colitis represents with induced cell death pathways and increased IL1B expression despite lower viral loads suggestive of increased epithelial stress. Conclusions Altogether our analysis shows that the IBD epithelium is not more prone to SARS-CoV-2 infection and that several antiviral response genes are intrinsically activated in IBD. Moreover, ulcerative colitis and Crohn's disease exhibit specific transcriptional differences which might explain the differing COVID-19 outcomes between IBD phenotypes. SARS-CoV-2 intestinal organoids RNA sequencing inflammatory bowel diseases Crohn’s disease ulcerative colitis transcriptional response to viral infection IL1B Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Inflammatory bowel diseases (IBD), represented by Crohn’s disease (CD) and ulcerative colitis (UC), are chronic inflammatory disorders that occur in genetically susceptible individuals because of a dysregulated intestinal immune homeostasis ( 1 ). IBD patients are prone to viral infections also driven by the immuno-suppression used to treat IBD ( 2 , 3 ). This vulnerability has raised concerns whether IBD patients represent a population at risk for worse COVID-19 outcomes. Although the incidence of COVID-19 in IBD patients is comparable to that of the general population ( 4 , 5 ), IBD patients show increased gastrointestinal (GI) symptoms during COVID-19 ( 6 ), while active IBD and corticosteroids in combination with biologics seem to increase COVID-19 severity ( 5 – 8 ). The GI tract represents a productive reservoir for SARS-CoV-2. A high expression of viral entry factors in enterocytes, like the host receptor ACE2, facilitates infection, virus amplification and faecal shedding ( 9 – 11 ). Viral persistence in the GI tract likely perpetuates dissemination and pandemic spread ( 12 , 13 ). Genetic alterations in IBD can be found in genes related to immunity, and experimental models suggest that a combination of these genetic changes with viral infections drive the chronic inflammatory pathology of IBD ( 14 – 16 ). In addition, the expression of key SARS-CoV-2 entry-related genes is altered in IBD and differs between the small and large intestines ( 17 – 19 ). Since ACE2 is considered to be tissue-protective with anti-inflammatory capabilities ( 20 ), it's altered expression in IBD and during SARS-CoV-2 infection might aggravate the inflammatory pathology ( 21 , 22 ). For all these reasons, it is likely that SARS-CoV-2 infection of the GI tract has profound functional consequences and might impact IBD patients differently compared to healthy persons, which might also lead to differing long term consequences. Indeed, recent data suggest that SARS-CoV-2 can persist in the GI mucosa of IBD patients over prolonged periods, which correlates with post-acute COVID-19 symptoms ( 23 ). In the current study, we used small and large intestinal organoids from IBD patients and controls to comparatively assess infection and the transcriptional response of epithelial cells as an entry site for SARS-CoV-2. We showed that IBD epithelia are generally not more prone to infection, but respond differently compared to non-IBD epithelia, which is dependent on the IBD phenotype. Importantly, intrinsic changes in the transcriptional response to virus infection seem to counteract SARS-CoV-2 in IBD, with specific differences between CD and UC. Materials and methods Ethics approval Generation and use of organoids were approved by the ethics committee of Medical University Innsbruck, Austria (ethics vote no.: AN4994 323/4.4). All patients gave their informed written consent for participation in this study. SARS-CoV-2 isolation was done during an autopsy study ( 24 ) approved by the ethics committee of the Medical University of Graz (EK-number: 32–362 ex 19/20). Human intestinal organoid culture Intestinal organoids were cultured from biopsy specimens retrieved during endoscopy of IBD patients and healthy controls at the University Hospital Innsbruck. 11 IBD patients and 11 controls were included. Their characteristics are summarized in Table S1 . Organoid generation was performed with IntestiCult Organoid Growth Medium (STEMCELL Technologies Inc., Vancouver, Canada) with an adapted protocol. Two to three ileal or colonic biopsies, taken from an inflamed site in case of IBD patients, were flushed with ice cold PBS and minced into small pieces with a sterile blade. Tissues were then transferred to 15-ml tubes with 5 ml of Gentle Cell Dissociation Reagent (GCDR, STEMCELL Technologies) and incubated at 4°C on a rocking platform for 30 min. After centrifugation (290 x g, 5 min, 4°C), the tissue suspension was transferred to 1 ml of ice cold DMEM (Gibco, ThermoFisher Scientific Inc., Waltham, Massachusetts, US) with 1% (vol/vol) bovine serum albumin (Roche, Germany). The suspension was gently mixed and passed through a 70 µm cell strainer (Corning, Merck, Darmstadt, Germany). The cell suspensions containing whole intestinal crypts were seeded in 50 µl Growth Factor Reduced Matrigel matrix (Corning Inc., Corning, New York, US) on a pre-warmed 24-well plate and allowed to solidify for 10 min at 37°C, after which 500 µl IntestiCult Growth Medium supplemented with 100 U/ml penicillin and 100 µg/ml streptomycin (Biochrom, Berlin, Germany) was added. Medium was exchanged every two days and organoids were passaged every 5–10 days depending on their growth, with a maximum of 12 passages. During passaging, ileal organoids could be disrupted by pipetting only, as opposed to colonic organoids that needed chemical disruption with Gentle Cell Dissociation Reagent (GCDR) according to the manufacturer’s protocol (STEMCELL Technologies). To create monolayers, organoids were harvested from 24 well plates and processed according to the protocol by STEMCELL Technologies. In brief, organoids were mechanically disrupted by pipetting, trypsinized, washed and seeded in a 96 well plates pre-coated with 5% Matrigel. Four monolayers were created for each patient in order to have two replicates for infections and controls, respectively. Virus isolation and culture The used SARS-CoV-2 strain originated from an autopsy study performed at the first pandemic peak ( 24 ). The strain hCoV-19/Austria/Graz-MUG5 (B.1.5/O/20A) originated from a lung swab from a patient with concomitant high-level intestinal SARS-CoV-2 carriage. The virus strain was propagated using Vero CCL-81 cells. To prepare the inoculum for organoid infections, Vero CCL-81 cell supernatants were centrifuged at 1500 rcf for 10 min at RT and SARS-CoV-2 particles were quantified by using qPCR as described ( 24 ). Organoid infection Organoid infection followed protocols adapted from Lamers et al. ( 25 ) and Ettayebi et al. ( 26 ). Briefly, the viral inoculum was prepared by diluting the virus corresponding to 10 5 PFU in serum-free (AD) medium containing Advanced DMEM/F-12 (ThermoFisher Scientific Inc.), 1M HEPES (Merck), 1% GlutaMAX (ThermoFisher Scientific) and 1% Pen/Strep (Thermo Fisher Scientific). Conditioned medium from Vero CLL-81 cells without virus was used as control for uninfected (mock) samples. Monolayers in 96-well plates were infected on day 13 after seeding. They were washed with 150 µl of AD medium ahead of the infection and then treated with 100 µl of inoculum or mock medium. Infection dose corresponded to a multiplicity of infection (MOI) of 0.1. Monolayers were incubated in an atmosphere containing 5% CO 2 at 37°C for 2 h to allow for the virus to attach. Supernatants were then removed and the cells were washed twice with 200 µl AD medium for 5 minutes. 150 µl of IntestiCult™ medium was added to monolayers and incubated for further 48 hrs. 200 µl of TRIzol (ThermoFisher Scientific) was added to each monolayer well thereafter and replicates were collected separately. Cell lysates in TRIzol were vortexed once and immediately frozen at -20°C until further processing. RNA isolation and sequencing Total RNA was isolated with TRIzol (ThermoFisher Scientific) according to manufacturer’s protocol. RNA quality was controlled by using a Bioanalyzer instrument (Agilent) and sequencing libraries were prepared from 25-250ng of total RNA per sample following Roche’s strand specific “KAPA RNA Hyper Prep Kit + RiboErase (HMR)” library preparation protocol for double indexed Illumina libraries. Briefly, the rRNA fraction was depleted using complementary DNA oligonucleotides; rRNA-DNA hybrids and DNA baits were removed by treatment with RNase H and DNase. RNA was heat-fragmented and subjected to first strand synthesis using random priming. The second strand was synthesized incorporating dUTP instead of dTTP to preserve strand information. After A-tailing Illumina sequencing compatible adapters carrying unique dual indices were ligated (NEXTFLEX® Unique Dual Index Barcodes). Following bead-based clean-up steps the libraries were amplified using 13 cycles of PCR. Library quality and size was checked with the qBit, Bioanalyzer (Agilent) and qPCR. Sequencing was carried out on an Illumina NovaSeq6000 system in PE100bp mode yielding between 20–40 million fragments per sample. Following base calling, adaptor clipping was performed using cutadapt 2.4 ( 27 ). Data was mapped against the hg38.p12 genome including the SARS-CoV-2 genome (strain A9351) using STAR v2.7.5a ( 28 ). Gene-wise read count tables were used for analysis with DESeq2( 29 ) and transformed to visualize sample distances by principal component analysis (PCA). Viral load Viral gene counts were normalized by the R package DESeq2 ( 29 ), summed by sample and log 2 transformed. To compare the groups regarding the viral load, a mean of monolayer replicates was used resulting in a single value per patient. For SARS-CoV-2 quantification 12 ng of total RNA were used for RT-qPCR using N2 primers (Eurofins Genomics, Ebersberg, Germany) in combination with the One-Step QuantiTect® SYBR Green RT-PCR Kit (QIAGEN GmbH, Hilden, Germany) on a Rotor Gene® Q cycler (QIAGEN GmbH, Hilden, Germany) according to the manufacturer’s instructions. Human ß-actin (hACTB) served as housekeeping gene for normalization. The primer and probe set for viral RNA quantification consisted of the 2019-nCoV_N2-F 2019-nCoV_N2 forward primer 5’- TTA CAA ACA TTG GCC GCA AA -3’, the 2019-nCoV_N2-R 2019-nCoV_N2 reverse primer 5’- GCG CGA CAT TCC GAA GAA − 3’, the hACTB forward primer 5’ - GAG CAC AGA GCC TCG CCT TT -3’ recommended by the CDC ( https://stacks.cdc.gov/view/cdc/84525 ) and the the hACTB reverse primer 5’ – TCA TCA TCC GTG GTG AGC TGG – 3’. A commercially available SARS-CoV-2 RNA copy number standard (VR-1986D genomic RNA from 2019 Novel Coronavirus, Lot: 70035624, ATCC, U.K.) was serially diluted and analysed by RT-qPCR to provide a standard curve. The resulting Ct-values were plotted against ln[copy numbers] and the equation obtained from a simple linear regression analysis was used to calculate the copy numbers from the Ct-values (Eq. 1). Eq. 1: Standard curve to calculate between Ct -values and viral copy numbers y= -1,5x + 34,82. The ∂Ct was calculated for SARS-CoV-2 against the housekeeping gene hACTB to quantify the amount of virus in the samples. To extrapolate the virus load per epithelial cell the approximation of 20 ng total RNA content per cell was used as described ( 30 ). Differential gene expression analysis Differences in gene expression were analysed using the R package DESeq2 ( 29 ). Two monolayer replicates per patient and treatment condition were pooled during the analysis after confirming a high correlation between replicates (Suppl. Figure 1). The effects of SARS-CoV-2 infection were determined for each group separately and by pairing the infected with the control sample originating from the same patient (paired analysis in DESeq2). If the uninfected or infected sample was discarded due to low sequencing quality, this patient was removed from the subsequent analyses resulting in the final number of patients (6 h-ileum, 5 CD ileum, 5 h-colon, 5 UC colon) assessing infection effects. When assessing the differences in gene expression due to IBD, control samples from an IBD group were compared to control samples from the corresponding healthy organ, i.e. h-ileum and CD, as well as h-colon for UC, resulting in the final number of included patients (6 h-ileum, 6 CD ileum, 5 h-colon, 5 UC colon). A meta-cohort ( 31 ) was included to further underscore the differential expression patterns in IBD. Pre-processing was done according to the authors performed with STAR v2.7.5a. The gene count table (accession: GSE197698) was retrieved and analysed identical as described above. Colonic organoid data was combined, regardless of colonic site noted in the metadata. Technical replicates were merged based on the metadata provided ( 31 ). Pathway analyses For each group, two types of functional pathway analyses were performed using the R package clusterProfiler ( 32 ) and the Reactome pathway database ( 33 ). Over-enrichment analysis (ORA) was based on a list of DEGs upon SARS-CoV-2 infection with P < 0.05. Gene set enrichment analysis (GSEA) ( 34 ) was based on all expressed genes pre-ranked by their P value with the sign of LFC. The rank metric was calculated by the following formula: $$\:-{\text{l}\text{o}\text{g}}_{10}\left(P\:\text{v}\text{a}\text{l}\text{u}\text{e}\right)\times\:\frac{\text{L}\text{F}\text{C}}{\left|\text{L}\text{F}\text{C}\right|}$$ Statistical analysis GraphPad Prism and R were used for data analysis and imaging. All data are represented as mean ± SD if not otherwise specified. Statistics of differential gene expression were calculated with the Wald test. Other statistical significance testing employed the Mann-Whitney-U-Test and 1way ANOVA with uncorrected Fisher's LSD. Correlation analyses employed Pearson correlation. P values < 0.05 were considered statistically significant. Benjamini-Hochberg (BH) procedure with a False Discovery Rate (FDR) < 0.1 was used to determine the adjusted P values ( Padj ) in the differential gene expression analysis. Results SARS-CoV-2 infection of ileum and colon organoids from IBD patients and controls Intestinal organoids were successfully cultivated from 5 patients with CD (CD ileum), 5 patients with UC (UC colon), as well as 6 healthy ileum (h-ileum) and 5 healthy colon (h-colon) controls (Fig. 1 ). From the 3D organoids, 2D organoid monolayers were generated to model a luminal SARS-CoV-2 infection. Infection was performed with a SARS-CoV-2 strain (B.1.5/O/20A) isolated from a COVID-19 patient from the first pandemic wave ( 24 ). Monolayers were infected with a MOI of 0.1 and collected 48 hours post-infection (p.i.) based on previous protocols ( 25 , 26 ). Experimental set-up consisted of four organoid monolayers per patient, two infected and two uninfected allowing for paired downstream analyses. RNA-seq generated from organoid monolayers yielded on average 29 million reads per monolayer (SD = 3.7 M). High correlation of RNA-seq reads from monolayer duplicates allowed for their pooling in the analyses process (Suppl. Figure 1). Tissue origin followed by IBD status but not SARS-CoV-2 infection are the major discriminators in deep transcriptomic analyses Principal component analysis (PCA) based on gene counts separated organoid-derived monolayers most according to tissue origin (Fig. 2 A). H-ileum clearly separated from h-colon and UC colon (PC1, 36.3% variation) while CD ileum samples were more dispersed and more similar to colon samples (Fig. 2 A, Suppl. Figure 2A). SARS-CoV-2 infection induced only small variations in PCA. The major separators in PCA were genes differentiating small and large intestines, for example, the colon marker MUC12 or the small intestinal marker APOB (Fig. 2 B). IBD samples were separated from healthy controls mostly by PC4 (5.61% variation) driven by differential gene expression of UGT2B17 , APOA4, CEACAM7 and NXPE1 , known to be altered in IBD ( 15 , 35 – 37 ). Organoids retained typical expression signatures of their original tissues including markers of stemness, enterocytes, Paneth cells, enteroendocrine cells and goblet cells, also evident in IBD organoids (Fig. 2 C). Interestingly, three CD ileum cases clustered together with colon samples, suggesting that CD can alter gene expression to a more colon-like phenotype in at least certain individuals ( 38 ). Of note, the level of mitochondrial transcripts used as a surrogate marker for cell damage ( 39 ) was not significantly different between the organoid groups (Suppl. Figure 2B). SARS-CoV-2 infection is highest in healthy ileum while IBD status does not increase viral loads We sought for mild infection levels not to overburden organoids, which would likely lead to increased cell damage potentially obscuring assessment of transcriptional effects ( 25 , 26 ). Viral transcripts derived from RNA-seq were evenly distributed along the SARS-CoV-2 genome (Fig. 3 A). 16.76 viral gene counts (median; IQR = 11.23–26.40) compared to 14.75 × 10 6 human gene counts (median; IQR = 14.33 × 10 6 - 15.60 × 10 6 ) were detected per infected single monolayer. Viral transcripts were most abundant in HC ileum, followed by CD ileum, HC colon and UC colon, respectively (Fig. 3 B). Using a SARS-CoV-2 standard and extrapolating viral loads by use of normalized RNA inputs in qPCR, between 0.003 to 39 viral copies per host cell were determined (suppl. Figure 3; see materials for calculation used). Variation of viral loads was higher in healthy organoids (suppl. Figure 3), but infection levels were less in IBD organoids. Furthermore, qPCR using human ACTB as a reference confirmed that IBD organoids showed lower infection than their healthy counterparts (Fig. 3 B). Higher viral loads in the ileum might be because of the higher ACE2 expression in the small intestine compared to colon ( 19 ). SARS-CoV-2 cell entry is facilitated by the host receptor ACE2 and the proteases CTSL, CTSB, furin, TMPRSS2/4/11D/13 and NRP1 ( 40 – 45 ). ACE2 expression was highest in h-ileum (Fig. 3 C). Its expression in our organoid model mirrored the known decrease in the inflamed ileum of IBD patients, as well as differences reported between ileum and colon ( 19 , 46 ). Conversely, ACE2 expression is reported to be increased in the colon upon inflammation ( 46 ), which corresponds to a non-significant trend of increased ACE2 expression in UC colon organoids compared to h-colon. TMPRSS2 expression, the major protease facilitating viral cell surface entry ( 41 ), was significantly increased in CD ileum compared to h-ileum, again corroborating findings from CD patients ( 47 ). Expression of CTSL and CTSB , both proteases important for endosomal entry of the virus ( 41 , 48 ), followed the same trend as ACE2 . TMPRSS4 , FURIN and NRP1 were similarly expressed across all groups, while TMPRSS11D was not detectable (Suppl. Figure 4). Notably, SARS-CoV-2 infection did not significantly alter expression of any investigated entry gene in our model (Fig. 3 C, Suppl. Figure 4). Correlation analysis of viral load and the expression of entry genes revealed a significant positive correlation of ACE2 , CTSL and CTSB with viral load, but only in organoids from controls and not in IBD, suggesting that SARS-CoV-2 entry and propagation might be altered in IBD (Fig. 3 D, Suppl. Figure 5). In summary, our organoid model recapitulated the expression of SARS-CoV-2 entry genes in small and large intestines and showed changes typically occurring in IBD. However, although the IBD phenotype modulates the expression of entry genes, it does not increase SARS-CoV-2 infection levels. SARS-CoV-2 infection impacts gene expression differently depending on intestinal origin and IBD status Our study employed organoids from 21 different individuals. Because of the already inherent inter-individual heterogeneity, we used only moderately permissive thresholds ( P < 0.05, FDR < 0.1) in downstream differential gene expression analyses ( 49 ). Notably, our study design allowed for the comparison of paired infected and uninfected organoids originating from the same individual reducing the influence of inter-patient variation (Fig. 1 ); 995 differentially expressed genes (DEG) were found in h-ileum organoids upon infection, 802 DEGs in CD ileum, 760 in h-colon and 895 in UC colon ( P < 0.05, suppl. File 1). H-ileum showed most unique DEGs which is consistent with the dominant infection of this organoid group (Fig. 4 A, Suppl. Figure 6A, suppl. File 1). Organoids from healthy controls shared more DEGs than IBD organoids upon infection, indicating a greater inter-individual variation in IBD (Suppl. Figure 6B-C, suppl. File 1). Over-representation pathway analysis of DEGs (ORA; Reactome ( 33 )) revealed altered pathways of immunity, central cellular functions and signalling cascades upon infection. Surprisingly, enriched pathways were often shared between h-ileum and UC colon while depleted pathways seemed more specific for tissue type and IBD status (Fig. 4 B, suppl. File 2). These findings were mirrored in the gene set enrichment analysis which calls also subtle gene expression changes ( 34 ) (GSEA; Reactome; suppl. File 3, Suppl. Figure 7). Grouping the identified pathways into biological themes showed an enrichment of extracellular matrix organization, cell death, metabolism, immunity and signalling cascades upon infection (Suppl. Figure 8). Amongst these were FOXO-mediated transcription, TGF-β, EGF, MAPK and NOTCH signalling, which are implicated in the inflammatory response and known to be altered by coronavirus infection ( 50 – 54 ). Circadian clock-related genes showed a consistent and strong upregulation in all organoid groups, a process which controls host-virus interactions and which can be manipulated by viruses ( 55 ) including SARS-CoV-2 ( 56 ). Cell death pathways were stronger induced in h-ileum compared to CD ileum, consistent with the increased infection level in the former, however, they were also stronger induced in UC colon compared to h-colon despite a lower infection level in UC (Fig. 4 C). Amongst the top induced genes implicated in programmed cell death were NET1 ( Padj = 0.001) binding to CARD and regulating NF-κB activation ( 57 ) and BIRC3 ( Padj = 0.029) a proposed IBD susceptibility gene regulating proinflammatory cytokine expression through NF-κB and MAPK pathways ( 58 ). Thus, SARS-CoV-2 leads to a pronounced induction of cell death pathways specifically in UC (suppl. File 4). Coherent to this finding was also a difference in defence signalling pathways like interferon type I (alpha/beta) signalling, which was induced in UC colon but not in h-colon (Fig. 4 C). Taken together, SARS-CoV-2 infection influenced gene expression of organoids differently, depending on tissue origin and IBD status. H-ileum and UC colon showed many commonalities and UC colon likely reacts with increased epithelial stress upon SARS-CoV-2 infection. Expression of genes implicated in the cellular response to viral infection is intrinsically altered in IBD IBD is characterized by altered expression of genes implicated in immunity ( 59 ). These alterations are central to the pathogenesis of IBD but might also influence the cellular reaction and susceptibility to infections. Comparison of uninfected IBD organoids to uninfected controls revealed 904 DEGs in CD ileum and 822 DEGs in UC colon ( P < 0.05, Padj < 0.1; Suppl. Figure 9, suppl. File 5). We next focused our analysis to genes implicated in the host defence against viruses, including the Gene Ontology gene sets ‘response to virus’ (GO:0009615) and ‘type I interferon signalling pathway’ (GO:0060337) with type III interferons and their receptors amounting 330 genes in total (Fig. 5 A, Tab. S6). Of these genes, 44 were nominally differentially expressed ( P < 0.05) in CD ileum compared to h-ileum, and 29 genes in UC colon compared to h-colon (Fig. 5 B, suppl. File 6). These genes could be functionally categorized into interferon-stimulated genes (ISGs), factors for recognition of viral RNA or with direct antiviral activity, factors regulating cellular defence mechanisms, cytokines, chemokines and their receptors, antigen presentation, autophagy and cell death (Fig. 5 C, suppl. File 6). Comparing uninfected IBD organoids to uninfected healthy controls revealed the viral RNA sensors OASL ( Padj = 0.007) and OAS1 ( Padj = 0.015), which degrade viral RNA and activate the retinoic acid-inducible gene I (RIG-I) leading to a type I interferon (IFN) response ( 60 ), significantly upregulated in CD ileum (Fig. 5 A). OAS1 was shown to be protective against severe COVID-19 ( 61 ). BST2 , a potent anti-SARS-CoV-2 factor inhibiting budding of virus particles from the cell surface ( 62 ) showed also a trend towards upregulation in CD ileum ( P = 0.006). Upregulated in uninfected CD was also SEC14L1 which impairs RIG-I signalling ( 63 ) ( Padj < 0.001). Downregulated in uninfected CD ileum were TKFC (syn.: DAK, Padj = 0.009), a negative regulator of antiviral signalling ( 64 ), and the heparan sulphate biosynthesis gene EXT1 ( Padj = 0.031), which represents an ancillary SARS-CoV-2 entry factor ( 65 ). Together, 5 out of 6 top changed response to virus genes in uninfected CD ileum compared to h-ileum likely impair SARS-CoV-2 infection (Fig. 5 C, 6 A). In uninfected UC colon compared to h-colon, we found the interferon regulatory transcription factor IRF7 ( Padj = 0.010) and the antiviral and pro-apoptotic factor XAF1 ( 66 ) ( Padj = 0.013) upregulated (Fig. 6 B, suppl. File 6). IRF7 is crucial in propagating signalling cascades initiated by viral infection and normally shows low constitutive expression in enterocytes ( 19 , 67 ). Interestingly, MX2 , an ISG with antiviral activity ( 68 ) and reported to be induced in COVID-19 ( 69 ), was also upregulated in UC colon ( Padj = 0.013) but borderline downregulated in CD ileum ( P = 0.022), indicating subtle differences between IBD phenotypes. Downregulated in uninfected UC colon was the apoptosis regulator BCL2 ( Padj = 0.011), known to be colitis-protective and whose viral homologs propagate infection by inhibiting premature apoptosis of infected cells ( 70 , 71 ). Notably, BCL2 inhibition is thought to counteract SARS-CoV-2 infection ( 72 ). Also, borderline downregulated was DDIT4 ( P = 0.003), which inhibits virus propagation by suppressing mTOR activity ( 73 , 74 ). Again, 4 out of 5 top changed response to virus genes likely counteract SARS-CoV-2 infection in baseline UC colon (Fig. 5 C, 6 B). Notably, reanalysis of a meta-cohort comparing IBD organoids to small and large intestinal organoids from UC and CD patients and of healthy controls ( 31 ) confirmed the induction of several viral response genes in IBD including BST2 , OASL , IFI6 or IFIT1 (Suppl. Figure 10; suppl. File 7). In summary, several genes with antiviral action are induced in IBD intestinal epithelia. This might be a factor contributing to the decreased viral loads detected in IBD organoids. However, the expression of these response to virus genes are different between IBD phenotypes. Expression of genes implicated in the response to viral infection are different between ileum and colon and influenced by IBD status upon SARS-CoV-2 challenge We next assessed the effect of SARS-CoV-2 on the transcriptional response in organoids (Suppl. Figure 10, suppl. File 6). Infection changed 29 of the viral response genes in h-ileum, 13 in CD ileum, 12 in h-colon and 26 in UC colon (Fig. 5 C). In CD ileum, 9 were up- and 4 downregulated and 3 of them were already altered in uninfected CD ileum. In UC colon, 25 were up- and 1 downregulated and 5 of them were already altered in uninfected UC colon (Fig. 5 B-C). Similar to findings from pathway analyses, h-ileum and UC colon showed a stronger antiviral response compared to CD ileum or h-colon. Among top induced genes upon infection were IFIT1 and IFIT2 , known to sequester 5'PPP-capped viral RNAs in host cells, thereby inhibiting virus amplification ( 75 , 76 ). Both genes were upregulated upon infection in h-ileum ( IFIT1 P = 0.002; IFIT2 , P = 0.0003) and UC colon ( IFIT1 , P = 0.0005; IFIT2 , P = 0.004). IFIT1 was already induced in uninfected UC colon compared to H colon ( P = 0.005). In infected CD ileum, IFIT1 showed the opposite trend ( P = 0.008). IFIT2 was induced in h-ileum upon infection ( P = 0.0003), in addition to the pro-apoptotic mitophagy gene BNIP3L ( Padj = 0.003). The consistent activation of IFIT genes suggests that these viral RNA sensing and degrading factors are central in the intestinal epithelial response to SARS-CoV-2 infection. Two borderline induced genes in h-ileum but not in CD ileum upon infection were ITGB6 ( P = 0.026), a possible alternative receptor for SARS-CoV-2 ( 77 ), and SERINC3 ( P = 0.018), a known antiviral defence factor and a suggested risk gene for severe COVID-19 ( 78 ). Antigen presentation also seemed to be modified in IBD organoids. The HLA class I genes HLA-B ( P = 0.034), a known IBD risk gene ( 79 ), and HLA-H ( P = 0.036), were borderline induced in baseline CD ileum but were not further induced upon infection in this organoid group, while they showed borderline induction in infected h-ileum ( HLA-B , P = 0.009; HLA-H , P = 0.006). Conversely, HLA-G ( P < 0.001) was induced in UC colon upon infection but not in h-colon (Fig. 5 C). The gene with the strongest induction in UC colon upon infection was IL1Β ( P = 0.0003). Interestingly, IL-1B is a downstream effector of the inflammasome and of regulated cell death pathways, the latter induced in UC colon organoids. Moreover, IL-1B is a cytokine that induces IGSs and is insensitive to viral immune escape strategies ( 80 ). As opposed to its beneficial effects against infection, it was recently shown to drive chronic intestinal inflammation in UC ( 81 , 82 ). Several response to virus genes were nominally upregulated in both UC colon and h-colon upon infection, however, certain genes were found to be induced upon infection only in UC colon but not h-colon, such as IFIT1 ( P < 0.0005) and DDIT4 ( P = 0.0004), which were already differentially expressed in baseline UC compared to h-colon. Infection with SARS-CoV-2 in h-colon triggered also the expression of a cytidine deaminase APOBEC1 , known to inhibit viral replication ( 83 ) ( P = 0.013), while it was already increased in uninfected baseline UC ( P = 0.002). In summary, our comparative analyses showed that the transcriptional response of small and large intestinal epithelia to SARS-CoV-2 infection is altered in IBD. Figure 6 summarizes the main transcriptional alterations relevant to infection. In CD ileum, genes implicated in antigen presentation, direct antiviral activity, cytokine/chemokine signalling as well as regulated cell death and autophagy were already upregulated prior to infection. In UC colon compared to healthy colon, such an induction is less evident. Upon infection, h-ileum reacts stronger and/or preserves its transcriptional response to virus to a greater extent compared to CD ileum. This difference possibly originates from the higher viral load found in h-ileum. However, UC colon responds transcriptionally stronger upon SARS-CoV-2 challenge despite a slightly lower viral load compared to h-colon. This reaction includes the induction of cell death pathways and of IL1Β expression, which is intriguing since this cytokine seems to drive inflammation in UC ( 81 ) and COVID-19 outcomes in UC patients seem to be worse compared to CD ( 84 ). Discussion The GI tract represents a niche for productive SARS-CoV-2 infection. IBD is characterized by altered immune reactions and, therefore, it is likely that SARS-CoV-2 impacts the GI tract of IBD patients differently compared to healthy individuals. In addition, genetic differences between CD and UC might differentially impact the response to SARS-CoV-2. We used small and large intestinal organoids from IBD patients and controls to comparatively assess susceptibility and transcriptional responses to SARS-CoV-2 infection. We showed that IBD phenotype does not increase SARS-CoV-2 infection levels, which is likely associated with a depressed expression of SARS-CoV-2 entry factors (e.g. ACE2 and CTSL/B ) most prominent in CD ileum, but also with an upregulation of several antiviral defence genes potentially counteracting viral propagation in IBD. However, transcriptional differences between IBD phenotypes in response to SARS-CoV-2 exist, which might lead to varying disease outcomes between CD and UC patients. ACE2, the host receptor of SARS-CoV-2 ( 85 ), is expressed apically in enterocytes ( 40 ) indicating that virus attachment and uptake via the lumen is a dominant infection route in the GI tract. This was modelled in our 2D organoids ( 86 , 87 ). SARS-CoV-2 entry factors are reported to be altered in IBD ileum ( 17 , 46 , 47 , 88 , 89 ) and we recapitulated a reduced ACE2 expression in CD ileum organoids compared to h-ileum, which correlates with reduced infection levels. In colon, ACE2 expression increases with the inflammatory tone ( 17 , 46 , 47 , 88 – 90 ) and we also noted an increase of ACE2 expression in UC colon compared to h-colon. Despite that, the lowest infection levels were noted in this organoid group. The proteases TMPRSS2 and CTSL/B, which prime the S protein to facilitate binding to ACE2 ( 48 , 85 ), are also reported to be altered in IBD ( 17 , 18 , 46 , 47 , 91 ) and their expression was mostly recapitulated in our organoids. Entry genes were not transcriptionally changed by SARS-CoV-2 infection in our model, which is different to some other studies ( 92 , 93 ). Importantly, infection levels were not increased in our IBD organoids. We noticed a positive correlation of viral load with the expression of entry factors ACE2 and CTSL/B only in non-IBD organoids, indicating that infection levels and entry factors are functionally linked in healthy epithelia but this relation is possibly skewed in IBD. We cultured organoids from multiple IBD and non-IBD individuals which allowed us to at least partially capture the heterogeneity of IBD and identify the major transcriptional changes in response to SARS-CoV-2. Extracellular matrix organization (ECMO) was strongly enriched in all organoid groups. Besides being involved in infection processes ( 94 ), ECMO is crucial for the healing response after injury. However, this process can also lead to fibrosis when overstimulated, especially when auxiliary signalling pathways like TGF-β, EGFR and NOTCH, are co-activated ( 94 ), which was the case in CD organoids. In COVID-19, lung fibrosis emerges after acute lung damage and represents a severe functional impairment and also fibrosis of the kidneys are reported in COVID-19 ( 95 , 96 ). In parallel, intestinal fibrosis is a feared complication of IBD, especially in CD, appearing as a consequence of chronic inflammation ( 97 ). Therefore, SARS-CoV-2 infection of epithelia might initiate the development of fibrosis in the GI tract, which might aggravate IBD sequels. Inflammatory signalling pathways were also induced upon infection including interleukin-, TNF- and neurotrophin signalling. The last pathway was strongly induced in infected CD ileum. Besides activating pain, neurotrophins strengthen the epithelial barrier during viral infections, reduce apoptosis and induce TGF-β signalling ( 98 , 99 ). Neurotrophin signalling is reported to be induced in IBD ( 100 , 101 ), and it is likely that its activation might be implicated in the development of intestinal pain recognized in a substantial fraction of COVID-19 patients. Interestingly, chemokine signalling was only induced in non-IBD organoids, in contrast to interferon signalling, which was induced in h-ileum and UC colon upon infection. Similarly, apoptosis, necroptosis and pyroptosis were enriched only in h-ileum and UC colon. SARS-CoV-2 induces a carefully calibrated caspase-8 activation, leading to the activation of cell-death pathways, which support IL-1β secretion ( 102 ). Necroptosis, apoptosis and IL1B expression were upregulated in UC colon upon infection despite the lowest viral load in this organoid group. This reaction was not evident in CD organoids. Recently, a specific subgroup of IBD patients were described, wherein IL-1β-driven neutrophil recruitment perpetuates chronic inflammation in UC. These patients lack responsiveness to anti-TNF therapy ( 81 ) but IL-1β blockage can efficiently suppress inflammation in a murine model ( 82 ). Future research should clarify whether UC patients might generally overreact upon SARS-CoV-2 infection compared to CD patients ( 7 , 84 ) and whether this is driven by IL-1β. SARS-CoV-2 induces an interferon response in intestinal organoids that is stronger than from SARS-CoV ( 25 , 103 , 104 ) and comparable to other intestinal viruses like rotavirus, enterovirus and norovirus ( 105 – 107 ). SARS-CoV-2 induces low amounts of type I and III interferon transcripts in intestinal organoids ( 25 , 93 , 103 ) and interferon transcripts were not detectable in our experiment. It was recently shown that interferons can be actively suppressed by SARS-CoV-2 ( 108 ). By comparing uninfected IBD to non-IBD organoids, we were able to discern the transcriptional changes of genes involved in the host response to virus already present in IBD. CD ileum organoids showed an induction of several antiviral response genes implicated in antigen presentation (e.g. HLA-B ), direct antiviral activity and recognition (e.g. OAS1/L ), cell death (e.g., FADD ) as well as induction of cyto- and chemokines (e.g. TNF ). In UC, there was also an induction of direct antiviral activity and recognition (e.g. MX2, IFIT1 ) and important initiators of defence mechanisms (e.g. IRF7 ), but in contrast to CD, response to virus-genes were more often decreased. Together with a reduced expression of SARS-CoV-2 entry factors, CD phenotype likely has a benefit and counteracts infection and viral propagation in ileal epithelial cells. Additionally, CD organoids were generally less reactive in response to the virus compared to UC. In UC, a strong induction of cell death pathways and IL1β expression might suppress the virus, but can also lead to increased epithelial stress. Recently, viral persistence in the GI tract was reported in IBD and linked to postacute-COVID-19 symptoms ( 23 ). Whether the transcriptional changes identified in our model are implicated in the development of long-term sequels, such as the post-acute COVID-19 syndrome, warrants further investigations. Our study has several limitations. Although we used a larger number of individual organoids compared to other studies ( 25 , 43 ), our numbers are still too small to capture the entire genetic heterogeneity prevalent in IBD and potentially influencing SARS-CoV-2 infection ( 15 ). Consistent with an individual response to infection, a large variability in viral infection levels were noted, although variations were generally higher in healthy organoids. This variability compelled us to apply less stringent statistical thresholds not to obscure potentially relevant signals which might have otherwise been lost ( 49 ). However, this approach might have also increased the rate of spurious signals. Nevertheless, the consistent call of genes included in specific cellular reaction pathways suggests plausibility of our findings to a large extend. In addition, different infection doses and infection times would also have impacted the transcriptional response, all of these variations were not assessed. Moreover, it is important to note that immune cells, the connective tissue and IBD medications altogether likely influence COVID-19 intestinal pathology in-vivo . Conclusions Our model proved feasible to analyse virus-epithelial interactions of a newly emerging pathogen and to assess intestinal susceptibility in health and IBD. The model system of intestinal organoids represents intact epithelial tissue with no additional inflammatory stimuli but with the (epi-) genetic repertoire prevalent in IBD. The fact that infection with SARS-CoV-2 did not lead to higher viral burden in IBD organoids falls in line with the current clinical observation that IBD patients do not seem to be more prone to SARS-CoV-2 infection. Still, we observed differential responses to the virus in IBD organoids such as a stronger activation of neurotrophin and fibrosis-related pathways in CD ileum, and interferon signalling and regulated cell death in UC colon. Our study indicates that intestinal epithelial cells in IBD have a heightened antiviral state due to persisting changes in gene expression. Together with reductions of ACE2 and CTSL/B levels in CD ileum, this could protect against SARS-CoV-2 infection. On the other hand, as seen in UC colon marked with the lowest viral load, it could lead to a possibly detrimental overreaction of the IBD epithelium. These findings should prompt further investigations whether SARS-CoV-2 or other enteric viruses differently favour GI symptoms, IBD complications or long-term sequels in IBD subtypes. Declarations Acknowledgments We are grateful for the technical support from Martina Loibner, Julia Kirchner and Iris Kreuzmann. The valuable contributions from the BSL-3 and the routine laboratories of Institute of Pathology and the Core Facility for Imaging at the Center for Medical Research at Medical University of Graz, as well as the Division of Internal Medicine of the Medical University of Innsbruck are highly acknowledged. Disclosure statement The authors declare no competing interests. Funding G.G. and A. M. acknowledge funding from the Austrian Science Fund (FWF) [doi.org/10.55776/COE7 and DK-MOLIN W1241]. A.M. is supported by the Christian Doppler research foundation. Author contributions Conceptualization, G.G., A.M., B.J.; Methodology, G.G., A.M., B.J., P.W.; Formal Analysis, B.J., S.B., P.W., P.S., M.H; Investigation, B.J., S.B., P.S., P.W., M.H., N.P., C.W., S.W., M.A., S.E., G.G.; Resources, G.G., A.M., B.T., K.Z.; Data Curation, B.J., S.B.; Writing – Original Draft, B.J., G.G.; Writing – Review & Editing, K.Z., A.M., P.W., , B.J., G.G.; Visualization, B.J., P.W., S.B.; Supervision, G.G., A.M., Funding Acquisition, G.G., A.M., B.T.,K.Z. Data availability statement The RNAseq data have been deposited in NCBI-GEO under the accession number GSE208684. Further information and requests for resources should be directed to the lead contact Gregor Gorkiewicz ( [email protected] ). References Guan Q. A Comprehensive Review and Update on the Pathogenesis of Inflammatory Bowel Disease. J Immunol Res. 2019;2019:7247238. Dorrington AM, Selinger CP, Parkes GC, Smith M, Pollok RC, Raine T. The Historical Role and Contemporary Use of Corticosteroids in Inflammatory Bowel Disease. J Crohns Colitis. 2020;14(9):1316–29. Craviotto V, Furfaro F, Loy L, Zilli A, Peyrin-Biroulet L, Fiorino G, et al. Viral infections in inflammatory bowel disease: Tips and tricks for correct management. World J Gastroenterol. 2021;27(27):4276–97. Aziz M, Fatima R, Haghbin H, Lee-Smith W, Nawras A. The Incidence and Outcomes of COVID-19 in IBD Patients: A Rapid Review and Meta-analysis. Inflamm Bowel Dis. 2020;26(10):e132–3. Derikx L, Lantinga MA, de Jong DJ, van Dop WA, Creemers RH, Romkens TEH, et al. Clinical Outcomes of Covid-19 in Patients With Inflammatory Bowel Disease: A Nationwide Cohort Study. J Crohns Colitis. 2021;15(4):529–39. Ungaro RC, Brenner EJ, Agrawal M, Zhang X, Kappelman MD, Colombel JF, et al. Impact of Medications on COVID-19 Outcomes in Inflammatory Bowel Disease: Analysis of More Than 6000 Patients From an International Registry. Gastroenterology. 2022;162(1):316–e95. Roy S, Sheikh SZ, Furey TS. A machine learning approach identifies 5-ASA and ulcerative colitis as being linked with higher COVID-19 mortality in patients with IBD. Sci Rep. 2021;11(1):16522. Ricciuto A, Lamb CA, Benchimol EI, Walker GJ, Kennedy NA, Kuenzig ME, et al. Inflammatory Bowel Disease Clinical Activity is Associated with COVID-19 Severity Especially in Younger Patients. J Crohns Colitis. 2022;16(4):591–600. Xiao F, Tang M, Zheng X, Liu Y, Li X, Shan H. Evidence for Gastrointestinal Infection of SARS-CoV-2. Gastroenterology. 2020;158(6):1831–3. e3. Yeo C, Kaushal S, Yeo D. Enteric involvement of coronaviruses: is faecal-oral transmission of SARS-CoV-2 possible? Lancet Gastroenterol Hepatol. 2020;5(4):335–7. Livanos AE, Jha D, Cossarini F, Gonzalez-Reiche AS, Tokuyama M, Aydillo T, et al. Intestinal Host Response to SARS-CoV-2 Infection and COVID-19 Outcomes in Patients With Gastrointestinal Symptoms. Gastroenterology. 2021;160(7):2435–e5034. Wu Y, Guo C, Tang L, Hong Z, Zhou J, Dong X, et al. Prolonged presence of SARS-CoV-2 viral RNA in faecal samples. Lancet Gastroenterol Hepatol. 2020;5(5):434–5. Amman F, Markt R, Endler L, Hupfauf S, Agerer B, Schedl A, et al. Viral variant-resolved wastewater surveillance of SARS-CoV-2 at national scale. Nat Biotechnol. 2022;40(12):1814–22. Cadwell K, Patel KK, Maloney NS, Liu TC, Ng AC, Storer CE, et al. Virus-plus-susceptibility gene interaction determines Crohn's disease gene Atg16L1 phenotypes in intestine. Cell. 2010;141(7):1135–45. Jostins L, Ripke S, Weersma RK, Duerr RH, McGovern DP, Hui KY, et al. Host-microbe interactions have shaped the genetic architecture of inflammatory bowel disease. Nature. 2012;491(7422):119–24. Axelrad JE, Cadwell KH, Colombel JF, Shah SC. Systematic review: gastrointestinal infection and incident inflammatory bowel disease. Aliment Pharmacol Ther. 2020;51(12):1222–32. Nowak JK, Lindstrom JC, Kalla R, Ricanek P, Halfvarson J, Satsangi J, Age. Inflammation, and Disease Location Are Critical Determinants of Intestinal Expression of SARS-CoV-2 Receptor ACE2 and TMPRSS2 in Inflammatory Bowel Disease. Gastroenterology. 2020;159(3):1151–4. e2. Burgueno JF, Reich A, Hazime H, Quintero MA, Fernandez I, Fritsch J, et al. Expression of SARS-CoV-2 Entry Molecules ACE2 and TMPRSS2 in the Gut of Patients With IBD. Inflamm Bowel Dis. 2020;26(6):797–808. Uhlen M, Fagerberg L, Hallstrom BM, Lindskog C, Oksvold P, Mardinoglu A, et al. Proteomics. Tissue-based map of the human proteome. Science. 2015;347(6220):1260419. Garg M, Angus PW, Burrell LM, Herath C, Gibson PR, Lubel JS. Review article: the pathophysiological roles of the renin-angiotensin system in the gastrointestinal tract. Aliment Pharmacol Ther. 2012;35(4):414–28. Zhang H, Penninger JM, Li Y, Zhong N, Slutsky AS. Angiotensin-converting enzyme 2 (ACE2) as a SARS-CoV-2 receptor: molecular mechanisms and potential therapeutic target. Intensive Care Med. 2020;46(4):586–90. Garg M, Royce SG, Tikellis C, Shallue C, Batu D, Velkoska E, et al. Imbalance of the renin-angiotensin system may contribute to inflammation and fibrosis in IBD: a novel therapeutic target? Gut. 2020;69(5):841–51. Zollner A, Koch R, Jukic A, Pfister A, Meyer M, Rossler A, et al. Postacute COVID-19 is Characterized by Gut Viral Antigen Persistence in Inflammatory Bowel Diseases. Gastroenterology. 2022;163(2):495–506. e8. Zacharias M, Kashofer K, Wurm P, Regitnig P, Schutte M, Neger M, et al. Host and microbiome features of secondary infections in lethal covid-19. iScience. 2022;25(9):104926. Lamers MM, Beumer J, van der Vaart J, Knoops K, Puschhof J, Breugem TI, et al. SARS-CoV-2 productively infects human gut enterocytes. Science. 2020;369(6499):50–4. Ettayebi K, Crawford SE, Murakami K, Broughman JR, Karandikar U, Tenge VR, et al. Replication of human noroviruses in stem cell-derived human enteroids. Science. 2016;353(6306):1387–93. Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. 2011. 2011;17(1):3. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29(1):15–21. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550. Han F, Lillard SJ. In-situ sampling and separation of RNA from individual mammalian cells. Anal Chem. 2000;72(17):4073–9. Jelinsky SA, Derksen M, Bauman E, Verissimo CS, van Dooremalen WTM, Roos JL, et al. Molecular and Functional Characterization of Human Intestinal Organoids and Monolayers for Modeling Epithelial Barrier. Inflamm Bowel Dis. 2023;29(2):195–206. Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 2012;16(5):284–7. Jassal B, Matthews L, Viteri G, Gong C, Lorente P, Fabregat A, et al. The reactome pathway knowledgebase. Nucleic Acids Res. 2020;48(D1):D498–503. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005;102(43):15545–50. Maddipatla SC, Kolachala VL, Venkateswaran S, Dodd AF, Pelia RS, Geem D, et al. Assessing Cellular and Transcriptional Diversity of Ileal Mucosa Among Treatment-Naive and Treated Crohn's Disease. Inflamm Bowel Dis. 2023;29(2):274–85. Schniers A, Goll R, Pasing Y, Sorbye SW, Florholmen J, Hansen T. Ulcerative colitis: functional analysis of the in-depth proteome. Clin Proteom. 2019;16:4. Saiz-Gonzalo G, Hanrahan N, Rossini V, Singh R, Ahern M, Kelleher M, et al. Regulation of CEACAM Family Members by IBD-Associated Triggers in Intestinal Epithelial Cells, Their Correlation to Inflammation and Relevance to IBD Pathogenesis. Front Immunol. 2021;12:655960. Weiser M, Simon JM, Kochar B, Tovar A, Israel JW, Robinson A, et al. Molecular classification of Crohn's disease reveals two clinically relevant subtypes. Gut. 2018;67(1):36–42. Ilicic T, Kim JK, Kolodziejczyk AA, Bagger FO, McCarthy DJ, Marioni JC, et al. Classification of low quality cells from single-cell RNA-seq data. Genome Biol. 2016;17:29. Zhang H, Kang Z, Gong H, Xu D, Wang J, Li Z, et al. Digestive system is a potential route of COVID-19: an analysis of single-cell coexpression pattern of key proteins in viral entry process. Gut. 2020;69(6):1010–8. Padmanabhan P, Desikan R, Dixit NM. Targeting TMPRSS2 and Cathepsin B/L together may be synergistic against SARS-CoV-2 infection. PLoS Comput Biol. 2020;16(12):e1008461. Johnson BA, Xie X, Bailey AL, Kalveram B, Lokugamage KG, Muruato A, et al. Loss of furin cleavage site attenuates SARS-CoV-2 pathogenesis. Nature. 2021;591(7849):293–9. Zang R, Gomez Castro MF, McCune BT, Zeng Q, Rothlauf PW, Sonnek NM et al. TMPRSS2 and TMPRSS4 promote SARS-CoV-2 infection of human small intestinal enterocytes. Sci Immunol. 2020;5(47). Kishimoto M, Uemura K, Sanaki T, Sato A, Hall WW, Kariwa H et al. TMPRSS11D and TMPRSS13 Activate the SARS-CoV-2 Spike Protein. Viruses. 2021;13(3). Daly JL, Simonetti B, Klein K, Chen KE, Williamson MK, Anton-Plagaro C, et al. Neuropilin-1 is a host factor for SARS-CoV-2 infection. Science. 2020;370(6518):861–5. Potdar AA, Dube S, Naito T, Li K, Botwin G, Haritunians T, et al. Altered Intestinal ACE2 Levels Are Associated With Inflammation, Severe Disease, and Response to Anti-Cytokine Therapy in Inflammatory Bowel Disease. Gastroenterology. 2021;160(3):809–22. e7. Suarez-Farinas M, Tokuyama M, Wei G, Huang R, Livanos A, Jha D, et al. Intestinal Inflammation Modulates the Expression of ACE2 and TMPRSS2 and Potentially Overlaps With the Pathogenesis of SARS-CoV-2-related Disease. Gastroenterology. 2021;160(1):287–e30120. Zhao MM, Yang WL, Yang FY, Zhang L, Huang WJ, Hou W, et al. Cathepsin L plays a key role in SARS-CoV-2 infection in humans and humanized mice and is a promising target for new drug development. Signal Transduct Target Ther. 2021;6(1):134. Karakasheva TA, Zhou Y, Xie HM, Soto GE, Johnson TD, Stoltz MA, et al. Patient-derived Colonoids From Disease-spared Tissue Retain Inflammatory Bowel Disease-specific Transcriptomic Signatures. Gastro Hep Adv. 2023;2(6):830–42. Denney L, Branchett W, Gregory LG, Oliver RA, Lloyd CM. Epithelial-derived TGF-beta1 acts as a pro-viral factor in the lung during influenza A infection. Mucosal Immunol. 2018;11(2):523–35. Alsamman AM, Zayed H. The transcriptomic profiling of SARS-CoV-2 compared to SARS, MERS, EBOV, and H1N1. PLoS ONE. 2020;15(12):e0243270. Ferreira-Gomes M, Kruglov A, Durek P, Heinrich F, Tizian C, Heinz GA, et al. SARS-CoV-2 in severe COVID-19 induces a TGF-beta-dominated chronic immune response that does not target itself. Nat Commun. 2021;12(1):1961. Vagapova ER, Lebedev TD, Prassolov VS. Viral fibrotic scoring and drug screen based on MAPK activity uncovers EGFR as a key regulator of COVID-19 fibrosis. Sci Rep. 2021;11(1):11234. Baindara P, Sarker MB, Earhart AP, Mandal SM, Schrum AG. NOTCH signaling in COVID-19: a central hub controlling genes, proteins, and cells that mediate SARS-CoV-2 entry, the inflammatory response, and lung regeneration. Front Cell Infect Microbiol. 2022;12:928704. Borrmann H, McKeating JA, Zhuang X. The Circadian Clock and Viral Infections. J Biol Rhythms. 2021;36(1):9–22. Tang X, Xue D, Zhang T, Nilsson-Payant BE, Carrau L, Duan X, et al. A multi-organoid platform identifies CIART as a key factor for SARS-CoV-2 infection. Nat Cell Biol. 2023;25(3):381–9. Vessichelli M, Ferravante A, Zotti T, Reale C, Scudiero I, Picariello G, et al. Neuroepithelial transforming gene 1 (Net1) binds to caspase activation and recruitment domain (CARD)- and membrane-associated guanylate kinase-like domain-containing (CARMA) proteins and regulates nuclear factor kappaB activation. J Biol Chem. 2012;287(17):13722–30. Eguchi R, Karim MB, Hu P, Sato T, Ono N, Kanaya S, et al. An integrative network-based approach to identify novel disease genes and pathways: a case study in the context of inflammatory bowel disease. BMC Bioinformatics. 2018;19(1):264. Holgersen K, Kutlu B, Fox B, Serikawa K, Lord J, Hansen AK, et al. High-resolution gene expression profiling using RNA sequencing in patients with inflammatory bowel disease and in mouse models of colitis. J Crohns Colitis. 2015;9(6):492–506. Zhu J, Zhang Y, Ghosh A, Cuevas RA, Forero A, Dhar J, et al. Antiviral activity of human OASL protein is mediated by enhancing signaling of the RIG-I RNA sensor. Immunity. 2014;40(6):936–48. Zhou S, Butler-Laporte G, Nakanishi T, Morrison DR, Afilalo J, Afilalo M, et al. A Neanderthal OAS1 isoform protects individuals of European ancestry against COVID-19 susceptibility and severity. Nat Med. 2021;27(4):659–67. Dolskiy AA, Bodnev SA, Nazarenko AA, Smirnova AM, Pyankova OG, Matveeva AK, et al. Deletion of BST2 Cytoplasmic and Transmembrane N-Terminal Domains Results in SARS-CoV, SARS-CoV-2, and Influenza Virus Production Suppression in a Vero Cell Line. Front Mol Biosci. 2020;7:616798. Li MT, Di W, Xu H, Yang YK, Chen HW, Zhang FX, et al. Negative regulation of RIG-I-mediated innate antiviral signaling by SEC14L1. J Virol. 2013;87(18):10037–46. Diao F, Li S, Tian Y, Zhang M, Xu LG, Zhang Y, et al. Negative regulation of MDA5- but not RIG-I-mediated innate antiviral signaling by the dihydroxyacetone kinase. Proc Natl Acad Sci U S A. 2007;104(28):11706–11. Baggen J, Persoons L, Vanstreels E, Jansen S, Van Looveren D, Boeckx B, et al. Genome-wide CRISPR screening identifies TMEM106B as a proviral host factor for SARS-CoV-2. Nat Genet. 2021;53(4):435–44. Han Y, Bai X, Liu S, Zhu J, Zhang F, Xie L, et al. XAF1 Protects Host against Emerging RNA Viruses by Stabilizing IRF1-Dependent Antiviral Immunity. J Virol. 2022;96(17):e0077422. Ciancanelli MJ, Huang SX, Luthra P, Garner H, Itan Y, Volpi S, et al. Infectious disease. Life-threatening influenza and impaired interferon amplification in human IRF7 deficiency. Science. 2015;348(6233):448–53. Liu Z, Pan Q, Ding S, Qian J, Xu F, Zhou J, et al. The interferon-inducible MxB protein inhibits HIV-1 infection. Cell Host Microbe. 2013;14(4):398–410. Bizzotto J, Sanchis P, Abbate M, Lage-Vickers S, Lavignolle R, Toro A, et al. SARS-CoV-2 Infection Boosts MX1 Antiviral Effector in COVID-19 Patients. iScience. 2020;23(10):101585. Polster BM, Pevsner J, Hardwick JM. Viral Bcl-2 homologs and their role in virus replication and associated diseases. Biochim Biophys Acta. 2004;1644(2–3):211–27. Mizushima T, Arakawa S, Sanada Y, Yoshino I, Miyazaki D, Urushima H, et al. Inhibition of epithelial cell death by Bcl-2 improved chronic colitis in IL-10 KO mice. Am J Pathol. 2013;183(6):1936–44. Kim YJ, Witwit H, Cubitt B, de la Torre JC. Inhibitors of Anti-apoptotic Bcl-2 Family Proteins Exhibit Potent and Broad-Spectrum Anti-mammarenavirus Activity via Cell Cycle Arrest at G0/G1 Phase. J Virol. 2021;95(24):e0139921. Tiwarekar V, Wohlfahrt J, Fehrholz M, Scholz CJ, Kneitz S, Schneider-Schaulies J. APOBEC3G-Regulated Host Factors Interfere with Measles Virus Replication: Role of REDD1 and Mammalian TORC1 Inhibition. J Virol. 2018;92(17). Huynh H, Levitz R, Huang R, Kahn JS. mTOR kinase is a therapeutic target for respiratory syncytial virus and coronaviruses. Sci Rep. 2021;11(1):24442. Pichlmair A, Lassnig C, Eberle CA, Gorna MW, Baumann CL, Burkard TR, et al. IFIT1 is an antiviral protein that recognizes 5'-triphosphate RNA. Nat Immunol. 2011;12(7):624–30. Li D, Swaminathan S. Human IFIT proteins inhibit lytic replication of KSHV: A new feed-forward loop in the innate immune system. PLoS Pathog. 2019;15(2):e1007609. Sigrist CJ, Bridge A, Le Mercier P. A potential role for integrins in host cell entry by SARS-CoV-2. Antiviral Res. 2020;177:104759. Xu S, Zheng Z, Pathak JL, Cheng H, Zhou Z, Chen Y, et al. The Emerging Role of the Serine Incorporator Protein Family in Regulating Viral Infection. Front Cell Dev Biol. 2022;10:856468. Lee JC, Biasci D, Roberts R, Gearry RB, Mansfield JC, Ahmad T, et al. Genome-wide association study identifies distinct genetic contributions to prognosis and susceptibility in Crohn's disease. Nat Genet. 2017;49(2):262–8. Orzalli MH, Smith A, Jurado KA, Iwasaki A, Garlick JA, Kagan JC. An Antiviral Branch of the IL-1 Signaling Pathway Restricts Immune-Evasive Virus Replication. Mol Cell. 2018;71(5):825–40. e6. Friedrich M, Pohin M, Jackson MA, Korsunsky I, Bullers SJ, Rue-Albrecht K, et al. IL-1-driven stromal-neutrophil interactions define a subset of patients with inflammatory bowel disease that does not respond to therapies. Nat Med. 2021;27(11):1970–81. Liso M, Verna G, Cavalcanti E, De Santis S, Armentano R, Tafaro A, et al. Interleukin 1beta Blockade Reduces Intestinal Inflammation in a Murine Model of Tumor Necrosis Factor-Independent Ulcerative Colitis. Cell Mol Gastroenterol Hepatol. 2022;14(1):151–71. Gonzalez MC, Suspene R, Henry M, Guetard D, Wain-Hobson S, Vartanian JP. Human APOBEC1 cytidine deaminase edits HBV DNA. Retrovirology. 2009;6:96. Singh AK, Jena A, Kumar MP, Sharma V, Sebastian S. Risk and outcomes of coronavirus disease in patients with inflammatory bowel disease: A systematic review and meta-analysis. United Eur Gastroenterol J. 2021;9(2):159–76. Hoffmann M, Kleine-Weber H, Schroeder S, Kruger N, Herrler T, Erichsen S, et al. SARS-CoV-2 Cell Entry Depends on ACE2 and TMPRSS2 and Is Blocked by a Clinically Proven Protease Inhibitor. Cell. 2020;181(2):271–80. e8. Guo Y, Wang B, Gao H, Gao L, Hua R, Xu JD. ACE2 in the Gut: The Center of the 2019-nCoV Infected Pathology. Front Mol Biosci. 2021;8:708336. Hikmet F, Mear L, Edvinsson A, Micke P, Uhlen M, Lindskog C. The protein expression profile of ACE2 in human tissues. Mol Syst Biol. 2020;16(7):e9610. Li XZ, Qiu Y, Jeffery L, Liu F, Feng R, He JS, et al. Down-Regulation of Colonic ACE2 Expression in Patients With Inflammatory Bowel Disease Responding to Anti-TNF Therapy: Implications for COVID-19. Front Med (Lausanne). 2020;7:613475. Verstockt B, Verstockt S, Abdu Rahiman S, Ke BJ, Arnauts K, Cleynen I, et al. Intestinal Receptor of SARS-CoV-2 in Inflamed IBD Tissue Seems Downregulated by HNF4A in Ileum and Upregulated by Interferon Regulating Factors in Colon. J Crohns Colitis. 2021;15(3):485–98. Toyonaga T, Araba KC, Kennedy MM, Keith BP, Wolber EA, Beasley C, et al. Increased colonic expression of ACE2 associates with poor prognosis in Crohn's disease. Sci Rep. 2021;11(1):13533. Menzel K, Hausmann M, Obermeier F, Schreiter K, Dunger N, Bataille F, et al. Cathepsins B, L and D in inflammatory bowel disease macrophages and potential therapeutic effects of cathepsin inhibition in vivo. Clin Exp Immunol. 2006;146(1):169–80. Ziegler CGK, Allon SJ, Nyquist SK, Mbano IM, Miao VN, Tzouanas CN, et al. SARS-CoV-2 Receptor ACE2 Is an Interferon-Stimulated Gene in Human Airway Epithelial Cells and Is Detected in Specific Cell Subsets across Tissues. Cell. 2020;181(5):1016–e3519. Triana S, Metz-Zumaran C, Ramirez C, Kee C, Doldan P, Shahraz M, et al. Single-cell analyses reveal SARS-CoV-2 interference with intrinsic immune response in the human gut. Mol Syst Biol. 2021;17(4):e10232. Tomlin H, Piccinini AM. A complex interplay between the extracellular matrix and the innate immune response to microbial pathogens. Immunology. 2018;155(2):186–201. Huang WJ, Tang XX. Virus infection induced pulmonary fibrosis. J Transl Med. 2021;19(1):496. Jansen J, Reimer KC, Nagai JS, Varghese FS, Overheul GJ, de Beer M, et al. SARS-CoV-2 infects the human kidney and drives fibrosis in kidney organoids. Cell Stem Cell. 2022;29(2):217–31. e8. Zidar N, Langner C, Jerala M, Bostjancic E, Drobne D, Tomazic A. Pathology of Fibrosis in Crohn's Disease-Contribution to Understanding Its Pathogenesis. Front Med (Lausanne). 2020;7:167. Hagbom M, De Faria FM, Winberg ME, Westerberg S, Nordgren J, Sharma S et al. Neurotrophic Factors Protect the Intestinal Barrier from Rotavirus Insult in Mice. mBio. 2020;11(1). Morel L, Domingues O, Zimmer J, Michel T. Revisiting the Role of Neurotrophic Factors in Inflammation. Cells. 2020;9(4). di Mola FF, Friess H, Zhu ZW, Koliopanos A, Bley T, Di Sebastiano P, et al. Nerve growth factor and Trk high affinity receptor (TrkA) gene expression in inflammatory bowel disease. Gut. 2000;46(5):670–9. Sochal M, Malecka-Panas E, Gabryelska A, Fichna J, Talar-Wojnarowska R, Szmyd B, et al. Brain-derived neurotrophic factor is elevated in the blood serum of Crohn's disease patients, but is not influenced by anti-TNF-alpha treatment-A pilot study. Neurogastroenterol Motil. 2021;33(6):e13978. Li S, Zhang Y, Guan Z, Li H, Ye M, Chen X, et al. SARS-CoV-2 triggers inflammatory responses and cell death through caspase-8 activation. Signal Transduct Target Ther. 2020;5(1):235. Zhao X, Li C, Liu X, Chiu MC, Wang D, Wei Y, et al. Human Intestinal Organoids Recapitulate Enteric Infections of Enterovirus and Coronavirus. Stem Cell Rep. 2021;16(3):493–504. Stanifer ML, Pervolaraki K, Boulant S. Differential Regulation of Type I and Type III Interferon Signaling. Int J Mol Sci. 2019;20(6). Drummond CG, Bolock AM, Ma C, Luke CJ, Good M, Coyne CB. Enteroviruses infect human enteroids and induce antiviral signaling in a cell lineage-specific manner. Proc Natl Acad Sci U S A. 2017;114(7):1672–7. Saxena K, Simon LM, Zeng XL, Blutt SE, Crawford SE, Sastri NP, et al. A paradox of transcriptional and functional innate interferon responses of human intestinal enteroids to enteric virus infection. Proc Natl Acad Sci U S A. 2017;114(4):E570–9. Hosmillo M, Chaudhry Y, Nayak K, Sorgeloos F, Koo BK, Merenda A et al. Norovirus Replication in Human Intestinal Epithelial Cells Is Restricted by the Interferon-Induced JAK/STAT Signaling Pathway and RNA Polymerase II-Mediated Transcriptional Responses. mBio. 2020;11(2). Shemesh M, Aktepe TE, Deerain JM, McAuley JL, Audsley MD, David CT, et al. SARS-CoV-2 suppresses IFNbeta production mediated by NSP1, 5, 6, 15, ORF6 and ORF7b but does not suppress the effects of added interferon. PLoS Pathog. 2021;17(8):e1009800. Supplementary Files SupplFile1Differentialgeneexpressionuponinfection.xlsx Supplementary files Supplementary File 1, Differential gene expression upon infection SupplFile2PathwayanalysisORA.xlsx Supplementary File 2, Pathway analysis ORA Reactome SupplFile3PathwayanalysisGSEA.xlsx Supplementary File 3, Pathway analysis GSEA Reactome SupplFile4CellDeathrelatedgenes.xlsx Supplemental File 4, Cell Death related genes SupplFile5DifferentialgeneexpressionIBD.xlsx Supplementary File 5, Differential gene expression IBD SupplFile6Responsetovirusgenesetanalyses.xlsx Supplementary File 6, Response to virus gene set analyses SupplFile7IBDorganoidmetacohort.xlsx Supplemental File 7, IBD organoid metacohort (31) Supplementaltablesandfigures.pdf Supplemental information legends Table S1. Characteristics of healthy controls and IBD patients for generation of intestinal organoids Supplementary Figure 1 – Correlation of monolayer replicates. Each plot shows correlation of normalized gene counts of two organoid-derived monolayer replicates from the same patient. Red dots represent genes aligned to SARS-CoV-2 genome and black represent normalized human reads. Supplementary Figure 2 – Characteristics of uninfected and infected organoids. A) Principal component analysis of h-ileum, CD ileum, h-colon and UC colon, infected with SARS-COV-2 or uninfected. Sample distances are calculated from transformed normalized gene counts and visualized on PC1-PC5 explaining variance percentage as stated. Each dot represents pooled monolayer replicates from one patient, either infected or uninfected. B) Expression of mitochondrial genes as a proxy for cell damage is not different between uninfected and SARS-CoV-2 infected healthy and IBD organoids. Dots represent single organoid monolayer replicates. Mean of log(normalized gene counts) with SD; Mann-Whitney-U-Test. N(patients) = 5-6; CD = Crohn’s disease; UC = ulcerative colitis; ile = h-ileum; col = h-colon. Supplementary Figure 3 - qPCR quantification of viral loads using a SARS-CoV-2 RNA copy number standard. Higher variations are evident in healthy organoids compared to IBD organoids. The mean value is shown as a line in each panel. Supplementary Figure 4 – Expression of TMPRSS4 , FURIN and NRP1 do not differ between the groups. Each dot represents pooled organoid monolayer replicates from one patient. Mean of normalized gene counts with SD; 1way ANOVA, uncorrected Fisher's LSD on following comparisons: h-ileum vs. CD ileum, h-ileum vs. h-colon, h-colon vs. UC colon. N(patients) = 5-6; CD = Crohn’s disease; UC = ulcerative colitis; * P < 0.05, ** P <0.01, *** P < 0.001, **** P < 0.0001. Supplementary Figure 5 – Correlation analysis, extended. Viral load correlates to normalized gene counts of SARS-CoV-2 entry-related genes in samples: 1) all combined, 2) by group, 3) by organ. Each dot represents one patient, pooled organoid monolayer replicates. Viral load is defined as the sum of viral genes. Pearson correlation of transformed normalized gene counts. N(patients) = 5-6; CD = Crohn’s disease; UC = ulcerative colitis. Supplementary Figure 6 – Shared and unique DEGs upon infection with SARS-CoV-2. A) Upregulated (up) and downregulated (down) DEGs ( Padj < 0.1) minimally overlap between healthy and IBD organoids separated by organ. Upregulated and downregulated DEGs with B) P < 0.05 and C) Padj < 0.1 overlap more between organoids from colon and ileum of healthy origin as opposed to IBD organoids. Statistics of differential gene expression were calculated with the Wald test. N(patients) = 5-6; CD = Crohn’s disease; UC = ulcerative colitis; IBD = inflammatory bowel disease. Supplementary Figure 7 – Enrichment map of gene set enrichment analysis (GSEA, Reactome) after SARS-CoV-2 infection of intestinal organoids. Upregulated pathways are shown in red and downregulated pathways in blue ( Padj < 0.05). Pathways which share genes contributing to the enrichment score are connected with a line. Size of circles corresponds to the number of contributing genes. N(patients) = 5-6; CD = Crohn’s disease; UC = ulcerative colitis. Supplementary Figure 8 – SARS-CoV-2 infection impacts gene expression differently depending on intestinal origin and IBD status of organoids. Gene set enrichment analysis (GSEA; Reactome) based on sign of log2-fold change and P values of all detected genes upon infection. Results (Suppl. File 3) were combined for simplicity. Pathways were selected based on their prevalence across groups or relevance to viral infections before grouping into common biological themes. Statistics of differential expression were calculated with the Wald test. N(patients) = 5-6, CD = Crohn’s disease, UC = ulcerative colitis. Supplementary Figure 9 – Organoids from IBD patients are transcriptionally different from healthy organoids. Volcano plots of differential gene expression in uninfected A) CD ileum compared to h-ileum and B) UC colon compared to h-colon. Dots represent genes; horizontal dashed line marks P value of 0.05; vertical dashed lines mark LFC of 0.4. Statistics of differential gene expression were calculated with the Wald test. N(patients) = 5-6; CD = Crohn’s disease; UC = ulcerative colitis; FC = fold change; NS = non-significant. Supplementary Figure 10 – Response to virus gene set analysed in the IBD organoid metacohort (31). Volcano plots of differential gene expression of CD vs healthy ileum (left) and UC vs. healthy colon (right). Supplementary Figure 11 – Organ and health status influence expression of genes involved in response to virus after SARS-CoV-2 infection of organoids. Volcano plots of differential gene expression upon infection. Dots represent genes; horizontal dashed line marks P value of 0.05; vertical dashed lines mark log 2 FC of -/+0.6. Statistics of differential gene expression were calculated with the Wald test. N(patients) = 5-6; CD = Crohn’s disease; UC = ulcerative colitis; FC = fold change; GOI = gene of interest. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8029502","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":544437223,"identity":"c5febfda-420f-4996-831f-d8db0aa71a90","order_by":0,"name":"Barbara Jelusic","email":"","orcid":"","institution":"Medical University of Graz: Medizinische Universitat Graz","correspondingAuthor":false,"prefix":"","firstName":"Barbara","middleName":"","lastName":"Jelusic","suffix":""},{"id":544437224,"identity":"6b641430-2475-42cc-a0a8-2a27738deb75","order_by":1,"name":"Stefan 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Four 2D monolayer replicates were created from the 3D organoids of each patient. Two replicates were inoculated with SARS-CoV-2. Samples from monolayers were collected after 48 hours and subjected to RNA sequencing. Differential gene expression analyses between ileum vs. colon, IBD vs. healthy and infected vs. uninfected samples were performed from each patient.\u003c/p\u003e","description":"","filename":"fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-8029502/v1/5d7727f4f75c98f74b9b42c1.png"},{"id":96787812,"identity":"cc8278a1-e000-4e90-bbe8-eefdb9f610aa","added_by":"auto","created_at":"2025-11-26 06:27:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":414512,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTissue origin followed by IBD status but not SARS-CoV-2 infection are the major discriminators in deep transcriptomic analyses. A)\u003c/strong\u003e Principal component analysis (PCA). Sample distances were calculated from transformed normalized gene counts. Ellipses indicate 95% confidence intervals around sample groups. Each dot represents pooled organoid monolayer replicates from one patient, either treated with SARS-CoV-2 or uninfected (un). \u003cstrong\u003eB)\u003c/strong\u003e Genes that are top 10% contributors to variation along PC1 and PC4. A darker-grey marks a stronger influence on a PC. \u003cstrong\u003eC)\u003c/strong\u003e Heatmap with sample clustering showing cell type marker genes for small and large intestines. Marker genes are stratified into stem/transit-amplifying cells (TA), mature enterocytes, Paneth cells, enteroendocrine cells and goblet cells. Row Z-Score is calculated based on transformed normalized counts. N(patients) = 5-6; CD = Crohn’s disease; UC = ulcerative colitis.\u003c/p\u003e","description":"","filename":"fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-8029502/v1/3a6e22853a9a6b9a21062894.png"},{"id":96916596,"identity":"d708c4f5-8ec7-4612-982a-72383fe95fa6","added_by":"auto","created_at":"2025-11-27 14:08:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":269219,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSARS-CoV-2 infection is highest in healthy ileum while IBD status does not increase viral load.\u003c/strong\u003e \u003cstrong\u003eA)\u003c/strong\u003eDistribution of viral reads along the SARS-CoV-2 genome. Cumulative coverage of plus and minus strand transcripts is shown (median in bold). \u003cstrong\u003eB)\u003c/strong\u003e Viral load in 2D organoid-based monolayers 48 hours post infection determined by viral transcripts normalized by reads per million (rpm) and summed per sample (mean ± SD). qPCR of SARS-CoV-2 compared to human ß-actin (ACTB; Mann Whitney U test). \u003cstrong\u003eC) \u003c/strong\u003eSARS-CoV-2 entry gene expression in uninfected and infected 2D organoid groups (mean ± SD; 1way ANOVA with uncorrected Fisher's LSD on following comparisons: h-ileum vs. CD ileum, h-ileum vs. h-colon, h-colon vs. UC colon. \u003cstrong\u003eD)\u003c/strong\u003e Viral load in 2D cultures correlates to SARS-CoV-2 entry genes only in non-IBD groups (Pearson correlation of transformed normalized gene counts). All panels: each dot represents one patient, pooled monolayer replicates. N(patients) = 5-6; CD = Crohn’s disease; UC = ulcerative colitis, * \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, ** \u003cem\u003eP\u003c/em\u003e \u0026lt;0.01, *** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001, **** \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-8029502/v1/502c43c07d26756adb6c2fa3.png"},{"id":96787810,"identity":"d2f684dd-7c29-4424-b77f-45e503d62404","added_by":"auto","created_at":"2025-11-26 06:27:48","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":360566,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSARS-CoV-2 infection impacts gene expression differently depending on intestinal origin and IBD status of organoids.\u003c/strong\u003e \u003cstrong\u003eA)\u003c/strong\u003e Venn diagram specifying upregulated (up) and downregulated (down) DEGs (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) in SARS-CoV-2-infected 2D organoid monolayers compared to uninfected controls. \u003cstrong\u003eB)\u003c/strong\u003e \u0026nbsp;Over-representation analysis (ORA; Reactome) of most significantly changed pathways based on up- (left) and downregulated (right) DEGs upon infection. \u003cstrong\u003eC)\u003c/strong\u003e Cell death and defence signalling pathways. Apoptosis, necrosis and interferon alpha/beta signalling were enriched only in h-ileum and UC colon upon infection. Normalized enrichment scores determined by GSEA are shown for chosen enriched pathways. Statistics of differential expression were calculated with the Wald test. N(patients) = 5-6, CD = Crohn’s disease, UC = ulcerative colitis.\u003c/p\u003e","description":"","filename":"fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-8029502/v1/fdd0278c702fdfbefcfb3dde.png"},{"id":96787816,"identity":"6f42cee1-d584-48dc-b38e-6561497ea09a","added_by":"auto","created_at":"2025-11-26 06:27:48","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":319894,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExpression of genes implicated in the cellular response to viral infection is intrinsically altered in IBD, different between ileum and colon and influenced by IBD status upon SARS-CoV-2 challenge. A)\u003c/strong\u003e Volcano plot showing DEGs of uninfected CD ileum vs. h-ileum (left) and UC colon vs. h-colon (right). Coloured dots represent response to virus-related genes; horizontal dashed line marks \u003cem\u003eP\u003c/em\u003e value of 0.05. Bolded genes remained significant after BH correction with FDR = 0.1. \u003cstrong\u003eB)\u003c/strong\u003e Sankey plot visualizing the amount of upregulated and downregulated DEGs in baseline IBD vs. healthy compared to infected vs. uninfected in CD ileum and UC colon. \u003cstrong\u003eC)\u003c/strong\u003e DEGs (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) from the created gene set ‘response to virus’ in (yellow) IBD organoids vs. non-IBD/healthy organoids and in (orange) SARS-CoV-2 infected organoids vs. uninfected. Heatmap shows log\u003csub\u003e2\u003c/sub\u003efold changes (LFC) with \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05. Number (N) of significantly different genes per comparison is shown. Statistics of differential expression were calculated with the Wald test. N(patients) = 5-6, CD = Crohn’s disease, UC = ulcerative colitis, GOI = gene of interest.\u003c/p\u003e","description":"","filename":"fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-8029502/v1/a6b6263057e29b7ffc6c68a4.png"},{"id":96787813,"identity":"f478f0b7-a1b9-4508-9818-6386fd0a4971","added_by":"auto","created_at":"2025-11-26 06:27:48","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":396743,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProposed influences of IBD status and SARS-CoV-2 infection on expression of ‘response to virus’ genes.\u003c/strong\u003e DEGs (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) from the created gene set ‘response to virus’ were categorized into genes important for antigen presentation, anti-viral activity or recognition, cell death, cytokine/chemokines as well as SARS-CoV-2 entry factors. \u003cstrong\u003eA) \u003c/strong\u003eUninfected CD ileum organoids compared to uninfected h-ileum organoids.\u003cstrong\u003e B)\u003c/strong\u003e Uninfected UC colon organoids compared to uninfected h-colon organoids. \u003cstrong\u003eC) \u003c/strong\u003eSARS-CoV-2 infection in h-ileum and CD ileum.\u003cstrong\u003e D)\u003c/strong\u003e SARS-CoV-2 infection in h-colon and UC colon. Sizes of triangles correspond to the percentage of DEGs (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) that came from a particular organoid group and comparison, i.e., which percent of DEGs in that category are changed in a particular group (see also Fig. 5C). Categories contain different number of DEGs. Chosen DEGs (\u003cem\u003ePadj\u003c/em\u003e \u0026lt; 0.1) including SARS-CoV-2 entry genes are individually depicted in ellipses. Statistics of differential expression were calculated with the Wald test. N(patients) = 5-6; CD = Crohn’s disease; UC = ulcerative colitis.\u003c/p\u003e","description":"","filename":"fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-8029502/v1/8425cb66f69719d109085152.png"},{"id":100594564,"identity":"55152692-a4dd-465f-9d24-463c6daf6b26","added_by":"auto","created_at":"2026-01-19 13:42:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3398393,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8029502/v1/efcb86a5-b72a-4711-9323-96403ee5404b.pdf"},{"id":96916332,"identity":"3e96060d-29bf-456b-8816-115dcbed3a04","added_by":"auto","created_at":"2025-11-27 14:08:28","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":5976555,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary files\u003c/p\u003e\n\u003cp\u003eSupplementary File 1, Differential gene expression upon infection\u003c/p\u003e","description":"","filename":"SupplFile1Differentialgeneexpressionuponinfection.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8029502/v1/a4ac17e88c4e6c212b282ec2.xlsx"},{"id":96917076,"identity":"a97f69fd-8c9a-4f3a-86c3-c61c9f91ba77","added_by":"auto","created_at":"2025-11-27 14:09:14","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":119855,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary File 2, Pathway analysis ORA Reactome\u003c/p\u003e","description":"","filename":"SupplFile2PathwayanalysisORA.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8029502/v1/56c8c47d6a9a8b14709c529b.xlsx"},{"id":96915216,"identity":"dce426a4-f4ae-4fd4-81d3-f2c996650116","added_by":"auto","created_at":"2025-11-27 14:06:59","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":238022,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary File 3, Pathway analysis GSEA Reactome\u003c/p\u003e","description":"","filename":"SupplFile3PathwayanalysisGSEA.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8029502/v1/c08c62adc4d2b97563c9c3bd.xlsx"},{"id":96914975,"identity":"ea6b3beb-012d-44bb-8cc5-c58a4b866cb5","added_by":"auto","created_at":"2025-11-27 14:06:41","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":28190,"visible":true,"origin":"","legend":"\u003cp\u003eSupplemental File 4, Cell Death related genes\u003c/p\u003e","description":"","filename":"SupplFile4CellDeathrelatedgenes.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8029502/v1/c9290d2d65e20b876a055b95.xlsx"},{"id":96787834,"identity":"ee82d515-14c0-4635-b0d2-9d1481f4fe10","added_by":"auto","created_at":"2025-11-26 06:27:49","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":4618299,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary File 5, Differential gene expression IBD\u003c/p\u003e","description":"","filename":"SupplFile5DifferentialgeneexpressionIBD.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8029502/v1/bfae92c79e55f422556f3e70.xlsx"},{"id":96915940,"identity":"b890853f-e1cc-43cd-ac51-2ffe6d71f0f2","added_by":"auto","created_at":"2025-11-27 14:07:48","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":190857,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary File 6, Response to virus gene set analyses\u003c/p\u003e","description":"","filename":"SupplFile6Responsetovirusgenesetanalyses.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8029502/v1/5830c4acedf277b6f4e5ce01.xlsx"},{"id":96916156,"identity":"a9098018-b061-4d5e-abfe-3e9f049f0fe0","added_by":"auto","created_at":"2025-11-27 14:08:07","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":5955126,"visible":true,"origin":"","legend":"\u003cp\u003eSupplemental File 7, IBD organoid metacohort (31)\u003c/p\u003e","description":"","filename":"SupplFile7IBDorganoidmetacohort.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8029502/v1/a9aada59edd98373c055329f.xlsx"},{"id":96787821,"identity":"73c18881-a3c4-4aee-8555-c8b079db2902","added_by":"auto","created_at":"2025-11-26 06:27:49","extension":"pdf","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":9208400,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplemental information legends\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S1. Characteristics of healthy controls and IBD patients for generation of intestinal organoids\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Supplementary Figure 1\u003c/strong\u003e – \u003cstrong\u003eCorrelation of monolayer replicates.\u003c/strong\u003e Each plot shows correlation of normalized gene counts of two organoid-derived monolayer replicates from the same patient. Red dots represent genes aligned to SARS-CoV-2 genome and black represent normalized human reads.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eSupplementary Figure 2\u003c/strong\u003e \u003cstrong\u003e– Characteristics of uninfected and infected organoids.\u003c/strong\u003e \u003cstrong\u003eA)\u003c/strong\u003e Principal component analysis of h-ileum, CD ileum, h-colon and UC colon, infected with SARS-COV-2 or uninfected. Sample distances are calculated from transformed normalized gene counts and visualized on PC1-PC5 explaining variance percentage as stated. Each dot represents pooled monolayer replicates from one patient, either infected or uninfected. \u003cstrong\u003eB)\u003c/strong\u003e Expression of mitochondrial genes as a proxy for cell damage is not different between uninfected and SARS-CoV-2 infected healthy and IBD organoids. Dots represent single organoid monolayer replicates. Mean of log(normalized gene counts) with SD; Mann-Whitney-U-Test. N(patients) = 5-6; CD = Crohn’s disease; UC = ulcerative colitis; ile = h-ileum; col = h-colon.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eSupplementary Figure 3 - qPCR quantification of viral loads using a SARS-CoV-2 RNA copy number standard. \u003c/strong\u003eHigher variations are evident in healthy organoids compared to IBD organoids. The mean value is shown as a line in each panel.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eSupplementary Figure 4 –\u003c/strong\u003e \u003cstrong\u003eExpression of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eTMPRSS4\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e, \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eFURIN\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e and \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eNRP1 \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003edo not differ between the groups.\u003c/strong\u003e Each dot represents pooled organoid monolayer replicates from one patient. Mean of normalized gene counts with SD; 1way ANOVA, uncorrected Fisher's LSD on following comparisons: h-ileum vs. CD ileum, h-ileum vs. h-colon, h-colon vs. UC colon. N(patients) = 5-6; CD = Crohn’s disease; UC = ulcerative colitis; * \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, ** \u003cem\u003eP\u003c/em\u003e \u0026lt;0.01, *** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001, **** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Figure 5\u003c/strong\u003e – \u003cstrong\u003eCorrelation analysis, extended. \u003c/strong\u003eViral load correlates to normalized gene counts of SARS-CoV-2 entry-related genes in samples: 1) all combined, 2) by group, 3) by organ. Each dot represents one patient, pooled organoid monolayer replicates. Viral load is defined as the sum of viral genes. Pearson correlation of transformed normalized gene counts. N(patients) = 5-6; CD = Crohn’s disease; UC = ulcerative colitis.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eSupplementary Figure 6 – Shared and unique DEGs upon infection with SARS-CoV-2.\u003c/strong\u003e \u003cstrong\u003eA)\u003c/strong\u003e Upregulated (up) and downregulated (down) DEGs (\u003cem\u003ePadj \u003c/em\u003e\u0026lt; 0.1) minimally overlap between healthy and IBD organoids separated by organ. Upregulated and downregulated DEGs with \u003cstrong\u003eB) \u003c/strong\u003e\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05 and \u003cstrong\u003eC)\u003c/strong\u003e \u003cem\u003ePadj\u003c/em\u003e \u0026lt; 0.1 overlap more between organoids from colon and ileum of healthy origin as opposed to IBD organoids. Statistics of differential gene expression were calculated with the Wald test. N(patients) = 5-6; CD = Crohn’s disease; UC = ulcerative colitis; IBD = inflammatory bowel disease.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eSupplementary Figure 7 – Enrichment map of gene set enrichment analysis (GSEA, Reactome) after SARS-CoV-2 infection of intestinal organoids. \u003c/strong\u003eUpregulated pathways are shown in red and downregulated pathways in blue (\u003cem\u003ePadj\u003c/em\u003e\u0026nbsp;\u0026lt; 0.05). Pathways which share genes contributing to the enrichment score are connected with a line. Size of circles corresponds to the number of contributing genes.\u0026nbsp;N(patients) = 5-6; CD = Crohn’s disease; UC = ulcerative colitis.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eSupplementary Figure 8 – SARS-CoV-2 infection impacts gene expression differently depending on intestinal origin and IBD status of organoids. \u003c/strong\u003eGene set enrichment analysis (GSEA; Reactome) based on sign of log2-fold change and \u003cem\u003eP\u003c/em\u003e values of all detected genes upon infection. Results (Suppl. File 3) were combined for simplicity. Pathways were selected based on their prevalence across groups or relevance to viral infections before grouping into common biological themes. Statistics of differential expression were calculated with the Wald test. N(patients) = 5-6, CD = Crohn’s disease, UC = ulcerative colitis.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eSupplementary Figure 9\u003c/strong\u003e \u003cstrong\u003e– Organoids from IBD patients are transcriptionally different from healthy organoids.\u003c/strong\u003e Volcano plots of differential gene expression in uninfected \u003cstrong\u003eA)\u003c/strong\u003e CD ileum compared to h-ileum and \u003cstrong\u003eB)\u003c/strong\u003e UC colon compared to h-colon. Dots represent genes; horizontal dashed line marks \u003cem\u003eP \u003c/em\u003evalue of 0.05; vertical dashed lines mark LFC of 0.4. Statistics of differential gene expression were calculated with the Wald test. N(patients) = 5-6; CD = Crohn’s disease; UC = ulcerative colitis; FC = fold change; NS = non-significant.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eSupplementary Figure 10\u003c/strong\u003e \u003cstrong\u003e– Response to virus gene set analysed in the IBD organoid metacohort (31). \u003c/strong\u003eVolcano plots of differential gene expression of CD vs healthy ileum (left) and UC vs. healthy colon (right).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eSupplementary Figure 11\u003c/strong\u003e \u003cstrong\u003e– Organ and health status influence expression of genes involved in response to virus after SARS-CoV-2 infection of organoids. \u003c/strong\u003eVolcano plots of differential gene expression upon infection. Dots represent genes; horizontal dashed line marks \u003cem\u003eP\u003c/em\u003e value of 0.05; vertical dashed lines mark log\u003csub\u003e2\u003c/sub\u003e FC of -/+0.6. Statistics of differential gene expression were calculated with the Wald test. N(patients) = 5-6; CD = Crohn’s disease; UC = ulcerative colitis; FC = fold change; GOI = gene of interest.\u003c/p\u003e","description":"","filename":"Supplementaltablesandfigures.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8029502/v1/97003da0a1210cbe52b00a35.pdf"}],"financialInterests":"","formattedTitle":"Reduced SARS-CoV-2 infection levels and pathotype specific altered antiviral transcriptional response in IBD intestinal organoids","fulltext":[{"header":"Introduction","content":"\u003cp\u003eInflammatory bowel diseases (IBD), represented by Crohn\u0026rsquo;s disease (CD) and ulcerative colitis (UC), are chronic inflammatory disorders that occur in genetically susceptible individuals because of a dysregulated intestinal immune homeostasis (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). IBD patients are prone to viral infections also driven by the immuno-suppression used to treat IBD (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). This vulnerability has raised concerns whether IBD patients represent a population at risk for worse COVID-19 outcomes. Although the incidence of COVID-19 in IBD patients is comparable to that of the general population (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), IBD patients show increased gastrointestinal (GI) symptoms during COVID-19 (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), while active IBD and corticosteroids in combination with biologics seem to increase COVID-19 severity (\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe GI tract represents a productive reservoir for SARS-CoV-2. A high expression of viral entry factors in enterocytes, like the host receptor ACE2, facilitates infection, virus amplification and faecal shedding (\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Viral persistence in the GI tract likely perpetuates dissemination and pandemic spread (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Genetic alterations in IBD can be found in genes related to immunity, and experimental models suggest that a combination of these genetic changes with viral infections drive the chronic inflammatory pathology of IBD (\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). In addition, the expression of key SARS-CoV-2 entry-related genes is altered in IBD and differs between the small and large intestines (\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Since ACE2 is considered to be tissue-protective with anti-inflammatory capabilities (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e), it's altered expression in IBD and during SARS-CoV-2 infection might aggravate the inflammatory pathology (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). For all these reasons, it is likely that SARS-CoV-2 infection of the GI tract has profound functional consequences and might impact IBD patients differently compared to healthy persons, which might also lead to differing long term consequences. Indeed, recent data suggest that SARS-CoV-2 can persist in the GI mucosa of IBD patients over prolonged periods, which correlates with post-acute COVID-19 symptoms (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn the current study, we used small and large intestinal organoids from IBD patients and controls to comparatively assess infection and the transcriptional response of epithelial cells as an entry site for SARS-CoV-2. We showed that IBD epithelia are generally not more prone to infection, but respond differently compared to non-IBD epithelia, which is dependent on the IBD phenotype. Importantly, intrinsic changes in the transcriptional response to virus infection seem to counteract SARS-CoV-2 in IBD, with specific differences between CD and UC.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eEthics approval\u003c/h2\u003e\u003cp\u003eGeneration and use of organoids were approved by the ethics committee of Medical University Innsbruck, Austria (ethics vote no.: AN4994 323/4.4). All patients gave their informed written consent for participation in this study. SARS-CoV-2 isolation was done during an autopsy study (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e) approved by the ethics committee of the Medical University of Graz (EK-number: 32\u0026ndash;362 ex 19/20).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eHuman intestinal organoid culture\u003c/h3\u003e\n\u003cp\u003eIntestinal organoids were cultured from biopsy specimens retrieved during endoscopy of IBD patients and healthy controls at the University Hospital Innsbruck. 11 IBD patients and 11 controls were included. Their characteristics are summarized in \u003cem\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/em\u003e. Organoid generation was performed with IntestiCult Organoid Growth Medium (STEMCELL Technologies Inc., Vancouver, Canada) with an adapted protocol. Two to three ileal or colonic biopsies, taken from an inflamed site in case of IBD patients, were flushed with ice cold PBS and minced into small pieces with a sterile blade. Tissues were then transferred to 15-ml tubes with 5 ml of Gentle Cell Dissociation Reagent (GCDR, STEMCELL Technologies) and incubated at 4\u0026deg;C on a rocking platform for 30 min. After centrifugation (290 x g, 5 min, 4\u0026deg;C), the tissue suspension was transferred to 1 ml of ice cold DMEM (Gibco, ThermoFisher Scientific Inc., Waltham, Massachusetts, US) with 1% (vol/vol) bovine serum albumin (Roche, Germany). The suspension was gently mixed and passed through a 70 \u0026micro;m cell strainer (Corning, Merck, Darmstadt, Germany). The cell suspensions containing whole intestinal crypts were seeded in 50 \u0026micro;l Growth Factor Reduced Matrigel matrix (Corning Inc., Corning, New York, US) on a pre-warmed 24-well plate and allowed to solidify for 10 min at 37\u0026deg;C, after which 500 \u0026micro;l IntestiCult Growth Medium supplemented with 100 U/ml penicillin and 100 \u0026micro;g/ml streptomycin (Biochrom, Berlin, Germany) was added. Medium was exchanged every two days and organoids were passaged every 5\u0026ndash;10 days depending on their growth, with a maximum of 12 passages. During passaging, ileal organoids could be disrupted by pipetting only, as opposed to colonic organoids that needed chemical disruption with Gentle Cell Dissociation Reagent (GCDR) according to the manufacturer\u0026rsquo;s protocol (STEMCELL Technologies). To create monolayers, organoids were harvested from 24 well plates and processed according to the protocol by STEMCELL Technologies. In brief, organoids were mechanically disrupted by pipetting, trypsinized, washed and seeded in a 96 well plates pre-coated with 5% Matrigel. Four monolayers were created for each patient in order to have two replicates for infections and controls, respectively.\u003c/p\u003e\n\u003ch3\u003eVirus isolation and culture\u003c/h3\u003e\n\u003cp\u003eThe used SARS-CoV-2 strain originated from an autopsy study performed at the first pandemic peak (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). The strain hCoV-19/Austria/Graz-MUG5 (B.1.5/O/20A) originated from a lung swab from a patient with concomitant high-level intestinal SARS-CoV-2 carriage. The virus strain was propagated using Vero CCL-81 cells. To prepare the inoculum for organoid infections, Vero CCL-81 cell supernatants were centrifuged at 1500 rcf for 10 min at RT and SARS-CoV-2 particles were quantified by using qPCR as described (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eOrganoid infection\u003c/h3\u003e\n\u003cp\u003eOrganoid infection followed protocols adapted from Lamers \u003cem\u003eet al.\u003c/em\u003e (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e) and Ettayebi \u003cem\u003eet al.\u003c/em\u003e (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Briefly, the viral inoculum was prepared by diluting the virus corresponding to 10\u003csup\u003e5\u003c/sup\u003e PFU in serum-free (AD) medium containing Advanced DMEM/F-12 (ThermoFisher Scientific Inc.), 1M HEPES (Merck), 1% GlutaMAX (ThermoFisher Scientific) and 1% Pen/Strep (Thermo Fisher Scientific). Conditioned medium from Vero CLL-81 cells without virus was used as control for uninfected (mock) samples. Monolayers in 96-well plates were infected on day 13 after seeding. They were washed with 150 \u0026micro;l of AD medium ahead of the infection and then treated with 100 \u0026micro;l of inoculum or mock medium. Infection dose corresponded to a multiplicity of infection (MOI) of 0.1. Monolayers were incubated in an atmosphere containing 5% CO\u003csub\u003e2\u003c/sub\u003e at 37\u0026deg;C for 2 h to allow for the virus to attach. Supernatants were then removed and the cells were washed twice with 200 \u0026micro;l AD medium for 5 minutes. 150 \u0026micro;l of IntestiCult\u0026trade; medium was added to monolayers and incubated for further 48 hrs. 200 \u0026micro;l of TRIzol (ThermoFisher Scientific) was added to each monolayer well thereafter and replicates were collected separately. Cell lysates in TRIzol were vortexed once and immediately frozen at -20\u0026deg;C until further processing.\u003c/p\u003e\n\u003ch3\u003eRNA isolation and sequencing\u003c/h3\u003e\n\u003cp\u003eTotal RNA was isolated with TRIzol (ThermoFisher Scientific) according to manufacturer\u0026rsquo;s protocol. RNA quality was controlled by using a Bioanalyzer instrument (Agilent) and sequencing libraries were prepared from 25-250ng of total RNA per sample following Roche\u0026rsquo;s strand specific \u0026ldquo;KAPA RNA Hyper Prep Kit\u0026thinsp;+\u0026thinsp;RiboErase (HMR)\u0026rdquo; library preparation protocol for double indexed Illumina libraries. Briefly, the rRNA fraction was depleted using complementary DNA oligonucleotides; rRNA-DNA hybrids and DNA baits were removed by treatment with RNase H and DNase. RNA was heat-fragmented and subjected to first strand synthesis using random priming. The second strand was synthesized incorporating dUTP instead of dTTP to preserve strand information. After A-tailing Illumina sequencing compatible adapters carrying unique dual indices were ligated (NEXTFLEX\u0026reg; Unique Dual Index Barcodes). Following bead-based clean-up steps the libraries were amplified using 13 cycles of PCR. Library quality and size was checked with the qBit, Bioanalyzer (Agilent) and qPCR. Sequencing was carried out on an Illumina NovaSeq6000 system in PE100bp mode yielding between 20\u0026ndash;40\u0026nbsp;million fragments per sample. Following base calling, adaptor clipping was performed using cutadapt 2.4 (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Data was mapped against the hg38.p12 genome including the SARS-CoV-2 genome (strain A9351) using STAR v2.7.5a (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Gene-wise read count tables were used for analysis with DESeq2(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e) and transformed to visualize sample distances by principal component analysis (PCA).\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eViral load\u003c/h2\u003e\u003cp\u003eViral gene counts were normalized by the R package DESeq2 (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e), summed by sample and log\u003csub\u003e2\u003c/sub\u003e transformed. To compare the groups regarding the viral load, a mean of monolayer replicates was used resulting in a single value per patient. For SARS-CoV-2 quantification 12 ng of total RNA were used for RT-qPCR using N2 primers (Eurofins Genomics, Ebersberg, Germany) in combination with the One-Step QuantiTect\u0026reg; SYBR Green RT-PCR Kit (QIAGEN GmbH, Hilden, Germany) on a Rotor Gene\u0026reg; Q cycler (QIAGEN GmbH, Hilden, Germany) according to the manufacturer\u0026rsquo;s instructions. Human \u0026szlig;-actin (hACTB) served as housekeeping gene for normalization. The primer and probe set for viral RNA quantification consisted of the 2019-nCoV_N2-F 2019-nCoV_N2 forward primer 5\u0026rsquo;- TTA CAA ACA TTG GCC GCA AA -3\u0026rsquo;, the 2019-nCoV_N2-R 2019-nCoV_N2 reverse primer 5\u0026rsquo;- GCG CGA CAT TCC GAA GAA \u0026minus;\u0026thinsp;3\u0026rsquo;, the hACTB forward primer 5\u0026rsquo; - GAG CAC AGA GCC TCG CCT TT -3\u0026rsquo; recommended by the CDC (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://stacks.cdc.gov/view/cdc/84525\u003c/span\u003e\u003cspan address=\"https://stacks.cdc.gov/view/cdc/84525\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and the the hACTB reverse primer 5\u0026rsquo; \u0026ndash; TCA TCA TCC GTG GTG AGC TGG \u0026ndash; 3\u0026rsquo;. A commercially available SARS-CoV-2 RNA copy number standard (VR-1986D genomic RNA from 2019 Novel Coronavirus, Lot: 70035624, ATCC, U.K.) was serially diluted and analysed by RT-qPCR to provide a standard curve. The resulting Ct-values were plotted against ln[copy numbers] and the equation obtained from a simple linear regression analysis was used to calculate the copy numbers from the Ct-values (Eq.\u0026nbsp;1). Eq.\u0026nbsp;1: Standard curve to calculate between Ct -values and viral copy numbers y= -1,5x\u0026thinsp;+\u0026thinsp;34,82. The \u0026part;Ct was calculated for SARS-CoV-2 against the housekeeping gene hACTB to quantify the amount of virus in the samples. To extrapolate the virus load per epithelial cell the approximation of 20 ng total RNA content per cell was used as described (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eDifferential gene expression analysis\u003c/h3\u003e\n\u003cp\u003eDifferences in gene expression were analysed using the R package DESeq2 (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Two monolayer replicates per patient and treatment condition were pooled during the analysis after confirming a high correlation between replicates (Suppl. Figure\u0026nbsp;1). The effects of SARS-CoV-2 infection were determined for each group separately and by pairing the infected with the control sample originating from the same patient (paired analysis in DESeq2). If the uninfected or infected sample was discarded due to low sequencing quality, this patient was removed from the subsequent analyses resulting in the final number of patients (6 h-ileum, 5 CD ileum, 5 h-colon, 5 UC colon) assessing infection effects. When assessing the differences in gene expression due to IBD, control samples from an IBD group were compared to control samples from the corresponding healthy organ, i.e. h-ileum and CD, as well as h-colon for UC, resulting in the final number of included patients (6 h-ileum, 6 CD ileum, 5 h-colon, 5 UC colon). A meta-cohort (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e) was included to further underscore the differential expression patterns in IBD. Pre-processing was done according to the authors performed with STAR v2.7.5a. The gene count table (accession: GSE197698) was retrieved and analysed identical as described above. Colonic organoid data was combined, regardless of colonic site noted in the metadata. Technical replicates were merged based on the metadata provided (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003ePathway analyses\u003c/h3\u003e\n\u003cp\u003eFor each group, two types of functional pathway analyses were performed using the R package clusterProfiler (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e) and the Reactome pathway database (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Over-enrichment analysis (ORA) was based on a list of DEGs upon SARS-CoV-2 infection with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Gene set enrichment analysis (GSEA) (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e) was based on all expressed genes pre-ranked by their \u003cem\u003eP\u003c/em\u003e value with the sign of LFC. The rank metric was calculated by the following formula:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:-{\\text{l}\\text{o}\\text{g}}_{10}\\left(P\\:\\text{v}\\text{a}\\text{l}\\text{u}\\text{e}\\right)\\times\\:\\frac{\\text{L}\\text{F}\\text{C}}{\\left|\\text{L}\\text{F}\\text{C}\\right|}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eGraphPad Prism and R were used for data analysis and imaging. All data are represented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD if not otherwise specified. Statistics of differential gene expression were calculated with the Wald test. Other statistical significance testing employed the Mann-Whitney-U-Test and 1way ANOVA with uncorrected Fisher's LSD. Correlation analyses employed Pearson correlation. \u003cem\u003eP\u003c/em\u003e values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant. Benjamini-Hochberg (BH) procedure with a False Discovery Rate (FDR)\u0026thinsp;\u0026lt;\u0026thinsp;0.1 was used to determine the adjusted \u003cem\u003eP\u003c/em\u003e values (\u003cem\u003ePadj\u003c/em\u003e) in the differential gene expression analysis.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eSARS-CoV-2 infection of ileum and colon organoids from IBD patients and controls\u003c/h2\u003e\u003cp\u003eIntestinal organoids were successfully cultivated from 5 patients with CD (CD ileum), 5 patients with UC (UC colon), as well as 6 healthy ileum (h-ileum) and 5 healthy colon (h-colon) controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). From the 3D organoids, 2D organoid monolayers were generated to model a luminal SARS-CoV-2 infection. Infection was performed with a SARS-CoV-2 strain (B.1.5/O/20A) isolated from a COVID-19 patient from the first pandemic wave (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Monolayers were infected with a MOI of 0.1 and collected 48 hours post-infection (p.i.) based on previous protocols (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Experimental set-up consisted of four organoid monolayers per patient, two infected and two uninfected allowing for paired downstream analyses. RNA-seq generated from organoid monolayers yielded on average 29\u0026nbsp;million reads per monolayer (SD\u0026thinsp;=\u0026thinsp;3.7 M). High correlation of RNA-seq reads from monolayer duplicates allowed for their pooling in the analyses process (Suppl. Figure\u0026nbsp;1).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eTissue origin followed by IBD status but not SARS-CoV-2 infection are the major discriminators in deep transcriptomic analyses\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePrincipal component analysis (PCA) based on gene counts separated organoid-derived monolayers most according to tissue origin (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). H-ileum clearly separated from h-colon and UC colon (PC1, 36.3% variation) while CD ileum samples were more dispersed and more similar to colon samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, Suppl. Figure\u0026nbsp;2A). SARS-CoV-2 infection induced only small variations in PCA. The major separators in PCA were genes differentiating small and large intestines, for example, the colon marker \u003cem\u003eMUC12\u003c/em\u003e or the small intestinal marker \u003cem\u003eAPOB\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). IBD samples were separated from healthy controls mostly by PC4 (5.61% variation) driven by differential gene expression of \u003cem\u003eUGT2B17\u003c/em\u003e, \u003cem\u003eAPOA4, CEACAM7\u003c/em\u003e and \u003cem\u003eNXPE1\u003c/em\u003e, known to be altered in IBD (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Organoids retained typical expression signatures of their original tissues including markers of stemness, enterocytes, Paneth cells, enteroendocrine cells and goblet cells, also evident in IBD organoids (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Interestingly, three CD ileum cases clustered together with colon samples, suggesting that CD can alter gene expression to a more colon-like phenotype in at least certain individuals (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Of note, the level of mitochondrial transcripts used as a surrogate marker for cell damage (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e) was not significantly different between the organoid groups (Suppl. Figure\u0026nbsp;2B).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eSARS-CoV-2 infection is highest in healthy ileum while IBD status does not increase viral loads\u003c/h2\u003e\u003cp\u003eWe sought for mild infection levels not to overburden organoids, which would likely lead to increased cell damage potentially obscuring assessment of transcriptional effects (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Viral transcripts derived from RNA-seq were evenly distributed along the SARS-CoV-2 genome (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). 16.76 viral gene counts (median; IQR\u0026thinsp;=\u0026thinsp;11.23\u0026ndash;26.40) compared to 14.75 \u0026times; 10\u003csup\u003e6\u003c/sup\u003e human gene counts (median; IQR\u0026thinsp;=\u0026thinsp;14.33 \u0026times; 10\u003csup\u003e6\u003c/sup\u003e- 15.60 \u0026times; 10\u003csup\u003e6\u003c/sup\u003e) were detected per infected single monolayer. Viral transcripts were most abundant in HC ileum, followed by CD ileum, HC colon and UC colon, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Using a SARS-CoV-2 standard and extrapolating viral loads by use of normalized RNA inputs in qPCR, between 0.003 to 39 viral copies per host cell were determined (suppl. Figure\u0026nbsp;3; see materials for calculation used). Variation of viral loads was higher in healthy organoids (suppl. Figure\u0026nbsp;3), but infection levels were less in IBD organoids. Furthermore, qPCR using human \u003cem\u003eACTB\u003c/em\u003e as a reference confirmed that IBD organoids showed lower infection than their healthy counterparts (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eHigher viral loads in the ileum might be because of the higher \u003cem\u003eACE2\u003c/em\u003e expression in the small intestine compared to colon (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). SARS-CoV-2 cell entry is facilitated by the host receptor ACE2 and the proteases CTSL, CTSB, furin, TMPRSS2/4/11D/13 and NRP1 (\u003cspan additionalcitationids=\"CR41 CR42 CR43 CR44\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). \u003cem\u003eACE2\u003c/em\u003e expression was highest in h-ileum (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Its expression in our organoid model mirrored the known decrease in the inflamed ileum of IBD patients, as well as differences reported between ileum and colon (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). Conversely, \u003cem\u003eACE2\u003c/em\u003e expression is reported to be increased in the colon upon inflammation (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e), which corresponds to a non-significant trend of increased \u003cem\u003eACE2\u003c/em\u003e expression in UC colon organoids compared to h-colon. \u003cem\u003eTMPRSS2\u003c/em\u003e expression, the major protease facilitating viral cell surface entry (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e), was significantly increased in CD ileum compared to h-ileum, again corroborating findings from CD patients (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). Expression of \u003cem\u003eCTSL\u003c/em\u003e and \u003cem\u003eCTSB\u003c/em\u003e, both proteases important for endosomal entry of the virus (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e), followed the same trend as \u003cem\u003eACE2\u003c/em\u003e. \u003cem\u003eTMPRSS4\u003c/em\u003e, \u003cem\u003eFURIN\u003c/em\u003e and \u003cem\u003eNRP1\u003c/em\u003e were similarly expressed across all groups, while \u003cem\u003eTMPRSS11D\u003c/em\u003e was not detectable (Suppl. Figure\u0026nbsp;4). Notably, SARS-CoV-2 infection did not significantly alter expression of any investigated entry gene in our model (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, Suppl. Figure\u0026nbsp;4). Correlation analysis of viral load and the expression of entry genes revealed a significant positive correlation of \u003cem\u003eACE2\u003c/em\u003e, \u003cem\u003eCTSL\u003c/em\u003e and \u003cem\u003eCTSB\u003c/em\u003e with viral load, but only in organoids from controls and not in IBD, suggesting that SARS-CoV-2 entry and propagation might be altered in IBD (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD, Suppl. Figure\u0026nbsp;5). In summary, our organoid model recapitulated the expression of SARS-CoV-2 entry genes in small and large intestines and showed changes typically occurring in IBD. However, although the IBD phenotype modulates the expression of entry genes, it does not increase SARS-CoV-2 infection levels.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eSARS-CoV-2 infection impacts gene expression differently depending on intestinal origin and IBD status\u003c/h2\u003e\u003cp\u003eOur study employed organoids from 21 different individuals. Because of the already inherent inter-individual heterogeneity, we used only moderately permissive thresholds (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.1) in downstream differential gene expression analyses (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). Notably, our study design allowed for the comparison of paired infected and uninfected organoids originating from the same individual reducing the influence of inter-patient variation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e); 995 differentially expressed genes (DEG) were found in h-ileum organoids upon infection, 802 DEGs in CD ileum, 760 in h-colon and 895 in UC colon (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, suppl. File 1). H-ileum showed most unique DEGs which is consistent with the dominant infection of this organoid group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, Suppl. Figure\u0026nbsp;6A, suppl. File 1). Organoids from healthy controls shared more DEGs than IBD organoids upon infection, indicating a greater inter-individual variation in IBD (Suppl. Figure\u0026nbsp;6B-C, suppl. File 1). Over-representation pathway analysis of DEGs (ORA; Reactome (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e)) revealed altered pathways of immunity, central cellular functions and signalling cascades upon infection. Surprisingly, enriched pathways were often shared between h-ileum and UC colon while depleted pathways seemed more specific for tissue type and IBD status (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, suppl. File 2). These findings were mirrored in the gene set enrichment analysis which calls also subtle gene expression changes (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e) (GSEA; Reactome; suppl. File 3, Suppl. Figure\u0026nbsp;7). Grouping the identified pathways into biological themes showed an enrichment of extracellular matrix organization, cell death, metabolism, immunity and signalling cascades upon infection (Suppl. Figure\u0026nbsp;8). Amongst these were FOXO-mediated transcription, TGF-β, EGF, MAPK and NOTCH signalling, which are implicated in the inflammatory response and known to be altered by coronavirus infection (\u003cspan additionalcitationids=\"CR51 CR52 CR53\" citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). Circadian clock-related genes showed a consistent and strong upregulation in all organoid groups, a process which controls host-virus interactions and which can be manipulated by viruses (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e) including SARS-CoV-2 (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e). Cell death pathways were stronger induced in h-ileum compared to CD ileum, consistent with the increased infection level in the former, however, they were also stronger induced in UC colon compared to h-colon despite a lower infection level in UC (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Amongst the top induced genes implicated in programmed cell death were \u003cem\u003eNET1\u003c/em\u003e (\u003cem\u003ePadj\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) binding to CARD and regulating NF-κB activation (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e) and \u003cem\u003eBIRC3\u003c/em\u003e (\u003cem\u003ePadj\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.029) a proposed IBD susceptibility gene regulating proinflammatory cytokine expression through NF-κB and MAPK pathways (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e). Thus, SARS-CoV-2 leads to a pronounced induction of cell death pathways specifically in UC (suppl. File 4). Coherent to this finding was also a difference in defence signalling pathways like interferon type I (alpha/beta) signalling, which was induced in UC colon but not in h-colon (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Taken together, SARS-CoV-2 infection influenced gene expression of organoids differently, depending on tissue origin and IBD status. H-ileum and UC colon showed many commonalities and UC colon likely reacts with increased epithelial stress upon SARS-CoV-2 infection.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eExpression of genes implicated in the cellular response to viral infection is intrinsically altered in IBD\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIBD is characterized by altered expression of genes implicated in immunity (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e). These alterations are central to the pathogenesis of IBD but might also influence the cellular reaction and susceptibility to infections. Comparison of uninfected IBD organoids to uninfected controls revealed 904 DEGs in CD ileum and 822 DEGs in UC colon (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003cem\u003ePadj\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1; Suppl. Figure\u0026nbsp;9, suppl. File 5). We next focused our analysis to genes implicated in the host defence against viruses, including the Gene Ontology gene sets \u0026lsquo;response to virus\u0026rsquo; (GO:0009615) and \u0026lsquo;type I interferon signalling pathway\u0026rsquo; (GO:0060337) with type III interferons and their receptors amounting 330 genes in total (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, Tab. S6). Of these genes, 44 were nominally differentially expressed (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in CD ileum compared to h-ileum, and 29 genes in UC colon compared to h-colon (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB, suppl. File 6). These genes could be functionally categorized into interferon-stimulated genes (ISGs), factors for recognition of viral RNA or with direct antiviral activity, factors regulating cellular defence mechanisms, cytokines, chemokines and their receptors, antigen presentation, autophagy and cell death (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC, suppl. File 6). Comparing uninfected IBD organoids to uninfected healthy controls revealed the viral RNA sensors \u003cem\u003eOASL\u003c/em\u003e (\u003cem\u003ePadj\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007) and \u003cem\u003eOAS1\u003c/em\u003e (\u003cem\u003ePadj\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015), which degrade viral RNA and activate the retinoic acid-inducible gene I (RIG-I) leading to a type I interferon (IFN) response (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e), significantly upregulated in CD ileum (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). \u003cem\u003eOAS1\u003c/em\u003e was shown to be protective against severe COVID-19 (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e). \u003cem\u003eBST2\u003c/em\u003e, a potent anti-SARS-CoV-2 factor inhibiting budding of virus particles from the cell surface (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e) showed also a trend towards upregulation in CD ileum (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006). Upregulated in uninfected CD was also \u003cem\u003eSEC14L1\u003c/em\u003e which impairs RIG-I signalling (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e) (\u003cem\u003ePadj\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Downregulated in uninfected CD ileum were \u003cem\u003eTKFC\u003c/em\u003e (syn.: DAK, \u003cem\u003ePadj\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009), a negative regulator of antiviral signalling (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e), and the heparan sulphate biosynthesis gene \u003cem\u003eEXT1\u003c/em\u003e (\u003cem\u003ePadj\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.031), which represents an ancillary SARS-CoV-2 entry factor (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e). Together, 5 out of 6 top changed response to virus genes in uninfected CD ileum compared to h-ileum likely impair SARS-CoV-2 infection (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). In uninfected UC colon compared to h-colon, we found the interferon regulatory transcription factor \u003cem\u003eIRF7\u003c/em\u003e (\u003cem\u003ePadj\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.010) and the antiviral and pro-apoptotic factor \u003cem\u003eXAF1\u003c/em\u003e (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e) (\u003cem\u003ePadj\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013) upregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB, suppl. File 6). \u003cem\u003eIRF7\u003c/em\u003e is crucial in propagating signalling cascades initiated by viral infection and normally shows low constitutive expression in enterocytes (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e). Interestingly, \u003cem\u003eMX2\u003c/em\u003e, an ISG with antiviral activity (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e) and reported to be induced in COVID-19 (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e), was also upregulated in UC colon (\u003cem\u003ePadj\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013) but borderline downregulated in CD ileum (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.022), indicating subtle differences between IBD phenotypes. Downregulated in uninfected UC colon was the apoptosis regulator \u003cem\u003eBCL2\u003c/em\u003e (\u003cem\u003ePadj\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011), known to be colitis-protective and whose viral homologs propagate infection by inhibiting premature apoptosis of infected cells (\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e). Notably, BCL2 inhibition is thought to counteract SARS-CoV-2 infection (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e). Also, borderline downregulated was \u003cem\u003eDDIT4\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003), which inhibits virus propagation by suppressing mTOR activity (\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e). Again, 4 out of 5 top changed response to virus genes likely counteract SARS-CoV-2 infection in baseline UC colon (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Notably, reanalysis of a meta-cohort comparing IBD organoids to small and large intestinal organoids from UC and CD patients and of healthy controls (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e) confirmed the induction of several viral response genes in IBD including \u003cem\u003eBST2\u003c/em\u003e, \u003cem\u003eOASL\u003c/em\u003e, \u003cem\u003eIFI6\u003c/em\u003e or \u003cem\u003eIFIT1\u003c/em\u003e (Suppl. Figure\u0026nbsp;10; suppl. File 7). In summary, several genes with antiviral action are induced in IBD intestinal epithelia. This might be a factor contributing to the decreased viral loads detected in IBD organoids. However, the expression of these response to virus genes are different between IBD phenotypes.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eExpression of genes implicated in the response to viral infection are different between ileum and colon and influenced by IBD status upon SARS-CoV-2 challenge\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe next assessed the effect of SARS-CoV-2 on the transcriptional response in organoids (Suppl. Figure\u0026nbsp;10, suppl. File 6). Infection changed 29 of the viral response genes in h-ileum, 13 in CD ileum, 12 in h-colon and 26 in UC colon (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). In CD ileum, 9 were up- and 4 downregulated and 3 of them were already altered in uninfected CD ileum. In UC colon, 25 were up- and 1 downregulated and 5 of them were already altered in uninfected UC colon (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB-C). Similar to findings from pathway analyses, h-ileum and UC colon showed a stronger antiviral response compared to CD ileum or h-colon. Among top induced genes upon infection were \u003cem\u003eIFIT1\u003c/em\u003e and \u003cem\u003eIFIT2\u003c/em\u003e, known to sequester 5'PPP-capped viral RNAs in host cells, thereby inhibiting virus amplification (\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e). Both genes were upregulated upon infection in h-ileum (\u003cem\u003eIFIT1 P\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002; \u003cem\u003eIFIT2\u003c/em\u003e, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0003) and UC colon (\u003cem\u003eIFIT1\u003c/em\u003e, P\u0026thinsp;=\u0026thinsp;0.0005; \u003cem\u003eIFIT2\u003c/em\u003e, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004). \u003cem\u003eIFIT1\u003c/em\u003e was already induced in uninfected UC colon compared to H colon (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005). In infected CD ileum, \u003cem\u003eIFIT1\u003c/em\u003e showed the opposite trend (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008). \u003cem\u003eIFIT2\u003c/em\u003e was induced in h-ileum upon infection (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0003), in addition to the pro-apoptotic mitophagy gene \u003cem\u003eBNIP3L\u003c/em\u003e (\u003cem\u003ePadj\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003). The consistent activation of \u003cem\u003eIFIT\u003c/em\u003e genes suggests that these viral RNA sensing and degrading factors are central in the intestinal epithelial response to SARS-CoV-2 infection. Two borderline induced genes in h-ileum but not in CD ileum upon infection were \u003cem\u003eITGB6\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.026), a possible alternative receptor for SARS-CoV-2 (\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e), and \u003cem\u003eSERINC3\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018), a known antiviral defence factor and a suggested risk gene for severe COVID-19 (\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e). Antigen presentation also seemed to be modified in IBD organoids. The HLA class I genes \u003cem\u003eHLA-B\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.034), a known IBD risk gene (\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e), and \u003cem\u003eHLA-H\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.036), were borderline induced in baseline CD ileum but were not further induced upon infection in this organoid group, while they showed borderline induction in infected h-ileum (\u003cem\u003eHLA-B\u003c/em\u003e, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009; \u003cem\u003eHLA-H\u003c/em\u003e, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006). Conversely, \u003cem\u003eHLA-G\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) was induced in UC colon upon infection but not in h-colon (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). The gene with the strongest induction in UC colon upon infection was \u003cem\u003eIL1Β\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0003). Interestingly, IL-1B is a downstream effector of the inflammasome and of regulated cell death pathways, the latter induced in UC colon organoids. Moreover, IL-1B is a cytokine that induces IGSs and is insensitive to viral immune escape strategies (\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e). As opposed to its beneficial effects against infection, it was recently shown to drive chronic intestinal inflammation in UC (\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSeveral response to virus genes were nominally upregulated in both UC colon and h-colon upon infection, however, certain genes were found to be induced upon infection only in UC colon but not h-colon, such as \u003cem\u003eIFIT1\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0005) and \u003cem\u003eDDIT4\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0004), which were already differentially expressed in baseline UC compared to h-colon. Infection with SARS-CoV-2 in h-colon triggered also the expression of a cytidine deaminase \u003cem\u003eAPOBEC1\u003c/em\u003e, known to inhibit viral replication (\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013), while it was already increased in uninfected baseline UC (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002).\u003c/p\u003e\u003cp\u003eIn summary, our comparative analyses showed that the transcriptional response of small and large intestinal epithelia to SARS-CoV-2 infection is altered in IBD. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e summarizes the main transcriptional alterations relevant to infection. In CD ileum, genes implicated in antigen presentation, direct antiviral activity, cytokine/chemokine signalling as well as regulated cell death and autophagy were already upregulated prior to infection. In UC colon compared to healthy colon, such an induction is less evident. Upon infection, h-ileum reacts stronger and/or preserves its transcriptional response to virus to a greater extent compared to CD ileum. This difference possibly originates from the higher viral load found in h-ileum. However, UC colon responds transcriptionally stronger upon SARS-CoV-2 challenge despite a slightly lower viral load compared to h-colon. This reaction includes the induction of cell death pathways and of \u003cem\u003eIL1Β\u003c/em\u003e expression, which is intriguing since this cytokine seems to drive inflammation in UC (\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e) and COVID-19 outcomes in UC patients seem to be worse compared to CD (\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe GI tract represents a niche for productive SARS-CoV-2 infection. IBD is characterized by altered immune reactions and, therefore, it is likely that SARS-CoV-2 impacts the GI tract of IBD patients differently compared to healthy individuals. In addition, genetic differences between CD and UC might differentially impact the response to SARS-CoV-2. We used small and large intestinal organoids from IBD patients and controls to comparatively assess susceptibility and transcriptional responses to SARS-CoV-2 infection. We showed that IBD phenotype does not increase SARS-CoV-2 infection levels, which is likely associated with a depressed expression of SARS-CoV-2 entry factors (e.g. \u003cem\u003eACE2\u003c/em\u003e and \u003cem\u003eCTSL/B\u003c/em\u003e) most prominent in CD ileum, but also with an upregulation of several antiviral defence genes potentially counteracting viral propagation in IBD. However, transcriptional differences between IBD phenotypes in response to SARS-CoV-2 exist, which might lead to varying disease outcomes between CD and UC patients.\u003c/p\u003e\u003cp\u003eACE2, the host receptor of SARS-CoV-2 (\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e), is expressed apically in enterocytes (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e) indicating that virus attachment and uptake via the lumen is a dominant infection route in the GI tract. This was modelled in our 2D organoids (\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e). SARS-CoV-2 entry factors are reported to be altered in IBD ileum (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e, \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e) and we recapitulated a reduced \u003cem\u003eACE2\u003c/em\u003e expression in CD ileum organoids compared to h-ileum, which correlates with reduced infection levels. In colon, \u003cem\u003eACE2\u003c/em\u003e expression increases with the inflammatory tone (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan additionalcitationids=\"CR89\" citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e) and we also noted an increase of \u003cem\u003eACE2\u003c/em\u003e expression in UC colon compared to h-colon. Despite that, the lowest infection levels were noted in this organoid group. The proteases TMPRSS2 and CTSL/B, which prime the S protein to facilitate binding to ACE2 (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e), are also reported to be altered in IBD (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e) and their expression was mostly recapitulated in our organoids. Entry genes were not transcriptionally changed by SARS-CoV-2 infection in our model, which is different to some other studies (\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e, \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e). Importantly, infection levels were not increased in our IBD organoids. We noticed a positive correlation of viral load with the expression of entry factors \u003cem\u003eACE2\u003c/em\u003e and \u003cem\u003eCTSL/B\u003c/em\u003e only in non-IBD organoids, indicating that infection levels and entry factors are functionally linked in healthy epithelia but this relation is possibly skewed in IBD.\u003c/p\u003e\u003cp\u003eWe cultured organoids from multiple IBD and non-IBD individuals which allowed us to at least partially capture the heterogeneity of IBD and identify the major transcriptional changes in response to SARS-CoV-2. Extracellular matrix organization (ECMO) was strongly enriched in all organoid groups. Besides being involved in infection processes (\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e), ECMO is crucial for the healing response after injury. However, this process can also lead to fibrosis when overstimulated, especially when auxiliary signalling pathways like TGF-β, EGFR and NOTCH, are co-activated (\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e), which was the case in CD organoids. In COVID-19, lung fibrosis emerges after acute lung damage and represents a severe functional impairment and also fibrosis of the kidneys are reported in COVID-19 (\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e, \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e). In parallel, intestinal fibrosis is a feared complication of IBD, especially in CD, appearing as a consequence of chronic inflammation (\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e). Therefore, SARS-CoV-2 infection of epithelia might initiate the development of fibrosis in the GI tract, which might aggravate IBD sequels.\u003c/p\u003e\u003cp\u003eInflammatory signalling pathways were also induced upon infection including interleukin-, TNF- and neurotrophin signalling. The last pathway was strongly induced in infected CD ileum. Besides activating pain, neurotrophins strengthen the epithelial barrier during viral infections, reduce apoptosis and induce TGF-β signalling (\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e, \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e). Neurotrophin signalling is reported to be induced in IBD (\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e, \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e), and it is likely that its activation might be implicated in the development of intestinal pain recognized in a substantial fraction of COVID-19 patients. Interestingly, chemokine signalling was only induced in non-IBD organoids, in contrast to interferon signalling, which was induced in h-ileum and UC colon upon infection. Similarly, apoptosis, necroptosis and pyroptosis were enriched only in h-ileum and UC colon. SARS-CoV-2 induces a carefully calibrated caspase-8 activation, leading to the activation of cell-death pathways, which support IL-1β secretion (\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e). Necroptosis, apoptosis and \u003cem\u003eIL1B\u003c/em\u003e expression were upregulated in UC colon upon infection despite the lowest viral load in this organoid group. This reaction was not evident in CD organoids. Recently, a specific subgroup of IBD patients were described, wherein IL-1β-driven neutrophil recruitment perpetuates chronic inflammation in UC. These patients lack responsiveness to anti-TNF therapy (\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e) but IL-1β blockage can efficiently suppress inflammation in a murine model (\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e). Future research should clarify whether UC patients might generally overreact upon SARS-CoV-2 infection compared to CD patients (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e) and whether this is driven by IL-1β. SARS-CoV-2 induces an interferon response in intestinal organoids that is stronger than from SARS-CoV (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e, \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e) and comparable to other intestinal viruses like rotavirus, enterovirus and norovirus (\u003cspan additionalcitationids=\"CR106\" citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e). SARS-CoV-2 induces low amounts of type I and III interferon transcripts in intestinal organoids (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e, \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e) and interferon transcripts were not detectable in our experiment. It was recently shown that interferons can be actively suppressed by SARS-CoV-2 (\u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e108\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBy comparing uninfected IBD to non-IBD organoids, we were able to discern the transcriptional changes of genes involved in the host response to virus already present in IBD. CD ileum organoids showed an induction of several antiviral response genes implicated in antigen presentation (e.g. \u003cem\u003eHLA-B\u003c/em\u003e), direct antiviral activity and recognition (e.g. \u003cem\u003eOAS1/L\u003c/em\u003e), cell death (e.g., \u003cem\u003eFADD\u003c/em\u003e) as well as induction of cyto- and chemokines (e.g. \u003cem\u003eTNF\u003c/em\u003e). In UC, there was also an induction of direct antiviral activity and recognition (e.g. \u003cem\u003eMX2, IFIT1\u003c/em\u003e) and important initiators of defence mechanisms (e.g. \u003cem\u003eIRF7\u003c/em\u003e), but in contrast to CD, response to virus-genes were more often decreased. Together with a reduced expression of SARS-CoV-2 entry factors, CD phenotype likely has a benefit and counteracts infection and viral propagation in ileal epithelial cells. Additionally, CD organoids were generally less reactive in response to the virus compared to UC. In UC, a strong induction of cell death pathways and \u003cem\u003eIL1β\u003c/em\u003e expression might suppress the virus, but can also lead to increased epithelial stress. Recently, viral persistence in the GI tract was reported in IBD and linked to postacute-COVID-19 symptoms (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Whether the transcriptional changes identified in our model are implicated in the development of long-term sequels, such as the post-acute COVID-19 syndrome, warrants further investigations.\u003c/p\u003e\u003cp\u003eOur study has several limitations. Although we used a larger number of individual organoids compared to other studies (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e), our numbers are still too small to capture the entire genetic heterogeneity prevalent in IBD and potentially influencing SARS-CoV-2 infection (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Consistent with an individual response to infection, a large variability in viral infection levels were noted, although variations were generally higher in healthy organoids. This variability compelled us to apply less stringent statistical thresholds not to obscure potentially relevant signals which might have otherwise been lost (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). However, this approach might have also increased the rate of spurious signals. Nevertheless, the consistent call of genes included in specific cellular reaction pathways suggests plausibility of our findings to a large extend. In addition, different infection doses and infection times would also have impacted the transcriptional response, all of these variations were not assessed. Moreover, it is important to note that immune cells, the connective tissue and IBD medications altogether likely influence COVID-19 intestinal pathology \u003cem\u003ein-vivo\u003c/em\u003e.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur model proved feasible to analyse virus-epithelial interactions of a newly emerging pathogen and to assess intestinal susceptibility in health and IBD. The model system of intestinal organoids represents intact epithelial tissue with no additional inflammatory stimuli but with the (epi-) genetic repertoire prevalent in IBD. The fact that infection with SARS-CoV-2 did not lead to higher viral burden in IBD organoids falls in line with the current clinical observation that IBD patients do not seem to be more prone to SARS-CoV-2 infection. Still, we observed differential responses to the virus in IBD organoids such as a stronger activation of neurotrophin and fibrosis-related pathways in CD ileum, and interferon signalling and regulated cell death in UC colon. Our study indicates that intestinal epithelial cells in IBD have a heightened antiviral state due to persisting changes in gene expression. Together with reductions of \u003cem\u003eACE2\u003c/em\u003e and \u003cem\u003eCTSL/B\u003c/em\u003e levels in CD ileum, this could protect against SARS-CoV-2 infection. On the other hand, as seen in UC colon marked with the lowest viral load, it could lead to a possibly detrimental overreaction of the IBD epithelium. These findings should prompt further investigations whether SARS-CoV-2 or other enteric viruses differently favour GI symptoms, IBD complications or long-term sequels in IBD subtypes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful for the technical support from Martina Loibner, Julia Kirchner and Iris Kreuzmann. The valuable contributions from the BSL-3 and the routine laboratories of Institute of Pathology and the Core Facility for Imaging at the Center for Medical Research at Medical University of Graz, as well as the Division of Internal Medicine of the Medical University of Innsbruck are highly acknowledged.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eG.G. and A. M. acknowledge funding from the Austrian Science Fund (FWF) [doi.org/10.55776/COE7 and DK-MOLIN W1241]. A.M. is supported by the Christian Doppler research foundation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, G.G., A.M., B.J.; Methodology, G.G., A.M., B.J., P.W.; Formal Analysis, B.J., S.B., P.W., P.S., M.H; Investigation, B.J., S.B., P.S., P.W., M.H., N.P., C.W., S.W., M.A., S.E., G.G.; Resources, G.G., A.M., B.T., K.Z.; Data Curation, B.J., S.B.; Writing \u0026ndash; Original Draft, B.J., G.G.; Writing \u0026ndash; Review \u0026amp; Editing, K.Z., A.M., P.W., , B.J., G.G.; Visualization, B.J., P.W., S.B.; Supervision, G.G., A.M., Funding Acquisition, G.G., A.M., B.T.,K.Z.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe RNAseq data have been deposited in NCBI-GEO under the accession number GSE208684. Further information and requests for resources should be directed to the lead contact Gregor Gorkiewicz ([email protected]).\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGuan Q. A Comprehensive Review and Update on the Pathogenesis of Inflammatory Bowel Disease. J Immunol Res. 2019;2019:7247238.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDorrington AM, Selinger CP, Parkes GC, Smith M, Pollok RC, Raine T. The Historical Role and Contemporary Use of Corticosteroids in Inflammatory Bowel Disease. J Crohns Colitis. 2020;14(9):1316\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCraviotto V, Furfaro F, Loy L, Zilli A, Peyrin-Biroulet L, Fiorino G, et al. Viral infections in inflammatory bowel disease: Tips and tricks for correct management. World J Gastroenterol. 2021;27(27):4276\u0026ndash;97.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAziz M, Fatima R, Haghbin H, Lee-Smith W, Nawras A. The Incidence and Outcomes of COVID-19 in IBD Patients: A Rapid Review and Meta-analysis. Inflamm Bowel Dis. 2020;26(10):e132\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDerikx L, Lantinga MA, de Jong DJ, van Dop WA, Creemers RH, Romkens TEH, et al. Clinical Outcomes of Covid-19 in Patients With Inflammatory Bowel Disease: A Nationwide Cohort Study. J Crohns Colitis. 2021;15(4):529\u0026ndash;39.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eUngaro RC, Brenner EJ, Agrawal M, Zhang X, Kappelman MD, Colombel JF, et al. Impact of Medications on COVID-19 Outcomes in Inflammatory Bowel Disease: Analysis of More Than 6000 Patients From an International Registry. Gastroenterology. 2022;162(1):316\u0026ndash;e95.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRoy S, Sheikh SZ, Furey TS. A machine learning approach identifies 5-ASA and ulcerative colitis as being linked with higher COVID-19 mortality in patients with IBD. Sci Rep. 2021;11(1):16522.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRicciuto A, Lamb CA, Benchimol EI, Walker GJ, Kennedy NA, Kuenzig ME, et al. Inflammatory Bowel Disease Clinical Activity is Associated with COVID-19 Severity Especially in Younger Patients. J Crohns Colitis. 2022;16(4):591\u0026ndash;600.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXiao F, Tang M, Zheng X, Liu Y, Li X, Shan H. Evidence for Gastrointestinal Infection of SARS-CoV-2. Gastroenterology. 2020;158(6):1831\u0026ndash;3. e3.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYeo C, Kaushal S, Yeo D. Enteric involvement of coronaviruses: is faecal-oral transmission of SARS-CoV-2 possible? Lancet Gastroenterol Hepatol. 2020;5(4):335\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLivanos AE, Jha D, Cossarini F, Gonzalez-Reiche AS, Tokuyama M, Aydillo T, et al. Intestinal Host Response to SARS-CoV-2 Infection and COVID-19 Outcomes in Patients With Gastrointestinal Symptoms. Gastroenterology. 2021;160(7):2435\u0026ndash;e5034.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWu Y, Guo C, Tang L, Hong Z, Zhou J, Dong X, et al. Prolonged presence of SARS-CoV-2 viral RNA in faecal samples. Lancet Gastroenterol Hepatol. 2020;5(5):434\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAmman F, Markt R, Endler L, Hupfauf S, Agerer B, Schedl A, et al. Viral variant-resolved wastewater surveillance of SARS-CoV-2 at national scale. Nat Biotechnol. 2022;40(12):1814\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCadwell K, Patel KK, Maloney NS, Liu TC, Ng AC, Storer CE, et al. Virus-plus-susceptibility gene interaction determines Crohn's disease gene Atg16L1 phenotypes in intestine. Cell. 2010;141(7):1135\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJostins L, Ripke S, Weersma RK, Duerr RH, McGovern DP, Hui KY, et al. Host-microbe interactions have shaped the genetic architecture of inflammatory bowel disease. Nature. 2012;491(7422):119\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAxelrad JE, Cadwell KH, Colombel JF, Shah SC. Systematic review: gastrointestinal infection and incident inflammatory bowel disease. Aliment Pharmacol Ther. 2020;51(12):1222\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNowak JK, Lindstrom JC, Kalla R, Ricanek P, Halfvarson J, Satsangi J, Age. Inflammation, and Disease Location Are Critical Determinants of Intestinal Expression of SARS-CoV-2 Receptor ACE2 and TMPRSS2 in Inflammatory Bowel Disease. Gastroenterology. 2020;159(3):1151\u0026ndash;4. e2.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBurgueno JF, Reich A, Hazime H, Quintero MA, Fernandez I, Fritsch J, et al. Expression of SARS-CoV-2 Entry Molecules ACE2 and TMPRSS2 in the Gut of Patients With IBD. Inflamm Bowel Dis. 2020;26(6):797\u0026ndash;808.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eUhlen M, Fagerberg L, Hallstrom BM, Lindskog C, Oksvold P, Mardinoglu A, et al. Proteomics. Tissue-based map of the human proteome. Science. 2015;347(6220):1260419.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGarg M, Angus PW, Burrell LM, Herath C, Gibson PR, Lubel JS. Review article: the pathophysiological roles of the renin-angiotensin system in the gastrointestinal tract. Aliment Pharmacol Ther. 2012;35(4):414\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang H, Penninger JM, Li Y, Zhong N, Slutsky AS. Angiotensin-converting enzyme 2 (ACE2) as a SARS-CoV-2 receptor: molecular mechanisms and potential therapeutic target. Intensive Care Med. 2020;46(4):586\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGarg M, Royce SG, Tikellis C, Shallue C, Batu D, Velkoska E, et al. Imbalance of the renin-angiotensin system may contribute to inflammation and fibrosis in IBD: a novel therapeutic target? Gut. 2020;69(5):841\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZollner A, Koch R, Jukic A, Pfister A, Meyer M, Rossler A, et al. Postacute COVID-19 is Characterized by Gut Viral Antigen Persistence in Inflammatory Bowel Diseases. Gastroenterology. 2022;163(2):495\u0026ndash;506. e8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZacharias M, Kashofer K, Wurm P, Regitnig P, Schutte M, Neger M, et al. Host and microbiome features of secondary infections in lethal covid-19. iScience. 2022;25(9):104926.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLamers MM, Beumer J, van der Vaart J, Knoops K, Puschhof J, Breugem TI, et al. SARS-CoV-2 productively infects human gut enterocytes. Science. 2020;369(6499):50\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEttayebi K, Crawford SE, Murakami K, Broughman JR, Karandikar U, Tenge VR, et al. Replication of human noroviruses in stem cell-derived human enteroids. Science. 2016;353(6306):1387\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMartin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. 2011. 2011;17(1):3.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29(1):15\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLove MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHan F, Lillard SJ. In-situ sampling and separation of RNA from individual mammalian cells. Anal Chem. 2000;72(17):4073\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJelinsky SA, Derksen M, Bauman E, Verissimo CS, van Dooremalen WTM, Roos JL, et al. Molecular and Functional Characterization of Human Intestinal Organoids and Monolayers for Modeling Epithelial Barrier. Inflamm Bowel Dis. 2023;29(2):195\u0026ndash;206.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 2012;16(5):284\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJassal B, Matthews L, Viteri G, Gong C, Lorente P, Fabregat A, et al. The reactome pathway knowledgebase. Nucleic Acids Res. 2020;48(D1):D498\u0026ndash;503.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSubramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005;102(43):15545\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMaddipatla SC, Kolachala VL, Venkateswaran S, Dodd AF, Pelia RS, Geem D, et al. Assessing Cellular and Transcriptional Diversity of Ileal Mucosa Among Treatment-Naive and Treated Crohn's Disease. Inflamm Bowel Dis. 2023;29(2):274\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchniers A, Goll R, Pasing Y, Sorbye SW, Florholmen J, Hansen T. Ulcerative colitis: functional analysis of the in-depth proteome. Clin Proteom. 2019;16:4.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSaiz-Gonzalo G, Hanrahan N, Rossini V, Singh R, Ahern M, Kelleher M, et al. Regulation of CEACAM Family Members by IBD-Associated Triggers in Intestinal Epithelial Cells, Their Correlation to Inflammation and Relevance to IBD Pathogenesis. Front Immunol. 2021;12:655960.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWeiser M, Simon JM, Kochar B, Tovar A, Israel JW, Robinson A, et al. Molecular classification of Crohn's disease reveals two clinically relevant subtypes. Gut. 2018;67(1):36\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIlicic T, Kim JK, Kolodziejczyk AA, Bagger FO, McCarthy DJ, Marioni JC, et al. Classification of low quality cells from single-cell RNA-seq data. Genome Biol. 2016;17:29.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang H, Kang Z, Gong H, Xu D, Wang J, Li Z, et al. Digestive system is a potential route of COVID-19: an analysis of single-cell coexpression pattern of key proteins in viral entry process. Gut. 2020;69(6):1010\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePadmanabhan P, Desikan R, Dixit NM. Targeting TMPRSS2 and Cathepsin B/L together may be synergistic against SARS-CoV-2 infection. PLoS Comput Biol. 2020;16(12):e1008461.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJohnson BA, Xie X, Bailey AL, Kalveram B, Lokugamage KG, Muruato A, et al. Loss of furin cleavage site attenuates SARS-CoV-2 pathogenesis. Nature. 2021;591(7849):293\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZang R, Gomez Castro MF, McCune BT, Zeng Q, Rothlauf PW, Sonnek NM et al. TMPRSS2 and TMPRSS4 promote SARS-CoV-2 infection of human small intestinal enterocytes. Sci Immunol. 2020;5(47).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKishimoto M, Uemura K, Sanaki T, Sato A, Hall WW, Kariwa H et al. TMPRSS11D and TMPRSS13 Activate the SARS-CoV-2 Spike Protein. Viruses. 2021;13(3).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDaly JL, Simonetti B, Klein K, Chen KE, Williamson MK, Anton-Plagaro C, et al. Neuropilin-1 is a host factor for SARS-CoV-2 infection. Science. 2020;370(6518):861\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePotdar AA, Dube S, Naito T, Li K, Botwin G, Haritunians T, et al. Altered Intestinal ACE2 Levels Are Associated With Inflammation, Severe Disease, and Response to Anti-Cytokine Therapy in Inflammatory Bowel Disease. Gastroenterology. 2021;160(3):809\u0026ndash;22. e7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSuarez-Farinas M, Tokuyama M, Wei G, Huang R, Livanos A, Jha D, et al. Intestinal Inflammation Modulates the Expression of ACE2 and TMPRSS2 and Potentially Overlaps With the Pathogenesis of SARS-CoV-2-related Disease. Gastroenterology. 2021;160(1):287\u0026ndash;e30120.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhao MM, Yang WL, Yang FY, Zhang L, Huang WJ, Hou W, et al. Cathepsin L plays a key role in SARS-CoV-2 infection in humans and humanized mice and is a promising target for new drug development. Signal Transduct Target Ther. 2021;6(1):134.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKarakasheva TA, Zhou Y, Xie HM, Soto GE, Johnson TD, Stoltz MA, et al. Patient-derived Colonoids From Disease-spared Tissue Retain Inflammatory Bowel Disease-specific Transcriptomic Signatures. Gastro Hep Adv. 2023;2(6):830\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDenney L, Branchett W, Gregory LG, Oliver RA, Lloyd CM. Epithelial-derived TGF-beta1 acts as a pro-viral factor in the lung during influenza A infection. Mucosal Immunol. 2018;11(2):523\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlsamman AM, Zayed H. The transcriptomic profiling of SARS-CoV-2 compared to SARS, MERS, EBOV, and H1N1. PLoS ONE. 2020;15(12):e0243270.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFerreira-Gomes M, Kruglov A, Durek P, Heinrich F, Tizian C, Heinz GA, et al. SARS-CoV-2 in severe COVID-19 induces a TGF-beta-dominated chronic immune response that does not target itself. Nat Commun. 2021;12(1):1961.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVagapova ER, Lebedev TD, Prassolov VS. Viral fibrotic scoring and drug screen based on MAPK activity uncovers EGFR as a key regulator of COVID-19 fibrosis. Sci Rep. 2021;11(1):11234.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBaindara P, Sarker MB, Earhart AP, Mandal SM, Schrum AG. NOTCH signaling in COVID-19: a central hub controlling genes, proteins, and cells that mediate SARS-CoV-2 entry, the inflammatory response, and lung regeneration. Front Cell Infect Microbiol. 2022;12:928704.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBorrmann H, McKeating JA, Zhuang X. The Circadian Clock and Viral Infections. J Biol Rhythms. 2021;36(1):9\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTang X, Xue D, Zhang T, Nilsson-Payant BE, Carrau L, Duan X, et al. A multi-organoid platform identifies CIART as a key factor for SARS-CoV-2 infection. Nat Cell Biol. 2023;25(3):381\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVessichelli M, Ferravante A, Zotti T, Reale C, Scudiero I, Picariello G, et al. Neuroepithelial transforming gene 1 (Net1) binds to caspase activation and recruitment domain (CARD)- and membrane-associated guanylate kinase-like domain-containing (CARMA) proteins and regulates nuclear factor kappaB activation. J Biol Chem. 2012;287(17):13722\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEguchi R, Karim MB, Hu P, Sato T, Ono N, Kanaya S, et al. An integrative network-based approach to identify novel disease genes and pathways: a case study in the context of inflammatory bowel disease. BMC Bioinformatics. 2018;19(1):264.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHolgersen K, Kutlu B, Fox B, Serikawa K, Lord J, Hansen AK, et al. High-resolution gene expression profiling using RNA sequencing in patients with inflammatory bowel disease and in mouse models of colitis. J Crohns Colitis. 2015;9(6):492\u0026ndash;506.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhu J, Zhang Y, Ghosh A, Cuevas RA, Forero A, Dhar J, et al. Antiviral activity of human OASL protein is mediated by enhancing signaling of the RIG-I RNA sensor. Immunity. 2014;40(6):936\u0026ndash;48.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhou S, Butler-Laporte G, Nakanishi T, Morrison DR, Afilalo J, Afilalo M, et al. A Neanderthal OAS1 isoform protects individuals of European ancestry against COVID-19 susceptibility and severity. Nat Med. 2021;27(4):659\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDolskiy AA, Bodnev SA, Nazarenko AA, Smirnova AM, Pyankova OG, Matveeva AK, et al. Deletion of BST2 Cytoplasmic and Transmembrane N-Terminal Domains Results in SARS-CoV, SARS-CoV-2, and Influenza Virus Production Suppression in a Vero Cell Line. Front Mol Biosci. 2020;7:616798.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi MT, Di W, Xu H, Yang YK, Chen HW, Zhang FX, et al. Negative regulation of RIG-I-mediated innate antiviral signaling by SEC14L1. J Virol. 2013;87(18):10037\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDiao F, Li S, Tian Y, Zhang M, Xu LG, Zhang Y, et al. Negative regulation of MDA5- but not RIG-I-mediated innate antiviral signaling by the dihydroxyacetone kinase. Proc Natl Acad Sci U S A. 2007;104(28):11706\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBaggen J, Persoons L, Vanstreels E, Jansen S, Van Looveren D, Boeckx B, et al. Genome-wide CRISPR screening identifies TMEM106B as a proviral host factor for SARS-CoV-2. Nat Genet. 2021;53(4):435\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHan Y, Bai X, Liu S, Zhu J, Zhang F, Xie L, et al. XAF1 Protects Host against Emerging RNA Viruses by Stabilizing IRF1-Dependent Antiviral Immunity. J Virol. 2022;96(17):e0077422.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCiancanelli MJ, Huang SX, Luthra P, Garner H, Itan Y, Volpi S, et al. Infectious disease. Life-threatening influenza and impaired interferon amplification in human IRF7 deficiency. Science. 2015;348(6233):448\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu Z, Pan Q, Ding S, Qian J, Xu F, Zhou J, et al. The interferon-inducible MxB protein inhibits HIV-1 infection. Cell Host Microbe. 2013;14(4):398\u0026ndash;410.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBizzotto J, Sanchis P, Abbate M, Lage-Vickers S, Lavignolle R, Toro A, et al. SARS-CoV-2 Infection Boosts MX1 Antiviral Effector in COVID-19 Patients. iScience. 2020;23(10):101585.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePolster BM, Pevsner J, Hardwick JM. Viral Bcl-2 homologs and their role in virus replication and associated diseases. Biochim Biophys Acta. 2004;1644(2\u0026ndash;3):211\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMizushima T, Arakawa S, Sanada Y, Yoshino I, Miyazaki D, Urushima H, et al. Inhibition of epithelial cell death by Bcl-2 improved chronic colitis in IL-10 KO mice. Am J Pathol. 2013;183(6):1936\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKim YJ, Witwit H, Cubitt B, de la Torre JC. Inhibitors of Anti-apoptotic Bcl-2 Family Proteins Exhibit Potent and Broad-Spectrum Anti-mammarenavirus Activity via Cell Cycle Arrest at G0/G1 Phase. J Virol. 2021;95(24):e0139921.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTiwarekar V, Wohlfahrt J, Fehrholz M, Scholz CJ, Kneitz S, Schneider-Schaulies J. APOBEC3G-Regulated Host Factors Interfere with Measles Virus Replication: Role of REDD1 and Mammalian TORC1 Inhibition. J Virol. 2018;92(17).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuynh H, Levitz R, Huang R, Kahn JS. mTOR kinase is a therapeutic target for respiratory syncytial virus and coronaviruses. Sci Rep. 2021;11(1):24442.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePichlmair A, Lassnig C, Eberle CA, Gorna MW, Baumann CL, Burkard TR, et al. IFIT1 is an antiviral protein that recognizes 5'-triphosphate RNA. Nat Immunol. 2011;12(7):624\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi D, Swaminathan S. Human IFIT proteins inhibit lytic replication of KSHV: A new feed-forward loop in the innate immune system. PLoS Pathog. 2019;15(2):e1007609.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSigrist CJ, Bridge A, Le Mercier P. A potential role for integrins in host cell entry by SARS-CoV-2. Antiviral Res. 2020;177:104759.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXu S, Zheng Z, Pathak JL, Cheng H, Zhou Z, Chen Y, et al. The Emerging Role of the Serine Incorporator Protein Family in Regulating Viral Infection. Front Cell Dev Biol. 2022;10:856468.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLee JC, Biasci D, Roberts R, Gearry RB, Mansfield JC, Ahmad T, et al. Genome-wide association study identifies distinct genetic contributions to prognosis and susceptibility in Crohn's disease. Nat Genet. 2017;49(2):262\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOrzalli MH, Smith A, Jurado KA, Iwasaki A, Garlick JA, Kagan JC. An Antiviral Branch of the IL-1 Signaling Pathway Restricts Immune-Evasive Virus Replication. Mol Cell. 2018;71(5):825\u0026ndash;40. e6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFriedrich M, Pohin M, Jackson MA, Korsunsky I, Bullers SJ, Rue-Albrecht K, et al. IL-1-driven stromal-neutrophil interactions define a subset of patients with inflammatory bowel disease that does not respond to therapies. Nat Med. 2021;27(11):1970\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiso M, Verna G, Cavalcanti E, De Santis S, Armentano R, Tafaro A, et al. Interleukin 1beta Blockade Reduces Intestinal Inflammation in a Murine Model of Tumor Necrosis Factor-Independent Ulcerative Colitis. Cell Mol Gastroenterol Hepatol. 2022;14(1):151\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGonzalez MC, Suspene R, Henry M, Guetard D, Wain-Hobson S, Vartanian JP. Human APOBEC1 cytidine deaminase edits HBV DNA. Retrovirology. 2009;6:96.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSingh AK, Jena A, Kumar MP, Sharma V, Sebastian S. Risk and outcomes of coronavirus disease in patients with inflammatory bowel disease: A systematic review and meta-analysis. United Eur Gastroenterol J. 2021;9(2):159\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHoffmann M, Kleine-Weber H, Schroeder S, Kruger N, Herrler T, Erichsen S, et al. SARS-CoV-2 Cell Entry Depends on ACE2 and TMPRSS2 and Is Blocked by a Clinically Proven Protease Inhibitor. Cell. 2020;181(2):271\u0026ndash;80. e8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGuo Y, Wang B, Gao H, Gao L, Hua R, Xu JD. ACE2 in the Gut: The Center of the 2019-nCoV Infected Pathology. Front Mol Biosci. 2021;8:708336.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHikmet F, Mear L, Edvinsson A, Micke P, Uhlen M, Lindskog C. The protein expression profile of ACE2 in human tissues. Mol Syst Biol. 2020;16(7):e9610.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi XZ, Qiu Y, Jeffery L, Liu F, Feng R, He JS, et al. Down-Regulation of Colonic ACE2 Expression in Patients With Inflammatory Bowel Disease Responding to Anti-TNF Therapy: Implications for COVID-19. Front Med (Lausanne). 2020;7:613475.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVerstockt B, Verstockt S, Abdu Rahiman S, Ke BJ, Arnauts K, Cleynen I, et al. Intestinal Receptor of SARS-CoV-2 in Inflamed IBD Tissue Seems Downregulated by HNF4A in Ileum and Upregulated by Interferon Regulating Factors in Colon. J Crohns Colitis. 2021;15(3):485\u0026ndash;98.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eToyonaga T, Araba KC, Kennedy MM, Keith BP, Wolber EA, Beasley C, et al. Increased colonic expression of ACE2 associates with poor prognosis in Crohn's disease. Sci Rep. 2021;11(1):13533.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMenzel K, Hausmann M, Obermeier F, Schreiter K, Dunger N, Bataille F, et al. Cathepsins B, L and D in inflammatory bowel disease macrophages and potential therapeutic effects of cathepsin inhibition in vivo. Clin Exp Immunol. 2006;146(1):169\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZiegler CGK, Allon SJ, Nyquist SK, Mbano IM, Miao VN, Tzouanas CN, et al. SARS-CoV-2 Receptor ACE2 Is an Interferon-Stimulated Gene in Human Airway Epithelial Cells and Is Detected in Specific Cell Subsets across Tissues. Cell. 2020;181(5):1016\u0026ndash;e3519.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTriana S, Metz-Zumaran C, Ramirez C, Kee C, Doldan P, Shahraz M, et al. Single-cell analyses reveal SARS-CoV-2 interference with intrinsic immune response in the human gut. Mol Syst Biol. 2021;17(4):e10232.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTomlin H, Piccinini AM. A complex interplay between the extracellular matrix and the innate immune response to microbial pathogens. Immunology. 2018;155(2):186\u0026ndash;201.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuang WJ, Tang XX. Virus infection induced pulmonary fibrosis. J Transl Med. 2021;19(1):496.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJansen J, Reimer KC, Nagai JS, Varghese FS, Overheul GJ, de Beer M, et al. SARS-CoV-2 infects the human kidney and drives fibrosis in kidney organoids. Cell Stem Cell. 2022;29(2):217\u0026ndash;31. e8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZidar N, Langner C, Jerala M, Bostjancic E, Drobne D, Tomazic A. Pathology of Fibrosis in Crohn's Disease-Contribution to Understanding Its Pathogenesis. Front Med (Lausanne). 2020;7:167.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHagbom M, De Faria FM, Winberg ME, Westerberg S, Nordgren J, Sharma S et al. Neurotrophic Factors Protect the Intestinal Barrier from Rotavirus Insult in Mice. mBio. 2020;11(1).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMorel L, Domingues O, Zimmer J, Michel T. Revisiting the Role of Neurotrophic Factors in Inflammation. Cells. 2020;9(4).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003edi Mola FF, Friess H, Zhu ZW, Koliopanos A, Bley T, Di Sebastiano P, et al. Nerve growth factor and Trk high affinity receptor (TrkA) gene expression in inflammatory bowel disease. Gut. 2000;46(5):670\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSochal M, Malecka-Panas E, Gabryelska A, Fichna J, Talar-Wojnarowska R, Szmyd B, et al. Brain-derived neurotrophic factor is elevated in the blood serum of Crohn's disease patients, but is not influenced by anti-TNF-alpha treatment-A pilot study. Neurogastroenterol Motil. 2021;33(6):e13978.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi S, Zhang Y, Guan Z, Li H, Ye M, Chen X, et al. SARS-CoV-2 triggers inflammatory responses and cell death through caspase-8 activation. Signal Transduct Target Ther. 2020;5(1):235.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhao X, Li C, Liu X, Chiu MC, Wang D, Wei Y, et al. Human Intestinal Organoids Recapitulate Enteric Infections of Enterovirus and Coronavirus. Stem Cell Rep. 2021;16(3):493\u0026ndash;504.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStanifer ML, Pervolaraki K, Boulant S. Differential Regulation of Type I and Type III Interferon Signaling. Int J Mol Sci. 2019;20(6).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDrummond CG, Bolock AM, Ma C, Luke CJ, Good M, Coyne CB. Enteroviruses infect human enteroids and induce antiviral signaling in a cell lineage-specific manner. Proc Natl Acad Sci U S A. 2017;114(7):1672\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSaxena K, Simon LM, Zeng XL, Blutt SE, Crawford SE, Sastri NP, et al. A paradox of transcriptional and functional innate interferon responses of human intestinal enteroids to enteric virus infection. Proc Natl Acad Sci U S A. 2017;114(4):E570\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHosmillo M, Chaudhry Y, Nayak K, Sorgeloos F, Koo BK, Merenda A et al. Norovirus Replication in Human Intestinal Epithelial Cells Is Restricted by the Interferon-Induced JAK/STAT Signaling Pathway and RNA Polymerase II-Mediated Transcriptional Responses. mBio. 2020;11(2).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShemesh M, Aktepe TE, Deerain JM, McAuley JL, Audsley MD, David CT, et al. SARS-CoV-2 suppresses IFNbeta production mediated by NSP1, 5, 6, 15, ORF6 and ORF7b but does not suppress the effects of added interferon. PLoS Pathog. 2021;17(8):e1009800.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"SARS-CoV-2, intestinal organoids, RNA sequencing, inflammatory bowel diseases, Crohn’s disease, ulcerative colitis, transcriptional response to viral infection, IL1B","lastPublishedDoi":"10.21203/rs.3.rs-8029502/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8029502/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eIBD is characterized by altered immune reactions and infections are thought to trigger chronic inflammation in IBD. The gut represents a productive reservoir for SARS-CoV-2 and the aforementioned factors together with immunosuppression used to treat IBD are likely influencing the outcomes of IBD patients with COVID-19.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe used large and small intestinal organoids from ulcerative colitis and Crohn's disease patients and controls to comparatively assess infection levels and transcriptional response of the gut epithelium during SARS-CoV-2 infection.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eOur analysis showed that IBD epithelia exhibit reduced viral loads compared to controls associated with a reduced expression of SARS-CoV-2 entry factors including the host receptor ACE2. Moreover, several genes implicated in the epithelial response to viral infection are intrinsically altered in IBD potentially counteracting viral propagation. Notably, differences between IBD phenotypes exist wherein ulcerative colitis represents with induced cell death pathways and increased \u003cem\u003eIL1B\u003c/em\u003e expression despite lower viral loads suggestive of increased epithelial stress.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eAltogether our analysis shows that the IBD epithelium is not more prone to SARS-CoV-2 infection and that several antiviral response genes are intrinsically activated in IBD. Moreover, ulcerative colitis and Crohn's disease exhibit specific transcriptional differences which might explain the differing COVID-19 outcomes between IBD phenotypes.\u003c/p\u003e","manuscriptTitle":"Reduced SARS-CoV-2 infection levels and pathotype specific altered antiviral transcriptional response in IBD intestinal organoids","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-26 06:27:44","doi":"10.21203/rs.3.rs-8029502/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4dd39090-bc93-4fb8-9690-a02383cc00d7","owner":[],"postedDate":"November 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-16T15:09:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-26 06:27:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8029502","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8029502","identity":"rs-8029502","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}