Identification of differentially expressed genes in ulcerative colitis and verification in a colitis mouse model by bioinformatics analyses

Evaluating the significance of ECSCR in the diagnosis of ulcerative colitis and drug efficacy assessment

Background

The main challenge in diagnosing and treating ulcerative colitis (UC) has prompted this study to discover useful biomarkers and understand the underlying molecular mechanisms.

Methods

In this study, transcriptomic data from intestinal mucosal biopsies underwent Robust Rank Aggregation (RRA) analysis to identify differential genes. These genes intersected with UC key genes from Weighted Gene Co-expression Network Analysis (WGCNA). Machine learning identified UC signature genes, aiding predictive model development. Validation involved external data for diagnostic, progression, and drug efficacy assessment, along with ELISA testing of clinical serum samples.

Results

RRA integrative analysis identified 251 up-regulated and 211 down-regulated DEGs intersecting with key UC genes in WGCNA, yielding 212 key DEGs. Subsequently, five UC signature biomarkers were identified by machine learning based on the key DEGs-THY1, SLC6A14, ECSCR, FAP, and GPR109B. A logistic regression model incorporating these five genes was constructed. The AUC values for the model set and internal validation data were 0.995 and 0.959, respectively. Mechanistically, activation of the IL-17 signaling pathway, TNF signaling pathway, PI3K-Akt signaling pathway in UC was indicated by KEGG and GSVA analyses, which were positively correlated with the signature biomarkers. Additionally, the expression of the signature biomarkers was strongly correlated with various UC types and drug efficacy in different datasets. Notably, ECSCR was found to be upregulated in UC serum and exhibited a positive correlation with neutrophil levels in UC patients.

Conclusions

THY1, SLC6A14, ECSCR, FAP, and GPR109B can serve as potential biomarkers of UC and are closely related to signaling pathways associated with UC progression. The discovery of these markers provides valuable information for understanding the molecular mechanisms of UC.

Differential Impact of Smoking on Methylome and Transcriptome in Crohn's Disease and Ulcerative Colitis

BACKGROUND

Smoking is an environmental factor that differentially impacts Crohn's disease (CD) and ulcerative colitis (UC). The mechanism of impact of smoking on disease risk and clinical outcomes remains to be established.

METHODS

This study used a prospective cohort of patients with CD or UC. Self-reported smoking status was validated using serum cotinine measurement. We profiled methylation changes in peripheral blood using the Illumina Methylation BeadChip. Transcriptomic profiling was performed on ileal and colonic tissue using an Illumina TruSeq platform. We compared the methylation and transcriptional changes in current, former, and never smokers stratified by disease type.

RESULTS

Our cohort included 200 patients with CD or UC with methylation profiles and 160 with transcriptomic data. The mean serum cotinine level was higher in current compared with former or never smokers. Epigenetic changes common to both CD and UC included hypomethylation at AHRR. Smoking-associated MGAT3 hypomethylation was associated with severe disease course only in UC, while IER3 hypomethylation was associated with worse course only in CD. Smoking downregulated several inflammatory pathways in UC. Current smoking in CD but not in UC was associated with upregulation of several genes mediating Paneth cell function. Genes with opposite direction of effects in CD and UC include HSD3B2 and GSTA1.

CONCLUSIONS

Our findings suggest both common and differential effects of cigarette smoking on CD and UC. Paneth cell dysfunction may mediate adverse impact of smoking on CD. Bile acid and oxidative stress pathways may be relevant for the differential effect of smoking on CD and UC.

Management of ulcerative colitis by dichloroacetate: Impact on NFATC1/NLRP3/IL1B signaling based on bioinformatics analysis combined with in vivo experimental verification

The inflammatory response in ulcerative colitis (UC) could be relieved by the conventional immunomodulatory agents; 5-aminosalicylic acid, corticosteroids, or azathioprine. However, the low remission rates and the intolerance to these agents necessitate investigation of gene expression signature in UC that could influence the therapeutic efficacy of drugs, as well as the interference with persistence genes by novel therapeutic option. Three microarray datasets (GSE66407, GSE38713 and GSE14580) from the NCBI-GEO database were utilized. Differentially expressed genes between samples of patients with UC and healthy ones were analyzed using R software. In addition, in vivo study using oxazolone-induced UC in BALB/c mice was carried out to investigate the proposed therapeutic efficacy of dichloroacetate (DCA). The bioinformatics analysis revealed the persistence of NLRP3, NFATC1, and IL1B in UC despite treatment with common therapeutic agents. DCA administration to oxazolone-treated mice showed remarkable interference with those persistence genes. Western blotting analysis for NLRP3, NFATC1, nuclear/total NF-κB, and cleaved caspase-1 revealed the ability of DCA to reduce the expression levels of these proteins in oxazolone-treated mice. Additionally, the inflammatory cytokines IL-1β and IL-13 were reduced in colonic tissue by DCA treatment. The therapeutic efficacy of DCA was further confirmed by the apparent reduction in histopathological scoring, disease activity index, and the normalization of colon length. Therefore, DCA could be suggested as a novel and promising therapeutic option in UC based on its ability to interfere with the persistence of NFATC1/NLRP3/IL1B signaling. That merits further safety/toxicological pre-clinical assessment and update of bioavailability/metabolism data prior to clinical investigation.

Network pharmacology‑based investigation of potential targets of triptonodiol acting on non-small-cell lung cancer

BACKGROUND

Triptonodiol is a very promising antitumor drug candidate extracted from the Chinese herbal remedy Tripterygium wilfordii Hook. F., and related studies are underway.

METHODS

To explore the mechanism of triptonodiol for lung cancer treatment, we used network pharmacology, molecular docking, and ultimately protein validation. Gene ontology (GO) analysis and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway enrichment analysis were performed through the David database. Molecular docking was performed using PyMoL2.3.0 and AutoDock Vina software. After screening, the major targets of triptonodiol were identified for the treatment of lung cancer. Target networks were established, Protein-protein interaction (PPI) network topology was analyzed, then KEGG pathway enrichment analysis was performed. Useful proteins were screened by survival analysis, and Western blot analysis was performed.

RESULTS

Triptonodiol may regulate cell proliferation, drug resistance, metastasis, anti-apoptosis, etc., by acting on glycogen synthase kinase 3 beta (GSK3B), protein kinase C (PKC), p21-activated kinase (PAK), and other processes. KEGG pathway enrichment analysis showed that these targets were associated with tumor, erythroblastic oncogene B (ErbB) signaling, protein phosphorylation, kinase activity, etc. Molecular docking showed that the target protein GSK has good binding activity to the main active component of triptonodiol. The protein abundance of GSK3B was significantly downregulated in non-small-cell lung cancer cells H1299 and A549 treated with triptonodiol for 24 h.

CONCLUSION

The cellular-level studies combined with network pharmacology and molecular docking approaches provide new ideas for the development and therapeutic application of triptonodiol, and identify it as a potential GSK inhibitor.

A novel therapeutic approach to prevent Helicobacter pylori induced gastric cancer using networking biology, molecular docking, and simulation approaches

Helicobacter pylori infects 50% of the world population and in 80% of cases, the infection progresses to the point where an ulcer develops leading to gastric cancer (GC). This study aimed to prevent GC by predicting Hub genes that are inducing GC. Furthermore, the study objective was to screen inhibitory molecules that block the function of predicted genes through several biophysical approaches. These proteins, such as Mucin 4 (MUC4) and Baculoviral IAP repeat containing 3 (BIRC3), had LogFC values of 2.28 and 3.39, respectively, and were found to be substantially expressed in those who had H. pylori infection. The MUC4 and BIRC3 inhibit apoptosis of infected cells and promote cancerous cell survival. The proteins were examined for their Physico-chemical characteristics, 3D structure and secondary structure analysis, solvent assessable surface area (SASA), active site identification, and network analysis. The MUC4 and BIRC3 expression was inhibited by docking eighty different compounds collected from the ZINC database. Fifty-seven compounds were successfully docked into the active site resulting in the lowest binding energy scores. The ZINC585267910 and ZINC585268691 compounds showed the lowest binding energy of -8.5 kcal/mol for MUC4 and -7.1 kcal/mol for BIRC3, respectively, and were considered best-docked solutions for molecular dynamics simulations. The mean root mean square deviation (RMSD) value for the ZINC585267910-MUC4 complex was 0.86 Å and the ZINC585268691-BIRC3 complex was 1.01 Å. The net MM/GBSA energy value of the ZINC585267910-MUC4 complex estimated was -46.84 kcal/mol and that of the ZINC585268691-BIRC3 complex was -44.84 kcal/mol. In a nutshell, the compounds might be investigated further as an inhibitor of the said proteins to stop the progress of GC induced by H. pylori.Communicated by Ramaswamy H. Sarma.

Prediction of anti-TNF therapy failure in ulcerative colitis patients by ensemble machine learning: A prospective study

Nowadays, anti-TNF therapy remarkably improves the medical management of ulcerative colitis (UC), but approximately 40 % of patients do not respond to this treatment. In this study, we used 79 anti-TNF-naive patients with moderate-to-severe UC from four cohorts to discover alternative therapeutic targets and develop a personalized medicine approach that can diagnose UC non-responders (UCN) prior to receiving anti-TNF therapy. To this end, two microarray data series were integrated to create a discovery cohort with 35 UC samples. A comprehensive gene expression and functional analysis was performed and identified 313 significantly altered genes, among which IL6 and INHBA were highlighted as overexpressed genes in the baseline mucosal biopsies of UCN, whose cooperation may lead to a decrease in the Tregs population. Besides, screening the abundances of immune cell subpopulations showed neutrophils' accumulation increasing the inflammation. Furthermore, the correlation of KRAS signaling activation with unresponsiveness to anti-TNF mAb was observed using network analysis. Using 50x repeated 10-fold cross-validation LASSO feature selection and a stack ensemble machine learning algorithm, a five-mRNA prognostic panel including IL13RA2, HCAR3, CSF3, INHBA, and MMP1 was introduced that could predict the response of UC patients to anti-TNF antibodies with an average accuracy of 95.3 %. The predictive capacity of the introduced biomarker panel was also validated in two independent cohorts (44 UC patients). Moreover, we presented a distinct immune cell landscape and gene signature for UCN to anti-TNF drugs and further studies should be considered to make this predictive biomarker panel and therapeutic targets applicable in the clinical setting.

Computer-Aided Imaging Analysis of Probe-Based Confocal Laser Endomicroscopy With Molecular Labeling and Gene Expression Identifies Markers of Response to Biological Therapy in IBD Patients: The Endo-Omics Study

BACKGROUND

We aimed to predict response to biologics in inflammatory bowel disease (IBD) using computerized image analysis of probe confocal laser endomicroscopy (pCLE) in vivo and assess the binding of fluorescent-labeled biologics ex vivo. Additionally, we investigated genes predictive of anti-tumor necrosis factor (TNF) response.

METHODS

Twenty-nine patients (15 with Crohn's disease [CD], 14 with ulcerative colitis [UC]) underwent colonoscopy with pCLE before and 12 to 14 weeks after starting anti-TNF or anti-integrin α4β7 therapy. Biopsies were taken for fluorescein isothiocyanate-labeled infliximab and vedolizumab staining and gene expression analysis. Computer-aided quantitative image analysis of pCLE was performed. Differentially expressed genes predictive of response were determined and validated in a public cohort.

RESULTS

In vivo, vessel tortuosity, crypt morphology, and fluorescein leakage predicted response in UC (area under the receiver-operating characteristic curve [AUROC], 0.93; accuracy 85%, positive predictive value [PPV] 89%; negative predictive value [NPV] 75%) and CD (AUROC, 0.79; accuracy 80%; PPV 75%; NPV 83%) patients. Ex vivo, increased binding of labeled biologic at baseline predicted response in UC (UC) (AUROC, 83%; accuracy 77%; PPV 89%; NPV 50%) but not in Crohn's disease (AUROC 58%). A total of 325 differentially expressed genes distinguished responders from nonresponders, 86 of which fell within the most enriched pathways. A panel including ACTN1, CXCL6, LAMA4, EMILIN1, CRIP2, CXCL13, and MAPKAPK2 showed good prediction of anti-TNF response (AUROC >0.7).

CONCLUSIONS

Higher mucosal binding of the drug target is associated with response to therapy in UC. In vivo, mucosal and microvascular changes detected by pCLE are associated with response to biologics in inflammatory bowel disease. Anti-TNF-responsive UC patients have a less inflamed and fibrotic state pretreatment. Chemotactic pathways involving CXCL6 or CXCL13 may be novel targets for therapy in nonresponders.

Identifying biomarkers associated with the diagnosis of ulcerative colitis via bioinformatics and machine learning

BACKGROUND

Ulcerative colitis (UC) is an idiopathic inflammatory disease with an increasing incidence. This study aimed to identify potential UC biomarkers and associated immune infiltration characteristics.

METHODS

Two datasets (GSE87473 and GSE92415) were merged to obtain 193 UC samples and 42 normal samples. Using R, differentially expressed genes (DEGs) between UC and normal samples were filtered out, and their biological functions were investigated using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses. Promising biomarkers were identified using least absolute shrinkage selector operator regression and support vector machine recursive feature elimination, and their diagnostic efficacy was evaluated through receiver operating characteristic (ROC) curves. Finally, CIBERSORT was used to investigate the immune infiltration characteristics in UC, and the relationship between the identified biomarkers and various immune cells was examined.

RESULTS

We found 102 DEGs, of which 64 were significantly upregulated, and 38 were significantly downregulated. The DEGs were enriched in pathways associated with interleukin-17, cytokine-cytokine receptor interaction and viral protein interactions with cytokines and cytokine receptors, among others. Using machine learning methods and ROC tests, we confirmed DUOX2, DMBT1, CYP2B7P, PITX2 and DEFB1 to be essential diagnostic genes for UC. Immune cell infiltration analysis revealed that all five diagnostic genes were correlated with regulatory T cells, CD8 T cells, activated and resting memory CD4 T cells, activated natural killer cells, neutrophils, activated and resting mast cells, activated and resting dendritic cells and M0, M1 and M2 macrophages.

CONCLUSIONS

DUOX2, DMBT1, CYP2B7P, PITX2 and DEFB1 were identified as prospective biomarkers for UC. A new perspective on understanding the progression of UC may be provided by these biomarkers and their relationship with immune cell infiltration.

Genome-Wide Analysis of Long Noncoding RNAs in Porcine Intestine during Weaning Stress

Long noncoding RNAs (lncRNAs) play crucial roles in various biological processes, and they are considered to be closely associated with the pathogenesis of intestinal diseases. However, the role and expression of lncRNAs in intestinal damage during weaning stress remain unknown. Herein, we investigated the expression profiles of jejunal tissue from weaning piglets at 4 and 7 d after weaning (groups W4 and W7, respectively) and from suckling piglets on the same days (groups S4 and S7, respectively). Genome-wide analysis of lncRNAs was also performed using RNA sequencing technology. A total of 1809 annotated lncRNAs and 1612 novel lncRNAs were obtained from the jejunum of piglets. In W4 vs. S4, a total of 331 lncRNAs showed significant differential expression, and a total of 163 significantly differentially expressed lncRNAs (DElncRNAs) was identified in W7 vs. S7. Biological analysis indicated that DElncRNAs were involved in intestinal diseases, inflammation, and immune functions, and were mainly enriched in the Jak-STAT signaling pathway, inflammatory bowel disease, T cell receptor signaling pathway, B cell receptor signaling pathway and intestinal immune network for IgA production. Moreover, we found that lnc_000884 and target gene KLF5 were significantly upregulated in the intestine of weaning piglets. The overexpression of lnc_000884 also significantly promoted the proliferation and depressed apoptosis of IPEC-J2 cells. This result suggested that lnc_000884 may contribute to repairing intestinal damage. Our study identified the characterization and expression profile of lncRNAs in the small intestine of weaning piglets and provided new insights into the molecular regulation of intestinal damage during weaning stress.

Identification of Novel Core Genes Involved in Malignant Transformation of Inflamed Colon Tissue Using a Computational Biology Approach and Verification in Murine Models

Inflammatory bowel disease (IBD) is a complex and multifactorial systemic disorder of the gastrointestinal tract and is strongly associated with the development of colorectal cancer. Despite extensive studies of IBD pathogenesis, the molecular mechanism of colitis-driven tumorigenesis is not yet fully understood. In the current animal-based study, we report a comprehensive bioinformatics analysis of multiple transcriptomics datasets from the colon tissue of mice with acute colitis and colitis-associated cancer (CAC). We performed intersection of differentially expressed genes (DEGs), their functional annotation, reconstruction, and topology analysis of gene association networks, which, when combined with the text mining approach, revealed that a set of key overexpressed genes involved in the regulation of colitis (C3, Tyrobp, Mmp3, Mmp9, Timp1) and CAC (Timp1, Adam8, Mmp7, Mmp13) occupied hub positions within explored colitis- and CAC-related regulomes. Further validation of obtained data in murine models of dextran sulfate sodium (DSS)-induced colitis and azoxymethane/DSS-stimulated CAC fully confirmed the association of revealed hub genes with inflammatory and malignant lesions of colon tissue and demonstrated that genes encoding matrix metalloproteinases (acute colitis: Mmp3, Mmp9; CAC: Mmp7, Mmp13) can be used as a novel prognostic signature for colorectal neoplasia in IBD. Finally, using publicly available transcriptomics data, translational bridge interconnecting of listed colitis/CAC-associated core genes with the pathogenesis of ulcerative colitis, Crohn's disease, and colorectal cancer in humans was identified. Taken together, a set of key genes playing a core function in colon inflammation and CAC was revealed, which can serve both as promising molecular markers and therapeutic targets to control IBD and IBD-associated colorectal neoplasia.

Identification of gene signatures associated with ulcerative colitis and the association with immune infiltrates in colon cancer

Background

Inflammatory bowel diseases, including ulcerative colitis (UC) and Crohn's disease, are some of the most common inflammatory disorders of the gastrointestinal tract. The dysfunction of the immune system in the intestines is suggested to be the underlying cause of the pathogenesis of UC. However, the mechanisms regulating these dysfunctional immune cells and inflammatory phenotypes are still unclear.

Methods

The differential expression analysis on microarray datasets were performed including GSE24287, GSE87466, GSE102133, and GSE107499, including 376 samples. "Gene Ontology" and "Kyoto Encyclopedia of Genes and Genomes" pathway enrichment analyses were conducted to identify the common differentially expressed genes (DEGs) in these datasets and explore their underlying biological mechanisms. Further algorithms like "Cell-type Identification by Estimating Relative Subsets of RNA Transcripts" were used to determine the infiltration status of immune cells in patients with UC. "Cytoscape" and "Gene Set Enrichment Analysis" were used to screen for hub genes and to investigate their biological mechanisms. The Tumor Immune Estimation Resource database was used to study the correlation between hub genes and infiltrating immune cells in patients with UC. A total of three hub genes, CCL3, MMP3, and TIMP1, were identified using Cytoscape.

Results

A positive correlation was observed between these hub genes and patients with active UC. These genes served as a biomarker for active UC. Moreover, a decrease in CCL3, MMP3, and TIMP1 expression was observed in the mucosa of the intestine of patients with active UC who responded to Golimumab therapy. In addition, results show a significant positive correlation between CCL3, MMP3, and TIMP1 expression and different immune cell types including dendritic cells, macrophages, CD8+ T cells, and neutrophils in patients with colon cancer. Moreover, CCL3, MMP3, and TIMP1 expression were strongly correlated with different immune cell markers.

Conclusion

Study results show the involvement of hub genes like CCL3, MMP3, and TIMP1 in the pathogenesis of UC. These genes could serve as a novel pharmacological regulator of UC. These could be used as a therapeutic target for treating patients with UC and may serve as biomarkers for immune cell infiltration in colon cancer.

Network pharmacology and in vivo experiment-based strategy to investigate mechanisms of JingFangFuZiLiZhong formula for ulcerative colitis

BACKGROUND

Ulcerative colitis (UC), a chronic inflammatory disease, often cause carcinogenesis, disability, and intestinal perforation. The JingFangFuZiLiZhong formula (JFFZLZ) shows a good effect against UC in the clinic. Hence, we aim to investigate the mechanisms between JFFZLZ and UC via network pharmacology data mining and in vivo experiments.

METHODS

We obtained active constituents and related targets from public databases. The overlapped genes between JFFZLZ and UC targets were further analysed by enrichment analysis. The active constituents and hub targets were used to construct molecule docking analysis. We finally screened out nine hub targets and their expressions were verified in the Gene Expression Omnibus database and UC rats' colon tissues after JFFZLZ treatment.

RESULTS

The results implied that JFFZLZ mainly regulated signal transduction, metabolites production, and inflammation pathways. The expression of STAT3, CXCL8, IL6, CXCL12, TNF, TP53, and PTPN11 were both upregulated in colon tissues of UC patients and UC rats. While RELA, EGFR, and TP53 were downregulated in UC patients, but upregulated in UC rats. Furthermore, JFFZLZ could repair UC rats' colon mucosal damage and promote the healing of ulcers via regulating the hub targets.

CONCLUSION

These results elucidated that the anti-UC effect of JFFZLZ was closely related to the inhibition of inflammatory response, inhibition of oxidative stress, and repairing colon mucosal damage through different signal pathways. The findings could contribute to a better understanding of the regulation mechanisms in JFFZLZ against UC.Key messagesJFFZLZ could reduce the inflammatory infiltration and repair UC rats' colon mucosal damage.Through the network pharmacology-based strategy and public database mining, we obtained the hub targets and key pathways between JFFZLF and UC.The mechanism of JFFZLZ against UC was inhibition of inflammatory response and oxidative stress by regulating the expression of the hub targets.

Deciphering the Mechanism of YuPingFeng Granules in Treating Pneumonia: A Network Pharmacology and Molecular Docking Study

Objective

YuPingFeng Granules (YPFGs) is an herbal formula clinically used in China for more than 100 years to treat pneumonia. Nevertheless, the mechanism of YPFG in pneumonia treatment has not been established. This network pharmacology-based strategy has been performed to elucidate active compounds as well as mechanisms of YPFG in pneumonia treatment.

Methods

First, active compounds of YPFG were identified in the traditional Chinese medicine systems pharmacology (TCMSP) database, and then the targets related to the active compounds were obtained from TCMSP and Swiss Target Prediction databases. Next, using DisGeNET, DrugBank, and GeneCards databases, we got therapeutic targets of pneumonia and common targets between pneumonia targets and YPFG. After that, a protein-protein interaction (PPI) network of pneumonia composed of common targets was built to analyze the interactions among these targets, which focused on screening for hub targets by topology. Then, online software and the ClusterProfiler package were utilized for the enrichment analysis of gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) data. Finally, the visualization software of Autodock was used for molecular docking among the hub target proteins.

Results

10 hub genes were selected by comparing the GO and KEGG functions of pneumonia targets with those of the common targets of YPFG and pneumonia. By using molecular docking technology, a total of 3 active ingredients have been verified as being able to combine closely with 6 hub targets and contribute to their therapeutic effects.

Conclusion

This research explored the multigene pharmacological mechanism of action of YPFG against pneumonia through network pharmacology. The findings present new ideas for studying the mechanism of action of Chinese medicine against pneumonia caused by bacteria.

Identification of Potential Biomarkers and Immune Infiltration Characteristics in Ulcerative Colitis by Combining Results from Two Machine Learning Algorithms

Objective

This study was designed to identify potential biomarkers for ulcerative colitis (UC) and analyze the immune infiltration characteristics in UC.

Methods

Datasets containing human UC and normal control tissues (GSE87466, GSE107597, and GSE13367) were downloaded from the GEO database. Then, the GSE87466 and GSE107597 datasets were merged, and the differentially expressed genes (DEGs) between UC and normal control tissues were screened out by the “limma R” package. The LASSO regression model and support vector machine recursive feature elimination (SVM-RFE) were performed to screen out the best biomarkers. The GSE13367 dataset was used as a validation cohort, and the receiver operating characteristic curve (ROC) was used to evaluate the diagnostic performance. Finally, the immune infiltration characteristics in UC were explored by CIBERSORT, and we further analyzed the correlation between potential biomarkers and different immune cells.

Results

A total of 76 DEGs were screened out, among which 56 genes were upregulated and 20 genes were downregulated. Functional enrichment analysis revealed that these DEGs were mainly involved in immune response, chemokine signaling, IL−17 signaling, cytokine receptor interactions, inflammatory bowel disease, etc. ABCG2, HSPB3, SLC6A14, and VNN1 were identified as potential biomarkers for UC and validated in the GSE13367 dataset (AUC = 0.889, 95% CI: 0.797~0.961). Immune infiltration analysis by CIBERSORT revealed that there were significant differences in immune infiltration characteristics between UC and normal control tissues. A high level of memory B cells, γδ T cells, activated mast cells, M1 macrophages, neutrophils, etc. were found in the UC group, while a high level of M2 type macrophages, resting mast cells, eosinophils, CD8+ T cells, etc. were found in the normal control group.

Conclusion

ABCG2, HSPB3, SLC6A14, and VNN 1 were identified as potential biomarkers for UC. There was an obvious difference in immune infiltration between UC and normal control tissues, which may provide help to guide individualized treatment and develop new research directions.

Artemisinin Alleviates Intestinal Inflammation and Metabolic Disturbance in Ulcerative Colitis Rats Induced by DSS

Objective

This study is aimed to reveal the possible mechanisms of artemisinin in the treatment of ulcerative colitis (UC) through bioinformatics analysis and experimental verification in UC model rats.

Methods

Firstly, we searched two microarray data of the Gene Expression Omnibus (GEO) database to explore the differentially expressed genes (DEGs) between UC samples and normal samples. Then, we selected DEGs for gene ontology (GO) function enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The acute UC model of rats was established by using 3.5% dextran sulfate sodium (DSS) for 10 days to verify the core pathway. Finally, we evaluated the therapeutic effect of artemisinin at the molecular level and used metabonomics to study the endogenous metabolites in the rat serum.

Results

We screened in the GEO database and selected two eligible microarray datasets, GSE36807 and GSE9452. We performed GO function and KEGG pathway enrichment analyses of DEGs and found that these DEGs were mainly enriched in the inflammatory response, immune response, and IL-17 and NF-κB signaling pathways. Finally, we verified the IL-17 signaling pathway and key cytokines, and ELISA and immunohistochemical results showed that artemisinin could downregulate the expression of proinflammatory cytokines such as IL-1β and IL-17 in the IL-17 signaling pathway and upregulate the expression of the anti-inflammatory cytokine PPAR-γ. Metabolomics analysis showed that 33 differential metabolites were identified in the artemisinin group (AG) compared to the model group (MG). Differential metabolites were mainly involved in alanine, aspartate, and glutamate metabolism and synthesis and degradation of ketone bodies.

Conclusion

In this study, we found that artemisinin can significantly inhibit the inflammatory response in UC rats and regulate metabolites and related metabolic pathways. This study provides a foundation for further research on the mechanism of artemisinin in the treatment of UC.

SKAP2 suppresses inflammation-mediated tumorigenesis by regulating SHP-1 and SHP-2

Inflammatory bowel diseases, like ulcerative colitis and Crohn's disease are frequently accompanied by colorectal cancers. However, the mechanisms underlying colitis-associated cancers are not fully understood. Src Kinase Associated Phosphoprotein 2 (SKAP2), a substrate of Src family kinases, is highly expressed in macrophages. Here, we examined the effects of SKAP2 on inflammatory responses in a mouse model of tumorigenesis with colitis induced by azoxymethane/dextran sulfate sodium. SKAP2 knockout increased the severity of colitis and tumorigenesis, as well as lipopolysaccharide (LPS) induced acute inflammation. SKAP2 attenuated inflammatory signaling in macrophages induced by uptake of cancer cell-derived exosomes. SKAP2-/- mice were characterized by the activation of NF-κB signaling and the upregulation and release of cytokines including TNFα, IL-1β, IL-6, CXCL-9/-10/-13, and sICAM1; SKAP2 overexpression attenuated NF-κB activation. Mechanistically, SKAP2 formed a complex with the SHP-1 tyrosine phosphatase via association with the Sirpα transmembrane receptor. SKAP2 also physically associated with the TIR domain of MyD88, TIRAP, and TRAM, adaptors of toll-like receptor 4 (TLR4). SKAP2-mediated recruitment of the Sirpα/SHP-1 complex to TLR4 attenuated inflammatory responses, whereas direct interaction of SKAP2 with SHP-2 decreased SHP-2 activation. SHP-2 is required for efficient NF-κB activation and suppresses the TRAM/TRIF-INFβ pathway; therefore, SKAP2-mediated SHP-2 inhibition affected two signaling axes from TLR4. The present findings indicate that SKAP2 prevents excess inflammation by inhibiting the TLR4-NF-κB pathway, and it activates the TLR4-IFNβ pathway through SHP-1 and SHP-2, thereby suppressing inflammation-mediated tumorigenesis.

Response to Anti-α4β7 Blockade in Patients With Ulcerative Colitis Is Associated With Distinct Mucosal Gene Expression Profiles at Baseline

BACKGROUND

Improving treatment outcomes with biological therapy is a demanding current need for patients with inflammatory bowel disease. Discovery of pretreatment prognostic indicators of response may facilitate patient selection and increase long-term remission rates. We aimed to identify baseline mucosal gene expression profiles with predictive value for subsequent response to or failure of treatment with the monoclonal antibody against integrin α4β7, vedolizumab, in patients with active ulcerative colitis (UC).

METHODS

Mucosal expression of 84 immunological and inflammatory genes was quantified in RNA extracted from colonic biopsies before vedolizumab commencement and compared between patients with or without response to treatment. Significantly differentiated genes were further validated in a larger patient cohort and within available public data sets, and their functional profiles were studied accordingly.

RESULTS

In the discovery cohort, we identified 21 genes with a statistically significant differential expression between 54-week responders and nonresponders to vedolizumab. Our validation study allowed us to recognize a "core" mucosal profile that was preserved in both discovery and validation cohorts and in the public database. The applied functional annotation and analysis revealed candidate dysregulated pathways in nonresponders to vedolizumab, including immune cell trafficking, TNF receptor superfamily members mediating noncanonical NF-kB pathway, in addition to interleukin signaling, MyD88 signaling, and toll-like receptors (TLRs) cascade.

CONCLUSIONS

Nonresponse to vedolizumab in UC is associated with specific pretreatment gene-expression mucosal signatures and dysregulation of particular immunological and inflammatory pathways. Baseline mucosal and/or systemic molecular profiling may help in the optimal stratification of patients to receive vedolizumab for active UC.

The Mechanism Action of German Chamomile (Matricaria recutita L.) in the Treatment of Eczema: Based on Dose-Effect Weight Coefficient Network Pharmacology

To determine the active ingredients in German chamomile volatile oil and the mechanism of action in the treatment of eczema, this study used two parameters (ingredient content and oil–water partition coefficient) and established a new network pharmacology method based on the dose–effect weight coefficient. Through the new network pharmacology method, we found that German chamomile volatile oil regulated T-cell lymphatic subpopulations to inhibit the Th17 cell differentiation signaling pathway. This resulted in a reduction of interleukin 17 (IL-17), thereby inhibiting the activation of the nuclear factor kappa beta (NF-κB) and MAPK pathways, decreasing the secretion of the pro-inflammatory factors (tumor necrosis factor alpha (TNF-α) and interleukin 6 (IL-6)), and reducing inflammation. In this study, a new dose–effect relationship synergistic network pharmacology method was established to provide a new method for the screening of effective ingredients and pathways of drugs, and to provide a basis for the follow-up studies of German chamomile volatile oil in the treatment of eczema.

Integrated Analysis of Multiple Microarray Studies to Identify Novel Gene Signatures in Ulcerative Colitis

Background: Ulcerative colitis (UC) is a chronic, complicated, inflammatory disease with an increasing incidence and prevalence worldwide. However, the intrinsic molecular mechanisms underlying the pathogenesis of UC have not yet been fully elucidated.

Methods: All UC datasets published in the GEO database were analyzed and summarized. Subsequently, the robust rank aggregation (RRA) method was used to identify differentially expressed genes (DEGs) between UC patients and controls. Gene functional annotation and PPI network analysis were performed to illustrate the potential functions of the DEGs. Some important functional modules from the protein-protein interaction (PPI) network were identified by molecular complex detection (MCODE), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG), and analyses were performed. The results of CytoHubba, a plug for integrated algorithm for biomolecular interaction networks combined with RRA analysis, were used to identify the hub genes. Finally, a mouse model of UC was established by dextran sulfate sodium salt (DSS) solution to verify the expression of hub genes.

Results: A total of 6 datasets met the inclusion criteria (GSE38713, GSE59071, GSE73661, GSE75214, GSE87466, GSE92415). The RRA integrated analysis revealed 208 significant DEGs (132 upregulated genes and 76 downregulated genes). After constructing the PPI network by MCODE plug, modules with the top three scores were listed. The CytoHubba app and RRA identified six hub genes: LCN2, CXCL1, MMP3, IDO1, MMP1, and S100A8. We found through enrichment analysis that these functional modules and hub genes were mainly related to cytokine secretion, immune response, and cancer progression. With the mouse model, we found that the expression of all six hub genes in the UC group was higher than that in the control group (P < 0.05).

Conclusion: The hub genes analyzed by the RRA method are highly reliable. These findings improve the understanding of the molecular mechanisms in UC pathogenesis.

Prioritizing Pain-Associated Targets with Machine Learning

While hundreds of genes have been associated with pain, much of the molecular mechanisms of pain remain unknown. As a result, current analgesics are limited to few clinically validated targets. Here, we trained a machine learning (ML) ensemble model to predict new targets for 17 categories of pain. The model utilizes features from transcriptomics, proteomics, and gene ontology to prioritize targets for modulating pain. We focused on identifying novel G-protein-coupled receptors (GPCRs), ion channels, and protein kinases because these proteins represent the most successful drug target families. The performance of the model to predict novel pain targets is 0.839 on average based on AUROC, while the predictions for arthritis had the highest accuracy (AUROC = 0.929). The model predicts hundreds of novel targets for pain; for example, GPR132 and GPR109B are highly ranked GPCRs for rheumatoid arthritis. Overall, gene-pain association predictions cluster into three groups that are enriched for cytokine, calcium, and GABA-related cell signaling pathways. These predictions can serve as a foundation for future experimental exploration to advance the development of safer and more effective analgesics.

Limited Impact of 6-Mercaptopurine on Inflammation-Induced Chemokines Expression Profile in Primary Cultures of Enteric Nervous System

Increasing evidences indicate that the enteric nervous system (ENS) and enteric glial cells (EGC) play important regulatory roles in intestinal inflammation. Mercaptopurine (6-MP) is a cytostatic compound clinically used for the treatment of inflammatory bowel diseases (IBD), such as ulcerative colitis and Crohn's disease. However, potential impacts of 6-MP on ENS response to inflammation have not been evaluated yet. In this study, we aimed to gain deeper insights into the profile of inflammatory mediators expressed by the ENS and on the potential anti-inflammatory impact of 6-MP in this context. Genome-wide expression analyses were performed on ENS primary cultures exposed to lipopolysaccharide (LPS) and 6-MP alone or in combination. Differential expression of main hits was validated by quantitative real-time PCR (qPCR) using a cell line for EGC. ENS cells expressed a broad spectrum of cytokines and chemokines of the C-X-C motif ligand (CXCL) family under inflammatory stress. Induction of Cxcl5 and Cxcl10 by inflammatory stimuli was confirmed in EGC. Inflammation-induced protein secretion of TNF-α and Cxcl5 was partly inhibited by 6-MP in ENS primary cultures but not in EGC. Further work is required to identify the cellular mechanisms involved in this regulation. These findings extend our knowledge of the anti-inflammatory properties of 6-MP related to the ENS and in particular of the EGC-response to inflammatory stimuli.

Efficacy and safety evaluation of hyperbaric oxygen therapy for patients with ulcerative colitis: A protocol of systematic review and meta-analysis

BACKGROUND

Ulcerative colitis (UC) belongs to chronic colitis whose etiology and pathogenesis still have remained unclear. Hyperbaric oxygen therapy (HBOT) has been demonstrated to be effective for UC therapy. Still, evidence of its efficacy and safety is inconclusive. The purpose of the protocol is to evaluate the efficacy and safety of HBOT in UC therapy.

METHODS

This systematic review will retrieve studies that meet the requirements in Embase, MEDLINE, PubMed, Web of Science, Cochrane Library Central Register of Controlled Trials, the Chinese Biomedical Literature Database (CBM), China national knowledge infrastructure database (CNKI), Wei Pu database, Wan fang database, SinoMed, Google scholar, and Baidu Scholar from their inception to November 2020. Two authors are to be independent in their article selection, data collection, and research quality assessments. The primary outcome is the clinical effectiveness. And the secondary outcomes will include 4 criteria. RevMan 5.3 software will be utilized for analysis of the data.

RESULTS

The results of this study are to be submitted via a peer-reviewed journal.

CONCLUSIONS

The study is to assess the effectiveness and safety of HBOT for UC and provide valid and reliable evidence regarding HBOT for UC.

INPLASY REGISTRATION NUMBER

INPLASY2020100118.

Potential Biomarkers Associated with Differential Manifestations of Ulcerative Colitis (UC) in Uyghur and Han Population in China

BACKGROUND

Ulcerative colitis (UC) is one of the main subtypes of inflammatory bowel disease (IBD). The incidence of UC in the Xinjiang region is relatively high in China and the manifestations of UC in Uyghur and Han patients are usually differential. This study aimed to identify potential biomarkers of UC.

METHODS

All miRNA and mRNA were extracted from the tissue samples obtained from participants in Xinjiang. Differential expression analysis was performed on all mRNAs and miRNAs. The target genes of miRNAs were predicted via three databases. The clusterProfiler package was used for GO and KEGG pathway enrichment analysis.

RESULTS

Preliminarily, four miRNAs and 15 genes were associated with the differential manifestations of UC in Uyghur and Han patients. Through the co-expression network construction and further screening in more samples, two miRNAs (hsa-miR-141-5p and hsa-miR-378a-5p) and three genes (ARNTL2, CLDN1 and SLC6A14) were found to be more crucial. These 15 genes were enriched in tight junction, NF-κB, and several other pathways.

CONCLUSION

Two miRNAs (hsa-miR-141-5p and hsa-miR-378a-5p) and three genes (ARNTL2, CLDN1, and SLC6A14) associated with the differential manifestations of UC in Uyghur and Han population were identified, which were potential biomarkers.