Meta-Analysis of Expression Profiling Data Indicates Need for Combinatorial Biomarkers in Pediatric Ulcerative Colitis

Identification and experimental validation of immune-related gene PPARG is involved in ulcerative colitis

BACKGROUND

The pathophysiology of ulcerative colitis (UC) is believed to be heavily influenced by immunology, which presents challenges for both diagnosis and treatment. The main aims of this study are to deepen our understanding of the immunological characteristics associated with the disease and to identify valuable biomarkers for diagnosis and treatment.

METHODS

The UC datasets were sourced from the GEO database and were analyzed using unsupervised clustering to identify different subtypes of UC. Twelve machine learning algorithms and Deep learning model DNN were developed to identify potential UC biomarkers, with the LIME and SHAP methods used to explain the models' findings. PPI network is used to verify the identified key biomarkers, and then a network connecting super enhancers, transcription factors and genes is constructed. Single-cell sequencing technology was utilized to investigate the role of Peroxisome Proliferator Activated Receptor Gamma (PPARG) in UC and its correlation with macrophage infiltration. Furthermore, alterations in PPARG expression were validated through Western blot (WB) and immunohistochemistry (IHC) in both in vitro and in vivo experiments.

RESULT

By utilizing bioinformatics techniques, we were able to pinpoint PPARG as a key biomarker for UC. The expression of PPARG was significantly reduced in cell models, UC animal models, and colitis models induced by dextran sodium sulfate (DSS). Interestingly, overexpression of PPARG was able to restore intestinal barrier function in H2O2-induced IEC-6 cells. Additionally, immune-related differentially expressed genes (DEGs) allowed for efficient classification of UC samples into neutrophil and mitochondrial metabolic subtypes. A diagnostic model incorporating the three disease-specific genes PPARG, PLA2G2A, and IDO1 demonstrated high accuracy in distinguishing between the UC group and the control group. Furthermore, single-cell analysis revealed that decreased PPARG expression in colon tissue may contribute to the polarization of M1 macrophages through activation of inflammatory pathways.

CONCLUSION

In conclusion, PPARG, a gene related to immunity, has been established as a reliable potential biomarker for the diagnosis and treatment of UC. The immune response it controls plays a key role in the progression and development of UC by enabling interaction between characteristic biomarkers and immune infiltrating cells.

The Prokineticin System in Inflammatory Bowel Diseases: A Clinical and Preclinical Overview

Inflammatory bowel disease (IBD) includes Crohn's disease (CD) and ulcerative colitis (UC), which are characterized by chronic inflammation of the gastrointestinal (GI) tract. IBDs clinical manifestations are heterogeneous and characterized by a chronic relapsing-remitting course. Typical gastrointestinal signs and symptoms include diarrhea, GI bleeding, weight loss, and abdominal pain. Moreover, the presence of pain often manifests in the remitting disease phase. As a result, patients report a further reduction in life quality. Despite the scientific advances implemented in the last two decades and the therapies aimed at inducing or maintaining IBDs in a remissive condition, to date, their pathophysiology still remains unknown. In this scenario, the importance of identifying a common and effective therapeutic target for both digestive symptoms and pain remains a priority. Recent clinical and preclinical studies have reported the prokineticin system (PKS) as an emerging therapeutic target for IBDs. PKS alterations are likely to play a role in IBDs at multiple levels, such as in intestinal motility, local inflammation, ulceration processes, localized abdominal and visceral pain, as well as central nervous system sensitization, leading to the development of chronic and widespread pain. This narrative review summarized the evidence about the involvement of the PKS in IBD and discussed its potential as a druggable target.

Characterization of prokineticin system in Crohn's disease pathophysiology and pain, and its modulation by alcohol abuse: A preclinical study

BACKGROUND

Crohn's disease-(CD) pathogenesis is still unknown and chronic pain is a frequent symptom in CD-patients. Identifying novel therapeutic targets and predisposing factors is a primary goal. In this regard, prokineticin system-(PKS) appears a promising target.

AIMS AND METHODS

TNBS-model was used. DAI, abdominal and visceral pain, and muscle strength were monitored. CD-mice were sacrificed at two times (day 7 and 14 after TNBS) in order to identify PKS involvement in CD pathophysiology and pain. PKS characterization was performed in mesenteric lymph nodes-(MLN), colon, myenteric plexus-(MP), dorsal root ganglia-(DRGs) and spinal cord-(SC). Inflammation/neuroinflammation was also assessed in the same tissues. In order to evaluate alcohol abuse as a possible trigger for CD and its effect on PKS activation, naïve mice were administered (oral-gavage) with ethanol for 10 consecutive days. PKS as well as inflammation/neuroinflammation were evaluated in MLN, colon and MP.

RESULTS

TNBS treated-mice showed a rapid increase in DAI, abdominal/visceral hypersensitivity and a progressive strength loss. In all tissue analysed of CD-mice, a quick and significant increase of mRNA of PKs and PKRs was observed, associated with an increase of pro-inflammatory cytokines (IL-1β, IL-6 and TNFα) and macrophage/glia markers (iba1, CD11b and GFAP) levels. In alcohol abuse model, ethanol induced in colon and MP a significant PKS activation accompanied by inflammation/neuroinflammation.

CONCLUSIONS

We can assume that PKS may be involved in CD development and pain. Furthermore, alcohol appears to activate PKS and may be a trigger factor for CD.

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.

Integrated multiple microarray studies by robust rank aggregation to identify immune-associated biomarkers in Crohn's disease based on three machine learning methods

Crohn's disease (CD) is a complex autoimmune disorder presumed to be driven by complex interactions of genetic, immune, microbial and even environmental factors. Intrinsic molecular mechanisms in CD, however, remain poorly understood. The identification of novel biomarkers in CD cases based on larger samples through machine learning approaches may inform the diagnosis and treatment of diseases. A comprehensive analysis was conducted on all CD datasets of Gene Expression Omnibus (GEO); our team then used the robust rank aggregation (RRA) method to identify differentially expressed genes (DEGs) between controls and CD patients. PPI (protein‒protein interaction) network and functional enrichment analyses were performed to investigate the potential functions of the DEGs, with molecular complex detection (MCODE) identifying some important functional modules from the PPI network. Three machine learning algorithms, support vector machine-recursive feature elimination (SVM-RFE), random forest (RF), and least absolute shrinkage and selection operator (LASSO), were applied to determine characteristic genes, which were verified by ROC curve analysis and immunohistochemistry (IHC) using clinical samples. Univariable and multivariable logistic regression were used to establish a machine learning score for diagnosis. Single-sample GSEA (ssGSEA) was performed to examine the correlation between immune infiltration and biomarkers. In total, 5 datasets met the inclusion criteria: GSE75214, GSE95095, GSE126124, GSE179285, and GSE186582. Based on RRA integrated analysis, 203 significant DEGs were identified (120 upregulated genes and 83 downregulated genes), and MCODE revealed some important functional modules in the PPI network. Machine learning identified LCN2, REG1A, AQP9, CCL2, GIP, PROK2, DEFA5, CXCL9, and NAMPT; AQP9, PROK2, LCN2, and NAMPT were further verified by ROC curves and IHC in the external cohort. The final machine learning score was defined as [Expression level of AQP9 × (2.644)] + [Expression level of LCN2 × (0.958)] + [Expression level of NAMPT × (1.115)]. ssGSEA showed markedly elevated levels of dendritic cells and innate immune cells, such as macrophages and NK cells, in CD, consistent with the gene enrichment results that the DEGs are mainly involved in the IL-17 signaling pathway and humoral immune response. The selected biomarkers analyzed by the RRA method and machine learning are highly reliable. These findings improve our understanding of the molecular mechanisms of CD pathogenesis.

Biomarkers for Optimization and Personalization of Anti-TNFs in Pediatric Inflammatory Bowel Disease

The use of biological drugs has improved outcomes in pediatric inflammatory bowel disease (IBD). Prediction of the response to biological drugs would be extremely useful in IBD, and even more so in children, who are still growing physically and psychologically. Specific clinical, biochemical, and genetic parameters are considered predictive of response to biological drugs, although few studies have been carried out in children with IBD. In this review, we present current evidence on biological treatments used in pediatric IBD and the available biomarkers of response. We examine demographics, clinical characteristics, biomarkers (genetic, genomic, and cellular), and microbiota.

The Inflammatory Bowel Disease Transcriptome and Metatranscriptome Meta-Analysis (IBD TaMMA) framework

Inflammatory bowel disease (IBD) is a class of chronic disorders whose etiogenesis is still unknown. Despite the high number of IBD-related omics studies, the RNA-sequencing data produced results that are hard to compare because of the experimental variability and different data analysis approaches. We here introduce the IBD Transcriptome and Metatranscriptome Meta-Analysis (TaMMA) framework, a comprehensive survey of publicly available IBD RNA-sequencing datasets. IBD TaMMA is an open-source platform where scientists can explore simultaneously the freely available IBD-associated transcriptomics and microbial profiles thanks to its interactive interface, resulting in a useful tool to the IBD community.

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.

Relationship between Metalloprotease-7 and -14 and Tissue Inhibitor of Metalloprotease 1 Expression by Mucosal Stromal Cells and Colorectal Cancer Development in Inflammatory Bowel Disease

Colorectal carcinoma (CRC) associated with inflammatory bowel disease (IBD) is an example of an inflammation-related cancer. Matrix metalloproteases (MMP) are known to be associated with both processes. The aim of the study was to compare the expression of MMP-7, MMP-14 and tissue inhibitor of metalloproteases-1 (TIMP-1) in sporadic CRC- and IBD-associated CRC, and to compare the expression in inflamed and non-inflamed colonic tissue samples from IBD patients without or with associated CRC. An immunohistochemical study of MMP-7, -14 and TIMP-1 was performed on sporadic CRC (n = 86), IBD-associated CRC (n = 23) and colorectal mucosa of non-tumor samples from IBD patients without (n = 47) and with (n = 23) associated CRC. These factors were more frequently expressed by cancer-associated fibroblasts (CAF) from IBD-associated CRC than by CAF from CRC not associated with IBD. Regarding the inflamed tissue of IBD patients, Crohn’s disease (CD) patients with CRC development showed a higher expression of MMP-14 by fibroblasts and by mononuclear inflammatory cells (MICs) than CD patients without CRC development. In non-inflamed tissue samples, MMP-7 associated with fibroblasts and MICs, and TIMP-1 associated with MICs, were more frequently expressed in CD patients with CRC development than in CD patients without CRC development. Our data suggest that these factor expressions by stromal cells may be biological markers of CRC development risk in IBD patients.