Exploring the molecular mechanisms and shared gene signatures between celiac disease and ulcerative colitis based on bulk RNA and single-cell sequencing: Experimental verification.

Many immune-mediated diseases have the common genetic basis, as an autoimmune disorder, celiac disease (CeD) primarily affects the small intestine, and is caused by the ingestion of gluten in genetically susceptible individuals. As for ulcerative colitis (UC), which most likely involves a complex interplay between some components of the commensal microbiota and other environmental factors in its origin. These two autoimmune diseases share a specific target organ, the bowel. The etiology and immunopathogenesis of both conditions characterized by chronic intestinal inflammation, ulcerative colitis and celiac disease, are not completely understood. Both are complex diseases with genetics and the environmental factors contributing to dysregulation of innate and adaptive immune responses, leading to chronic inflammation and disease. This study is designed to further clarify the relationship between UC and CeD. The GEO database was used to download gene expression profiles for CeD (GSE112102) and UC (GSE75214). The GSEA KEGG pathway analysis revealed that immune-related pathways were significantly associated with both diseases. Further, we screened 187 shared differentially expressed genes (DEGs) of the two diseases. Gene Ontology (GO) and WikiPathways were carried out to perform the biological process and pathway enrichment analysis. Subsequently, based on the DEGs, the least absolute shrinkage and selection operator (LASSO) analysis was performed to screen for the diagnostic biomarkers of the diseases. Moreover, single-cell RNA-sequencing (RNA-seq) data from five colonic propria with UC showed that REG4 expression was present in Goblet cell, Enteroendocrine cell, and Epithelial. Finally, our work identified REG4 is the shared gene of UC and CeD via external data validation, cellular experiments, and immunohistochemistry. In conclusion, our study elucidated that abnormal immune response could be the common pathogenesis of UC and CeD, and REG4 might be a key potential biomarker and therapeutic target for the comorbidity of these two diseases.

Identification and validation of shared gene signature of kidney renal clear cell carcinoma and COVID-19.

Background: COVID-19 is a severe infectious disease caused by the SARS-CoV-2 virus, and previous studies have shown that patients with kidney renal clear cell carcinoma (KIRC) are more susceptible to SARS-CoV-2 infection than the general population. Nevertheless, their co-pathogenesis remains incompletely elucidated. Methods: We obtained shared genes between these two diseases based on public datasets, constructed a prognostic risk model consisting of hub genes, and validated the accuracy of the model using internal and external validation sets. We further analyzed the immune landscape of the prognostic risk model, investigated the biological functions of the hub genes, and detected their expression in renal cell carcinoma cells using qPCR. Finally, we searched the candidate drugs associated with hub gene-related targets from DSigDB and CellMiner databases. Results: We obtained 156 shared genes between KIRC and COVID-19 and constructed a prognostic risk model consisting of four hub genes. Both shared genes and hub genes were highly enriched in immune-related functions and pathways. Hub genes were significantly overexpressed in COVID-19 and KIRC. ROC curves, nomograms, etc., showed the reliability and robustness of the risk model, which was validated in both internal and external datasets. Moreover, patients in the high-risk group showed a higher proportion of immune cells, higher expression of immune checkpoint genes, and more active immune-related functions. Finally, we identified promising drugs for COVID-19 and KIRC, such as etoposide, fulvestrant, and topotecan. Conclusion: This study identified and validated four shared genes for KIRC and COVID-19. These genes are associated with immune functions and may serve as potential prognostic biomarkers for KIRC. The shared pathways and genes may provide new insights for further mechanistic research and treatment of comorbidities.

Identification of the shared gene signatures and molecular mechanisms between multiple sclerosis and non-small cell lung cancer.

Introduction: The association between multiple sclerosis (MS) and non-small cell lung cancer (NSCLC) has been the subject of investigation in clinical cohorts, yet the molecular mechanisms underpinning this relationship remain incompletely understood. To address this, our study aimed to identify shared genetic signatures, shared local immune microenvironment, and molecular mechanisms between MS and NSCLC. Methods: We selected multiple Gene Expression Omnibus (GEO) datasets, including GSE19188, GSE214334, GSE199460, and GSE148071, to obtain gene expression levels and clinical information from patients or mice with MS and NSCLC. We employed Weighted Gene Co-expression Network Analysis (WGCNA) to investigate co-expression networks linked to MS and NSCLC and used single-cell RNA sequencing (scRNA-seq) analysis to explore the local immune microenvironment of MS and NSCLC and identify possible shared components. Results: Our analysis identified the most significant shared gene in MS and NSCLC, phosphodiesterase 4A (PDE4A), and we analyzed its expression in NSCLC patients and its impact on patient prognosis, as well as its molecular mechanism. Our results demonstrated that high expression of PDE4A was associated with poor prognoses in NSCLC patients, and Gene Set Enrichment Analysis (GSEA) revealed that PDE4A is involved in immune-related pathways and has a significant regulatory effect on human immune responses. We further observed that PDE4A was closely linked to the sensitivity of several chemotherapy drugs. Conclusion: Given the limitation of studies investigating the molecular mechanisms underlying the correlation between MS and NSCLC, our findings suggest that there are shared pathogenic processes and molecular mechanisms between these two diseases and that PDE4A represents a potential therapeutic target and immune-related biomarker for patients with both MS and NSCLC.

Shared Genes of PPARG and NOS2 in Alzheimer's Disease and Ulcerative Colitis Drive Macrophages and Microglia Polarization: Evidence from Bioinformatics Analysis and Following Validation.

Emerging evidence shows that peripheral systemic inflammation, such as inflammatory bowel disease (IBD), has a close even interaction with central nervous disorders such as Alzheimer's disease (AD). This study is designed to further clarify the relationship between AD and ulcerative colitis (UC, a subclass of IBD). The GEO database was used to download gene expression profiles for AD (GSE5281) and UC (GSE47908). Bioinformatics analysis included GSEA, KEGG pathway, Gene Ontology (GO), WikiPathways, PPI network, and hub gene identification. After screening the shared genes, qRT-PCR, Western blot, and immunofluorescence were used to verify the reliability of the dataset and further confirm the shared genes. GSEA, KEGG, GO, and WikiPathways suggested that PPARG and NOS2 were identified as shared genes and hub genes by cytoHubba in AD and UC and further validated via qRT-PCR and Western blot. Our work identified PPARG and NOS2 are shared genes of AD and UC. They drive macrophages and microglia heterogeneous polarization, which may be potential targets for treating neural dysfunction induced by systemic inflammation and vice versa.

When type II diabetes mellitus meets COVID-19-Identification of the shared gene signatures and biological mechanism between the two diseases.

BACKGROUND: According to current studies, more than 20% of all patients diagnosed with COVID-19 globally have diabetes. Further, the mortality rate of these patients is 7.3%. Compared with non-diabetic COVID-19 patients, diabetic COVID-19 patients have higher rates of mortality and severe infection, suggesting that diabetes is associated with the severity of COVID-19 infection. This study aimed to analyse the relationship and susceptibility factors between COVID-19 and T2DM. METHODS: Using bioinformatics methods, potential targets for COVID-19 and T2DM were screened from GeneCards database. Potential targets of COVID-19 and T2DM were mapped to each other to identify overlapping targets, and a PPI network was constructed to extract the core target. The clusterProfiler package in R was used to analyse the function and pathway that core target involved. GO enrichment and KEGG pathway analysis were used to elucidate the correlation between COVID-19 and T2DM. RESULTS: A total of 277 potential pathogenic targets of COVID-19 were found, 282 potential targets were found for T2DM. Mapping of the potential COVID-19 and T2DM targets revealed 53 overlapping targets, with TNF as the core target. IL-17 signalling pathway was the most significant KEGG pathway involving TNF. CONCLUSIONS: The inflammatory cytokine, TNF, was identified as a core target between COVID-19 and T2DM, which induces inflammatory response mainly through the IL-17 signalling pathway, leading to aggravation of infection and increased difficulty in blood glucose control. This study provides a reference for further exploring the potential correlation and endogenous mechanisms between two seemingly independent and unrelated diseases-T2DM and COVID-19.

Transcriptomic identification of key genes in Pacific oysters Crassostrea gigas responding to major abiotic and biotic stressors.

Oysters are commercially important intertidal filter-feeding species. Mass mortality events of oysters often occur due to environmental stresses, such as exposure to fluctuating temperatures, salinity, and air, as well as to metal pollution and pathogen infection. Here, RNA-seq data were used to identify shared and specific responsive genes by differential gene expression analysis and weighted gene co-expression network analysis. A total of 18 up-regulated and 10 down-regulated shared responsive genes were identified corresponding to five different stressors. Total 27 stressor-specific genes for temperature, 11 for salinity, 80 for air exposure, 51 for metal pollution, and 636 for Vibrio mediterranei pathogen stress were identified in oysters. Elongin-ß was identified as a crucial gene for thermal stress response. Some HSP70s were determined to be shared responsive genes while others were specific to thermal tolerance. The proteins encoded by these stress-related genes should be further investigated to characterize their physiological functions. In addition, the uncharacterized proteins and ncRNAs that were identified may be involved in species-specific stress-response and regulatory mechanisms. This study identified specific genes related to stressors relevant to oyster cultivation. These findings provide useful information for new selective breeding strategies using a data driven method.

Shared and Cell-Type-Specific Gene Expression Patterns Associated With Autism Revealed by Integrative Regularized Non-Negative Matrix Factorization.

Human brain-related disorders, such as autism spectrum disorder (ASD), are often characterized by cell heterogeneity, as the cell atlas of brains consists of diverse cell types. There are commonality and specificity in gene expression among different cell types of brains; hence, there may also be commonality and specificity in dysregulated gene expression affected by ASD among brain cells. Moreover, as genes interact together, it is important to identify shared and cell-type-specific ASD-related gene modules for studying the cell heterogeneity of ASD. To this end, we propose integrative regularized non-negative matrix factorization (iRNMF) by imposing a new regularization based on integrative non-negative matrix factorization. Using iRNMF, we analyze gene expression data of multiple cell types of the human brain to obtain shared and cell-type-specific gene modules. Based on ASD risk genes, we identify shared and cell-type-specific ASD-associated gene modules. By analyzing these gene modules, we study the commonality and specificity among different cell types in dysregulated gene expression affected by ASD. The shared ASD-associated gene modules are mostly relevant to the functioning of synapses, while in different cell types, different kinds of gene functions may be specifically dysregulated in ASD, such as inhibitory extracellular ligand-gated ion channel activity in GABAergic interneurons and excitatory postsynaptic potential and ionotropic glutamate receptor signaling pathway in glutamatergic neurons. Our results provide new insights into the molecular mechanism and pathogenesis of ASD. The identification of shared and cell-type-specific ASD-related gene modules can facilitate the development of more targeted biomarkers and treatments for ASD.

Unravelling the Shared Genetic Mechanisms Underlying 18 Autoimmune Diseases Using a Systems Approach.

Autoimmune diseases (AiDs) are complex heterogeneous diseases characterized by hyperactive immune responses against self. Genome-wide association studies have identified thousands of single nucleotide polymorphisms (SNPs) associated with several AiDs. While these studies have identified a handful of pleiotropic loci that confer risk to multiple AiDs, they lack the power to detect shared genetic factors residing outside of these loci. Here, we integrated chromatin contact, expression quantitative trait loci and protein-protein interaction (PPI) data to identify genes that are regulated by both pleiotropic and non-pleiotropic SNPs. The PPI analysis revealed complex interactions between the shared and disease-specific genes. Furthermore, pathway enrichment analysis demonstrated that the shared genes co-occur with disease-specific genes within the same biological pathways. In conclusion, our results are consistent with the hypothesis that genetic risk loci associated with multiple AiDs converge on a core set of biological processes that potentially contribute to the emergence of polyautoimmunity.

Correlation in gene expression between the aggravation of chronic obstructive pulmonary disease and the occurrence of complications.

Aggravation of the chronic obstructive pulmonary disease (COPD) often leads to a slew of complications, but the correlation between COPD aggravation and the complications on the basis of molecular level remains unclear. In this study, gene expression profiles of COPD in patients at early and aggravation stages were collected and differentially-expressed genes were selected. Meanwhile, gene expression data implicated in COPD complications were analyzed to establish a regulatory network of COPD aggravation and COPD related complications. In addition, the gene enrichment function of DAVID6.7 was utilized to evaluate the similarities between COPD aggravation and COPD complications in term of biological process. By analyzing the genes of COPD aggravation and the COPD complications, we found 18 genes highly related to COPD aggravation, among which haptoglobin (HP) was correlated with 14 complications, followed by ADRB2, LCK and CA1, which were related to 13, 11 and 11 complications, respectively. As far as the complications concerned, obesity was regulated by 17 of the 18 genes, which indicated that there was a close correlation between COPD aggravation and obesity. Meanwhile, lung cancer, diabetes and heart failure were regulated by 15, 15 and 14 genes, respectively, among the 18 selected genes. This study suggested the driver genes of COPD aggravation were capable of extensively regulating COPD complications, which would provide a theoretical basis for development of cures for COPD and its complications.

Identification of 67 Pleiotropic Genes Associated With Seven Autoimmune/Autoinflammatory Diseases Using Multivariate Statistical Analysis.

Although genome-wide association studies (GWAS) have a dramatic impact on susceptibility locus discovery, this univariate approach has limitations in detecting complex genotype-phenotype correlations. Multivariate analysis is essential to identify shared genetic risk factors acting through common biological mechanisms of autoimmune/autoinflammatory diseases. In this study, GWAS summary statistics, including 41,274 single nucleotide polymorphisms (SNPs) located in 11,516 gene regions, were analyzed to identify shared variants of seven autoimmune/autoinflammatory diseases using the metaCCA method. Gene-based association analysis was used to refine the pleiotropic genes. In addition, GO term enrichment analysis and protein-protein interaction network analysis were applied to explore the potential biological functions of the identified genes. A total of 4,962 SNPs (P < 1.21 × 10-6) and 1,044 pleotropic genes (P < 4.34 × 10-6) were identified by metaCCA analysis. By screening the results of gene-based P-values, we identified the existence of 27 confirmed pleiotropic genes and highlighted 40 novel pleiotropic genes that achieved statistical significance in the metaCCA analysis and were also associated with at least one autoimmune/autoinflammatory in the VEGAS2 analysis. Using the metaCCA method, we identified novel variants associated with complex diseases incorporating different GWAS datasets. Our analysis may provide insights for the development of common therapeutic approaches for autoimmune/autoinflammatory diseases based on the pleiotropic genes and common mechanisms identified.