Identification of genes and functional coexpression modules closely related to ulcerative colitis by gene datasets analysis

Fmo5 plays a sex-specific role in goblet cell maturation and mucus barrier formation

Background and Aims

The intestine plays a key role in metabolism, nutrient and water absorption, and provides both physical and immunological defense against dietary and luminal antigens. The protective mucosal lining in the intestine is a critical component of intestinal barrier that when compromised, can lead to increased permeability, a defining characteristic of inflammatory bowel disease (IBD), among other intestinal diseases. Here, we define a new role for the flavin-containing monooxygenase (FMO) family of enzymes in maintaining a healthy intestinal epithelium.

Methods

Using Caenorhabditis elegans we measure intestinal barrier function, actin expression, and intestinal damage response. In mice, we utilize an intestine-specific, tamoxifen- inducible knockout model of the mammalian homolog of Cefmo-2 , Fmo5, and assess histology, mucus barrier thickness, and goblet cell physiology. We also treat mice with the ER chaperone Tauroursodeoxycholic acid (TUDCA).

Results

In nematodes, we find Cefmo-2 is necessary and sufficient for intestinal barrier function, intestinal actin expression, and is induced by intestinal damage. In mice, we find striking changes to the intestine within two weeks following Fmo5 disruption. Alterations include sex-dependent changes in colon epithelial histology, goblet cell localization, and mucus barrier formation. These changes are significantly more severe in female mice, mirroring differences observed in IBD patients. Furthermore, we find increased protein folding stress in Fmo5 knockout animals and successfully rescue the severe female phenotype with addition of a chemical ER chaperone.

Conclusions

Together, our results identify a highly conserved and novel role for Fmo5 in the mammalian intestine and support a key role for Fmo5 in maintenance of ER/protein homeostasis and proper mucus barrier formation.

Identification of the hub genes in polycystic ovary syndrome based on disease-associated molecule network

Revealing the key genes involved in polycystic ovary syndrome (PCOS) and elucidating its pathogenic mechanism is of extreme importance for the development of targeted clinical therapy for PCOS. Investigating disease by integrating several associated and interacting molecules in biological systems will make it possible to discover new pathogenic genes. In this study, an integrative disease-associated molecule network, combining protein-protein interactions and protein-metabolites interactions (PPMI) network was constructed based on the PCOS-associated genes and metabolites systematically collected. This new PPMI strategy identified several potential PCOS-associated genes, which have unreported in previous publications. Moreover, the systematic analysis of five benchmarks data sets indicated the DERL1 was identified as downregulated in PCOS granulosa cell and has good classification performance between PCOS patients and healthy controls. CCR2 and DVL3 were upregulated in PCOS adipose tissues and have good classification performance. The expression of novel gene FXR2 identified in this study is significantly increased in ovarian granulosa cells of PCOS patients compared with controls via quantitative analysis. Our study uncovers substantial differences in the PCOS-specific tissue and provides a plethora of information on dysregulated genes and metabolites that are linked to PCOS. This knowledgebase could have the potential to benefit the scientific and clinical community. In sum, the identification of novel gene associated with PCOS provides valuable insights into the underlying molecular mechanisms of PCOS and could potentially lead to the development of new diagnostic and therapeutic strategies.

Identification of diagnostic biomarks and immune cell infiltration in ulcerative colitis

We aimed to explore diagnostic biomarks and immune cell infiltration characteristics in ulcerative colitis (UC). We used the dataset GSE38713 as the training set and dataset GSE94648 as the test set. A total of 402 differentially expressed genes (DEGs) were obtained from GSE38713. Annotating, visualizing, and integrating discovery of these differential genes was performed using Gene Ontology (GO), Kyoto Gene and Genome Encyclopedia Pathway (KEGG), and Gene Set Enrichment Analysis (GSEA). Protein-protein interaction networks were constructed from the STRING database, and protein functional modules were identified using the CytoHubba plugin of Cytoscape. Random forest and LASSO regression were used to screen for UC-related diagnostic markers, and ROC curves were generated to validate their diagnostic value. The composition of 22 immune cells was analyzed, and the immune cell infiltration in UC was analyzed using CIBERSORT. Results: Seven diagnostic markers associated with UC were identified: TLCD3A, KLF9, EFNA1, NAAA,WDR4, CKAP4, and CHRNA1. Immune cell infiltration assessment revealed that macrophages M1, activated dendritic cells, and neutrophil cells infiltrated relatively more compared to normal control samples. Our results suggest a new functional feature of UC and suggest potential biomarkers for UC through comprehensive analysis of integrated gene expression data.

Screening of characteristic genes in ulcerative colitis by integrating gene expression profiles

Background

This study aimed to screen the feature modules and characteristic genes related to ulcerative colitis (UC) and construct a support vector machine (SVM) classifier to distinguish UC patients.

Methods

Four datasets that contained UC and control samples were obtained from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) with consistency were screened via the MetaDE method. The weighted gene coexpression network (WGCNA) was used to distinguish significant modules based on the four datasets. The protein–protein interaction network was established based on intersection genes. Enrichment analysis of Gene Ontology (GO) biological processes (BPs) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were established based on DAVID. An SVM combined with recursive feature elimination was also applied to construct a disease classifier for the disease diagnosis of UC patients. The efficacy of the SVM classifier was evaluated through receiver operating characteristic curves.

Results

Twelve highly preserved modules were obtained using the WGCNA, and 2009 DEGs with significant consistency were selected using the MetaDE method. Sixteen significantly related GO BPs and 12 KEGG pathways were obtained, such as cytokine-cytokine receptor interaction, cell adhesion molecules, and leukocyte transendothelial migration. Subsequently, 41 genes were used to construct an SVM classifier, such as CXCL1, CCR2, IL1B, and IL1A. The area under the curve (AUC) was 0.999 in the training dataset, whereas the AUC was 0.886, 0.790, and 0.819 in the validation set (GSE65114, GSE37283, and GSE36807, respectively).

Conclusions

An SVM classifier based on feature genes might correctly identify healthy people or UC patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12876-021-01940-0.

Meta-analysis of gene expression disease signatures in colonic biopsy tissue from patients with ulcerative colitis

Publicly available ulcerative colitis (UC) gene expression datasets from observational studies and clinical trials include inherently heterogeneous disease characteristics and methodology. We used meta-analysis to identify a robust UC gene signature from inflamed biopsies. Eight gene expression datasets derived from biopsy tissue samples from noninflammatory bowel disease (IBD) controls and areas of active inflammation from patients with UC were publicly available. Expression- and meta-data were downloaded with GEOquery. Differentially expressed genes (DEG) in individual datasets were defined as those with fold change > 1.5 and a Benjamini–Hochberg adjusted P value < .05. Meta-analysis of all DEG used a random effects model. Reactome pathway enrichment analysis was conducted. Meta-analysis identified 946 up- and 543 down-regulated genes in patients with UC compared to non-IBD controls (1.2 and 1.7 times fewer up- and down-regulated genes than the median of the individual datasets). Top-ranked up- and down-regulated DEG were LCN2 and AQP8. Multiple immune-related pathways (e.g., ‘Chemokine receptors bind chemokine’ and ‘Interleukin-10 signaling’) were significantly up-regulated in UC, while ‘Biological oxidations’ and ‘Fatty acid metabolism’ were downregulated. A web-based data-mining tool with the meta-analysis results was made available (https://premedibd.com/genes.html). A UC inflamed biopsy disease gene signature was derived. This signature may be an unbiased reference for comparison and improve the efficiency of UC biomarker studies by increasing confidence for identification of disease-related genes and pathways.

Expression and Clinical Significance of BCL2 Interacting Protein 3 Like in Multiple Myeloma

Multiple myeloma (MM) is one of the main blood disorders threatening human health today. This study aimed to examine the expression of BCL-2/adenovirus E1B 19 kDa-interacting protein 3-like (BNIP3L) in patients with MM and explore its mechanisms in silico. Bone marrow samples (n = 36 from patients with MM and n = 12 from healthy donors) were used to conduct BNIP3L expression analysis using immunohistochemistry. Microarray or RNA sequencing data from the Sequence Read Archive, Gene Expression Omnibus, and ArrayExpress databases were used to appraise BNIP3L expression and its prognostic role in patients with MM. The co-expressed genes of BNIP3L were identified for enrichment and protein-protein interaction (PPI) analyses to determine the associated signaling pathways. Immunohistochemistry indicated that BNIP3L expression in bone marrow of patients with MM was significantly lower than that in bone marrow of healthy donors. BNIP3L mRNA expression was also significantly lower in patients with MM than in healthy donors. The overall standard mean difference (SMD) for downregulation of BNIP3L was −0.62 [−1.17, −0.06], and the area under the curve was 0.81 [0.78, 0.85] based on a total of 694 MM cases. The overall survival analysis demonstrated that BNIP3L levels could act as an independent protective indicator of MM patient survival (HR = 0.79). Moreover, 261 co-expressed genes of BNIP3L were confirmed and found to be mainly involved in the adipocytokine signaling pathway. We preliminarily proved that downregulation of BNIP3L may play an important role in the occurrence and development of MM, and the promoting cancer capacity may be related to the pathway of adipocytokine signaling pathway.

Upregulation of ATIC in multiple myeloma tissues based on tissue microarray and gene microarrays

PURPOSE

Multiple myeloma (MM) is characterized by the malignant proliferation of plasma cells, which produce a monoclonal immunoglobulin protein. The role of 5-aminoimidazole-4-carboxamide ribonucleotide formyltransferase/IMP cyclohydrolase (ATIC) has not yet been well studied in the area of MM. Thus, in the current study, we sought to examine the expression levels, including mRNA and protein levels of ATIC in MM.

METHODS

Multiple myeloma microarray and RNA-seq data were screened from the SRA, GEO, ArrayExpress, and Oncomine databases. The mRNA level of ATIC was extracted from the high throughput data, and the prognostic value was studied. The protein level of ATIC was also detected by in-house immunohistochemistry on a tissue microarray. Potential signaling pathways were enriched with ATIC-related genes in MM.

RESULTS

Both the mRNA and protein levels of ATIC were significantly upregulated in MM samples as compared to normal samples. Furthermore, the summarized Standardized Mean Difference was 1.66 with 674 cases of MM based on 10 independent studies including the in-house tissue microarray. The overall hazard ratio of ATIC in MM was 1.7 with 1631 cases of MM based on five microarrays. In the KEGG pathway analysis, the ATIC-related genes were mainly enriched in the pathway of complement and coagulation cascades.

CONCLUSION

We provided the first evidence supporting the upregulation of ATIC may play an essential part in the tumorigenesis and development of MM. The promoting cancer capacity may be related to the pathway of complement and coagulation cascades.

Carboxypeptidase A6 was identified and validated as a novel potential biomarker for predicting the occurrence of active ulcerative colitis

Ulcerative colitis (UC) is a chronic, highly heterogeneous intestinal inflammation with changes in epithelial function and tissue damage. However, the pathogenesis is still unclear between active UC and inactive UC. Herein, weighted gene co‐expression network analysis was applied to explore the gene modules related to active UC. Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were used to further investigate the underlying mechanism of selected genes. We found that in the blue module (r = −.72), carboxypeptidase A6 (CPA6) was chosen to validate because of its high intra‐modular connectivity and module membership. In the test sets, the expression level of CPA6 was down‐regulated in active UC compared with inactive UC and normal colon. Furthermore, CPA6 expression was decreased primarily in the descending colon and only in mucosa affected by active UC. The receiver operating characteristic curve indicated that CPA6 expression had a performed well in diagnosing active UC from inactive UC (area under the curve = 0.99). Importantly, anti‐tumour necrosis factor (TNF) treatment (infliximab and golimumab) significantly increased the CPA6 expression. Finally, GSEA and GSVA found that extracellular matrix receptor, inflammatory response and epithelial‐mesenchymal transition were highly enriched in active UC with low CPA6 expression. In conclusion, CPA6 was identified and validated as a novel potential biomarker for predicting the occurrence of active UC, probably through regulating extracellular matrix or immune response.