Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive lung disease, but its pathogenesis is still unclear. Bioinformatics methods were used to explore the differentially expressed genes (DEGs) and to elucidate the pathogenesis of IPF at the genetic level. The microarray datasets GSE110147 and GSE53845 were downloaded from the Gene Expression Omnibus (GEO) database and analyzed using GEO2R to obtain the DEGs. The DEGs were further analyzed for Gene Ontology (GO) and Kyoto Encyclopedia of Genomes (KEGG) pathway enrichment using the DAVID database. Then, using the STRING database and Cytoscape, a protein-protein interaction (PPI) network was created and the hub genes were selected. In addition, lung tissue from a mouse model was validated. Lastly, the network between the target microRNAs (miRNAs) and the hub genes was constructed with NetworkAnalyst. A summary of 240 genes were identified as DEGs, and functional analysis highlighted their role in cell adhesion molecules and ECM-receptor interactions in IPF. In addition, eight hub genes were selected. Four of these hub genes (VCAM1, CDH2, SPP1, and POSTN) were screened for animal validation. The IHC and RT-qPCR of lung tissue from a mouse model confirmed the results above. Then, miR-181b-5p, miR-4262, and miR-155-5p were predicted as possible key miRNAs. Eight hub genes may play a key role in the development of IPF. Four of the hub genes were validated in animal experiments. MiR-181b-5p, miR-4262, and miR-155-5p may be involved in the pathophysiological processes of IPF by interacting with hub genes.
Idiopathic Pulmonary Fibrosis , MicroRNAs , Animals , Mice , Gene Regulatory Networks , Idiopathic Pulmonary Fibrosis/genetics , Protein Interaction Maps/genetics , Computational Biology , Disease Models, Animal , MicroRNAs/genetics
Previous studies have reported a correlation between the dysregulation of intestinal microbiota and the occurrence of asthma. This study aimed to investigate the effect of probiotic Lactobacillus rhamnosus 76 (LR76) on ovalbumin (OVA)-allergic mice and the mechanism of LR76 affecting mucus secretion in asthma. OVA-allergic mice were supplemented with LR76, and 16HBE cells induced by interleukin-13 (IL-13) were treated with LR76 supernatant (LR76-s) to observe the effect of LR76. In OVA-sensitized mice, LR76 alleviated the inflammatory cell infiltration in lung tissue and reduced the inflammatory cell counts of BALF. The expression level of mRNA, including Il4, Il5, Il13, Il25, Tgfb1, Il10, and Ifng, was decreased in the lung tissue of mice in the LR76 group compared with the OVA group. MUC5AC expression was down-regulated, while SCGB1A1 was up-regulated in the lung tissue of OVA-allergic mice after being supplemented with LR76 and in 16HBE cells induced by IL-13 after incubating with LR76-s. LR76 and LR76-s down-regulated the expression of proteins, including STAT6, p-STAT6, and SPDEF, and mRNA of STAT6 and SPDEF. In conclusion, LR76 alleviated airway inflammation and Th2 response in OVA-allergic mice and improved the mucus secretion of mouse lung tissue and 16HBE cells in the asthma model by down-regulating STAT6/SPDEF pathway.
Asthma , Hypersensitivity , Lacticaseibacillus rhamnosus , Animals , Mice , Asthma/therapy , Asthma/metabolism , Bronchoalveolar Lavage Fluid , Disease Models, Animal , Hypersensitivity/metabolism , Inflammation/metabolism , Interleukin-13/genetics , Lung/metabolism , Mice, Inbred BALB C , Mucus , Ovalbumin/adverse effects , RNA, Messenger/metabolism , Transcription Factors/metabolism , Humans
Background: The frequent exacerbator phenotype of acute exacerbations of chronic obstructive pulmonary disease (AECOPD) is characterized by experiencing at least two exacerbations per year, leading to a significant economic burden on healthcare systems worldwide. Although several biomarkers have been shown to be effective in assessing AECOPD severity in recent years, there is a lack of studies on markers to predict the frequent exacerbator phenotype of AECOPD. The current study aimed to develop a new predictive model for the frequent exacerbator phenotype of AECOPD based on rapid, inexpensive, and easily obtained routine markers. Methods: This was a single-center, retrospective study that enrolled a total of 2,236 AECOPD patients. The participants were divided into two groups based on the frequency of exacerbations: infrequent group (n=1,827) and frequent group (n=409). They underwent a complete blood count, as well as blood biochemistry, blood lipid and coagulation testing, and general characteristics were also recorded. Univariate analysis and binary multivariate logistic regression analyses were used to explore independent risk factors for the frequent exacerbator phenotype of AECOPD, which could be used as components of a new predictive model. The receiver operator characteristic (ROC) curve was used to assess the predictive value of the new model, which consisted of all significant risk factors predicting the primary outcome. The nomogram risk prediction model was established using R software. Results: Age, gender, length of stay (LOS), neutrophils, monocytes, eosinophils, direct bilirubin (DBil), gamma-glutamyl transferase (GGT), and the glucose-to-lymphocyte ratio (GLR) were independent risk factors for the frequent exacerbator phenotype of AECOPD. The area under the curve (AUC) of the new predictive model was 0.681 [95% confidence interval (CI): 0.653-0.708], and the sensitivity was 63.6% (95% CI: 58.9-68.2%) and the specificity was 65.0% (95% CI: 60.3-69.6%). Conclusions: A new predictive model based on demographic characteristics and blood parameters can be used to predict the frequency of acute exacerbations in the management of chronic obstructive pulmonary disease (COPD).
BACKGROUND AND AIMS: Recent studies have shown that changes in the intestinal microbiota contribute to the pathogenesis of irritable bowel syndrome (IBS). This study aimed to investigate the characteristics of the fecal and intestinal mucosal microbiota in IBS patients, and the correlation between microbiota and clinical manifestations. METHODS: Fecal and intestinal mucosal samples were collected from 14 constipation-predominant IBS (IBS-C) patients, 20 diarrhea-predominant IBS (IBS-D) patients, and 20 healthy controls (HCs). 16S rRNA gene sequencing and fluorescence in situ hybridization were used for the analysis of samples. RESULTS: Community richness and diversity of the fecal microbiota in IBS patients were significantly reduced compared with the HCs. The mucosal samples in IBS patients showed decreased Bifidobacterium and increased Bacteroides caccae compared with HCs; Eubacterium and Roseburia were decreased in IBS-C patients and increased in IBS-D patients. A comparison of the fecal and mucosal microbiota in IBS patients showed significantly increased Bifidobacterium in fecal samples and a decrease in mucosal samples in IBS-C patients; Bacteroides caccae and Roseburia were significantly reduced in fecal samples and increased in mucosal samples of IBS patients. A correlation between microbiota and clinical manifestations in IBS patients showed that Bacteroides caccae and Roseburia in fecal samples and Bifidobacterium and Eubacterium in mucosal samples were associated with abdominal pain and distention. CONCLUSIONS: Distinct differences exist between the fecal and intestinal mucosal microbiota in IBS patients, with the changes in the latter appearing more consistent with the pathophysiology of IBS. Changes in intestinal microbiota were associated with the clinical manifestations in IBS.
Gastrointestinal Microbiome , Irritable Bowel Syndrome , Microbiota , Humans , Irritable Bowel Syndrome/complications , Gastrointestinal Microbiome/genetics , RNA, Ribosomal, 16S/genetics , In Situ Hybridization, Fluorescence , Diarrhea/etiology , Feces/microbiology , Clostridiales
Studies have shown that SQLE is highly expressed in a variety of tumours and promotes tumour progression. However, the role of SQLE in pancreatic cancer (PC) has not been reported. Here, we aim to study the role and molecular mechanism of SQLE in PC. Immunohistochemistry and functional experiments showed that SQLE was highly expressed in PC tissues and promoted the proliferation and invasion of PC cells. Terbinafine, an inhibitor of SQLE, inhibited this effect. In order to further study the upstream mechanism that regulates SQLE, we used bioinformatics technology to lock miR-133b and lncRNA-TTN-AS. In situ hybridization was used to detect the expression of miR-133b and lncRNA-TTN-AS1 in PC tissues. The luciferase reporter gene experiment was used to confirm the binding of miR-133b and lncRNA-TTN-AS1. The results showed that miR-133b was down-regulated in PC tissues and negatively correlated with the expression of SQLE. LncRNA-TTN-AS1 was upregulated in pancreatic cancer tissues and positively correlated with the expression of SQLE. Luciferase gene reporter gene analysis confirmed lncRNA-TTN-AS1 directly binded to miR-133b. Therefore, we propose that targeting the lncRNA-TTN-AS1/miR-133b/SQLE axis is expected to provide new ideas for the clinical treatment of PC patients.
MicroRNAs , Pancreatic Neoplasms , RNA, Long Noncoding , Squalene Monooxygenase , Humans , Cell Line, Tumor , Cell Movement/genetics , Cell Proliferation/genetics , Connectin/genetics , Gene Expression Regulation, Neoplastic , MicroRNAs/genetics , Pancreatic Neoplasms/genetics , Pancreatic Neoplasms/pathology , RNA, Long Noncoding/genetics , Squalene Monooxygenase/genetics , Pancreatic Neoplasms
DEAD-Box Helicase 5(DDX5), also known as P68, is one of the founding members of the DEAD-Box helicase superfamily and it plays a key role in RNA metabolism. Several studies have reported that DDX5 is involved in many types of tumors through abnormal expression, but the detailed mechanism of DDX5 in esophageal squamous cell carcinoma (ESCC) has not been elucidated. In this study, we demonstrate that the level of DDX5 is a negative prognostic factor for ESCC. The obtained results indicated that decreased expression of DDX5 inhibits ESCC cell proliferation and metastasis. Further experiments suggested that CDK2, Cyclin D1 and Vimentin were downregulated, while E-cadherin was upregulated after DDX5 was knocked down. In addition, DDX5 was positively correlated with the expression of BIP, phospho-eIF2α, phospho-PERK and P62, suggesting that knockdown of DDX5 can inhibit endoplasmic reticulum(ER) stress and promote the recovery of autophagy flux. Therefore, this study demonstrates that the downregulation of DDX5 in ESSC correlates to lower malignancy and presents a novel target for the development of new treatment strategies.
Autophagy/genetics , DEAD-box RNA Helicases/genetics , Endoplasmic Reticulum Stress/genetics , Esophageal Neoplasms/pathology , Esophageal Squamous Cell Carcinoma/pathology , Animals , Cell Line, Tumor , Cell Movement/genetics , Cell Proliferation/genetics , Esophageal Neoplasms/genetics , Esophageal Neoplasms/mortality , Esophageal Squamous Cell Carcinoma/genetics , Esophageal Squamous Cell Carcinoma/mortality , Gene Expression Regulation, Neoplastic , Humans , Mice, Inbred BALB C , Prognosis , Tissue Array Analysis
Background: The development of heart failure (HF) remains a common complication following an acute myocardial infarction (AMI), and is associated with substantial adverse outcomes. However, the specific predictive biomarkers and candidate therapeutic targets for post-infarction HF have not been fully established. We sought to perform a weighted gene co-expression network analysis (WGCNA) to identify key modules, hub genes, and possible regulatory targets involved in the development of HF following AMI. Methods: Genes exhibiting the most (top 50%) variation in expression levels across samples in a GSE59867 dataset were imported to the WGCNA. Gene Ontology and pathway enrichment analyses were performed on genes identified in the key module by Metascape. Gene regulatory networks were constructed using the microarray probe reannotation and bioinformatics database. Hub genes were screened out from the key module and validated using other datasets. Results: A total of 10,265 most varied genes and six modules were identified between AMI patients who developed HF within 6 months of follow-up and those who did not. Specifically, the blue module was found to be the most significantly related to the development of post-infarction HF. Functional enrichment analysis revealed that the blue module was primarily associated with the inflammatory response, immune system, and apoptosis. Seven transcriptional factors, including SPI1, ZBTB7A, IRF8, PPARG, P65, KLF4, and Fos, were identified as potential regulators of the expression of genes identified in the blue module. Further, non-coding RNAs, including miR-142-3p and LINC00537, were identified as having close interactions with genes from the blue module. A total of six hub genes (BCL3, HCK, PPIF, S100A9, SERPINA1, and TBC1D9B) were identified and validated for their predictive value in identifying future HFs. Conclusions: By using the WGCNA, we provide new insights into the underlying molecular mechanism and molecular markers correlated with HF development following an AMI, which may serve to improve risk stratification, therapeutic decisions, and prognosis prediction in AMI patients.
