Construction and Bioinformatics Analysis of ceRNA Regulatory Networks in Idiopathic Pulmonary Fibrosis.

Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive form of pulmonary fibrosis of unknown etiology. Despite ongoing research, there is currently no cure for this disease. Recent studies have highlighted the significance of competitive endogenous RNA (ceRNA) regulatory networks in IPF development. Therefore, this study investigated the ceRNA network associated with IPF pathogenesis. We obtained gene expression datasets (GSE32538, GSE32537, GSE47460, and GSE24206) from the Gene Expression Omnibus (GEO) database and analyzed them using bioinformatics tools to identify differentially expressed messenger RNAs (DEmRNAs), microRNAs (DEmiRNAs), and long non-coding RNAs (DElncRNA). For DEmRNAs, we conducted an enrichment analysis, constructed protein-protein interaction networks, and identified hub genes. Additionally, we predicted the target genes of differentially expressed mRNAs and their interacting long non-coding RNAs using various databases. Subsequently, we screened RNA molecules with ceRNA regulatory relations in the lncACTdb database based on the screening results. Furthermore, we performed disease and functional enrichment analyses and pathway prediction for miRNAs in the ceRNA network. We also validated the expression levels of candidate DEmRNAs through quantitative real-time reverse transcriptase polymerase chain reaction and analyzed the correlation between the expression of these candidate DEmRNAs and the percent predicted pre-bronchodilator forced vital capacity [%predicted FVC (pre-bd)]. We found that three ceRNA regulatory axes, specifically KCNQ1OT1/XIST/NEAT1-miR-20a-5p-ITGB8, XIST-miR-146b-5p/miR-31-5p- MMP16, and NEAT1-miR-31-5p-MMP16, have the potential to significantly affect IPF progression. Further examination of the underlying regulatory mechanisms within this network enhances our understanding of IPF pathogenesis and may aid in the identification of diagnostic biomarkers and therapeutic targets.

An integrated meta-omics approach reveals the different response mechanisms of two anammox bacteria towards fluoroquinolone antibiotics.

The emerging fluoroquinolone antibiotics (FQs) are highly influential in nitrogen removal from livestock wastewater. However, beyond the capability of nitrogen removal, little is known about the molecular mechanisms (e.g., shift of core metabolism and energy allocation) of different anaerobic ammonium-oxidizing bacteria (AnAOB) under continuous FQ stress. This study investigated the effects of ciprofloxacin, ofloxacin and their mixture at concentrations detected in livestock wastewater on two key anammox species in membrane bioreactors. It was found 20 µg/L FQs promoted nitrogen removal efficiency and community stability, and42-51 % of FQs were removed simultaneously. Integrated meta-omics analysis revealed varied gene expression patterns between the two dominant AnAOB, Candidatus Brocadia sapporoensis (B AnAOB) and Candidatus Kuenenia stuttgartiensis (K AnAOB). The nitrogen metabolic processes were bolstered in B AnAOB, while those involved in anammox pathway of K AnAOB were inhibited. This difference was tentatively attributed to the up-regulation of reactive oxygen species scavenger genes (ccp and dxf) and FQ resistance gene (qnrB72) in B AnAOB. Importantly, most enhanced core biosynthesis/metabolism of AnAOB and close cross-feeding with accompanying bacteria were also likely to contribute to their higher levels of biomass yield and metabolism activity under FQ stress. This finding suggests that B AnAOB has the advantage of higher nitrogen metabolism capacity over K AnAOB in livestock wastewater containing FQs, which is helpful for efficient and stable nitrogen removal by the functional anammox species.

A selective CB2R agonist (JWH133) protects against pulmonary fibrosis through inhibiting FAK/ERK/S100A4 signaling pathways.

BACKGROUND: The combination of the endocannabinoid system (ECS) and the type 2 cannabinoid receptor (CB2R) can activate various signal pathways, leading to distinct pathophysiological roles. This interaction has gained significant attention in recent research on fibrosis diseases. Focal adhesion kinase (FAK) plays a crucial role in regulating signals from growth factor receptors and Integrins. It is also involved in the transformation of fibroblasts into myofibroblasts. This study aims to investigate the impact of the CB2R agonist JWH133 on lung fibrosis and its potential to alleviate pulmonary fibrosis in mice through the FAK pathway. METHODS: The C57 mice were categorized into five groups: control, BLM, BLM + JWH133, BLM + JWH133 + NC, and BLM + JWH133 + FAK groups.JWH133 was administered to mice individually or in conjunction with the FAK vector. After 21 days, pathological changes in mouse lung tissues, inflammatory factor levels, hydroxyproline levels, and collagen contents were evaluated. Moreover, the levels of the FAK/ERK/S100A4 pathway-related proteins were measured. RESULTS: JWH133 treatment decreased inflammatory factor levels, attenuated pathological changes, and reduced extracellular matrix accumulation in the mouse model of bleomycin-induced pulmonary fibrosis; however, these effects were reversed by FAK. JWH133 attenuated fibrosis by regulating the FAK/ERK/S100A4 pathway. CONCLUSIONS: The results presented in this study show that JWH133 exerts a protective effect against pulmonary fibrosis by inhibiting the FAK/ERK/S100A4 pathway.Therefore, JWH133 holds promise as a potential therapeutic target for pulmonary fibrosis.

Identification of immune biomarkers associated with basement membranes in idiopathic pulmonary fibrosis and their pan-cancer analysis.

Idiopathic pulmonary fibrosis (IPF) is a chronic progressive interstitial lung disease of unknown etiology, characterized by diffuse alveolitis and alveolar structural damage. Due to the short median survival time and poor prognosis of IPF, it is particularly urgent to find new IPF biomarkers. Previous studies have shown that basement membranes (BMs) are associated with the development of IPF and tumor metastasis. However, there is still a lack of research on BMs-related genes in IPF. Therefore, we investigated the expression level of BMs genes in IPF and control groups, and explored their potential as biomarkers for IPF diagnosis. In this study, the GSE32537 and GSE53845 datasets were used as training sets, while the GSE24206, GSE10667 and GSE101286 datasets were used as validation sets. In the training set, seven immune biomarkers related to BMs were selected by differential expression analysis, machine learning algorithm (LASSO, SVM-RFE, Randomforest) and ssGSEA analysis. Further ROC analysis confirmed that seven BMs-related genes played an important role in IPF. Finally, four immune-related Hub genes (COL14A1, COL17A1, ITGA10, MMP7) were screened out. Then we created a logistic regression model of immune-related hub genes (IHGs) and used a nomogram to predict IPF risk. The nomogram model was evaluated to have good reliability and validity, and ROC analysis showed that the AUC value of IHGs was 0.941 in the training set and 0.917 in the validation set. Pan-cancer analysis showed that IHGs were associated with prognosis, immune cell infiltration, TME, and drug sensitivity in 33 cancers, suggesting that IHGs may be potential targets for intervention in human diseases including IPF and cancer.

Molecular insights into enhanced nitrogen removal induced by trace fluoroquinolone antibiotics in an anammox system.

It has been widely reported that fluoroquinolones (FQs) can affect the anaerobic ammonium oxidization (anammox) microorganisms, which interferes with the performance of nitrogen removal from wastewater. However, the metabolic mechanism of anammox microorganisms responding to FQs has rarely been explored. In this study, it was found that 20 µg/L FQs promoted the nitrogen removal performance of anammox microorganisms in batch exposure assays, and 36-51% of FQs were removed simultaneously. Combined metabolomics and genome-resolved metagenomic analysis revealed up-regulated carbon fixation in anammox bacteria (AnAOB), while purine and pyrimidine metabolism, protein generation and transmembrane transport were enhanced in AnAOB and symbiotic bacteria by 20 µg/L FQs. Consequently, hydrazine dehydrogenation, nitrite reduction, and ammonium assimilation were bolstered, improving the nitrogen removal efficiency of the anammox system. These results revealed the potential roles of specific microorganisms in response to emerging FQs and provided further information for practical application of anammox technology in wastewater treatment.

Comprehensive analysis of an endoplasmic reticulum stress-related gene prediction model and immune infiltration in idiopathic pulmonary fibrosis.

Background: Idiopathic pulmonary fibrosis (IPF) is a chronic progressive interstitial lung disease. This study aimed to investigate the involvement of endoplasmic reticulum stress (ERS) in IPF and explore its correlation with immune infiltration. Methods: ERS-related differentially expressed genes (ERSRDEGs) were identified by intersecting differentially expressed genes (DEGs) from three Gene Expression Omnibus datasets with ERS-related gene sets. Gene Set Variation Analysis and Gene Ontology were used to explore the potential biological mechanisms underlying ERS. A nomogram was developed using the risk signature derived from the ERSRDEGs to perform risk assessment. The diagnostic value of the risk signature was evaluated using receiver operating characteristics, calibration, and decision curve analyses. The ERS score of patients with IPF was measured using a single-sample Gene Set Enrichment Analysis (ssGSEA) algorithm. Subsequently, a prognostic model based on the ERS scores was established. The proportion of immune cell infiltration was assessed using the ssGSEA and CIBERSORT algorithms. Finally, the expression of ERSRDEGs was validated in vivo and in vitro via RT-qPCR. Results: This study developed an 8-ERSRDEGs signature. Based on the expression of these genes, we constructed a diagnostic nomogram model in which agouti-related neuropeptide had a significantly greater impact on the model. The area under the curve values for the predictive value of the ERSRDEGs signature were 0.975 and 1.000 for GSE70866 and GSE110147, respectively. We developed a prognostic model based on the ERS scores of patients with IPF. Furthermore, we classified patients with IPF into two subtypes based on their signatures. The RT-qPCR validation results supported the reliability of most of our conclusions. Conclusion: We developed and verified a risk model using eight ERSRDEGs. These eight genes can potentially affect the progression of IPF by regulating ERS and immune responses.