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BackgroundIdiopathic pulmonary fibrosis (IPF) is a chronic progressive interstitial lung disease characterized by unexplained irreversible pulmonary fibrosis. Although the etiology of IPF is unclear, studies have shown that it is related to telomere length shortening. However, the prognostic value of telomere-related genes in IPF has not been investigated.MethodsWe utilized the GSE10667 and GSE110147 datasets as the training set, employing differential expression analysis and weighted gene co-expression network analysis (WGCNA) to screen for disease candidate genes. Then, we used consensus clustering analysis to identify different telomere patterns. Next, we used summary data-based mendelian randomization (SMR) analysis to screen core genes. We further evaluated the relationship between core genes and overall survival and lung function in IPF patients. Finally, we performed immune infiltration analysis to reveal the changes in the immune microenvironment of IPF.ResultsThrough differential expression analysis and WGCNA, we identified 35 significant telomere regulatory factors. Consensus clustering analysis revealed two distinct telomere patterns, consisting of cluster A (n = 26) and cluster B (n = 19). Immune infiltration analysis revealed that cluster B had a more active immune microenvironment, suggesting its potential association with IPF. Using GTEx eQTL data, our SMR analysis identified two genes with potential causal associations with IPF, including GPA33 (PSMR = 0.0013; PHEIDI = 0.0741) and MICA (PSMR = 0.0112; PHEIDI = 0.9712). We further revealed that the expression of core genes is associated with survival time and lung function in IPF patients. Finally, immune infiltration analysis revealed that NK cells were downregulated and plasma cells and memory B cells were upregulated in IPF. Further correlation analysis showed that GPA33 expression was positively correlated with NK cells and negatively correlated with plasma cells and memory B cells.ConclusionOur study provides a new perspective for the role of telomere dysfunction and immune infiltration in IPF and identifies potential therapeutic targets. Further research may reveal how core genes affect cell function and disease progression, providing new insights into the complex mechanisms of IPF.

Abstract 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.

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.

BackgroundIdiopathic 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.MethodsERS-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.ResultsThis 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.ConclusionWe 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.

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.