Integrated analysis of the molecular mechanisms in idiopathic pulmonary fibrosis

Treatment of idiopathic pulmonary fibrosis and progressive pulmonary fibrosis: A position statement from the Thoracic Society of Australia and New Zealand 2023 revision

Idiopathic pulmonary fibrosis (IPF) is a progressive disease leading to significant morbidity and mortality. In 2017 the Thoracic Society of Australia and New Zealand (TSANZ) and Lung Foundation Australia (LFA) published a position statement on the treatment of IPF. Since that time, subsidized anti-fibrotic therapy in the form of pirfenidone and nintedanib is now available in both Australia and New Zealand. More recently, evidence has been published in support of nintedanib for non-IPF progressive pulmonary fibrosis (PPF). Additionally, there have been numerous publications relating to the non-pharmacologic management of IPF and PPF. This 2023 update to the position statement for treatment of IPF summarizes developments since 2017 and reaffirms the importance of a multi-faceted approach to the management of IPF and progressive pulmonary fibrosis.

Development of a Novel Biomarker for the Progression of Idiopathic Pulmonary Fibrosis

The progression of idiopathic pulmonary fibrosis (IPF) is diverse and unpredictable. We identified and validated a new biomarker for IPF progression. To identify a candidate gene to predict progression, we assessed differentially expressed genes in patients with advanced IPF compared with early IPF and controls in three lung sample cohorts. Candidate gene expression was confirmed using immunohistochemistry and Western blotting of lung tissue samples from an independent IPF clinical cohort. Biomarker potential was assessed using an enzyme-linked immunosorbent assay of serum samples from the retrospective validation cohort. We verified that the final candidate gene reflected the progression of IPF in a prospective validation cohort. In the RNA-seq comparative analysis of lung tissues, CD276, COL7A1, CTSB, GLI2, PIK3R2, PRAF2, IGF2BP3, and NUPR1 were up-regulated, and ADAMTS8 was down-regulated in the samples of advanced IPF. Only CTSB showed significant differences in expression based on Western blotting (n = 12; p < 0.001) and immunohistochemistry between the three groups of the independent IPF cohort. In the retrospective validation cohort (n = 78), serum CTSB levels were higher in the progressive group (n = 25) than in the control (n = 29, mean 7.37 ng/mL vs. 2.70 ng/mL, p < 0.001) and nonprogressive groups (n = 24, mean 7.37 ng/mL vs. 2.56 ng/mL, p < 0.001). In the prospective validation cohort (n = 129), serum CTSB levels were higher in the progressive group than in the nonprogressive group (mean 8.30 ng/mL vs. 3.00 ng/mL, p < 0.001). After adjusting for baseline FVC, we found that CTSB was independently associated with IPF progression (adjusted OR = 2.61, p < 0.001). Serum CTSB levels significantly predicted IPF progression (AUC = 0.944, p < 0.001). Serum CTSB level significantly distinguished the progression of IPF from the non-progression of IPF or healthy control.

An explainable machine learning-driven proposal of pulmonary fibrosis biomarkers

Pulmonary fibrosing diseases are in the very epicenter of biomedical research both due to their increasing prevalence and their association with SARS-CoV-2 infections. Research of idiopathic pulmonary fibrosis, the most lethal among the interstitial lung diseases, is in need for new biomarkers and potential disease targets, a goal that could be accelerated using machine learning techniques. In this study, we have used Shapley values to explain the decisions made by an ensemble learning model trained to classify samples to an either pulmonary fibrosis or steady state based on the expression values of deregulated genes. This process resulted in a full and a laconic set of features capable of separating phenotypes to an at least equal degree as previously published marker sets. Indicatively, a maximum increase of 6% in specificity and 5% in Mathew's correlation coefficient was achieved. Evaluation with an additional independent dataset showed our feature set having a greater generalization potential than the rest. Ultimately, the proposed gene lists are expected not only to serve as new sets of diagnostic marker elements, but also as a target pool for future research initiatives.

Construction of a TFs-miRNA-mRNA network related to idiopathic pulmonary fibrosis

Background

The transcription factors (TFs)-microRNA (miRNA)-messenger RNA (mRNA) network plays an important role in a variety of diseases. However, the relationship between the TFs-miRNA-mRNA network and idiopathic pulmonary fibrosis (IPF) remains unclear.

Methods

The GSE110147 and GSE53845 datasets from the Gene Expression Omnibus (GEO) database were used to process differentially expressed genes (DEGs) analysis, gene set enrichment analysis (GSEA), weighted gene co-expression network analysis (WGCNA), as well as Gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. The GSE13316 dataset was used to perform differentially expressed miRNAs (DEMs) analysis and TFs prediction. Finally, a TFs-miRNA-mRNA network related to IPF was constructed, and its function was evaluated by Gene Ontology (GO) and KEGG analyses. Also, 19 TFs in the network were verified by quantitative real time polymerase chain reaction (qRT-PCR).

Results

Through our analysis, 53 DEMs and 2,630 DEGs were screened. The GSEA results suggested these genes were mainly related to protein digestion and absorption. The WGCNA results showed that these DEGs were divided into eight modules, and the GO and KEGG analyses results of blue module genes showed that these 86 blue module genes were mainly enriched in cilium assembly and cilium organization. Moreover, a TFs-miRNA-mRNA network comprising 25 TFs, 11 miRNAs, and 60 mRNAs was constructed. Ultimately, the functional enrichment analysis showed that the TFs-miRNA-mRNA network was mainly related to the cell cycle and the phosphatidylinositol 3 kinase-protein kinase B (PI3K-Akt) signaling pathway. Furthermore, experimental verification of the TFs showed that ARNTL, TRIM28, EZH2, BCOR, and ASXL1 were sufficiently up-regulated in the transforming growth factor (TGF)-β1 treatment groups, while BCL6, BHLHE40, FOXA1, and EGR1 were significantly down-regulated.

Conclusions

The novel TFs-miRNA-mRNA network that we constructed could provide new insights into the underlying molecular mechanisms of IPF. ARNTL, TRIM28, EZH2, BCOR, ASXL1, BCL6, BHLHE40, FOXA1, and EGR1 may play important roles in IPF and become effective biomarkers for diagnosis and treatment.

Comprehensive analyses of the microRNA-messenger RNA-transcription factor regulatory network in mouse and human renal fibrosis

Objective: The aim of this study was to construct a microRNA (miRNA)-messenger RNA (mRNA)-transcription factor (TF) regulatory network and explore underlying molecular mechanisms, effective biomarkers, and drugs in renal fibrosis (RF). Methods: A total of six datasets were downloaded from Gene Expression Omnibus. "Limma" and "DESeq2" packages in R software and GEO2R were applied to identify the differentially expressed miRNAs and mRNAs (DEmiRNAs and DEmRNAs, respectively). The determination and verification of DEmiRNAs and DEmRNAs were performed through the integrated analysis of datasets from five mouse 7 days of unilateral ureteral obstruction datasets and one human chronic kidney disease dataset and the Human Protein Atlas (http://www.proteinatlas.org). Target mRNAs of DEmiRNAs and TFs were predicted by prediction databases and the iRegulon plugin in Cytoscape, respectively. A protein-protein interaction network was constructed using STRING, Cytoscape v3.9.1, and CytoNCA. Functional enrichment analysis was performed by DIANA-miRPath v3.0 and R package "clusterProfiler." A miRNA-mRNA-TF network was established using Cytoscape. Receiver operating characteristic (ROC) curve analysis was used to examine the diagnostic value of the key hub genes. Finally, the Comparative Toxicogenomics Database and Drug-Gene Interaction database were applied to identify potential drugs. Results: Here, 4 DEmiRNAs and 11 hub genes were determined and confirmed in five mouse datasets, of which Bckdha and Vegfa were further verified in one human dataset and HPA, respectively. Moreover, Bckdha and Vegfa were also predicted by miR-125a-3p and miR-199a-5p, respectively, in humans as in mice. The sequences of miR-125a-3p and miR-199a-5p in mice were identical to those in humans. A total of 6 TFs were predicted to regulate Bckdha and Vegfa across mice and humans; then, a miRNA-mRNA-TF regulatory network was built. Subsequently, ROC curve analysis showed that the area under the curve value of Vegfa was 0.825 (p = 0.002). Finally, enalapril was identified to target Vegfa for RF therapy. Conclusion: Pax2, Pax5, Sp1, Sp2, Sp3, and Sp4 together with Bckdha-dependent miR-125a-3p/Vegfa-dependent miR-199a-5p formed a co-regulatory network enabling Bckdha/Vegfa to be tightly controlled in the underlying pathogenesis of RF across mice and humans. Vegfa could act as a potential novel diagnostic marker and might be targeted by enalapril for RF therapy.

Zinc Deficiency Aggravates Oxidative Stress Leading to Inflammation and Fibrosis in Lung of Mice

Zinc (Zn) is an essential trace element for the body. Studies have confirmed that Zn deficiency can cause oxidative stress. The purpose of the present study was designed to investigate the effect of Zn on fibrosis in lung of mice and its mechanism. Mice were fed with different Zn levels dietary, then we found that the Zn-deficient diet induced a decrease of Zn level in lung tissue. The results also revealed the alveolar structure hyperemia and an inflammatory exudated in the alveolar cavity. Moreover, immunohistochemical results showed that the expression of α-smooth muscle actin (α-SMA) increased. And the Sirius red staining indicated an increase in collagen with Zn deficiency. Furthermore, oxygen radicals (ROS) levels were significantly increased, and the antioxidants were significantly decreased. Meanwhile, inflammatory factors (TNF-α and IL-1β) were remarkably increased, and the ELISA results showed that collagen I, III, and IV and fibronectin (FN) were increased. In addition, the expressions of matrix metalloproteinases (MMPs) and tissue inhibitors of metalloproteinase (TIMPs) were detected by qPCR. The results showed that the expression of TIMPs was increased but the expression of MMPs was decreased. The results of the experiment in vitro were consistent with that in vivo. All the results indicated that Zn deficiency aggravated the oxidative stress response of lung tissue to induce inflammation, leading to fibrosis in lung.

Identification of three hub genes related to the prognosis of idiopathic pulmonary fibrosis using bioinformatics analysis

Background: Idiopathic pulmonary fibrosis (IPF) is a chronic respiratory disease characterized by peripheral distribution of bilateral pulmonary fibrosis that is more pronounced at the base. IPF has a short median survival time and a poor prognosis. Therefore, it is necessary to identify effective prognostic indicators to guide the treatment of patients with IPF. Methods: We downloaded microarray data of bronchoalveolar lavage cells from the Gene Expression Omnibus (GEO), containing 176 IPF patients and 20 controls. The top 5,000 genes in the median absolute deviation were classified into different color modules using weighted gene co-expression network analysis (WGCNA), and the modules significantly associated with both survival time and survival status were identified as prognostic modules. We used Lasso Cox regression and multivariate Cox regression to search for hub genes related to prognosis from the differentially expressed genes (DEGs) in the prognostic modules and constructed a risk model and nomogram accordingly. Moreover, based on the risk model, we divided IPF patients into high-risk and low-risk groups to determine the biological functions and immune cell subtypes associated with the prognosis of IPF using gene set enrichment analysis and immune cell infiltration analysis. Results: A total of 153 DEGs located in the prognostic modules, three (TPST1, MRVI1, and TM4SF1) of which were eventually defined as prognostic hub genes. A risk model was constructed based on the expression levels of the three hub genes, and the accuracy of the model was evaluated using time-dependent receiver operating characteristic (ROC) curves. The areas under the curve for 1-, 2-, and 3-year survival rates were 0.862, 0.885, and 0.833, respectively. The results of enrichment analysis showed that inflammation and immune processes significantly affected the prognosis of patients with IPF. The degree of mast and natural killer (NK) cell infiltration also increases the prognostic risk of IPF. Conclusions: We identified three hub genes as independent molecular markers to predict the prognosis of patients with IPF and constructed a prognostic model that may be helpful in promoting therapeutic gains for IPF patients.