Systems biology analysis of lung fibrosis-related genes in the bleomycin mouse model

Activation of ILC2s through constitutive IFNγ signaling reduction leads to spontaneous pulmonary fibrosis

Pulmonary fibrosis (PF), a condition characterized by inflammation and collagen deposition in the alveolar interstitium, causes dyspnea and fatal outcomes. Although the bleomycin-induced PF mouse model has improved our understanding of exogenous factor-induced fibrosis, the mechanism governing endogenous factor-induced fibrosis remains unknown. Here, we find that Ifngr1-/-Rag2-/- mice, which lack the critical suppression factor for group 2 innate lymphoid cells (ILC2), develop PF spontaneously. The onset phase of fibrosis includes ILC2 subpopulations with a high Il1rl1 (IL-33 receptor) expression, and fibrosis does not develop in ILC-deficient or IL-33-deficient mice. Although ILC2s are normally localized near bronchioles and blood vessels, ILC2s are increased in fibrotic areas along with IL-33 positive fibroblasts during fibrosis. Co-culture analysis shows that activated-ILC2s directly induce collagen production from fibroblasts. Furthermore, increased IL1RL1 and decreased IFNGR1 expressions are confirmed in ILC2s from individuals with idiopathic PF, highlighting the applicability of Ifngr1-/-Rag2-/- mice as a mouse model for fibrosis research.

Proteomic Fingerprint of Lung Fibrosis Progression and Response to Therapy in Bleomycin-Induced Mouse Model

Idiopathic pulmonary fibrosis (IPF) is a chronic lung disease characterized by the aberrant accumulation of extracellular matrix in the lungs. nintedanib is one of the two FDA-approved drugs for IPF treatment; however, the exact pathophysiological mechanisms of fibrosis progression and response to therapy are still poorly understood. In this work, the molecular fingerprint of fibrosis progression and response to nintedanib treatment have been investigated by mass spectrometry-based bottom-up proteomics in paraffin-embedded lung tissues from bleomycin-induced (BLM) pulmonary fibrosis mice. Our proteomics results unveiled that (i) samples clustered depending on the tissue fibrotic grade (mild, moderate, and severe) and not on the time course after BLM treatment; (ii) the dysregulation of different pathways involved in fibrosis progression such as the complement coagulation cascades, advanced glycation end products (AGEs) and their receptors (RAGEs) signaling, the extracellular matrix-receptor interaction, the regulation of actin cytoskeleton, and ribosomes; (iii) Coronin 1A (Coro1a) as the protein with the highest correlation when evaluating the progression of fibrosis, with an increased expression from mild to severe fibrosis; and (iv) a total of 10 differentially expressed proteins (p_adj-value ≤ 0.05 and Fold change ≤-1.5 or ≥1.5), whose abundance varied in the base of the severity of fibrosis (mild and moderate), were modulated by the antifibrotic treatment with nintedanib, reverting their trend. Notably, nintedanib significantly restored lactate dehydrogenase B (Ldhb) expression but not lactate dehydrogenase A (Ldha). Notwithstanding the need for further investigations to validate the roles of both Coro1a and Ldhb, our findings provide an extensive proteomic characterization with a strong relationship with histomorphometric measurements. These results unveil some biological processes in pulmonary fibrosis and drug-mediated fibrosis therapy.

A potential three-gene-based diagnostic signature for idiopathic pulmonary fibrosis

Background: Idiopathic pulmonary fibrosis (IPF) is a life-threatening disease whose etiology remains unknown. This study aims to explore diagnostic biomarkers and pathways involved in IPF using bioinformatics analysis. Methods: IPF-related gene expression datasets were retrieved and downloaded from the NCBI Gene Expression Omnibus database. Differentially expressed genes (DEGs) were screened, and weighted correlation network analysis (WGCNA) was performed to identify key module and genes. Functional enrichment analysis was performed on genes in the clinically significant module. Then least absolute shrinkage and selection operator (LASSO) logistic regression and support vector machine-recursive feature elimination (SVM-RFE) algorithms were run to screen candidate biomarkers. The expression and diagnostic value of the biomarkers in IPF were further validated in external test datasets (GSE110147). Results: 292 samples and 1,163 DEGs were screened to construct WGCNA. In WGCNA, the blue module was identified as the key module, and 59 genes in this module correlated highly with IPF. Functional enrichment analysis of blue module genes revealed the importance of extracellular matrix-associated pathways in IPF. IL13RA2, CDH3, and COMP were identified as diagnostic markers of IPF via LASSO and SVM-RFE. These genes showed good diagnostic value for IPF and were significantly upregulated in IPF. Conclusion: This study indicates that IL13RA2, CDH3, and COMP could serve as diagnostic signature for IPF and might offer new insights in the underlying diagnosis of IPF.

FibROAD: a manually curated resource for multi-omics level evidence integration of fibrosis research

Organ fibrosis represents a vital health threat that substantially contributes to yearly mortality rates. While a considerable amount of research has been conducted on fibrosis, these reports have only focused on specific organs as affected within distinct disorders. Accordingly, results from such studies have been unable to provide a comprehensive understanding of the pathological processes involved. Here, we describe the development of FibROAD, an open-access database that integrates evidence from fibrosis-associated disorders as obtained from both the literature and multi-omics data. This resource will greatly assist both researchers and clinicians in the comprehension and treatment of this condition. FibROAD currently involves an assembly of 232 strong evidence-based fibrosis-related genes (FRGs) as garnered from 909 PubMed publications and contains lists of multi-omics data from > 4000 samples including RNA-seq, single-cell RNA-seq, miRNA-seq, ChIP-seq, ATAC-seq MeDIP-seq and MBD-seq as obtained from 17 different organs in 5 species. Results from integrative analyses as obtained using FibROAD have demonstrated that FRGs can be indicators for a wide range of organ fibrosis and reveal potential pro-fibrotic candidate genes for fibrosis research. In conclusion, FibROAD serves as a convenient platform where researchers can acquire integrated evidence and a more comprehensive understanding of fibrosis-related disorders.

Database URL  https://www.fibroad.org

Identification and Validation of Aging-Related Genes in Idiopathic Pulmonary Fibrosis

Aging plays a significant role in the occurrence and development of idiopathic pulmonary fibrosis (IPF). In this study, we aimed to identify and verify potential aging-associated genes involved in IPF using bioinformatic analysis. The mRNA expression profile dataset GSE150910 available in the Gene Expression Omnibus (GEO) database and R software were used to identify the differentially expressed aging-related genes involved in IPF. Hub gene expression was validated by other GEO datasets. Gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed on differentially expressed aging-related genes. Subsequently, aging-related genes were further screened using three techniques (least absolute shrinkage and selection operator (LASSO) regression, support vector machine, and random forest), and the receiver operating characteristic curves were plotted based on screening results. Finally, real-time quantitative polymerase chain reaction (qRT-PCR) was performed to verify the RNA expression of the six differentially expressed aging-related genes using the blood samples of patients with IPF and healthy individuals. Sixteen differentially expressed aging-related genes were detected, of which the expression of 12 were upregulated and four were downregulated. GO and KEGG enrichment analyses indicated the presence of several enriched terms related to senescence and apoptotic mitochondrial changes. Further screening by LASSO regression, support vector machine, and random forest identified six genes (IGF1, RET, IGFBP2, CDKN2A, JUN, and TFAP2A) that could serve as potential diagnostic biomarkers for IPF. Furthermore, qRT-PCR analysis indicated that among the above-mentioned six aging-related genes, only the expression levels of IGF1, RET, and IGFBP2 in patients with IPF and healthy individuals were consistent with the results of bioinformatic analysis. In conclusion, bioinformatics analysis identified 16 potential aging-related genes associated with IPF, and clinical sample validation suggested that among these, IGF1, RET, and IGFBP2 might play a role in the incidence and prognosis of IPF. Our findings may help understand the pathogenesis of IPF.