Cluster analysis of transcriptomic datasets to identify endotypes of idiopathic pulmonary fibrosis

Noninvasive assessment of the lung inflammation-fibrosis axis by targeted imaging of CMKLR1

Precision management of fibrotic lung diseases is challenging due to their diverse clinical trajectories and lack of reliable biomarkers for risk stratification and therapeutic monitoring. Here, we validated the accuracy of CMKLR1 as an imaging biomarker of the lung inflammation-fibrosis axis. By analyzing single-cell RNA sequencing datasets, we demonstrated CMKLR1 expression as a transient signature of monocyte-derived macrophages (MDMφ) enriched in patients with idiopathic pulmonary fibrosis (IPF). Consistently, we identified MDMφ as the major driver of the uptake of CMKLR1-targeting peptides in a murine model of bleomycin-induced lung fibrosis. Furthermore, CMKLR1-targeted positron emission tomography in the murine model enabled quantification and spatial mapping of inflamed lung regions infiltrated by CMKLR1-expressing macrophages and emerged as a robust predictor of subsequent lung fibrosis. Last, high CMKLR1 expression by bronchoalveolar lavage cells identified an inflammatory endotype of IPF with poor survival. Our investigation supports the potential of CMKLR1 as an imaging biomarker for endotyping and risk stratification of fibrotic lung diseases.

In vitro co-culture studies and the crucial role of fibroblast-immune cell crosstalk in IPF pathogenesis

IPF is a fatal lung disease characterized by intensive remodeling of lung tissue leading to respiratory failure. The remodeling in IPF lungs is largely characterized by uncontrolled fibrosis. Fibroblasts and their contractile phenotype the myofibroblast are the main cell types responsible for typical wound healing responses, however in IPF, these responses are aberrant and result in the overactivation of fibroblasts which contributes to the inelasticity of the lung leading to a decrease in lung function. The specific mechanisms behind IPF pathogenesis have been elusive, but recently the innate and adaptive immunity have been implicated in the fibrotic processes of the disease. In connection with this, several in vitro co-culture models have been used to investigate the specific interactions occurring between fibroblasts and immune cells and how this contributes to the pathobiology of IPF. In this review, we discuss the in vitro models that have been used to examine the abnormal interactions between fibroblasts and cells of the innate and adaptive immune system, and how these contribute to the fibrotic processes in the lungs of IPF patients.

Identification and Validation of Genes Exhibiting Dynamic Alterations in Response to Bleomycin-Induced Pulmonary Fibrosis

Idiopathic pulmonary fibrosis (IPF) carries a high mortality rate and has a poor prognosis. The pathogenesis of pulmonary fibrosis (PF) is highly related to dysregulation of multiple RNAs. This study aims to identify and validate dysregulated RNAs that exhibited dynamic alterations in response to bleomycin (BLM)-induced PF. The results will provide therapeutic targets for patients suffering from IPF. Whole transcriptomic profiles of BLM-induced PF were obtained through high-throughput RNA sequencing. miRNA profiling was downloaded from GSE45789 database in the Gene Expression Omnibus (GEO). We identified the differentially expressed RNAs (DERNAs) that exhibited dynamic alterations in response to BLM-induced PF. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) pathway enrichment analysis were conducted to discovery regulatory processes of PF. Weighted gene co-expression network analysis (WGCNA), protein-protein interaction (PPI) analysis, and co-expression analysis were performed to identify key genes and pathogenic pattern during the progression of PF. MiRanda, miRcode, and TargetScan were utilized to predict target relationships in the potential competing endogenous RNA (ceRNA) network. The results were verified by qRT-PCR analysis. In the context of BLM-induced PF, this study identified a total of 167 differentially expressed messenger RNAs (DEmRNAs), 115 differentially expressed long non-coding RNAs (DElncRNAs), 45 differentially expressed circular RNAs (DEcircRNAs), and 87 differentially expressed microRNAs (DEmiRNAs). These RNA molecules showed dynamic alterations in response to BLM-induced PF. These DEmRNAs exhibited a predominant association with the biological processes pertaining to the organization of extracellular matrix. A regulatory network was built in PF, encompassing 31 DEmRNAs, 18 DE lncRNAs, 13 DEcircRNAs, and 13 DEmiRNAs. Several DERNA molecules were subjected to validate using additional BLM-induced PF model. The outcomes of this validation process shown a strong correlation with the results obtained from RNA sequencing analysis. The GSE213001 dataset was utilized to validate the expression levels and diagnostic efficacy of four specific hub mRNAs (CCDC80, CLU, COL5A1, and COL6A3) in individuals diagnosed with PF. In this study, we identified and validated several RNA molecules that exhibited dynamic alternations in response to BLM-induced PF. These dysregulated RNAs participated in the pathogenesis of PF and can be used as therapeutic targets for early-stage IPF. Although more work must be done to confirm the results, our study may provide directions for future studies.

Lung immune signatures define two groups of end-stage IPF patients

BACKGROUND

The role of the immune system in the pathobiology of Idiopathic Pulmonary Fibrosis (IPF) is controversial.

METHODS

To investigate it, we calculated immune signatures with Gene Set Variation Analysis (GSVA) and applied them to the lung transcriptome followed by unbiased cluster analysis of GSVA immune-enrichment scores, in 109 IPF patients from the Lung Tissue Research Consortium (LTRC). Results were validated experimentally using cell-based methods (flow cytometry) in lung tissue of IPF patients from the University of Pittsburgh (n = 26). Finally, differential gene expression and hypergeometric test were used to explore non-immune differences between clusters.

RESULTS

We identified two clusters (C#1 and C#2) of IPF patients of similar size in the LTRC dataset. C#1 included 58 patients (53%) with enrichment in GSVA immune signatures, particularly cytotoxic and memory T cells signatures, whereas C#2 included 51 patients (47%) with an overall lower expression of GSVA immune signatures (results were validated by flow cytometry with similar unbiased clustering generation). Differential gene expression between clusters identified differences in cilium, epithelial and secretory cell genes, all of them showing an inverse correlation with the immune response signatures. Notably, both clusters showed distinct features despite clinical similarities.

CONCLUSIONS

In end-stage IPF lung tissue, we identified two clusters of patients with very different levels of immune signatures and gene expression but with similar clinical characteristics. Weather these immune clusters differentiate diverse disease trajectories remains unexplored.

Topological data analysis identifies molecular phenotypes of idiopathic pulmonary fibrosis

BACKGROUND

Idiopathic pulmonary fibrosis (IPF) is a debilitating, progressive disease with a median survival time of 3-5 years. Diagnosis remains challenging and disease progression varies greatly, suggesting the possibility of distinct subphenotypes.

METHODS AND RESULTS

We analysed publicly available peripheral blood mononuclear cell expression datasets for 219 IPF, 411 asthma, 362 tuberculosis, 151 healthy, 92 HIV and 83 other disease samples, totalling 1318 patients. We integrated the datasets and split them into train (n=871) and test (n=477) cohorts to investigate the utility of a machine learning model (support vector machine) for predicting IPF. A panel of 44 genes predicted IPF in a background of healthy, tuberculosis, HIV and asthma with an area under the curve of 0.9464, corresponding to a sensitivity of 0.865 and a specificity of 0.89. We then applied topological data analysis to investigate the possibility of subphenotypes within IPF. We identified five molecular subphenotypes of IPF, one of which corresponded to a phenotype enriched for death/transplant. The subphenotypes were molecularly characterised using bioinformatic and pathway analysis tools identifying distinct subphenotype features including one which suggests an extrapulmonary or systemic fibrotic disease.

CONCLUSIONS

Integration of multiple datasets, from the same tissue, enabled the development of a model to accurately predict IPF using a panel of 44 genes. Furthermore, topological data analysis identified distinct subphenotypes of patients with IPF which were defined by differences in molecular pathobiology and clinical characteristics.