Metabolomic analysis of fibrotic mice combined with public RNA-Seq human lung data reveal potential diagnostic biomarker candidates for lung fibrosis

Diagnostic Application of Bronchoalveolar Lavage Fluid Analysis in Cases of Idiopathic Pulmonary Fibrosis in which Diagnosis Cannot Be Confirmed by High-Resolution Computed Tomography

PURPOSE

Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive fibrotic lung disorder characterized by dry cough, fatigue, and exacerbated dyspnea. The prognosis of IPF is notably unfavorable, becoming extremely poor when the disease advances acutely. Effective therapeutic intervention is essential to mitigate disease progression; hence, early diagnosis and treatment are paramount. When high-resolution computed tomography (HRCT) reveals usual interstitial pneumonia (UIP), a diagnosis of IPF can be established. However, when HRCT fails to conclusively confirm IPF, the diagnostic pathway becomes intricate and necessitates a multidisciplinary approach involving clinicians, radiologists, and pathologists. Consequently, the objective of this study was to investigate new diagnostic approaches through bronchoalveolar lavage (BAL) analysis.

METHODS

BAL is a commonly utilized diagnostic tool for interstitial lung diseases. We review the application of bronchoalveolar lavage (BALF) in idiopathic pulmonary fibrotic disease, emphasizing that the cellular and solute composition of the lower respiratory tract offers valuable insights.

RESULTS

This review delineates the advancements in diagnosing IPF cases that remain indeterminate via HRCT, leveraging BALF analysis. In contrast to surgical lung biopsy, BAL is minimally invasive and offers potential diagnostic utility through the identification of specific BALF biomarkers.

CONCLUSION

Augment the clinical diagnostic armamentarium for IPF, particularly in scenarios where HRCT findings are inconclusive.

Multi-omics integration reveals a nonlinear signature that precedes progression of lung fibrosis

Objectives

Idiopathic pulmonary fibrosis (IPF) is a devastating progressive interstitial lung disease with poor outcomes. While decades of research have shed light on pathophysiological mechanisms associated with the disease, our understanding of the early molecular events driving IPF and its progression is limited. With this study, we aimed to model the leading edge of fibrosis using a data-driven approach.

Methods

Multiple omics modalities (transcriptomics, metabolomics and lipidomics) of healthy and IPF lung explants representing different stages of fibrosis were combined using an unbiased approach. Multi-Omics Factor Analysis of datasets revealed latent factors specifically linked with established fibrotic disease (Factor1) and disease progression (Factor2).

Results

Features characterising Factor1 comprised well-established hallmarks of fibrotic disease such as defects in surfactant, epithelial-mesenchymal transition, extracellular matrix deposition, mitochondrial dysfunction and purine metabolism. Comparatively, Factor2 identified a signature revealing a nonlinear trajectory towards disease progression. Molecular features characterising Factor2 included genes related to transcriptional regulation of cell differentiation, ciliogenesis and a subset of lipids from the endocannabinoid class. Machine learning models, trained upon the top transcriptomics features of each factor, accurately predicted disease status and progression when tested on two independent datasets.

Conclusion

This multi-omics integrative approach has revealed a unique signature which may represent the inflection point in disease progression, representing a promising avenue for the identification of therapeutic targets aimed at addressing the progressive nature of the disease.

Proteomics analysis of lung tissue reveals protein makers for the lung injury of adjuvant arthritis rats

Lung injury is one of the common extra‑articular lesions in rheumatoid arthritis (RA). Due to its insidious onset and no obvious clinical symptoms, it can be easily dismissed in the early stage of diagnosis, which is one of the reasons that leads to a decline of the quality of life and subsequent death of patients with RA. However, its pathogenesis is still unclear and there is a lack of effective therapeutic targets. In the present study, tandem mass tag‑labeled proteomics was used to research the lung tissue proteins in RA model (adjuvant arthritis, AA) rats that had secondary lung injury. The aim of the present study was to identify the differentially expressed proteins related to RA‑lung injury, determine their potential role in the pathogenesis of RA‑lung injury and provide potential targets for clinical treatment. Lung tissue samples were collected from AA‑lung injury and normal rats. The differentially expressed proteins (DEPs) were identified by tandem mass spectrometry. Bioinformatic analysis was used to assess the biological processes and signaling pathways associated with these DEPs. A total of 310 DEPs were found, of which 244 were upregulated and 66 were downregulated. KEGG anlysis showed that 'fatty acid degradation', 'fatty acid metabolism', 'fatty acid elongation', 'complement and coagulation cascades', 'peroxisome proliferator‑activated receptor signaling pathway' and 'hypoxia‑inducible factor signaling pathway' were significantly upregulated in the lung tissues of AA‑lung injury. Immunofluorescence staining confirmed the increased expression of clusterin, serine protease inhibitors and complement 1qc in lung tissue of rats with AA lung injury. In the present study, the results revealed the significance of certain DEPs (for example, C9, C1qc and Clu) in the occurrence and development of RA‑lung injury and provided support through experiments to identify potential biomarkers for the early diagnosis and prevention of RA‑lung injury.

Radiogenomics Reveals Correlation between Quantitative Texture Radiomic Features of Biparametric MRI and Hypoxia-Related Gene Expression in Men with Localised Prostate Cancer

OBJECTIVES

To perform multiscale correlation analysis between quantitative texture feature phenotypes of pre-biopsy biparametric MRI (bpMRI) and targeted sequence-based RNA expression for hypoxia-related genes.

MATERIALS AND METHODS

Images from pre-biopsy 3T bpMRI scans in clinically localised PCa patients of various risk categories (n = 15) were used to extract textural features. The genomic landscape of hypoxia-related gene expression was obtained using post-radical prostatectomy tissue for targeted RNA expression profiling using the TempO-sequence method. The nonparametric Games Howell test was used to correlate the differential expression of the important hypoxia-related genes with 28 radiomic texture features. Then, cBioportal was accessed, and a gene-specific query was executed to extract the Oncoprint genomic output graph of the selected hypoxia-related genes from The Cancer Genome Atlas (TCGA). Based on each selected gene profile, correlation analysis using Pearson's coefficients and survival analysis using Kaplan-Meier estimators were performed.

RESULTS

The quantitative bpMR imaging textural features, including the histogram and grey level co-occurrence matrix (GLCM), correlated with three hypoxia-related genes (ANGPTL4, VEGFA, and P4HA1) based on RNA sequencing using the TempO-Seq method. Further radiogenomic analysis, including data accessed from the cBioportal genomic database, confirmed that overexpressed hypoxia-related genes significantly correlated with a poor survival outcomes, with a median survival ratio of 81.11:133.00 months in those with and without alterations in genes, respectively.

CONCLUSION

This study found that there is a correlation between the radiomic texture features extracted from bpMRI in localised prostate cancer and the hypoxia-related genes that are differentially expressed. The analysis of expression data based on cBioportal revealed that these hypoxia-related genes, which were the focus of the study, are linked to an unfavourable survival outcomes in prostate cancer patients.

Untargeted metabolomics and transcriptomics identified glutathione metabolism disturbance and PCS and TMAO as potential biomarkers for ER stress in lung

Endoplasmic reticulum (ER) stress is a cellular state that results from the overload of unfolded/misfolded protein in the ER that, if not resolved properly, can lead to cell death. Both acute lung infections and chronic lung diseases have been found related to ER stress. Yet no study has been presented integrating metabolomic and transcriptomic data from total lung in interpreting the pathogenic state of ER stress. Total mouse lungs were used to perform LC-MS and RNA sequencing in relevance to ER stress. Untargeted metabolomics revealed 16 metabolites of aberrant levels with statistical significance while transcriptomics revealed 1593 genes abnormally expressed. Enrichment results demonstrated the injury ER stress inflicted upon lung through the alteration of multiple critical pathways involving energy expenditure, signal transduction, and redox homeostasis. Ultimately, we have presented p-cresol sulfate (PCS) and trimethylamine N-oxide (TMAO) as two potential ER stress biomarkers. Glutathione metabolism stood out in both omics as a notably altered pathway that believed to take important roles in maintaining the redox homeostasis in the cells critical for the development and relief of ER stress, in consistence with the existing reports.

The role of metabolic reprogramming and de novo amino acid synthesis in collagen protein production by myofibroblasts: implications for organ fibrosis and cancer

Fibrosis is a pathologic condition resulting from aberrant wound healing responses that lead to excessive accumulation of extracellular matrix components, distortion of organ architecture, and loss of organ function. Fibrotic disease can affect every organ system; moreover, fibrosis is an important microenvironmental component of many cancers, including pancreatic, cervical, and hepatocellular cancers. Fibrosis is also an independent risk factor for cancer. Taken together, organ fibrosis contributes to up to 45% of all deaths worldwide. There are no approved therapies that halt or reverse fibrotic disease, highlighting the great need for novel therapeutic targets. At the heart of almost all fibrotic disease is the TGF-β-mediated differentiation of fibroblasts into myofibroblasts, the primary cell type responsible for the production of collagen and other matrix proteins and distortion of tissue architecture. Recent advances, particularly in the field of lung fibrosis, have highlighted the role that metabolic reprogramming plays in the pathogenic phenotype of myofibroblasts, particularly the induction of de novo amino acid synthesis pathways that are required to support collagen matrix production by these cells. In this review, we will discuss the metabolic changes associated with myofibroblast differentiation, focusing on the de novo production of glycine and proline, two amino acids which compose over half of the primary structure of collagen protein. We will also discuss the important role that synthesis of these amino acids plays in regulating cellular redox balance and epigenetic state.

There is detectable variation in the lipidomic profile between stable and progressive patients with idiopathic pulmonary fibrosis (IPF)

Background

Idiopathic pulmonary fibrosis (IPF) is a chronic interstitial lung disease characterized by fibrosis and progressive loss of lung function. The pathophysiological pathways involved in IPF are not well understood. Abnormal lipid metabolism has been described in various other chronic lung diseases including asthma and chronic obstructive pulmonary disease (COPD). However, its potential role in IPF pathogenesis remains unclear.

Methods

In this study, we used ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS) to characterize lipid changes in plasma derived from IPF patients with stable and progressive disease. We further applied a data-independent acquisition (DIA) technique called SONAR, to improve the specificity of lipid identification.

Results

Statistical modelling showed variable discrimination between the stable and progressive subjects, revealing differences in the detection of triglycerides (TG) and phosphatidylcholines (PC) between progressors and stable IPF groups, which was further confirmed by mass spectrometry imaging (MSI) in IPF tissue.

Conclusion

This is the first study to characterise lipid metabolism between stable and progressive IPF, with results suggesting disparities in the circulating lipidome with disease progression.