Project description:Idiopathic pulmonary fibrosis (IPF) is a chronic and progressive fibrosing interstitial lung disease that is unresponsive to current therapy. While it carries a median survival of less than 3 years its rate of progression varies widely between patients. We hypothesized that studying the gene expression profiles of physiologically stable patients and those in which the disease progressed rapidly after the initial diagnosis would aid in the search for biomarkers and contribute to the understanding of disease pathogenesis. We generated 12 Idiopathic Pulmonary Fibrosis (IPF) lung parenchyma SAGE profiles. Initial cluster analysis including 8 other public available lung SAGE libraries verified that the IPF transcriptome is distinct from normal lung tissue and other lung diseases like COPD. In order to identify candidate markers of disease progression we segregated the IPF SAGE profiles in two groups based on clinical parameters regarding lung volume and lung function.
Project description:The response to a 4 hour treatment with TGFbeta (4 ng/ml) was evaluated in lung fibroblasts derived from three controls (normal periphery of resected tumor), open lung biopsies from three patients with idiopathic pulmonary fibrosis (usual interstitial pneumonia pattern on biopsy) and from three patients with fibrosing alveolitis associated with systemic sclerosis (fibrotic non specific interstitial pneumonia pattern on biopsy). Lung fibroblasts were grown to confluence in DMEM with 10% fetal calf serum. At confluence, lung fibroblasts were serum-deprived overnight, and exposed to either 4 ng/ml of activated TGF-Ã1 (R&D Systems) or serum-free culture medium with 0.1% BSA for four hours.
Project description:Idiopathic pulmonary fibrosis (IPF) is a chronic and progressive fibrosing interstitial lung disease that is unresponsive to current therapy. While it carries a median survival of less than 3 years its rate of progression varies widely between patients. We hypothesized that studying the gene expression profiles of physiologically stable patients and those in which the disease progressed rapidly after the initial diagnosis would aid in the search for biomarkers and contribute to the understanding of disease pathogenesis.
Project description:Pulmonary fibrosis includes a variety of underlying causes of fibrosing interstitial lung diseases (ILDs). Pulmonary fibrosis is usually considered to be related to chronic inflammation of lung tissue and excessive proliferation of fibroblasts, however, the mechanism is complex and unclear.To study the underlying molecular mechanisms, we further applied high-throughput sequencing to analyze the differentially expressed genes in rat lung tissues induced by Bleomycin.
Project description:Kynureninase is a member of a large family of catalytically diverse but structurally homologous pyridoxal 5'-phosphate (PLP) dependent enzymes known as the aspartate aminotransferase superfamily or alpha-family. The Homo sapiens and other eukaryotic constitutive kynureninases preferentially catalyze the hydrolytic cleavage of 3-hydroxy-l-kynurenine to produce 3-hydroxyanthranilate and l-alanine, while l-kynurenine is the substrate of many prokaryotic inducible kynureninases. The human enzyme was cloned with an N-terminal hexahistidine tag, expressed, and purified from a bacterial expression system using Ni metal ion affinity chromatography. Kinetic characterization of the recombinant enzyme reveals classic Michaelis-Menten behavior, with a Km of 28.3 +/- 1.9 microM and a specific activity of 1.75 micromol min-1 mg-1 for 3-hydroxy-dl-kynurenine. Crystals of recombinant kynureninase that diffracted to 2.0 A were obtained, and the atomic structure of the PLP-bound holoenzyme was determined by molecular replacement using the Pseudomonas fluorescens kynureninase structure (PDB entry 1qz9) as the phasing model. A structural superposition with the P. fluorescens kynureninase revealed that these two structures resemble the "open" and "closed" conformations of aspartate aminotransferase. The comparison illustrates the dynamic nature of these proteins' small domains and reveals a role for Arg-434 similar to its role in other AAT alpha-family members. Docking of 3-hydroxy-l-kynurenine into the human kynureninase active site suggests that Asn-333 and His-102 are involved in substrate binding and molecular discrimination between inducible and constitutive kynureninase substrates.
Project description:Antifibrotic therapy with nintedanib is the clinical mainstay in the treatment of progressive fibrosing interstitial lung disease (ILD). High-dimensional medical image analysis, known as radiomics, provides quantitative insights into organ-scale pathophysiology, generating digital disease fingerprints. Here, we used an integrative analysis of radiomic and proteomic profiles (radioproteomics) to assess whether changes in radiomic signatures can stratify the degree of antifibrotic response to nintedanib in (experimental) fibrosing ILD. Unsupervised clustering of delta radiomic profiles revealed two distinct imaging phenotypes in mice treated with nintedanib, contrary to conventional densitometry readouts, which showed a more uniform response. Integrative analysis of delta radiomics and proteomics demonstrated that these phenotypes reflected different treatment response states, as further evidenced on transcriptional and cellular levels. Importantly, radioproteomics signatures paralleled disease- and drug related biological pathway activity with high specificity, including extracellular matrix (ECM) remodeling, cell cycle activity, wound healing, and metabolic activity. Evaluation of the preclinical molecular response-defining features, particularly those linked to ECM remodeling, in a cohort of nintedanib-treated fibrosing ILD patients, accurately stratified patients based on their extent of lung function decline. In conclusion, delta radiomics has great potential to serve as a non-invasive and readily accessible surrogate of molecular response phenotypes in fibrosing ILD. This could pave the way for personalized treatment strategies and improved patient outcomes. References: Hallal, Mahmoud, Sophie Braga-Lagache, Jovana Jankovic, Cedric Simillion, Rémy Bruggmann, Anne-Christine Uldry, Ramanjaneyulu Allam, Manfred Heller, and Nicolas Bonadies. 2021. “Inference of Kinase-Signaling Networks in Human Myeloid Cell Line Models by Phosphoproteomics Using Kinase Activity Enrichment Analysis (KAEA).” BMC Cancer 21 (1): 789.