A functional genomic model for predicting prognosis in idiopathic pulmonary fibrosis.

BACKGROUND: The course of disease for patients with idiopathic pulmonary fibrosis (IPF) is highly heterogeneous. Prognostic models rely on demographic and clinical characteristics and are not reproducible. Integrating data from genomic analyses may identify novel prognostic models and provide mechanistic insights into IPF. METHODS: Total RNA of peripheral blood mononuclear cells was subjected to microarray profiling in a training (45 IPF individuals) and two independent validation cohorts (21 IPF/10 controls, and 75 IPF individuals, respectively). To identify a gene set predictive of IPF prognosis, we incorporated genomic, clinical, and outcome data from the training cohort. Predictor genes were selected if all the following criteria were met: 1) Present in a gene co-expression module from Weighted Gene Co-expression Network Analysis (WGCNA) that correlated with pulmonary function (p < 0.05); 2) Differentially expressed between observed "good" vs. "poor" prognosis with fold change (FC) >1.5 and false discovery rate (FDR) < 2%; and 3) Predictive of mortality (p < 0.05) in univariate Cox regression analysis. "Survival risk group prediction" was adopted to construct a functional genomic model that used the IPF prognostic predictor gene set to derive a prognostic index (PI) for each patient into either high or low risk for survival outcomes. Prediction accuracy was assessed with a repeated 10-fold cross-validation algorithm and independently assessed in two validation cohorts through multivariate Cox regression survival analysis. RESULTS: A set of 118 IPF prognostic predictor genes was used to derive the functional genomic model and PI. In the training cohort, high-risk IPF patients predicted by PI had significantly shorter survival compared to those labeled as low-risk patients (log rank p < 0.001). The prediction accuracy was further validated in two independent cohorts (log rank p < 0.001 and 0.002). Functional pathway analysis revealed that the canonical pathways enriched with the IPF prognostic predictor gene set were involved in T-cell biology, including iCOS, T-cell receptor, and CD28 signaling. CONCLUSIONS: Using supervised and unsupervised analyses, we identified a set of IPF prognostic predictor genes and derived a functional genomic model that predicted high and low-risk IPF patients with high accuracy. This genomic model may complement current prognostic tools to deliver more personalized care for IPF patients.

Probing ECM remodeling in idiopathic pulmonary fibrosis via second harmonic generation microscopy analysis of macro/supramolecular collagen structure.

Idiopathic pulmonary fibrosis (IPF) is a progressive disease with poor prognosis with short lifespan following diagnosis as patients have limited effective treatment options. A fundamental limitation is a lack of knowledge of the underlying collagen alterations in the disease, as this could lead to better diagnostics, prognostics, and measures of treatment efficacy. While the fibroses is the primary presentation of the disease, the collagen architecture has not been well studied beyond standard histology. Here, we used several metrics based on second harmonic generation (SHG) microscopy and optical scattering measurements to characterize the subresolution collagen assembly in human IPF and normal lung tissues. Using SHG directional analysis, we found that while collagen synthesis is increased in IPF, the resulting average fibril architecture is more disordered than in normal tissue. Wavelength-dependent optical scattering measurements lead to the same conclusion, and both optical approaches are consistent with ultrastructural analysis. SHG circular dichroism revealed significant differences in the net chirality between the fibrotic and normal collagen, where the former has a more randomized helical structure. Collectively, the measurements reveal significant changes in the collagen macro/supramolecular structure in the abnormal fibrotic collagen, and we suggest these alterations can serve as biomarkers for IPF diagnosis and progression.

Investigating Fibroblast-Induced Collagen Gel Contraction Using a Dynamic Microscale Platform.

Mechanical forces have long been recognized as fundamental drivers in biological processes, such as embryogenesis, tissue formation and disease regulation. The collagen gel contraction (CGC) assay has served as a classic tool in the field of mechanobiology to study cell-induced contraction of extracellular matrix (ECM), which plays an important role in inflammation and wound healing. In a conventional CGC assay, cell-laden collagen is loaded into a cell culture vessel (typically a well plate) and forms a disk-shaped gel adhering to the bottom of the vessel. The decrement in diameter or surface area of the gel is used as a parameter to quantify the degree of cell contractility. In this study, we developed a microscale CGC assay with an engineered well plate insert that uses surface tension forces to load and manipulate small volumes (14 µL) of cell-laden collagen. The system is easily operated with two pipetting steps and the microscale device moves dynamically as a result of cellular forces. We used a straightforward one-dimensional measurement as the gel contraction readout. We adapted a conventional lung fibroblast CGC assay to demonstrate the functionality of the device, observing significantly more gel contraction when human lung fibroblasts were cultured in serum-containing media vs. serum-free media (p ≤ 0.05). We further cocultured eosinophils and fibroblasts in the system, two important cellular components that lead to fibrosis in asthma, and observed that soluble factors from eosinophils significantly increase fibroblast-mediated gel contraction (p ≤ 0.01). Our microscale CGC device provides a new method for studying downstream ECM effects of intercellular cross talk using 7- to 35-fold less cell-laden gel than traditional CGC assays.