About This Project

Many patients recover from acute lung injury, while others develop life-threatening pulmonary fibrosis. We hypothesize that endothelial injury activates inflammatory and metabolic pathways that drive fibrotic remodeling and predict disease progression. This project combines bioinformatics, machine learning, and laboratory validation to identify these pathways, discover early biomarkers, and reveal therapeutic targets for preventing irreversible lung scarring.

Ask the Scientists

What is the context of this research?

Acute lung injury (ALI) can progress to pulmonary fibrosis, a condition characterized by irreversible lung scarring and impaired respiratory function. Previous studies have shown that endothelial injury promotes inflammation, fibroblast activation, and extracellular matrix deposition, which are key drivers of fibrosis. However, the molecular mechanisms linking endothelial dysfunction to fibrotic remodeling remain unclear.

This project addresses this knowledge gap by integrating genomic data analysis, machine learning, and laboratory validation to identify the genes and pathways involved in endothelial–fibroblast crosstalk. We hypothesize that endothelial injury activates distinct inflammatory and metabolic pathways that drive extracellular matrix remodeling and serve as early biomarkers of fibrotic progression. Identifying these mechanisms could improve early diagnosis and reveal new therapeutic targets.

What is the significance of this project?

Pulmonary fibrosis is a devastating disease with limited treatment options and no reliable way to predict which patients will develop progressive lung scarring after acute lung injury. By the time fibrosis is diagnosed, significant and often irreversible damage has already occurred.

This project addresses a critical gap in our understanding of how endothelial injury triggers extracellular matrix remodeling and fibrosis. By integrating genomic data, machine learning, and experimental validation, we aim to identify the key molecular pathways and biomarkers involved in this transition.

The resulting datasets, biomarker candidates, and mechanistic insights will provide valuable resources for the scientific community, helping researchers better understand fibrosis development and potentially accelerating the discovery of earlier diagnostic tools and new therapeutic targets.

What are the goals of the project?

This project will analyze two publicly available genomic datasets representing endothelial injury (GSE306296) and extracellular matrix remodeling (GSE281481) to identify shared molecular mechanisms involved in pulmonary fibrosis. Differential gene expression, pathway enrichment, protein–protein interaction network analysis, and machine-learning approaches will be used to identify shared pathways and prioritize candidate biomarkers. Preliminary analyses have already identified 51 shared differentially expressed genes and 10 hub genes, from which the most promising biomarkers will be selected. These candidates will then be validated using independent datasets and experimentally in an inflammatory endothelial–fibroblast co-culture model by RT-qPCR. This integrated approach aims to identify robust biomarkers and molecular pathways that predict fibrosis progression and reveal potential therapeutic targets.