New insights into the natural history of bronchopulmonary dysplasia from proteomics and multiplexed immunohistochemistry

Bronchopulmonary dysplasia: analysis and validation of ferroptosis-related diagnostic biomarkers and immune cell infiltration features

BACKGROUND

Early and precise diagnosis of bronchopulmonary dysplasia (BPD) is essential to improve the prognosis of preterm infants with BPD. Studying ferroptosis-related genes for diagnostic markers of BPD was the objective of this study.

METHODS

Using the GEO database and the FerrDb database, we obtained the GSE32472 dataset and screened the ferroptosis-related differentially expressed mRNAs (FRDE-mRNAs). By using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG), possible biological functions and pathways were identified for FRDE-mRNAs. Three machine learning algorithms (LASSO, SVM-RFE, Random Forest) were used to recognize hub genes, as well as CIBERSORT for exploring the immune landscape of BPD and controls. Functional predictions for hub genes were made using single-gene gene set enrichment analysis (GSEA).

RESULTS

Twenty three FRDE-mRNAs were obtained and were mainly involved in autophagy, fatty acid metabolism and ferroptosis. The four hub genes (LPIN1, ACADSB, WIPI1 and SLC7A11) screened were utilized to construct a diagnostic nomogram. The receiver operating characteristic (ROC) curves and calibration curves demonstrateld that the nomogram exhibited good predictive performance. Eight types of immune cell markers differed significantly between BPD and controls.

CONCLUSION

We developed a diagnostic model for BPD, which could facilitate the early diagnosis and timely intervention of BPD.

IMPACT

The role of ferroptosis in bronchopulmonary dysplasia is rarely reported. The ferroptosis-related genes (LPIN1, ACADSB, WIPI1 and SLC7A11) we identified could serve as early diagnostic biomarkers for BPD. Immune cell infiltration features in BPD and signaling pathways associated with marker genes give new insight into the disease process and provide a basis for further research.

Unagi: Deep Generative Model for Deciphering Cellular Dynamics and In-Silico Drug Discovery in Complex Diseases

Human diseases are characterized by intricate cellular dynamics. Single-cell sequencing provides critical insights, yet a persistent gap remains in computational tools for detailed disease progression analysis and targeted in-silico drug interventions. Here, we introduce UNAGI, a deep generative neural network tailored to analyze time-series single-cell transcriptomic data. This tool captures the complex cellular dynamics underlying disease progression, enhancing drug perturbation modeling and discovery. When applied to a dataset from patients with Idiopathic Pulmonary Fibrosis (IPF), UNAGI learns disease-informed cell embeddings that sharpen our understanding of disease progression, leading to the identification of potential therapeutic drug candidates. Validation via proteomics reveals the accuracy of UNAGI's cellular dynamics analyses, and the use of the Fibrotic Cocktail treated human Precision-cut Lung Slices confirms UNAGI's predictions that Nifedipine, an antihypertensive drug, may have antifibrotic effects on human tissues. UNAGI's versatility extends to other diseases, including a COVID dataset, demonstrating adaptability and confirming its broader applicability in decoding complex cellular dynamics beyond IPF, amplifying its utility in the quest for therapeutic solutions across diverse pathological landscapes.