Unravelling the transcriptomic characteristics of bronchoalveolar lavage in post-covid pulmonary fibrosis

Abstract

Background: Post-Covid Pulmonary Fibrosis (PCPF) has emerged as a significant global issue associated with a poor quality of life and significant morbidity. Currently, our understanding of the molecular pathways of PCPF is limited. Hence, in this study, we performed whole transcriptome sequencing of the RNA isolated from the bronchoalveolar lavage (BAL) samples of PCPF and compared it with idiopathic pulmonary fibrosis (IPF) and non-ILD (Interstitial Lung Disease) control to understand the gene expression profile and associated pathways.

Methods: BAL samples from PCPF (n = 3), IPF (n = 3), and non-ILD Control (n = 3) (individuals with apparent healthy lung without interstitial lung disease) groups were obtained and RNA were isolated for whole transcriptomic sequencing. Differentially Expressed Genes (DEGs) were determined followed by functional enrichment analysis and qPCR validation.

Results: A panel of differentially expressed genes were identified in bronchoalveolar lavage fluid cells (BALF) of PCPF as compare to control and IPF. Our analysis revealed dysregulated pathways associated with cell cycle regulation, immune responses, and neuroinflammatory processes. Real-time validation further supported these findings. The PPI network and module analysis shed light on potential biomarkers and underscore the complex interplay of molecular mechanisms in PCPF. The comparison of PCPF and IPF identified a significant downregulation of pathways that were more prominent in IPF.

Conclusion: This investigation provides crucial insights into the molecular mechanism of PCPF and also outlines avenues for prospective research and the development of therapeutic approaches.

Keywords: BAL; CIBERSORTX; GSEA; IPF; PCPF; RNA-Seq.

Fig. 1

Summarizes the RNA-Seq analysis of PCPF and control samples. a shows a t-SNE plot highlighting the distinct separation of PCPF (red) and control (blue) samples based on their gene expression profiles. b presents a hierarchical clustering dendrogram, demonstrating clear grouping of PCPF and control samples. c is a volcano plot showing differentially expressed genes (DEGs) between the two groups, with upregulated genes in red, downregulated genes in green, and non-significant genes in gray. Finally, d is a heatmap of DEGs, with a color gradient representing gene expression levels (red for upregulation and green for downregulation

Fig. 2

The figure displays enrichment plots from Gene Set Enrichment Analysis (GSEA) highlighting key pathways differentially enriched between experimental groups. E2F Targets, G2M Checkpoint, Mitotic Spindle, MTORC1 Signaling, and Allograft Rejection are positively enriched, indicating their activation in the PCPF. Conversely, pathways such as KRAS Signaling Down, Complement, Myogenesis, Hypoxia, and Epithelial Mesenchymal Transition are negatively enriched, suggesting their suppression

Fig. 3

Top Gene Ontology (BP, CC, and MF) terms and KEGG pathways in PCPF compared to control

Fig. 4

a. qRT-PCR validation of top DEG in PCPF vs. Non-ILD Control (BAL sample), b. qRT-PCR validation of top DEG in PCPF vs. Healthy Control (PB sample)

Fig. 5

a Shows a PCA plot with clear separation between IPF (red) and PCPF (blue). b volcano plot highlighting significantly upregulated (red) and downregulated (green) genes in IPF. c presents a hierarchical clustering dendrogram showing distinct sample groupings by expression similarity. d displays a heatmap of differentially expressed genes, with red and green indicating upregulation and downregulation, respectively, emphasizing distinct molecular signatures in IPF

Fig. 6

Top Gene Ontology (BP, CC, and MF) terms and KEGG pathways in PCPF compared to IPF

Fig. 7

Venn diagram of number of DEGs common in IPF and PCPF