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. 2022 Apr 21;23(1):97.
doi: 10.1186/s12931-022-02013-w.

Lung tissue shows divergent gene expression between chronic obstructive pulmonary disease and idiopathic pulmonary fibrosis

Auyon J Ghosh  1   2 , Brian D Hobbs  1   2   3 , Jeong H Yun  1   2   3 , Aabida Saferali  1 , Matthew Moll  1   2 , Zhonghui Xu  1 , Robert P Chase  1 , Jarrett Morrow  1   3 , John Ziniti  1 , Frank Sciurba  4 , Lucas Barwick  5 , Andrew H Limper  6 , Kevin Flaherty  7 , Gerard Criner  8 , Kevin K Brown  9 , Robert Wise  10 , Fernando J Martinez  11 , Daniel McGoldrick  12 , Michael H Cho  1   2   3 , Dawn L DeMeo  1   2   3 , Edwin K Silverman  1   2   3 , Peter J Castaldi  1   3 ; NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium; Craig P Hersh  13   14   15
Collaborators, Affiliations

Lung tissue shows divergent gene expression between chronic obstructive pulmonary disease and idiopathic pulmonary fibrosis

Auyon J Ghosh et al. Respir Res. .

Abstract

Background: Chronic obstructive pulmonary disease (COPD) and idiopathic pulmonary fibrosis (IPF) are characterized by shared exposures and clinical features, but distinct genetic and pathologic features exist. These features have not been well-studied using large-scale gene expression datasets. We hypothesized that there are divergent gene, pathway, and cellular signatures between COPD and IPF.

Methods: We performed RNA-sequencing on lung tissues from individuals with IPF (n = 231) and COPD (n = 377) compared to control (n = 267), defined as individuals with normal spirometry. We grouped the overlapping differential expression gene sets based on direction of expression and examined the resultant sets for genes of interest, pathway enrichment, and cell composition. Using gene set variation analysis, we validated the overlap group gene sets in independent COPD and IPF data sets.

Results: We found 5010 genes differentially expressed between COPD and control, and 11,454 genes differentially expressed between IPF and control (1% false discovery rate). 3846 genes overlapped between IPF and COPD. Several pathways were enriched for genes upregulated in COPD and downregulated in IPF; however, no pathways were enriched for genes downregulated in COPD and upregulated in IPF. There were many myeloid cell genes with increased expression in COPD but decreased in IPF. We found that the genes upregulated in COPD but downregulated in IPF were associated with lower lung function in the independent validation cohorts.

Conclusions: We identified a divergent gene expression signature between COPD and IPF, with increased expression in COPD and decreased in IPF. This signature is associated with worse lung function in both COPD and IPF.

Keywords: COPD; IPF; RNA sequencing.

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Conflict of interest statement

EKS received grant support from GlaxoSmithKline and Bayer. CPH reports grant support from Boehringer-Ingelheim, Novartis, Bayer and Vertex, outside of this study. PJC has received grant support from GlaxoSmithKline and Bayer and consulting fees from GlaxoSmithKline and Novartis. MHC has received grant support from GlaxoSmithKline and Bayer, consulting fees from Genentech and AstraZeneca, and speakingfees from Illumina. DLD has received from the National Institutes of Health, the Alpha 1 Foundation and Bayer and honoraria from Novartis. KKB has received grants from NHLBI, serves on the board of the Open Source Imaging Consoritum (OSIC), and personal fees from Biogen, Galecto, Third Pole, Galapagos, Boehringer Ingelheim, Theravance, Pliant, Blade Therapeutics, Huitai Biomedicine, Lilly, Dispersol, DevPro Biopharma, Sanofi, Bristol Myers Squibb, and Humanetics outside the submitted work. LB has received contract funding from NHLBI, during the conduct of the study. RW has received personal fees from AstraZeneca, grants and personal fees from Boehringer Ingelheim, personal fees from Contrafect, personal fees from Roche-Genentech, personal fees from Merck, grants and personal fees from Verona, personal fees from Mylan/Theravance, non-financial support from Propeller Health, grants from Sanofi-Aventis, personal fees from AbbVie, personal fees from GSK, personal fees from ChemRx, personal fees from Kiniksa, personal fees from Bristol Myers Squibb, personal fees from Galderma, personal fees from Kamada, personal fees from Pulmonx, personal fees from Kinevant, personal fees from PureTech, personal fees from Arrowhead, personal fees from VaxArt, personal fees from Polarean, outside the submitted work. KF has received personal fees from Boehringer Ingelheim, personal fees from Roche/Genentech, personal fees from Bellerophon, personal fees from Respivant, personal fees from Blade Therapeutics, personal fees from Shionogi, outside the submitted work. FJM has received personal fees from GlaxoSmithKline, personal fees from AstraZeneca, personal fees from Boehringer Ingelheim, personal fees from Raziel, during the conduct of the study; personal fees and non-financial support from AstraZeneca, personal fees and non-financial support from Boehringer Ingelheim, non-financial support from ProterrixBio, personal fees, non-financial support and other from Genentech, personal fees and non-financial support from GlaxoSmithKline, personal fees from MD Magazine, personal fees from Methodist Hospital Brooklyn, personal fees and non-financial support from Miller Communications, personal fees and non-financial support from National Society for Continuing Education, personal fees from New York University, personal fees and non-financial support from PeerView Communications, personal fees and non-financial support from Chiesi, personal fees and non-financial support from Sunovion, personal fees from UpToDate, personal fees from WebMD/MedScape, other from Afferent/Merck, non-financial support from Gilead, non-financial support from Nitto, personal fees from Patara/Respivant, other from Biogen, other from Veracyte, non-financial support from Zambon, personal fees from American Thoracic Society, grants from NIH, personal fees and non-financial support from Physicians Education Resource, personal fees from Rockpointe, other from Prometic, grants from Rare Disease Healthcare Communications, personal fees and other from Bayer, other from Bridge Biotherapeutics, personal fees and non-financial support from Canadian Respiratory Network, grants from ProMedior/Roche, personal fees and non-financial support from Teva, personal fees from CME Outfitters, personal fees and non-financial support from Csl Behring, personal fees from Dartmouth University, personal fees from DevPro, from Gala, personal fees from Integritas, personal fees from IQVIA, personal fees from Projects in Knowledge, personal fees and non-financial support from Sanofi/Regeneron, from twoXAR, personal fees from Vindico, other from AbbVie, personal fees from Academy for Continuing Healthcare Learning, outside the submitted work. All other authors report nothing to disclose.

Figures

Fig. 1
Fig. 1
Principal component analysis (PCA) plots for lung tissue RNA sequencing data and volcano plots of differential expression results. Principal component analysis (PCA) plots for lung tissue RNA sequencing data and volcano plots of differential expression results. A PCA plot for control samples (orange) vs. IPF samples (blue). B PCA plot for control samples (orange) vs. COPD samples (blue). C Volcano plot of IPF vs. control differential expression results. FDR < 0.01 results shown in blue and results that did not meet FDR threshold are shown in orange. D Volcano plot of COPD vs. control differential expression results
Fig. 2
Fig. 2
Overlap of IPF and COPD vs. control differentially expressed genes. A Venn diagram of differentially expressed genes between IPF vs. control and COPD vs. control. B Scatterplot of overlapping differentially expressed genes with log2 fold change of IPF vs. control on x axis and log2 fold change of COPD vs. control on y axis. Genes with increased expression in IPF and COPD (Group 1) are in green; genes with increased expression in IPF but decreased expression in COPD (Group 2) are in red; genes with decreased expression in IPF but increased expression in COPD (Group 3) are in blue; genes with decreased expression in IPF and COPD (Group 4) are in orange
Fig. 3
Fig. 3
Hallmark pathway enrichment. A Heatmap of enrichment scores for differentially expressed genes in IPF and COPD vs. control with hierarchical clustering. Cells in red denote positive enrichment score whereas cells in blue denote negative enrichment score. B Density plot of genes ranks in IPF on x axis and gene ranks in COPD on y axis in TNF-alpha signaling via NFKB pathway. Color gradient represents the number of genes at the given rank coordinates, where darker shades of red denote the location with the highest number of genes and lighter shades of yellow denote the location with the lowest number genes. C Density plot of genes ranks in IPF on x axis and gene ranks in COPD on y axis in epithelial mesenchymal transition pathway. D Density plot of gene ranks in IPF on x axis and gene ranks in COPD on y axis in inflammatory response pathway. E Density plot of gene ranks in IPF on x axis and gene ranks in COPD on y axis in TGF Beta signaling pathway
Fig. 4
Fig. 4
Cell category composition of differentially expressed genes and cell deconvolution in IPF and COPD. A Histogram of differentially expressed genes in LTRC in each overlap group by single cell RNA sequencing (scRNASeq) defined cell category [26]. Cell category genes were defined by top 5 scRNASeq genes differentially expressed by each cell type within each category. Group 1: genes with increased expression in IPF and COPD; Group 2: genes with increased expression in IPF but decreased expression in COPD; Group 3: genes with decreased expression in IPF but increased expression in COPD; Group 4: genes with decreased expression in IPF and COPD. B Selected COPD-associated deconvoluted cell type proportion distributions across COPD, IPF, and control samples. C Selected IPF-associated deconvoluted cell type proportion distributions across COPD, IPF, and control samples
Fig. 5
Fig. 5
Association of GSVA scores from overlap groups with FEV1% predicted in LTRC. Scatterplots with trend lines of association of GSVA scores from overlap groups with FEV1% predicted in LTRC subjects. Turquoise represents COPD cases, purple represents IPF cases, and gray represents all other diagnoses. Trend lines are colored to represent overlap group. Spearman correlation coefficient and p value are shown for each association. Group 1: genes with increased expression in IPF and COPD; Group 2: genes with increased expression in IPF but decreased expression in COPD; Group 3: genes with decreased expression in IPF but increased expression in COPD; Group 4: genes with decreased expression in IPF and COPD. A Association of GSVA score from Group 1 genes with FEV1% predicted. B Association of GSVA score from Group 2 genes with FEV1% predicted. C Association of GSVA score from Group 3 with FEV1% predicted. D Association of GSVA score from Group 4 with FEV1% predicted. GSVA gene set variation analysis, LTRC Lung Tissue Research Consortium, COPD chronic obstructive pulmonary disease, IPF idiopathic pulmonary fibrosis, FEV1 forced expiratory volume over 1 s
Fig. 6
Fig. 6
Association of GSVA scores from overlap groups with DLCO % predicted in LTRC. Scatterplots with trend lines of association of GSVA scores from overlap groups with DLCO % predicted in LTRC subjects. Turquoise represents COPD cases, purple represents IPF cases, and gray represents all other diagnoses. Trend lines are colored to represent overlap group. Spearman correlation coefficient and p value are shown for each association. Group 1: genes with increased expression in IPF and COPD; Group 2: genes with increased expression in IPF but decreased expression in COPD; Group 3: genes with decreased expression in IPF but increased expression in COPD; Group 4: genes with decreased expression in IPF and COPD. A Association of GSVA score from Group 1 genes with DLCO % predicted. B Association of GSVA score from Group 2 genes with DLCO % predicted. C Association of GSVA score from Group 3 with DLCO % predicted. D Association of GSVA score from Group 4 with DLCO % predicted. GSVA gene set variation analysis, LTRC Lung Tissue Research Consortium, COPD chronic obstructive pulmonary disease, IPF idiopathic pulmonary fibrosis, DLCO diffusion capacity of carbon monoxide
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