Proteomics and metabonomics analyses of Covid-19 complications in patients with pulmonary fibrosis

Jianrong Yang, Chunxia Chen, Wan Chen, Luying Huang, Zhao Fu, Kun Ye, Liwen Lv, Zhihuang Nong, Xing Zhou, Wensheng Lu, Mei Zhong, Jianrong Yang, Chunxia Chen, Wan Chen, Luying Huang, Zhao Fu, Kun Ye, Liwen Lv, Zhihuang Nong, Xing Zhou, Wensheng Lu, Mei Zhong

Abstract

Pulmonary fibrosis is a devastating disease, and the pathogenesis of this disease is not completely clear. Here, the medical records of 85 Covid-19 cases were collected, among which fibrosis and progression of fibrosis were analyzed in detail. Next, data independent acquisition (DIA) quantification proteomics and untargeted metabolomics were used to screen disease-related signaling pathways through clustering and enrichment analysis of the differential expression of proteins and metabolites. The main imaging features were lesions located in the bilateral lower lobes and involvement in five lobes. The closed association pathways were FcγR-mediated phagocytosis, PPAR signaling, TRP-inflammatory pathways, and the urea cycle. Our results provide evidence for the detection of serum biomarkers and targeted therapy in patients with Covid-19.

Conflict of interest statement

The authors declare no competing interests.

© 2021. The Author(s).

Figures

Figure 1
Figure 1
Representative chest CT images. (a) Ground-glass opacities in the dorsal segment of the left lower lobe of lung were seen in a 44-year-old female on admission, and partial absorption of lung lesions before discharge (b), Multiple fibrotic stripes shadows in dorsal segment of the lower lobe of the right lung were seen in a 62-year-old male after symptom onset (c), however, no obvious changes were observed before discharge (d, e), multiple fibrotic stripes shadows and patchy dense shadow (exudative lesion) were shown in a 65-year-old severe male on admission. (f) The exudative lesions were less and the fibrosis lesions were more than before in 20 days after hospitalization.
Figure 2
Figure 2
Differentially expressed proteins (DEPs) were identified and clustered in Covid-19 patients with pulmonary fibrosis. (a) Volcano plots of total protein levels. Red dots, significantly upregulated proteins; green dots, significantly downregulated proteins. (b) Cluster chart of DEPs. Red rows, significantly upregulated proteins; blue rows present significantly downregulated proteins.
Figure 3
Figure 3
Quality control of samples in Covid-19 patients with pulmonary fibrosis. (a, b) Coefficient of variation (CV) plots for positive and negative ion modes. (c,d) PLS-DA model plots for positive and negative ion modes. A, Covid-19 patients without pulmonary fibrosis (n = 6). B, Covid-19 patients with pulmonary fibrosis (n = 22).
Figure 4
Figure 4
Positive differentially expressed metabolites were identified, clustered and enriched in Covid-19 patients with pulmonary fibrosis. (a) Volcano plots of positive compounds. Red dots, significantly upregulated metabolites; green dots, significantly downregulated metabolites. (b) Heatmap of different positive metabolites (“pheatmap” package in R software v3.5.0, https://www.r-project.org/). Each row denotes a different metabolite, and each column denotes a sample. A, Covid-19 patients without pulmonary fibrosis (n = 6). B, Covid-19 patients with pulmonary fibrosis (n = 22). (c) Bubble plot of KEGG enrichment of positive metabolic pathways. The size of the circle dot denotes the number of different metabolites. RichFactor is defined as the number of differential metabolites annotated to the pathway divided by all identified metabolites annotated to the pathway.
Figure 5
Figure 5
Differentially expressed proteins (DEPs) were identified and analyzed using proteomics in Covid-19 patients with progressive pulmonary fibrosis. (a) Volcano plots of total protein level. (b) Cluster chart of DEPs. (c) Enrichment analysis of DEPs by GO function. (d) Bubble plot of the top 20 KEGG enrichment pathways of DEPs. RichFactor is defined as the number of differential metabolites annotated to the pathways divided by all identified metabolites annotated to the pathway. (e) Pathway network of DEPs (“igraph” package in R software v3.5.0, https://www.r-project.org/). The red and blue dots represent up-regulated and down-regulated differential proteins, respectively. The purple balls represent the top 10 pathways of enrichment. The area is directly proportional to the enrichment degree.
Figure 6
Figure 6
Positive differentially expressed metabolites were identified and analyzed using proteomics in Covid-19 patients with progressive pulmonary fibrosis. (a) PLS-DA model plot for positive ion mode. (b) Volcano plots of positive compounds. (c) Heatmap of different positive metabolites (“pheatmap” package in R software v3.5.0, https://www.r-project.org/). (d) Bubble plot of the top 20 KEGG enrichment of positive metabolic pathways. C, Nonprogressive pulmonary fibrosis of Covid-19 patients. D, Progressive pulmonary fibrosis of Covid-19 patients. RichFactor is defined as the number of differential metabolites annotated to the pathway divided by all identified metabolites annotated to the pathway.
Figure 7
Figure 7
Association analysis of proteomics and metabolomics in Covid-19 patients with progressive pulmonary fibrosis. (a) Cluster heat map of the correlation between proteomics and metabolomics (“pheatmap” package in R software v3.5.0, https://www.r-project.org/). Each row denotes a differentially expressed metabolite, and each column denote a DEP. Blue, negative correlation; Red, positive correlation. (b) Concentric circles of correlation between differential proteins and differential metabolites. If the angle between a differential protein and differential metabolite is an acute angle (less than 90 degrees), the correlation is positive. If the angle between the differential protein and the differential metabolite is an oblique angle (greater than 90 degrees and less than 180 degrees), the correlation is negative. Starting from the center of the circle, the lines are connected to the differential metabolites and differential proteins. The longer the connection length, the stronger the relationship.

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