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. 2022 Jul 22:10:854425.
doi: 10.3389/fcell.2022.854425. eCollection 2022.

Comprehensive Analysis of Quantitative Proteomics With DIA Mass Spectrometry and ceRNA Network in Intrahepatic Cholestasis of Pregnancy

Dajun Fang  1 , Yan Fang  1 , Weiqiang Zhang  1 , Yun Xiang  1 , Xi Cheng  1 , Mingfeng Liang  1 , Huimin Xia  1
Affiliations

Comprehensive Analysis of Quantitative Proteomics With DIA Mass Spectrometry and ceRNA Network in Intrahepatic Cholestasis of Pregnancy

Dajun Fang et al. Front Cell Dev Biol. .

Abstract

Background: Intrahepatic cholestasis of pregnancy (ICP) is a pregnancy-specific complication characterized by pruritus without skin damage and jaundice. The poor perinatal outcomes include fetal distress, preterm birth, and unexpected intrauterine death. However, the mechanism of ICP leading to poor prognosis is still unclear. Methods: We analyzed 10 ICP and 10 normal placental specimens through quantitative proteomics of data-independent acquisition (DIA) to screen and identify differentially expressed proteins. GO, KEGG, COG/KOG, StringDB, InterProScan, Metascape, BioGPS, and NetworkAnalyst databases were used in this study. PITA, miRanda, TargetScan, starBase, and LncBase Predicted v.2 were used for constructing a competing endogenous RNA (ceRNA) network. Cytoscape was used for drawing regulatory networks, and cytoHubba was used for screening core nodes. The ICP rat models were used to validate the pathological mechanism. Results: GO, KEGG, and COG/KOG functional enrichment analysis results showed the differentially expressed proteins participated in autophagy, autophagosome formation, cofactor binding, JAK-STAT signaling pathway, and coenzyme transport and metabolism. DisGeNET analysis showed that these differentially expressed proteins were associated with red blood cell disorder and slow progression. We further analyzed first 12 proteins in the upregulated and downregulated differentially expressed proteins and incorporated clinicopathologic parameters. Our results showed HBG1, SPI1, HBG2, HBE1, FOXK1, KRT72, SLC13A3, MBD2, SP9, GPLD1, MYH7, and BLOC1S1 were associated with ICP development. ceRNA network analysis showed that MBD2, SPI1, FOXK1, and SLC13A3 were regulated by multiple miRNAs and lncRNAs. Conclusion: ICP was associated with autophagy. The ceRNA network of MBD2, SPI1, FOXK1, and SLC13A3 was involved in ICP progression, and these core proteins might be potential target.

Keywords: competing endogenous RNA (ceRNA) network; intrahepatic cholestasis of pregnancy; quantitative proteomics; regulatory mechanism; target therapy.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Functional enrichment analysis of differentially expressed proteins based on quantitative proteomics. (A) Results of OPLS-DA analysis. (B) Heatmap of ICP differentially expressed proteins. (C) Volcano map of ICP differentially expressed proteins. (D) Histogram about the number of differentially expressed proteins. (E) GO analysis of ICP differentially expressed proteins. (F) KEGG pathway analysis of ICP differentially expressed proteins. (G) Domain enrichment analysis of ICP differentially expressed proteins. (H) KOG enrichment analysis of ICP differentially expressed proteins.
FIGURE 2
FIGURE 2
Selection of core proteins from the PPI network. (A) PPI network of ICP differentially expressed proteins. (A) Results from the STRING database. (B) Results from the Cytoscape database. Red color: upregulated proteins; blue color: downregulated proteins. (B) Network about enriched terms of ICP differentially expressed proteins in the Metascape database (color by cluster ID). (C) MCODE analysis of ICP differentially expressed proteins and functional enrichment analysis of proteins in MCODE 1/2. (D) Summary of enrichment analysis in Cell Type Signature. (E) Summary of enrichment analysis in DisGeNET. (F) PPI network by value of protein expression difference.
FIGURE 3
FIGURE 3
Expression of core proteins. (A) Expression of upregulated proteins. (B) Expression of downregulated proteins. Error bars, SD. *p < 0.05; **p < 0.01; ***p < 0.001.
FIGURE 4
FIGURE 4
Tissue specificity of the protein expression. (A) Summary of enrichment analysis in PaGenBase. (B) Expression positioning of SIGLEC6, HBG2, and GH2 in the BioGPS database.
FIGURE 5
FIGURE 5
ceRNA and transcription factor regulatory network of 12 clinically significant proteins. (A) Transcription factor regulatory network of 12 clinically significant proteins. (B) Table of transcription factor regulatory network and 12 clinically significant proteins ranked by degree and betweenness. (C) Three independent miRNA target databases were used to predict the potential miRNAs for SPI1, FOXK1, SLC13A3, and MBD2. (D) lncRNA–miRNA network.
FIGURE 6
FIGURE 6
Validation of in vitro and in vivo experimental models. (A) HE results of the placental explant. NC Group: normal placenta; ICP group: normal placenta was stimulated for 48 h with 100 uM cholid acid. (B) Western blotting results and gray histogram of the placental explant. NC group: normal placenta; ICP group: the normal placenta was stimulated for 48 h with 100 uM cholid acid. (C)Appearance of fetal and placenta in ICP and normal pregnancy rat models. (D) Proportion of mature fetuses in ICP and normal pregnancy rat models. SD. *p < 0.05; **p < 0.01; ***p < 0.001.
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