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

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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.

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Most cited references 37

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Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.

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The STRING database in 2021: customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets

Damian Szklarczyk, Annika Gable, Katerina C. Nastou … (2020)

Abstract Cellular life depends on a complex web of functional associations between biomolecules. Among these associations, protein–protein interactions are particularly important due to their versatility, specificity and adaptability. The STRING database aims to integrate all known and predicted associations between proteins, including both physical interactions as well as functional associations. To achieve this, STRING collects and scores evidence from a number of sources: (i) automated text mining of the scientific literature, (ii) databases of interaction experiments and annotated complexes/pathways, (iii) computational interaction predictions from co-expression and from conserved genomic context and (iv) systematic transfers of interaction evidence from one organism to another. STRING aims for wide coverage; the upcoming version 11.5 of the resource will contain more than 14 000 organisms. In this update paper, we describe changes to the text-mining system, a new scoring-mode for physical interactions, as well as extensive user interface features for customizing, extending and sharing protein networks. In addition, we describe how to query STRING with genome-wide, experimental data, including the automated detection of enriched functionalities and potential biases in the user's query data. The STRING resource is available online, at https://string-db.org/.

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NetworkAnalyst 3.0: a visual analytics platform for comprehensive gene expression profiling and meta-analysis

Guangyan Zhou, Othman Soufan, Jessica D Ewald … (2019)

Abstract The growing application of gene expression profiling demands powerful yet user-friendly bioinformatics tools to support systems-level data understanding. NetworkAnalyst was first released in 2014 to address the key need for interpreting gene expression data within the context of protein-protein interaction (PPI) networks. It was soon updated for gene expression meta-analysis with improved workflow and performance. Over the years, NetworkAnalyst has been continuously updated based on community feedback and technology progresses. Users can now perform gene expression profiling for 17 different species. In addition to generic PPI networks, users can now create cell-type or tissue specific PPI networks, gene regulatory networks, gene co-expression networks as well as networks for toxicogenomics and pharmacogenomics studies. The resulting networks can be customized and explored in 2D, 3D as well as Virtual Reality (VR) space. For meta-analysis, users can now visually compare multiple gene lists through interactive heatmaps, enrichment networks, Venn diagrams or chord diagrams. In addition, users have the option to create their own data analysis projects, which can be saved and resumed at a later time. These new features are released together as NetworkAnalyst 3.0, freely available at https://www.networkanalyst.ca.

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Author and article information

Contributors

Xi Cheng: URI : https://loop.frontiersin.org/people/1874674/overview

Huimin Xia: URI : https://loop.frontiersin.org/people/739956/overview

Journal

Journal ID (nlm-ta): Front Cell Dev Biol

Journal ID (iso-abbrev): Front Cell Dev Biol

Journal ID (publisher-id): Front. Cell Dev. Biol.

Title: Frontiers in Cell and Developmental Biology

Publisher: Frontiers Media S.A.

ISSN (Electronic): 2296-634X

Publication date (Electronic): 22 July 2022

Publication date Collection: 2022

Volume: 10

Electronic Location Identifier: 854425

Affiliations

Department of Obstetrics and Gynecology , Guangzhou Women and Children’s Medical Center , Guangdong Provincial Clinical Research Center for Child Health , Guangzhou Medical University , Guangzhou, China

Author notes

Edited by: Mohammad Taheri, Institute of Human Genetics, Germany

Reviewed by: Yanxing Wei, University of Toronto, Canada

Abbas Salihi, Salahaddin University, Iraq

Thomas Zheng, College of Medicine Phoenix, University of Arizona, United States

*Correspondence: Huimin Xia, xia-huimin@ 123456foxmail.com

[ † ]

These authors have contributed equally to this work

This article was submitted to Evolutionary Developmental Biology, a section of the journal Frontiers in Cell and Developmental Biology

Article

Publisher ID: 854425

DOI: 10.3389/fcell.2022.854425

PMC ID: 9354660

PubMed ID: 35938169

SO-VID: c6a9e9a8-ff72-439d-b17f-7bfbd86b8569

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This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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

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Most cited references 37

Cytoscape: a software environment for integrated models of biomolecular interaction networks.

The STRING database in 2021: customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets

NetworkAnalyst 3.0: a visual analytics platform for comprehensive gene expression profiling and meta-analysis

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