Insights into potential mechanisms of asthma patients with COVID-19: A study based on the gene expression profiling of bronchoalveolar lavage fluid

Jiang, Yong; Yan, Qian; Liu, Chengxin; Peng, Chen-Wen; Zheng, Wenjiang; Zhuang, Hongfa; Huang, Hui-Ting; Liu, Qiong; Liao, Huili; Zhan, Shaofeng; Liu, Xiaohong; Huang, Xiufang

doi:10.1016/j.compbiomed.2022.105601

Cited by 11 publications

(4 citation statements)

References 131 publications

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“…Ultimately, EEF1A1, EGR1, UBA52, DDX5 and IRF8 might be the key shared pathogenesis-related genes in COVID-19 and asthma. Moreover, LY294002, wortmannin, PD98059 and heparin were found to be potential agents for treating COVID-19 in the context of asthma [ 39 ]. The research method of this study was similar to ours, but the results were different.…”

Section: Discussionmentioning

confidence: 99%

Bioinformatic analysis and preliminary validation of potential therapeutic targets for COVID-19 infection in asthma patients

Liu

Duo

et al. 2022

Cell Commun Signal

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Background Severe acute respiratory syndrome coronavirus 2 causes coronavirus disease 19 (COVID-19). The number of confirmed cases of COVID-19 is also rapidly increasing worldwide, posing a significant challenge to human safety. Asthma is a risk factor for COVID-19, but the underlying molecular mechanisms of the asthma–COVID-19 interaction remain unclear. Methods We used transcriptome analysis to discover molecular biomarkers common to asthma and COVID-19. Gene Expression Omnibus database RNA-seq datasets (GSE195599 and GSE196822) were used to identify differentially expressed genes (DEGs) in asthma and COVID-19 patients. After intersecting the differentially expressed mRNAs, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to identify the common pathogenic molecular mechanism. Bioinformatic methods were used to construct protein–protein interaction (PPI) networks and identify key genes from the networks. An online database was used to predict interactions between transcription factors and key genes. The differentially expressed long noncoding RNAs (lncRNAs) in the GSE195599 and GSE196822 datasets were intersected to construct a competing endogenous RNA (ceRNA) regulatory network. Interaction networks were constructed for key genes with RNA-binding proteins (RBPs) and oxidative stress-related proteins. The diagnostic efficacy of key genes in COVID-19 was verified with the GSE171110 dataset. The differential expression of key genes in asthma was verified with the GSE69683 dataset. An asthma cell model was established with interleukins (IL-4, IL-13 and IL-17A) and transfected with siRNA-CXCR1. The role of CXCR1 in asthma development was preliminarily confirmed. Results By intersecting the differentially expressed genes for COVID-19 and asthma, 393 common DEGs were obtained. GO and KEGG enrichment analyses of the DEGs showed that they mainly affected inflammation-, cytokine- and immune-related functions and inflammation-related signaling pathways. By analyzing the PPI network, we obtained 10 key genes: TLR4, TLR2, MMP9, EGF, HCK, FCGR2A, SELP, NFKBIA, CXCR1, and SELL. By intersecting the differentially expressed lncRNAs for COVID-19 and asthma, 13 common differentially expressed lncRNAs were obtained. LncRNAs that regulated microRNAs (miRNAs) were mainly concentrated in intercellular signal transduction, apoptosis, immunity and other related functional pathways. The ceRNA network suggested that there were a variety of regulatory miRNAs and lncRNAs upstream of the key genes. The key genes could also bind a variety of RBPs and oxidative stress-related genes. The key genes also had good diagnostic value in the verification set. In the validation set, the expression of key genes was statistically significant in both the COVID-19 group and the asthma group compared with the healthy control group. CXCR1 expression was upregulated in asthma cell models, and interference with CXCR1 expression significantly reduced cell viability. Conclusions Key genes may become diagnostic and predictive biomarkers of outcomes in COVID-19 and asthma.

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Section: Discussionmentioning

confidence: 99%

Bioinformatic analysis and preliminary validation of potential therapeutic targets for COVID-19 infection in asthma patients

Liu

Duo

et al. 2022

Cell Commun Signal

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“…Picciotto et al indicated that UBA52 is rapidly upregulated after T-cell activation (de Picciotto et al, 2022), and it may be involved in effector T-cell activation. In addition, UBA52 was found to be highly expressed in patients with COVID-19 (Jiang et al, 2022), which may be related to COVID-19 pathogenesis. Thus, the differential expression of UBA52 in blood T cells helps to distinguish different prime-boost vaccination strategies.…”

Section: Classification Rules In Immune Cellsmentioning

confidence: 93%

Immune responses of different COVID-19 vaccination strategies by analyzing single-cell RNA sequencing data from multiple tissues using machine learning methods

Ren

et al. 2023

Front. Genet.

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Multiple types of COVID-19 vaccines have been shown to be highly effective in preventing SARS-CoV-2 infection and in reducing post-infection symptoms. Almost all of these vaccines induce systemic immune responses, but differences in immune responses induced by different vaccination regimens are evident. This study aimed to reveal the differences in immune gene expression levels of different target cells under different vaccine strategies after SARS-CoV-2 infection in hamsters. A machine learning based process was designed to analyze single-cell transcriptomic data of different cell types from the blood, lung, and nasal mucosa of hamsters infected with SARS-CoV-2, including B and T cells from the blood and nasal cavity, macrophages from the lung and nasal cavity, alveolar epithelial and lung endothelial cells. The cohort was divided into five groups: non-vaccinated (control), 2*adenovirus (two doses of adenovirus vaccine), 2*attenuated (two doses of attenuated virus vaccine), 2*mRNA (two doses of mRNA vaccine), and mRNA/attenuated (primed by mRNA vaccine, boosted by attenuated vaccine). All genes were ranked using five signature ranking methods (LASSO, LightGBM, Monte Carlo feature selection, mRMR, and permutation feature importance). Some key genes that contributed to the analysis of immune changes, such as RPS23, DDX5, PFN1 in immune cells, and IRF9 and MX1 in tissue cells, were screened. Afterward, the five feature sorting lists were fed into the feature incremental selection framework, which contained two classification algorithms (decision tree [DT] and random forest [RF]), to construct optimal classifiers and generate quantitative rules. Results showed that random forest classifiers could provide relative higher performance than decision tree classifiers, whereas the DT classifiers provided quantitative rules that indicated special gene expression levels under different vaccine strategies. These findings may help us to develop better protective vaccination programs and new vaccines.

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“…COVID-19-specific genes might be potentially high-activity gene signatures of COVID-19. For example, NPM1 was present in the COVID-19-associated protein-protein interaction (PPI) network module and associated with enhanced viral replication of COVID-19 [71,72]. COVID-19 and IAV coexpressed genes might be associated with essential biological functions of B cells; for example, IGHG3 and IGLC2 were B cell marker genes [73].…”

Section: Plos Biologymentioning

confidence: 99%

scapGNN: A graph neural network–based framework for active pathway and gene module inference from single-cell multi-omics data

Han,

Wang,

Situ

et al. 2023

PLoS Biol

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Although advances in single-cell technologies have enabled the characterization of multiple omics profiles in individual cells, extracting functional and mechanistic insights from such information remains a major challenge. Here, we present scapGNN, a graph neural network (GNN)-based framework that creatively transforms sparse single-cell profile data into the stable gene–cell association network for inferring single-cell pathway activity scores and identifying cell phenotype–associated gene modules from single-cell multi-omics data. Systematic benchmarking demonstrated that scapGNN was more accurate, robust, and scalable than state-of-the-art methods in various downstream single-cell analyses such as cell denoising, batch effect removal, cell clustering, cell trajectory inference, and pathway or gene module identification. scapGNN was developed as a systematic R package that can be flexibly extended and enhanced for existing analysis processes. It provides a new analytical platform for studying single cells at the pathway and network levels.

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Insights into potential mechanisms of asthma patients with COVID-19: A study based on the gene expression profiling of bronchoalveolar lavage fluid

Cited by 11 publications

References 131 publications

Bioinformatic analysis and preliminary validation of potential therapeutic targets for COVID-19 infection in asthma patients

Bioinformatic analysis and preliminary validation of potential therapeutic targets for COVID-19 infection in asthma patients

Immune responses of different COVID-19 vaccination strategies by analyzing single-cell RNA sequencing data from multiple tissues using machine learning methods

scapGNN: A graph neural network–based framework for active pathway and gene module inference from single-cell multi-omics data

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