Identification of hub glutamine metabolism-associated genes and immune characteristics in pre-eclampsia

• Published in: PloS one 2024-05, Vol.19 (5), p.e0303471-e0303471
• Main Authors: Mao, Yan, Li, Xinye, Ren, Rui, Yuan, Yue, Wang, Li, Zhang, Xuehong
• Format: Article
• Language: English
• Subjects: Adult; B cells; Bioinformatics; Biology and Life Sciences; Biomarkers; Cesarean section; Cluster analysis; Computational Biology - methods; Computer and Information Sciences; Datasets; Diagnosis; Energy metabolism; Female; Gene expression; Gene Expression Profiling; Gene Ontology; Gene Regulatory Networks; Gene set enrichment analysis; Genes; Genomes; Genomics; Glutamine; Glutamine - metabolism; Health aspects; Helper cells; Humans; Immune system; Immunohistochemistry; Immunotherapy; Infiltration; Lymphocytes; Lymphocytes B; Medicine and Health Sciences; Messenger RNA; Metabolism; Modules; mRNA; Network analysis; Neutrophils; Pathogenesis; Physical Sciences; Physiological aspects; Placenta - immunology; Placenta - metabolism; Pre-eclampsia; Pre-Eclampsia - genetics; Pre-Eclampsia - immunology; Preeclampsia; Pregnancy; Pregnancy complications; Pregnant women; Protein interaction; Protein Interaction Maps - genetics; Protein-protein interactions; Proteins; Risk factors; Sulfur; Sulfur compounds; Women
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0303471

Preeclampsia (PE) is a severe complication of unclear pathogenesis associated with pregnancy. This research aimed to elucidate the properties of immune cell infiltration and potential biomarkers of PE based on bioinformatics analysis. Two PE datasets were imported from the Gene ExpressioOmnibus (GEO) and screened to identify differentially expressed genes (DEGs). Significant module genes were identified by weighted gene co-expression network analysis (WGCNA). DEGs that interacted with key module genes (GLu-DEGs) were analyzed further by Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses. The diagnostic value of the genes was assessed using receiver operating characteristic (ROC) curves and protein-protein interaction (PPI) networks were constructed using GeneMANIA, and GSVA analysis was performed using the MSigDB database. Immune cell infiltration was analyzed using the TISIDB database, and StarBase and Cytoscape were used to construct an RBP-mRNA network. The identified hub genes were validated in two independent datasets. For further confirmation, placental tissue from healthy pregnant women and women with PE were collected and analyzed using both RT-qPCR and immunohistochemistry. A total of seven GLu-DEGs were obtained and were found to be involved in pathways associated with the transport of sulfur compounds, PPAR signaling, and energy metabolism, shown by GO and KEGG analyses. GSVA indicated significant increases in adipocytokine signaling. Furthermore, single-sample Gene Set Enrichment Analysis (ssGSEA) indicated that the levels of activated B cells and T follicular helper cells were significantly increased in the PE group and were negatively correlated with GLu-DEGs, suggesting their potential importance. In summary, the results showed a correlation between glutamine metabolism and immune cells, providing new insights into the understandingPE pathogenesis and furnishing evidence for future advances in the treatment of this disease.