Coding and Noncoding RNA Expression Profiles of Spleen CD4+ T Lymphocytes in Mice With Echinococcosis

Abstract

Background: Cystic echinococcosis (CE) is a severe and neglected zoonotic disease, which is caused by Echinococcus granulosus sensu lato and poses health and socioeconomic hazards. RNA molecules play important roles in genetic coding, translation, regulation, and gene expression and are classified into noncoding RNAs, such as long noncoding RNAs (lncRNAs), miRNAs, and circular RNAs (circRNAs), and coding RNAs (mRNAs) based on whether they encode proteins.Methods: Peripheral blood serum from E. granulosus-infected and uninfected female BALB/c mice was used to measure IFN-γ, IL-2, IL-4, IL-6, IL-10, IL-17A, and TNF-α levels using the cytometric bead array mouse Th1/Th2/Th17 cytokine kit. mRNA, lncRNA, miRNA, and circRNA profiles were analyzed in spleen CD4+ T cells from the two groups of mice using high-throughput sequencing.Results: The results showed that the levels of serum IFN-γ, IL-2, IL-4, IL-6, IL-10, IL-17A, and TNF-α were significantly higher in the CE mice than in the control group (P < 0. 01). A total of 1,758 known mRNAs, 37 miRNAs, 175 lncRNAs, and 22 circRNAs were differentially expressed between infected and uninfected mice (|fold change| ≥ 0.585, P < 0.05). These differentially expressed molecules were closely related to the JAK/STAT, mitogen-activated protein kinase, p53, and PI3K/Akt signaling pathways, cell cycle, and metabolic pathways.Conclusions: E. granulosus infection caused significant increases in the IFN-γ, IL-2, IL-4, IL-6, IL-10, IL-17A, and TNF-α levels in the peripheral blood of mice, as well as significant changes in the expression levels of a variety of coding and noncoding RNAs. A further study of these trends and pathways may help clarify the pathogenesis of CE and provide new insights into the prevention and treatment of this infection.

Bioinformatics and systems biology approaches to identify the effects of COVID-19 on neurodegenerative diseases: A review

“…To identify DEGs in the dataset, after converting some counts data into fpkm data via SangerBox [24–26] (http://vip.sangerbox.com/), a difference-in-difference analysis using limma R and combined with the Benjamini-Hochberg false discovery rate method was used to screen for statistically significant genes and limit false positives. [27,28] Genes with a screening-adjusted P ≤ .05 and log 2 FC ≥ 0.585 (fold change = 1.5) [29–31] were identified as DEGs. Genes common to both datasets of the same tissue for the same disease were selected for ND as DEGs for that disease to improve precision.…”