miR-139-5p protects septic mice with acute lung injury by inhibiting Toll-like receptor 4/Myeloid differentiation factor 88/Nuclear factor-κB signaling pathway

Zhang, Xiuxiu; Liu, Xin; Chang, Rui; Li, Yue

doi:10.6061/clinics/2021/e2484

Cited by 4 publications

(2 citation statements)

References 24 publications

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“…miR-139-5p was upregulated in SARS-CoV-2 infected cells [59] and was associated with COVID-19 severity [60]. MiR-139-5p could reduce the production of proinflammatory cytokines and chemokines by targeting genes, such as MyD88, c-FOS, and Rap1b, and mediating NF-κB and STAT3 signaling pathways [97][98][99][100]. This result might be explained by the overexpression of miR-139-5p in COVID-19 patients, which is the organism's stress response to infectious inflammation.…”

Section: Analysis Of the Key Biomarkersmentioning

confidence: 99%

Identifying MicroRNA Markers That Predict COVID-19 Severity Using Machine Learning Methods

Ren

Guo

Kong

et al. 2022

Life

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Individuals with the SARS-CoV-2 infection may experience a wide range of symptoms, from being asymptomatic to having a mild fever and cough to a severe respiratory impairment that results in death. MicroRNA (miRNA), which plays a role in the antiviral effects of SARS-CoV-2 infection, has the potential to be used as a novel marker to distinguish between patients who have various COVID-19 clinical severities. In the current study, the existing blood expression profiles reported in two previous studies were combined for deep analyses. The final profiles contained 1444 miRNAs in 375 patients from six categories, which were as follows: 30 patients with mild COVID-19 symptoms, 81 patients with moderate COVID-19 symptoms, 30 non-COVID-19 patients with mild symptoms, 137 patients with severe COVID-19 symptoms, 31 non-COVID-19 patients with severe symptoms, and 66 healthy controls. An efficient computational framework containing four feature selection methods (LASSO, LightGBM, MCFS, and mRMR) and four classification algorithms (DT, KNN, RF, and SVM) was designed to screen clinical miRNA markers, and a high-precision RF model with a 0.780 weighted F1 was constructed. Some miRNAs, including miR-24-3p, whose differential expression was discovered in patients with acute lung injury complications brought on by severe COVID-19, and miR-148a-3p, differentially expressed against SARS-CoV-2 structural proteins, were identified, thereby suggesting the effectiveness and accuracy of our framework. Meanwhile, we extracted classification rules based on the DT model for the quantitative representation of the role of miRNA expression in differentiating COVID-19 patients with different severities. The search for novel biomarkers that could predict the severity of the disease could aid in the clinical diagnosis of COVID-19 and in exploring the specific mechanisms of the complications caused by SARS-CoV-2 infection. Moreover, new therapeutic targets for the disease may be found.

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Section: Analysis Of the Key Biomarkersmentioning

confidence: 99%

Identifying MicroRNA Markers That Predict COVID-19 Severity Using Machine Learning Methods

Ren

Guo

Kong

et al. 2022

Life

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“…Myeloid differentiation factor 88 (MYD88) is a key connector molecule in the toll-like receptor (TLR) signaling pathway, regulating upstream information transmission and various disease development (15,16). It has been shown that MYD88 acts through the NF-κB signaling pathway in sepsis-induced acute lung injury (17). The present study suggests that MYD88 may be targeted by miR-944 to participate in SIC and inflammatory responses.…”

Section: Introductionmentioning

confidence: 99%

Circ_0003907 Modulates Sepsis-Induced Myocardial Injury via Enhancing Myd88/Nlrp3/Nf-Κb Axis by Sponging Mir-944

Lv,

Liu,

Wang

et al. 2023

Shock

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Background Sepsis-induced cardiomyopathy (SIC) is a common complication of sepsis with high morbidity and mortality, but lacks specific therapy. The purpose of this study was to investigate the role of circularRNA_0003907 (circ_0003907) in myocardium injury induced by sepsis. Methods In this experiment, human AC16 cells were treated with lipopolysaccharide (LPS) to induce an in vitro cardiomyocyte injury model. Expression of circ_0003907, microRNA-944 (miR-944), and MYD88 was detected using quantitative real-time PCR (qRT-PCR). Cell proliferation and apoptosis were assessed using 3-(4, 5-dimethylthiazol-2-yl)-2, 5-diphenyltetrazolium Bromide (MTT), thymidine analog 5-ethynyl-2’-deoxyuridine (EdU), and flow cytometry assays. Secretions of pro-inflammatory cytokines interleukin (IL)-6 and tumor necrosis factor-α (TNF-α) were detected using ELISA kits. Products of malondialdehyde (MDA) and malondialdehyde (MDA) were measured using Special assay kits. Protein levels of cyclin D1, cleaved caspase-3, MYD88, NLRP3, P65, and IκBα were determined using western blot assay. After being predicted using Circineractome and starBase, the interaction between miR-944 and circ_0003907 or MYD88 was confirmed using dual-luciferase reporter and RNA Immunoprecipitation (RIP) assays. Results Circ_0003907 expression was increased in serum from SIC patients and in LPS-treated AC16 cells. Circ_0003907 knockdown might abolish LPS-triggered proliferation inhibition, and the promotion of apoptosis, inflammatory response, and oxidative stress in AC16 cells. In mechanism, circ_0003907 acted as a sponge for miR-944 to increase MYD88 expression. Meanwhile, the absence of circ_0003907 induced miR-944 expression and suppressed MYD88/NLRP3/NF-κB levels. Conclusion Circ_0003907 sponged miR-944 to aggravate LPS-induced AC16 cell dysfunction via activating the MYD88/NLRP3/NF-κB axis during sepsis, which might provide a new direction for the treatment of SIC.

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Lipopolysaccharide-induced lung cell inflammation and apoptosis are enhanced by circ_0003420/miR-424-5p/TLR4 axis via inactivating the NF-κB signaling pathway

Yang

Zhang²,

Zhao³

2022

Transplant Immunology

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miR-139-5p protects septic mice with acute lung injury by inhibiting Toll-like receptor 4/Myeloid differentiation factor 88/Nuclear factor-κB signaling pathway

Cited by 4 publications

References 24 publications

Identifying MicroRNA Markers That Predict COVID-19 Severity Using Machine Learning Methods

Identifying MicroRNA Markers That Predict COVID-19 Severity Using Machine Learning Methods

Circ_0003907 Modulates Sepsis-Induced Myocardial Injury via Enhancing Myd88/Nlrp3/Nf-Κb Axis by Sponging Mir-944

Lipopolysaccharide-induced lung cell inflammation and apoptosis are enhanced by circ_0003420/miR-424-5p/TLR4 axis via inactivating the NF-κB signaling pathway

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