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  • Frontiers Media SA  (4)
  • 1
    Online Resource
    Online Resource
    Table_3.DOCX
    Frontiers Media SA ; 2021
    In:  Frontiers in Cell and Developmental Biology Vol. 9 ( 2021-11-19)
    Details
    In: Frontiers in Cell and Developmental Biology, Frontiers Media SA, Vol. 9 ( 2021-11-19)
    Abstract: Progressive or chronic renal diseases arise from a process of destructive renal fibrosis. Therefore, the molecular basis of renal fibrosis has attracted increasing attention. In this investigation, we set out to elucidate the potential interaction among long non-coding RNA ENST00000453774.1 (lncRNA 74.1), microRNA-324-3p (miR-324-3p), and NRG1, and to investigate their roles in the context of cellular autophagy and renal fibrosis. We collected 30 renal fibrosis tissue samples for analysis. In other studies, HK-2 cells were stimulated with TGF-β1 to induce a cell model of renal fibrosis, followed by alteration on the expression of lncRNA 74.1, miR-324-3p, or NRG1, or by the addition of AKT activator SC79 in the HK-2 cells. The expression levels of lncRNA 74.1, miR-324-3p, NRG1, autophagy-related proteins (ATG5, ATG7, LC3II/I, and P62), and the corresponding fibrosis markers (Collagen I, Fibronectin, and α-SMA) were subsequently determined using various assay methods. In addition, the proportion of LC3 positive cells and number of autophagosomes were recorded. Results revealed that lncRNA 74.1 and NRG1 were poorly expressed and miR-324-3p was highly expressed in renal fibrosis tissues and modeled cells. LncRNA 74.1 could bind to miR-324-3p, which led to upregulated NRG1 expression and inhibition of the PI3K/AKT signaling pathway. Meanwhile, overexpression of lncRNA 74.1 or down-regulation of miR-324-3p increased the levels of ATG5, ATG7, LC3II, and LC3I, and decreased levels of P62, Collagen I, Fibronectin, and α-SMA, accompanied by elevated proportions of LC3 positive cells and autophagosomes. Findings concur in showing that lncRNA 74.1 could induce cellular autophagy and alleviate renal fibrosis by regulating the miR-324-3p-mediated NRG1/PI3K/AKT axis. This axis may thus present a potential molecular target in renal fibrosis treatment.
    Type of Medium: Online Resource
    ISSN: 2296-634X
    URL: Article
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    DOI: 10.3389/fcell.2021.580754
    DOI: 10.3389/fcell.2021.580754.s001
    DOI: 10.3389/fcell.2021.580754.s002
    DOI: 10.3389/fcell.2021.580754.s003
    DOI: 10.3389/fcell.2021.580754.s004
    DOI: 10.3389/fcell.2021.580754.s005
    DOI: 10.3389/fcell.2021.580754.s006
    DOI: 10.3389/fcell.2021.580754.s007
    DOI: 10.3389/fcell.2021.580754.s008
    DOI: 10.3389/fcell.2021.580754.s009
    DOI: 10.3389/fcell.2021.580754.s010
    DOI: 10.3389/fcell.2021.580754.s011
    DOI: 10.3389/fcell.2021.580754.s012
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2021
    detail.hit.zdb_id: 2737824-X
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  • 2
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    Fluorofenidone Alleviates Renal Fibrosis by Inhibiting Necroptosis Through RIPK3/MLKL Pathway
    Frontiers Media SA ; 2020
    In:  Frontiers in Pharmacology Vol. 11 ( 2020-12-16)
    Details
    In: Frontiers in Pharmacology, Frontiers Media SA, Vol. 11 ( 2020-12-16)
    Abstract: Cell death and sterile inflammation are major mechanisms of renal fibrosis, which eventually develop into end-stage renal disease. “Necroptosis” is a type of caspase-independent regulated cell death, and sterile inflammatory response caused by tissue injury is strongly related to necrosis. Fluorofenidone (AKF-PD) is a novel compound shown to ameliorate renal fibrosis and associated inflammation. We investigated whether AKF-PD could alleviate renal fibrosis by inhibiting necroptosis. Unilateral ureteral obstruction (UUO) was used to induce renal tubulointerstitial fibrosis in C57BL/6J mice. AKF-PD (500 mg/kg) or necrostatin-1 (Nec-1; 1.65 mg/kg) was administered simultaneously for 3 and 7 days. Obstructed kidneys and serum were harvested after euthanasia. AKF-PD and Nec-1 ameliorated renal tubular damage, inflammatory-cell infiltration, and collagen deposition, and the expression of proinflammatory factors (interlukin-1β, tumor necrosis factor [TNF]-α) and chemokines (monocyte chemoattractant protein-1) decreased. AKF-PD or Nec-1 treatment protected renal tubular epithelial cells from necrosis and reduced the release of lactate dehydrogenase in serum. Simultaneously, production of receptor-interacting protein kinase (RIPK)3 and mixed lineage kinase domain-like protein (MLKL) was also reduced 3 and 7 days after UUO. AKF-PD and Nec-1 significantly decreased the percentage of cell necrosis, inhibiting the phosphorylation of MLKL and RIPK3 in TNF-α- and Z-VAD–stimulated human proximal tubular epithelial (HK-2) cells. In conclusion, AKF-PD and Nec-1 have effective anti-inflammatory and antifibrotic activity in UUO-induced renal tubulointerstitial fibrosis, potentially mediated by the RIPK3/MLKL pathway.
    Type of Medium: Online Resource
    ISSN: 1663-9812
    URL: Article
    DOI: 10.3389/fphar.2020.534775
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2020
    detail.hit.zdb_id: 2587355-6
    SSG: 15,3
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  • 3
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    Table_4.docx
    Frontiers Media SA ; 2023
    In:  Frontiers in Immunology Vol. 14 ( 2023-3-21)
    Details
    In: Frontiers in Immunology, Frontiers Media SA, Vol. 14 ( 2023-3-21)
    Abstract: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the main cause of COVID-19, causing hundreds of millions of confirmed cases and more than 18.2 million deaths worldwide. Acute kidney injury (AKI) is a common complication of COVID-19 that leads to an increase in mortality, especially in intensive care unit (ICU) settings, and chronic kidney disease (CKD) is a high risk factor for COVID-19 and its related mortality. However, the underlying molecular mechanisms among AKI, CKD, and COVID-19 are unclear. Therefore, transcriptome analysis was performed to examine common pathways and molecular biomarkers for AKI, CKD, and COVID-19 in an attempt to understand the association of SARS-CoV-2 infection with AKI and CKD. Three RNA-seq datasets (GSE147507, GSE1563, and GSE66494) from the GEO database were used to detect differentially expressed genes (DEGs) for COVID-19 with AKI and CKD to search for shared pathways and candidate targets. A total of 17 common DEGs were confirmed, and their biological functions and signaling pathways were characterized by enrichment analysis. MAPK signaling, the structural pathway of interleukin 1 (IL-1), and the Toll-like receptor pathway appear to be involved in the occurrence of these diseases. Hub genes identified from the protein–protein interaction (PPI) network, including DUSP6, BHLHE40, RASGRP1, and TAB2, are potential therapeutic targets in COVID-19 with AKI and CKD. Common genes and pathways may play pathogenic roles in these three diseases mainly through the activation of immune inflammation. Networks of transcription factor (TF)–gene, miRNA–gene, and gene–disease interactions from the datasets were also constructed, and key gene regulators influencing the progression of these three diseases were further identified among the DEGs. Moreover, new drug targets were predicted based on these common DEGs, and molecular docking and molecular dynamics (MD) simulations were performed. Finally, a diagnostic model of COVID-19 was established based on these common DEGs. Taken together, the molecular and signaling pathways identified in this study may be related to the mechanisms by which SARS-CoV-2 infection affects renal function. These findings are significant for the effective treatment of COVID-19 in patients with kidney diseases.
    Type of Medium: Online Resource
    ISSN: 1664-3224
    URL: Article
    URL: Article
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    URL: Article
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    DOI: 10.3389/fimmu.2023.961642
    DOI: 10.3389/fimmu.2023.961642.s001
    DOI: 10.3389/fimmu.2023.961642.s002
    DOI: 10.3389/fimmu.2023.961642.s003
    DOI: 10.3389/fimmu.2023.961642.s004
    DOI: 10.3389/fimmu.2023.961642.s005
    DOI: 10.3389/fimmu.2023.961642.s006
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2023
    detail.hit.zdb_id: 2606827-8
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  • 4
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    DataSheet_5.pdf
    Frontiers Media SA ; 2023
    In:  Frontiers in Immunology Vol. 14 ( 2023-4-3)
    Details
    In: Frontiers in Immunology, Frontiers Media SA, Vol. 14 ( 2023-4-3)
    Abstract: Sepsis-associated acute kidney injury (S-AKI) is considered to be associated with high morbidity and mortality, a commonly accepted model to predict mortality is urged consequently. This study used a machine learning model to identify vital variables associated with mortality in S-AKI patients in the hospital and predict the risk of death in the hospital. We hope that this model can help identify high-risk patients early and reasonably allocate medical resources in the intensive care unit (ICU). Methods A total of 16,154 S-AKI patients from the Medical Information Mart for Intensive Care IV database were examined as the training set (80%) and the validation set (20%). Variables (129 in total) were collected, including basic patient information, diagnosis, clinical data, and medication records. We developed and validated machine learning models using 11 different algorithms and selected the one that performed the best. Afterward, recursive feature elimination was used to select key variables. Different indicators were used to compare the prediction performance of each model. The SHapley Additive exPlanations package was applied to interpret the best machine learning model in a web tool for clinicians to use. Finally, we collected clinical data of S-AKI patients from two hospitals for external validation. Results In this study, 15 critical variables were finally selected, namely, urine output, maximum blood urea nitrogen, rate of injection of norepinephrine, maximum anion gap, maximum creatinine, maximum red blood cell volume distribution width, minimum international normalized ratio, maximum heart rate, maximum temperature, maximum respiratory rate, minimum fraction of inspired O 2 , minimum creatinine, minimum Glasgow Coma Scale, and diagnosis of diabetes and stroke. The categorical boosting algorithm model presented significantly better predictive performance [receiver operating characteristic (ROC): 0.83] than other models [accuracy (ACC): 75%, Youden index: 50%, sensitivity: 75%, specificity: 75%, F1 score: 0.56, positive predictive value (PPV): 44%, and negative predictive value (NPV): 92%] . External validation data from two hospitals in China were also well validated (ROC: 0.75). Conclusions After selecting 15 crucial variables, a machine learning-based model for predicting the mortality of S-AKI patients was successfully established and the CatBoost model demonstrated best predictive performance.
    Type of Medium: Online Resource
    ISSN: 1664-3224
    URL: Article
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    DOI: 10.3389/fimmu.2023.1140755
    DOI: 10.3389/fimmu.2023.1140755.s001
    DOI: 10.3389/fimmu.2023.1140755.s002
    DOI: 10.3389/fimmu.2023.1140755.s003
    DOI: 10.3389/fimmu.2023.1140755.s004
    DOI: 10.3389/fimmu.2023.1140755.s005
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2023
    detail.hit.zdb_id: 2606827-8
    Location Call Number Limitation Availability
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