ACE2 Expressed on Myeloid Cells Alleviates Sepsis-Induced Acute Liver Injury via the Ang-(1–7)–Mas Receptor Axis

Liu, Lei; Li, Ya; Li, Jia-Xin; Xiao, Xue; Wan, Tian-Tian; Li, Hui-Hua; Guo, Shu-Bin

doi:10.1007/s10753-023-01949-5

Cited by 3 publications

References 50 publications

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ACE2 alleviates sepsis-induced cardiomyopathy through inhibiting M1 macrophage via NF-κB/STAT1 signals

Xiao,

Li,

et al. 2024

Cell Biol Toxicol

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Angiotensin-converting enzyme 2 (ACE2), a crucial element of the renin-angiotensin system (RAS), metabolizes angiotensin II into Ang (1–7), which then combines with the Mas receptor (MasR) to fulfill its protective role in various diseases. Nevertheless, the involvement of ACE2 in sepsis-induced cardiomyopathy (SIC) is still unexplored. In this study, our results revealed that CLP surgery dramatically impaired cardiac function accompanied with disruption of the balance between ACE2-Ang (1–7) and ACE-Ang II axis in septic heart tissues. Moreover, ACE2 knockin markedly alleviated sepsis induced RAS disorder, cardiac dysfunction and improved survival rate in mice, while ACE2 knockout significantly exacerbates these outcomes. Adoptive transfer of bone marrow cells and in vitro experiments showed the positive role of myeloid ACE2 by mitigating oxidative stress, inflammatory response, macrophage polarization and cardiomyocyte apoptosis by blocking NF-κB and STAT1 signals. However, the beneficial impacts were nullified by MasR antagonist A779. Collectively, these findings showed that ACE2 alleviated SIC by inhibiting M1 macrophage via activating the Ang (1–7)-MasR axis, highlight that ACE2 might be a promising target for the management of sepsis and SIC patients.

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ACE2 alleviates sepsis-induced cardiomyopathy through inhibiting M1 macrophage via NF-κB/STAT1 signals

Xiao,

Li,

et al. 2024

Cell Biol Toxicol

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Unveiling shared diagnostic biomarkers and molecular mechanisms between T2DM and sepsis: Insights from bioinformatics to experimental assays

Weng,

Shi,

et al. 2024

The FASEB Journal

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Septic patients with T2DM were prone to prolonged recovery and unfavorable prognoses. Thus, this study aimed to pinpoint potential genes related to sepsis with T2DM and develop a predictive model for the disease. The candidate genes were screened using protein–protein interaction networks (PPI) and machine learning algorithms. The nomogram and receiver operating characteristic curve were developed to assess the diagnostic efficiency of the biomarkers. The relationship between sepsis and immune cells was analyzed using the CIBERSORT algorithm. The biomarkers were validated by qPCR and western blotting in basic experiments, and differences in organ damage in mice were studied. Three genes (MMP8, CD177, and S100A12) were identified using PPI and machine learning algorithms, demonstrating strong predictive capabilities. These biomarkers presented significant differences in gene expression patterns between diseased and healthy conditions. Additionally, the expression levels of biomarkers in mouse models and blood samples were consistent with the findings of the bioinformatics analysis. The study elucidated the common molecular mechanisms associated with the pathogenesis of T2DM and sepsis and developed a gene signature‐based prediction model for sepsis. These findings provide new targets for the diagnosis and intervention of sepsis complicated with T2DM.

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Identification of novel prognostic targets in acute kidney injury using bioinformatics and next generation sequencing data analysis

Vastrad,

Vastrad

2024

Preprint

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Acute kidney injury (AKI) is a type of renal disease occurs frequently in hospitalized patients, which may cause abnormal renal function and structure with increase in serum creatinine level with or without reduced urine output. With the incidence of AKI is increasing. However, the molecular mechanisms of AKI have not been elucidated. It is significant to further explore the molecular mechanisms of AKI. We downloaded the GSE139061 next generation sequencing (NGS) dataset from the Gene Expression Omnibus (GEO) database. Limma R bioconductor package was used to screen the differentially expressed genes (DEGs). Then, the enrichment analysis of DEGs in Gene Ontology (GO) function and REACTOME pathways was analyzed by g:Profiler. Next, the protein-protein interaction (PPI) network and modules was constructed and analyzed, and the hub genes were identified. Next, the miRNA-hub gene regulatory network and TF-hub gene regulatory network were built. We also validated the identified hub genes via receiver operating characteristic (ROC) curve analysis. Overall, 956 DEGs were identified, including 478 up regulated and 478 down regulated genes. The enrichment functions and pathways of DEGs involve primary metabolic process, small molecule metabolic process, striated muscle contraction and metabolism. Topological analysis of the PPI network and module revealed that hub genes, including PPP1CC, RPS2, MDFI, BMI1, RPL23A, VCAM1, ALB, SNCA, DPP4 and RPL26L1, might be involved in the development of AKI. miRNA-hub gene and TF-hub gene regulatory networks revealed that miRNAs and TFs including hsa-mir-510-3p, hsa-mir-6086 and mir-146a-5p, MAX and PAX2, might be involved in the development of AKI. Various known and newtherapeutic targets were obtained via network analysis. The results of the current investigation might be beneficial for the diagnosis and treatment of AKI.

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ACE2 Expressed on Myeloid Cells Alleviates Sepsis-Induced Acute Liver Injury via the Ang-(1–7)–Mas Receptor Axis

Cited by 3 publications

References 50 publications

ACE2 alleviates sepsis-induced cardiomyopathy through inhibiting M1 macrophage via NF-κB/STAT1 signals

ACE2 alleviates sepsis-induced cardiomyopathy through inhibiting M1 macrophage via NF-κB/STAT1 signals

Unveiling shared diagnostic biomarkers and molecular mechanisms between T2DM and sepsis: Insights from bioinformatics to experimental assays

Identification of novel prognostic targets in acute kidney injury using bioinformatics and next generation sequencing data analysis

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