MicroRNA-30a inhibits cell proliferation in a sepsis-induced acute kidney injury model by targeting the YAP-TEAD complex.

Background: Acute kidney injury (AKI) is a primary feature of renal complications in patients with sepsis. MicroRNA (miRNA/miR)-30a is an essential regulator of cardiovascular diseases, tumors, phagocytosis, and other physical processes, but whether it participates in sepsis-induced AKI (sepsis-AKI) is unknown. We aimed to elucidate the functions and molecular mechanism underlying miR-30a activity in sepsis-AKI. Methods: The classical cecal ligation and puncture (CLP) method and lipopolysaccharide (LPS)-induced Human Kidney 2 (HK-2) cells were used to establish in vivo and in vitro sepsis-AKI models. Specific pathogen-free and mature male Sprague-Dawley (SD) rats, aged 6-8 weeks (weight 200-250 g), were randomly divided into five-time phase subgroups. Fluid resuscitation with 30 mL/kg 37 °C saline was administered after the operation, without antibiotics. Formalin-fixed, paraffin-embedded kidney sections were stained with hematoxylin and eosin. SD rat kidney tissue samples were collected for analysis by real-time quantitative polymerase chain reaction and enzyme-linked immunosorbent assay. HK-2 cells were transfected with hsa-miR-30a-3p mimics or inhibitors, and compared with untreated normal controls. RNA, protein, and cell viability were evaluated by quantitative reverse transcription-polymerase chain reaction (qRT-PCR), western blot, and cell counting kit-8 methods. A Dual-Luciferase Assay Kit (Promega) was used to measure luciferase activity 48 h after transfection with miR-30a-3p mimics. Results: Expression levels of miR-30a-3p and miR-30a-5p in renal tissues of the sepsis group were significantly reduced at 12 h and 24 h (P <0.05). Tumor necrosis factor-α (TNF-α) and interleukin-1ß (IL-1ß) were significantly increased in renal tissue 3 h after the operation in rats (P <0.05), and gradually decreased 6 h, 12 h, and 24 h after CLP. Levels of miR-30a-5p and miR-30a-3p were significantly down-regulated at 3 h after LPS treatment (P <0.05), and gradually decreased in HK-2 cells. One hour after LPS (10 µg/mL) treatment, TNF-α and IL-1ß levels in HK-2 cells were significantly up-regulated (P < 0.05), and they were markedly down-regulated after 3 h (P <0.05). IL-6 expression levels began to rise after LPS treatment of cells, peaked at 6 h (P <0.05), and then decreased to the initial level within a few hours. Stimulation with 10 µg/mL LPS promoted HK-2 cells proliferation, which was inhibited after miR-30a-3p-mimic transfection. Bioinformatics prediction identified 37 potential miR-30a-3p target genes, including transcriptional enhanced associate domain 1 (TEAD1). After transfection of HK-2 cells with miR-30a-3p mimics and miR-30a-3p inhibitor, TEAD1 transcript was significantly up- and down-regulated, respectively (both P <0.05). After LPS treatment (24 h), expression of TEAD1 in the inhibitors group was significantly increased (P <0.01), while that in the mimics group was significantly suppressed (P <0.01). In the dual luciferase reporter experiment, miR-30a-3p overexpression decreased fluorescence intensity (P <0.01) from TEAD1-wt-containing plasmids, but did not influence fluorescence intensity from TEAD1-muta-containing plasmids. LPS may promote HK-2 cells proliferation through the miR-30a-3p/TEAD1 pathway. Conclusion: In a background of expression of inflammatory factors, including TNF-α, IL-1ß, and IL-6, which were transiently increased in the sepsis-AKI model, miR-30a was down-regulated. Down-regulated miR-30a-3p may promote cell proliferation by targeting TEAD1 in LPS-induced HK-2 cells, demonstrating its potential as a biomarker for early sepsis-AKI diagnosis.

Development of a nomogram for predicting in-hospital mortality in patients with liver cirrhosis and sepsis.

In this study, we aimed to investigate the risk factors associated with in-hospital mortality in patients with cirrhosis and sepsis, establish and validate the nomogram. This retrospective study included patients diagnosed with liver cirrhosis and sepsis in the Medical Information Mart for Intensive Care IV (MIMIC-IV). Models were compared by the area under the curve (AUC), integrated discriminant improvement (IDI), net reclassification index (NRI) and decision curve analysis (DCA). A total of 1,696 patients with cirrhosis and sepsis were included in the final cohort. Our final model included the following 9 variables: age, heartrate, total bilirubin (TBIL), glucose, sodium, anion gap (AG), fungal infections, mechanical ventilation, and vasopressin. The nomogram were constructed based on these variables. The AUC values of the nomograms were 0.805 (95% CI 0.776-0.833), which provided significantly higher discrimination compared to that of SOFA score [0.684 (95% CI 0.647-0.720)], MELD-Na [0.672 (95% CI 0.636-0.709)] and ABIC [0.674(95% CI 0.638-0.710)]. We established the first nomogram for predicting in-hospital mortality in patients with liver cirrhosis and sepsis based on these factors. This nomogram can performs well and facilitates clinicians to identify people at high risk of in-hospital mortality.

MicroRNA-30a-5p Promotes Chronic Heart Failure in Rats by Targeting Sirtuin-1 to Activate the Nuclear Factor-κB/NOD-Like Receptor 3 Signaling Pathway.

OBJECTIVE: MicroRNA-30a-5p (miR-30a-5p) has been identified as a marker of heart failure; however, its functional mechanisms in chronic heart failure (CHF) remain unknown. We aim to investigate the role of miR-30a-5p targeting sirtuin-1 (SIRT1) in myocardial remodeling in CHF via the nuclear factor-κB/NOD-like receptor 3 (NF-κB/NLRP3) signaling pathway. METHODS: CHF rat models were established using aortic coarctation. The expression of miR-30a-5p, SIRT1, and the NF-κB/NLRP3 signaling pathway-related factors in CHF rats was determined. The CHF rats were then respectively treated with altered miR-30a-5p or SIRT1 to explore their roles in cardiac function, myocardial function, inflammatory response, pathological changes, and cardiomyocyte apoptosis. The binding relation between miR-30a-5p and SIRT1 was confirmed. RESULTS: MiR-30a-5p was upregulated whereas SIRT1 was downregulated in myocardial tissues of CHF rats. MiR-30a-5p inhibition or SIRT1 overexpression improved cardiac and myocardial function, and suppressed the inflammatory response, alleviated pathological changes and inhibited cardiomyocyte apoptosis in CHF rats. MiR-30a-5p targeted SIRT1 to regulate the NF-κB/NLRP3 signaling pathway. In CHF rats, downregulated miR-30a-5p and silenced SIRT1 could reverse the beneficial effects of downregulated miR-30a-5p. CONCLUSION: Inhibited miR-30a-5p inhibits CHF progression via the SIRT1-mediated NF-κB/NLRP3 signaling pathway.

Using Machine Learning Algorithms to Predict Candidaemia in ICU Patients With New-Onset Systemic Inflammatory Response Syndrome.

Background: Distinguishing ICU patients with candidaemia can help with the precise prescription of antifungal drugs to create personalized guidelines. Previous prediction models of candidaemia have primarily used traditional logistic models and had some limitations. In this study, we developed a machine learning algorithm trained to predict candidaemia in patients with new-onset systemic inflammatory response syndrome (SIRS). Methods: This retrospective, observational study used clinical information collected between January 2013 and December 2017 from three hospitals. The ICU patient data were used to train 4 machine learning algorithms-XGBoost, Support Vector Machine (SVM), Random Forest (RF), ExtraTrees (ET)-and a logistic regression (LR) model to predict patients with candidaemia. Results: Of the 8,002 cases of new-onset SIRS (in 7,932 patients) included in the analysis, 137 new-onset SIRS cases (in 137 patients) were blood culture positive for candidaemia. Risk factors, such as fungal colonization, diabetes, acute kidney injury, the total number of parenteral nutrition days and renal replacement therapy, were important predictors of candidaemia. The XGBoost machine learning model outperformed the other models in distinguishing patients with candidaemia [XGBoost vs. SVM vs. RF vs. ET vs. LR; area under the curve (AUC): 0.92 vs. 0.86 vs. 0.91 vs. 0.90 vs. 0.52, respectively]. The XGBoost model had a sensitivity of 84%, specificity of 89% and negative predictive value of 99.6% at the best cut-off value. Conclusions: Machine learning algorithms can potentially predict candidaemia in the ICU and have better efficiency than previous models. These prediction models can be used to guide antifungal treatment for ICU patients when SIRS occurs.

PAI-1/PIAS3/Stat3/miR-34a forms a positive feedback loop to promote EMT-mediated metastasis through Stat3 signaling in Non-small cell lung cancer.

AIM: This study intented to clarify the intracellular effect of PAI-1 on Non-small cell lung cancer (NSCLC) metastasis and the precise mechanism involved. METHODS: The metastatic properties of NSCLC cells were determined by transwell assays and wound-healing assay in vitro. The mRNA and protein expressions of genes were analyzed by Real-time qPCR and western blot, respectively. Pulmonary metastasis model of NSCLC cells was established to evaluate the pro-metastasis effect of PAI-1 and anti-metastatic effect of miR-34a in vivo. The gene targets of miR-34a were confirmed by luciferase reporter assays. Chromatin immunoprecipitation assay was employed to detect the transcriptional regulation of miR-34a. Co-immunoprecipitation assay was performed to observe the interaction of proteins. RESULTS: PAI-1, which was elevated in NSCLC patients with recurrence and metastasis, augmented NSCLC metastasis and was negatively related to the prognosis of NSCLC. miR-34a, which was decreased in NSCLC patients with metastasis, attenuated NSCLC metastasis and was positively correlated with the prognosis of NSCLC. Moreover, PAI-1 was identified as the target gene of miR-34a and activated the Stat3 signaling pathway to promote epithelial-mesenchymal transition (EMT) in NSCLC cells. PAI-1 interacted with PIAS3 to regulate Stat3-dependent gene expression and miR-34a was transcriptionally suppressed by Stat3 to form a positive regulatory loop through Stat3 signaling. CONCLUSION: Our findings suggest that PAI-1 and miR-34a, which can be clinically utilized as biomarkers for the clinical prognosis or diagnosis of NSCLC, are potential targets for the treatment of NSCLC.

[The effect of ulinastatin on gene expression of brain tissue in septic rat].

OBJECTIVE: To detect the differences in gene expression of brain tissue in septic rats with ulinastatin (UTI) preconditioning with DNA microarray. METHODS: Forty-five male Wistar rats were equally divided into control group, sepsis group, and UTI group by means of random number table. In UTI group the rats were treated with intramuscular injection of UTI (100 kU/kg) 1 hour before cecal ligation and puncture (CLP). In sepsis group and control group intramuscular balanced solution (5 ml/kg) instead of UTI was given. Septic rat model was reproduced by CLP. The control group underwent a simulated operation without CLP. Gene expression profile was studied by using RatRef-12 rat gene expression profile microarray to detect the changes in gene expression pattern of rat brain tissue after CLP. Then related computer software was used to screen and analyze the relationship between the sepsis/UTI group and control group. Finally, the difference between the sepsis group and UTI group was analyzed. RESULTS: In 22 523 genes, 55 differential genes were found between sepsis group and control group, accounting for 0.244%. Among them 47 genes showed down-regulation, with 23 known functional genes; 8 genes showed up-regulation, with 6 known functional genes. Eighty-two differential genes were found between UTI group and control group, accounting for 0.364%. Among them 66 genes showed down-regulation, with 39 known functional genes; 16 genes showed up-regulation, with 8 known functional genes. When sepsis group and UTI group compared with control group, 19 genes showed the same degree of regulation,with 18 genes (Adora2a, Avp, Cart, Gng7, Myh7, Oxt, Pde1b, Pdyn, Prkcd, Prkch, Rgs9, Rxrg, Six3, Slc17a6, Slco1a5, Sostdc1, Tac1, Ttr) showed down-regulation and 1 gene (S100a8) showed up-regulation. CONCLUSION: UTI preconditioning can partly adjust abnormal expression of genes in the brain tissue of rat with sepsis in the presence of excessive inflammation and immune suppression. UTI has some degree of protective effect on brain at the genetic level. Meanwhile, there is certain self regulation in the body during sepsis.

[Effect of ulinastatin preconditioning on gene expression profile of heart tissue in a rat sepsis model].

OBJECTIVE: To observe the regulatory effect of ulinastatin (UTI) preconditioning on gene expression of heart tissue in septic rats by DNA microarrays. METHODS: Forty-five male Wistar rats were equally divided into control group, sepsis group and UTI group by means of random number table. Cecal ligation and puncture (CLP) was used to reproduce rat sepsis model. The control group only experienced a simulated operation without CLP. In UTI group the rats were treated with intramuscular injection of UTI 100 kU/kg 1 hour before CLP. In sepsis group and control group balanced electrolyte solution (5 ml/kg) was given. Gene expression spectrum was studied with RatRef-12 rat gene expression profile microarray to detect the changes in gene expression pattern of rat heart tissue after CLP. Genes with fluorescent signal of Cy3/Cy5 of ratio average (RA)>2.0 or RA<0.5 were identified as differential genes, and those highly correlated to sepsis and UTI groups were screened by means of related computer software to analyze their relationship. RESULTS: In 22 523 genes, 418 differential genes were found in sepsis group compared with control group, accounting for 1.856%, and among them 200 genes showed up-regulation, with 84 known functional genes, and 43 of which only showed up-regulation in sepsis group, but normal in UTI group. Two hundred and eighteen genes showed down-regulation, with 74 known functional genes, 37 of which only showed down-regulation in sepsis group, but normal in UTI group. Two hundred and two differential genes were found in UTI group compared with control group, accounting for 0.897%, and among them 111 genes showed up-regulation, with 57 known functional genes, and 17 of which only showed up-regulation in UTI group, but normal in sepsis group. Ninety-one genes showed down-regulation, with 48 known functional genes, 18 of which only showed down-regulation in UTI group, but normal in sepsis group. Compared with the control group, in both UTI group and sepsis group, 41 of known functional genes showed up-regulation, and 37 showed down-regulation. CONCLUSION: UTI preconditioning can ameliorate the damage to heart tissue in rat sepsis model, thus it has a protective effect on heart, and its mechanism may be attributable to regulatory effect of UTI on expression of stress reaction, cell signal transduction, energy metabolism, immune reaction and other related genes.

[Effect of ulinastatin preconditioning on gene expression profile of kidney tissue in a rat sepsis model].

OBJECTIVE: To investigate the modulation effect of ulinastatin (UTI) preconditioning on gene expression of kidney tissue in septic rats by DNA microarrays. METHODS: Forty-five male Wistar rats were divided into control group, sepsis group and UTI group, with 15 rats in each group by means of random number table. Cecal ligation and puncture (CLP) was used to reproduce rat sepsis model. The control group only experienced a simulated operation without CLP. In UTI group the rats were treated with intramuscular injection of UTI (100 kU/kg). In sepsis group and control group intramuscular balanced solution (5 ml/kg) was given. Gene expression spectrum was studied with oligonucleotide gene expression profile microarray that contained 22 523 rat cDNA clones to detect the changes in gene expression pattern of rat kidney tissue 24 hours after CLP. Genes with fluorescent signal of Cy3/Cy5 of ratio average (RA)>2.0 or RA<0.5 were identified as differential genes, then those highly correlated to sepsis and UTI were screened by means of related computer software, and their relationship was analyzed. RESULTS: Three hundred and twenty-seven differential genes were found in sepsis group/control group, accounting for 1.45%, and among them 181 genes showed up-regulation,with 78 known functional genes, and 146 genes showed down-regulation, with 51 known functional genes. One hundred and twenty-seven differential genes were found in UTI group/sepsis group, accounting for 0.56%, and among them 41 genes showed up-regulation, with 14 known functional genes, and 86 genes showed down-regulation, with 37 known functional genes. Twenty-two genes were down-regulated in sepsis group/control group but up-regulated in UTI group/sepsis group, with 11 known functional genes, 51 genes were up-regulated in sepsis group/control group but down-regulated in UTI group/sepsis group, with 24 known functional genes. CONCLUSION: UTI preconditioning can alleviate the damage of kidney tissue in rat sepsis model, thus showing a protective effect on kidney, and the mechanism may be attributable to effect of UTI on modulation of immune reaction, energy metabolism, inflammatory reaction, signal transduction, defense reaction, oxidation-reduction reaction, DNA replication, and transcription related genes.

[An investigation of changes in gene expression profile of heart tissue in a rat sepsis model].

OBJECTIVE: To investigate the changes in gene expression spectrum of heart tissue in septic rats by DNA microarrays. METHODS: Thirty male Wistar rats were randomly divided into sepsis group and control group with 15 rats in each group. Cecal ligation and puncture (CLP) was used to reproduce rat sepsis model, and the success of reproduction was confirmed by examining the heart tissue with transmission electron microscope. Gene expression spectrum was studied with oligonucleotide gene expression profile microarray that contained 22 523 rat cDNA clones to detect the changes in gene expression pattern of rat heart tissue 24 hours after CLP. Genes with fluorescent signal of Cy3/Cy5 of ratio average (RA)>2.0 or RA<0.5 were identified as differential genes, then those that high correlated to sepsis were screened by means of related computer software, and their relationship was analyzed. RESULTS: Electron microscopic examination of heart tissue demonstrated that the sepsis model was successfully reproduced. Compared to the controls, gene expression of 418 genes of heart tissue of septic rat were changed 24 hours after CLP, accounting for 1.86%, and among them 200 genes showed up-regulation, 218 genes with down-regulation. Among known functional genes, 84 genes up-regulated and 74 genes down-regulated. They were related with a range of genetic functions, such as acute stress reaction, signal transduction, immune response, energy metabolism related genes etc. CONCLUSION: There are a series of changes in gene expression in heart tissue apparently induced by sepsis rat. DNA microarray technology provides a new tool for rapidly analyzing them.