Midkine promotes renal fibrosis by stabilizing C/EBPß to facilitate endothelial-mesenchymal transition.

Numerous myofibroblasts are arisen from endothelial cells (ECs) through endothelial to mesenchymal transition (EndMT) triggered by TGF-ß. However, the mechanism of ECs transforms to a different subtype, or whether there exists an intermediate state of ECs remains unclear. In present study, we demonstrate Midkine (MDK) mainly expressed by CD31 + ACTA2+ECs going through partial EndMT contribute greatly to myofibroblasts by spatial and single-cell transcriptomics. MDK is induced in TGF-ß treated ECs, which upregulates C/EBPß and increases EndMT genes, and these effects could be reversed by siMDK. Mechanistically, MDK promotes the binding ability of C/EBPß with ACTA2 promoter by stabilizing the C/EBPß protein. In vivo, knockout of Mdk or conditional knockout of Mdk in ECs reduces EndMT markers and significantly reverses fibrogenesis. In conclusion, our study provides a mechanistic link between the induction of EndMT by TGF-ß and MDK, which suggests that blocking MDK provides potential therapeutic strategies for renal fibrosis.

Machine learning models to predict red blood cell transfusion in patients undergoing mitral valve surgery.

BACKGROUND: Red blood cell (RBC) transfusion therapy has been widely used in surgery, and has yielded excellent treatment outcomes. However, in some instances, the demand for RBC transfusion is assessed by doctors based on their experience. In this study, we use machine learning models to predict the need for RBC transfusion during mitral valve surgery to guide the surgeon's assessment of the patient's need for intraoperative blood transfusion. METHODS: We retrospectively reviewed 698 cases of isolated mitral valve surgery with and without combined tricuspid valve operation. Seventy percent of the database was used as the training set and the remainder as the testing set for 13 machine learning algorithms to build a model to predict the need for intraoperative RBC transfusion. According to the characteristic value of model mining, we analyzed the risk-related factors to determine the main effects of variables influencing the outcome. RESULTS: A total of 166 patients of the cases considered had undergone intraoperative RBC transfusion (24.52%). Of the 13 machine learning algorithms, CatBoost delivered the best performance, with an AUC of 0.888 (95% CI: 0.845-0.909) in testing set. Further analysis using the CatBoost model revealed that hematocrit (<37.81%), age (>64 y), body weight (<59.92 kg), body mass index (BMI) (<22.56 kg/m2), hemoglobin (<122.6 g/L), type of surgery (median thoracotomy surgery), height (<160.61 cm), platelet (>194.12×109/L), RBC (<4.08×1012/L), and gender (female) were the main risk-related factors for RBC transfusion. A total of 204 patients were tested, 177 of whom were predicted accurately (86.8%). CONCLUSIONS: Machine learning models can be used to accurately predict the outcomes of RBC transfusion, and should be used to guide surgeons in clinical practice.

Myeloid-derived suppressor cell (MDSC) key genes analysis in rat anti-CD28-induced immune tolerance kidney transplantation.

BACKGROUND: In the field of transplantation, inducing immune tolerance in recipients is of great importance. Blocking co-stimulatory molecule using anti-CD28 antibody could induce tolerance in a rat kidney transplantation model. Myeloid-derived suppressor cells (MDSCs) reveals strong immune suppressive abilities in kidney transplantation. Here we analyzed key genes of MDSCs leading to transplant tolerance in this model. METHODS: Microarray data of rat gene expression profiles under accession number GSE28545 in the Gene Expression Omnibus (GEO) database were analyzed. Running the LIMMA package in R language, the differentially expressed genes (DEGs) were found. Enrichment analysis of the DEGs was conducted in the Database for Annotation, Visualization and Integrated Discovery (DAVID) database to explore gene ontology (GO) annotation and their Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Their protein-protein interactions (PPIs) were provided by STRING database and was visualized in Cytoscape. Hub genes were carried out by CytoHubba. RESULTS: Three hundred and thirty-eight DEGs were exported, including 27 upregulated and 311 downregulated genes. The functions and KEGG pathways of the DEGs were assessed and the PPI network was constructed based on the string interactions of the DEGs. The network was visualized in Cytoscape; the entire PPI network consisted of 192 nodes and 469 edges. Zap70, Cdc42, Stat1, Stat4, Ccl5 and Cxcr3 were among the hub genes. CONCLUSIONS: These key genes, corresponding proteins and their functions may provide valuable background for both basic and clinical research and could be the direction of future studies in immune tolerance, especially those examining immunocyte-induced tolerance.

Histone Methylation Inhibitor DZNep Ameliorated the Renal Ischemia-Reperfusion Injury via Inhibiting TIM-1 Mediated T Cell Activation.

Renal ischemia-reperfusion injury (IRI) after renal transplantation often leads to the loss of kidney graft function. However, there is still a lack of efficient regimens to prevent or alleviate renal IRI. Our study focused on the renoprotective effect of 3-Deazaneplanocin A (DZNep), which is a histone methylation inhibitor. We found that DZNep significantly alleviated renal IRI by suppressing nuclear factor kappa-B (NF-κB), thus inhibiting the expression of inflammatory factors in renal tubular epithelial cells in vivo or in vitro. After treatment with DZNep, T cell activation was impaired in the spleen and kidney, which correlated with the downregulated expression of T-cell immunoglobulin mucin (TIM)-1 on T cells and TIM-4 in macrophages. In addition, pretreatment with DZNep was not sufficient to protect the kidney, while administration of DZNep from before to after surgery significantly ameliorated IRI. Our findings suggest that DZNep can be a novel strategy for preventing renal IRI following kidney transplantation.

Single-cell Sequencing in the Field of Stem Cells.

Variation and heterogeneity between cells are the basic characteristics of stem cells. Traditional sequencing analysis methods often cover up this difference. Single-cell sequencing technology refers to the technology of high-throughput sequencing analysis of genomes at the single-cell level. It can effectively analyze cell heterogeneity and identify a small number of cell populations. With the continuous progress of cell sorting, nucleic acid extraction and other technologies, single-cell sequencing technology has also made great progress. Encouraging new discoveries have been made in stem cell research, including pluripotent stem cells, tissue-specific stem cells and cancer stem cells. In this review, we discuss the latest progress and future prospects of single-cell sequencing technology in the field of stem cells.

Association of pvl gene with incomplete hemolytic phenotype in clinical Staphylococcus aureus.

Purpose: This experiment aimed to evaluate the correlation between the hemolytic phenotype of Staphylococcus aureus and pvl gene in terms of characteristics of antibiotic resistance. Materials and methods: Two-hundred and eleven strains of hospital-acquired S. aureus and their bacterial susceptibility to 20 antibiotics were determined by MicroScan WalkAway96. All strains were cultured on Columbia sheep blood agar plates for 24 hours and then underwent ten passages for investigation of their hemolytic phenotypes. S. aureus produced incomplete ß-hemolytic phenotype, termed as S. aureus strains with incomplete hemolytic phenotype (SIHP). The pvl gene was identified by PCR amplification followed by DNA sequencing. Statistical analyses of the data were performed using SPSS version 16.0 software. Results: Fifty-two (24.64%) strains were confirmed to maintain the incomplete hemolytic phenotype of S. aureus (SIHP). Meanwhile, 15 (7.11%) of 211 strains were found to carry the pvl gene, and eight of the 15 strains were SIHP. Compared with S. aureus strains with complete hemolytic phenotype (SCHP), SIHP showed higher susceptibility to seven of the 20 antibiotics (oxacillin, ciprofloxacin, gentamicin, ceftriaxone, cefoxitin, levofloxacin, and moxifloxacin) (P<0.05). The pvl-positive bacteria had a higher rate of resistance to four antibiotics (rifampin, ciprofloxacin, levofloxacin, and moxifloxacin) in comparison with the pvl-negative strains (P<0.05). Conclusion: SIHP had a high frequency of pvl gene. The pvl-positive isolates showed less resistance to rifampin, ciprofloxacin, levofloxacin, and moxifloxacin. Additionally, the majority of SIHP isolates (61.54%) were methicillin-resistant S. aureus. SIHP strains had significantly higher antibiotic resistance to cefoxitin when compared with SCHP, while SCHP strains had a high rate of antibiotic resistance to ciprofloxacin, gentamicin, ceftriaxone, levofloxacin, and moxifloxacin. The results may help to provide medical advice for selection of antibiotics for patients with SIHP-associated infections.