Identification of necroptosis-related features in diabetic nephropathy and analysis of their immune microenvironent and inflammatory response.

Background: Diabetic nephropathy (DN) was considered a severe microvascular complication of diabetes, which was recognized as the second leading cause of end-stage renal diseases. Therefore, identifying several effective biomarkers and models to diagnosis and subtype DN is imminent. Necroptosis, a distinct form of programmed cell death, has been established to play a critical role in various inflammatory diseases. Herein, we described the novel landscape of necroptosis in DN and exploit a powerful necroptosis-mediated model for the diagnosis of DN. Methods: We obtained three datasets (GSE96804, GSE30122, and GSE30528) from the Gene Expression Omnibus (GEO) database and necroptosis-related genes (NRGs) from the GeneCards website. Via differential expression analysis and machine learning, significant NRGs were identified. And different necroptosis-related DN subtypes were divided using consensus cluster analysis. The principal component analysis (PCA) algorithm was utilized to calculate the necroptosis score. Finally, the logistic multivariate analysis were performed to construct the necroptosis-mediated diagnostic model for DN. Results: According to several public transcriptomic datasets in GEO, we obtained eight significant necroptosis-related regulators in the occurrence and progress of DN, including CFLAR, FMR1, GSDMD, IKBKB, MAP3K7, NFKBIA, PTGES3, and SFTPA1 via diversified machine learning methods. Subsequently, employing consensus cluster analysis and PCA algorithm, the DN samples in our training set were stratified into two diverse necroptosis-related subtypes based on our eight regulators' expression levels. These subtypes exhibited varying necroptosis scores. Then, we used various functional enrichment analysis and immune infiltration analysis to explore the biological background, immune landscape and inflammatory status of the above subtypes. Finally, a necroptosis-mediated diagnostic model was exploited based on the two subtypes and validated in several external verification datasets. Moreover, the expression level of our eight regulators were verified in the singe-cell level and glomerulus samples. And we further explored the relationship between the expression of eight regulators and the kidney function of DN. Conclusion: In summary, our necroptosis scoring model and necroptosis-mediated diagnostic model fill in the blank of the relationship between necroptosis and DN in the field of bioinformatics, which may provide novel diagnostic insights and therapy strategies for DN.

Identification of Hub Genes and Typing of Tuberculosis Infections Based on Autophagy-Related Genes.

Tuberculosis (TB) caused by Mycobacterium tuberculosis is one of the leading causes of morbidity and death in humans worldwide. Some autophagy genes associated with TB and some miRNAs regulating TB have been found, but the identification of autophagy-related genes in M. tuberculosis remains to be explored. Forty-seven autophagy-related genes differentially expressed in TB were identified in this study by analysis of TB-related datasets in the Gene Expression Omnibus (GEO) and autophagy-related genes in the Human Autophagy Database. The potential crucial genes affecting TB were found through the protein-protein interaction (PPI) network, and the possible pathways affected by these genes were verified. Analysis of the PPI network of miRNAs associated with M. tuberculosis infection and their target genes revealed that hsa-let-7, hsa-mir-155, hsa-mir-206, hsa-mir-26a, hsa-mir-30a, and hsa-mir-32 may regulate the expression of multiple autophagy-related genes (MAPK8, UVRAG, UKL2, and GABARAPL1) alone or in combination. Subsequently, Cytoscape was utilized to screen the differentially expressed genes related to autophagy. The hub genes (GABARAPL1 and ULK2) affecting TB were identified. Combined with Gene Set Enrichment Analysis (GSEA), the signaling pathways affected by the hub genes were verified. Finally, we divided TB patients into two subgroups based on autophagy-related genes, and the immune microenvironment of patients in different subgroups was significantly different. Our study found two autophagy-related hub genes that could affect TB and divide TB samples into two subgroups. This finding is of great significance for TB treatment and provides new ideas for exploring the pathogenesis of M. tuberculosis.

Association of chemotactic chemokine ligand 5 rs2107538 polymorphism with tuberculosis susceptibility: A meta-analysis.

A meta-analysis was carried out in this study by summarizing relevant research to evaluate the relationship between rs2107538 polymorphism in the chemotactic chemokine ligand 5 (CCL5) gene and tuberculosis (TB) susceptibility. Published studies were retrieved from PubMed, Embase, and CNKI databases using the keywords 'CCL5', 'TB', and 'polymorphism'. Nine studies involving 2584 patients with TB and 2265 controls were included in the current meta-analysis. The combined results suggested that the CCL5 rs2107538 polymorphism was correlated with TB susceptibility (recessive model: OR = 1.45, 95% CI = 1.02-2.07). Subgroup analysis according to race indicated that such correlation could be detected in Caucasians (CT versus CC: OR = 1.53, 95% CI = 1.20-1.95; dominant model: OR = 1.58, 95% CI = 1.25-1.99), but not in East Asian, South Asian, and South African populations. In conclusion, the results of our meta-analysis suggest that CCL5 rs2107538 polymorphism might contribute to the risk of TB, especially in Caucasians. Well-designed studies with more subjects will be required for further validation of these results.

Introducing molecular testing of pyrazinamide susceptibility improves multidrug-resistant tuberculosis treatment outcomes: a prospective cohort study.

The current treatment for multidrug-resistant tuberculosis (MDR-TB) takes a lengthy period of 18-24 months and has a poor cure rate of 50-60%. A multicenter, prospective cohort study was conducted to assess the role of testing for molecular susceptibility to pyrazinamide (PZA) in optimising treatment for MDR-TB.We assigned 76 patients to an optimised molecular susceptibility group and 159 patients to a regular treatment group where PZA susceptibility was not determined. Of these patients, 152 were matched after propensity score matching (76 in the optimised group and 76 in the regular group). Treatment success rate was measured in the propensity-matched cohort as the primary outcome.Patients in the optimised group achieved a higher treatment success rate than those in the regular group (76.3% versus 55.3%, p=0.006). Of 51 patients with isolates that were susceptible to PZA and who were receiving a 12-month regimen, 42 (82.4%) were treated successfully. The optimised group showed faster culture conversion than the regular group (p=0.024). After exclusion of pre-extensively drug-resistant TB (pre-XDR-TB), the treatment outcome in the optimised group was still better than the regular group (83.1% versus 62.1%, p=0.009).Introducing molecular susceptibility testing for PZA improved the treatment outcomes for MDR-TB without the use of new drugs. Introducing PZA for patients with PZA-susceptible (PZA-S) MDR-TB allows the current regimen to be shortened to 12 months with comparable success rates to the World Health Organization (WHO) recommended shorter regimen.

[Molecular markers related to prognosis of acute myeloid leukemia-review].

Numerous genetic abnormalities which can not be identified by cytogenetic detection (e.g., gene mutations, gene expression abnormalities) have been gradually found, which means that the further molecular classification of AML (acute myeloid leukemia) with distinctive prognosis have arrived. For example, mutations of the transcription factor (CCAAT enhancer binding factor alpha, C/EBPalpha) or nucleophosmin-1 (NPM1) may predict better prognosis, whereas partial tandem duplications of the MLL gene (MLL-PTD), internal tandem duplications of FLT3 (FLT3-ITD) or mutations of WT1 gene confer worse prognosis. This review focuses on the features and relationship of these genetic abnormalities, as well as their influence on the prognosis of AML.

[Relation between glucosylceramide synthase and multidrug resistance in leukemia cells].

This study was purposed to explore the expression of glucosylceramide synthase (GCS) in human leukemia cells and its relationship with multidrug resistance. RT-PCR was used to analyze peripheral blood samples from 53 leukemia patients with multidrug resistance/non-resistance, and to detect the expression level of GCS gene in HL-60 cells and HL-60/ADR cells, the expression level was compared with the level of mdr-1. The expressions of GCS protein and P-gp protein in HL-60 cells and HL-60/ADR cells were assayed by Western blot analysis. The results showed that the relative optical density ratio of GCS gene amplified bands in samples of leukemia patients with drug-resistance was significantly higher than that in samples of leukemia patients with drug non-resistance group (P < 0.05), meanwhile the significant enhancement of optical density value of GCS gene amplified bands accompanied by high expression of mdr-1 gene. Their correlation showed positive (P < 0.01, r = 0.6). The GCS mRNA and protein were overexpressed in HL-60/ADR cells, and their expression levels were obviously higher than that in HL-60 cells, meanwhile the expression of mdr-1 mRNA and P-gp also significantly increased in HL-60/ADR cells. It is concluded that the high level of GCS in leukemia patients possibly is associated with multidrug resistance of leukemia cells.