Protective Efficacy of Vitamins C and E on p,p′-DDT-Induced Cytotoxicity via the ROS-Mediated Mitochondrial Pathway and NF-κB/FasL Pathway

Dichlorodiphenoxytrichloroethane (DDT) is a known persistent organic pollutant and liver damage toxicant. However, there has been little emphasis on the mechanism underlying liver damage toxicity of DDT and the relevant effective inhibitors. Hence, the present study was conducted to explore the protective effects of vitamin C (VC) and vitamin E (VE) on the cytotoxicity of DDT in HL-7702 cells and elaborate the specific molecular mechanisms. The results demonstrated that p,p′-DDT exposure at over 10 µM depleted cell viability of HL-7702 cells and led to cell apoptotic. p,p′-DDT treatment elevated the level of reactive oxygen species (ROS) generation, induced mitochondrial membrane potential, and released cytochrome c into the cytosol, with subsequent elevations of Bax and p53, along with suppression of Bcl-2. In addition, the activations of caspase-3 and -8 were triggered. Furthermore, p,p′-DDT promoted the expressions of NF-κB and FasL. When the cells were exposed to the NF-κB inhibitor (PDTC), the up-regulated expression of FasL was attenuated. Strikingly, these alterations caused by DDT treatment were prevented or reversed by the addition of VC or VE, and the protective effects of co-treatment with VC and VE were higher than the single supplement with p,p′-DDT. Taken together, these findings provide novel experimental evidences supporting that VC or/and VE could reduce p,p′-DDT-induced cytotoxicity of HL-7702 cells via the ROS-mediated mitochondrial pathway and NF-κB/FasL pathway

Identification of apoptosis-related gene signatures as potential biomarkers for differentiating active from latent tuberculosis via bioinformatics analysis

BackgroundApoptosis is associated with the pathogenesis of Mycobacterium tuberculosis infection. This study aims to identify apoptosis-related genes as biomarkers for differentiating active tuberculosis (ATB) from latent tuberculosis infection (LTBI).MethodsThe tuberculosis (TB) datasets (GSE19491, GSE62525, and GSE28623) were downloaded from the Gene Expression Omnibus (GEO) database. The diagnostic biomarkers differentiating ATB from LTBI were identified by combining the data of protein-protein interaction network, differentially expressed gene, Weighted Gene Co-Expression Network Analysis (WGCNA), and receiver operating characteristic (ROC) analyses. Machine learning algorithms were employed to validate the diagnostic ability of the biomarkers. Enrichment analysis for biomarkers was established, and potential drugs were predicted. The association between biomarkers and N6-methyladenosine (m6A) or 5-methylated cytosine (m5C) regulators was evaluated.ResultsSix biomarkers including CASP1, TNFSF10, CASP4, CASP5, IFI16, and ATF3 were obtained for differentiating ATB from LTBI. They showed strong diagnostic performances, with area under ROC (AUC) values > 0.7. Enrichment analysis demonstrated that the biomarkers were involved in immune and inflammation responses. Furthermore, 24 drugs, including progesterone and emricasan, were predicted. The correlation analysis revealed that biomarkers were positively correlated with most m6A or m5C regulators.ConclusionThe six ARGs can serve as effective biomarkers differentiating ATB from LTBI and provide insight into the pathogenesis of Mycobacterium tuberculosis infection

Complete genome sequence of the virulent Klebsiella pneumoniae Phage Geezett infecting multidrug-resistant clinical strains

Geezett was isolated from hospital sewage in Hangzhou, China, and exhibits lytic activity against clinical isolates of the nosocomial pathogen Klebsiella pneumoniae. The bacteriophage is a myovirus and has a double-stranded DNA (dsDNA) genome 50,707 bp long, containing 79 open reading frames (ORFs)

Artificial intelligence-aided rapid and accurate identification of clinical fungal infections by single-cell Raman spectroscopy

Integrating artificial intelligence and new diagnostic platforms into routine clinical microbiology laboratory procedures has grown increasingly intriguing, holding promises of reducing turnaround time and cost and maximizing efficiency. At least one billion people are suffering from fungal infections, leading to over 1.6 million mortality every year. Despite the increasing demand for fungal diagnosis, current approaches suffer from manual bias, long cultivation time (from days to months), and low sensitivity (only 50% produce positive fungal cultures). Delayed and inaccurate treatments consequently lead to higher hospital costs, mobility and mortality rates. Here, we developed single-cell Raman spectroscopy and artificial intelligence to achieve rapid identification of infectious fungi. The classification between fungi and bacteria infections was initially achieved with 100% sensitivity and specificity using single-cell Raman spectra (SCRS). Then, we constructed a Raman dataset from clinical fungal isolates obtained from 94 patients, consisting of 115,129 SCRS. By training a classification model with an optimized clinical feedback loop, just 5 cells per patient (acquisition time 2 s per cell) made the most accurate classification. This protocol has achieved 100% accuracies for fungal identification at the species level. This protocol was transformed to assessing clinical samples of urinary tract infection, obtaining the correct diagnosis from raw sample-to-result within 1 h

Co‐evolutionary adaptations of Acinetobacter baumannii and a clinical carbapenemase‐encoding plasmid during carbapenem exposure

Abstract: OXA‐23 is the predominant carbapenemase in carbapenem‐resistant Acinetobacter baumannii. The co‐evolutionary dynamics of A. baumannii and OXA‐23‐encoding plasmids are poorly understood. Here, we transformed A. baumannii ATCC 17978 with pAZJ221, a blaOXA−23‐containing plasmid from clinical A. baumannii isolate A221, and subjected the transformant to experimental evolution in the presence of a sub‐inhibitory concentration of imipenem for nearly 400 generations. We used population sequencing to track genetic changes at six time points and evaluated phenotypic changes. Increased fitness of evolving populations, temporary duplication of blaOXA−23 in pAZJ221, interfering allele dynamics, and chromosomal locus‐level parallelism were observed. To characterize genotype‐to‐phenotype associations, we focused on six mutations in parallel targets predicted to affect small RNAs and a cyclic dimeric (3′ → 5′) GMP‐metabolizing protein. Six isogenic mutants with or without pAZJ221 were engineered to test for the effects of these mutations on fitness costs and plasmid kinetics, and the evolved plasmid containing two copies of blaOXA−23 was transferred to ancestral ATCC 17978. Five of the six mutations contributed to improved fitness in the presence of pAZJ221 under imipenem pressure, and all but one of them impaired plasmid conjugation ability. The duplication of blaOXA−23 increased host fitness under carbapenem pressure but imposed a burden on the host in antibiotic‐free media relative to the ancestral pAZJ221. Overall, our study provides a framework for the co‐evolution of A. baumannii and a clinical blaOXA−23‐containing plasmid in the presence of imipenem, involving early blaOXA−23 duplication followed by chromosomal adaptations that improved the fitness of plasmid‐carrying cells