HSAF-induced antifungal effects in Candida albicans through ROS-mediated apoptosis

Heat-stable antifungal factor (HSAF) belongs to polycyclic tetramate macrolactams (PTMs), which inhibits many fungal pathogens and is effective in inhibiting Candida albicans (C. albicans). In this study, we found that HSAF induced the apoptosis of C. albicans SC5314 through inducing the production of reactive oxygen species (ROS). Nevertheless, we validated the efficacy of HSAF against candidiasis caused by C. albicans in a murine model in vivo,and HSAF significantly improved survival and reduced fungal burden compared to vehicles. A molecular dynamics (MD) simulation was also investigated, revealing the theoretical binding mode of HSAF to the β-tubulin of C. albicans. This study first found PTMs-induced fungal apoptosis through ROS accumulation in C. albicans and its potential as a novel agent for fungicides

Iterative Assembly of Two Separate Polyketide Chains by the Same Single-module Bacterial Polyketide Synthase in the Biosynthesis of HSAF

HSAF (1) was isolated from the biocontrol agent Lysobacter enzymogenes (Figure 1).[1-4] This bacterial metabolite belongs to polycyclic tetramate macrolactams (PTM) that are emerging as a new class of natural products with distinct structural features. [5, 6] HSAF exhibits a potent antifungal activity and shows a novel mode of action.[1-4] The HSAF biosynthetic gene cluster contains only a single-module hybrid polyketide synthasenonribosomal peptide synthetase (PKS-NRPS), although the PTM scaffold is apparently derived from two separate hexaketide chains and an ornithine residue.[1-4] This suggests that the same PKS module would act not only iteratively, but also separately, in order to link the two hexaketide chains with the NRPS-activated ornithine to form the characteristic PTM scaffold. Recently, the Gulder group reported heterologous expression of the ikarugamycin (4) biosynthetic gene cluster in E. coli,[7] and the Zhang group reported the enzymatic mechanism for formation of the inner 5-memebered ring and demonstrated the polyketide origin of the ikarugamycin skeleton.[8] Ikarugamycin is a Streptomyces-derived PTM which has a 5,6,5-tricyclic system (Figure 1). Both the Gulder and Zhang groups showed that a three-gene cluster is sufficient for ikarugamycin biosynthesis. Despite the progress, this iterative polyketide biosynthetic mechanism had not been demonstrated using purified PKS and NRPS. In addition, HSAF has a 5,5,6-tricyclic system, and its gene cluster contains at least six genes.[3] Finally, unlike most PTM compounds, HSAF is produced by a Gramnegative bacterium, L. enzymogenes. Here, we report the heterologous production of HSAF analogs in Gram-positive Streptomyces hosts, in which the native PKS have been deleted. We also obtained evidence for the formation of the polyene tetramate intermediate in Streptomyces when only the single-module hybrid PKS-NRPS gene was expressed. Finally, we showed the in vitro production of the polyene tetramate using the individually purified PKS and NRPS. The results provide direct evidence for this iterative polyketide biosynthetic mechanism that is likely general for the PTM-type hybrid polyketide-peptides

lncRNA ZEB1-AS1 Mediates Oxidative Low-Density Lipoprotein-Mediated Endothelial Cells Injury by Post-transcriptional Stabilization of NOD2

Oxidized-low density lipoprotein (ox-LDL) can induce injury of endothelial cells, causing atherosclerosis, which is an important initial event in several cardiovascular diseases. Long non-coding RNAs (lncRNAs) have emerged as regulators of diverse biological processes, but their specific biological functions and biochemical mechanisms in ox-LDL-induced endothelial cell injury have not been well investigated. Here, we describe the initial functional analysis of a poorly characterized human lncRNA ZEB1 antisense 1 (ZEB1-AS1). We found that ox-LDL treatment could induce a decreased cell viability and an increased cell apoptosis in endothelial cells, and knockdown of ZEB1-AS1 significantly reversed this effect. Mechanistically, ox-LDL treatment could sequester p53 from binding to ZEB1-AS1 promoter region, causing transcriptional activation and upregulation of ZEB1-AS1. Moreover, enhanced ZEB1-AS1 could upregulate Nucleotide-Binding Oligomerization Domain 2 (NOD2) expression through recruiting leucine-rich pentatricopeptide repeat motif-containing protein (LRPPRC) to stabilize NOD2 mRNA. Experimental data showed that knockdown of NOD2 or LRPPRC dramatically abrogated the functional role of ZEB1-AS1 in ox-LDL-induced endothelial cell injury. In summary, we demonstrated that lncRNA ZEB1-AS1 regulates the ox-LDL-induced endothelial cell injury via an LRPPRC-dependent mRNA stabilization mechanism. Therefore, ZEB1-AS1 may serve as a multi-potency target to overcome endothelial cell injury, atherosclerosis and other cardiovascular diseases

An oxidative stress biomarkers predict prognosis in gastric cancer patients receiving immune checkpoint inhibitor

ObjectiveThe development and advance of gastric cancer are inextricably linked to oxidative and antioxidant imbalance. Although immunotherapy has been shown to be clinically effective, the link between oxidative stress and gastric cancer patients treated with immune checkpoint inhibitor (ICIs) remains unknown. This study aims at looking into the prognostic value of oxidative stress scores in gastric cancer patients treated with ICIs.MethodsBy taking the propagation to receiver operating characteristic (ROC) we got the best cut-off values, and divided 265 patients receiving ICIs and chemotherapy into high and low GC-Integrated Oxidative Stress Score (GIOSS) groups. We also used Kaplan-Meier and COX regression models to investigate the relationship between oxidative stress biomarkers and prognosis.ResultsThrough both univariate and multivariate analyses, it’s shown that GIOSS severs as an independent prognostic factor for progression-free survival (PFS) and Overall survival (OS). Based on GIOSS cutoff values, patients with high GIOSS levels, compared to those with low levels exhibited shorter PFS and OS, both in the high GIOSS group, which performed poorly in the ICIs subgroup and other subgroup analyses.ConclusionGIOSS is a biomarker that responds to systemic oxidative stress in the body and can predict prognosis in patients with gastric cancer who are taking ICIs. Additionally, it might come to medical professionals’ aid in making more effective or more suitable treatment plans for gastric cancer

Synthesis of carbon-supported PdSn–SnO2 nanoparticles with different degrees of interfacial contact and enhanced catalytic activities for formic acid oxidation

The conjunction of the PdSn alloy and SnO2 is of interest for improving catalytic activity in formic acid oxidation (FAO). Here, we report the synthesis of PdSn–SnO2 nanoparticles and a study of their catalytic FAO activity. Different degrees of interfacial contact between SnO2 and PdSn were obtained using two different stabilizers (sodium citrate and EDTA) during the reduction process in catalyst preparation. Compared to the PdSn alloy, PdSn–SnO2 supported on carbon black showed enhanced FAO catalytic activity due to the presence of SnO2 species. It was also found that interfacial contact between the PdSn alloy and the SnO2 phase has an impact on the activity towards CO oxidation and FAO.Web of Scienc

Psoriasis comorbid with atherosclerosis meets in lipid metabolism

Psoriasis (PSO) is a common skin disease affecting approximately 1%–3% of the population, and the incidence rate is increasing yearly. PSO is associated with a dramatically increased risk of cardiovascular disease, the most common of which is atherosclerosis (AS). In the past, inflammation was considered to be the triggering factor of the two comorbidities, but in recent years, studies have found that lipid metabolism disorders increase the probability of atherosclerosis in patients with psoriasis. In this review, we discuss epidemiological studies, clinical treatment methods, risk factors, and lipid metabolism of psoriasis and atherosclerosis comorbidities

An Improved Squirrel Search Algorithm for Global Function Optimization

An improved squirrel search algorithm (ISSA) is proposed in this paper. The proposed algorithm contains two searching methods, one is the jumping search method, and the other is the progressive search method. The practical method used in the evolutionary process is selected automatically through the linear regression selection strategy, which enhances the robustness of squirrel search algorithm (SSA). For the jumping search method, the ‘escape’ operation develops the search space sufficiently and the ‘death’ operation further explores the developed space, which balances the development and exploration ability of SSA. Concerning the progressive search method, the mutation operation fully preserves the current evolutionary information and pays more attention to maintain the population diversity. Twenty-one benchmark functions are selected to test the performance of ISSA. The experimental results show that the proposed algorithm can improve the convergence accuracy, accelerate the convergence speed as well as maintain the population diversity. The statistical test proves that ISSA has significant advantages compared with SSA. Furthermore, compared with five other intelligence evolutionary algorithms, the experimental results and statistical tests also show that ISSA has obvious advantages on convergence accuracy, convergence speed and robustness

A Diagnosis Model for College Teachers' Teaching Ability Based on Big Data and its Evaluation

The mining of big data provides new ideas, methods, and technical support for the evaluation of college teachers’ teaching ability. Existing studies generally over-emphasize outcome evaluations and the evaluation methods are not scientific or objective enough, thus the evaluation results are often trapped in large errors and single pattern of manifestation. To overcome such defects, this paper took college English teaching as an example to develop a diagnosis model for college teachers’ teaching ability based on big data and evaluate its feasibility. At first, the evaluation indexes of college teachers' teaching ability were determined and the entropy weight method was adopted to assign weight values to the evaluation indexes. Then, based on the Gradient Boosted Decision Tree (GBDT), the diagnosis model was constructed and the steps were detailed. After that, an improved Particle Swarm Optimization (PSO) algorithm was adopted to optimize the proposed model. At last, experimental results proved the feasibility of the proposed diagnosis model