Impact of LuxS on virulence and pathogenicity in Klebsiella pneumoniae exhibiting varied mucoid phenotypes.

How the LuxS/AI-2 quorum sensing (QS) system influences the pathogenicity of K. pneumoniae is complicated by the heterogeneity of the bacterial mucoid phenotypes. This study aims to explore the LuxS-mediated regulation of the pathogenicity of K. pneumoniae with diverse mucoid phenotypes, including hypermucoid, regular-mucoid, and nonmucoid. The wild-type, luxS knockout, and complemented strains of three K. pneumoniae clinical isolates with distinct mucoid phenotypes were constructed. The results revealed the downregulation of virulence genes of regular-mucoid, and nonmucoid but not hypermucoid strains. The deletion of luxS reduced the pathogenicity of the regular-mucoid, and nonmucoid strains in mice; while in hypermucoid strain, luxS knockout reduced virulence in late growth but enhanced virulence in the early growth phase. Furthermore, the absence of luxS led the regular-mucoid and nonmucoid strains to be more sensitive to the host cell defense, and less biofilm-productive than the wild-type at both the low and high-density growth state. Nevertheless, luxS knockout enhanced the resistances to adhesion and phagocytosis by macrophage as well as serum-killing, of hypermucoid K. pneumoniae at its early low-density growth state, while it was opposite to those in its late high-density growth phase. Collectively, our results suggested that LuxS plays a crucial role in the pathogenicity of K. pneumoniae, and it is highly relevant to the mucoid phenotypes and growth phases of the strains. LuxS probably depresses the capsule in the early low-density phase and promotes the capsule, biofilm, and pathogenicity during the late high-density phase, but inhibits lipopolysaccharide throughout the growth phase, in K. pneumoniae.IMPORTANCECharacterizing the regulation of physiological functions by the LuxS/AI-2 quorum sensing (QS) system in Klebsiella pneumoniae strains will improve our understanding of this important pathogen. The genetic heterogeneity of K. pneumoniae isolates complicates our understanding of its pathogenicity, and the association of LuxS with bacterial pathogenicity has remained poorly addressed in K. pneumoniae. Our results demonstrated strain and growth phase-dependent variation in the contributions of LuxS to the virulence and pathogenicity of K. pneumoniae. Our findings provide new insights into the important contribution of the LuxS/AI-2 QS system to the networks that regulate the pathogenicity of K. pneumoniae. Our study will facilitate our understanding of the regulatory mechanisms of LuxS/AI-2 QS on the pathogenicity of K. pneumoniae under the background of their genetic heterogeneity and help develop new strategies for diminished bacterial virulence within the clinical K. pneumoniae population.

Azomycin Orchestrate Colistin-Resistant Enterobacter cloacae Complex's Colistin Resistance Reversal In Vitro and In Vivo.

The Enterobacter cloacae complex (ECC) is a group of nosocomial pathogens that pose a challenge in clinical treatment due to its intrinsic resistance and the ability to rapidly acquire resistance. Colistin was reconsidered as a last-resort antibiotic for combating multidrug-resistant ECC. However, the persistent emergence of colistin-resistant (COL-R) pathogens impedes its clinical efficacy, and novel treatment options are urgently needed. We propose that azomycin, in combination with colistin, restores the susceptibility of COL-R ECC to colistin in vivo and in vitro. Results from the checkerboard susceptibility, time-killing, and live/dead bacterial cell viability tests showed strong synergistic antibacterial activity in vitro. Animal infection models suggested that azomycin-colistin enhanced the survival rate of infected Galleria mellonella and reduced the bacterial load in the thighs of infected mice, highlighting its superior in vivo synergistic antibacterial activity. Crystal violet staining and scanning electron microscopy unveiled the in vitro synergistic antibiofilm effects of azomycin-colistin. The safety of azomycin and azomycin-colistin at experimental concentrations was confirmed through cytotoxicity tests and an erythrocyte hemolysis test. Azomycin-colistin stimulated the production of reactive oxygen species in COL-R ECC and inhibited the PhoPQ two-component system to combat bacterial growth. Thus, azomycin is feasible as a colistin adjuvant against COL-R ECC infection.

The Properties of Linezolid, Rifampicin, and Vancomycin, as Well as the Mechanism of Action of Pentamidine, Determine Their Synergy against Gram-Negative Bacteria.

Combining pentamidine with Gram-positive-targeting antibiotics has been proven to be a promising strategy for treating infections from Gram-negative bacteria (GNB). However, which antibiotics pentamidine can and cannot synergize with and the reasons for the differences are unclear. This study aimed to identify the possible mechanisms for the differences in the synergy of pentamidine with rifampicin, linezolid, tetracycline, erythromycin, and vancomycin against GNB. Checkerboard assays were used to detect the synergy of pentamidine and the different antibiotics. To determine the mechanism of pentamidine, fluorescent labeling assays were used to measure membrane permeability, membrane potential, efflux pump activity, and reactive oxygen species (ROS); the LPS neutralization assay was used to evaluate the target site; and quantitative PCR was used to measure changes in efflux pump gene expression. Our results revealed that pentamidine strongly synergized with rifampicin, linezolid, and tetracycline and moderately synergized with erythromycin, but did not synergize with vancomycin against E. coli, K. pneumoniae, E. cloacae, and A. baumannii. Pentamidine increased the outer membrane permeability but did not demolish the outer and inner membranes, which exclusively permits the passage of hydrophobic, small-molecule antibiotics while hindering the entry of hydrophilic, large-molecule vancomycin. It dissipated the membrane proton motive force and inactivated the efflux pump, allowing the intracellular accumulation of antimicrobials that function as substrates of the efflux pump, such as linezolid. These processes resulted in metabolic perturbation and ROS production which ultimately was able to destroy the bacteria. These mechanisms of action of pentamidine on GNB indicate that it is prone to potentiating hydrophobic, small-molecule antibiotics, such as rifampicin, linezolid, and tetracycline, but not hydrophilic, large-molecule antibiotics like vancomycin against GNB. Collectively, our results highlight the importance of the physicochemical properties of antibiotics and the specific mechanisms of action of pentamidine for the synergy of pentamidine-antibiotic combinations. Pentamidine engages in various pathways in its interactions with GNB, but these mechanisms determine its specific synergistic effects with certain antibiotics against GNB. Pentamidine is a promising adjuvant, and we can optimize drug compatibility by considering its functional mechanisms.

Clinical impact of the type VI secretion system on clinical characteristics, virulence and prognosis of Acinetobacter baumannii during bloodstream infection.

The type VI secretion system (T6SS) has been regarded as a late-model virulence factor widely distributed in Acinetobacter baumannii (A. baumannii). This study aimed to elucidate the clinical manifestations, the genetic background and microbiological characteristics of A. baumannii isolates causing bloodstream infection (BSI), and assessed the impact of T6SS carrying state on the clinical course. In this study, Clinical samples of A. baumannii causing BSI were collected from a teaching hospital in China from 2016 to 2020 and a retrospective cohort was conducted. Experimental strains were categorized into T6SS positive and negative groups through PCR targeting on hcp gene. The antimicrobials sensitivity test, virulence genes, biofilm formation ability, serum resistance of A. baumannii strains and Galleria mellonella infection model were investigated. Independent risk factors for T6SS+ A. baumannii BSI and Kaplan-Meier curve through follow-up survey were analyzed. A total of 182 A. baumannii strains were isolated from patients with BSI during 5 years and the medical records of all patients were retrospectively reviewed. The proportion of T6SS+ isolates was 62.64% (114/182), which exhibited significantly higher resistance rates of commonly used antibacterial drugs compared to T6SS- group. We found that T6SS+ A. baumannii strains had significantly weaker biofilm formation ability compared to T6SS- A. baumannii. Despite no difference in the positivity rate of tested virulence genes in two groups, T6SS+ strains exhibited higher resistance to the serum and increased virulence in vivo compared to T6SS- strains, indicating that T6SS is likely to enhance the survival and invasive capabilities of A. baumannii in vivo. Indwelling catheter, respiratory diseases, ICU history, white blood cell count and percentage of neutrophils increasing were independent risk factors for T6SS+ A. baumannii BSI. At last, the Kaplan-Meier curve confirmed a higher mortality rate associated with T6SS+ A. baumannii BSI, suggesting that the presence of T6SS may serve as a prognostic factor for mortality. In conclusion, our study revealed that T6SS+ A. baumannii exhibited distinct clinical features, characterized by high antimicrobial resistance and enhanced virulence, providing valuable insights for clinical treatment considerations.

A novel fast method for identifying the origin of Maojian using NIR spectroscopy with deep learning algorithms.

Maojian is one of China's traditional famous teas. There are many Maojian-producing areas in China. Because of different producing areas and production processes, different Maojian have different market prices. Many merchants will mix Maojian in different regions for profit, seriously disrupting the healthy tea market. Due to the similar appearance of Maojian produced in different regions, it is impossible to make a quick and objective distinction. It often requires experienced experts to identify them through multiple steps. Therefore, it is of great significance to develop a rapid and accurate method to identify different regions of Maojian to promote the standardization of the Maojian market and the development of detection technology. In this study, we propose a new method based on Near infra-red (NIR) with deep learning algorithms to distinguish different origins of Maojian. In this experiment, the NIR spectral data of Maojian from different origins are combined with the back propagation neural network (BPNN), improved AlexNet, and improved RepSet models for classification. Among them, improved RepSet has the highest accuracy of 99.30%, which is 8.67% and 0.70% higher than BPNN and improved AlexNet, respectively. The overall results show that it is feasible to use NIR and deep learning methods to quickly and accurately identify Maojian from different origins and prove an effective alternative method to discriminate different origins of Maojian.

Raman spectroscopy combined with machine learning algorithms to detect adulterated Suichang native honey.

Zhejiang Suichang native honey, which is included in the list of China's National Geographical Indication Agricultural Products Protection Project, is very popular. This study proposes a method of Raman spectroscopy combined with machine learning algorithms to accurately detect low-concentration adulterated Suichang native honey. In this study, the native honey collected by local beekeepers in Suichang was selected for adulteration detection. The spectral data was compressed by Savitzky-Golay smoothing and partial least squares (PLS) in sequence. The PLS features taken for further analysis were selected according to the contribution rate. In this study, three classification modeling methods including support vector machine, probabilistic neural network and convolutional neural network were adopted to correctly classify pure and adulterated honey samples. The total accuracy was 100%, 100% and 99.75% respectively. The research result shows that Raman spectroscopy combined with machine learning algorithms has great potential in accurately detecting adulteration of low-concentration honey.

Recognition of chronic renal failure based on Raman spectroscopy and convolutional neural network.

PURPOSE: Chronic renal failure (CRF) is a disease with a high morbidity rate that can develop into uraemia, resulting in a series of complications, such as dyspnoea, mental disorders, hypertension, and heart failure. CRF may be controlled clinically by drug intervention. Therefore, early diagnosis and control of the disease are of great significance for the treatment and prevention of chronic renal failure. Based on the complexity of CRF diagnosis, this study aims to explore a new rapid and noninvasive diagnostic method. METHODS: In this experiment, the serum Raman spectra of samples from 47 patients with CRF and 53 normal subjects were obtained. In this study, Serum Raman spectra of healthy and CRF patients were identified by a Convolutional Neural Network (CNN) and compared with the results of identified by an Improved AlexNet. In addition, different amplitude of noise were added to the spectral data of the samples to explore the influence of a small random noise on the experimental results. RESULTS: A CNN and an Improved AlexNet was used to classify the spectra, and the accuracy was 79.44 % and 95.22 % respectively. And the addition of noise did not significantly interfere with the classification accuracy. CONCLUSION: The accuracy of CNN of this study can be as high as 95.22 %, which greatly improves its accuracy and reliability, compared to 89.7 % in the previous study. The results of this study show that the combination of serum Raman spectrum and CNN can be used in the diagnosis of CRF, and small random noise will not cause serious interference to the data analysis results.