Phentolamine Significantly Enhances Macrolide Antibiotic Antibacterial Activity against MDR Gram-Negative Bacteria.

OBJECTIVES: Multidrug-resistant (MDR) Gram-negative bacterial infections have limited treatment options due to the impermeability of the outer membrane. New therapeutic strategies or agents are urgently needed, and combination therapies using existing antibiotics are a potentially effective means to treat these infections. In this study, we examined whether phentolamine can enhance the antibacterial activity of macrolide antibiotics against Gram-negative bacteria and investigated its mechanism of action. METHODS: Synergistic effects between phentolamine and macrolide antibiotics were evaluated by checkerboard and time-kill assays and in vivo using a Galleria mellonella infection model. We utilized a combination of biochemical tests (outer membrane permeability, ATP synthesis, ΔpH gradient measurements, and EtBr accumulation assays) with scanning electron microscopy to clarify the mechanism of phentolamine enhancement of macrolide antibacterial activity against Escherichia coli. RESULTS: In vitro tests of phentolamine combined with the macrolide antibiotics erythromycin, clarithromycin, and azithromycin indicated a synergistic action against E. coli test strains. The fractional concentration inhibitory indices (FICI) of 0.375 and 0.5 indicated a synergic effect that was consistent with kinetic time-kill assays. This synergy was also seen for Salmonella typhimurium, Klebsiella pneumoniae, and Actinobacter baumannii but not Pseudomonas aeruginosa. Similarly, a phentolamine/erythromycin combination displayed significant synergistic effects in vivo in the G. mellonella model. Phentolamine added singly to bacterial cells also resulted in direct outer membrane damage and was able to dissipate and uncouple membrane proton motive force from ATP synthesis that, resulted in enhanced cytoplasmic antibiotic accumulation via reduced efflux pump activity. CONCLUSIONS: Phentolamine potentiates macrolide antibiotic activity via reducing efflux pump activity and direct damage to the outer membrane leaflet of Gram-negative bacteria both in vitro and in vivo.

MALDI-TOF MS for rapid detection and differentiation between Tet(X)-producers and non-Tet(X)-producing tetracycline-resistant Gram-negative bacteria.

The extensive use of tetracycline antibiotics has led to the widespread presence of tetracycline-resistance genes in Gram-negative bacteria and this poses serious threats to human and animal health. In our previous study, we reported a method for rapid detection of Tet(X)-producers using MALDI-TOF MS. However, there have been multiple machineries involved in tetracycline resistance including efflux pump, and ribosomal protection protein. Our previous demonstrated the limitation in probing the non-Tet(X)-producing tetracycline-resistant strains. In this regard, we further developed a MALDI-TOF MS method to detect and differentiate Tet(X)-producers and non-Tet(X)-producing tetracycline-resistant strains. Test strains were incubated with tigecycline and oxytetracycline in separate tubes for 3 h and then analyzed spectral peaks of tigecycline, oxytetracycline, and their metabolite. Strains were distinguished using MS ratio for [metabolite/(metabolite+ tigecycline or oxytetracycline)]. Four control strains and 319 test strains were analyzed and the sensitivity was 98.90% and specificity was 98.34%. This was consistent with the results obtained from LC-MS/MS analysis. Interestingly, we also found that the reactive oxygen species (ROS) produced by tetracycline-susceptible strains were able to promote the degradation of oxytetracycline. Overall, the MALDITet(X)-plus test represents a rapid and reliable method to detect Tet(X)-producers, non-Tet(X)-producing tetracycline-resistant strains, and tetracycline-susceptible strains.

Rapid Screening of Essential Oils as Substances Which Enhance Antibiotic Activity Using a Modified Well Diffusion Method.

Antimicrobial resistance is recognized as one of the major global health challenges of the 21st century. Synergistic combinations for antimicrobial therapies can be a good strategy for the treatment of multidrug resistant infections. We examined the ability of a group of 29 plant essential oils as substances which enhance the antibiotic activity. We used a modified well diffusion method to establish a high-throughput screening method for easy and rapid identification of high-level enhancement combinations against bacteria. We found that 25 essential oils possessed antibacterial activity against Escherichia Coli ATCC 25922 and methicillin-resistant Staphylococcus aureus (MRSA) 43300 with MICs that ranged from 0.01% to 2.5% v/v. We examined 319 (11 × 29) combinations in a checkerboard assay with E. Coli ATCC 25922 and MRSA 43300, and the result showed that high-level enhancement combinations were 48 and 44, low-level enhancement combinations were 214 and 211, and no effects combinations were 57 and 64, respectively. For further verification we randomly chose six combinations that included orange and Petitgrain essential oils in a standard time-killing assay. The results are in great agreement with those of the well diffusion assays. Therefore, the modified diffusion method was a rapid and effective method to screen high-level enhancement combinations of antibiotics and essential oils.

The Comparison of Spectral Classification Based on DBN, BP Neural Network and SVM.

The stellar classification was an important research field for understanding the formation and evolution of stars and galaxies. With large sky surveys and its massive data, the speed and accuracy of the celestial automatic classification was very important. The depth confidence neural network (DBN), support vector machines (SVM) and BP neural networks used in the star classification were compared in this paper. And the applicability of star classification with these three methods was analyzed. First, K, F stars are classified according to the depth of confidence neural network and BP neural network and support vector machine.Then the K1, K3, K5 sub-type and F2, F5, F9 sub-type were separately identified. Finally, the data which did not belong to the k sub-type were excluded by a secondary classification model based on SVM support vector machine . The results shows that: the depth of belief networks is better for K, F-type star classification, but it is poor for K, F sub-type classification results; The recognition rate of SVM is high for the K, F-type stars and the classification effects of this method is better for K, F-type stars than the corresponding sub-type stars by comparison; The recognition rate of BP neural network is ordinary general for K, F-type stars and their sub-types. The experiment showed that the accuracy of excluding non-k-sub-type data can be up to 100% which indicates that the unknown spectral data can be screened and classified with SVM.

Apelin Enhances Brown Adipogenesis and Browning of White Adipocytes.

Brown adipose tissue expends energy in the form of heat via the mitochondrial uncoupling protein UCP1. Recent studies showed that brown adipose tissue is present in adult humans and may be exploited for its anti-obesity and anti-diabetes actions. Apelin is an adipocyte-derived hormone that plays important roles in energy metabolism. Here, we report that apelin-APJ signaling promotes brown adipocyte differentiation by increasing the expressions of brown adipogenic and thermogenic transcriptional factors via the PI3K/Akt and AMPK signaling pathways. It is also found that apelin relieves the TNFα inhibition on brown adipogenesis. In addition, apelin increases the basal activity of brown adipocytes, as evidenced by the increased PGC1α and UCP1 expressions, mitochondrial biogenesis, and oxygen consumption. Finally, we provide both in vitro and in vivo evidence that apelin is able to increase the brown-like characteristics in white adipocytes. This study, for the first time, reveals the brown adipogenic and browning effects of apelin and suggests a potential therapeutic route to combat obesity and related metabolic disorders.