Effects of artificial honey and epigallocatechin-3-gallate on streptococcus pyogenes

Rapid in situ identification of honey authenticity based on RP-Nano-ESI-MS using online desalting

High-content sugar in honey frequently results in severe matrix effects and requires complex pretreatment prior to analysis, posing significant challenges for the rapid analysis of honey. In this study, the reversal polarity nano-electrospray ionization mass spectrometry (RP-Nano-ESI-MS) analysis was developed for the direct evaluation of honey samples. The results indicated that RP-Nano-ESI-MS significantly mitigated the matrix effects induced by high-content sugar through the implementation of online desalting. Furthermore, RP-Nano-ESI-MS has been proven capable of not only differentiating acacia honey adulterated with 10% rape honey, but also effectively distinguishing six types of honey and exhibiting remarkable proficiency in detecting honey adulteration and botanical traceability. Additionally, RP-Nano-ESI-MS exhibited strong quantitative abilities, effectively characterizing variations in amino acid composition among six types of honey with high stability and reproducibility. Our studies underscore the significant potential of RP-Nano-ESI-MS for its rapid in situ analysis of sugar-rich foods like honey, especially in their authenticity verification.

Metabolomics and network pharmacology reveal the mechanism of Castanopsis honey against Streptococcus pyogenes

Streptococcus pyogenes (GAS) is one of the most virulent and infectious bacteria, severely threatening health and lives of people worldwide. Honey has been proven to have effective capability against GAS, but the underlying metabolites and mechanisms are still unclear. In this study, the Castanopsis honey (CH) showed significant antibacterial ability compared to other seven kinds of honey and artificial honey. Furthermore, the antibacterial metabolites and their targets in CH were screened by combined method of metabolomics, network pharmacology, and molecular docking. The results suggested that the activities of two antioxidant enzymes, glutathione peroxidase and tyrosyl tRNA synthetase identified as the primary targets, were significantly inhibited by CH, which significantly increased the level of oxidative stress in GAS. The results revealed a possibly novel mechanism regulating the oxidative stress and inhibits the growth in bacteria, providing strong experimental evidence to support the further development of CH as a novel antibacterial agent.

Efficient detection of Streptococcus pyogenes based on recombinase polymerase amplification and lateral flow strip

PURPOSE

This article aims to establish a rapid visual method for the detection of Streptococcus pyogenes (GAS) based on recombinase polymerase amplification (RPA) and lateral flow strip (LFS).

METHODS

Utilizing speB of GAS as a template, RPA primers were designed, and basic RPA reactions were performed. To reduce the formation of primer dimers, base mismatch was introduced into primers. The probe was designed according to the forward primer, and the RPA-LFS system was established. According to the color results of the reaction system, the optimum reaction temperature and time were determined. Thirteen common clinical standard strains and 14 clinical samples of GAS were used to detect the selectivity of this method. The detection limit of this method was detected by using tenfold gradient dilution of GAS genome as template. One hundred fifty-six clinical samples were collected and compared with qPCR method and culture method. Kappa index and clinical application evaluation of the RPA-LFS were carried out.

RESULTS

The enhanced RPA-LFS method demonstrates the ability to complete the amplification process within 6 min at 33 °C. This method exhibits a high analytic sensitivity, with the lowest detection limit of 0.908 ng, and does not exhibit cross-reaction with other pathogenic bacteria.

CONCLUSIONS

The utilization of RPA and LFS allows for efficient and rapid testing of GAS, thereby serving as a valuable method for point-of-care testing.