Abstract
Syringa oblata Lindl. (S. oblata) has been used in herbal medicines for treating bacterial diseases. It is also thought to inhibit Streptococcus suis (S. suis) biofilm formation. However, due to the inherent nature of the complexity in its chemical properties, it is difficult to understand the possible bioactive ingredients of S. oblata. The spectrum-effect relationships method was applied to screen the main active ingredients in S. oblata obtained from Heilongjiang Province based on gray relational analysis. The results revealed that Sub-MICs obtained from 10 batches of S. oblata could inhibit biofilm formation by S. suis. Gray relational analysis revealed variations in the contents of 15 main peaks and rutin was discovered to be the main active ingredient. Then, the function of rutin was further verified by inhibiting S. suis biofilm formation using crystal violet staining. Computational studies revealed that rutin may target the chloramphenicol acetyltransferase protein in the biofilm formation of S. suis. In conclusion, this study revealed that the spectrum-effect relationships and computational studies are useful tools to associate the active ingredient with the potential anti-biofilm effects of S. oblata. Here, our findings would provide foundation for the further understanding of the mechanism of S. oblata intervention in biofilm formation.
Highlights
To date, herbal medicines are gaining more and more attention in recent years because of the availability of multiple chemical ingredients (Yi et al, 2014)
These results indicated that S1 and S2 which were located in oil field with poor soil conditions and a low temperature region respectively had the lowest degree of similarity (Liao et al, 2015)
The results revealed that the 1/2 Minimum Inhibitory Concentration (MIC) of 10 batches of the S. oblate samples had the ability to significantly inhibit the formation of S. suis biofilm (p < 0.05) (Figure 5A) compared with positive control
Summary
Herbal medicines are gaining more and more attention in recent years because of the availability of multiple chemical ingredients (Yi et al, 2014). It is very difficult to determine the main ingredients that provide the therapeutic effects in herbal medicines (Kong et al, 2011). It is well-known that spectrum-effect relationships analysis (Kong et al, 2009b) and chemical fingerprints have been used to determine the main ingredients in herbal medicines. Similarity analysis, clustering analysis, PCA and gray relational analysis may be able to elucidate the association between therapeutic effect and main component in herbal medicines
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