Synergy of clavine alkaloid ‘chanoclavine’ with tetracycline against multi-drug-resistant E. coli

Abstract

The emergence of multi drug resistance (MDR) in Gram-negative bacteria (GNB) and lack of novel classes of antibacterial agents have raised an immediate need to identify antibacterial agents, which can reverse the phenomenon of MDR. The purpose of present study was to evaluate synergy potential and understanding the drug resistance reversal mechanism of chanoclavine isolated from Ipomoea muricata against the multi-drug-resistant clinical isolate of Escherichia coli (MDREC). Although chanoclavine did not show antibacterial activity of its own, but in combination, it could reduce the minimum inhibitory concentration (MIC) of tetracycline (TET) up to 16-folds. Chanoclavine was found to inhibit the efflux pumps which seem to be ATPase-dependent. In real-time expression analysis, chanoclavine showed down-regulation of different efflux pump genes and decreased the mutation prevention concentration of tetracycline. Further, in silico docking studies revealed significant binding affinity of chanoclavine with different proteins known to be involved in drug resistance. In in silico ADME/toxicity studies, chanoclavine was found safe with good intestinal absorption, aqueous solubility, medium blood–brain barrier (BBB), no CYP 2D6 inhibition, no hepatotoxicity, no skin irritancy, and non-mutagenic indicating towards drug likeliness of this molecule. Based on these observations, it is hypothesized that chanoclavine might be inhibiting the efflux of tetracycline from MDREC and thus enabling the more availability of tetracycline inside the cell for its action.

Keywords:

• multi-drug-resistant Escherichia coli (MDREC)
• chanoclavine
• efflux pump inhibitor
• in silico ADMET
• antibacterial combinations

Acknowledgments

The authors are thankful to Director, CSIR-CIMAP for providing the facilities. Financial support from CSIR Network project BSC-0203, and in house project MLP-02 was gratefully acknowledged. GRD and AM are thankful to Council of Scientific and Industrial Research for fellowship (CSIR-SRF). Authors are thankful to Dr Surendra Kumar and National Institute of Supercomputing and Network/Korea Institute of Science and Technology for in silico help.