(567e) A Computational Approach to Identifying Synergistic Compounds for Treating Antimicrobial-Resistant Pathogens

Antimicrobial resistance (AMR) is increasingly a threat to global public health [1]. AMR-related infections limit the efficacy of lifesaving antibiotic therapy necessary for the treatment of infectious disease and limit the success of advanced surgical procedures like organ transplants [2, 3]. To combat AMR pathogens, either new antibiotics or the cocktails of existing antibiotics with inhibitors of antimicrobial resistance proteins should be explored. Since the development of new antibiotic has been slowed down significantly recently, identifying compounds/antimicrobials with synergistic effects becomes the major approach to addressing the AMR crisis. In this work, we demonstrate a computational approach to identifying plant compounds that have synergistic effect with Fosfomycin to inhibit Listeria monocytogenes, a food-borne pathogen that could cause listeriosis disease, especially on immune-compromised people [4, 5]. Experimental results further validate the our finding.

L. monocytogenes was found to infect and adversely affect patients’ liver and spleen. In addition, L. monocytogenes can penetrate blood-brain barrier and blood-placenta barriers to harm the central neural system of pregnant woman and infant [4]. Since L. monocytogenes is an intracellular pathogen, antimicrobials used to treat listeriosis should be able to be transported into host cells. Penicillin, ampicillin and amoxicillin were commonly used antibiotics in the treatment of listeriosis [6]. However, antibiotic resistance genes have been continuously found in Listeria strains. For example, the strain that has resistance to Penicillin G was isolated from vegetables in 2016 [4, 7]. As a natural product, Fosfomycin was found effective against clinical isolates of L. monocytogenes and used as a novel therapeutic antibiotic for listeriosis clinical treatment [8]. In addition, Fosfomycin is able to penetrate the blood-brain barrier and reach clinically relevant concentrations. Thus, it has the potential to eliminate L. monocytogenes which would cause neuron damage[9]. However, stronger Fosfomycin resistance was found in the L. monocytogenes isolates with Fosfomycin resistance proteins detected [10]. In particular, a resistant gene FosX (LMO1702,402bp) was identified and expressed in L. monocytogenes EGDe (strain ATCC BAA-679), a typical well-studied strain [11]. The FosX enzyme catalyzed the hydrolysis of Fosfomycin and resulted in the Fosfomycin resistance in L. monocytogenes EGDe [5, 10]. Therefore, there is an urgent need to identify compounds that can inhibit FosX enzyme to revive the efficacy of Fosfomycin to treat L. monocytogenes.

Automated molecular docking is the most commonly used computational approach that evaluates the binding of small-molecule ligands like compounds to a target receptor with a known crystal 3D structure [12]. Molecular docking provides an avenue for a high-throughput virtual screening of ligands, and it has been widely implemented in drug discovery research for hit identification[13]. Docking programs have been improved recently to provide more accurate prediction on ligand-target binding by optimizing docking algorithms and scoring functions [14]. Among those existing docking programs, Molsoft ICM was evaluated with 93% accuracy in flexible docking and 90% successful rate in covalent docking. This was significantly better than the performance of Autodock, DOCK, FlexX, Gold, FITTED and MOE [13-15]. Since structures and activities of the FosX protein in L. monocytogenes have been well studied, we used an integrated ICM-docking and experimental approach to identify FosX inhibitors that are of synergistic effect with Fosfomycin in treating resistant L. monocytogenes. Specifically, automated ligand docking was implemented to perform virtual screening of Indofine natural-product database and FDA-approved drugs to identified potential inhibitors. In vitro bacterial growth inhibition test was then utilized to verify the effectiveness of identified compounds combined with Fosfomycin on inhibiting the resistant L. monocytogenes. We demonstrated that two phenolic acids, i.e., caffeic acid and chlorogenic acid, were predicted as high-affinity FosX inhibitors from the ligand-docking platform. Experiments with these compounds indicated that: the cocktail of either caffeic acid (1.5mg/mL) or chlorogenic acid (3mg/mL) with Fosfomycin (50mg/L) was able to significantly inhibit the growth of the pathogen [16]. The finding of this work implies that the combination of Fosfomycin with either caffeic acid or chlorogenic acid is of potential to be used in the clinical treatment of Listeria infections.

References

1. Velez, R. and E. Sloand, Combating antibiotic resistance, mitigating future threats and ongoing initiatives. J Clin Nurs, 2016. 25(13-14): p. 1886-9.
2. Michael, G.B., et al., Emerging issues in antimicrobial resistance of bacteria from food-producing animals. Future Microbiol, 2015. 10(3): p. 427-43.
3. Davies, J. and D. Davies, Origins and evolution of antibiotic resistance. Microbiol Mol Biol Rev, 2010. 74(3): p. 417-33.
4. Radoshevich, L. and P. Cossart, Listeria monocytogenes: towards a complete picture of its physiology and pathogenesis. Nat Rev Microbiol, 2018. 16(1): p. 32-46.
5. Scortti, M., et al., Epistatic control of intrinsic resistance by virulence genes in Listeria. PLoS Genet, 2018. 14(9): p. e1007525.
6. Temple, M.E. and M.C. Nahata, Treatment of Listeriosis. Annals of Pharmacotherapy, 2000. 34(5): p. 656-661.
7. de Vasconcelos Byrne, V., et al., Occurrence and antimicrobial resistance patterns of Listeria monocytogenes isolated from vegetables. Brazilian Journal of Microbiology, 2016. 47(2): p. 438-443.
8. Lepe, J.A., et al., In vitro and intracellular activities of fosfomycin against clinical strains of Listeria monocytogenes. International Journal of Antimicrobial Agents, 2014. 43(2): p. 135-139.
9. Falagas, M.E., et al., Fosfomycin. Clin Microbiol Rev, 2016. 29(2): p. 321-47.
10. Fillgrove, K.L., et al., Mechanistic diversity of fosfomycin resistance in pathogenic microorganisms. J Am Chem Soc, 2003. 125(51): p. 15730-1.
11. Fillgrove, K.L., et al., Structure and mechanism of the genomically encoded fosfomycin resistance protein, FosX, from Listeria monocytogenes. Biochemistry, 2007. 46(27): p. 8110-20.
12. Meng, X.Y., et al., Molecular docking: a powerful approach for structure-based drug discovery. Curr Comput Aided Drug Des, 2011. 7(2): p. 146-57.
13. Kitchen, D.B., et al., Docking and scoring in virtual screening for drug discovery: methods and applications. Nat Rev Drug Discov, 2004. 3(11): p. 935-49.
14. Lionta, E., et al., Structure-Based Virtual Screening for Drug Discovery: Principles, Applications and Recent Advances. Current Topics in Medicinal Chemistry, 2014. 14(16): p. 1923-1938.
15. Scarpino, A., G.G. Ferenczy, and G.M. Keserű, Comparative Evaluation of Covalent Docking Tools. Journal of Chemical Information and Modeling, 2018. 58(7): p. 1441-1458.
16. Zhang, F., et al., Synergistic Effect of Chlorogenic Acid and Caffeic Acid with Fosfomycin in Growth Inhibition of a Resistant Listeria monocytogenes Strain. ACS Omega, 2020. https://dx.doi.org/10.1021/acsomega.0c00352.