Antimycobacterial Activities of Cryptolepis sanguinolenta, Lantana camara, Zanthoxylum leprieurii Modeled as a Function of Their Fingerprints for Active Compounds Identification

Abstract

There is a pressing need to discover novel anti-tuberculosis agents to combat emerging drug-resistant strains. Cryptolepis sanguinolenta, Lantana camara, and Zanthoxylum leprieurii have been identified as potential sources of anti-tuberculosis (TB) drug candidates. Previous studies have examined the metabolites and metabolic pathways in mycobacterial strains affected by methanolic extracts of these plants, but the specific active compounds responsible for the antimycobacterial activity, the effect on affected metabolites and metabolic pathways of mycobacterial cell cultures, remain unclear. Untargeted metabolic fingerprinting may help identify the active compounds. The objective of this study was to model the antimycobacterial activity of methanolic extracts of C. sanguinolenta, L. camara, and Z. leprieurii as a function of their UHPLC-MS fingerprints and determine whether specific peaks (compounds) in the fingerprints contributed significantly to the activity. In this study, fingerprints of 18 methanolic extracts from C. sanguinolenta roots, L. camara leaves, and Z. leprieurii stem barks were obtained with ultra-high-performance liquid chromatography–mass spectrometry (UHPLC-MS). The minimal inhibitory concentrations (MICs) of these extracts against a pan-sensitive M. tuberculosis strain were determined using a resazurin-based microdilution assay. Fingerprints were processed and analyzed using regions of interest–multivariate curve resolution (ROIMCR). Partial least squares (PLS) regression was employed to model the MICs. Potential active compounds, including cryptolepine (from C. sanguinolenta), verbascoside (from L. camara), and isofagaridine (from Z. leprieurii), were identified as antimycobacterial compounds. These compounds likely influence mycobacterial metabolic processes, including cell wall synthesis, protein production, nucleotide metabolism, and energy generation. Further investigations are required to validate our findings.

Keywords

untargeted metabolite fingerprinting - multivariate calibration - antimycobacterial phytochemicals and their mechanisms - Cryptolepis sanguinolenta (Apocynaceae) - Lantana camara (Verbenaceae) - Zanthoxylum leprieurii (Rutaceae)

• References

• 1 Raviglione M, Maher D. Ending infectious diseases in the era of Sustainable Development Goals. Porto Biomed J 2017; 2: 140-142
• 2 Jamshidi-Kia F, Lorigooini Z, Amini-Khoei H. Medicinal plants: Past history and future perspective. J Herbmed Pharmacol 2018; 7: 1-7
• 3 Gibbons S, Fallah F, Wright CW. Cryptolepine hydrochloride: A potent antimycobacterial alkaloid derived from Cryptolepis sanguinolenta . Phytother Res 2003; 17: 434-436
• 4 Tuyiringire N, Mugisha Taremwa I, Tusubira D, Munyampundu JP, Mambo Muvunyi C, Vander Heyden Y. In vitro antimycobacterial activity of medicinal plants Lantana camara, Cryptolepis sanguinolenta, and Zanthoxylum leprieurii . J Clin Tuberc Other Mycobact Dis 2022; 27: 100307
• 5 Tuyiringire N, Deyno S, Weisheit A, Tolo CU, Tusubira D, Munyampundu JP, Ogwang PE, Mambo Muvunyi C, Vander Heyden Y. Three promising antimycobacterial medicinal plants reviewed as potential sources of drug hit candidates against multidrug-resistant tuberculosis. Tuberculosis 2020; 124: 101987
• 6 Bunalema L, Fotso GW, Waako P, Tabuti J, Yeboah SO. Potential of Zanthoxylum leprieurii as a source of active compounds against drug resistant Mycobacterium tuberculosis . BMC Complement Altern Med 2017; 17: 4-9
• 7 Hughes JP, Rees SS, Kalindjian SB, Philpott KL. Principles of early drug discovery. Br J Pharmacol 2011; 162: 1239-1249
• 8 Kumar A, Chettiar S, Parish T. Current challenges in drug discovery for tuberculosis. Expert Opin Drug Discov 2017; 12: 1-4
• 9 Van Nederkassel AM, Daszykowski M, Massart DL, Vander Heyden Y. Prediction of total green tea antioxidant capacity from chromatograms by multivariate modeling. J Chromatogr A 2005; 1096: 177-186
• 10 Ben Ahmed Z, Yousfi M, Viaene J, Dejaegher B, Demeyer K, Mangelings D, Vander Heyden Y. Antioxidant activities of Pistacia atlantica extracts modeled as a function of chromatographic fingerprints in order to identify antioxidant markers. Microchem J 2016; 128: 208-217
• 11 Segers K, Declerck S, Mangelings D, Vander Heyden Y, Van Eeckhaut A. Analytical techniques for metabolomic studies: A review. Bioanalysis 2019; 11: 2297-2318
• 12 Marr S, Hageman JA, Wehrens R, van Dam NM, Bruelheide H, Neumann S. LC-MS based plant metabolic profiles of thirteen grassland species grown in diverse neighbourhoods. Sci Data 2021; 8: 52
• 13 Long WJ, Wu HL, Wang T, Dong MY, Yu RQ. Exploiting second-order advantage from mathematically modeled liquid chromatography–mass spectrometry data for simultaneous determination of polyphenols in Chinese propolis. Microchem J 2020; 157: 105003
• 14 Gorrochategui E, Jaumot J, Tauler R. ROIMCR: A powerful analysis strategy for LC-MS metabolomic datasets. BMC Bioinformatics 2019; 20: 256
• 15 de León L, Beltrán B, Moujir L. Antimicrobial activity of 6-oxophenolic triterpenoids. Mode of action against Bacillus subtilis . Planta Med 2005; 71: 313-319
• 16 Attia YM, El-Kersh DM, Wagdy HA, Elmazar MM. Verbascoside: Identification, quantification, and potential sensitization of colorectal cancer cells to 5-FU by targeting PI3K/AKT pathway. Sci Rep 2018; 8: 1-12
• 17 Shi C, Ma Y, Tian L, Li J, Qiao G, Liu C, Cao W, Liang C. Verbascoside: An efficient and safe natural antibacterial adjuvant for preventing bacterial contamination of fresh meat. Molecules 2022; 27: 4943
• 18 Desbois AP, Smith VJ. Antibacterial free fatty acids: Activities, mechanisms of action and biotechnological potential. Appl Microbiol Biotechnol 2010; 85: 1629-1642
• 19 Khameneh B, Iranshahy M, Soheili V, Fazly Bazzaz BS. Review on plant antimicrobials: A mechanistic viewpoint. Antimicrob Resist Infect Control 2019; 8: 118
• 20 Tudu CK, Bandyopadhyay A, Kumar M, Das Radha T, Nandy S, Ghorai M, Gopalakrishnan AV, Proćków J, Dey A. Unravelling the pharmacological properties of cryptolepine and its derivatives: A mini-review insight. Naunyn-Schmiedebergʼs Arch Pharmacol 2023; 396: 229-238
• 21 Cimanga K, De Bruyne T, Pieters L, Totte J, Tona L, Kambu K, Berghe DV, Vlietinck AJ. Antibacterial and antifungal activities of neocryptolepine, biscryptolepine and cryptoquindoline, alkaloids isolated from Cryptolepis sanguinolenta . Phytomedicine 1998; 5: 209-214
• 22 Mimee B, Labbé C, Pelletier R, Bélanger RR. Antifungal activity of flocculosin, a novel glycolipid isolated from Pseudozyma flocculosa . Antimicrob Agents Chemother 2005; 49: 1597-1599
• 23 Begum S, Wahab A, Siddiqui BS, Qamar F. Nematicidal constituents of the aerial parts of Lantana camara . J Nat Prod 2000; 63: 765-767
• 24 Ishikawa T. Benzo[c]phenanthridine bases and their antituberculosis activity. Med Res Rev 2001; 21: 61-72
• 25 Okagu IU, Ndefo JC, Aham EC, Udenigwe CC. Zanthoxylum species: A comprehensive review of traditional uses, phytochemistry, pharmacological and nutraceutical applications. Molecules 2021; 26: 4023
• 26 Tagousop CN, Tamokou J, Ekom SE, Ngnokam D, Voutquenne-Nazabadioko L. Antimicrobial activities of flavonoid glycosides from Graptophyllum grandulosum and their mechanism of antibacterial action. BMC Complement Altern Med 2018; 18: 252
• 27 Xu J, Jin DQ, Guo Y, Xie C, Ma Y, Yamakuni T, Ohizumi Y. New myrsinol diterpenes from Euphorbia prolifera and their inhibitory activities on LPS-induced NO production. Bioorg Med Chem Lett 2012; 22: 3612-3618
• 28 Sieniawska E, Sawicki R, Golus J, Georgiev MI. Untargeted metabolomic exploration of the Mycobacterium tuberculosis stress response to cinnamon essential oil. Biomolecules 2020; 10: 357
• 29 Sawicki R, Widelski J, Okińczyc P, Truszkiewicz W, Golus J, Sieniawska E. Exposure to Nepalese propolis alters the metabolic state of Mycobacterium tuberculosis . Front Microbiol 2022; 13: 929476
• 30 Palomino JC, Martin A, Camacho M, Guerra H, Swings J, Portaels F. Resazurin microtiter assay plate: Simple and inexpensive method for detection of drug resistance in Mycobacterium tuberculosis . Antimicrob Agents Chemother 2002; 46: 2720-2722
• 31 David H, Levy-Frebault V, Thorel MF. Méthodes de Laboratoire pour Mycobacteriologie. Paris: Institut Pasteur; 1989
• 32 Woods GL, Brown-Elliott BA, Conville PS, Desmond EP, Hall GS, Lin G, Pfyffer GE, Ridderhof JC, Siddiqi SH, Wallace jr. RJ. Susceptibility Testing of Mycobacteria, Nocardiae, and Other Aerobic Actinomycetes. 3rd ed.. ed. Rockville Pike, USA; Wayne (PA): Clinical and Laboratory Standards Institute; 2018
• 33 Theodoridis G, Gika HG, Wilson ID. Mass spectrometry-based holistic analytical approaches for metabolite profiling in systems biology studies. Mass Spectrom Rev 2011; 30: 884-906
• 34 Gika HG, Zisi C, Theodoridis G, Wilson ID. Protocol for quality control in metabolomic profiling of biological fluids by U(H)PLC-SM. J Chromatogr B Analyt Technol Biomed Life Sci 2016; 1008: 15-25
• 35 Gorrochategui E, Jaumot J, Lacorte S, Tauler R. Data analysis strategies for targeted and untargeted LC-MS metabolomic studies: Overview and workflow. TrAC 2016; 82: 425-442
• 36 Kumar K. Partial Least Square (PLS) analysis: Most favorite tool in chemometrics to build a calibration model. Resonance 2021; 26: 429-442
• 37 van den Berg RA, Hoefsloot HCJ, Westerhuis JA, Smilde AK, van der Werf MJ. Centering, scaling, and transformations: Improving the biological information content of metabolomics data. BMC Genomics 2006; 7: 142-157
• 38 Kharyuk P, Nazarenko D, Oseledets I, Rodin I, Shpigun O, Tsitsilin A, Lavrentyev M. Employing fingerprinting of medicinal plants by means of LC-MS and machine learning for species identification task. Sci Rep 2018; 8: 17053