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Current Medicinal Chemistry

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ISSN (Print): 0929-8673
ISSN (Online): 1875-533X

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Review Article

The Role of Mass Spectrometry in the Discovery of Antibiotics and Bacterial Resistance Mechanisms: Proteomics and Metabolomics Approaches

Author(s): Miguel Cuevas-Cruz, Ulises Hernández-Guzmán, Poulette Carolina Álvarez-Rosales, Meike Schnabel, Saúl Gómez-Manzo and Roberto Arreguín-Espinosa*

Volume 30, Issue 1, 2023

Published on: 13 May, 2022

Page: [30 - 58] Pages: 29

DOI: 10.2174/0929867329666220329090822

Price: $65

Abstract

The abuse and incorrect administration of antibiotics has resulted in an increased proliferation of bacteria that exhibit drug resistance. The emergence of resistant bacteria has become one of the biggest health concerns globally, and an enormous effort has been made to combat them. However, despite the efforts, the emergence of resistant strains is rapidly increasing, while the discovery of new classes of antibiotics has lagged. For this reason, it is pivotal to acquire a more detailed knowledge of bacterial resistance mechanisms and the mechanism of action of substances with antibacterial effects to identify biomarkers, therapeutic targets, and the development of new antibiotics. Metabolomics and proteomics, combined with mass spectrometry for data acquisition, are suitable techniques and have already been applied successfully. This review presents basic aspects of the metabolomic and proteomic approaches and their application for the elucidation of bacterial resistance mechanisms.

Keywords: Mass spectrometry, proteomics, metabolomics, antibiotics, bacterial resistance, proliferation.

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[1]
Sukhum, K.V.; Diorio-Toth, L.; Dantas, G.; Louis, S.; Louis, S.; Louis, S. Genomic and metagenomic approaches for predictive surveillance of emerging pathogens and antibiotic resistance. Clin. Pharmacol. Ther., 2019, 106(3), 512-524.
[http://dx.doi.org/10.1002/cpt.1535] [PMID: 31172511]
[2]
Stuart, K.A.; Welsh, K.; Walker, M.C.; Edrada-Ebel, R. Metabolomic tools used in marine natural product drug discovery. Expert Opin. Drug Discov., 2020, 15(4), 499-522.
[http://dx.doi.org/10.1080/17460441.2020.1722636] [PMID: 32026730]
[3]
Khameneh, B.; Iranshahy, M.; Soheili, V.; Fazly Bazzaz, B.S.; Bazzaz, F. Review on plant antimicrobials: A mechanistic viewpoint. Antimicrob. Resist. Infect. Control, 2019, 8(1), 118.
[http://dx.doi.org/10.1186/s13756-019-0559-6] [PMID: 31346459]
[4]
Langford, B.J.; So, M.; Raybardhan, S.; Leung, V.; Westwood, D.; MacFadden, D.R.; Soucy, J.R.; Daneman, N. Bacterial co-infection and secondary infection in patients with COVID-19: A living rapid review and meta-analysis. Clin. Microbiol. Infect., 2020, 26(12), 1622-1629.
[http://dx.doi.org/10.1016/j.cmi.2020.07.016] [PMID: 32711058]
[5]
Wu, C.; Kim, H.K.; van Wezel, G.P.; Choi, Y.H. Metabolomics in the natural products field--a gateway to novel antibiotics. Drug Discov. Today. Technol., 2015, 13, 11-17.
[http://dx.doi.org/10.1016/j.ddtec.2015.01.004] [PMID: 26190678]
[6]
Hoerr, V.; Duggan, G.E.; Zbytnuik, L.; Poon, K.K.H.; Große, C.; Neugebauer, U.; Methling, K.; Löffler, B.; Vogel, H.J. Characterization and prediction of the mechanism of action of antibiotics through NMR metabolomics. BMC Microbiol., 2016, 16(1), 82.
[http://dx.doi.org/10.1186/s12866-016-0696-5] [PMID: 27159970]
[7]
Hug, J.J.; Bader, C.D.; Remškar, M.; Cirnski, K.; Müller, R. Concepts and methods to access novel antibiotics from Actinomycetes. Antibiotics (Basel), 2018, 7(2), 44.
[http://dx.doi.org/10.3390/antibiotics7020044] [PMID: 29789481]
[8]
Wong, F.; Wilson, S.; Helbig, R.; Hegde, S.; Aftenieva, O.; Zheng, H.; Liu, C.; Pilizota, T.; Garner, E.C.; Amir, A.; Renner, L.D. Understanding beta-lactam-induced lysis at the single-cell level. Front. Microbiol., 2021, 12, 712007.
[http://dx.doi.org/10.3389/fmicb.2021.712007] [PMID: 34421870]
[9]
Li, H.; Wang, Y.; Meng, Q.; Wang, Y.; Xia, G.; Xia, X.; Shen, J. Comprehensive proteomic and metabolomic profiling of mcr-1-mediated colistin resistance in Escherichia coli. Int. J. Antimicrob. Agents, 2019, 53(6), 795-804.
[http://dx.doi.org/10.1016/j.ijantimicag.2019.02.014] [PMID: 30811973]
[10]
Kapoor, G.; Saigal, S.; Elongavan, A. Action and resistance mechanisms of antibiotics: A guide for clinicians. J. Anaesthesiol. Clin. Pharmacol., 2017, 33(3), 300-305.
[http://dx.doi.org/10.4103/joacp.JOACP_349_15] [PMID: 29109626]
[11]
Wehrli, W.; Staehelin, M. Actions of the rifamycins. Bacteriol. Rev., 1971, 35(3), 290-309.
[http://dx.doi.org/10.1128/br.35.3.290-309.1971] [PMID: 5001420]
[12]
Oliveira, P.F.M.; Guidetti, B.; Chamayou, A.; André-Barrès, C.; Madacki, J.; Korduláková, J.; Mori, G.; Orena, B.S.; Chiarelli, L.R.; Pasca, M.R.; Lherbet, C.; Carayon, C.; Massou, S.; Baron, M.; Baltas, M. Mechanochemical synthesis and biological evaluation of novel isoniazid derivatives with potent antitubercular activity. Molecules, 2017, 22(9), 1457.
[http://dx.doi.org/10.3390/molecules22091457] [PMID: 28862683]
[13]
Zhang, A.; Li, W.; Liu, X.; Wu, M.; Xuan, G. Synthesis, biological evaluation and in silico studies of several substituted benzene sulfonamides as potential antibacterial agents. J. Phys. Conf. Ser., 2020, 1624(2), 022058.
[http://dx.doi.org/10.1088/1742-6596/1624/2/022058]
[14]
Blair, J.M.; Webber, M.A.; Baylay, A.J.; Ogbolu, D.O.; Piddock, L.J. Molecular mechanisms of antibiotic resistance. Nat. Rev. Microbiol., 2015, 13(1), 42-51.
[http://dx.doi.org/10.1039/c0cc05111j]
[15]
Munita, J.M.; Arias, C.A. Mechanisms of antibiotic resistance. Microbiol. Spectr., 2016, 4(2), 482-501.
[http://dx.doi.org/10.1128/microbiolspec.VMBF-0016-2015] [PMID: 27227291]
[16]
Bronzwaer, S. L. A. M.; Cars, O.; Buchholz, U.; Mölstad, S.; Goettsch, W.; Veldhuijzen, I. K.; Degener, J. E. The relationship between antimicrobial use and antimicrobial resistance in Europe. Emerg. Infect. Dis., 2002, 8(3), 278-282.
[http://dx.doi.org/10.3201/eid0803.010192]
[17]
Ribeiro, B.; Fonseca, P.; Calado, R.C. Antibiotics antibiotic discovery : Where have we come from, where do we go? Antibiotics (Basel), 2019, 8(2), 45.
[http://dx.doi.org/10.3390/antibiotics8020045]
[18]
Pitout, J.D.D.; Gregson, D.B.; Poirel, L.; McClure, J.A.; Le, P.; Church, D.L. Detection of Pseudomonas aeruginosa producing metallo-β-lactamases in a large centralized laboratory. J. Clin. Microbiol., 2005, 43(7), 3129-3135.
[http://dx.doi.org/10.1128/JCM.43.7.3129-3135.2005] [PMID: 16000424]
[19]
Correa-Martínez, C.L.; Idelevich, E.A.; Sparbier, K.; Kostrzewa, M.; Becker, K. Rapid detection of extended-spectrum b -Lactamases (ESBL) and AmpC b -Lactamases in Enterobacterales : Development of a screening panel using the MALDI-TOF- MS-based direct-on-target microdroplet growth assay. Front. Microbiol., 2019, 10, 13.
[http://dx.doi.org/10.3389/fmicb.2019.00013] [PMID: 30733710]
[20]
Nosrati, M.; Dey, D.; Mehrani, A.; Strassler, S.E.; Zelinskaya, N.; Hoffer, E.D.; Stagg, S.M.; Dunham, C.M.; Conn, G.L. Functionally critical residues in the aminoglycoside resistance-associated methyltransferase RmtC play distinct roles in 30S substrate recognition. J. Biol. Chem., 2019, 294(46), 17642-17653.
[http://dx.doi.org/10.1074/jbc.RA119.011181] [PMID: 31594862]
[21]
Alcala, A.; Ramirez, G.; Solis, A.; Kim, Y.; Tan, K.; Luna, O.; Nguyen, K.; Vazquez, D.; Ward, M.; Zhou, M.; Mulligan, R.; Maltseva, N.; Kuhn, M.L. Structural and functional characterization of three Type B and C chloramphenicol acetyltransferases from Vibrio species. Protein Sci., 2020, 29(3), 695-710.
[http://dx.doi.org/10.1002/pro.3793] [PMID: 31762145]
[22]
Ghosh, A.; Roymahapatra, G.; Paul, D.; Mandal, S.M. Theoretical analysis of bacterial efflux pumps inhibitors: Strategies in-search of competent molecules and develop next. Comput. Biol. Chem., 2020, 87, 107275.
[http://dx.doi.org/10.1016/j.compbiolchem.2020.107275] [PMID: 32438117]
[23]
Chernov, V.M.; Chernova, O.A.; Mouzykantov, A.A.; Lopukhov, L.L.; Aminov, R.I. Omics of antimicrobials and antimicrobial resistance. Expert Opin. Drug Discov., 2019, 14(5), 455-468.
[http://dx.doi.org/10.1080/17460441.2019.1588880] [PMID: 30884978]
[24]
Panter, F.; Bader, D.; Rolf, M. Synergizing the potential of bacterial genomics and metabolomics to find novel antibiotics. Chem. Sci., 2021, 12(17), 5994-6010.
[http://dx.doi.org/10.1039/D0SC06919A]
[25]
Gorlenko, C.L.; Kiselev, H.Y.; Budanova, E.V.; Zamyatnin, A.A., Jr; Ikryannikova, L.N. Plant secondary metabolites in the battle of drugs and drug-resistant bacteria: New heroes or worse clones of antibiotics? Antibiotics (Basel), 2020, 9(4), 170.
[http://dx.doi.org/10.3390/antibiotics9040170] [PMID: 32290036]
[26]
Rodrigues, K.F.; Hesse, M.; Werner, C. Antimicrobial activities of secondary metabolites produced by endophytic fungi from Spondias mombin. J. Basic Microbiol., 2000, 40(4), 261-7.
[27]
Darabpour, E.; Ardakani, M.R.; Motamedi, H.; Ronagh, M.T. Isolation of a potent antibiotic producer bacterium, especially against MRSA, from Northern Region of the Persian Gulf. Gulf. Bosn. J. Basic Med. Sci., 2012, 12(2), 108-21.
[http://dx.doi.org/10.17305/bjbms.2012.2509]
[28]
Biemann, K.; Sanchez, J. Laying the groundwork for proteomics: Mass spectrometry from 1958 to 1988. J. Proteomics, 2014, 107, 62-70.
[http://dx.doi.org/10.1016/j.jprot.2014.01.008] [PMID: 24448399]
[29]
Singhal, N.; Kumar, M.; Kanaujia, P.K.; Virdi, J.S.; Graham, D.W. MALDI-TOF mass spectrometry: An emerging technology for microbial identification and diagnosis. Front. Microbiol., 2015, 6, 791.
[http://dx.doi.org/10.3389/fmicb.2015.00791] [PMID: 26300860]
[30]
Snyder, D.T.; Fedick, P.W.; Cooks, R.G. Multigenerational collision-induced dissociation for characterization of organic compounds. Anal. Chem., 2016, 88(19), 9572-9581.
[http://dx.doi.org/10.1021/acs.analchem.6b02209] [PMID: 27622856]
[31]
Bizzini, A.; Greub, G. Matrix-assisted laser desorption ionization time-of-flight mass spectrometry, a revolution in clinical microbial identification. Clin. Microbiol. Infect., 2010, 16(11), 1614-1619.
[http://dx.doi.org/10.1111/j.1469-0691.2010.03311.x] [PMID: 20636422]
[32]
Lavigne, J.P.; Espinal, P.; Dunyach-Remy, C.; Messad, N.; Pantel, A.; Sotto, A. Mass spectrometry: A revolution in clinical microbiology? Clin. Chem. Lab. Med., 2013, 51(2), 257-270.
[http://dx.doi.org/10.1515/cclm-2012-0291] [PMID: 23072853]
[33]
Carbonnelle, E.; Beretti, J.L.; Cottyn, S.; Quesne, G.; Berche, P.; Nassif, X.; Ferroni, A. Rapid identification of Staphylococci isolated in clinical microbiology laboratories by matrix-assisted laser desorption ionization-time of flight mass spectrometry. J. Clin. Microbiol., 2007, 45(7), 2156-2161.
[http://dx.doi.org/10.1128/JCM.02405-06] [PMID: 17507519]
[34]
Stephan, R.; Cernela, N.; Ziegler, D.; Pflüger, V.; Tonolla, M.; Ravasi, D.; Fredriksson-Ahomaa, M.; Hächler, H. Rapid species specific identification and subtyping of Yersinia enterocolitica by MALDI-TOF mass spectrometry. J. Microbiol. Methods, 2011, 87(2), 150-153.
[http://dx.doi.org/10.1016/j.mimet.2011.08.016] [PMID: 21914454]
[35]
He, Y.; Chang, T.C.; Li, H.; Shi, G.; Tang, Y.W. Matrix-assisted laser desorption ionization time-of-flight mass spectrometry and database for identification of Legionella species. Can. J. Microbiol., 2011, 57(7), 533-538.
[http://dx.doi.org/10.1139/w11-039] [PMID: 21767077]
[36]
Wang, J.; Chen, W.F.; Li, Q.X. Rapid identification and classification of Mycobacterium spp. using whole-cell protein barcodes with matrix assisted laser desorption ionization time of flight mass spectrometry in comparison with multigene phylogenetic analysis. Anal. Chim. Acta, 2012, 716, 133-137.
[http://dx.doi.org/10.1016/j.aca.2011.12.016] [PMID: 22284888]
[37]
Sandalakis, V.; Psaroulaki, A.; De Bock, P.J.; Christidou, A.; Gevaert, K.; Tsiotis, G.; Tselentis, Y. Investigation of rifampicin resistance mechanisms in Brucella abortus using MS-driven comparative proteomics. J. Proteome Res., 2012, 11(4), 2374-2385.
[http://dx.doi.org/10.1021/pr201122w] [PMID: 22360387]
[38]
Kempf, M.; Bakour, S.; Flaudrops, C.; Berrazeg, M.; Brunel, J.M.; Drissi, M.; Mesli, E.; Touati, A.; Rolain, J.M. Rapid detection of carbapenem resistance in Acinetobacter baumannii using matrix-assisted laser desorption ionization-time of flight mass spectrometry. PLoS One, 2012, 7(2), e31676.
[http://dx.doi.org/10.1371/journal.pone.0031676] [PMID: 22359616]
[39]
Yoon, E.J.; Jeong, S.H. MALDI-TOF mass spectrometry technology as a tool for the rapid diagnosis of antimicrobial resistance in bacteria. Antibiotics (Basel), 2021, 10(8), 1-13.
[http://dx.doi.org/10.3390/antibiotics10080982] [PMID: 34439032]
[40]
Lee, D. Y.; Bowen, B. P.; Northen, T. R. Mass spectrometry–based metabolomics, analysis of metabolite-protein interactions, and imaging. Biotechniques, 2010, 49(2), 557-565.
[http://dx.doi.org/10.2144/000113451]
[41]
Wang, R.; Yin, Y.; Zhu, Z.J. Advancing untargeted metabolomics using data-independent acquisition mass spectrometry technology. Anal. Bioanal. Chem., 2019, 411(19), 4349-4357.
[http://dx.doi.org/10.1007/s00216-019-01709-1] [PMID: 30847570]
[42]
van der Laan, T.; Boom, I.; Maliepaard, J.; Dubbelman, A.C.; Harms, A.C.; Hankemeier, T. Data-independent acquisition for the quantification and identification of metabolites in plasma. Metabolites, 2020, 10(12), 1-14.
[http://dx.doi.org/10.3390/metabo10120514] [PMID: 33353236]
[43]
Xu, T.; Hu, C.; Xuan, Q.; Xu, G. Recent advances in analytical strategies for mass spectrometry-based lipidomics. Anal. Chim. Acta, 2020, 1137, 156-169.
[http://dx.doi.org/10.1016/j.aca.2020.09.060] [PMID: 33153599]
[44]
Wilkins, M.R.; Sanchez, J.C.; Gooley, A.A.; Appel, R.D.; Humphery-Smith, I.; Hochstrasser, D.F.; Williams, K.L. Progress with proteome projects: Why all proteins expressed by a genome should be identified and how to do it. Biotechnol. Genet. Eng. Rev., 1996, 13(1), 19-50.
[http://dx.doi.org/10.1080/02648725.1996.10647923] [PMID: 8948108]
[45]
Aslam, B.; Basit, M.; Nisar, M.A.; Khurshid, M.; Rasool, M.H. Proteomics: Technologies and their applications. J. Chromatogr. Sci., 2017, 55(2), 182-196.
[http://dx.doi.org/10.1093/chromsci/bmw167] [PMID: 28087761]
[46]
Anderson, N.L.; Anderson, N.G. Proteome and proteomics: New technologies, new concepts, and new words. Electrophoresis, 1998, 19(11), 1853-1861.
[http://dx.doi.org/10.1002/elps.1150191103] [PMID: 9740045]
[47]
Kellner, R. Proteomics. concepts and perspectives. Fresenius J. Anal. Chem., 2000, 366(6-7), 517-524.
[http://dx.doi.org/10.1007/s002160051547] [PMID: 11225764]
[48]
Marko-Varga, G.; Fehniger, T.E. Proteomics and disease--the challenges for technology and discovery. J. Proteome Res., 2004, 3(2), 167-178.
[http://dx.doi.org/10.1021/pr049958+] [PMID: 15113092]
[49]
Sharar, M.; Saied, E.M.; Rodriguez, M.C.; Arenz, C.; Montes-Bayón, M.; Linscheid, M.W. Elemental labelling and mass spectrometry for the specific detection of sulfenic acid groups in model peptides: A proof of concept. Anal. Bioanal. Chem., 2017, 409(8), 2015-2027.
[http://dx.doi.org/10.1007/s00216-016-0149-x] [PMID: 28097376]
[50]
Patterson, S.D.; Aebersold, R.H. Proteomics: The first decade and beyond. Nat. Genet., 2003, 33(3S), 311-323.
[http://dx.doi.org/10.1038/ng1106] [PMID: 12610541]
[51]
Luepke, K.H.; Mohr, J.F., III The antibiotic pipeline: Reviving research and development and speeding drugs to market. Expert Rev. Anti Infect. Ther., 2017, 15(5), 425-433.
[http://dx.doi.org/10.1080/14787210.2017.1308251] [PMID: 28306360]
[52]
Jean Beltran, P.M.; Federspiel, J.D.; Sheng, X.; Cristea, I.M. Proteomics and integrative omic approaches for understanding host-pathogen interactions and infectious diseases. Mol. Syst. Biol., 2017, 13(3), 922.
[http://dx.doi.org/10.15252/msb.20167062] [PMID: 28348067]
[53]
Peng, B.; Li, H.; Peng, X. Proteomics approach to understand bacterial antibiotic resistance strategies. Expert Rev. Proteomics, 2019, 16(10), 829-839.
[http://dx.doi.org/10.1080/14789450.2019.1681978] [PMID: 31618606]
[54]
Gagarinova, A.; Phanse, S.; Cygler, M.; Babu, M. Insights from protein-protein interaction studies on bacterial pathogenesis. Expert Rev. Proteomics, 2017, 14(9), 779-797.
[http://dx.doi.org/10.1080/14789450.2017.1365603] [PMID: 28786313]
[55]
Cianciotto, N.P.; White, R.C. Expanding role of Type II secretion in bacterial pathogenesis and beyond. Infect. Immun., 2017, 85(5), e00014-17.
[http://dx.doi.org/10.1128/IAI.00014-17] [PMID: 28264910]
[56]
Martin, J.K., II; Sheehan, J.P.; Bratton, B.P.; Moore, G.M.; Mateus, A.; Li, S.H.J.; Kim, H.; Rabinowitz, J.D.; Typas, A.; Savitski, M.M.; Wilson, M.Z.; Gitai, Z. A dual-mechanism antibiotic kills gram-negative bacteria and avoids drug resistance. Cell, 2020, 181(7), 1518-1532.e14.
[http://dx.doi.org/10.1016/j.cell.2020.05.005] [PMID: 32497502]
[57]
Paes, J.A.; Machado, L.D.P.N.; Dos Anjos Leal, F.M.; De Moraes, S.N.; Moura, H.; Barr, J.R.; Ferreira, H.B. Comparative proteomics of two Mycoplasma hyopneumoniae strains and Mycoplasma flocculare identified potential porcine enzootic pneumonia determinants. Virulence, 2018, 9(1), 1230-1246.
[http://dx.doi.org/10.1080/21505594.2018.1499379] [PMID: 30027802]
[58]
Kim, M.S.; Pinto, S.M.; Getnet, D.; Nirujogi, R.S.; Manda, S.S.; Chaerkady, R.; Madugundu, A.K.; Kelkar, D.S.; Isserlin, R.; Jain, S.; Thomas, J.K.; Muthusamy, B.; Leal-Rojas, P.; Kumar, P.; Sahasrabuddhe, N.A.; Balakrishnan, L.; Advani, J.; George, B.; Renuse, S.; Selvan, L.D.; Patil, A.H.; Nanjappa, V.; Radhakrishnan, A.; Prasad, S.; Subbannayya, T.; Raju, R.; Kumar, M.; Sreenivasamurthy, S.K.; Marimuthu, A.; Sathe, G.J.; Chavan, S.; Datta, K.K.; Subbannayya, Y.; Sahu, A.; Yelamanchi, S.D.; Jayaram, S.; Rajagopalan, P.; Sharma, J.; Murthy, K.R.; Syed, N.; Goel, R.; Khan, A.A.; Ahmad, S.; Dey, G.; Mudgal, K.; Chatterjee, A.; Huang, T.C.; Zhong, J.; Wu, X.; Shaw, P.G.; Freed, D.; Zahari, M.S.; Mukherjee, K.K.; Shankar, S.; Mahadevan, A.; Lam, H.; Mitchell, C.J.; Shankar, S.K.; Satishchandra, P.; Schroeder, J.T.; Sirdeshmukh, R.; Maitra, A.; Leach, S.D.; Drake, C.G.; Halushka, M.K.; Prasad, T.S.K.; Hruban, R.H.; Kerr, C.L.; Bader, G.D.; Iacobuzio-Donahue, C.A.; Gowda, H.; Pandey, A. A draft map of the human proteome. Nature, 2014, 509(7502), 575-581.
[http://dx.doi.org/10.1038/nature13302] [PMID: 24870542]
[59]
Schwanhäusser, B.; Busse, D.; Li, N.; Dittmar, G.; Schuchhardt, J.; Wolf, J.; Chen, W.; Selbach, M. Global quantification of mammalian gene expression control. Nature, 2011, 473(7347), 337-342.
[http://dx.doi.org/10.1038/nature10098] [PMID: 21593866]
[60]
Geladaki, A.; Kočevar Britovšek, N.; Breckels, L.M.; Smith, T.S.; Vennard, O.L.; Mulvey, C.M.; Crook, O.M.; Gatto, L.; Lilley, K.S. Combining LOPIT with differential ultracentrifugation for high-resolution spatial proteomics. Nat. Commun., 2019, 10(1), 331.
[http://dx.doi.org/10.1038/s41467-018-08191-w] [PMID: 30659192]
[61]
Potel, C.M.; Lin, M.H.; Heck, A.J.R.; Lemeer, S. Widespread bacterial protein histidine phosphorylation revealed by mass spectrometry-based proteomics. Nat. Methods, 2018, 15(3), 187-190.
[http://dx.doi.org/10.1038/nmeth.4580] [PMID: 29377012]
[62]
Mateus, A.; Kurzawa, N.; Becher, I.; Sridharan, S.; Helm, D.; Stein, F.; Typas, A.; Savitski, M.M. Thermal proteome profiling for interrogating protein interactions. Mol. Syst. Biol., 2020, 16(3), e9232.
[http://dx.doi.org/10.15252/msb.20199232] [PMID: 32133759]
[63]
Vranakis, I.; Goniotakis, I.; Psaroulaki, A.; Sandalakis, V.; Tselentis, Y.; Gevaert, K.; Tsiotis, G. Proteome studies of bacterial antibiotic resistance mechanisms. J. Proteomics, 2014, 97, 88-99.
[http://dx.doi.org/10.1016/j.jprot.2013.10.027] [PMID: 24184230]
[64]
Lima, T.B.; Pinto, M.F.S.; Ribeiro, S.M.; de Lima, L.A.; Viana, J.C.; Gomes Júnior, N.; Cândido, E.S.; Dias, S.C.; Franco, O.L. Bacterial resistance mechanism: What proteomics can elucidate. FASEB J., 2013, 27(4), 1291-1303.
[http://dx.doi.org/10.1096/fj.12-221127] [PMID: 23349550]
[65]
Tsakou, F.; Jersie-Christensen, R.; Jenssen, H.; Mojsoska, B. The role of proteomics in bacterial response to antibiotics. Pharmaceuticals (Basel), 2020, 13(9), 1-27.
[http://dx.doi.org/10.3390/ph13090214] [PMID: 32867221]
[66]
Mateus, A.; Bobonis, J.; Kurzawa, N.; Stein, F.; Helm, D.; Hevler, J.; Typas, A.; Savitski, M.M. Thermal proteome profiling in bacteria: Probing protein state in vivo. Mol. Syst. Biol., 2018, 14(7), e8242.
[http://dx.doi.org/10.15252/msb.20188242] [PMID: 29980614]
[67]
Savitski, M.M.; Zinn, N.; Faelth-Savitski, M.; Poeckel, D.; Gade, S.; Becher, I.; Muelbaier, M.; Wagner, A.J.; Strohmer, K.; Werner, T.; Melchert, S.; Petretich, M.; Rutkowska, A.; Vappiani, J.; Franken, H.; Steidel, M.; Sweetman, G.M.; Gilan, O.; Lam, E.Y.N.; Dawson, M.A.; Prinjha, R.K.; Grandi, P.; Bergamini, G.; Bantscheff, M. Multiplexed proteome dynamics profiling reveals mechanisms controlling protein homeostasis. Cell, 2018, 173(1), 260-274.e25.
[http://dx.doi.org/10.1016/j.cell.2018.02.030] [PMID: 29551266]
[68]
Saei, A.A.; Beusch, C.M.; Sabatier, P.; Wells, J.A.; Gharibi, H.; Meng, Z.; Chernobrovkin, A.; Rodin, S.; Näreoja, K.; Thorsell, A.G.; Karlberg, T.; Cheng, Q.; Lundström, S.L.; Gaetani, M.; Végvári, Á.; Arnér, E.S.J.; Schüler, H.; Zubarev, R.A. System-wide identification and prioritization of enzyme substrates by thermal analysis. Nat. Commun., 2021, 12(1), 1296.
[http://dx.doi.org/10.1038/s41467-021-21540-6] [PMID: 33637753]
[69]
Becher, I.; Andrés-Pons, A.; Romanov, N.; Stein, F.; Schramm, M.; Baudin, F.; Helm, D.; Kurzawa, N.; Mateus, A.; Mackmull, M.T.; Typas, A.; Müller, C.W.; Bork, P.; Beck, M.; Savitski, M.M. Pervasive protein thermal stability variation during the cell cycle. Cell, 2018, 173(6), 1495-1507.e18.
[http://dx.doi.org/10.1016/j.cell.2018.03.053] [PMID: 29706546]
[70]
Dziekan, J.M.; Yu, H.; Chen, D.; Dai, L.; Wirjanata, G.; Larsson, A.; Prabhu, N.; Sobota, R.M.; Bozdech, Z.; Nordlund, P. Identifying purine nucleoside phosphorylase as the target of quinine using cellular thermal shift assay. Sci. Transl. Med., 2019, 11(473), eaau3174.
[http://dx.doi.org/10.1126/scitranslmed.aau3174] [PMID: 30602534]
[71]
Mathieson, T.; Franken, H.; Kosinski, J.; Kurzawa, N.; Zinn, N.; Sweetman, G.; Poeckel, D.; Ratnu, V.S.; Schramm, M.; Becher, I.; Steidel, M.; Noh, K.M.; Bergamini, G.; Beck, M.; Bantscheff, M.; Savitski, M.M. Systematic analysis of protein turnover in primary cells. Nat. Commun., 2018, 9(1), 689.
[http://dx.doi.org/10.1038/s41467-018-03106-1] [PMID: 29449567]
[72]
Reinhard, F.B.M.; Eberhard, D.; Werner, T.; Franken, H.; Childs, D.; Doce, C.; Savitski, M.F.; Huber, W.; Bantscheff, M.; Savitski, M.M.; Drewes, G. Thermal proteome profiling monitors ligand interactions with cellular membrane proteins. Nat. Methods, 2015, 12(12), 1129-1131.
[http://dx.doi.org/10.1038/nmeth.3652] [PMID: 26524241]
[73]
Sridharan, S.; Kurzawa, N.; Werner, T.; Günthner, I.; Helm, D.; Huber, W.; Bantscheff, M.; Savitski, M.M. Proteome-wide solubility and thermal stability profiling reveals distinct regulatory roles for ATP. Nat. Commun., 2019, 10(1), 1155.
[http://dx.doi.org/10.1038/s41467-019-09107-y] [PMID: 30858367]
[74]
Ochoa, D.; Jarnuczak, A.F.; Viéitez, C.; Gehre, M.; Soucheray, M.; Mateus, A.; Kleefeldt, A.A.; Hill, A.; Garcia-Alonso, L.; Stein, F.; Krogan, N.J.; Savitski, M.M.; Swaney, D.L.; Vizcaíno, J.A.; Noh, K.M.; Beltrao, P. The functional landscape of the human phosphoproteome. Nat. Biotechnol., 2020, 38(3), 365-373.
[http://dx.doi.org/10.1038/s41587-019-0344-3] [PMID: 31819260]
[75]
Peng, H.; Guo, H.; Pogoutse, O.; Wan, C.; Hu, L.Z.; Ni, Z.; Emili, A. An unbiased chemical proteomics method identifies FabI as the primary target of 6-OH-BDE-47. Environ. Sci. Technol., 2016, 50(20), 11329-11336.
[http://dx.doi.org/10.1021/acs.est.6b03541] [PMID: 27682841]
[76]
Perrin, J.; Werner, T.; Kurzawa, N.; Rutkowska, A.; Childs, D.D.; Kalxdorf, M.; Poeckel, D.; Stonehouse, E.; Strohmer, K.; Heller, B.; Thomson, D.W.; Krause, J.; Becher, I.; Eberl, H.C.; Vappiani, J.; Sevin, D.C.; Rau, C.E.; Franken, H.; Huber, W.; Faelth-Savitski, M.; Savitski, M.M.; Bantscheff, M.; Bergamini, G. Identifying drug targets in tissues and whole blood with thermal-shift profiling. Nat. Biotechnol., 2020, 38(3), 303-308.
[http://dx.doi.org/10.1038/s41587-019-0388-4] [PMID: 31959954]
[77]
Werner, T.; Becher, I.; Sweetman, G.; Doce, C.; Savitski, M.M.; Bantscheff, M. High-resolution enabled TMT 8-plexing. Anal. Chem., 2012, 84(16), 7188-7194.
[http://dx.doi.org/10.1021/ac301553x] [PMID: 22881393]
[78]
Savitski, M.M.; Reinhard, F.B.M.; Franken, H.; Werner, T.; Savitski, M.F.; Eberhard, D.; Martinez Molina, D.; Jafari, R.; Dovega, R.B.; Klaeger, S.; Kuster, B.; Nordlund, P.; Bantscheff, M.; Drewes, G. Tracking cancer drugs in living cells by thermal profiling of the proteome. Science, 2014, 346(6205), 1255784.
[http://dx.doi.org/10.1126/science.1255784] [PMID: 25278616]
[79]
Franken, H.; Mathieson, T.; Childs, D.; Sweetman, G.M.A.; Werner, T.; Tögel, I.; Doce, C.; Gade, S.; Bantscheff, M.; Drewes, G.; Reinhard, F.B.M.; Huber, W.; Savitski, M.M. Thermal proteome profiling for unbiased identification of direct and indirect drug targets using multiplexed quantitative mass spectrometry. Nat. Protoc., 2015, 10(10), 1567-1593.
[http://dx.doi.org/10.1038/nprot.2015.101] [PMID: 26379230]
[80]
Kurzawa, N.; Becher, I.; Sridharan, S.; Franken, H.; Mateus, A.; Anders, S.; Bantscheff, M.; Huber, W.; Savitski, M.M. A computational method for detection of ligand-binding proteins from dose range thermal proteome profiles. Nat. Commun., 2020, 11(1), 5783.
[http://dx.doi.org/10.1038/s41467-020-19529-8] [PMID: 33188197]
[81]
Li, J.; Cai, Z.; Bomgarden, R.D.; Pike, I.; Kuhn, K.; Rogers, J.C.; Roberts, T.M.; Gygi, S.P.; Paulo, J.A. TMTpro-18plex: The expanded and complete set of tmtpro reagents for sample multiplexing. J. Proteome Res., 2021, 20(5), 2964-2972.
[http://dx.doi.org/10.1021/acs.jproteome.1c00168] [PMID: 33900084]
[82]
Gaetani, M.; Sabatier, P.; Saei, A.A.; Beusch, C.M.; Yang, Z.; Lundström, S.L.; Zubarev, R.A. Proteome integral solubility alteration: A high-throughput proteomics assay for target deconvolution. J. Proteome Res., 2019, 18(11), 4027-4037.
[http://dx.doi.org/10.1021/acs.jproteome.9b00500] [PMID: 31545609]
[83]
Li, J.; Van Vranken, J.G.; Paulo, J.A.; Huttlin, E.L.; Gygi, S.P. Selection of heating temperatures improves the sensitivity of the proteome integral solubility alteration assay. J. Proteome Res., 2020, 19(5), 2159-2166.
[http://dx.doi.org/10.1021/acs.jproteome.0c00063] [PMID: 32243163]
[84]
Noor, Z.; Ahn, S.B.; Baker, M.S.; Ranganathan, S.; Mohamedali, A. Mass spectrometry-based protein identification in proteomics-a review. Brief. Bioinform., 2021, 22(2), 1620-1638.
[http://dx.doi.org/10.1093/bib/bbz163] [PMID: 32047889]
[85]
Hinkson, I.V.; Elias, J.E. The dynamic state of protein turnover: It’s about time. Trends Cell Biol., 2011, 21(5), 293-303.
[http://dx.doi.org/10.1016/j.tcb.2011.02.002] [PMID: 21474317]
[86]
Bisht, K.; Wakeman, C.A. Discovery and therapeutic targeting of differentiated biofilm subpopulations. Front. Microbiol., 2019, 10, 1908.
[http://dx.doi.org/10.3389/fmicb.2019.01908] [PMID: 31507548]
[87]
Belle, A.; Tanay, A.; Bitincka, L.; Shamir, R.; O’Shea, E.K. Quantification of protein half-lives in the budding yeast proteome. Proc. Natl. Acad. Sci. USA, 2006, 103(35), 13004-13009.
[http://dx.doi.org/10.1073/pnas.0605420103] [PMID: 16916930]
[88]
Schoenheimer, R.; Ratner, S.; Rittenberg, D. Studies in protein metabolism. J. Biol. Chem., 1939, 130(2), 703-732.
[http://dx.doi.org/10.1016/S0021-9258(18)73540-0]
[89]
Doherty, M.K.; Hammond, D.E.; Clague, M.J.; Gaskell, S.J.; Beynon, R.J. Turnover of the human proteome: Determination of protein intracellular stability by dynamic SILAC. J. Proteome Res., 2009, 8(1), 104-112.
[http://dx.doi.org/10.1021/pr800641v] [PMID: 18954100]
[90]
Selbach, M.; Schwanhäusser, B.; Thierfelder, N.; Fang, Z.; Khanin, R.; Rajewsky, N. Widespread changes in protein synthesis induced by microRNAs. Nature, 2008, 455(7209), 58-63.
[http://dx.doi.org/10.1038/nature07228] [PMID: 18668040]
[91]
Ross, A.B.; Langer, J.D.; Jovanovic, M. Proteome turnover in the spotlight: Approaches, applications, and perspectives. Mol. Cell. Proteomics, 2021, 20, 100016.
[http://dx.doi.org/10.1074/mcp.R120.002190] [PMID: 33556866]
[92]
Boisvert, F.M.; Ahmad, Y.; Gierliński, M.; Charrière, F.; Lamont, D.; Scott, M.; Barton, G.; Lamond, A.I. A quantitative spatial proteomics analysis of proteome turnover in human cells. Mol. Cell. Proteomics, 2012, 11(3), 011429.
[http://dx.doi.org/10.1074/mcp.M111.011429] [PMID: 21937730]
[93]
Welle, K.A.; Zhang, T.; Hyrohorenko, J.R.; Shen, S.; Qu, J.; Ghaemmaghami, S. Time-resolved analysis of proteome dynamics by TMT-SILAC hyperplexing. Mol. Cell. Proteomics, 2016, 15(12), 3551-3563.
[http://dx.doi.org/10.1074/mcp.M116.063230] [PMID: 27765818]
[94]
Jayapal, K.P.; Sui, S.; Philp, R.J.; Kok, Y.J.; Yap, M.G.S.; Griffin, T.J.; Hu, W.S. Multitagging proteomic strategy to estimate protein turnover rates in dynamic systems. J. Proteome Res., 2010, 9(5), 2087-2097.
[http://dx.doi.org/10.1021/pr9007738] [PMID: 20184388]
[95]
Brenes, A.; Hukelmann, J.; Bensaddek, D.; Lamond, A.I. Multibatch TMT reveals false positives, batch effects and missing values. Mol. Cell. Proteomics, 2019, 18(10), 1967-1980.
[http://dx.doi.org/10.1074/mcp.RA119.001472] [PMID: 31332098]
[96]
Moradali, M.F.; Ghods, S.; Rehm, B.H.A. Pseudomonas aeruginosa lifestyle: A paradigm for adaptation, survival, and persistence. Front. Cell. Infect. Microbiol., 2017, 7, 39.
[http://dx.doi.org/10.3389/fcimb.2017.00039] [PMID: 28261568]
[97]
Chua, S.L.; Yam, J.K.H.; Hao, P.; Adav, S.S.; Salido, M.M.; Liu, Y.; Givskov, M.; Sze, S.K.; Tolker-Nielsen, T.; Yang, L. Selective labelling and eradication of antibiotic-tolerant bacterial populations in Pseudomonas aeruginosa biofilms. Nat. Commun., 2016, 7(1), 10750.
[http://dx.doi.org/10.1038/ncomms10750] [PMID: 26892159]
[98]
Forsberg, E.M.; Huan, T.; Rinehart, D.; Benton, H.P.; Warth, B.; Hilmers, B.; Siuzdak, G. Data processing, multi-omic pathway mapping, and metabolite activity analysis using XCMS Online. Nat. Protoc., 2018, 13(4), 633-651.
[http://dx.doi.org/10.1038/nprot.2017.151] [PMID: 29494574]
[99]
Han, J.; Datla, R.; Chan, S.; Borchers, C.H. Mass spectrometry-based technologies for high-throughput metabolomics. Bioanalysis, 2009, 1(9), 1665-1684.
[http://dx.doi.org/10.4155/bio.09.158] [PMID: 21083110]
[100]
Markley, J.L.; Brüschweiler, R.; Edison, A.S.; Eghbalnia, H.R.; Powers, R.; Raftery, D.; Wishart, D.S. The future of NMR-based metabolomics. Curr. Opin. Biotechnol., 2017, 43, 34-40.
[http://dx.doi.org/10.1016/j.copbio.2016.08.001] [PMID: 27580257]
[101]
Kałużna-Czaplińska, J. Current medical research with the application of coupled techniques with mass spectrometry. Med. Sci. Monit., 2011, 17(5), RA117-RA123.
[http://dx.doi.org/10.12659/MSM.881756] [PMID: 21525822]
[102]
Imperlini, E.; Santorelli, L.; Orrù, S.; Scolamiero, E.; Ruoppolo, M.; Caterino, M. Mass Spectrometry-Based metabolomic and proteomic strategies in organic acidemias. BioMed Res. Int., 2016, 2016, 9210408.
[http://dx.doi.org/10.1155/2016/9210408] [PMID: 27403441]
[103]
Covington, B.C.; McLean, J.A.; Bachmann, B.O. Comparative mass spectrometry-based metabolomics strategies for the investigation of microbial secondary metabolites. Nat. Prod. Rep., 2017, 34(1), 6-24.
[http://dx.doi.org/10.1039/C6NP00048G] [PMID: 27604382]
[104]
Chang, H.Y.; Colby, S.M.; Du, X.; Gomez, J.D.; Helf, M.J.; Kechris, K.; Kirkpatrick, C.R.; Li, S.; Patti, G.J.; Renslow, R.S.; Subramaniam, S.; Verma, M.; Xia, J.; Young, J.D. A practical guide to metabolomics software development. Anal. Chem., 2021, 93(4), 1912-1923.
[http://dx.doi.org/10.1021/acs.analchem.0c03581] [PMID: 33467846]
[105]
Huber, F.; Ridder, L.; Verhoeven, S.; Spaaks, J.H.; Diblen, F.; Rogers, S.; van der Hooft, J.J.J. Spec2Vec: Improved mass spectral similarity scoring through learning of structural relationships. PLOS Comput. Biol., 2021, 17(2), e1008724.
[http://dx.doi.org/10.1371/journal.pcbi.1008724] [PMID: 33591968]
[106]
Ludwig, K.R.; Hummon, A.B. Mass spectrometry for the discovery of biomarkers of sepsis. Mol. Biosyst., 2017, 13(4), 648-664.
[http://dx.doi.org/10.1039/C6MB00656F] [PMID: 28207922]
[107]
Ivanisevic, J.; Want, E.J. From samples to insights into metabolism: Uncovering biologically relevant information in LC-HRMS metabolomics data. Metabolites, 2019, 9(12), 1-30.
[http://dx.doi.org/10.3390/metabo9120308] [PMID: 31861212]
[108]
Bartel, J.; Krumsiek, J.; Theis, F.J. Statistical methods for the analysis of high-throughput metabolomics data. Comput. Struct. Biotechnol. J., 2013, 4(5), e201301009.
[http://dx.doi.org/10.5936/csbj.201301009] [PMID: 24688690]
[109]
Yang, J.Y.; Sanchez, L.M.; Rath, C.M.; Liu, X.; Boudreau, P.D.; Bruns, N.; Glukhov, E.; Wodtke, A.; de Felicio, R.; Fenner, A.; Wong, W.R.; Linington, R.G.; Zhang, L.; Debonsi, H.M.; Gerwick, W.H.; Dorrestein, P.C. Molecular networking as a dereplication strategy. J. Nat. Prod., 2013, 76(9), 1686-1699.
[http://dx.doi.org/10.1021/np400413s] [PMID: 24025162]
[110]
Kusonmano, K.; Vongsangnak, W.; Chumnanpuen, P. Informatics for metabolomics. Adv. Exp. Med. Biol., 2016, 939, 91-115.
[http://dx.doi.org/10.1007/978-981-10-1503-8_5] [PMID: 27807745]
[111]
Rab, E.; Kekos, D.; Roussis, V.; Ioannou, E. α-Pyrone polyketides from streptomyces ambofaciens bi0048, an endophytic actinobacterial strain isolated from the red alga Laurencia glandulifera. Mar. Drugs, 2017, 15(12), E389.
[http://dx.doi.org/10.3390/md15120389] [PMID: 29240664]
[112]
Yang, L.J.; Peng, X.Y.; Zhang, Y.H.; Liu, Z.Q.; Li, X.; Gu, Y.C.; Shao, C.L.; Han, Z.; Wang, C.Y. Antimicrobial and antioxidant polyketides from a deep-sea-derived fungus aspergillus versicolor SH0105. Mar. Drugs, 2020, 18(12), 636.
[http://dx.doi.org/10.3390/md18120636] [PMID: 33322355]
[113]
Maglangit, F.; Fang, Q.; Leman, V.; Soldatou, S.; Ebel, R.; Kyeremeh, K.; Deng, H. Accramycin A, a new aromatic polyketide, from the soil bacterium, Streptomyces sp. MA37. Molecules, 2019, 24(18), 1-11.
[http://dx.doi.org/10.3390/molecules24183384] [PMID: 31533358]
[114]
Baindara, P.; Nayudu, N.; Korpole, S. Whole genome mining reveals a diverse repertoire of lanthionine synthetases and lanthipeptides among the genus Paenibacillus. J. Appl. Microbiol., 2020, 128(2), 473-490.
[http://dx.doi.org/10.1111/jam.14495] [PMID: 31633851]
[115]
Metelev, M.; Arseniev, A.; Bushin, L.B.; Kuznedelov, K.; Artamonova, T.O.; Kondratenko, R.; Khodorkovskii, M.; Seyedsayamdost, M.R.; Severinov, K. Acinetodin and Klebsidin, RNA Polymerase targeting lasso peptides produced by human isolates of Acinetobacter gyllenbergii and Klebsiella pneumoniae. ACS Chem. Biol., 2017, 12(3), 814-824.
[http://dx.doi.org/10.1021/acschembio.6b01154] [PMID: 28106375]
[116]
Imai, Y.; Meyer, K.J.; Iinishi, A.; Favre-Godal, Q.; Green, R.; Manuse, S.; Caboni, M.; Mori, M.; Niles, S.; Ghiglieri, M.; Honrao, C.; Ma, X.; Guo, J.J.; Makriyannis, A.; Linares-Otoya, L.; Böhringer, N.; Wuisan, Z.G.; Kaur, H.; Wu, R.; Mateus, A.; Typas, A.; Savitski, M.M.; Espinoza, J.L.; O’Rourke, A.; Nelson, K.E.; Hiller, S.; Noinaj, N.; Schäberle, T.F.; D’Onofrio, A.; Lewis, K. A new antibiotic selectively kills Gram-negative pathogens. Nature, 2019, 576(7787), 459-464.
[http://dx.doi.org/10.1038/s41586-019-1791-1] [PMID: 31747680]
[117]
Collin, F.; Maxwell, A. The microbial toxin microcin B17: Prospects for the development of new antibacterial agents. J. Mol. Biol., 2019, 431(18), 3400-3426.
[http://dx.doi.org/10.1016/j.jmb.2019.05.050] [PMID: 31181289]
[118]
Berditsch, M.; Trapp, M.; Afonin, S.; Weber, C.; Misiewicz, J.; Turkson, J.; Ulrich, A.S. Antimicrobial peptide gramicidin S is accumulated in granules of producer cells for storage of bacterial phosphagens. Sci. Rep., 2017, 7(1), 44324.
[http://dx.doi.org/10.1038/srep44324] [PMID: 28295017]
[119]
Mankelow, D.P.; Neilan, B.A. Non-ribosomal peptide antibiotics. Expert Opin. Ther. Pat., 2000, 10(10), 1583-1591.
[http://dx.doi.org/10.1517/13543776.10.10.1583]
[120]
Tan, S.; Moore, G.; Nodwell, J. Put a bow on it: Knotted antibiotics take center stage. Antibiotics (Basel), 2019, 8(3), E117.
[http://dx.doi.org/10.3390/antibiotics8030117] [PMID: 31405236]
[121]
Huddleston, J.R. Horizontal gene transfer in the human gastrointestinal tract: Potential spread of antibiotic resistance genes. Infect. Drug Resist., 2014, 7, 167-176.
[http://dx.doi.org/10.2147/IDR.S48820] [PMID: 25018641]
[122]
Da Silva, L.; Collino, S.; Cominetti, O.; Martin, F.P.; Montoliu, I.; Moreno, S.O.; Corthesy, J.; Kaput, J.; Kussmann, M.; Monteiro, J.P.; Guiraud, S.P. High-throughput method for the quantitation of metabolites and co-factors from homocysteine-methionine cycle for nutritional status assessment. Bioanalysis, 2016, 8(18), 1937-1949.
[http://dx.doi.org/10.4155/bio-2016-0112] [PMID: 27558871]
[123]
Blaženović, I.; Kind, T.; Ji, J.; Fiehn, O. Software tools and approaches for compound identification of LC-MS/MS data in metabolomics. Metabolites, 2018, 8(2), E31.
[http://dx.doi.org/10.3390/metabo8020031] [PMID: 29748461]
[124]
van der Hooft, J.J.J.; Mohimani, H.; Bauermeister, A.; Dorrestein, P.C.; Duncan, K.R.; Medema, M.H. Linking genomics and metabolomics to chart specialized metabolic diversity. Chem. Soc. Rev., 2020, 49(11), 3297-3314.
[http://dx.doi.org/10.1039/D0CS00162G] [PMID: 32393943]
[125]
Soldatou, S.; Eldjárn, G.H.; Ramsay, A.; van der Hooft, J.J.J.; Hughes, A.H.; Rogers, S.; Duncan, K.R. Comparative metabologenomics analysis of polar actinomycetes. Mar. Drugs, 2021, 19(2), 1-21.
[http://dx.doi.org/10.3390/md19020103] [PMID: 33578887]
[126]
Parkinson, E.I.; Tryon, J.H.; Goering, A.W.; Ju, K.S.; McClure, R.A.; Kemball, J.D.; Zhukovsky, S.; Labeda, D.P.; Thomson, R.J.; Kelleher, N.L.; Metcalf, W.W. Discovery of the tyrobetaine natural products and their biosynthetic gene cluster via metabologenomics. ACS Chem. Biol., 2018, 13(4), 1029-1037.
[http://dx.doi.org/10.1021/acschembio.7b01089] [PMID: 29510029]
[127]
Goering, A.W.; McClure, R.A.; Doroghazi, J.R.; Albright, J.C.; Haverland, N.A.; Zhang, Y.; Ju, K.S.; Thomson, R.J.; Metcalf, W.W.; Kelleher, N.L. Metabologenomics: Correlation of microbial gene clusters with metabolites drives discovery of a nonribosomal peptide with an unusual amino acid monomer. ACS Cent. Sci., 2016, 2(2), 99-108.
[http://dx.doi.org/10.1021/acscentsci.5b00331] [PMID: 27163034]
[128]
Chu, L.; Huang, J.; Muhammad, M.; Deng, Z.; Gao, J. Genome mining as a biotechnological tool for the discovery of novel marine natural products. Crit. Rev. Biotechnol., 2020, 40(5), 571-589.
[http://dx.doi.org/10.1080/07388551.2020.1751056] [PMID: 32308042]
[129]
Crüsemann, M. Coupling mass spectral and genomic information to improve bacterial natural product discovery workflows. Mar. Drugs, 2021, 19(3), 142.
[http://dx.doi.org/10.3390/md19030142] [PMID: 33807702]
[130]
Russell, A.H.; Truman, A.W. Genome mining strategies for ribosomally synthesised and post-translationally modified peptides. Comput. Struct. Biotechnol. J., 2020, 18, 1838-1851.
[http://dx.doi.org/10.1016/j.csbj.2020.06.032] [PMID: 32728407]
[131]
Hudson, G.A.; Mitchell, D.A. RiPP antibiotics: Biosynthesis and engineering potential. Curr. Opin. Microbiol., 2018, 45, 61-69.
[http://dx.doi.org/10.1016/j.mib.2018.02.010] [PMID: 29533845]
[132]
Delgado, M.A.; Rintoul, M.R.; Farías, R.N.; Salomón, R.A. Escherichia coli RNA polymerase is the target of the cyclopeptide antibiotic microcin J25. J. Bacteriol., 2001, 183(15), 4543-4550.
[http://dx.doi.org/10.1128/JB.183.15.4543-4550.2001] [PMID: 11443089]
[133]
Vincent, P.A.; Delgado, M.A.; Farías, R.N.; Salomón, R.A. Inhibition of Salmonella enterica serovars by microcin J25. FEMS Microbiol. Lett., 2004, 236(1), 103-107.
[http://dx.doi.org/10.1111/j.1574-6968.2004.tb09634.x] [PMID: 15212798]
[134]
Choules, M.P.; Wolf, N.M.; Lee, H.; Anderson, J.R.; Grzelak, E.M.; Wang, Y.; Ma, R.; Gao, W.; McAlpine, J.B.; Jin, Y.Y.; Cheng, J.; Lee, H.; Suh, J.W.; Duc, N.M.; Paik, S.; Choe, J.H.; Jo, E.K.; Chang, C.L.; Lee, J.S.; Jaki, B.U.; Pauli, G.F.; Franzblau, S.G.; Cho, S. Rufomycin Targets ClpC1 Proteolysis in Mycobacterium tuberculosis and M. abscessus. Antimicrob. Agents Chemother., 2019, 63(3), 1-46.
[http://dx.doi.org/10.1128/AAC.02204-18] [PMID: 30602512]
[135]
Manam, R.R.; Teisan, S.; White, D.J.; Nicholson, B.; Grodberg, J.; Neuteboom, S.T.C.; Lam, K.S.; Mosca, D.A.; Lloyd, G.K.; Potts, B.C.M. Lajollamycin, a nitro-tetraene spiro-β-lactone-γ-lactam antibiotic from the marine actinomycete Streptomyces nodosus. J. Nat. Prod., 2005, 68(2), 240-243.
[http://dx.doi.org/10.1021/np049725x] [PMID: 15730252]
[136]
Agrawal, S.; Acharya, D.; Adholeya, A.; Barrow, C.J.; Deshmukh, S.K. Nonribosomal peptides from marine microbes and their antimicrobial and anticancer potential. Front. Pharmacol., 2017, 8, 828.
[http://dx.doi.org/10.3389/fphar.2017.00828] [PMID: 29209209]
[137]
Tareq, F.S.; Kim, J.H.; Lee, M.A.; Lee, H.S.; Lee, Y.J.; Lee, J.S.; Shin, H.J. 125. Leodoglucomides A and B from a marine-derived bacterium Bacillus licheniformis. Org. Lett., 2013, 15(8), 2071.
[http://dx.doi.org/10.1021/ol4008603]
[138]
Duncan, K.R.; Crüsemann, M.; Lechner, A.; Sarkar, A.; Li, J.; Ziemert, N.; Wang, M.; Bandeira, N.; Moore, B.S.; Dorrestein, P.C.; Jensen, P.R. Molecular networking and pattern-based genome mining improves discovery of biosynthetic gene clusters and their products from Salinispora species. Chem. Biol., 2015, 22(4), 460-471.
[http://dx.doi.org/10.1016/j.chembiol.2015.03.010] [PMID: 25865308]
[139]
van der Lee, T.A.J.; Medema, M.H. Computational strategies for genome-based natural product discovery and engineering in fungi. Fungal Genet. Biol., 2016, 89, 29-36.
[http://dx.doi.org/10.1016/j.fgb.2016.01.006] [PMID: 26775250]
[140]
Mohimani, H.; Pevzner, P.A. Dereplication, sequencing and identification of peptidic natural products: From genome mining to peptidogenomics to spectral networks. Nat. Prod. Rep., 2016, 33(1), 73-86.
[http://dx.doi.org/10.1039/C5NP00050E] [PMID: 26497201]
[141]
Clarke, C.J.; Haselden, J.N. Metabolic profiling as a tool for understanding mechanisms of toxicity. Toxicol. Pathol., 2008, 36(1), 140-147.
[http://dx.doi.org/10.1177/0192623307310947] [PMID: 18337232]
[142]
Drapal, M.; Fraser, P.D. Metabolite Profiling: A tool for the biochemical characterisation of Mycobacterium sp. Microorganisms, 2019, 7(5), E148.
[http://dx.doi.org/10.3390/microorganisms7050148] [PMID: 31130621]
[143]
Cui, L.; Lu, H.; Lee, Y.H. Challenges and emergent solutions for LC-MS/MS based untargeted metabolomics in diseases. Mass Spectrom. Rev., 2018, 37(6), 772-792.
[http://dx.doi.org/10.1002/mas.21562] [PMID: 29486047]
[144]
Getahun, H.; Harrington, M.; O’Brien, R.; Nunn, P. Diagnosis of smear-negative pulmonary tuberculosis in people with HIV infection or AIDS in resource-constrained settings: Informing urgent policy changes. Lancet, 2007, 369(9578), 2042-2049.
[http://dx.doi.org/10.1016/S0140-6736(07)60284-0] [PMID: 17574096]
[145]
Wolk, D.M.; Kaleta, E.J.; Wysocki, V.H. PCR-electrospray ionization mass spectrometry: The potential to change infectious disease diagnostics in clinical and public health laboratories. J. Mol. Diagn., 2012, 14(4), 295-304.
[http://dx.doi.org/10.1016/j.jmoldx.2012.02.005] [PMID: 22584138]
[146]
Metzgar, D.; Frinder, M.; Lovari, R.; Toleno, D.; Massire, C.; Blyn, L.B.; Ranken, R.; Carolan, H.E.; Hall, T.A.; Moore, D.; Hansen, C.J.; Sampath, R.; Ecker, D.J. Broad-spectrum biosensor capable of detecting and identifying diverse bacterial and Candida species in blood. J. Clin. Microbiol., 2013, 51(8), 2670-2678.
[http://dx.doi.org/10.1128/JCM.00966-13] [PMID: 23761152]
[147]
Lau, S. K. P.; Lam, C. W.; Curreem, S. O. T.; Lee, K. C.; Lau, C. C. Y.; Chow, W. N.; Ngan, A. H. Y.; To, K. K. W.; Chan, J. F. W.; Hung, I. F. N.; Yam, W.C.; Yuen, K.Y.; Woo, P.C. Identification of specific metabolites in culture supernatant of Mycobacterium tuberculosis using metabolomics: Exploration of potential biomarkers. Emerg. Microbes Infect, 2015, 4(1), emi.2015.6.
[http://dx.doi.org/10.1038/emi.2015.6]
[148]
Tounta, V.; Liu, Y.; Cheyne, A.; Larrouy-Maumus, G. Metabolomics in infectious diseases and drug discovery. Mol. Omics, 2021, 17(3), 376-393.
[http://dx.doi.org/10.1039/D1MO00017A] [PMID: 34125125]
[149]
Allegretti, J.R.; Kearney, S.; Li, N.; Bogart, E.; Bullock, K.; Gerber, G.K.; Bry, L.; Clish, C.B.; Alm, E.; Korzenik, J.R. Recurrent Clostridium difficile infection associates with distinct bile acid and microbiome profiles. Aliment. Pharmacol. Ther., 2016, 43(11), 1142-1153.
[http://dx.doi.org/10.1111/apt.13616] [PMID: 27086647]
[150]
Tait, E.; Perry, J.D.; Stanforth, S.P.; Dean, J.R. Identification of volatile organic compounds produced by bacteria using HS-SPME-GC-MS. J. Chromatogr. Sci., 2014, 52(4), 363-373.
[http://dx.doi.org/10.1093/chromsci/bmt042] [PMID: 23661670]
[151]
Lawal, O.; Muhamadali, H.; Ahmed, W.M.; White, I.R.; Nijsen, T.M.E.; Goodacre, R.; Fowler, S.J. Headspace volatile organic compounds from bacteria implicated in ventilator-associated pneumonia analysed by TD-GC/MS. J. Breath Res., 2018, 12(2), 026002.
[http://dx.doi.org/10.1088/1752-7163/aa8efc] [PMID: 28947683]
[152]
Nizio, K.D.; Perrault, K.A.; Troobnikoff, A.N.; Ueland, M.; Shoma, S.; Iredell, J.R.; Middleton, P.G.; Forbes, S.L. In vitro volatile organic compound profiling using GC×GC-TOFMS to differentiate bacteria associated with lung infections: A proof-of-concept study. J. Breath Res., 2016, 10(2), 026008.
[http://dx.doi.org/10.1088/1752-7155/10/2/026008] [PMID: 27120170]
[153]
Neerincx, A.H.; Geurts, B.P.; Habets, M.F.J.; Booij, J.A.; van Loon, J.; Jansen, J.J.; Buydens, L.M.C.; van Ingen, J.; Mouton, J.W.; Harren, F.J.M.; Wevers, R.A.; Merkus, P.J.F.M.; Cristescu, S.M.; Kluijtmans, L.A.J. Identification of Pseudomonas aeruginosa and Aspergillus fumigatus mono- and co-cultures based on volatile biomarker combinations. J. Breath Res., 2016, 10(1), 016002.
[http://dx.doi.org/10.1088/1752-7155/10/1/016002] [PMID: 26824272]
[154]
Daoud, N.; Hamdoun, M.; Hannachi, H.; Gharsallah, C.; Mallekh, W.; Bahri, O. Antimicrobial susceptibility patterns of Escherichia coli among Tunisian outpatients with community-acquired urinary tract infection (2012-2018). Curr. Urol., 2020, 14(4), 200-205.
[http://dx.doi.org/10.1159/000499238] [PMID: 33488338]
[155]
Rêgo, A.M.; Alves da Silva, D.; Ferreira, N.V.; de Pina, L.C.; Evaristo, J.A.M.; Caprini Evaristo, G.P.; Nogueira, F.C.S.; Ochs, S.M.; Amaral, J.J.; Ferreira, R.B.R.; Antunes, L.C.M. Metabolic profiles of multidrug resistant and extensively drug resistant Mycobacterium tuberculosis unveiled by metabolomics. Tuberculosis (Edinb.), 2020, 2021, 126.
[http://dx.doi.org/10.1016/j.tube.2020.102043] [PMID: 33370646]
[156]
Fu, Q.; Liu, D.; Wang, Y.; Li, X.; Wang, L.; Yu, F.; Shen, J.; Xia, X. Metabolomic profiling of Campylobacter jejuni with resistance gene ermB by ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry and tandem quadrupole mass spectrometry. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci., 2018, 1079(1079), 62-68.
[http://dx.doi.org/10.1016/j.jchromb.2018.02.009] [PMID: 29453015]
[157]
Li, H.; Xia, X.; Li, X.; Naren, G.; Fu, Q.; Wang, Y.; Wu, C.; Ding, S.; Zhang, S.; Jiang, H.; Li, J.; Shen, J. Untargeted metabolomic pro fi ling of amphenicol-resistant Campylobacter jejuni by ultra-high-performance liquid chromatography. J. Proteome Res., 2015, 14(2), 1060-1068.
[http://dx.doi.org/10.1021/pr501061d] [PMID: 25491530]
[158]
Schelli, K.; Zhong, F.; Zhu, J. Comparative metabolomics revealing Staphylococcus aureus metabolic response to different antibiotics. Microb. Biotechnol., 2017, 10(6), 1764-1774.
[http://dx.doi.org/10.1111/1751-7915.12839] [PMID: 28815967]
[159]
Knoll, K.E.; Lindeque, Z.; Adeniji, A.A.; Oosthuizen, C.B.; Lall, N.; Loots, D.T. Elucidating the antimycobacterial mechanism of action of Decoquinate derivative RMB041 using metabolomics. Antibiotics (Basel), 2021, 10(6), 1-12.
[http://dx.doi.org/10.3390/antibiotics10060693] [PMID: 34200519]
[160]
Koen, N.; van Breda, S.V.; Loots, D.T. Elucidating the antimicrobial mechanisms of colistin sulfate on Mycobacterium tuberculosis using metabolomics. Tuberculosis (Edinb.), 2018, 111, 14-19.
[http://dx.doi.org/10.1016/j.tube.2018.05.001] [PMID: 30029899]
[161]
Knoll, K.E.; Lindeque, Z.; Adeniji, A.A.; Oosthuizen, C.B.; Lall, N.; Loots, D.T. Elucidating the antimycobacterial mechanism of action of ciprofloxacin using metabolomics. Microorganism, 2021, 9, 1158.
[http://dx.doi.org/10.3390/microorganisms9061158]
[162]
Hussein, M.; Karas, J.A.; Schneider-Futschik, E.K.; Chen, F.; Swarbrick, J.; Paulin, O.K.A.; Hoyer, D.; Baker, M.; Zhu, Y.; Li, J.; Velkov, T. The killing mechanism of teixobactin against methicillin-resistant staphylococcus aureus: An untargeted metabolomics study. mSystems, 2020, 5(3), 1-16.
[http://dx.doi.org/10.1128/mSystems.00077-20] [PMID: 32457238]
[163]
Mouton, J.W. Combination therapy as a tool to prevent emergence of bacterial resistance. Infection, 1999, 27(Suppl. 2), S24-S28.
[http://dx.doi.org/10.1007/BF02561666] [PMID: 10885823]
[164]
Han, B.; Zhang, Z.; Xie, Y.; Hu, X.; Wang, H.; Xia, W.; Wang, Y.; Li, H.; Wang, Y.; Sun, H. Multi-omics and temporal dynamics profiling reveal disruption of central metabolism in Helicobacter pylori on bismuth treatment. Chem. Sci. (Camb.), 2018, 9(38), 7488-7497.
[http://dx.doi.org/10.1039/C8SC01668B] [PMID: 30510674]
[165]
Tran, T.B.; Bergen, P.J.; Creek, D.J.; Velkov, T.; Li, J. Synergistic killing of polymyxin b in combination with the antineoplastic drug mitotane against polymyxin-susceptible and -resistant Acinetobacter baumannii: A metabolomic study. Front. Pharmacol., 2018, 9, 359.
[http://dx.doi.org/10.3389/fphar.2018.00359] [PMID: 29713282]
[166]
Maifiah, M.H.M.; Creek, D.J.; Nation, R.L.; Forrest, A.; Tsuji, B.T.; Velkov, T.; Li, J. Untargeted metabolomics analysis reveals key pathways responsible for the synergistic killing of colistin and doripenem combination against Acinetobacter baumannii. Sci. Rep., 2017, 7(1), 45527.
[http://dx.doi.org/10.1038/srep45527] [PMID: 28358014]
[167]
Lin, D.M.; Koskella, B.; Lin, H.C. Phage therapy: An alternative to antibiotics in the age of multi-drug resistance. World J. Gastrointest. Pharmacol. Ther., 2017, 8(3), 162-173.
[http://dx.doi.org/10.4292/wjgpt.v8.i3.162] [PMID: 28828194]
[168]
Chevallereau, A.; Blasdel, B.G.; De Smet, J.; Monot, M.; Zimmermann, M.; Kogadeeva, M.; Sauer, U.; Jorth, P.; Whiteley, M.; Debarbieux, L.; Lavigne, R. Next-generation "-omics" approaches reveal a massive alteration of host RNA metabolism during bacteriophage infection of Pseudomonas aeruginosa. PLoS Genet., 2016, 12(7), e1006134.
[http://dx.doi.org/10.1371/journal.pgen.1006134] [PMID: 27380413]
[169]
Diray-Arce, J.; Conti, M.G.; Petrova, B.; Kanarek, N.; Angelidou, A.; Levy, O. Integrative metabolomics to identify molecular signatures of responses to vaccines and infections. Metabolites, 2020, 10(12), 1-18.
[http://dx.doi.org/10.3390/metabo10120492] [PMID: 33266347]
[170]
Shi, D.; Mi, G.; Wang, M.; Webster, T.J. In vitro and ex vivo systems at the forefront of infection modeling and drug discovery. Biomaterials, 2019, 198, 228-249.
[http://dx.doi.org/10.1016/j.biomaterials.2018.10.030] [PMID: 30384974]
[171]
Matsunaga, S.; Nishiumi, S.; Tagawa, R.; Yoshida, M. Alterations in metabolic pathways in gastric epithelial cells infected with Helicobacter pylori. Microb. Pathog., 2018, 124, 122-129.
[http://dx.doi.org/10.1016/j.micpath.2018.08.033] [PMID: 30138760]
[172]
Buras, J.A.; Holzmann, B.; Sitkovsky, M. Animal models of sepsis: Setting the stage. Nat. Rev. Drug Discov., 2005, 4(10), 854-865.
[http://dx.doi.org/10.1038/nrd1854] [PMID: 16224456]
[173]
Teul, J.; Deja, S.; Celińska-Janowicz, K.; Ząbek, A.; Młynarz, P.; Barć, P.; Junka, A.; Smutnicka, D.; Bartoszewicz, M.; Pałka, J.; Miltyk, W. LC-QTOF-MS and 1H NMR metabolomics verifies potential use of Greater Omentum for Klebsiella pneumoniae biofilm eradication in rats. Pathogens, 2020, 9(5), E399.
[http://dx.doi.org/10.3390/pathogens9050399] [PMID: 32455691]
[174]
Nishiumi, S.; Yoshida, M.; Azuma, T. Alterations in metabolic pathways in stomach of mice infected with Helicobacter pylori. Microb. Pathog., 2017, 109, 78-85.
[http://dx.doi.org/10.1016/j.micpath.2017.05.027] [PMID: 28546118]
[175]
Chen, X.H.; Liu, S.R.; Peng, B.; Li, D.; Cheng, Z.X.; Zhu, J.X.; Zhang, S.; Peng, Y.M.; Li, H.; Zhang, T.T.; Peng, X.X. Exogenous l-valine promotes phagocytosis to kill multidrug-resistant bacterial pathogens. Front. Immunol., 2017, 8, 207.
[http://dx.doi.org/10.3389/fimmu.2017.00207] [PMID: 28321214]
[176]
Wozniak, J.M.; Mills, R.H.; Olson, J.; Caldera, J.R.; Sepich-Poore, G.D.; Carrillo-Terrazas, M.; Tsai, C.M.; Vargas, F.; Knight, R.; Dorrestein, P.C.; Liu, G.Y.; Nizet, V.; Sakoulas, G.; Rose, W.; Gonzalez, D.J. Mortality risk profiling of Staphylococcus aureus bacteremia by multi-omic serum analysis reveals early predictive and pathogenic signatures. Cell, 2020, 182(5), 1311-1327.e14.
[http://dx.doi.org/10.1016/j.cell.2020.07.040] [PMID: 32888495]
[177]
Moyne, O.; Castelli, F.; Bicout, D.J.; Boccard, J.; Camara, B.; Cournoyer, B.; Faudry, E.; Terrier, S.; Hannani, D.; Huot-Marchand, S.; Léger, C.; Maurin, M.; Ngo, T.D.; Plazy, C.; Quinn, R.A.; Attree, I.; Fenaille, F.; Toussaint, B.; Le Gouëllec, A. Metabotypes of Pseudomonas aeruginosa correlate with antibiotic resistance, virulence and clinical outcome in cystic fibrosis chronic infections. Metabolites, 2021, 11(2), 1-20.
[http://dx.doi.org/10.3390/metabo11020063] [PMID: 33494144]
[178]
Langley, R.J.; Wong, H.R. Early diagnosis of sepsis: Is an integrated omics approach the way forward? Mol. Diagn. Ther., 2017, 21(5), 525-537.
[http://dx.doi.org/10.1007/s40291-017-0282-z] [PMID: 28624903]
[179]
Hui, W.W.; Emerson, L.E.; Clapp, B.; Sheppe, A.E.; Sharma, J.; Del Castillo, J.; Ou, M.; Maegawa, G.H.B.; Hoffman, C.; Larkin Iii, J.; Pascual, D.W.; Edelmann, M.J. Antigen-encapsulating host extracellular vesicles derived from Salmonella-infected cells stimulate pathogen-specific Th1-type responses in vivo. PLoS Pathog., 2021, 17(5), e1009465.
[http://dx.doi.org/10.1371/journal.ppat.1009465] [PMID: 33956909]

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