MULTI-OMICS APPROACH TO THE STUDY OF MICROORGANISMS
DOI:
https://doi.org/10.26577/eb.2021.v89.i4.03Abstract
In this review multi-omics (transcriptomic and proteomic) research approaches that have been widely implemented in modern microbiology are examined. The transcriptomic approach is important for predicting the resistance of microorganisms to specific antibiotics, as well as for understanding the mechanisms of the emergence of antibiotic resistance. In this review, the issues of studying the transcriptional response in microorganisms after in vitro exposure to subinhibitory concentrations of antimicrobial drugs are most extensively examined. It has been shown that antibiotics induce both phenotypic and genetic changes in bacterial cells, contributing to the emergence of resistance to them. Likewise, a proteomics-based approach broadens understanding of the bacterial strategy for antibiotic resistance, as well as improved understanding of the mechanisms by which antimicrobial resistance emerges, which will facilitate controlling of the growing epidemic of antibiotic-resistant infections in the future. In this review, the advantages of using one of the proteomics approaches widely used in clinical microbiology, MALDI-TOF MS, are considered more extensively. It has been shown that this approach is a more powerful tool for studying the protein profile in comparison with other methods.
Thus, the development of high-throughput transcriptomic and proteomic methods made analysis of large datasets of mRNA and proteins possible, which allows identifying functionally significant networks of intermolecular interactions, and thereby allowed to expand the modern understanding of mechanisms underlying the emergence of resistance to antimicrobial drugs.
References
Cui H., Dhroso A., Johnson N., Korkin D. (2015) The variation game: Cracking complex genetic disorders with NGS and omics data. Methods, vol. 79–80, pp. 18–31. https://doi.org/10.1016/j.ymeth.04.018.
Hasin Y., Seldin M., Lusis A. (2017) Multi-omics approaches to disease. Genome Biol., vol. 18, no. 1, p. 83. https://doi.org/10.1186/s13059-017-1215-1.
Tsakou F., Jersie-Christensen R., Jenssen H., Mojsoska B.( 2020) The Role of Proteomics in Bacterial Response to Antibiotics. Pharmaceuticals, vol. 13, no. 9. https://doi.org/ 10.3390/ph13090214.
Gmuender H., Kuratli K., Gray C. P., Keck W., Evers S.(2001) Gene expression changes triggered by exposure of Haemophilus influenzae to novobiocin or ciprofloxacin: combined transcription and translation analysis. Genome Res., vol. 11, no. 1, pp. 28–42. https://doi.org/10.1101/gr.157701.
Chernov V. M., Chernova O. A., Mouzykantov A. A., Lopukhov L. L., Aminov R. I.(2019) Omics of antimicrobials and antimicrobial resistance. Expert Opin. Drug Discov., vol. 14, no. 5, pp. 455–468. https://doi.org/10.1080/17460441.2019.1588880.
Davidovich B. T., Solovyova N.V., Bashilova E.N. (2020) Endoecological aspects of antibiotic resistance: a literature review. Hum. Ecol., vol. 5, pp. 31–36. https://doi.org/10.33396 / 1728-0869-2020-5-31-36.
Bulgakova V.G., Vinogradova K.A, Orlova T.I., Kozhevin P.A. (2014) Antibiotic action as signaling molecules. Antibiot. Chemother., vol. 59, pp. 36–43.
Rogers P. D. et al. (2007) Gene expression profiling of the response of Streptococcus pneumoniae to penicillin. J. Antimicrob. Chemother., vol. 59, no. 4, pp. 616–626. https://doi.org/ 10.1093/jac/dkl560.
Linares J. F., Gustafsson I., Baquero F., Martinez J. L. (2006) Antibiotics as intermicrobial signaling agents instead of weapons. Proc. Natl. Acad. Sci., vol. 103, no. 51, pp. 19484–19489. https://doi.org/10.1073/pnas.0608949103.
Skindersoe M. E. et al. (2008) Effects of antibiotics on quorum sensing in Pseudomonas aeruginosa. Antimicrob. Agents Chemother., vol. 52, no. 10, pp. 3648–3663. https://doi.org/10.1128/AAC.01230-07.
Freitas M. C. R. et al. (2015) Bacteroides fragilis response to subinhibitory concentrations of antimicrobials includes different morphological, physiological and virulence patterns after in vitro selection. Microb. Pathog., vol. 78, pp. 103–113. https://doi.org/10.1016/j.micpath.2014.12.002.
Jeters R. T., Wang G.-R., Moon K., Shoemaker N. B., Salyers A. A. (2009) Tetracycline-associated transcriptional regulation of transfer genes of the Bacteroides conjugative transposon CTnDOT. J. Bacteriol., vol. 191, no. 20, pp. 6374–6382. https://doi.org/10.1128/JB.00739-09.
Gillespie S. H., Basu S., Dickens A. L., O’Sullivan D. M., McHugh T. D.( 2005) Effect of subinhibitory concentrations of ciprofloxacin on Mycobacterium fortuitum mutation rates. J. Antimicrob. Chemother., vol. 56, no. 2, pp. 344–348. https://doi.org/10.1093/jac/dki191.
Henderson-Begg S. K., Livermore D. M., Hall L. M. C. (2006) Effect of subinhibitory concentrations of antibiotics on mutation frequency in Streptococcus pneumoniae. J. Antimicrob. Chemother., vol. 57, no. 5, pp. 849–854. https://doi.org/10.1093/jac/dkl064.
de Freitas M. C. R. et al. (2016) Exploratory Investigation of Bacteroides fragilis Transcriptional Response during In vitro Exposure to Subinhibitory Concentration of Metronidazole. Front. Microbiol., vol. 7, p. 1465. https://doi.org/10.3389/fmicb.2016.01465.
Park A. J., Krieger J. R., Khursigara C. M. (2016) Survival proteomes: the emerging proteotype of antimicrobial resistance. FEMS Microbiol. Rev., vol. 40, no. 3, pp. 323–342. https://doi.org/10.1093/femsre/fuv051.
Lima T. B. et al. (2013) Bacterial resistance mechanism: what proteomics can elucidate. FASEB J., vol. 27, no. 4, pp. 1291–1303. https://doi.org/10.1096/fj.12-221127.
Kim S. W. et al. (2018) Outer membrane vesicles from β-lactam-resistant Escherichia coli enable the survival of β-lactam-susceptible E. coli in the presence of β-lactam antibiotics. Scientific reports, vol. 8, no. 1. Laboratory of Aquatic Animal Diseases, Institute of Animal Medicine, College of Veterinary Medicine, Gyeongsang National University, Jinju, 52828, Republic of Korea., p. 5402. https://doi.org/10.1038/s41598-018-23656-0.
Lata M., Sharma, D. Deo N., Tiwari P. K., Bisht D., Venkatesan K. (2015) Proteomic analysis of ofloxacin-mono resistant Mycobacterium tuberculosis isolates. J. Proteomics, vol. 127, no. Pt A, pp. 114–121. https://doi.org/10.1016/j.jprot.2015.07.031.
Zhang D., Li H., Lin X., Peng X. (2015) Outer membrane proteomics of kanamycin-resistant Escherichia coli identified MipA as a novel antibiotic resistance-related protein. FEMS Microbiol. Lett., vol. 362, no. 11. https://doi.org/10.1093/femsle/fnv074.
Ma Y., Guo C.,. Li H, Peng X.-X. (2013) Low abundance of respiratory nitrate reductase is essential for Escherichia coli in resistance to aminoglycoside and cephalosporin. J. Proteomics, vol. 87, pp. 78–88. https://doi.org/10.1016/j.jprot.2013.05.019.
Vranakis I.et al. (2014) Proteome studies of bacterial antibiotic resistance mechanisms. J. Proteomics, vol. 97, pp. 88–99. https://doi.org/10.1016/j.jprot.2013.10.027.
van Hoek A.,. Mevius D, Guerra B., Mullany P., Roberts A., Aarts H. (2011) Acquired Antibiotic Resistance Genes: An Overview. Front. Microbiol., vol. 2, p. 203. https://doi.org/10.3389/fmicb.2011.00203.
Peng X., Xu C., Ren H., Lin X., Wu L., Wang S. (2005) Proteomic Analysis of the Sarcosine-Insoluble Outer Membrane Fraction of Pseudomonas aeruginosa Responding to Ampicilin, Kanamycin, and Tetracycline Resistance. J. Proteome Res., vol. 4, no. 6, pp. 2257–2265. https://doi.org/10.1021/pr050159g.
Delcour A. H. (2009) Outer membrane permeability and antibiotic resistance. Biochim. Biophys. Acta - Proteins Proteomics, vol. 1794, no. 5, pp. 808–816. https://doi.org/10.1016/j.bbapap.2008.11.005.
Pérez-Llarena F. J, Bou G. (2009) Beta-lactamase inhibitors: the story so far. Curr. Med. Chem., vol. 16, no. 28, pp. 3740–3765. https://doi.org/10.2174/092986709789104957.
Pérez-Llarena F. J.,. Bou G. (2016) Proteomics As a Tool for Studying Bacterial Virulence and Antimicrobial Resistance. Front. Microbiol., vol. 7, p. 410. https://doi.org/10.3389/fmicb.2016.00410.
Poole K. (2004) Resistance to β-lactam antibiotics. Cell. Mol. Life Sci. C., vol. 61, no. 17, pp. 2200–2223. https://doi.org/10.1007/s00018-004-4060-9.
Egorov A. M., Ulyashova M. M., Rubtsova M. Y. (2018) Bacterial Enzymes and Antibiotic Resistance. Acta Naturae, vol. 10, no. 4, pp. 33–48.
Diniz C. G., Farias L. M., Carvalho M. A. R.,. Rocha E. R, Smith C. J. (2004) Differential gene expression in a Bacteroides fragilis metronidazole-resistant mutant. J. Antimicrob. Chemother., vol. 54, no. 1, pp. 100–108. https://doi.org/10.1093/jac/dkh256.
Nagy E., Becker S., Sóki J., Urbán E., Kostrzewa M. (2011) Differentiation of division I (cfiA-negative) and division II (cfiA-positive) Bacteroides fragilis strains by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. J. Med. Microbiol., vol. 60, no. Pt 11, pp. 1584–1590. https://doi.org/10.1099/jmm.0.031336-0.
Wybo I .et al. (2011) Differentiation of cfiA-Negative and cfiA-Positive Bacteroides fragilis Isolates by Matrix-Assisted Laser Desor. J. Clin. Microbiol., vol. 49, no. 5, pp. 1961 LP – 1964. https://doi.org/10.1128/JCM.02321-10.
Kozhakhmetova S. et al. (2021) Determinants of resistance in Bacteroides fragilis strain BFR_KZ01 isolated from a patient with peritonitis in Kazakhstan. J. Glob. Antimicrob. Resist., vol. 25, pp. 1–4. https://doi.org/10.1016/j.jgar.2021.02.022.
Oviaño M., Bou G. (2018) Matrix-Assisted Laser Desorption Ionization–Time of Flight Mass Spectrometry for the Rapid Detection of Antimicrobial Resistance Mechanisms and Beyond. Clin. Microbiol. Rev., vol. 32, no. 1, pp. e00037-18. https://doi.org/10.1128/CMR.00037-18.
Hrabák J., Chudáčková E., Walková R. (2013) Matrix-Assisted Laser Desorption Ionization–Time of Flight (MALDI-TOF) Mass Spectrometry for Detection of Antibiotic Resistance Mechanisms: from Research to Routine Diagnosis. Clin. Microbiol. Rev., vol. 26, no. 1, pp. 103 LP – 114. https://doi.org/10.1128/CMR.00058-12.
Johansson Å.,. Nagy E., Sóki J. (2014) Instant screening and verification of carbapenemase activity in Bacteroides fragilis in positive blood culture, using matrix-assisted laser desorption ionization--time of flight mass spectrometry. J. Med. Microbiol., vol. 63, no. Pt 8, pp. 1105–1110. https://doi.org/10.1099/jmm.0.075465-0.
Josten M. et al. (2014) Identification of agr-positive methicillin-resistant Staphylococcus aureus harbouring the class A mec complex by MALDI-TOF mass spectrometry. Int. J. Med. Microbiol., vol. 304, no. 8, pp. 1018–1023. https://doi.org/10.1016/j.ijmm.2014.07.005.
Imperi F. et al. (2009) Analysis of the periplasmic proteome of Pseudomonas aeruginosa, a metabolically versatile opportunistic pathogen. Proteomics, vol. 9, no. 7, pp. 1901–1915. https://doi.org/10.1002/pmic.200800618.
Beceiro A. et al. (2011) Phosphoethanolamine Modification of Lipid A in Colistin-Resistant Variants of Acinetobacter baumannii. Antimicrob. Agents Chemother., vol. 55, no. 7, pp. 3370 LP – 3379. https://doi.org/10.1128/AAC.00079-11.
Pusch W., Wurmbach J.-H., Thiele H., Kostrzewa M. (2002) MALDI-TOF mass spectrometry-based SNP genotyping. Pharmacogenomics, vol. 3, no. 4, pp. 537–548. https://doi.org/10.1517/14622416.3.4.537.
Jung J. S.et al. (2014) Rapid detection of antibiotic resistance based on mass spectrometry and stable isotopes. Eur. J. Clin. Microbiol. Infect. Dis., vol. 33, no. 6, pp. 949–955. https://doi.org/ 10.1007/s10096-013-2031-5.
Lange C., Schubert S., Jung J., Kostrzewa M., Sparbier K. (2014) Quantitative Matrix-Assisted Laser Desorption Ionization–Time of Flight Mass Spectrometry for Rapid Resistance Detection. J. Clin. Microbiol., vol. 52, no. 12, pp. 4155 LP – 4162. https://doi.org/ 10.1128/JCM.01872-14.
Cohen A. et al. (2015) A multifaceted ‘omics’ approach for addressing the challenge of antimicrobial resistance. Future Microbiol., vol. 10, no. 3, pp. 365–376. https://doi.org/ 10.2217/fmb.14.127.
Barantsevich E.P., Barantsevich N. E. (2014) Maldi-tof mass spectrometry in clinical microbiology. Transl. Med., vol. 3, pp. 23–28. https://doi.org/ 10.18705/2311-4495-2014-0-3-23-28.
