GC-MS-based metabolic profiling of Escherichia coli exposed to subinhibitory concentration of enoxacin

Objective: The rapid development of bacterial resistance to antibiotics is one of the most recent threats to human health. Understanding the metabolism of pathogens is essential to gain insights into the adaptation strategies that are required to deal with the host environment during infection and to identify new drug targets. In recent years omics technologies have offered new routes to understand adaptation and resistance mechanisms of pathogens against antibiotics at molecular level. In present work, we analyzed the metabolic response of Escherichia coli during treatment with enoxacin to identify metabolic adapdation processes. Methods: Gas chromatography/Mass spectroscopy (GC/MS) based metabolomics approach was used for metabolomics analysis. Metabolites were extracted using methanol: water co-solvent system. After derivatization process, Metabolites separated in GC column and analyzed in MS. MS-DIAL metabolomics and lipidomics platform was performed to analyze raw GC/ MS data. Metabolites were identified with retention index database and quantified relatively between control and enoxacin-treated groups. Statistically significant altered metabolites were investigated via pathway enrichment analysis. Results: Metabolite profile of control and enoxacintreated groups were compared to observe cellular adaptation against the antibiotic stress. Principal component analysis of GC/MS results showed that there was a dramatic shift at general metabolome structure under antibiotic stress. We identified 92 metabolites and statistical analysis indicated 36 metabolites altered significantly between experimental groups. Pathway enrichment analysis showed that amino acid biosynthesis, glycine, serine, and threonine metabolism, Aminoacyl-tRNA metabolism, purine metabolism, galactose metabolism was induced in E. coli exposed to subinhibitory concentration of enoxacin. Conclusion: In present study, we investigated metabolic adapdation of E. coli against enoxacininduced stress. Our findings may contribute to current literature to expand the understanding of bacteriaantibiotic interactions at metabolome level.

Enoksasin subinhibitör konsantrasyonu maruziyeti altında Escherichia coli’nin GC-MS bazlı metabolomik profillemesi

Amaç: Antibiyotiklere karşı hızla gelişen bakteri direnci insan sağlığını etkileyen en önemli tehlikelerden biri haline gelmiştir. Patojenlerin metabolizmalarının anlaşılması konak ortama karşı gerekli metabolik adaptasyonların anlaşılmasında ve yeni antimikrobiyal hedeflerin belirlenmesinde oldukça önemlidir. Son yıllarda gelişen omik teknolojiler patojenlerin antibiyotiklere karşı adaptasyon ve direnç süreçlerinin moleküler düzeyde incelenmesinde yeni fırsatlar sunmuştur. Bu çalışmada Escherichia coli’nin enoksasine karşı verdiği metabolik cevabı araştırarak metabolik adaptasyon süreçlerini belirlemeye çalıştık. Yöntem: Bu çalışmada metabolomik analiz için gaz kromatografisi/kütle spektroskopisi (GC/MS) bazlı metabolomik yaklaşım kullanılmıştır. Metabolitlerin ekstraksiyonunda methanol: su çözücü karışımı kullanılmıştır. Türevlendirme işlemi sonrasında metabolitler önce GC kolonunda ayrışmış daha sonrasında kütle spektroskopisinde analiz edilmiştir. Elde edilen ham GC/MS verilerinin biyoinformatik analizleri için MSDIAL metabolomik ve lipidomik platform kullanılmıştır. Metabolitlerin yapıları alıkonma indeksi veri bankasına göre yapılmıştır. İki grup arasında anlamlı olarak değişen metabolitler belirlendikten sonra yolak analizleri ile değerlendirmeler yapılmıştır. Bulgular: Bu çalışmada metabolik adaptasyonu anlamak için kontrol ve enoksasin maruziyeti altında bulunan E. coli hücrelerinin metabolit profilleri karşılaştırılmıştır. Temel bileşenler analizi (PCA) sonuçları enoksasin maruziyeti altında E. coli hücrelerinin metabolit yapısının dramatik şekilde değiştiğini göstermiştir. Yapılan analizlerde toplam 92 metabolitin yapısı aydınlatılmış ve bunlardan 36 tanesinin istatistiksel olarak anlamlı şekilde değiştiği belirlenmiştir. Miktarı değişen metabolitler yolak analizleri ile değerlendirilmiş ve enoksasin maruziyeti altında aminoasit biyosentezi, aminoaçiltRNA metabolizması, pürin metabolizması, glisin, serin ve treonin metabolizması ve galaktoz metabolizması gibi hücresel süreçlerin farklılaştığı tespit edilmiştir. Sonuç: Elde edilen sonuçlar enoksasin maruziyeti altında E. coli’de meydana gelen metabolik değişiklikleri ve antibiyotik kaynaklı stres koşullarında nasıl adapte olduğu hakkında bilgi sunmaktadır. Yapılan çalışmanın sonuçları antibiyotik-bakteri ilişkisinin anlaşılması ile ilgili olarak literatüre ve gelecek çalışmalara katkıda bulunacaktır.

PDF

___

1. Golkar Z, Bagasra O, Pace DG. Bacteriophage therapy: a potential solution for the antibiotic resistance crisis. J Infect Dev Ctries. 2014;8(2):129- 36.

2. Mills SD. When will the genomics investment pay off for antibacterial discovery? Biochem Pharmacol. 2006;71(7):1096-102.

3. Moloney MG. Natural Products as a Source for Novel Antibiotics. Trends Pharmacol Sci. 2016;37(8):689- 701.

4. McGettigan PA. Transcriptomics in the RNA-seq era. Curr Opin Chem Biol. 2013;17(1):4-11.

5. Pulido MR, Garcia-Quintanilla M, Gil-Marques ML, McConnell MJ. Identifying targets for antibiotic development using omics technologies. Drug Discov Today. 2016;21(3):465-72.

6. Vranakis I, Goniotakis I, Psaroulaki A, Sandalakis V, Tselentis Y, Gevaert K, et al. Proteome studies of bacterial antibiotic resistance mechanisms. J Proteomics. 2014;97:88-99.

7. Hoerr V, Duggan GE, Zbytnuik L, Poon KK, Grosse C, Neugebauer U, et al. Characterization and prediction of the mechanism of action of antibiotics through NMR metabolomics. BMC Microbiol. 2016;16:82.

8. Redgrave LS, Sutton SB, Webber MA, Piddock LJ. Fluoroquinolone resistance: mechanisms, impact on bacteria, and role in evolutionary success. Trends Microbiol. 2014;22(8):438-45.

9. Thabet L, Memmi M, Turki A, Messadi AA. The impact of fluoroquinolones use on antibiotic resistance in an intensive care burn department. Tunis Med. 2010;88(10):696-9.

10. Li W, Zhang S, Wang X, Yu J, Li Z, Lin W, et al. Systematically integrated metabonomic-proteomic studies of Escherichia coli under ciprofloxacin stress. J Proteomics. 2018;179:61-70.

11. Erickson KE, Otoupal PB, Chatterjee A. Transcriptome-Level Signatures in Gene Expression and Gene Expression Variability during Bacterial Adaptive Evolution. mSphere. 2017;2(1).

12. Patkari M, Mehra S. Transcriptomic study of ciprofloxacin resistance in Streptomyces coelicolor A3(2). Mol Biosyst. 2013;9(12):3101-16.

13. Gonulalan EM, Nemutlu E, Bayazeid O, Kocak E, Yalcin FN, Demirezer LO. Metabolomics and proteomics profiles of some medicinal plants and correlation with BDNF activity. Phytomedicine. 2019:152920.

14. Lu H, Que Y, Wu X, Guan T, Guo H. Metabolomics deciphered metabolic reprogramming required for biofilm formation. Sci Rep. 2019;9(1):13160.

15. Li T, Zhan Z, Lin Y, Lin M, Xie Q, Chen Y, et al. Biosynthesis of amino acids in xanthomonas oryzae pv. oryzae is essential to Its pathogenicity. Microorganisms. 2019;7(12).

16. Le CF, Gudimella R, Razali R, Manikam R, Sekaran SD. Transcriptome analysis of Streptococcus pneumoniae treated with the designed antimicrobial peptides, DM3. Sci Rep. 2016;6:26828.

17. Maass S, Otto A, Albrecht D, Riedel K, TrautweinSchult A, Becher D. Proteomic signatures of Clostridium difficile stressed with metronidazole, vancomycin, or fidaxomicin. Cells. 2018;7(11).

18. Zampieri M, Zimmermann M, Claassen M, Sauer U. Nontargeted metabolomics reveals the multilevel response to antibiotic perturbations. Cell Rep. 2017;19(6):1214-28.

19. Cheng ZX, Guo C, Chen ZG, Yang TC, Zhang JY, Wang J, et al. Glycine, serine and threonine metabolism confounds efficacy of complement-mediated killing. Nat Commun. 2019;10(1):3325.

20. Ye JZ, Lin XM, Cheng ZX, Su YB, Li WX, Ali FM, et al. Identification and efficacy of glycine, serine and threonine metabolism in potentiating kanamycinmediated killing of Edwardsiella piscicida. J Proteomics. 2018;183:34-44.

21. Schelli K, Zhong F, Zhu J. Comparative metabolomics revealing Staphylococcus aureus metabolic response to different antibiotics. Microb Biotechnol. 2017;10(6):1764-74.,

22. Kim S, Lee SW, Choi EC, Choi SY. AminoacyltRNA synthetases and their inhibitors as a novel family of antibiotics. Appl Microbiol Biotechnol. 2003;61(4):278-88.

23. Dorries K, Schlueter R, Lalk M. Impact of antibiotics with various target sites on the metabolome of Staphylococcus aureus. Antimicrob Agents Chemother. 2014;58(12):7151-63.

24. Cameron JC, Pakrasi HB. Glutathione facilitates antibiotic resistance and photosystem I stability during exposure to gentamicin in cyanobacteria. Appl Environ Microbiol. 2011;77(10):3547-50.

25. Iyer R, Delcour AH. Complex inhibition of OmpF and OmpC bacterial porins by p olyamines. J Biol Chem. 1997;272(30):18595-601.

26. Sarathy JP, Lee E, Dartois V. Polyamines inhibit porin-mediated fluoroquinolone uptake in mycobacteria. PLoS One. 2013;8(6):e65806.

27. Carlson-Banning KM, Chou A, Liu Z, Hamill RJ, Song Y, Zechiedrich L. Toward repurposing ciclopirox as an antibiotic against drug-resistant Acinetobacter baumannii, Escherichia coli, and Klebsiella pneumoniae. PLoS One. 2013;8(7):e69646.

28. Chai Y, Beauregard PB, Vlamakis H, Losick R, Kolter R. Galactose metabolism plays a crucial role in biofilm formation by Bacillus subtilis. mBio. 2012;3(4):e00184-12.

29. Yang Y, Mi J, Liang J, Liao X, Ma B, Zou Y, et al. Changes in the carbon metabolism of Escherichia coli during the evolution of doxycycline resistance. Front Microbiol. 2019;10:2506.

30. Lin X, Kang L, Li H, Peng X. Fluctuation of multiple metabolic pathways is required for Escherichia coli in response to chlortetracycline stress. Mol Biosyst. 2014;10(4):901-8.

31. Su YB, Peng B, Li H, Cheng ZX, Zhang TT, Zhu JX, et al. Pyruvate cycle increases aminoglycoside efficacy and provides respiratory energy in bacteria. Proc Natl Acad Sci U S A. 2018;115(7):E1578-E87.