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Characterization and prediction of the mechanism of action of antibiotics through NMR metabolomics.

Hoerr V, Duggan GE, Zbytnuik L, Poon KK, Große C, Neugebauer U, Methling K, Löffler B, Vogel HJ - BMC Microbiol. (2016)

Bottom Line: At the same time the numbers of new antibiotics reaching the market have decreased.Within the frame of our study the effects of five different and well-known antibiotic classes on the bacterial metabolome were investigated both by intracellular fingerprint and extracellular footprint analysis.While cell cultures affected by antibiotics that act on intracellular targets showed class-specific fingerprints, the metabolic footprints differed significantly only when antibiotics that target the cell wall were applied.

View Article: PubMed Central - PubMed

Affiliation: Institute of Medical Microbiology, Jena University Hospital, Erlanger Allee 101, D-07747, Jena, Germany. verena.hoerr@uni-jena.de.

ABSTRACT

Background: The emergence of antibiotic resistant pathogenic bacteria has reduced our ability to combat infectious diseases. At the same time the numbers of new antibiotics reaching the market have decreased. This situation has created an urgent need to discover novel antibiotic scaffolds. Recently, the application of pattern recognition techniques to identify molecular fingerprints in 'omics' studies, has emerged as an important tool in biomedical research and laboratory medicine to identify pathogens, to monitor therapeutic treatments or to develop drugs with improved metabolic stability, toxicological profile and efficacy. Here, we hypothesize that a combination of metabolic intracellular fingerprints and extracellular footprints would provide a more comprehensive picture about the mechanism of action of novel antibiotics in drug discovery programs.

Results: In an attempt to integrate the metabolomics approach as a classification tool in the drug discovery processes, we have used quantitative (1)H NMR spectroscopy to study the metabolic response of Escherichia coli cultures to different antibiotics. Within the frame of our study the effects of five different and well-known antibiotic classes on the bacterial metabolome were investigated both by intracellular fingerprint and extracellular footprint analysis. The metabolic fingerprints and footprints of bacterial cultures were affected in a distinct manner and provided complementary information regarding intracellular and extracellular targets such as protein synthesis, DNA and cell wall. While cell cultures affected by antibiotics that act on intracellular targets showed class-specific fingerprints, the metabolic footprints differed significantly only when antibiotics that target the cell wall were applied. In addition, using a training set of E. coli fingerprints extracted after treatment with different antibiotic classes, the mode of action of streptomycin, tetracycline and carbenicillin could be correctly predicted.

Conclusion: The metabolic profiles of E. coli treated with antibiotics with intracellular and extracellular targets could be separated in fingerprint and footprint analysis, respectively and provided complementary information. Based on the specific fingerprints obtained for different classes of antibiotics, the mode of action of several antibiotics could be predicted. The same classification approach should be applicable to studies of other pathogenic bacteria.

No MeSH data available.


Related in: MedlinePlus

Multivariate statistical analysis of metabolic fingerprints and footprints derived from E. coli cultures after antibiotic treatment. a PLS-DA scores plot (3 components, R2 = 0.76, Q2 = 0.65) of intracellular metabolic profiles of E. coli cultures after incubation with antibiotics with intra- (ciprofloxacin, kanamycin, doxycycline) and extracellular (ampicillin, cefalexin) action for 30 min. b PLS-DA scores plot (5 components, R2 = 0.88, Q2 = 0.49) of fingerprints treated with ampicillin and cefalexin compared to controls and c PLS-DA scores plot of footprints corresponding to a (7 components, R2 = 0.72, Q2 = 0.48). N = 6 for each compound
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Fig3: Multivariate statistical analysis of metabolic fingerprints and footprints derived from E. coli cultures after antibiotic treatment. a PLS-DA scores plot (3 components, R2 = 0.76, Q2 = 0.65) of intracellular metabolic profiles of E. coli cultures after incubation with antibiotics with intra- (ciprofloxacin, kanamycin, doxycycline) and extracellular (ampicillin, cefalexin) action for 30 min. b PLS-DA scores plot (5 components, R2 = 0.88, Q2 = 0.49) of fingerprints treated with ampicillin and cefalexin compared to controls and c PLS-DA scores plot of footprints corresponding to a (7 components, R2 = 0.72, Q2 = 0.48). N = 6 for each compound

Mentions: Ampicillin, cefalexin, doxycycline, ciprofloxacin and kanamycin are five antibiotics with different modes of action (Table 1). Ampicillin and cefalexin affect cell wall biosynthesis, doxycycline and kanamycin inhibit intracellular protein synthesis while ciprofloxacin inhibits DNA synthesis. To examine the metabolic effect of these antibiotics on E. coli, 1H NMR metabolite profiles of cell extracts were compared to those of controls treated with solvent (Fig. 3a, b). PLS-DA of the metabolic fingerprint profiles (Fig. 3a) resulted in a R2 and Q2 value of 76 and 65 % respectively for the first three components. Antibiotics with intracellular and extracellular modes of action were clearly separated along the first component which explained 32 % of the total variation. Antibiotics with different intracellular targets could be clearly separated from each other in different clusters. Comparison between 1H NMR-spectra of cell extracts derived from different intracellular targeted antibiotics showed changed levels of alanine, glutamate, acetamide as well as of energy metabolites such as ethanol, citrate, formate and isobutyrate (Fig. 4a, b). Based on the results of the corresponding PLS-DA, significantly increased and decreased levels of amino acids (Fig. 4c), energy metabolites (Fig. 4d) and stress induced metabolites (Fig. 4e) could additionally be identified and quantified. In contrast to antibiotics with intracellular mode of action, the effects of cell wall inhibitors on the metabolic fingerprint profiles were less pronounced and antibiotic profiles were similar to those of controls (Fig. 3a; b, 5 components, R2 = 0.88, Q2 = 0.49).Fig. 3


Characterization and prediction of the mechanism of action of antibiotics through NMR metabolomics.

Hoerr V, Duggan GE, Zbytnuik L, Poon KK, Große C, Neugebauer U, Methling K, Löffler B, Vogel HJ - BMC Microbiol. (2016)

Multivariate statistical analysis of metabolic fingerprints and footprints derived from E. coli cultures after antibiotic treatment. a PLS-DA scores plot (3 components, R2 = 0.76, Q2 = 0.65) of intracellular metabolic profiles of E. coli cultures after incubation with antibiotics with intra- (ciprofloxacin, kanamycin, doxycycline) and extracellular (ampicillin, cefalexin) action for 30 min. b PLS-DA scores plot (5 components, R2 = 0.88, Q2 = 0.49) of fingerprints treated with ampicillin and cefalexin compared to controls and c PLS-DA scores plot of footprints corresponding to a (7 components, R2 = 0.72, Q2 = 0.48). N = 6 for each compound
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4862084&req=5

Fig3: Multivariate statistical analysis of metabolic fingerprints and footprints derived from E. coli cultures after antibiotic treatment. a PLS-DA scores plot (3 components, R2 = 0.76, Q2 = 0.65) of intracellular metabolic profiles of E. coli cultures after incubation with antibiotics with intra- (ciprofloxacin, kanamycin, doxycycline) and extracellular (ampicillin, cefalexin) action for 30 min. b PLS-DA scores plot (5 components, R2 = 0.88, Q2 = 0.49) of fingerprints treated with ampicillin and cefalexin compared to controls and c PLS-DA scores plot of footprints corresponding to a (7 components, R2 = 0.72, Q2 = 0.48). N = 6 for each compound
Mentions: Ampicillin, cefalexin, doxycycline, ciprofloxacin and kanamycin are five antibiotics with different modes of action (Table 1). Ampicillin and cefalexin affect cell wall biosynthesis, doxycycline and kanamycin inhibit intracellular protein synthesis while ciprofloxacin inhibits DNA synthesis. To examine the metabolic effect of these antibiotics on E. coli, 1H NMR metabolite profiles of cell extracts were compared to those of controls treated with solvent (Fig. 3a, b). PLS-DA of the metabolic fingerprint profiles (Fig. 3a) resulted in a R2 and Q2 value of 76 and 65 % respectively for the first three components. Antibiotics with intracellular and extracellular modes of action were clearly separated along the first component which explained 32 % of the total variation. Antibiotics with different intracellular targets could be clearly separated from each other in different clusters. Comparison between 1H NMR-spectra of cell extracts derived from different intracellular targeted antibiotics showed changed levels of alanine, glutamate, acetamide as well as of energy metabolites such as ethanol, citrate, formate and isobutyrate (Fig. 4a, b). Based on the results of the corresponding PLS-DA, significantly increased and decreased levels of amino acids (Fig. 4c), energy metabolites (Fig. 4d) and stress induced metabolites (Fig. 4e) could additionally be identified and quantified. In contrast to antibiotics with intracellular mode of action, the effects of cell wall inhibitors on the metabolic fingerprint profiles were less pronounced and antibiotic profiles were similar to those of controls (Fig. 3a; b, 5 components, R2 = 0.88, Q2 = 0.49).Fig. 3

Bottom Line: At the same time the numbers of new antibiotics reaching the market have decreased.Within the frame of our study the effects of five different and well-known antibiotic classes on the bacterial metabolome were investigated both by intracellular fingerprint and extracellular footprint analysis.While cell cultures affected by antibiotics that act on intracellular targets showed class-specific fingerprints, the metabolic footprints differed significantly only when antibiotics that target the cell wall were applied.

View Article: PubMed Central - PubMed

Affiliation: Institute of Medical Microbiology, Jena University Hospital, Erlanger Allee 101, D-07747, Jena, Germany. verena.hoerr@uni-jena.de.

ABSTRACT

Background: The emergence of antibiotic resistant pathogenic bacteria has reduced our ability to combat infectious diseases. At the same time the numbers of new antibiotics reaching the market have decreased. This situation has created an urgent need to discover novel antibiotic scaffolds. Recently, the application of pattern recognition techniques to identify molecular fingerprints in 'omics' studies, has emerged as an important tool in biomedical research and laboratory medicine to identify pathogens, to monitor therapeutic treatments or to develop drugs with improved metabolic stability, toxicological profile and efficacy. Here, we hypothesize that a combination of metabolic intracellular fingerprints and extracellular footprints would provide a more comprehensive picture about the mechanism of action of novel antibiotics in drug discovery programs.

Results: In an attempt to integrate the metabolomics approach as a classification tool in the drug discovery processes, we have used quantitative (1)H NMR spectroscopy to study the metabolic response of Escherichia coli cultures to different antibiotics. Within the frame of our study the effects of five different and well-known antibiotic classes on the bacterial metabolome were investigated both by intracellular fingerprint and extracellular footprint analysis. The metabolic fingerprints and footprints of bacterial cultures were affected in a distinct manner and provided complementary information regarding intracellular and extracellular targets such as protein synthesis, DNA and cell wall. While cell cultures affected by antibiotics that act on intracellular targets showed class-specific fingerprints, the metabolic footprints differed significantly only when antibiotics that target the cell wall were applied. In addition, using a training set of E. coli fingerprints extracted after treatment with different antibiotic classes, the mode of action of streptomycin, tetracycline and carbenicillin could be correctly predicted.

Conclusion: The metabolic profiles of E. coli treated with antibiotics with intracellular and extracellular targets could be separated in fingerprint and footprint analysis, respectively and provided complementary information. Based on the specific fingerprints obtained for different classes of antibiotics, the mode of action of several antibiotics could be predicted. The same classification approach should be applicable to studies of other pathogenic bacteria.

No MeSH data available.


Related in: MedlinePlus