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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

Cell wall permeability of E. coli after antibiotic treatment investigated by fluorescence microscopy. Fluorescence microscopy of E. coli cultures incubated with streptomycin a and ampicillin b for 5, 15, 30 and 45 min and stained with SYTO9 and propidium iodide (PI). SYTO9 is membrane permeant and generally labels all bacteria in a population with a green fluorescence. PI is characterized by its red fluorescence and replaces the green fluorescence in cells with reduced membrane impermeability. Images after ampicillin treatment showed increased signs of cell debris in the red fluorescence channel at 30 and 45 min time point compared to samples after streptomycin treatment
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Fig6: Cell wall permeability of E. coli after antibiotic treatment investigated by fluorescence microscopy. Fluorescence microscopy of E. coli cultures incubated with streptomycin a and ampicillin b for 5, 15, 30 and 45 min and stained with SYTO9 and propidium iodide (PI). SYTO9 is membrane permeant and generally labels all bacteria in a population with a green fluorescence. PI is characterized by its red fluorescence and replaces the green fluorescence in cells with reduced membrane impermeability. Images after ampicillin treatment showed increased signs of cell debris in the red fluorescence channel at 30 and 45 min time point compared to samples after streptomycin treatment

Mentions: To better understand the metabolic changes, we characterized the permeability of the bacterial membrane by staining the bacteria with SYTO9 and propidium iodide (PI) after incubation with different antibiotics. SYTO9, which is membrane permeant, generally labels all bacteria in a population with a green fluorescence. In contrast, propidium iodide, characterized by its red fluorescence, is excluded by healthy cells due to membrane impermeability. In dead bacterial cells, both the displacement of SYTO9 by propidium iodide due to the stronger affinity of the latter for nucleic acids and the quenching of SYTO9 emission by fluorescence resonance energy transfer (FRET) is responsible for the replacement of the green fluorescence with the red one [37, 38]. The fluorescent bacteria were then quantified by flow cytometry (fluorescence-activated cell sorting, FACS; Fig. 5a, b) and visualized by fluorescence microscopy (Fig. 6a, b). Bacterial samples that had been incubated with cell wall affecting antibiotics such as ampicillin, carbenicillin, and cefalexin showed reduced membrane integrity, which allowed propidium iodide to cross the membrane and resulted in a significant increase in SYTO9/PI double stained population after 45 min (Fig. 5b). CFU counts in contrast were still unaffected after an antimicrobial incubation time of 30 min (Fig. 1b). A comparison of the corresponding dot plots of FSC (Forward SCatter) vs SSC (Side SCatter) for E. coli in the various antibiotics is shown in Fig. 5a. All cell wall affecting antibiotic samples displayed an increase in population outside the P1 gate where intact population of E. coli cells is located. Fluorescence microscopy confirmed the presence of a red fluorescent bacterial population already 15 min post incubation with ampicillin; these cells also showed the first signs of cell debris after an incubation time of 30 and 45 min in contrast to the control or samples treated with intracellular targeting antibiotics such as streptomycin (Fig. 6a, b).Fig. 5


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)

Cell wall permeability of E. coli after antibiotic treatment investigated by fluorescence microscopy. Fluorescence microscopy of E. coli cultures incubated with streptomycin a and ampicillin b for 5, 15, 30 and 45 min and stained with SYTO9 and propidium iodide (PI). SYTO9 is membrane permeant and generally labels all bacteria in a population with a green fluorescence. PI is characterized by its red fluorescence and replaces the green fluorescence in cells with reduced membrane impermeability. Images after ampicillin treatment showed increased signs of cell debris in the red fluorescence channel at 30 and 45 min time point compared to samples after streptomycin treatment
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig6: Cell wall permeability of E. coli after antibiotic treatment investigated by fluorescence microscopy. Fluorescence microscopy of E. coli cultures incubated with streptomycin a and ampicillin b for 5, 15, 30 and 45 min and stained with SYTO9 and propidium iodide (PI). SYTO9 is membrane permeant and generally labels all bacteria in a population with a green fluorescence. PI is characterized by its red fluorescence and replaces the green fluorescence in cells with reduced membrane impermeability. Images after ampicillin treatment showed increased signs of cell debris in the red fluorescence channel at 30 and 45 min time point compared to samples after streptomycin treatment
Mentions: To better understand the metabolic changes, we characterized the permeability of the bacterial membrane by staining the bacteria with SYTO9 and propidium iodide (PI) after incubation with different antibiotics. SYTO9, which is membrane permeant, generally labels all bacteria in a population with a green fluorescence. In contrast, propidium iodide, characterized by its red fluorescence, is excluded by healthy cells due to membrane impermeability. In dead bacterial cells, both the displacement of SYTO9 by propidium iodide due to the stronger affinity of the latter for nucleic acids and the quenching of SYTO9 emission by fluorescence resonance energy transfer (FRET) is responsible for the replacement of the green fluorescence with the red one [37, 38]. The fluorescent bacteria were then quantified by flow cytometry (fluorescence-activated cell sorting, FACS; Fig. 5a, b) and visualized by fluorescence microscopy (Fig. 6a, b). Bacterial samples that had been incubated with cell wall affecting antibiotics such as ampicillin, carbenicillin, and cefalexin showed reduced membrane integrity, which allowed propidium iodide to cross the membrane and resulted in a significant increase in SYTO9/PI double stained population after 45 min (Fig. 5b). CFU counts in contrast were still unaffected after an antimicrobial incubation time of 30 min (Fig. 1b). A comparison of the corresponding dot plots of FSC (Forward SCatter) vs SSC (Side SCatter) for E. coli in the various antibiotics is shown in Fig. 5a. All cell wall affecting antibiotic samples displayed an increase in population outside the P1 gate where intact population of E. coli cells is located. Fluorescence microscopy confirmed the presence of a red fluorescent bacterial population already 15 min post incubation with ampicillin; these cells also showed the first signs of cell debris after an incubation time of 30 and 45 min in contrast to the control or samples treated with intracellular targeting antibiotics such as streptomycin (Fig. 6a, b).Fig. 5

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