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LITHUANIAN UNIVERSITY OF HEALTH SCIENCES
MEDICAL ACADEMY
FACULTY OF PHARMACY
DEPARTMENT OF DRUG CHEMISTRY
JALAL K. NAOUS
THE IMPACT OF TRIMETHOPRIM AND NORSULFAZOL ON
THE METABOLIC PROFILE OF THE S.AUREUS (04-02981).
Final Master‘s thesis
Supervisor
Prof. Dr. Hiliaras Rodovičius
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LITHUANIAN UNIVERSITY OF HEALTH SCIENCES
MEDICAL ACADEMY
FACULTY OF PHARMACY
DEPARTMENT OF DRUG CHEMISTRY
APPROVED BY:
Dean of the Faculty of Pharmacy Prof. Dr. Ramunė Morkūnienė Date: 2018
THE IMPACT OF TRIMETHOPRIM AND NORSULFAZOL ON THE METABOLIC PROFILE OF THE S.AUREUS (04-02981)
Final Master‘s thesis
Supervisor Prof. Dr. Hiliaras Rodovičius Date: 2018
Reviewer The thesis was performed by
Master‘s student
Date: 2018 Jalal Khoder Naous Date: 2018
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Table of Contents
SUMMARY... 5 SANTRAUKA... 6 GRATITUDE ... 7 ABBREVIATIONS LIST... 8 TERMS ... 9 INTRODUCTION ... 10THE AIM AND OBJECTIVES OF THE THESIS ... 12
LITERATURE REVIEW ... 13
Metabolomics piece and pattern (sample) preparation. ... 15
Watering, freshening and extraction. ... 15
Derivatization. ... 15
Chromatography analysis. ... 17
Datum studies and dissection. ... 17
Folate biosynthesis pathway ... 18
LC-MS-based metabolomics ... 18
LC-MS/MS-based metabolite identification ... 20
LC-MS and MS/MS data variability ... 21
MATERIALS AND METHODS ... 22
Chemicals and Reagents ... 22
Sample Preparation ... 22
Chromatographic Method Evaluation ... 23
Optimized LC−MS Methods ... 24
Software for data analysis ... 25
Data Analysis Workflow ... 25
Statistical and Bioinformatics Analysis ... 26
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LC – MS analysis off S. aureus metabolic profile and metabolic fingerprint. ... 28
The impact of antibiotics on S. aureus metabolic profile ... 30
Discussion ... 45
CONCLUSIONS ... 46
PRACTICAL RECOMMENDATIONS ... 47
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SUMMARY
The impact of trimethoprim and norsulfazol on the metabolic profile of the S. aureus (04-02981). Faculty of Pharmacy V course student Jalal Khoder Naous. Supervisor prof. Dr. Hiliaras Rodovičius. Faculty of Pharmacy, Department of Drug Chemistry, Kaunas, 2018.
Aim: to establish the compared metabolic profiles of S. aureus strains (04-02981) treated with
trimethoprim and norsulfazol.
Objective: to perform antibiotic stress responses on S. aureus strains (04-02981) using trimethoprim
and norsulfazol. To identify the growth limiting metabolites resulting from a S. aureus strains (04-02981) batches after treatment with antibiotics (trimethoprim and norsulfazol). To elucidate the metabolic effects of treatment with antibiotics in S. aureus strains (04-029811) using LC-MS as metabolic fingerprinting, metabolic profiling and footprinting approaches respectively.
Methods: metabolic analysis was performed by LC-MS Q-TOF method. Untreated S. aureus samples
were prepared in 0, 9% NaCl, and treated samples – with 1 µM trimethoprim or 1 µM norsulfazol, respectively. For statistically analysis were chosen principal component analysis models. Metabolic profiling features selection statistical confidence level was chosen p<0.001, value of q<0.001, score of Q>99.995%, mass errors <5 ppm, and retention time <0.1%.
Results: Data were filtered by choosing only the features that were present in all samples in one of
the compared groups (i.e., in all untreated samples or in all samples after treated with one of the antibiotics – trimethoprim or norsulfazol, respectively). Subsequently, accurate masses of variables representing significant differences were searched against databases. Identification of compounds was performed by searching accurate masses of features against the data available in METLIN. After alignment the respective data matrices was showed 18574 and 15756 features for treated with trimethoprim and norsulfazol LC−MS, respectively. LC−MS data was filtered by choosing only those features present in all samples in one of the groups, and thus the data set was reduced to 1482 for trimethoprim and 1242 features for norsulfazol. After paired Student’s t-test, 68 variables were found significant for trimethoprim, 78 for norsulfazol, and LC– MS data were used to build the multivariate models. PCA models with prediction of controls and treated samples for LC−MS in positive mode are created.
Conclusion: The treated samples with two antibiotics trimethoprim and norsulfazol had shown
during testing and analyzing that the S. aureus metabolic profile was been influenced and the two antibiotics have a profound impact on metabolic pathways: DNA, RNA, glycolysis, amino acids and lipids biosynthesis of the S. aureus strain 04-02981.
Recommendations: do more in depth metabolomics change research, identifying all metabolites
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SANTRAUKA
Jalal K. Naous magistro baigiamasis darbas „Trimetoprimo ir norsulfazolio poveikis S. aureus
(04-02981) metabolominiam profiliui“. Mokslinio darbo vadovas prof. dr. Hiliaras Rodovičius. Lietuvos sveikatos mokslų universiteto, Farmacijos fakulteto, Vaistų chemijos katedra, Kaunas 2018.
Tyrimo tikslas: nustatyti ir palyginti metabolominį pokytį S. aureus (04-02981) padermėse veikiant
antibiotikais – trimetoprimu ir norsulfazoliu.
Tyrimo uždaviniai: Sukelti S. aureus (04-02981) padermėms metabolinį stresą veikiant antibiotikais
– trimetoprimu ir norsulfazoliu. Nustatyti bakterijų augimą stabdančius medžiagų apykaitos procesus S. aureus (04-029811) padermėse po paveikimo su antibiotikais (trimetoprimu ir norsulfazoliu). Identifikuoti paveiktą antibiotikais metabolinį S. aureus profilį naudojant tandeminę skysčių chromatografiją su masių spektrometrija.
Tyrimo metodai: Metabolinio profilio analizė atlikta taikant tandeminę skysčių chromatografiją su
masių spektrometrija. Tiksliai masei nustatyti panaudotas masių spektrometrinis kvadrupolinis – lėkio trukmės detektorius. S. aureus mėginiai buvo paruošti neigiamai kontrolei paveikus 0,9% NaCl tirpalu, o teigiamai – paveikus atitinkamai 1 µM antibiotikų (trimetoprimo ir norsulfazolio) tirpalais. Statistinei duomenų analizei pasirinkta pagrindinių komponenčių teorija. Analitės identifikuotos metaboliniame profilyje esant šiems statistiniams patikimumo lygmenims – p<0,001, q<0,001, Q skaičius >99,995%, tikslios masės nuokrypis <5 ppm ir sulaikymo laiko nuokrypis <0,1%.
Tyrimo rezultatai: Duomenų analizėje buvo panaudoti tik tie duomenys, kada analitė buvo tiksliai
identifikuota visuose mėginiuose (tiek paveiktuose izotoniniu vandeniu, tiek antibiotikų tirpalais). Atlikus chromatografinių rezultatų perklojimą buvo nustatyta, kad išviso kontrolėje ir paveiktuose mėginiuose analičių atitikimas atitinkamai trimetoprimo ir norsulfazolio grupėse yra 18574 ir 15756. Panaudojus rekursinės analizės algoritmus buvo atmesta daug nereikšmingų arba netikslių analičių, todėl atitinkamai sumažėjo iki 1482 ir 1242 metabolitų trimetoprimo ir norsulfazolio grupėje. Pritaikius porinį Stjudento t kriterijų statistiškai patikimai identifikuoti 68 ir 78 įvairūs edogeniniai metabolitai atitinkamai trimetoprimo ir norsulfazolio grupėse.
Tyrimo išvados: Apibendrinant metabolinio pokyčio tyrimo rezultatus, nustatyta, jog antimikrobinės
medžiagos (trimetoprimas ir norsulfazolis) turi didelę įtaką S. aureus padermės 04-02981 metabolizmui ir išgyvenimui. Tyrimo metu pastebėta, kad buvo paveikti ne tik DNR ir RNR sintezės keliai slopinant folio rūgšties biologinę sintezę, bet ir kiti. Atliekant metabolinio profilio tyrimą išsiaiškinta, jog trimetoprimas ir norsulfazolis veikia ir į glikolizę, aminorūgščių bei lipidų biosintezę.
Tyrimo rekomendacijos: atlikti detalesnius metabolitų pokyčių tyrimus, identifikuojant visų
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GRATITUDE
First of all, I would like to thank to my supervisor Prof. Dr. Hiliaras Rodovičius for his guidance, great support and kind advice throughout my master‘s thesis research . It was a real privilege and an honour for me to share of his exceptional scientific knowledge but also of his extraordinary human qualities.
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ABBREVIATIONS LIST
1-(NSA): Norsulfazol.2-(TMP): Trimethoprim. 3-(MS): Mass spectrometer.
4-(HPLC): High performance liquid chromatography. 5-(CV): Coefficient of variation.
6-(PCA): Principal component analysis.
7-(LC-MS): Liquid chromatography–mass spectrometry. 8-(ESI): electrospray ionization.
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TERMS
Metabolome: is the global collection of all low molecular weight metabolites that are produced by cells during metabolism, and provides a direct functional readout of cellular activity and physiological status. It reflects the combined exogenous effects of lifestyle and environmental factors, as well as the endogenous effects of genetic, developmental and pathological factors.
Metabolomics: is the global and overall study of the metabolome, the reference and repertoire of
biochemical (or small molecules) have presented in cells, tissues and body fluids. This study of comprehensive “omics” level is a rapidly growing domain that has the possibility and potential to have a deep and passionate influence and impact onto medical practice.
Metabonomics: is defined as "the quantitative measurement of the dynamic multiparametric
metabolic response of living systems to pathophysiological stimuli or genetic modification".
Metabolic fingerprinting: is a rapid and noninvasive analysis, representing a powerful approach for
the characterization of phenotypes and the distinction of specific metabolic states due to environmental alterations.
Mass spectrometer is typically composed of three major parts: ion source, mass analyzer, and
detector.
High performance liquid chromatography, as a versatile separation method, allows separation of
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INTRODUCTION
Bioinformatics apparatuses are desired to perform essential and fundamental functions such as statistical analyses and database implementations. Nowadays, they are also necessary needed for one of the most complicated assignments, helping researches determine which metabolites are the utmost biologically significant. This could be completely achieved out of assisting the correspondence and identification process, diminution feature abundancy, putting forward better candidates for tandem mass spectrometry (MS/MS), speeding up automating the workflow, disinvolving the element list through meta-analysis or multigroup analysis, or using isotopes and discipline mapping.
Meanwhile, metabolomics is the global and overall study of the metabolome, the reference and repertoire of biochemical (or small molecules) have presented in cells, tissues and body fluids. This study of comprehensive “omics” level is a rapidly growing domain that has the possibility and potential to have a deep and passionate influence and impact onto medical practice. The metabolic view produces and provides a significant and quantifiable readout of biochemical state from ordinary physiology to varied pathophysiology in a mode that is often not obvious from gene expression analyses. In meantime, the first thematic and objective of metabolomics ahead biomarker has been invented is to distinguish and recognize the most reliable metabolites that correspond with disease pathogenesis in other disorders of metabolism.
Therefore, one of the most substantial and significant aims and purposes of metabolomics analysis has been to appropriate metabolite analogy so they could be used for extra statistical and acquainted pathway analysis [1, 2].
Besides, steps to confirm the utmost dynamic and functional records and agendas for empirical layout, specimen extraction techniques, and datum conquest have promoted extending strong complex data sets [3–9]. As further is being desired of these data devices such as allocating conformity and biological significance to the countenances, bioinformatics is the region of metabolomics which is presently undergoing to the most prerequisite development .Indeed, some of these metabolites pretended to be disco ordinated in an assortment of infirmity and diseases such as acylcarnitines [7, 10, 11] and fatty acid [6, 12– 14].Likewise, as well as characterizing the right provenance of the biomarkers, it is as well significant to recognize their physiological part and how to exercise and utilize them as therapeutic objectives.
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THE AIM AND OBJECTIVES OF THE THESIS
Aim: to establish the compared metabolic profiles of S. aureus strains (04-02981) treated with trimethoprim and norsulfazol.
Objectives:
1. To perform antibiotic stress responses on S. aureus strains (04-02981) using trimethoprim and norsulfazol.
2. To identify the growth limiting metabolites resulting from a S. aureus strains (04-02981) batches after treatment with antibiotics (trimethoprim and norsulfazol).
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LITERATURE REVIEW
Automatic operation (automated) of metabolomics. The lineament similarity operation is the utmost time-consuming and complicated fraction and portion of the metabolomics workflow. After notes and observation of peaks and statistical dissection and analysis, MS/MS data requires to be obtained for the feature definitive identification and effectiveness, a procedure and process that could need days or might weeks to perform but can be potentially briefed and abbreviated through integration of metabolite profiling and identification into a single freelance and separate mass spectrometry method.
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Figure 1. Principal component analysis. Three different extraction protocols performed on the same
sample. Red line: metaphoshoric acid, green line: ethanol/methanol. Repetitions of the same extraction method (shape code) are near each other, indicating similarity, and extractions performed using different solvents (color code) are farther apart from one other, indicating the differences.
Therefore, in aim at (targeted) analysis, metabolites of attention are chosen beforehand testing with a view to test a special metabolic supposition and criterion chemical must subsist for convinced correspondence.
In addition, classical methods of knowing the quantity have been used and the statistical comparison of patterns is complete and performed per metabolites, such as degradation analysis or Student’s-test.
However, in non-aimed (non-targeted) analyses, metabolites of concern and attention are lighted on when instrumental and data analysis. A metabolic hypothesis may be suggested, but the particular metabolic phenotype is often quite completely, or slightly partially, anonymous.
There are five fundamental and essential proceedings in implementing GC*GC-TOFMS metabolomics researches and studies as summarized in Fig. 2, which contain:
1- Metabolomics piece and pattern (sample) preparation. 2- Watering, freshening and extraction.
3- Derivatization.
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Metabolomics piece and pattern (sample) preparation.
The metabolomics testing and researching could have been preceded by an assortment of tools (e.g., cultured, cells, tissue, urine), it is quite significant that the pattern (sample) elaboration is completely planned and controlled prior starting posterior procedure. For instance, cell samples could be acquired through culture or isolated from liquid samples.
The steadiness and stabilization of pattern could be influenced and impacted by: a) Disintegration, degeneration and degradation.
b) Alteration in pH.
c) Coagulate, thicken and clotting. d) Biochemistry mechanism.
e) It is significant realize, comprehend and divergence.
Foundation and incorporation confluent cultures, enumeration the cells, accurate and exact measuring their weights and corresponding the symmetry (homogenization) of the solid tissue, or schedule and organize the cell cycle are exemplary significance in sample preparation [4, 28, 29].
Watering, freshening and extraction.
Actually, it is used for quick and fast blocking the metabolism. Indeed, in chromatographic researches cellular metabolism should be watered before the admission and injection of the sample. However, subordinate analysis using GC*GC-TOFMS, therefore, the pattern should be cold before the further step (derivatization) and as a matter of choice must be speedy and dynamic in pausing metabolism and appropriate together with subsequent planning and analysis proceedings. The most favorable and optimum period framework for watering is the order of one second, and it is carried out at fading temperature to maintain applicable metabolic information [32].
Derivatization.
There is a type of method which is used for the imitative and derivatization by using methoximation phase which is followed by silylation step by using as reagents such as trimethylsilyl (TMS) or tertbutyldimethylsilyl (TBDMS).
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metabolomics studies. However, silylation reagents and the derivatization results are hydrophobic and which are sensible to mix and hydrolysis by aqueous (water). Therefore, getting ready and standing by the derivatization patterns desires all samples must be completely dehydrated and not wet and totally closed and locked to block and avoid incaution contact with even tiny amounts of steam. Therefore, there are two modes and steps for the procedure of the derizatization: Primary, pyridine has used as solution (solvent) for resolving methoxyamine hydrochloride. The reaction between methoxyamine reagents and ketone groups has taken place. Furthermore, the main rules of methoximation are to raise and accretion the volatility of ketone-consisting metabolites, even more, for opening the ring feature (structure) of carbohydrates and as a result, the chain which has opened conserve intermolecular transformation of hemiacetal carbohydrates to acetal carbohydrates when posterior step of silylation [33].
Figure 2. A general overview of the entire analysis method for metabolomics samples.
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in vapor pressure of metabolite, eliminate possible hydrogen-bonding sites, and also increases the thermal stability [30].
Indeed, the fundamental and essential objective of this procedure is amine functional group, hydroxyl and carboxyl groups [34].
Chromatography analysis.
Peak of capacity as is defined as a metrical of division interpretation which has used for straightening various divisions (separation) in technologies for implementation such as the reason for what is used here which means in metabolomics. For instance, it is a system which measured the numbers of peaks could be theoretically divided and separated in a presented chromatographic run, at unit ruling. In traditional D-GC and utilizing trade auto-injection, a peak ability of ~500 in a 45-min separation is obtained [35].
Likewise, The endeavors to raise the salient (peak) capacity and ability by decreasing and declining the extra column band extending due to admission (injection) could also elevate the salient capacity for conventional 1D-GC (35), however, an another column linked and attached by a calorific (thermal) adjustment system for GC*GC supplies extra chemical selectivity as well as immensely increased salient capacity for a global separation of metabolites [8]. In the meantime, the calorific system in GC*GC expands a genuine period thermal admission method, whereabouts the primary column has a commonalty impact that it is disturb the cryogenic and the calorific totally desorbed over the secondary column in the absence of either wastage, therefore, it is pointed out as gross switcher adjustment [36].
Indeed, in order to make extend and expand the show of the salient (peak), the quick scanning TOFMS has required for the disclosure with a GC*GC division (separation).In fact, representative be extended from ~50 ms to ~200 ms).
Datum studies and dissection.
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Folate biosynthesis pathway
Trimethoprim (TMP) is a pyrimidine derivative that competitively inhibits the production of dihydrofolate reductase. Trimethoprim binds much more avidly to bacterial dihydrofolate reductase than to the mammalian enzyme. The drug's overall effect is based on a sequential blockade of the various steps involved in microbial folate synthesis, which is necessary for the formation of purines and, ultimately, of deoxyribonucleic acid. Metabolomics is the comprehensive analysis of all metabolites in a biological system [33].Another related term, ‘‘metabolomics’’ was coined by Nicholson et al.[16]. to represent studies of changes in metabolic activities in response to pathophysiological stimuli or genetic modifications. Metabolomic investigations have been applied in various research areas including environmental and biological stress studies, functional genomics, biomarker discovery, and integrative systems biology [38, 39].
Metabolic analysis is typically categorized as two complementary methods: targeted and untargeted. The targeted approach focuses on identifying and quantifying selected metabolites (or metabolite classes), such as substrates of an enzyme, direct products of a protein, a particular class of compound or members of a particular pathway. In the targeted approach, the chemical properties of the investigated compounds are known, and sample preparation can be tailored to reduce matrix effects and interference from accompanying compounds.
LC-MS-based metabolomics
Once the metabolites are extracted, metabolomics data are acquired using specific analytical technologies.
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source fragmentation and are considered relatively tolerant to high buffer concentrations. These ionization approaches are complimentary to ESI for the analysis of non-polar and thermally stable compounds such as lipids.
Mass analyzers can be categorized as: quadrupole (e.g. Agilent 6100 Single Quadruple; Thermo MSQ plus), ion trap (IT, e.g. Thermo LTQ; Bruker Dalton amazon Ion trap; Agilent 6300 Ion trap), time-of-flight (TOF, e.g. Bruker Dalton MicrOTOF; AB Sciex Triple TOF; Agilent Accurate mass TOF), Orbitrap (Thermo Scientific) and Fourier transform ion cyclotron (FTICR, e.g. Bruker Apex FTICR; Thermo Scientific FT Ultra). Hybrid or tandem mass spectrometers refer to the combination of two or more analyzers. Modern high resolution mass spectrometers (HRMS), such as FTICR, Orbitrap, and TOF, can provide accurate mass measurements to facilitate metabolite identification and also provide accurate metabolite quantitation. In addition to resolving ions by their m/z values and obtaining estimates of their molecular masses, mass analyzers can further aid metabolite identification by acquiring highly resolved and accurate MS/ MS spectra.
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monolithic chromatography and ultra-performance LC (UPLC)] have achieved significant progress to improve peak resolution and expedite analysis [40].
LC-MS/MS-based metabolite identification
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thereby facilitating metabolite identification. Spectral matching mimics the manual verification of metabolite identity using the MS/MS spectrum. Instead of acquiring the MS/MS spectrum of the authentic compound each time, previously acquired MS/MS spectra of authentic compounds are assembled in a spectral library and used to compare with the spectra acquired from biological samples. Several spectral libraries have already been constructed and open to public[14, 43].
LC-MS and MS/MS data variability
Multiple factors contribute to the variability of LC-MS data. In addition to biological variability, which is inherent in biological studies involving multiple subjects, LC-MS data can exhibit significant variability because of analytical reasons. Sample preparation, instrument condition, or operation environment may introduce variations into the acquired data. The variations include drift of retention times, alteration of intensity values, and to a much less scale, drift of m/z values. To evaluate the analytical variability, it is recommended that QC samples are repeatedly analyzed throughout the entire LC-MS experiment [44].
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MATERIALS AND METHODS
Chemicals and Reagents
Water, methanol, acetonitrile, formic acid, ammonium formate, and ammonium acetate (99.999%), sodium chloride were purchased from Sigma–Aldrich (Schweiz) and were LC−MS grade except as noted. Dimethyl sulfoxide (DMSO), anhydrous (≥99.9%) were purchased from Sigma–Aldrich (Schweiz). ESI-L Low Concentration Tuning Mix 100 ml G1969-85000, LC/MS Performance verification standard, for positive mode G1946-85004 (Reserpine), LC/MS reference mass standard kit G1969-85003, which contains 2 mL ampoules of 5 mM purine, 1 M ammonium formate, 0.5 mM Tris(2,4,6-trifluoromethyl)-1,3,5-triazine (HP–0285), 0.1 mM Hexamethoxyphosphazine (HP–0321), 0.2 mM Hexakis (1H,1H,4H-hexafluorobutyloxy) phosphazine (HP–1221), Hexakis (1H,1H,6H-decafluorohexyloxy) phosphazine 0.2 mM (HP–1821), and 0.5 mM Hexakis (1H,1H,8H-tetradecafluoroctyoxy) phosphazine (HP–2421) were purchased from Agilent (Santa Clara, USA).
Sample Preparation
Bacterial samples were prepared due to previously described experimental protocols by Ting L., Yu Y. and Liu Y [45]:
1. Before processing, samples were assigned ID numbers that deidentified untreated and treated samples from S. aureus (strain 04–02981, proteome ID code UP000008681) [1].
2. 12 x 109 colony forming units (CFUs) of S. aureus (04–02981) microorganism cultures were inoculated (transferred) in to prepared Mueller–Hinton agar. The prepared culture was incubated at 37°C for 24 hours.
3. Antibiotics (trimethoprim and norsulfazol) were dissolved in DMSO to get stock solutions with 10 mM concentration. Stock solution were dissolved again with 7:3 acetonitrile/methanol mix 10 000 times. The final solutions concentrations were prepared 1 µM.
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5. After the incubation period, the untreated and treated S. aureus samples were lysed: mechanically homogenized and sonicated.
6. At the time of assay, control and treated samples were thawed on ice (–25°C). For proteins precipitation in preparation for LC–MS analysis 100 µl of S. aureus (04–02981) was combined with cold (–25°C) 400 µl of 7:3 acetonitrile/methanol mix.
7. The samples were vortexed, and then centrifuged (30 min at 6000g).
8. For reversed–phase (RP) LC−MS analysis, the supernatant was relocated to a clean vial and dried under a stream of nitrogen gas. The dried sample was re–formed in 50 µl of water and methanol and reassigned to an autosampler vial for analysis. All samples were treated in random order and were assigned to a random LC−MS run order using a computerized algorithm.
Chromatographic Method Evaluation
An initial investigation of three untreated and six treated with two different antibiotics S. aureus (04– 02981) samples was performed by LC−MS using reverse phase (RP) HPLC columns: Agilent Zorbax Rapid Resolution High Definition (RRHD) Eclipse Plus C18 1.8–µm, 50×2.1 mm ID, (p/n 959757–902) and Agilent InfinityLab Poroshell 120 Eclipse Plus C18 2.7–µm, 50×3.0 mm ID, (p/n 699975–302).
Table 1. LC – MS metabolic profiling analytical runs parameters.
Parameters Agilent 1260 Infinity LC system
Column Agilent Zorbax Rapid Resolution High Definition (RRHD) Eclipse Plus C18 1.8– µm, 50×2.1 mm ID, (p/n 959757–902)
Column temperature 50°C
Injection volume 1 µl + 10 seconds flush Injection speed 200 µl/min
Mobile phase A: 10 mM ammonium formate in water B: 10 mM ammonium formate in acetonitrile
Gradient 0 min 1 min 20 min 31 min 39 min 40 min
A: 90% 90% 10% 10% 90% 90%
B: 10% 10% 90% 90% 10% 10%
Flow rate 0.35 ml/min
Detection systems Agilent 1260 DAD (G4212B), range from 190 nm to 550 nm, 80 Hz Agilent 6530B Q–TOF mass range from 100 Da to 1700 Da
LC and MS stop time
40 minutes and 39.9 minutes Post time 15 minutes
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excellence. On the basis of these preliminary evaluations, chromatographic methods were further optimized for S. aureus metabolites profiling study using the Agilent Zorbax RRHD Eclipse Plus C18 column. Whole chromatographic parameters are in tables 1 and 2.
Optimized LC−MS Methods
For RP LC−MS, samples were investigated on an Agilent 1260 LC/6530 Q–TOF MS system (Agilent Technologies, Santa Clara, CA) using the 1.8 µm column. Each model was analyzed. For positive ion mode runs, mobile phase A was 100% water with 10 mM ammonium formate and mobile phase B was 100% acetonitrile with 10 mM ammonium formate
Table 2. Mass spectrometry parameters for metabolites detection
Parameters Agilent 6530 Q–TOF MS/MS system
Ionization mode Agilent Jetstream Dual electrospray ionization Ionization polarity Positive ionization
Capillary voltage 4250 V (+) Fragmentor voltage 250 V Drying gas temperature 350°C
Drying gas flow 13 l/min
Sheath gas temperature 280°C
Sheath gas flow 12 l/min
Nebulizer pressure 40 psig
MS scan range 100 – 1700 m/z
MS collision energy 0 – 40 V
MS acquisition rate/time 2 spectra/s; 500 ms/spectrum (Transients/spectrum 6737) MS/MS scan range 100 – 1700 m/z
MS/MS collision energy 0 – 40 V
MS/MS acquisition rate/time 3.25 spectra/s; 307.7 ms/spectrum (Transients/spectrum 3965) Isolation width Narrow (~1.3 m/z)
Reference masses (+): 121.050873 and 922.009798 Reference mass flow 10 µl/min
Instrument mode High Resolution (4 GHz, High Resolution mode)
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capillary voltage 4250 V, with internal reference mass correction enabled. Whole mass spectrometry parameters are in the table above.
Software for data analysis
It was used original data analysis software from Agilent and independent software for metabolic pathway networking from prof. Dr. Falk Schreiber:
1. Agilent MassHunter Qualitative Analysis (Qual) B.07.00; 2. Agilent MassHunter Profinder B.06.00, sp. 1;
3. Agilent Mass Profiler (MP) B.07.01;
4. Agilent MassHunter Molecular Structure Correlator (MSC) B.07.00;
5. Visualization and Analysis of Networks Containing Experimental Data (Vanted).
LC–MS/MS metabolic profiling study workflow:
Figure 3. LC–MS/MS metabolic profiling study workflow. High resolution LC–MS/MS Q–TOF
metabolic profiling study workflow for S. aureus (04–02981) strain, which are at the early growth stationary phase
Data Analysis Workflow
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retention time between samples. The software was configured to allow up to a 0.5 min RT shift or a 20 ppm mass shift between LC−MS runs. To reduce gaps in the data, recursive feature identification was then performed by searching the data a second time with the list of aligned features using the “Find by Formula” algorithm. Then, features that were present in over 70% and control samples were analyzed by principal component analysis (PCA), as described later in the Statistical and Bioinformatics Analysis part and ranked by loading plot scores and eigenvectors. The lists of features obtained from positive mode were ranked separately. The top 200 features from each mode were selected for further analysis. This threshold was chosen to allow a manageable number of features to be validated in a timely manner, while still retaining the vast majority of the features responsible for differentiating treated samples with antibiotics from control. Once the list of features was generated, to identify metabolites present in the samples, the features were searched against an in Agilent and Metlin commercial library of known metabolite standards that had been previously analyzed under identical LC−MS conditions. In many cases, the database searches resulted in multiple possible matches for each feature within a 5–ppm mass error window. Metabolite matches were ranked in order of ascending mass error and, among matches with equivalent mass error, in order of ascending. At this point, remaining features that did not match any database entries were not considered for further evaluation. Putative feature IDs from the database matches were validated or rejected by multiple searches of samples with authentic standards or by acquisition of targeted MS/MS data on the Q–TOF MS, followed by comparison with MS/MS spectra in the databases.
Statistical and Bioinformatics Analysis
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Figure 4. Schematic of the data processing workflow. Following sample extraction and
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RESULTS AND DISCUSSION
LC – MS analysis off S. aureus metabolic profile and metabolic fingerprint.
Raw data (TIC chromatogram has shown in figure 1) was cleaned of background noise and unrelated ions by the Molecular Feature Extraction (MFE) tool in the MassHunter Qualitative Analysis software (Agilent Technologies). The MFE algorithm groups ions related by charge state, isotopic distribution, and/or the presence of adducts and dimers by using the accuracy of the mass measurements. The MFE then creates a list of all possible components as represented by the full TOF mass spectral data. Each compound is characterized by mass, retention time, and abundance. Parameters selected for data extraction by the MFE were similar to those described previously. The background noise limit was set to 300 counts, and to find coeluting adducts of the same feature, the following adduct settings were applied: H+, Na+, [NH4]+, K+ in positive ionization. Neutral loss of water and phosphoric acid was also included.Figure 5. Overlaid TIC chromatogram. This multiple overlaid 3 untreated and 6 treated samples (3
are treated samples with TMP and 3 are treated samples with NSA respectively) total ion compounds chromatograms.
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norsulfazol, respectively). Subsequently, accurate masses of variables representing significant differences were searched against the publicly available databases. Identification of compounds detected by LC−MS was performed by searching accurate masses of features against the data available in METLIN
Figure 6. TCC chromatogram shows the whole compounds which were detected in one TIC analytical run.
After alignment the respective data matrices were showed 18574 and 15756 features for treated with trimethoprim and norsulfazol LC−MS, respectively. LC−MS data was filtered by choosing only those features present in all samples in one of the groups, and thus the data set was reduced to 1482 for trimethoprim and 1242 features for norsulfazol. After paired Student’s t-test, 68 variables were found significant for trimethoprim, 78 for norsulfazol, and LC–MS data were used to build the multivariate models. PCA models with prediction of controls and treated samples for LC−MS in positive mode are created. Good cluster of the treated and untreated groups and no strong outliers according to Hotelling’s T– squared distribution (T2) range plot were observed (figure 3).
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Next, the metabolites profiles of S. aureus (04-02981) which were grown in chemical defined medium (CDM) to late exponential phase (OD600 = 1) were investigated by LC/MS/MS. We were able to identify 59 in Norsufazol-antibiotic and 56 in Trimethoprim-antibiotic in S. aureus samples cultured chemically defined medium( CDM) which they were in general METLIN metabolites. Therefore, we were able to identify 40 and 30 in Norsulfazol (NSA) and Trimethoprim (TMP) respectively. The remaining compounds were quantified relative to CDM by their parent ion intensity [M H] + or other adducts.
By using this new method, all metabolites reported in Table (3) could be detected in a single LC/MS run within 40 minutes. With an automated Agilent MFG or Auto MS/MS features and analysis a more complex list of mass traces could be evaluated from the S.aureus samples.
The comparison chromatogram of a negative control detection with the antibiotic trimethoprim (TMP). The blue color is for TMP has treated the bacterial S. aureus samples and the red color for negative control which has been treated with 0.9 % NaCl solution.
The general chromatogram is shown in that graph down the treated and untreated metabolites abundance (chromatograph intensity) and retention time (RT).
Figure 8. TCC chromatogram between untreated and treated samples with TMP. The
chromatogram is shown the compounds retention time is similar, but the abundances are different because it is related to TMP pharmacological activity on S. aureus (04-02981)
The graph is shown the compounds retention time is similar, but the abundances are different.
The impact of antibiotics on S. aureus metabolic profile
This information could allow to provide a conclusion that the treated bacteria with the anti-bacterial (TMP) that the determine metabolic has changed.
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was detected under particular retention time (17.2) as it is matched with the chromatogram above with accurate mass (488.1773).
The comparison of treated and untreated was (-2.62) down. As well as, for 6-Methyltetrahydropterin was detected under aromatic compound degradation 4-Hydroxyphenylglyoxylate which was related indirectly in folic acid synthesis pathway. The retention time of 6-Methyltetrahydropterin was (19.116) with accurate mass (203.0795).Therefore, the compound p-Coumaroylagmatine with retention time (21.435) and accurate time (258.1462) were participated in folic acid synthesis pathway, even these nitrogen bases like CDPcholine, 2’-Deoxy-5-hydroxymethylcytidine-5’-diphosphate and UDP-2-acetamino-4-dehydro-2,6-dideoxyglucose with retention time RT: 31.183, 23.341 and 24.983 and accurate mass : 470.0962, 324.0624 and 588.0807 with log2(Ta/Ka) change : 0.6 up(Fold change:1.5),0.78 up (Fold change:1.7) and -1.01 down (Fold change:0.5), respectively.
Figure 9. TCC chromatogram between untreated and treated samples with NSA. The
chromatogram is shown the compounds retention time is similar, but the abundances are different because it is related to NSA pharmacological activity on S. aureus (04-02981)
The graph is shown the compounds retention time is similar but the abundances are different.
This information could allow to provide a conclusion that the treated bacteria with the anti-bacterial (Norsulfazol (NSA) that the determine metabolic has changed.
The individual metabolites has a specific retention time to identify the similarity with the standard for data bases (library) and as a result it has matched with the standard.
Example, 10-formyldihydrofolate was detected under particular retention time (17.2) as it is matched with the chromatogram above with accurate mass (488.1773).
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with accurate mass (203.0795).Therefore, the compound p-Coumaroylagmatine with retention time (21.435) and accurate time (258.1462) were participated in folic acid synthesis pathway, even these nitrogen bases like CDPcholine, 2’-Deoxy-5-hydroxymethylcytidine-5’-diphosphate and UDP-2-acetamino-4-dehydro-2,6-dideoxyglucose with retention time RT: 31.183, 23.341 and 24.983 and accurate mass : 470.0962, 324.0624 and 588.0807 with log2(Ta/Ka) change : 0.6 up(Fold change:1.5),0.78 up (Fold change:1.7) and -1.01 down (Fold change:0.5), respectively.
Figure 10. The chromatogram has shown the similarity in pharmacological action of the two antibiotics that we used in our study NSA and TMP
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Figure 11. The comparison of metabolites abundance in treated and untreated S. aureus (04-02981) samples. The blue color has chosen to present the control group in the experiment. The red color
was chosen to present the treated samples.
The relative size of the individual sample or dot has provided the abundance (mass spectrometry register intensity of the analyzing features).
The image above is related to the treated sample with NSA. The graph has shown the change and the comparison between the control (dots with blue color) and the treated samples (dots with red color), it has provided the change of average which means that the antibiotic had effected the DNA of bacteria synthesis. Which is in advance related to these metabolites: CDP-choline, 2’-Deoxy-5-hydroxymethylcytidine-5’-diphosphate and UDP-2-acetamino-4-dehydro-2, 6-dideoxyglucose.
Figure 12. The comparison of metabolites abundance in treated and untreated S. aureus (04-02981) samples. The blue color has chosen to present the control group in the experiment. The red color
was chosen to present the treated samples. The graph has shown the change and the comparison between the control (dots with blue color) and the treated samples (dots with red color), it has provided the change of average which means that the antibiotic had effected the DNA of bacteria synthesis
34
The image above is related to the treated sample with TMP. The graph has shown the change and the comparison between the control (dots with blue color) and the treated samples (dots with red color), it has provided the change of average which means that the antibiotic had effected the DNA of bacteria synthesis. Which is in advance related to these metabolites: CDPcholine, 2’-Deoxy-5-hydroxymethylcytidine-5’-diphosphate and UDP-2-acetamino-4-dehydro-2, 6-dideoxyglucose.
Figure 13. Comparison of average metabolic profile abundance of treated and untreated S. aureus samples. The graph has provided the average of control which is presented on X axe and the experiment
which is presented on Y axe. The black dots color is provide the average of six samples which are treated and untreated. The white dots color has provided statistically unreliable. At the image it rounded with blue circle.
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Figure 14. A comprehensive metabolic profile of peptides library in samples of treated and untreated with NSA in S. aureus. The purple dots which are circled in red circle have provide that the six
samples, three control samples and three treated samples, were been detected for the same metabolites. Figure 14 has provided a comprehensive metabolic profile of peptides library in samples of treated and untreated with NSA.
Figure 15. Six different samples in metabolic profile analysis of S. aureus (04-02981). Purple dots
36
Figure 15 is providing the peptides metabolome in six different samples which are untreated and treated with the NSA antibiotic in S. aureus.
Figure 16. Accurate mass deviation of untreated and treated S. aureus samples. The image has
provided the sample mass difference of the control samples (dots with blue color) and treated samples with NSA (with the red color) in S. aureus.
The deviation of the masses of six samples in normal distribution between (+5 ppm_-5ppm).
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Figure 17. Retention-Time deviation of treated and untreated samples of S. aureus. The image has
shown the sample retention time of untreated sample (blue dots color) and the treated sample with TMP (red dots color) in S. aureus. The standard retention scale has to be between -0.1 and +0.1 as it is marked in the graph.
The analyses of metabolic profile compounds show normal distribution of retention time between six treated and treated samples.
The samples treated and untreated have the same retention time, the difference might of retention time occur if the compound are isomers with same mass.
The table 1 has provided the result of the data analysis of the bacterial metabolites and the change in the bacterial metabolites after the S. aureus had been treated by the antibiotics TMP and NSA.
There are evidence in the changes in the metabolic pathway that has affected by the antibiotics, all the information are shown in the table (3). For example, the information that it is given in this table it shows the metabolites and the related pathways.
P-Coumaroylagmatine-[C04498], L-isoleucyl-L-proline-[C03127], (3Z)-Phycocyanobilin-[C05786] and Dimethylethanolamine-[C04308], these metabolites were related to arginine and proline metabolism (Glycine, serine and threonine metabolism/Glyoxylate and dicarboxylate metabolism) .Furthermore, 10-formyldihydrofolate (dihydrofolate)-[C03204],2'-Deoxy-5-hydroxymethyl-CDP-[C11038],UDP-2-ActD-4d26ddg-[C04613],Malyl-CoA-[C00904],Octanal-[C01545],6-Methyltetrahydropterin
38
and have a profound impact on DNA, RNA and Lipids metabolites of the S. aureus bacteria. Therefore, the metabolites were dominant and effected in the S.aureus which has an obvious changes in fold in NSA than TMP. For example, p-Coumaroylagmatine with changes in folds [-4.3], 10-formyldihydrofolate (dihydrofolate) with changes in folds [-6.3], CDP-choline with changes in folds [2.0], UDP-2-ActD-4d26ddg with change in folds [5.5], L-isoleucyl-L-proline with changes in folds [-5.3], Carbamic acid with changes in folds [-1.9],
(3Z)-Phycocyanobilin with change in folds [-2.0], 4 Hydroxyphenylglyoxylate with changes in folds [-5.1], Pyochelin with changes in folds [2.1] and 5–Me–dCMP with changes in folds [-1.6]. By contrast, the dominant metabolites of changes in folds of treated with TMP, for example, 5–HM–2'–dCDP with changes in folds [7.2], Malyl-CoA with changes in folds [-1.9], Diethyl Oxalpropionate with changes folds [-4.0], Methyl farnesoate with changes folds [-6.9] and 23DHBA with changes folds [-3.4].
39
Table 3. Detected metabolites of S. aureus and changes in fold between control and antimicrobial drugs (TMP and NAS).
Metabolites Metabolite species Observed
mass Theoretical mass Mass error, Δ ppm Retention time Log2(C/TMP) [Changes in folds (C/TMP)] Expr. Log2(C/NSA) [Changes in folds (C/NSA)] Expr. p-Coumaroylagmatine [M+H]+(-H2O) 259.1540 259.15534 5.16 21.435 -2.06 [-4.2] ↓ -2.09 [-4.3] ↓ 10-formyldihydrofolate (dihydrofolate) [M+NH4]+ 489.1824 489.18407 3.42 17.200 -2.62 [-6.1] ↓ -2.65 [-6.3] ↓ CDP-choline [M+H]+ 489.1163 489.11461 -3.46 31.183 0.82 [1.8] ↑ 1.02 [2.0] ↑ 5–HM–2'–dCDP [M+H]+ 418.0418 418.0411 -1.66 23.341 2.84 [7.2] ↑ 1.58 [3.0] ↑ UDP-2-ActD-4d26ddg [M+H]+ 590.0789 590.07828 -1.05 24.983 2.12 [4.3] ↑ 2.46 [5.5] ↑ Malyl-CoA [M+H]+ 884.1333 884.13344 0.12 21.114 -0.95 [-1.9] ↓ -0.64 [-1.6] ↓ L-isoleucyl-L-proline [2M+H]+ 457.3020 457.30206 0.16 20.77 -2.37 [-5.2] ↓ -2.40 [-5.3] ↓ Carbamic acid [2M+H]+ 140.0666 140.06658 -0.13 0.41 -0.7 [-1.6] ↓ -0.9 [-1.9] ↓ (3Z)-Phycocyanobilin [M+H]+ 587.2859 587.28641 0.87 21.122 -0.95 [-1.9] ↓ -0.99 [-2.0] ↓ 3-OH-vitamin K [M+H]+(-H2O) 451.3576 451.35706 -1.2 21.085 -1.5 [-2.8] ↓ -1.53 [-2.9] ↓ 2-OH-vitamin K [M+H]+(-H2O) 451.3571 451.35706 -0.09 22.629 -1.86 [-3.6] ↓ -1.89 [-3.7] ↓ 4-Hydroxyphenylglyoxylate [M+H]+(-H2O) 149.0226 149.02332 4.83 19.993 -2.31 [-5.0] ↓ -2.34 [-5.1] ↓ Pyochelin [M+H]+ 325.0675 325.06751 0.03 26.334 0.86 [1.8] ↑ 1.05 [2.1] ↑
Diethyl Oxalpropionate [M+H]+(-H2O) 185.0809 185.08084 -0.35 21.435 -1.99 [-4] ↓ -1.97 [-3.9] ↓
40
Figure 18. Multivariate statically analysis of S. aureus samples between treated and untreated. The
principle component analysis which has shown the 3 dots with blue and 3 dots with red color that are referenced to untreated and treated with antibiotic respectively. The different locations on graph for the 6 dots were explained that the antibiotics NSA and TMP respectively, had made a profound impact and a significant affect in the S. aureus metabolites.
41
Figure 19. Principle component analysis of S. aureus samples between treated and untreated. The
image provides the correlation and covariation of treated and untreated of S. aureus samples. The dark black dots those are inside the red circle were provide the merge of the treated and untreated samples. The covariance’s axe provides a measure of the force of the correlation between the samples.
The correlation and covariance were presented in the graph that it was supported the statistic metabolites of data analysis of for treated and untreated samples in S. aureus metabolites analyzing change by using two antibiotics TMP and NSA. The dark black dots those are marked by red dashes circle were provided statistically comparing of the treated and untreated samples in S. aureus. The covariance’s axe provides a measure of the strength of the correlation between the samples.
42
Table 4. Detected metabolites and their pathways for S. aureus
Metabolites KEGG entry Related pathways
p-Coumaroylagmatine C04498 Arginine and proline metabolism
10-formyldihydrofolate C03204 Folic acid metabolism (related to one carbon pool by folate)
CDP-choline C00307 Glycerophospholipid metabolism. Glycine, serine and threonine metabolism 5–HM–2'–dCDP C11038 Pyrimidine metabolism (related to DNA synthesis)
UDP-2-ActD-4d26ddg C04613 Amino sugar and nucleotide sugar metabolism
Malyl-CoA C00904 Methane metabolism, Carbon fixation pathways in prokaryotes, Glyoxylate and dicarboxylate metabolism (Energy and DNA metabolism)
L-isoleucyl-L-proline C03127 Arginine and proline metabolism
Carbamic acid C01563 Pyrimidine metabolism (related to DNA synthesis), Nitrogen metabolism (Glyoxylate metabolism, Carbon fixation pathways in prokaryotes)
(3Z)-Phycocyanobilin C05786 Porphyrin and chlorophyll metabolism (Glycine, serine and threonine metabolism) 3-OH-vitamin K C02785 Prenol lipids metabolism
2-OH-vitamin K C02793 Prenol lipids metabolism
4-Hydroxyphenylglyoxylate C03590 Methane metabolism, Carbon fixation pathways in prokaryotes, Glyoxylate and dicarboxylate metabolism (Energy and DNA metabolism), aminobenzoate degradation metabolism (related to citrate cycle and folate biosynthesis)
Pyochelin C12037 Biosynthesis of siderophore group nonribosomal peptides. Biosynthesis of secondary metabolites Diethyl Oxalpropionate C04067 Secondary metabolism of energy metabolism
HAP C05406 Secondary metabolism of energy metabolism
Octanal C01545 Fatty aldehydes metabolism (Fatty acyls metabolism) Methyl farnesoate C16503 Lipid metabolism
Pimelic acid C00666 Biotin metabolism (related to fatty acids and lysine biosynthesis, which important for citrate cycle) Dimethylethanolamine C04308 Glycerophospholipid metabolism
Dodecanamide C13831 Fatty amides metabolism (Fatty acyls metabolism) Tetrahydrobiopterin C00440 Folate biosynthesis
5–Me–dCMP C03495 Folate biosynthesis (also related to energy metabolism)
43
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The metabolites pathways are been shown in the graph to provide the place and how they are changed and the statically bars chart have supplied the change in comparison between the untreated bar chart (red color) and treated with two antibiotics under the two color blue and green for trimethoprim and norsulfazol respectively.
Under purine metabolism, 6-Hydroxymethyl-7,8-dihydropterin (C01300), is shown in the metabolic pathway, is detected and has provided the change and the bars chart has shown that C01300 was changes in fold (up) with trimethoprim and the changes in fold was (down) with norsulfazol in comparison with the untreated (red color) bar. Moreover, tetrahydrobiopterin (C00272) has provided as well the change in fold which they were down for both treated samples
Under phenylalanine, tyrosine and tryptophan biosynthesis, 7, 8-Dihydropterote (C00921), is detected and it has provided the changes in fold as the bars chart are provided and it is as well as the C01300 metabolites that is related to purine metabolism.
Under one carbon pool by folate, dihydrofolate (C00415), the bars chart have provided the changes in fold which were down for the treated samples in comparison to the untreated (red color bar).
Under benzoate degradation, 4-hydroxyphenylglyoxylate (C03590), has provided the change in the folate biosynthesis and the changes in fold were down for both treated samples.
Under nitrogen metabolism, S-Formylglutathione (C01031), has provided the changes in fold which was down for treated with trimethoprim and up for norsulfazol.
Under glyoxylate and dicarboxylate metabolism, which is related in folate biosynthesis, malyl-CoA (C04348), glyoxylate (C00048), 3-Phosphohydroxypyruvate (C03232) and phosphoenolpyruvate (C00074) have provided the change and the impact of the two antibiotics trimethoprim and norsulfazol on the metabolites profile of the bacteria.
45
Discussion
As a result of using trimethoprim and norsulfazol antibiotics to detect the metabolites profile changes in S. aureus, 6-Hydroxymethyl-7-8-dihydropterin (C01300) and tetrahydrobiopterin (C00272), which were provided the change and they are related to purine metabolism in the bacteria as well as in amino acid biosynthesis. However, there are lots of groups of antibiotics which are having a profound impact on the metabolites profile of S.aureus, e.g. beta lactams antibiotics such as rifampicin. Despite the differences of antibiotics groups are belonged to, a majority of the mutants belongs to metabolite production and regulation pathways such as purine biosynthesis. A study finding revealed that five purine biosynthesis genes (purB, purF, purH, purM, and SAUSA300_0147) are involved in S. aureus persistence. Nevertheless, complementation studies show that genes involved in purine biosynthesis are important for antibiotic and stress tolerance in S. aureus. Several mechanisms may be underlying the defect in persistence seen in purine biosynthesis mutants. For example, a defect in the purine biosynthesis pathway may lead to decreased downstream energy production, amino acid biosynthesis and urea cycle activation. Purine biosynthesis has also been shown to be associated with survival in stressed conditions such as vancomycin and daptomycin in other strains of S. aureus. The final step in purine nucleotide synthesis leads to AMP formation, increased AMP, and thus ATP energy levels, which were observed in antibiotic resistant strains. It is hypothesized that increased purine biosynthesis would allow for more energy used in generating polymers, one of the most energy demanding process in bacteria.
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CONCLUSIONS
1. The performance of the S. aureus (04-02981) bacteria stress responses by using the two antibiotic trimethoprim and norsulfazol in comparison with physiological water. The treated samples with two antibiotics trimethoprim and norsulfazol had shown during testing and analyzing that the S. aureus metabolic profile was been influenced and the two antibiotics have a profound impact on metabolic pathways: DNA, RNA, glycolysis, amino acids and lipids biosynthesis of the S. aureus strain 04-02981.
2. According to the results of this research it could be concluded the samples untreated and treated with trimethoprim and norsulfazol were detected metabolic profile changes: malyl-CoA (4.2 and 4.5, respectively in TMP and NSA groups), dihydrofolate (6.1 and 6.3, respectively in TMP and NSA), tetrahydrobiopterin (1.9 and 2, respectively in TMP and NSA).
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PRACTICAL RECOMMENDATIONS
48
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