Metabolomic experiments usually contain many different steps, each of which can strongly influence the obtained results. In this work, metabolic analyses of six bacterial strains were performed in ...light of three different bacterial cell disintegration methods. Three strains were gram-negative (Pseudomonas aeruginosa, Escherichia coli, and Klebsiella pneumoniae), and three were gram-positive (Corynebacterium glutamicum, Bacillus cereus, and Enterococcus faecalis). For extraction, the methanol-water extraction method (1:1) was chosen. To compare the efficiency of different cell disintegration methods, sonication, sand mill, and tissue lyser were used. For bacterial extract metabolite analysis,
H NMR together with univariate and multivariate analyses were applied. The obtained results showed that metabolite concentrations are strongly dependent on the cell lysing methodology used and are different for various bacterial strains. The results clearly show that one of the disruption methods gives the highest concentration for most identified compounds (e. g. sand mill for E. faecalis and tissue lyser for B. cereus). This study indicated that the comparison of samples prepared by different procedures can lead to false or imprecise results, leaving an imprint of the disintegration method. Furthermore, the presented results showed that NMR might be a useful bacterial strain identification and differentiation method. In addition to disintegration method comparison, the metabolic profiles of each elaborated strain were analyzed, and each exhibited its metabolic profile. Some metabolites were identified by the
H NMR method in only one strain. The results of multivariate data analyses (PCA) show that regardless of the disintegration method used, the strain group can be identified. Presented results can be significant for all types of microbial studies containing the metabolomic targeted and non-targeted analysis.
Metabolomic studies of Pseudomonas aeruginosa Mielko, Karolina Anna; Jabłoński, Sławomir Jan; Milczewska, Justyna ...
World journal of microbiology & biotechnology,
11/2019, Letnik:
35, Številka:
11
Journal Article
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Pseudomonas aeruginosa
is a common, Gram-negative environmental organism. It can be a significant pathogenic factor of severe infections in humans, especially in cystic fibrosis patients. Due to its ...natural resistance to antibiotics and the ability to form biofilms, infection with this pathogen can cause severe therapeutic problems. In recent years, metabolomic studies of
P. aeruginosa
have been performed. Therefore, in this review, we discussed recent achievements in the use of metabolomics methods in bacterial identification, differentiation, the interconnection between genome and metabolome, the influence of external factors on the bacterial metabolome and identification of new metabolites produced by
P. aeruginosa
. All of these studies may provide valuable information about metabolic pathways leading to an understanding of the adaptations of bacterial strains to a host environment, which can lead to new drug development and/or elaboration of new treatment and diagnostics strategies for
Pseudomonas
.
Graphic abstract
The photochromic properties of azobenzene, involving conformational changes occurring upon interaction with light, provide an excellent tool to establish new ways of selective regulation applied to ...biosystems. We report here on the binding of two water-soluble 4-(phenylazo)benzoic acid derivatives (Azo-2N and Azo-3N) with double stranded DNA and demonstrate that the photoisomerization of Azo-3N leads to changes in DNA structure. In particular, we show that stabilization and destabilization of the B-DNA secondary structure can be photochemically induced in situ by light. This photo-triggered process is fully reversible and could be an alternative pathway to control a broad range of biological processes. Moreover, we found that the bicationic Azo-3N exhibited a higher DNA-binding constant than the monocationic Azo-2N pointing out that the number of positive charges along the photosensitive polyamines chain plays a pivotal role in stabilizing the photochrome-DNA complex.
Abstract Volatile organic compounds (VOCs) are metabolites pivotal in determining the aroma of various products. A well-known VOC producer of industrial importance is Saccharomyces cerevisiae , ...partially responsible for flavor of beers and wines. We identified VOCs in beers produced by yeast strains characterized by improved aroma obtained in UV-induced mutagenesis. We observed significant increase in concentration of compounds in strains: 1214uv16 (2-phenylethyl acetate, 2- phenylethanol), 1214uv31 (2-ethyl henxan-1-ol), 1214uv33 (ethyl decanoate, caryophyllene). We observed decrease in production of 2-phenyethyl acetate in strain 1214uv33. Analysis of intracellular metabolites based on 1 H NMR revealed that intracellular phenylalanine concentration was not changed in strains producing more phenylalanine related VOCs (1214uv16 and 1214uv33), so regulation of this pathway seems to be more sophisticated than is currently assumed. Metabolome analysis surprisingly showed the presence of 3-hydroxyisobutyrate, a product of valine degradation, which is considered to be absent in S. cerevisiae . Our results show that our knowledge of yeast metabolism including VOC production has gaps regarding synthesis pathways for individual metabolites and regulation mechanisms. Detailed analysis of 1214uv16 and 1214uv33 may enhance our knowledge of the regulatory mechanisms of VOC synthesis in yeast, and analysis of strain 1214uv31 may reveal the pathway of 2-ethyl henxan-1-ol biosynthesis.
Abstract
There is a clear difference between severe brain damage and brain death. However, in clinical practice, the differentiation of these states can be challenging. Currently, there are no ...laboratory tools that facilitate brain death diagnosis. The aim of our study was to evaluate the utility of serum metabolomic analysis in differentiating coma patients (CP) from individuals with brain death (BD). Serum samples were collected from 23 adult individuals with established diagnosis of brain death and 24 patients in coma with Glasgow Coma Scale 3 or 4, with no other clinical symptoms of brain death for at least 7 days after sample collection. Serum metabolomic profiles were investigated using proton nuclear magnetic resonance (NMR) spectroscopy. The results obtained were examined by univariate and multivariate data analysis (PCA, PLS-DA, and OPLS-DA). Metabolic profiling allowed us to quantify 43 resonance signals, of which 34 were identified. Multivariate statistical modeling revealed a highly significant separation between coma patients and brain-dead individuals, as well as strong predictive potential. The findings not only highlight the potential of the metabolomic approach for distinguishing patients in coma from those in the state of brain death but also may provide an understanding of the pathogenic mechanisms underlying these conditions.
AIM:To evaluate the utility of serum and urine metabolomic analysis in diagnosing and monitoring of inflammatory bowel diseases(IBD).METHODS:Serum and urine samples were collected from 24 patients ...with ulcerative colitis(UC),19 patients with the Crohn’s disease(CD)and 17 healthy controls.The activity of UC was assessed with the Simple Clinical Colitis Activity Index,while the activity of CD was determined using the Harvey-Bradshaw Index.The analysis of serum and urine samples was performed using proton nuclear magnetic resonance(NMR)spectroscopy.All spectra were exported to Matlab for preprocessing which resulted in two data matrixes for serum and urine.Prior to the chemometric analysis,both data sets were unit variance scaled.The differences in metabolite fingerprints were assessed using partial least-squaresdiscriminant analysis(PLS-DA).Receiver operating characteristic curves and area under curves were used to evaluate the quality and prediction performance of the obtained PLS-DA models.Metabolites responsible for separation in models were tested using STATISTICA10 with the Mann-Whitney-Wilcoxon test and the Student’s t test(α=0.05).RESULTS:The comparison between the group of patients with active IBD and the group with IBD in remission provided good PLS-DA models(P value 0.002for serum and 0.003 for urine).The metabolites that allowed to distinguish these groups were:N-acetylated compounds and phenylalanine(up-regulated in serum),low-density lipoproteins and very low-density lipoproteins(decreased in serum)as well as glycine(increased in urine)and acetoacetate(decreased in urine).The significant differences in metabolomic profiles were also found between the group of patients with active IBD and healthy control subjects providing the PLS-DA models with a very good separation(P value<0.001 for serum and 0.003 for urine).The metabolites that were found to be the strongest biomarkers included in this case:leucine,isoleucine,3-hydroxybutyric acid,N-acetylated compounds,acetoacetate,glycine,phenylalanine and lactate(increased in serum),creatine,dimethyl sulfone,histidine,choline and its derivatives(decreased in serum),as well as citrate,hippurate,trigonelline,taurine,succinate and 2-hydroxyisobutyrate(decreased in urine).No clear separation in PLS-DA models was found between CD and UC patients based on the analysis of serum and urine samples,although one metabolite(formate)in univariate statistical analysis was significantly lower in serum of patients with active CD,and two metabolites(alanine and N-acetylated compounds)were significantly higher in serum of patients with CD when comparing jointly patients in the remission and active phase of the diseases.Contrary to the results obtained from the serum samples,the analysis of urine samples allowed to distinguish patients with IBD in remission from healthy control subjects.The metabolites of importance included in this case up-regulated acetoacetate and down-regulated citrate,hippurate,taurine,succinate,glycine,alanine and formate.CONCLUSION:NMR-based metabolomic fingerprinting of serum and urine has the potential to be a useful tool in distinguishing patients with active IBD from those in remission.
Systemic metabolic changes after renal transplantation reflect the key processes that are related to graft accommodation. In order to describe and better understand these changes, the
HNMR based ...metabolomics approach was used. The changes of 47 metabolites in the serum samples of 19 individuals were interpreted over time with respect to their levels prior to transplantation. Considering the specific repeated measures design of the experiments, data analysis was mainly focused on the multiple analyses of variance (ANOVA) methods such as ANOVA simultaneous component analysis and ANOVA-target projection. We also propose here the combined use of ANOVA and classification and regression trees (ANOVA-CART) under the assumption that a small set of metabolites the binary splits on which may better describe the graft accommodation processes over time. This assumption is very important for developing a medical protocol for evaluating a patient's health state. The results showed that besides creatinine, which is routinely used to monitor renal activity, the changes in levels of hippurate, mannitol and alanine may be associated with the changes in renal function during the post-transplantation recovery period. Specifically, the level of hippurate (or histidine) is more sensitive to any short-term changes in renal activity than creatinine.
Pseudomonas aeruginosa is a common human pathogen belonging to the ESKAPE group. The multidrug resistance of bacteria is a considerable problem in treating patients and may lead to increased ...morbidity and mortality rate. The natural resistance in these organisms is caused by the production of specific enzymes and biofilm formation, while acquired resistance is multifactorial. Precise recognition of potential antibiotic resistance on different molecular levels is essential. Metabolomics tools may aid in the observation of the flux of low molecular weight compounds in biochemical pathways yielding additional information about drug-resistant bacteria. In this study, the metabolisms of two P. aeruginosa strains were compared—antibiotic susceptible vs. resistant. Analysis was performed on both intra- and extracellular metabolites. The 1H NMR method was used together with multivariate and univariate data analysis, additionally analysis of the metabolic pathways with the FELLA package was performed. The results revealed the differences in P. aeruginosa metabolism of drug-resistant and drug-susceptible strains and provided direct molecular information about P. aeruginosa response for different types of antibiotics. The most significant differences were found in the turnover of amino acids. This study can be a valuable source of information to complement research on drug resistance in P. aeruginosa.
Urinary volatile compounds (VCs) have been recently assessed for disease diagnoses. They belong to very diverse chemical classes, and they are characterized by different volatilities, polarities and ...concentrations, complicating their analysis via a single analytical procedure. There remains a need for better, lower-cost methods for VC biomarker discovery. Thus, there is a strong need for alternative methods, enabling the detection of a broader range of VCs. Therefore, the main aim of this study was to optimize a simple and reliable liquid-liquid extraction (LLE) procedure for the analysis of VCs in urine using gas chromatography-mass spectrometry (GC-MS), in order to obtain the maximum number of responses. Extraction parameters such as pH, type of solvent and ionic strength were optimized. Moreover, the same extracts were analyzed using Proton Nuclear Magnetic Resonance Spectroscopy (
H-NMR), to evaluate the applicability of a single urine extraction for multiplatform purposes. After the evaluation of experimental conditions, an LLE protocol using 2 mL of urine in the presence of 2 mL of 1 M sulfuric acid and sodium sulphate extracted with dichloromethane was found to be optimal. The optimized method was validated with the external standards and was found to be precise and linear, and allowed for detection of >400 peaks in a single run present in at least 50% of six samples-considerably more than the number of peaks detected by solid-phase microextracton fiber pre-concentration-GC-MS (328 ± 6 vs. 234 ± 4).
H-NMR spectroscopy of the polar and non-polar extracts extended the range to >40 more (mainly low volatility compounds) metabolites (non-destructively), the majority of which were different from GC-MS. The more peaks detectable, the greater the opportunity of assessing a fingerprint of several compounds to aid biomarker discovery. In summary, we have successfully demonstrated the potential of LLE as a cheap and simple alternative for the analysis of VCs in urine, and for the first time the applicability of a single urine solvent extraction procedure for detecting a wide range of analytes using both GC-MS and
H-NMR analysis to enhance putative biomarker detection. The proposed method will simplify the transport between laboratories and storage of samples, as compared to intact urine samples.
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