<![CDATA[Current Metabolomics and Systems Biology (Discontinued) (Volume 8 - Issue 1)]]> https://benthamscience.com/journal/176 RSS Feed for Journals | BenthamScience EurekaSelect (+https://benthamscience.com) 2021-05-21 <![CDATA[Current Metabolomics and Systems Biology (Discontinued) (Volume 8 - Issue 1)]]> https://benthamscience.com/journal/176 <![CDATA[Cell Culture Studies: A Promising Approach to the Metabolomic Study of Human Aging]]>https://benthamscience.com/article/1149802021-05-21 <![CDATA[A Roadmap of Cancer: From the Historical Evidence to Recent Salivary Metabolites-based Nanobiosensor Diagnostic Devices]]>https://benthamscience.com/article/1076872021-05-21 <![CDATA[Comparison of Different Sample Preparation Techniques for Untargeted Metabolomics Utilizing Q-TOF LC/MS and MetaboAnalyst 4.0]]>https://benthamscience.com/article/1073392021-05-21Background: Profiling the whole metabolome with a single injection is not an easy process because the chemical and physical properties of metabolites are totally different with each other and the analytical methodologies and data mining procedures need lots of effort to make such an approach in real. This reality leads researchers to select an already applied methodology for metabolite profiling and analyze the samples through identical techniques.

Objective: In this study, it was focused on answer the question the sample preparation techniques on human blood samples prior to Q-TOF LC/MS analysis affect the number of detectable peaks and analyze the matched metabolites for these peaks. The results were compared with each other.

Methods: Precipitation of proteins with methanol, ultrafiltration (Amicon® Ultra 3 kDa 0.5 mL Centrifugal Filters), liquid-phase extraction (EXtrelut® NT 3 cartridges) and solid-phase extraction (Supelco HybridSPE®-Phospholipid Cartridge) were used for sample preparation on commercial pooled plasma samples. C18 column (Agilent Zorbax 1.8 μM, 50 x 2.1 mm) was used as the chromatography column. Q-TOF LC/MS analysis was performed on positive ionization mode. XCMS and MetaboAnalyst 4.0 - MS Peaks to Pathways utility were used to evaluate the raw data.

Results: Although the number of detected peaks through precipitation of proteins with methanol was the highest one (624 peaks), the detected peaks observed through ultrafiltration- based sample preparation technique matched with the highest number of metabolite peaks (151 metabolites). The number of the matched peaks with metabolites on liquid phase extraction (81 metabolites) was higher than the ones for solid phase extraction (29 metabolites).

Conclusion: The results in this study may provide a novel perspective for analytical chemists working with clinicians to select their sample preparation technique prior to Q-TOF LC/MS based untargeted metabolomic approaches.

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<![CDATA[Metabolite Profiling of Different Solvent Extracts of the Microalgae <i>Chlorella vulgaris</i> Via <sup>1</sup>H NMR-Based Metabolomics]]>https://benthamscience.com/article/1093262021-05-21Introduction: In the present study, profiling of the cultured Chlorella vulgaris metabolome was carried out via1H NMR metabolite profiling of 6 different solvent extracts. The results indicated that the six solvent extracts have metabolite profiles that are clearly different from each other.

Methods: Multivariate data analysis (MVA) reveals that ethyl acetate and ethanol extracts were well separated from the aqueous extract by PC1 while being well separated from each other by PC2. The same observations were seen with chloroform and 50% ethanol extracts. In contrast, the chemical shift signals for hexane extract clusters in-between that of chloroform and 50% ethanol, indicated that they have similar chemical profiles. Using partial least square discriminative analysis (PLS-DA), compounds responsible for the group separation were identified from the loading plot. Detailed examination of the loading plot shows that ethanol and ethyl acetate extracts contain significantly higher amounts of carotenoids, amino acids, vitamins and fatty acids. A total of 35 compounds were detected from the 6 different solvents upon which the ethanolic and ethyl acetate extracts were identified to contain more metabolites and in a wider range than the other organic solvent extracts.

Results: Hence, these two extracts would be more appropriate in metabolite extraction for analysis and for medicinal purposes. Therefore, NMR spectroscopy, in compliment with the right choice of solvent for extraction, could be utilized by relevant industries to evaluate and obtain maximum important metabolites in a shorter time.

Conclusion: In addition to possession of high diverse metabolites, the microalgae C. vulgaris could serve as an important functional food ingredient in the aquaculture industry and may possibly be considered as a source of biofuel.

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