scholarly journals Quenching for Microalgal Metabolomics: A Case Study on the Unicellular Eukaryotic Green Alga Chlamydomonas reinhardtii

Metabolites ◽  
2018 ◽  
Vol 8 (4) ◽  
pp. 72
Author(s):  
Rahul Kapoore ◽  
Seetharaman Vaidyanathan
Keyword(s):  
Chlamydomonas Reinhardtii ◽  
Methanol Concentration ◽  
Gas Chromatography Mass Spectrometry ◽  
Solvent Ratio ◽  
Aqueous Methanol ◽  
High Turnover ◽  
Metabolomics Data ◽  
Intracellular Metabolites ◽  
Metabolite Leakage ◽  
Quenching Time

Capturing a valid snapshot of the metabolome requires rapid quenching of enzyme activities. This is a crucial step in order to halt the constant flux of metabolism and high turnover rate of metabolites. Quenching with cold aqueous methanol is treated as a gold standard so far, however, reliability of metabolomics data obtained is in question due to potential problems connected to leakage of intracellular metabolites. Therefore, we investigated the influence of various parameters such as quenching solvents, methanol concentration, inclusion of buffer additives, quenching time and solvent to sample ratio on intracellular metabolite leakage from Chlamydomonas reinhardtii. We measured the recovery of twelve metabolite classes using gas chromatography mass spectrometry (GC-MS) in all possible fractions and established mass balance to trace the fate of metabolites during quenching treatments. Our data demonstrate significant loss of intracellular metabolites with the use of the conventional 60% methanol, and that an increase in methanol concentration or quenching time also resulted in higher leakage. Inclusion of various buffer additives showed 70 mM HEPES (4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid) to be suitable. In summary, we recommend quenching with 60% aqueous methanol supplemented with 70 mM HEPES (−40 °C) at 1:1 sample to quenching solvent ratio, as it resulted in higher recoveries for intracellular metabolites with subsequent reduction in the metabolite leakage for all metabolite classes.

Reactivity of proton and general acid with 1,3-bis-(4-methylphenyl)triazene in aqueous methanol

1990 ◽  
Vol 55 (11) ◽  
pp. 2701-2706 ◽  
Author(s):  
Oldřich Pytela ◽  
Taťjana Nevěčná ◽  
Jaromír Kaválek
Keyword(s):  
Benzoic Acid ◽  
Transition State ◽  
Rate Constant ◽  
Acid Catalysis ◽  
Methanol Concentration ◽  
Binary Solvent ◽  
Aqueous Methanol ◽  
Solvent Water ◽  
General Acid ◽  
Activated Complex

The effect of concentration of benzoic acid and composition of the binary solvent water-methanol on the rate of decomposition of 1,3-bis(4-methylphenyl)triazene has been studied. It has been found that both general acid catalysis by undissociated benzoic acid and catalysis by the proton are significant. The rate constant kHA of general acid catalysis decreases monotonously with decreasing amount of water in the mixture due to preferred solvation of the activated complex as compared with the educts. The rate constant kH of the catalysis by proton in its dependence on methanol concentration exhibits a minimum for 80% (by wt.) of methanol in the mixture. This phenomenon is caused by formation of the conjugated acid from more basic methanol and proton with simultaneous solvation by water and methanol; the particle thus formed is a weaker acid as compared with the complexes existing in water or in methanol. The kH value is higher in methanol than in water due to preferred solvation of the educts as compared with that of the transition state.

scholarly journals Chemometric analysis of comprehensive two dimensional gas chromatography–mass spectrometry metabolomics data

2017 ◽  
Vol 1488 ◽  
pp. 113-125 ◽  
Author(s):  
Yahya Izadmanesh ◽  
Elba Garreta-Lara ◽  
Jahan B. Ghasemi ◽  
Silvia Lacorte ◽  
Victor Matamoros ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Gas Chromatography ◽  
Chemometric Analysis ◽  
Gas Chromatography Mass Spectrometry ◽  
Two Dimensional ◽  
Metabolomics Data ◽  
Chromatography Mass Spectrometry

A New Approach of Outlier-robust Missing Value Imputation for Metabolomics Data Analysis

2018 ◽  
Vol 14 (1) ◽  
pp. 43-52 ◽  
Author(s):  
Nishith Kumar ◽  
Md. Aminul Hoque ◽  
Md. Shahjaman ◽  
S.M. Shahinul Islam ◽  
Md. Nurul Haque Mollah
Keyword(s):  
Mass Spectrometry ◽  
Data Analysis ◽  
Missing Values ◽  
Real Data ◽  
Gas Chromatography Mass Spectrometry ◽  
Data Generation ◽  
Metabolomics Data ◽  
Missing Value ◽  
Missing Value Imputation ◽  
Chromatography Mass Spectrometry

Background: Metabolomics data generation and quantification are different from other types of molecular “omics” data in bioinformatics. Mass spectrometry (MS) based (gas chromatography mass spectrometry (GC-MS), liquid chromatography mass spectrometry (LC-MS), etc.) metabolomics data frequently contain missing values that make some quantitative analysis complex. Typically metabolomics datasets contain 10% to 20% missing values that originate from several reasons, like analytical, computational as well as biological hazard. Imputation of missing values is a very important and interesting issue for further metabolomics data analysis. </P><P> Objective: This paper introduces a new algorithm for missing value imputation in the presence of outliers for metabolomics data analysis. </P><P> Method: Currently, the most well known missing value imputation techniques in metabolomics data are knearest neighbours (kNN), random forest (RF) and zero imputation. However, these techniques are sensitive to outliers. In this paper, we have proposed an outlier robust missing imputation technique by minimizing twoway empirical mean absolute error (MAE) loss function for imputing missing values in metabolomics data. Results: We have investigated the performance of the proposed missing value imputation technique in a comparison of the other traditional imputation techniques using both simulated and real data analysis in the absence and presence of outliers. Conclusion: Results of both simulated and real data analyses show that the proposed outlier robust missing imputation technique is better performer than the traditional missing imputation methods in both absence and presence of outliers.

scholarly journals Comparative genomics and metabolomics analysis of Riemerella anatipestifer strain CH-1 and CH-2

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jibin Liu ◽  
Anchun Cheng ◽  
Mingshu Wang ◽  
Mafeng Liu ◽  
Dekang Zhu ◽  
...  
Keyword(s):  
Large Scale ◽  
Hierarchical Cluster ◽  
Pathogenic Microorganism ◽  
Sequence Database ◽  
Metabolomics Data ◽  
Intracellular Metabolites ◽  
Riemerella Anatipestifer ◽  
Genome Sequence Database ◽  
Serotype 1 ◽  
Significant Mortality

AbstractRiemerella anatipestifer is a major pathogenic microorganism in poultry causing serositis with significant mortality. Serotype 1 and 2 were most pathogenic, prevalent, and liable over the world. In this study, the intracellular metabolites in R. anatipestifer strains RA-CH-1 (serotype 1) and RA-CH-2 (serotype 2) were identified by gas chromatography-mass spectrometer (GC–MS). The metabolic profiles were performed using hierarchical clustering and partial least squares discriminant analysis (PLS-DA). The results of hierarchical cluster analysis showed that the amounts of the detected metabolites were more abundant in RA-CH-2. RA-CH-1 and RA-CH-2 were separated by the PLS-DA model. 24 potential biomarkers participated in nine metabolisms were contributed predominantly to the separation. Based on the complete genome sequence database and metabolite data, the first large-scale metabolic models of iJL463 (RA-CH-1) and iDZ470 (RA-CH-2) were reconstructed. In addition, we explained the change of purine metabolism combined with the transcriptome and metabolomics data. The study showed that it is possible to detect and differentiate between these two organisms based on their intracellular metabolites using GC–MS. The present research fills a gap in the metabolomics characteristics of R. anatipestifer.

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