Metabolome Analysis of <i>Drosophila melanogaster</i> during Embryogenesis

<div><p>The <i>Drosophila melanogaster</i> embryo has been widely utilized as a model for genetics and developmental biology due to its small size, short generation time, and large brood size. Information on embryonic metabolism during developmental progression is important for further understanding the mechanisms of <i>Drosophila</i> embryogenesis. Therefore, the aim of this study is to assess the changes in embryos’ metabolome that occur at different stages of the <i>Drosophila</i> embryonic development. Time course samples of <i>Drosophila</i> embryos were subjected to GC/MS-based metabolome analysis for profiling of low molecular weight hydrophilic metabolites, including sugars, amino acids, and organic acids. The results showed that the metabolic profiles of <i>Drosophila</i> embryo varied during the course of development and there was a strong correlation between the metabolome and different embryonic stages. Using the metabolome information, we were able to establish a prediction model for developmental stages of embryos starting from their high-resolution quantitative metabolite composition. Among the important metabolites revealed from our model, we suggest that different amino acids appear to play distinct roles in different developmental stages and an appropriate balance in trehalose-glucose ratio is crucial to supply the carbohydrate source for the development of <i>Drosophila</i> embryo.</p></div

Partial Least Square was performed to construct a robust and accurate prediction model for <i>Drosophila</i> embryo developmental stages based on the metabolic data.

<p>Among 10 time points investigated, 4 time points including 4–6, 8–10, 12–14 and 16–18 hrs AEL were selected as the test set while the rest were used as training set. PLS regression was first performed with the training set by importing all compounds to the X-matrix while the actual AEL were imported to the Y-matrix. (<b>A</b>) A good correlation between metabolome data and developmental stages could be achieved. R² and Q²>0.9 indicated an excellent predictive model. (<b>B</b>) Cross-validation of the model using the test set was fitted onto the prediction model constructed using the training set. The root mean square error was calculated to point out how well the observed hrs AEL matched with the real hrs AEL. RMSEP was not so different from RMSEE, showing that the model was validated.</p

There is a high coincidence between the composition of metabolome and actual developmental stages of <i>Drosophila</i> embryo.

<p>Embryos were collected at 10 time points namely 0–2, 2–4, 4–6, 6–8, 8–10, 10–12, 12–14, 14–16, 16–18, 18–20 hrs AEL. All samples were analyzed in triplicates (n = 3). (<b>A</b>) PCA score plot shows 3 main clusters namely 0–4 hrs, 4–16 hrs and 16–20 hrs AEL, which is in total agreement with the developmental stages of <i>Drosophila</i> embryo (<b>B</b>) The loading plot shows the contributions of each metabolite on the discrimination in score plot according to the distance to the origin.</p

Heat map showing the widespread changes in metabolites during <i>Drosophila</i> embryogenesis.

<p>Metabolite levels were expressed relative to the average value of that metabolite throughout the development of embryo; the ratios were plotted on a color scale (top). Metabolites were discriminated hierarchically into three clusters which are represent for early (green), middle (blue) and late stage (red) of embryogenesis.</p

MOESM1 of A metabolomics-based strategy for identification of gene targets for phenotype improvement and its application to 1-butanol tolerance in Saccharomyces cerevisiae

Additional file 1: Supplemental tables. Full data for various tables and charts found in the text

Each metabolite has distinct contributions to the development of <i>Drosophila</i> embryos.

<p>(<b>A</b>) The regression coefficient plot shows the correlation trend of each metabolite. The negative correlation indicated the important role during early embryogenesis and vice versa. The relative intensity of metabolites that are important in sugar and amino acid metabolism are shown in (<b>B</b>) and (<b>C</b>), respectively. The peak intensity of each compound was normalized based on ribitol internal standard.</p

MRMPROBS: A Data Assessment and Metabolite Identification Tool for Large-Scale Multiple Reaction Monitoring Based Widely Targeted Metabolomics

We developed a new software program, MRMPROBS, for widely targeted metabolomics by using the large-scale multiple reaction monitoring (MRM) mode. The strategy became increasingly popular for the simultaneous analysis of up to several hundred metabolites at high sensitivity, selectivity, and quantitative capability. However, the traditional method of assessing measured metabolomics data without probabilistic criteria is not only time-consuming but is often subjective and makeshift work. Our program overcomes these problems by detecting and identifying metabolites automatically, by separating isomeric metabolites, and by removing background noise using a probabilistic score defined as the odds ratio from an optimized multivariate logistic regression model. Our software program also provides a user-friendly graphical interface to curate and organize data matrices and to apply principal component analyses and statistical tests. For a demonstration, we conducted a widely targeted metabolome analysis (152 metabolites) of propagating Saccharomyces cerevisiae measured at 15 time points by gas and liquid chromatography coupled to triple quadrupole mass spectrometry. MRMPROBS is a useful and practical tool for the assessment of large-scale MRM data available to any instrument or any experimental condition

MRMPROBS: A Data Assessment and Metabolite Identification Tool for Large-Scale Multiple Reaction Monitoring Based Widely Targeted Metabolomics

We developed a new software program, MRMPROBS, for widely targeted metabolomics by using the large-scale multiple reaction monitoring (MRM) mode. The strategy became increasingly popular for the simultaneous analysis of up to several hundred metabolites at high sensitivity, selectivity, and quantitative capability. However, the traditional method of assessing measured metabolomics data without probabilistic criteria is not only time-consuming but is often subjective and makeshift work. Our program overcomes these problems by detecting and identifying metabolites automatically, by separating isomeric metabolites, and by removing background noise using a probabilistic score defined as the odds ratio from an optimized multivariate logistic regression model. Our software program also provides a user-friendly graphical interface to curate and organize data matrices and to apply principal component analyses and statistical tests. For a demonstration, we conducted a widely targeted metabolome analysis (152 metabolites) of propagating Saccharomyces cerevisiae measured at 15 time points by gas and liquid chromatography coupled to triple quadrupole mass spectrometry. MRMPROBS is a useful and practical tool for the assessment of large-scale MRM data available to any instrument or any experimental condition

High-Throughput Simultaneous Analysis of Pesticides by Supercritical Fluid Chromatography Coupled with High-Resolution Mass Spectrometry

Recently, a generally applicable screening method for multiresidue pesticide analysis, which is simple, quick, and accurate and has a reliable performance, is becoming increasingly important for food safety and international trade. This paper proposes a high-throughput screening methodology that enables the detection of multiresidue pesticides using supercritical fluid chromatography coupled to a high-performance benchtop quadrupole Orbitrap mass spectrometry (SFC/Q Exactive) and an automated library-based detection. A total of 444 chemicals covering a wide polarity range (log<i>P</i>_ow from −4.2 to 7.7) and a wide molecular weight range (from 99.0 to 872.5) were analyzed simultaneously through a combination of high mass resolution (a value of <i>m</i>/Δ<i>m</i> = 70000), high mass accuracy (<5 ppm) with positive/negative polarity switching, and highly efficient separation by SFC. A total of 373 pesticides were detected in QuEChERS spinach extracts without dispersive solid phase extraction at the 10 μg kg^–1 level (provisional maximum residue limits in Japan). In conclusion, the developed analytical system is a potentially useful tool for practical multiresidue pesticide screening with high throughput (time for data acquisition, 72 samples per day; and time for data processing of 72 samples, approximately 45 min)

Alterations in hepatic essential fatty acid composition in response to the HP diet.

<p>Alterations in hepatic essential fatty acid composition in response to the HP diet.</p