An untargeted gas chromatography mass spectrometry metabolomics platform for marine polychaetes

Metabolomics on the study of marine organisms

BACKGROUND

Marine ecosystems are hosts to a vast array of organisms, being among the most richly biodiverse locations on the planet. The study of these ecosystems is very important, as they are not only a significant source of food for the world but also have, in recent years, become a prolific source of compounds with therapeutic potential. Studies of aspects of marine life have involved diverse fields of marine science, and the use of metabolomics as an experimental approach has increased in recent years. As part of the "omics" technologies, metabolomics has been used to deepen the understanding of interactions between marine organisms and their environment at a metabolic level and to discover new metabolites produced by these organisms.

AIM OF REVIEW

This review provides an overview of the use of metabolomics in the study of marine organisms. It also explores the use of metabolomics tools common to other fields such as plants and human metabolomics that could potentially contribute to marine organism studies. It deals with the entire process of a metabolomic study, from sample collection considerations, metabolite extraction, analytical techniques, and data analysis. It also includes an overview of recent applications of metabolomics in fields such as marine ecology and drug discovery and future perspectives of its use in the study of marine organisms.

KEY SCIENTIFIC CONCEPTS OF REVIEW

The review covers all the steps involved in metabolomic studies of marine organisms including, collection, extraction methods, analytical tools, statistical analysis, and dereplication. It aims to provide insight into all aspects that a newcomer to the field should consider when undertaking marine metabolomics.

Mining plant metabolomes: Methods, applications, and perspectives

Plants produce a variety of metabolites that are essential for plant growth and human health. To fully understand the diversity of metabolites in certain plants, lots of methods have been developed for metabolites detection and data processing. In the data-processing procedure, how to effectively reduce false-positive peaks, analyze large-scale metabolic data, and annotate plant metabolites remains challenging. In this review, we introduce and discuss some prominent methods that could be exploited to solve these problems, including a five-step filtering method for reducing false-positive signals in LC-MS analysis, QPMASS for analyzing ultra-large GC-MS data, and MetDNA for annotating metabolites. The main applications of plant metabolomics in species discrimination, metabolic pathway dissection, population genetic studies, and some other aspects are also highlighted. To further promote the development of plant metabolomics, more effective and integrated methods/platforms for metabolite detection and comprehensive databases for metabolite identification are highly needed. With the improvement of these technologies and the development of genomics and transcriptomics, plant metabolomics will be widely used in many fields.

Evaluation and optimization of sample pretreatment for GC/MS-based metabolomics in embryonic zebrafish

Metabolomics tactics have been applied in the research associated with embryonic zebrafish. However, the report regarding the evaluation of impacts of sample pretreatment on metabolomics results from zebrafish embryos is limited. In the present study, different data normalization approaches, extraction solvents, and extraction strategies for off-line derivatization gas chromatography coupled with mass spectrometry-based metabolomics analysis of zebrafish eleutheroembryos were evaluated and optimized. The results showed that, when 4-chlorophenylalanine normalization, sample homogenization and pure methanol combined with ultrasonic extraction were conducted, better repeatabilities, higher signals and broader coverages of detected metabolites can be achieved. The recovery and standard deviation of most standards were in the range of 82%-121% and 6.6%-12%, respectively, while the relative standard deviation of major detected metabolites ranged from 5.4% to 19%, indicating good extraction efficiencies and method precision. Under the developed method, 87 important endogenous metabolites such as citric acid and hypoxanthine were identified by universal databases or standards among 270 extracted metabolites, which consisted of sugars, amines, amino acids, nucleotides, fatty acids, and sterols. Therefore, the results could provide a proper pretreatment protocol for the analysis of wide-coverage metabolome in embryonic zebrafish. In addition, this study highlights the impact of normalization and extraction methods on the data quality of metabolomics analysis.

Olive oil quality classification and measurement of its organoleptic attributes by untargeted GC-MS and multivariate statistical-based approach

The capabilities of dynamic headspace entrainment followed by thermal desorption in combination with gas chromatography (GC) coupled to single quadrupole mass spectrometry (MS) have been tested for the determination of volatile components of olive oil. This technique has shown a great potential for olive oil quality classification by using an untargeted approach. The data processing strategy consisted of three different steps: component detection from GC-MS data using novel data treatment software PARADISe, a multivariate analysis using EZ-Info, and the creation of the statistical models. The great number of compounds determined enabled not only the development of a quality classification method as a complementary tool to the official established method "PANEL TEST" but also a correlation between these compounds and different types of defect. Classification method was finally validated using blind samples. An accuracy of 85% in oil classification was obtained, with 100% of extra virgin samples correctly classified.

Structural and functional measures of marine microbial communities: An experiment to assess implications for oil spill management

Microbial communities are ecologically important in aquatic environments and impacts on microbes have the potential to affect a number of functional processes. We have amended seawater with a crude oil and assessed changes in species composition as well as a measure of functional diversity (the ability of the community to utilise different carbon sources) and the community level metabolic signature. We found that there was a degree of functional redundancy in the community we tested. Oiled assemblages became less diverse and more dominated by specialist hydrocarbon degraders, carbon source utilisation increased initially but there was no change in metabolic signature in this small scale laboratory experiment. This study supports the decision framework around management of oil spills. This package of methods has the potential to be used in the testing and selection of new dispersants for use in oil spill response.

eRah: A Computational Tool Integrating Spectral Deconvolution and Alignment with Quantification and Identification of Metabolites in GC/MS-Based Metabolomics

Gas chromatography coupled to mass spectrometry (GC/MS) has been a long-standing approach used for identifying small molecules due to the highly reproducible ionization process of electron impact ionization (EI). However, the use of GC-EI MS in untargeted metabolomics produces large and complex data sets characterized by coeluting compounds and extensive fragmentation of molecular ions caused by the hard electron ionization. In order to identify and extract quantitative information on metabolites across multiple biological samples, integrated computational workflows for data processing are needed. Here we introduce eRah, a free computational tool written in the open language R composed of five core functions: (i) noise filtering and baseline removal of GC/MS chromatograms, (ii) an innovative compound deconvolution process using multivariate analysis techniques based on compound match by local covariance (CMLC) and orthogonal signal deconvolution (OSD), (iii) alignment of mass spectra across samples, (iv) missing compound recovery, and (v) identification of metabolites by spectral library matching using publicly available mass spectra. eRah outputs a table with compound names, matching scores and the integrated area of compounds for each sample. The automated capabilities of eRah are demonstrated by the analysis of GC-time-of-flight (TOF) MS data from plasma samples of adolescents with hyperinsulinaemic androgen excess and healthy controls. The quantitative results of eRah are compared to centWave, the peak-picking algorithm implemented in the widely used XCMS package, MetAlign, and ChromaTOF software. Significantly dysregulated metabolites are further validated using pure standards and targeted analysis by GC-triple quadrupole (QqQ) MS, LC-QqQ, and NMR. eRah is freely available at http://CRAN.R-project.org/package=erah .

The anti-atherosclerotic effect of tanshinol borneol ester using fecal metabolomics based on liquid chromatography-mass spectrometry

Tanshinol borneol ester (DBZ) is a novel experimental compound that consists of two chemical structural units from danshensu and borneol. It exhibits efficacious anti-ischemic and anti-atherosclerosis activities in rats. A fecal metabolomics based on Liquid Chromatography-Mass Spectrometry combined with clinical histopathology and blood lipid estimation was employed to assess the efficacy and the metabolic changes caused by administration of DBZ in atherosclerotic rats. There were the typical pathological features of atherosclerosis and significantly increased levels of TC, TG and LDL-C in the atherosclerotic rat group. Nevertheless, atherosclerotic rats administered both DBZ (at a dose of 40 mg kg(-1)) and simvastatin (at a dose of 20 mg kg(-1)) showed good therapeutic effects. The results of the metabolomics studies showed that 55 differential metabolites such as sebacic acid, enterodiol, nonanedioic acid, dodecanedioic acid, cholic acid, 13(S)-HPODE, deoxycholic acid, some phosphatidylglycerol and phosphatidic acids were found, indicating that abnormal metabolism occurred in the pathways of fatty acid oxidation, linoleic acid metabolism, bile acid biosynthesis and glycerophospholipid metabolism in atherosclerotic rats. Compared to those in the model group, the contents of 41 differential metabolites showed a tendency to recover to a healthy level after DBZ administration. Metabolomics studies suggested that DBZ exhibited good treatment efficacy against atherosclerosis by adjusting disturbed metabolic pathways related to atherosclerosis. This study could provide an experimental basis for DBZ's application to act as a candidate drug with anti-atherosclerosis activity.