Mark R. Viant

Proposed minimum reporting standards for chemical analysis

There is a general consensus that supports the need for standardized reporting of metadata or information describing large-scale metabolomics and other functional genomics data sets. Reporting of standard metadata provides a biological and empirical context for the data, facilitates experimental replication, and enables the re-interrogation and comparison of data by others. Accordingly, the Metabolomics Standards Initiative is building a general consensus concerning the minimum reporting standards for metabolomics experiments of which the Chemical Analysis Working Group (CAWG) is a member of this community effort. This article proposes the minimum reporting standards related to the chemical analysis aspects of metabolomics experiments including: sample preparation, experimental analysis, quality control, metabolite identification, and data pre-processing. These minimum standards currently focus mostly upon mass spectrometry and nuclear magnetic resonance spectroscopy due to the popularity of these techniques in metabolomics. However, additional input concerning other techniques is welcomed and can be provided via the CAWG on-line discussion forum at http://msi-workgroups.sourceforge.net/ or http://Msi-workgroups-feedback@lists.sourceforge.net. Further, community input related to this document can also be provided via this electronic forum.

Environmental metabolomics: a critical review and future perspectives

Environmental metabolomics is the application of metabolomics to characterise the interactions of organisms with their environment. This approach has many advantages for studying organism–environment interactions and for assessing organism function and health at the molecular level. As such, metabolomics is finding an increasing number of applications in the environmental sciences, ranging from understanding organismal responses to abiotic pressures, to investigating the responses of organisms to other biota. These interactions can be studied from individuals to populations, which can be related to the traditional fields of ecophysiology and ecology, and from instantaneous effects to those over evolutionary time scales, the latter enabling studies of genetic adaptation. This review provides a comprehensive and current overview of environmental metabolomics research. We begin with an overview of metabolomic studies into the effects of abiotic pressures on organisms. In the field of ecophysiology, studies on the metabolic responses to temperature, water, food availability, light and circadian rhythms, atmospheric gases and season are reviewed. A section on ecotoxicogenomics discusses research in aquatic and terrestrial ecotoxicology, assessing organismal responses to anthropogenic pollutants in both the laboratory and field. We then discuss environmental metabolomic studies of diseases and biotic–biotic interactions, in particular herbivory. Finally, we critically evaluate the contribution that metabolomics has made to the environmental sciences, and highlight and discuss recommendations to advance our understanding of the environment, ecology and evolution using a metabolomics approach.

Evaluation of metabolite extraction strategies from tissue samples using NMR metabolomics

Metabolomic analysis of tissue samples can be applied across multiple fields including medicine, toxicology, and environmental sciences. A thorough evaluation of several metabolite extraction procedures from tissues is therefore warranted. This has been achieved at two research laboratories using muscle and liver tissues from fish. Multiple replicates of homogenous tissues were extracted using the following solvent systems of varying polarities: perchloric acid, acetonitrile/water, methanol/water, and methanol/chloroform/water. Extraction of metabolites from ground wet tissue, ground dry tissue, and homogenized wet tissue was also compared. The hydrophilic metabolites were analyzed using 1-dimensional (1D) 1H nuclear magnetic resonance (NMR) spectroscopy and projections of 2-dimensional J-resolved (p-JRES) NMR, and the spectra evaluated using principal components analysis. Yield, reproducibility, ease, and speed were the criteria for assessing the quality of an extraction protocol for metabolomics. Both laboratories observed that the yields of low molecular weight metabolites were similar among the solvent extractions; however, acetonitrile-based extractions provided poorer fractionation and extracted lipids and macromolecules into the polar solvent. Extraction using perchloric acid produced the greatest variation between replicates due to peak shifts in the spectra, while acetonitrile-based extraction produced highest reproducibility. Spectra from extraction of ground wet tissues generated more macromolecules and lower reproducibility compared with other tissue disruption methods. The p-JRES NMR approach reduced peak congestion and yielded flatter baselines, and subsequently separated the metabolic fingerprints of different samples more clearly than by 1D NMR. Overall, single organic solvent extractions are quick and easy and produce reasonable results. However, considering both yield and reproducibility of the hydrophilic metabolites as well as recovery of the hydrophobic metabolites, we conclude that the methanol/chloroform/water extraction is the preferred method.

Improved classification accuracy in 1- and 2-dimensional NMR metabolomics data using the variance stabilising generalised logarithm transformation

BackgroundClassifying nuclear magnetic resonance (NMR) spectra is a crucial step in many metabolomics experiments. Since several multivariate classification techniques depend upon the variance of the data, it is important to first minimise any contribution from unwanted technical variance arising from sample preparation and analytical measurements, and thereby maximise any contribution from wanted biological variance between different classes. The generalised logarithm (glog) transform was developed to stabilise the variance in DNA microarray datasets, but has rarely been applied to metabolomics data. In particular, it has not been rigorously evaluated against other scaling techniques used in metabolomics, nor tested on all forms of NMR spectra including 1-dimensional (1D) 1H, projections of 2D 1H, 1H J-resolved (pJRES), and intact 2D J-resolved (JRES).ResultsHere, the effects of the glog transform are compared against two commonly used variance stabilising techniques, autoscaling and Pareto scaling, as well as unscaled data. The four methods are evaluated in terms of the effects on the variance of NMR metabolomics data and on the classification accuracy following multivariate analysis, the latter achieved using principal component analysis followed by linear discriminant analysis. For two of three datasets analysed, classification accuracies were highest following glog transformation: 100% accuracy for discriminating 1D NMR spectra of hypoxic and normoxic invertebrate muscle, and 100% accuracy for discriminating 2D JRES spectra of fish livers sampled from two rivers. For the third dataset, pJRES spectra of urine from two breeds of dog, the glog transform and autoscaling achieved equal highest accuracies. Additionally we extended the glog algorithm to effectively suppress noise, which proved critical for the analysis of 2D JRES spectra.ConclusionWe have demonstrated that the glog and extended glog transforms stabilise the technical variance in NMR metabolomics datasets. This significantly improves the discrimination between sample classes and has resulted in higher classification accuracies compared to unscaled, autoscaled or Pareto scaled data. Additionally we have confirmed the broad applicability of the glog approach using three disparate datasets from different biological samples using 1D NMR spectra, 1D projections of 2D JRES spectra, and intact 2D JRES spectra.

Two-dimensional J-resolved NMR spectroscopy: review of a key methodology in the metabolomics toolbox.

One-dimensional (1D) (1)H NMR spectroscopy remains a leading analytical technology in metabolomics. Advantages of this approach include relatively rapid spectral acquisition and NMR resonances that provide a direct measure of metabolite concentration based upon a single internal standard. Severe spectral congestion can, however, significantly hinder both metabolite identification and quantification. Two-dimensional (1)H J-resolved (JRES) NMR spectroscopy retains many of the benefits of 1D NMR, but additionally disperses the overlapping resonances into a second dimension, reducing congestion and increasing metabolite specificity. The usefulness of this approach to metabolomics was first realised six years ago, and since then it has been used in biological, medical and environmental studies of plants and animals. Here we provide a basic introduction to the 2D JRES NMR experiment and then discuss strategies for spectral acquisition and processing in the context of metabolomics applications, concluding with some key recommendations: acquisition using a double spin-echo sequence with excitation sculpting; processing using the SEM window function, tilting and symmetricising, optionally followed by a skyline projection. Strategies for implementing JRES spectroscopy into the metabolomics toolbox are then considered, including its roles in metabolic fingerprinting, metabolite identification and metabolite quantification. Public resources and data standards for JRES metabolomics are reviewed. We conclude by evaluating the advantages (e.g. increased spectral dispersion and confidence in metabolite identification; fully automated processing; reduced batch-to-batch variation) and disadvantages (e.g. longer acquisition times; higher technical variability; phase-twisted lineshapes resulting in quantification errors) of 2D JRES NMR vs the established 1D approach for metabolomics.

Aggregation and dispersion of silver nanoparticles in exposure media for aquatic toxicity tests

Silver nanoparticles (AgNPs) are currently being very widely used in industry, mainly because of their anti-bacterial properties, with applications in many areas. Once released into the environment, the mobility, bioavailability, and toxicity of AgNPs in any ecosystem are dominated by colloidal stability. There have been studies on the stability or the aggregation of various nanoparticles (NPs) under a range of environmental conditions, but there is little information on fully characterised AgNPs in media used in (eco)toxicity studies. In this study, monodisperse 7, 10 and 20 nm citrate-stabilised AgNPs were synthesised, characterised and then fractionated and sized by flow field-flow fractionation (FFF) and measured with dynamic light scattering (DLS) in different dilutions of the media recommended by OECD for Daphnia magna (water flea) toxicity testing. Stability of NPs was assessed over 24 h, and less so over 21 days, similar time periods to the OECD acute and chronic toxicity tests for D. magna. All particles aggregated quickly in the media with high ionic strength (media1), resulting in a loss of colour from the solution. The size of particles could be measured by DLS in most cases after 24h, although a fractogram by FFF could not be obtained due to aggregation and polydispersity of the sample. After diluting the media by a factor of 2, 5 or 10, aggregation was reduced, although the smallest NPs were unstable under all media conditions. Media diluted up to 10-fold in the absence of AgNPs did not induce any loss of mobility or fecundity in D. magna. These results confirm that standard OECD media causes aggregation of AgNPs, which result in changes in organism exposure levels and the nature of the exposed particles compared to exposure to fully dispersed particles. Setting aside questions of dose metrics, significant and substantial reduction in concentration over exposure period suggests that literature data are in the main improperly interpreted and nanoparticles are likely to have far greater biological effects than suggested thus far by poorly controlled exposures. We recommend that the standard OECD media is diluted by a factor of ca. 10 for use with these NPs and this test media, which reduces AgNP aggregation without affecting the viability of the text organism.

Fumarate Is Cardioprotective via Activation of the Nrf2 Antioxidant Pathway

Summary The citric acid cycle (CAC) metabolite fumarate has been proposed to be cardioprotective; however, its mechanisms of action remain to be determined. To augment cardiac fumarate levels and to assess fumarates cardioprotective properties, we generated fumarate hydratase (Fh1) cardiac knockout (KO) mice. These fumarate-replete hearts were robustly protected from ischemia-reperfusion injury (I/R). To compensate for the loss of Fh1 activity, KO hearts maintain ATP levels in part by channeling amino acids into the CAC. In addition, by stabilizing the transcriptional regulator Nrf2, Fh1 KO hearts upregulate protective antioxidant response element genes. Supporting the importance of the latter mechanism, clinically relevant doses of dimethylfumarate upregulated Nrf2 and its target genes, hence protecting control hearts, but failed to similarly protect Nrf2-KO hearts in an in vivo model of myocardial infarction. We propose that clinically established fumarate derivatives activate the Nrf2 pathway and are readily testable cytoprotective agents.

Integrating omic technologies into aquatic ecological risk assessment and environmental monitoring: hurdles, achievements, and future outlook.

Background In this commentary we present the findings from an international consortium on fish toxicogenomics sponsored by the U.K. Natural Environment Research Council (Fish Toxicogenomics—Moving into Regulation and Monitoring, held 21–23 April 2008 at the Pacific Environmental Science Centre, Vancouver, BC, Canada). Objectives The consortium from government agencies, academia, and industry addressed three topics: progress in ecotoxicogenomics, regulatory perspectives on roadblocks for practical implementation of toxicogenomics into risk assessment, and dealing with variability in data sets. Discussion Participants noted that examples of successful application of omic technologies have been identified, but critical studies are needed to relate molecular changes to ecological adverse outcome. Participants made recommendations for the management of technical and biological variation. They also stressed the need for enhanced interdisciplinary training and communication as well as considerable investment into the generation and curation of appropriate reference omic data. Conclusions The participants concluded that, although there are hurdles to pass on the road to regulatory acceptance, omics technologies are already useful for elucidating modes of action of toxicants and can contribute to the risk assessment process as part of a weight-of-evidence approach.

Identifying health impacts of exposure to copper using transcriptomics and metabolomics in a fish model.

Copper (Cu) is a micronutrient essential for the biochemical functioning of numerous processes in vertebrates but is also often present in the aquatic environment at concentrations able to cause adverse health effects in aquatic organisms. This study investigated the signaling pathways mediating the effects of exposure to Cu using a toxicogenomic approach in a fish model, the stickleback ( Gasterosteus aculeatus ). Freshwater-acclimated male fish were exposed via the water to Cu, including at environmentally relevant concentrations (3.2-128 microg of Cu/L for 4 days), and the biological responses explored through analyses of the hepatic transcriptome and metabolome and phenotypic end points, including assessment of DNA damage in blood cells. The Cu exposures resulted in DNA strand breaks in blood cells at all exposure concentrations and alterations in hepatic gene expression and metabolite concentrations in a concentration-dependent manner (from 10 microg of Cu/L). Genes associated with the cholesterol biosynthesis pathway were significantly over-represented and consistently down-regulated (at 128 microg of Cu/L), similar to that occurring in a mouse model for Wilsons disease. Additionally, inductions in metallothionein and catalase were also observed. The concentrations of NAD(+) and lactate increased significantly with the Cu exposure, consistent with a shift toward anaerobic metabolism, and these aligned closely with changes observed in gene expression. The pathways of Cu toxicity identified in our study support the conserved mechanisms of Cu toxicity from lower vertebrates to mammals, provide novel insights into the deleterious effects of Cu in fish, and further demonstrate the utility of fish as environmental sentinels for chemical impacts on both environmental and human health.

Optimized metabolite extraction from blood serum for 1H nuclear magnetic resonance spectroscopy

Blood serum is commonly used for clinical diagnostics because its protein composition bears a wealth of information about the health of an organism. More recently the analysis of the small molecule composition, the metabolome, has received increased attention because the metabolite composition is influenced by many diseases, by the administration of drugs and toxins, and by the diet and life style of an individual. When nuclear magnetic resonance spectroscopy is used as an analytical tool it is often preferable to remove catalytically active proteins, in particular for longer measurements, because metabolite concentrations are otherwise in constant flux. Here we have compared different protocols for the separation of proteins and metabolites, including precipitation methods and ultrafiltration. Whereas most extraction methods involving protein precipitation deplete some metabolites, ultrafiltration is superior in retaining metabolite concentrations and offers excellent reproducibility. We also describe a new method to recover the hydrophobic fraction for ultrafiltration with good reproducibility.