The future of NMR-based metabolomics

Application of metabolomics in toxicity evaluation of traditional Chinese medicines

Traditional Chinese medicines (TCM) have a long history of use because of its potential complementary therapy and fewer adverse effects. However, the toxicity and safety issues of TCM have drawn considerable attention in the past two decades. Metabolomics is an “omics” approach that aims to comprehensively analyze all metabolites in biological samples. In agreement with the holistic concept of TCM, metabolomics has shown great potential in efficacy and toxicity evaluation of TCM. Recently, a large amount of metabolomic researches have been devoted to exploring the mechanism of toxicity induced by TCM, such as hepatotoxicity, nephrotoxicity, and cardiotoxicity. In this paper, the application of metabolomics in toxicity evaluation of bioactive compounds, TCM extracts and TCM prescriptions are reviewed, and the potential problems and further perspectives for application of metabolomics in toxicological studies are also discussed.

Carbohydrate Background Removal in Metabolomics Samples

NMR-based metabolomics is a powerful tool to comprehensively monitor chemical processes in biological systems. Key to its success is the accurate and complete metabolite identification and quantification. Due to the inherent complexity of most metabolic mixtures, NMR peak overlap can make data analysis of 1D or even 2D NMR spectra challenging, especially for the ¹H spectral region from 3.2-4.5 ppm that is dominated by carbohydrates and their derivatives. To address this problem, we present an effective method for carbohydrate signal removal in complex metabolomics samples by oxidation via the addition of sodium periodate (NaIO₄). In an optional step, reaction products can be removed with hydrazide beads. The treated samples show substantially simplified 1D and 2D NMR spectra with their carbohydrate peaks removed, whereas noncarbohydrate peaks remain mostly unaffected. This allows the unrestricted detection of those metabolites that are otherwise obscured by carbohydrate signals. The method was first tested for metabolite model mixtures and then applied to urine and serum samples. It revealed a significant number of noncarbohydrates that were made unambiguously observable and identifiable by this method. The proposed protocol is simple and it is suitable for high-throughput sample treatment for the comprehensive metabolite identification in a broad range of samples.

Pharmacognosy in the digital era: shifting to contextualized metabolomics

Humans have co-evolved alongside numerous other organisms, some having a profound effect on health and nutrition. As the earliest pharmaceutical subject, pharmacognosy has evolved into a meta-discipline devoted to natural biomedical agents and their functional properties. While the acquisition of expanding data volumes is ongoing, contextualization is lagging. Thus, we assert that the establishment of an integrated and open databases ecosystem will nurture the discipline. After proposing an epistemological framework of knowledge acquisition in pharmacognosy, this study focuses on recent computational and analytical approaches. It then elaborates on the flux of research data, where good practices could foster the implementation of more integrated systems, which will in turn help shaping the future of pharmacognosy and determine its constitutional societal relevance.

Circulating tumour DNA, microRNA and metabolites in cerebrospinal fluid as biomarkers for central nervous system malignancies

Central nervous system (CNS) malignancies can be difficult to diagnose and many do not respond satisfactorily to existing therapies. Monitoring patients with CNS malignancies for treatment response and tumour recurrence can be challenging because of the difficulty and risks of brain biopsies, and the low specificity and sensitivity of the less invasive methodologies that are currently available. Uncertainty about tumour diagnosis or whether a tumour has responded to treatment or has recurred can cause delays in therapeutic decisions that can impact patient outcome. Therefore, there is an urgent need to develop and validate reliable and minimally invasive biomarkers for CNS tumours that can be used alone or in combination with current clinical practices. Blood-based biomarkers can be informative in the diagnosis and monitoring of various types of cancer. However, blood-based biomarkers have proven suboptimal for analysis of CNS tumours. In contrast, circulating biomarkers in cerebrospinal fluid (CSF), including circulating tumour DNA, microRNAs and metabolites, hold promise for accurate and minimally invasive assessment of CNS tumours. This review summarises the current understanding of these three types of CSF biomarkers and their potential use in neuro-oncologic clinical practice.

Exploratory machine-learned theoretical chemical shifts can closely predict metabolic mixture signals

Various chemical shift predictive methodologies have been studied and developed, but there remains the problem of prediction accuracy. Assigning the NMR signals of metabolic mixtures requires high predictive performance owing to the complexity of the signals. Here we propose a new predictive tool that combines quantum chemistry and machine learning. A scaling factor as the objective variable to correct the errors of 2355 theoretical chemical shifts was optimized by exploring 91 machine learning algorithms and using the partial structure of 150 compounds as explanatory variables. The optimal predictive model gave RMSDs between experimental and predicted chemical shifts of 0.2177 ppm for δ 1H and 3.3261 ppm for δ 13C in the test data; thus, better accuracy was achieved compared with existing empirical and quantum chemical methods. The utility of the predictive model was demonstrated by applying it to assignments of experimental NMR signals of a complex metabolic mixture.

Influence of Metabolite Extraction Methods on 1H-NMR-Based Metabolomic Profiling of Enteropathogenic Yersinia

Metabolite extraction is one of the critical steps in microbial metabolome analysis. It affects both the observed metabolite content and biological interpretation of the data. Several methods exist for metabolite extraction of microbes, but the literature is not consistent regarding the sample model, adequacy, and performance of each method. In this study, an optimal extraction protocol for Yersinia intracellular metabolites was investigated. The effect of five extraction protocols consisting of different extraction solvent systems (60% methanol, 100% methanol, acetonitrile/methanol/water (2:2:1), chloroform/methanol/water (2:1:1), and 60% ethanol) on Yersinia metabolic profiles were compared. The number of detected peaks, sample-to-sample variation, and metabolite yield were used as criteria. Extracted metabolites were analyzed by ¹H-NMR and principal component analysis (PCA), as well as partial least squares discriminant analysis (PLS-DA) multivariate statistics. The extraction protocol using 100% methanol as the extraction solvent provided the highest number of detected peaks for both Yersinia species analyzed, yielding more spectral information. Together with the reproducibility and spectrum quality, 100% methanol extraction was suitable for intracellular metabolite extraction from both species. However, depending on the metabolites of interest, other solvents might be more suitable for future studies, as distinct profiles were observed amongst the extraction methods.

Analytical Considerations of Stable Isotope Labelling in Lipidomics

Over the last two decades, lipids have come to be understood as far more than merely components of cellular membranes and forms of energy storage, and are now also being implicated to play important roles in a variety of diseases, with lipid biomarker research one of the most widespread applications of lipidomic techniques both in research and in clinical settings. Stable isotope labelling has become a staple technique in the analysis of small molecule metabolism and dynamics, as it is the only experimental setup by which biosynthesis, remodelling and degradation of biomolecules can be directly measured. Using state-of-the-art analytical technologies such as chromatography-coupled high resolution tandem mass spectrometry, the stable isotope label can be precisely localized and quantified within the biomolecules. The application of stable isotope labelling to lipidomics is however complicated by the diversity of lipids and the complexity of the necessary data analysis. This article discusses key experimental aspects of stable isotope labelling in the field of mass spectrometry-based lipidomics, summarizes current applications and provides an outlook on future developments and potential.

Multi 'omics comparison reveals metabolome biochemistry, not microbiome composition or gene expression, corresponds to elevated biogeochemical function in the hyporheic zone

Biogeochemical hotspots are pervasive at terrestrial-aquatic interfaces, particularly within groundwater-surface water mixing zones (hyporheic zones), and they are critical to understanding spatiotemporal variation in biogeochemical cycling. Here, we use multi 'omic comparisons of hotspots to low-activity sediments to gain mechanistic insight into hyporheic zone organic matter processing. We hypothesized that microbiome structure and function, as described by metagenomics and metaproteomics, would distinguish hotspots from low-activity sediments by shifting metabolism towards carbohydrate-utilizing pathways and elucidate discrete mechanisms governing organic matter processing in each location. We also expected these differences to be reflected in the metabolome, whereby hotspot carbon (C) pools and metabolite transformations therein would be enriched in sugar-associated compounds. In contrast to expectations, we found pronounced phenotypic plasticity in the hyporheic zone microbiome that was denoted by similar microbiome structure, functional potential, and expression across sediments with dissimilar metabolic rates. Instead, diverse nitrogenous metabolites and biochemical transformations characterized hotspots. Metabolomes also corresponded more strongly to aerobic metabolism than bulk C or N content only (explaining 67% vs. 42% and 37% of variation respectively), and bulk C and N did not improve statistical models based on metabolome composition alone. These results point to organic nitrogen as a significant regulatory factor influencing hyporheic zone organic matter processing. Based on our findings, we propose incorporating knowledge of metabolic pathways associated with different chemical fractions of C pools into ecosystem models will enhance prediction accuracy.

Metabolomics in the study of retinal health and disease

Metabolomics is the qualitative and quantitative assessment of the metabolites (small molecules < 1.5 kDa) in body fluids. The metabolites are the downstream of the genetic transcription and translation processes and also downstream of the interactions with environmental exposures; thus, they are thought to closely relate to the phenotype, especially for multifactorial diseases. In the last decade, metabolomics has been increasingly used to identify biomarkers in disease, and it is currently recognized as a very powerful tool with great potential for clinical translation. The metabolome and the associated pathways also help improve our understanding of the pathophysiology and mechanisms of disease. While there has been increasing interest and research in metabolomics of the eye, the application of metabolomics to retinal diseases has been limited, even though these are leading causes of blindness. In this manuscript, we perform a comprehensive summary of the tools and knowledge required to perform a metabolomics study, and we highlight essential statistical methods for rigorous study design and data analysis. We review available protocols, summarize the best approaches, and address the current unmet need for information on collection and processing of tissues and biofluids that can be used for metabolomics of retinal diseases. Additionally, we critically analyze recent work in this field, both in animal models and in human clinical disease, including diabetic retinopathy and age-related macular degeneration. Finally, we identify opportunities for future research applying metabolomics to improve our current assessment and understanding of mechanisms of vitreoretinal diseases, and to hence improve patient assessment and care.

Metabolomics in the clinic: A review of the shared and unique features of untargeted metabolomics for clinical research and clinical testing

Metabolomics is the untargeted measurement of the metabolome, which is composed of the complement of small molecules detected in a biological sample. As such, metabolomic analysis produces a global biochemical phenotype. It is a technology that has been utilized in the research setting for over a decade. The metabolome is directly linked to and is influenced by genetics, epigenetics, environmental factors, and the microbiome-all of which affect health. Metabolomics can be applied to human clinical diagnostics and to other fields such as veterinary medicine, nutrition, exercise, physiology, agriculture/plant biochemistry, and toxicology. Applications of metabolomics in clinical testing are emerging, but several aspects of its use as a clinical test differ from applications focused on research or biomarker discovery and need to be considered for metabolomics clinical test data to have optimum impact, be meaningful, and be used responsibly. In this review, we deconstruct aspects and challenges of metabolomics for clinical testing by illustrating the significance of test design, accurate and precise data acquisition, quality control, data processing, n-of-1 comparison to a reference population, and biochemical pathway analysis. We describe how metabolomics technology is integral to defining individual biochemical phenotypes, elaborates on human health and disease, and fits within the precision medicine landscape. Finally, we conclude by outlining some future steps needed to bring metabolomics into the clinical space and to be recognized by the broader medical and regulatory fields.

Nuclear magnetic resonance-based metabolomic investigation reveals metabolic perturbations in PM2.5-treated A549 cells

Exposure to PM_2.5 is associated with an increased risk of lung diseases, and oxidative damage is the main reason for PM_2.5-mediated lung injuries. However, little is known about the early molecular events in PM_2.5-induced lung toxicity. In the present study, the metabolites in PM_2.5-treated A549 cells were examined via a robust and nondestructive nuclear magnetic resonance (NMR)-based metabolic approach to clarify the molecular mechanism of PM_2.5-induced toxicity. NMR analysis revealed that 12 metabolites were significantly altered in PM_2.5-treated A549 cells, including up-regulation of alanine, valine, lactate, ω-6 fatty acids, and citrate and decreased levels of gamma-aminobutyric acid, acetate, leucine, isoleucine, D-glucose, lysine, and dimethylglycine. Pathway analysis demonstrated that seven metabolic pathways which included alanine, aspartate and glutamate metabolism, aminoacyl-tRNA biosynthesis, taurine and hypotaurine metabolism, arginine and proline metabolism, starch and sucrose metabolism, valine, leucine and isoleucine biosynthesis, and tricarboxylic acid cycle were mostly influenced. Our results indicate that NMR technique turns out to be a simple and reliable method for exploring the toxicity mechanism of air pollutant.

Network analysis of membranous glomerulonephritis based on metabolomics data

Membranous glomerulonephritis (MGN) is one of the most frequent causes of nephrotic syndrome in adults. It is characterized by the thickening of the glomerular basement membrane in the renal tissue. The current diagnosis of MGN is based on renal biopsy and the detection of antibodies to the few podocyte antigens. Due to the limitations of the current diagnostic methods, including invasiveness and the lack of sensitivity of the current biomarkers, there is a requirement to identify more applicable biomarkers. The present study aimed to identify diagnostic metabolites that are involved in the development of the disease using topological features in the component‑reaction‑enzyme‑gene (CREG) network for MGN. Significant differential metabolites in MGN compared with healthy controls were identified using proton nuclear magnetic resonance and gas chromatography‑mass spectrometry techniques, and multivariate analysis. The CREG network for MGN was constructed, and metabolites with a high centrality and a striking fold‑change in patients, compared with healthy controls, were introduced as putative diagnostic biomarkers. In addition, a protein‑protein interaction (PPI) network, which was based on proteins associated with MGN, was built and analyzed using PPI analysis methods, including molecular complex detection and ClueGene Ontology. A total of 26 metabolites were identified as hub nodes in the CREG network, 13 of which had salient centrality and fold‑changes: Dopamine, carnosine, fumarate, nicotinamide D‑ribonucleotide, adenosine monophosphate, pyridoxal, deoxyguanosine triphosphate, L‑citrulline, nicotinamide, phenylalanine, deoxyuridine, tryptamine and succinate. A total of 13 subnetworks were identified using PPI analysis. In total, two of the clusters contained seed proteins (phenylalanine‑4‑hydroxlylase and cystathionine γ‑lyase) that were associated with MGN based on the CREG network. The following biological processes associated with MGN were identified using gene ontology analysis: 'Pyrimidine‑containing compound biosynthetic process', 'purine ribonucleoside metabolic process', 'nucleoside catabolic process', 'ribonucleoside metabolic process' and 'aromatic amino acid family metabolic process'. The results of the present study may be helpful in the diagnostic and therapeutic procedures of MGN. However, validation is required in the future.

NMR metabolomics study of follicular fluid in women with cancer resorting to fertility preservation

PURPOSE

The purpose of this study was to evaluate the possible application of metabolomics to identify follicular fluid changes in cancer patients undergoing fertility preservation. Although metabolomics have been applied already in cancer studies, this is the first application on follicular fluid of cancer patients.

METHODS

We selected for the study ten patients with breast cancer and lymphoma who resorted to oocyte cryopreservation to preserve fertility and ten healthy women undergoing in vitro fertilization treatments. Follicular fluid was collected at the time of oocytes retrieval. Metabolomic analysis of follicular fluids was performed by 1H-nuclear magnetic resonance (NMR) spectroscopy in combination with multivariate analysis to interpret the spectral data. Univariate statistical analysis was applied to find correlations between patients' features and metabolites identified by NMR.

RESULTS

Partial least squares discriminant analysis allowed to discriminate samples from cancer patients and healthy controls. Univariate statistical analysis found significant correlations between patients' features and metabolites identified by NMR. This finding allowed to identify biomarkers to differentiate both healthy controls from cancer patients and the two different classes of oncological patients.

CONCLUSION

The follicular fluids of cancer patients display significant metabolic alterations in comparison to healthy subjects. NMR-based metabolomics could be a valid prognostic tool for identifying and selecting the best cryopreserved oocytes and improving the outcome prediction in cancer women undergoing in vitro fertilization.

Automated multicomponent phospholipid analysis using 31P NMR spectroscopy: example of vegetable lecithin and krill oil

Nuclear magnetic resonance (NMR) spectroscopy is widely applied in the field of metabolomics due to its quantitative nature and the reproducibility of data generated. However, one of the main challenges in routine NMR analysis is to obtain valuable information from large datasets of raw data in a high-throughput, automatic, and reproducible manner. In this study, a method to automatically annotate and quantify 12 phospholipids (PLs) in vegetable lecithin (soy, sunflower, rape) and krill oil is introduced. Automated routines were written in MATLAB environment for quantification of phosphatidylcholine (PC), phosphatidylinositol (PI), lyso-phosphatidylcholine (LPC), phosphatidylserine (PS), phosphatidylethanolamine (PE), diphosphatidylglycerol or cardiolipin (DPG), phosphatidylglycerol (PG), and lyso-phosphatidylethanolamine (LPE) in lecithin and of PC, PC-ether, LPC, PE, N-acyl phosphatidylethanolamine (APE), and LPE in krill oil matrix. The routine includes NMR spectra import, extraction of data points, peaking of local minima and local maxima in the data, integration, quantitation against internal standard, reporting of results as Word file, and their importing in our internal database. Our extensive studies on a representative set of more than 1000 lecithin (soy, rape, sunflower) and krill samples showed that the routine can automatically and accurately calculate the concentrations of all PLs. No systematic or proportional differences between automated and manual evaluation were detected. The developed automated program produces accurate results and requires less than 5 s for each analysis. This tool is already used in high-throughput PL analysis of krill and lecithin and will be adjusted to other matrices (egg, milk, chocolate, etc.) as well.

Evaluation of sample preparation methods for NMR-based metabolomics of cow milk

The quality of milk metabolome analyzed by nuclear magnetic resonance (NMR) is greatly influenced by the way samples are prepared. Although this analytical method is increasingly used to study milk metabolites, a thorough examination of available sample preparation protocols for milk has not been reported yet. We evaluated the performance of eight milk preparation methods namely (1) raw milk without any processing; (2) skimmed milk; (3) ultrafiltered milk; (4) skimming followed by ultrafiltration; (5) ultracentrifuged milk; (6) methanol; (7) dichloromethane; and (8) methanol/dichloromethane, in terms of spectra quality, repeatability, signal-to-noise ratio, extraction efficiency and yield criteria. A pooled sample of milk was used for all protocols. Skimming, ultracentrifugation and unprocessed milk protocols showed poor NMR spectra quality. Protocols involving multiple steps, namely methanol/dichloromethane extraction, and skimming followed by ultrafiltration produced inadequate results for signal-to-noise ratio parameter. Methanol and skimming associated to ultrafiltration provided good repeatability results compared to the other protocols. Chemical-based sample preparation protocols, particularly methanol, showed more efficient metabolite extraction compared to physical preparation methods. When considering all evaluation parameters, the methanol extraction protocol proved to be the best method. As a proof of utility, methanol protocol was then applied to milk samples from dairy cows fed a diet with or without a feed additive, showing a clear separation between the two groups of cows.

Combining Mass Spectrometry and NMR Improves Metabolite Detection and Annotation

Despite inherent complementarity, nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS) are routinely separately employed to characterize metabolomics samples. More troubling is the erroneous view that metabolomics is better served by exclusively utilizing MS. Instead, we demonstrate the importance of combining NMR and MS for metabolomics by using small chemical compound treatments of Chlamydomonas reinhardtii as an illustrative example. A total of 102 metabolites were detected (82 by gas chromatography-MS, 20 by NMR, and 22 by both techniques). Out of these, 47 metabolites of interest were identified: 14 metabolites were uniquely identified by NMR, and 16 metabolites were uniquely identified by GC-MS. A total of 17 metabolites were identified by both NMR and GC-MS. In general, metabolites identified by both techniques exhibited similar changes upon compound treatment. In effect, NMR identified key metabolites that were missed by MS and enhanced the overall coverage of the oxidative pentose phosphate pathway, Calvin cycle, tricarboxylic acid cycle, and amino acid biosynthetic pathways that informed on pathway activity in central carbon metabolism, leading to fatty-acid and complex-lipid synthesis. Our study emphasizes a prime advantage of combining multiple analytical techniques: the improved detection and annotation of metabolites.

Physiological changes for drought resistance in different species of Phyllanthus

The Phyllanthus genus is widely distributed in tropical and subtropical areas of the world and present several pharmacological applications. Drought is a restrictive factor for crop development and production, and is becoming a severe problem in many regions of the world. The species Phyllanthus amarus and Phyllanthus niruri were subjected to drought stress for varying periods of time (0, 3, 5, 7, and 10 days), and afterwards, leaves were collected and evaluated for physiological and biochemical responses, such as oxidative stress markers and drought-associated defense mechanisms. Results show that P. amarus has an endogenously higher level of variables of the oxidative/antioxidant metabolism, and P. niruri presents the most significant changes in those variables when compared to control and stressed plants. For both Phyllanthus species, drought stress induces higher levels of organic acids such as malic, succinic, and citric acids, and amino acids such as proline, GABA, alanine, and valine. Moreover, P. niruri plants respond with greater glucose and corilagin contents. Therefore, considering the evaluated metabolic changes, P. amarus is better adapted to drought-stress, while P. niruri presents an acclimation strategy that increases the corilagin levels induced by short-term drought stress.

1H NMR Metabolomics Identifies Underlying Inflammatory Pathology in Osteoarthritis and Rheumatoid Arthritis Synovial Joints

Despite osteoarthritis (OA) and rheumatoid arthritis (RA) being typically age-related, their underlying etiologies are markedly different. We used ¹H nuclear magnetic resonance (NMR) spectroscopy to identify differences in metabolite profiles in low volumes of OA and RA synovial fluid (SF). SF was aspirated from knee joints of 10 OA and 14 RA patients. 100 μL SF was analyzed using a 700 MHz Avance IIIHD Bruker NMR spectrometer with a TCI cryoprobe. Spectra were analyzed by Chenomx, Bruker TopSpin and AMIX software. Statistical analysis was undertaken using Metaboanalyst. 50 metabolites were annotated, including amino acids, saccharides, nucleotides and soluble lipids. Discriminant analysis identified group separation between OA and RA cohorts, with 32 metabolites significantly different between OA and RA SF (false discovery rate (FDR) < 0.05). Metabolites of glycolysis and the tricarboxylic acid cycle were lower in RA compared to OA; these results concur with higher levels of inflammation, synovial proliferation and hypoxia found in RA compared to OA. Elevated taurine in OA may indicate increased subchondral bone sclerosis. We demonstrate that quantifiable differences in metabolite abundance can be measured in low volumes of SF by ¹H NMR spectroscopy, which may be clinically useful to aid diagnosis and improve understanding of disease pathogenesis.

Changes in Neutrophil Metabolism upon Activation and Aging

Neutrophil activation is an important mechanism of host defense against pathogens. Chronic inflammation and autoimmunity are often associated with abnormalities in phenotype and functions of neutrophils. Since effector functions of immune cells during inflammation are tightly linked to their metabolic state, changes in neutrophil metabolome upon activation have been investigated in this study. Human neutrophils from healthy blood donors (n = 6) were treated either with tumor necrosis factor α (TNF-α) or lipopolysaccharide (LPS), whereas untreated neutrophils were used as control. Since apoptotic cells are abundant at sites of inflammation, the metabolome of aged, mainly apoptotic neutrophils was analyzed too. NMR spectroscopy of water-soluble metabolites revealed a clear distinction between aged neutrophils and neutrophils in control and activated samples. Higher levels of NAD⁺ (4- to 9-fold) and lower levels of ATP (0.3-fold), glutathione (0.8-fold), hypotaurine (0.8-fold), and phosphocholine (0.6-fold) were detected in aged neutrophils than in the other samples. Differences in metabolic profiles between LPS and TNF-α-stimulated cells as well as between stimulated and control neutrophils were statistically not significant. Replication with additional six blood donors confirmed increased NAD⁺ levels in aged cells compared to activated and control neutrophils.

Aging-related compensated hypogonadism: Role of metabolomic analysis in physiopathological and therapeutic evaluation

Aging is a complex process that increases the risk of chronic disease development. Hormonal and metabolic alterations occur with aging, such as androgen activity decrease. Studies aim to understand the role of testosterone replacement therapy (TRT) in males, however biomarkers and the metabolic responses to TRT are not well characterized. Therefore, the present study investigated TRT effect in young adult and aged rats by metabolomics. Male Wistar rats were divided into four groups: adult and adult + testo (6months), old and old + testo (25-27months). TRT animals received daily testosterone propionate (1 mg/kg/subcutaneous). TRT changed the testicular weight index decrease induced by aging but did not change the body weight and liver weight index. Sera were analyzed by liquid chromatograph high resolution mass spectrometry (LCMS/MS). Testosterone was quantified by target LCMS/MS. A total of 126 metabolites were detected with known identification altered by TRT by non-target metabolomics analysis. Multivariate statistics shows that all groups segregated individually after principal component analysis. The treatment with testosterone induced several metabolic alterations in adult and old rats that were summarized by variable importance on projection score, metabolite interaction and pathway analysis. Aging-related hypogonadism induces a pattern of systemic metabolic alterations that can be partially reversed by TRT, however, this treatment in aged rats induces novel alterations in some metabolites that are possible new targets for monitoring in patients submitted to TRT.

NMR-Based Metabolomic Approach To Elucidate the Differential Cellular Responses during Mitigation of Arsenic(III, V) in a Green Microalga

Nuclear magnetic resonance (NMR)-based metabolomic approach is a high-throughput fingerprinting technique that allows a rapid snapshot of metabolites without any prior knowledge of the organism. To demonstrate the applicability of NMR-based metabolomics in the field of microalgal-based bioremediation, novel freshwater microalga Scenedesmus sp. IITRIND2 that showed hypertolerance to As(III, V) was chosen for evaluating the metabolic perturbations during arsenic stress in both its oxidation states As(III) and As(V). Using NMR spectroscopy, we were able to identify and quantify an array of ∼45 metabolites, including amino acids, sugars, organic acids, phosphagens, osmolytes, nucleotides, etc. The NMR metabolomic experiments were complemented with various biophysical techniques to establish that the microalga tolerated the arsenic stress using a complex interplay of metabolites. The two different arsenic states distinctly influenced the microalgal cellular mechanisms due to their altered physicochemical properties. Eighteen differentially identified metabolites related to bioremediation of arsenic were then correlated to the major metabolic pathways to delineate the variable stress responses of microalga in the presence of As(III, V).

High-resolution gas chromatography/mass spectrometry metabolomics of non-human primate serum

RATIONALE

Metabolomics analyses using gas chromatography/mass spectrometry (GC/MS)-based metabolomics are heavily impeded by the lack of high-resolution mass spectrometers and limited spectral libraries to complement the excellent chromatography that GC platforms offer, a challenge that is being addressed with the implementation of high-resolution (HR) platforms such as 1D-GC/Orbitrap-MS.

METHODS

We used serum samples from a non-human primate (NHP), a baboon (Papio hamadryas), with suitable quality controls to quantify the chemical space using an advanced HRMS platform for confident metabolite identification and robust quantification to assess the suitability of the platform for routine clinical metabolomics research. In a complementary approach, we also analyzed the same serum samples using two-dimensional gas chromatography/time-of-flight mass spectrometry (2D-GC/TOF-MS) for metabolite identification and quantification following established standard protocols.

RESULTS

Overall, the 2D-GC/TOF-MS (~5000 peaks per sample) and 1D-GC/Orbitrap-MS (~500 peaks per sample) analyses enabled identification and quantification of a total of 555 annotated metabolites from the NHP serum with a spectral similarity score Rsim  ≥ 900 and signal-to-noise (S/N) ratio of >25. A common set of 30 metabolites with HMDB and KEGG IDs was quantified in the serum samples by both platforms where 2D-GC/TOF-MS enabled quantification of a total 384 metabolites (118 HMDB IDs) and 1D-GC/Orbitrap-MS analysis quantification of a total 200 metabolites (47 HMDB IDs). Thus, roughly 30-70% of the peaks remain unidentified or un-annotated across both platforms.

CONCLUSIONS

Our study provides insights into the benefits and limitations of the use of a higher mass resolution and mass accuracy instrument for untargeted GC/MS-based metabolomics with multi-dimensional chromatography in future studies addressing clinical conditions or exposome studies.

1H NMR metabolomics of microbial metabolites in the four MW agricultural biogas plant reactors: A case study of inhibition mirroring the acute rumen acidosis symptoms

In this study, nuclear magnetic resonance (¹H NMR) spectroscopic profiling was used to provide a more comprehensive view of microbial metabolites associated with poor reactor performance in a full-scale 4 MW mesophilic agricultural biogas plant under fully operational and also under inhibited conditions. Multivariate analyses were used to assess the significance of differences between reactors whereas artificial neural networks (ANN) were used to identify the key metabolites responsible for inhibition and their network of interaction. Based on the results of nm-MDS ordination the subsamples of each reactor were similar, but not identical, despite homogenization of the full-scale reactors before sampling. Hence, a certain extent of variability due to the size of the system under analysis was transferred into metabolome analysis. Multivariate analysis showed that fully active reactors were clustered separately from those containing inhibited reactor metabolites and were significantly different. Furthermore, the three distinct inhibited states were significantly different from each other. The inhibited metabolomes were enriched in acetate, caprylate, trimethylamine, thymine, pyruvate, alanine, xanthine and succinate. The differences in the metabolic fingerprint between inactive and fully active reactors observed in this study resembled closely the metabolites differentiating the (sub) acute rumen acidosis inflicted and healthy rumen metabolomes, creating thus favorable conditions for the growth and activity of pathogenic bacteria. The consistency of our data with those reported before for rumen ecosystems shows that ¹H NMR based metabolomics is a reliable approach for the evaluation of metabolic events at full-scale biogas reactors.

High-Resolution Magic Angle Spinning NMR-Based Metabolomics Revealing Metabolic Changes in Lung of Mice Infected with P. aeruginosa Consistent with the Degree of Disease Severity

Pseudomonas aeruginosa is a critical pathogen for human health, due to increased resistances to antibiotics and to nosocomial infections. There is an urgent need for tools allowing for better understanding mechanisms underlying the disease processes and for evaluating new therapeutic strategies with animal models. Here, we used a novel approach, applying high-resolution magic angle spinning nuclear magnetic resonance spectroscopy (HRMAS NMR) directly to lung biopsies of mice to better understand the impact of infection on the tissue at a molecular level. Mice were infected with two P. aeruginosa strains of different virulence levels. Statistical analysis applied to HRMAS NMR data allowed us to build a multivariate discriminant model to distinguish the lungs' metabolic profiles of mice, infected or not. Moreover, a second model was built to appreciate the degree of severity of infection, demonstrating sufficient sensitivity of HRMAS NMR-based metabolomics to investigate this type of infection. The metabolic features that discriminate infection statuses are dominated by some key differentially expressed metabolites that are related, respectively, to bacterial carbon metabolism (glycerophosphocholine) and to septic hypoxic stress response of host (succinate). Finally, to get closer to clinical and diagnosis issues, we proposed to build simple logistic regression models to predict the infection status on the basis of only one metabolite intensity. Thus, we have demonstrated that succinate intensity could discriminate the infected/noninfected status infection with a sensibility of 89% and a specificity of 95%, and leucine/isoleucine intensity could predict the severe/not severe status of infection with a sensibility of 100% and a specificity of 95%. We also looked for the interest of this model in order to predict the efficacy of anti- P. aeruginosa treatment. By HRMAS metabolomics analysis of lungs infected with P. aeruginosa after vaccination, we demonstrated that this model could be a useful tool to predict the efficacy of new anti- P. aeruginosa drugs. This metabolomics approach could therefore be useful both for the definition of biomarkers of severity of infection and for an earlier characterization of therapeutic efficacy.

Sepsis: Personalized Medicine Utilizing 'Omic' Technologies-A Paradigm Shift?

Sepsis has over the years proven a considerable challenge to physicians and researchers. Numerous pharmacological and non-pharmacological interventions have been tested in trials, but have unfortunately failed to improve the general prognosis. This has led to the speculation that the sepsis population may be too heterogeneous to be targeted with the traditional one treatment suits all’ approach. Recent advances in genetic and biochemical analyses now allow genotyping and biochemical characterisation of large groups of patients via the ‘omics’ technologies. These new opportunities could lead to a paradigm shift in the approach to sepsis towards personalised treatments with interventions targeted towards specific pathophysiological mechanisms activated in the patient. In this article, we review the potentials and pitfalls of using new advanced technologies to deepen our understanding of the clinical syndrome of sepsis.

A Metabolomics Approach to Pharmacotherapy Personalization

The optimization of drug therapy according to the personal characteristics of patients is a perspective direction in modern medicine. One of the possible ways to achieve such personalization is through the application of "omics" technologies, including current, promising metabolomics methods. This review demonstrates that the analysis of pre-dose metabolite biofluid profiles allows clinicians to predict the effectiveness of a selected drug treatment for a given individual. In the review, it is also shown that the monitoring of post-dose metabolite profiles could allow clinicians to evaluate drug efficiency, the reaction of the host to the treatment, and the outcome of the therapy. A comparative description of pharmacotherapy personalization (pharmacogenomics, pharmacoproteomics, and therapeutic drug monitoring) and personalization based on the analysis of metabolite profiles for biofluids (pharmacometabolomics) is also provided.

Metabolomics: a challenge for detecting and monitoring inborn errors of metabolism

Timely newborn screening and genetic profiling are crucial in early recognition and treatment of inborn errors of metabolism (IEMs). A proposed nosology of IEMs has inserted 1,015 well-characterized IEMs causing alterations in specific metabolic pathways. With the increasing expansion of metabolomics in clinical biochemistry and laboratory medicine communities, several research groups have focused their interest on the analysis of metabolites and their interconnections in IEMs. Metabolomics has the potential to extend metabolic information, thus allowing to achieve an accurate diagnosis for the individual patient and to discover novel IEMs. Structural and functional information on 247 metabolites associated with 147 IEMs and 202 metabolic pathways involved in various IEMs have been reported in the human metabolome data base (HMDB). For each metabolic gene, a new computational approach can be developed for predicting a set of metabolites, whose concentration is predicted to change after gene knockout in urine, blood and other biological fluids. Both targeted and untargeted mass spectrometry (MS)-based metabolomic approaches have been used to expand the range of disease-associate metabolites. The quantitative targeted approach, in conjunction with chemometrics, can be considered a basic tool for validating known diagnostic biomarkers in various metabolic disorders. The untargeted approach broadens the identification of new biomarkers in known IEMs and allows pathways analysis. Urine is an ideal biological fluid for metabolomics in neonatology; however, the lack of standardization of preanalytical phase may generate potential interferences in metabolomic studies. The integration of genomic and metabolomic data represents the current challenge for improving diagnosis and prognostication of IEMs. The goals consist in identifying both metabolically active loci and genes relevant to a disease phenotype, which means deriving disease-specific biological insights.

Metabolomics in chronic kidney disease: Strategies for extended metabolome coverage

Chronic kidney disease (CKD) is becoming a major public health issue as prevalence is increasing worldwide. It also represents a major challenge for the identification of new early biomarkers, understanding of biochemical mechanisms, patient monitoring and prognosis. Each metabolite contained in a biofluid or tissue may play a role as a signal or as a driver in the development or progression of the pathology. Therefore, metabolomics is a highly valuable approach in this clinical context. It aims to provide a representative picture of a biological system, making exhaustive metabolite coverage crucial. Two aspects can be considered: analytical and biological coverage. From an analytical point of view, monitoring all metabolites within one run is currently impossible. Multiple analytical techniques providing orthogonal information should be carried out in parallel for coverage improvement. The biological aspect of metabolome coverage can be enhanced by using multiple biofluids or tissues for in-depth biological investigation, as the analysis of a single sample type is generally insufficient for whole organism extrapolation. Hence, recording of signals from multiple sample types and different analytical platforms generates massive and complex datasets so that chemometric tools, including data fusion approaches and multi-block analysis, are key tools for extracting biological information and for discovery of relevant biomarkers. This review presents the recent developments in the field of metabolomic analysis, from sampling and analytical strategies to chemometric tools, dedicated to the generation and handling of multiple complementary metabolomic datasets enabling extended metabolite coverage to improve our biological knowledge of CKD.

What We Learned From Big Data for Autophagy Research

Autophagy is the process by which cytoplasmic components are engulfed in double-membraned vesicles before being delivered to the lysosome to be degraded. Defective autophagy has been linked to a vast array of human pathologies. The molecular mechanism of the autophagic machinery is well-described and has been extensively investigated. However, understanding the global organization of the autophagy system and its integration with other cellular processes remains a challenge. To this end, various bioinformatics and network biology approaches have been developed by researchers in the last few years. Recently, large-scale multi-omics approaches (like genomics, transcriptomics, proteomics, lipidomics, and metabolomics) have been developed and carried out specifically focusing on autophagy, and generating multi-scale data on the related components. In this review, we outline recent applications of in silico investigations and big data analyses of the autophagy process in various biological systems.

Microenvironmental regulation of cancer cell metabolism: implications for experimental design and translational studies

Cancers have an altered metabolism, and there is interest in understanding precisely how oncogenic transformation alters cellular metabolism and how these metabolic alterations can translate into therapeutic opportunities. Researchers are developing increasingly powerful experimental techniques to study cellular metabolism, and these techniques have allowed for the analysis of cancer cell metabolism, both in tumors and in ex vivo cancer models. These analyses show that, while factors intrinsic to cancer cells such as oncogenic mutations, alter cellular metabolism, cell-extrinsic microenvironmental factors also substantially contribute to the metabolic phenotype of cancer cells. These findings highlight that microenvironmental factors within the tumor, such as nutrient availability, physical properties of the extracellular matrix, and interactions with stromal cells, can influence the metabolic phenotype of cancer cells and might ultimately dictate the response to metabolically targeted therapies. In an effort to better understand and target cancer metabolism, this Review focuses on the experimental evidence that microenvironmental factors regulate tumor metabolism, and on the implications of these findings for choosing appropriate model systems and experimental approaches.

Applications of metabolomics to study cancer metabolism

Reprogrammed metabolism supports tumor growth and provides a potential source of therapeutic targets and disease biomarkers. Mass spectrometry-based metabolomics has emerged as a broadly informative technique for profiling metabolic features associated with specific oncogenotypes, disease progression, therapeutic liabilities and other clinically relevant aspects of tumor biology. In this review, we introduce the applications of metabolomics to study deregulated metabolism and metabolic vulnerabilities in cancer. We provide examples of studies that used metabolomics to discover novel metabolic regulatory mechanisms, including processes that link metabolic alterations with gene expression, protein function, and other aspects of systems biology. Finally, we discuss emerging applications of metabolomics for in vivo isotope tracing and metabolite imaging, both of which hold promise to advance our understanding of the role of metabolic reprogramming in cancer.

Metabolomic changes in vertebrate host during malaria disease progression

Metabolomics refers to top-down systems biological analysis of metabolites in biological specimens. Phenotypic proximity of metabolites makes them interesting candidates for studying biomarkers of environmental stressors such as parasitic infections. Moreover, the host-parasite interaction directly impinges upon metabolic pathways since the parasite uses the host metabolite pool as a biosynthetic resource. Malarial infection, although not recognized as a classic metabolic disorder, often leads to severe metabolic changes such as hypoglycemia and lactic acidosis. Thus, metabolomic analysis of the infection has become an invaluable tool for promoting a better understanding of the host-parasite interaction and for the development of novel therapeutics. In this review, we summarize the current knowledge obtained from metabolomic studies of malarial infection in rodent models and human patients. Metabolomic analysis of experimental rodent malaria has provided significant insights into the mechanisms of disease progression including utilization of host resources by the parasite, sexual dimorphism in metabolic phenotypes, and cellular changes in host metabolism. Moreover, these studies also provide proof of concept for prediction of cerebral malaria. On the other hand, metabolite analysis of patient biofluids generates extensive data that could be of use in identifying biomarkers of infection severity and in monitoring disease progression. Through the use of metabolomic datasets one hopes to assess crucial infection-specific issues such as clinical severity, drug resistance, therapeutic targets, and biomarkers. Also discussed are nascent or newly emerging areas of metabolomics such as pre-erythrocytic stages of the infection and the host immune response. This review is organized in four broad sections-methodologies for metabolomic analysis, rodent infection models, studies of human clinical specimens, and potential of immunometabolomics. Data summarized in this review should serve as a springboard for novel hypothesis testing and lead to a better understanding of malarial infection and parasite biology.

Illuminating the dark metabolome to advance the molecular characterisation of biological systems

BACKGROUND

The latest version of the Human Metabolome Database (v4.0) lists 114,100 individual entries. Typically, however, metabolomics studies identify only around 100 compounds and many features identified in mass spectra are listed only as 'unknown compounds'. The lack of ability to detect all metabolites present, and fully identify all metabolites detected (the dark metabolome) means that, despite the great contribution of metabolomics to a range of areas in the last decade, a significant amount of useful information from publically funded studies is being lost or unused each year. This loss of data limits our potential gain in knowledge and understanding of important research areas such as cell biology, environmental pollution, plant science, food chemistry and health and biomedical research. Metabolomics therefore needs to develop new tools and methods for metabolite identification to advance as a field.

AIM OF REVIEW

In this critical review, some potential issues with metabolite identification are identified and discussed. New and novel emerging technologies and tools which may contribute to expanding the number of compounds identified in metabolomics studies (thus illuminating the dark metabolome) are reviewed. The aim is to stimulate debate and research in the molecular characterisation of biological systems to drive forward metabolomic research.

KEY SCIENTIFIC CONCEPTS OF REVIEW

The work specifically discusses dynamic nuclear polarisation nuclear magnetic resonance spectroscopy (DNP-NMR), non-proton NMR active nuclei, two-dimensional liquid chromatography (2DLC) and Raman spectroscopy (RS). It is suggested that developing new methods for metabolomics with these techniques could lead to advances in the field and better characterisation of biological systems.

Early Diagnosis of Sepsis: Is an Integrated Omics Approach the Way Forward?

Sepsis remains one of the leading causes of death in the USA and it is expected to get worse as the population ages. Moreover, the standard of care, which recommends aggressive treatment with appropriate antibiotics, has led to an increase in multiple drug-resistant organisms. There is a dire need for the development of new antibiotics, improved antibiotic stewardship, and therapies that treat the host response. Development of new sepsis therapeutics has been a disappointment as no drugs are currently approved to treat the various complications from sepsis. Much of the failure has been blamed on animal models that do not accurately reflect the course of the disease. However, recent improvements in metabolomic, transcriptomic, genomic, and proteomic platforms have allowed for a broad-spectrum look at molecular changes in the host response using clinical samples. Integration of these multi-omic datasets allows researchers to perform systems biology approaches to identify novel pathophysiology of the disease. In this review, we highlight what is currently known about sepsis and how integrative omics has identified new diagnostic and predictive models of sepsis as well as novel mechanisms. These changes may improve patient care as well as guide future preclinical analysis of sepsis.

Response to dietary carbohydrates in European seabass (Dicentrarchus labrax) muscle tissue as revealed by NMR-based metabolomics

INTRODUCTION

Feed optimization is a key step to the environmental and economic sustainability of aquaculture, especially for carnivorous species. Plant-derived ingredients can contribute to reduce costs and nitrogenous effluents while sparing wild fish stocks. However, the metabolic use of carbohydrates from vegetable sources by carnivorous fish is still not completely understood.

OBJECTIVES

We aimed to study the effects of diets with carbohydrates of different digestibilities, gelatinized starch (DS) and raw starch (RS), in the muscle metabolome of European seabass (Dicentrarchus labrax).

METHODS

We followed an NMR-metabolomics approach, using two sample preparation procedures, the intact muscle (HRMAS) and the aqueous muscle extracts (¹H NMR), to compare the variations in muscle metabolome between the two diets.

RESULTS

In muscle, multivariate analysis revealed similar metabolome shifts for DS and RS diets, when compared with the control diet. HRMAS of intact muscle, which included both hydrophobic and hydrophilic metabolites, showed increased lipid in DS-fed fish by univariate analysis. Regardless of the nature of the starch, increased glycine and phenylalanine, and decreased proline were observed when compared to the Ctr diet. Combined univariate analysis of intact muscle and aqueous extracts indicated specific diet related changes in lipid and amino acid metabolism, consistent with increased dietary carbohydrate supplementation.

CONCLUSIONS

Due to differential sample processing, outputs differ in detail but provide complementary information. After tracing nutritional alterations by profiling fillet components, DS seems to be the most promising alternative to fishmeal-based diets in aquaculture. This approach should be reproducible for other farmed fish species and provide valuable information on nutritional and organoleptic properties of the final product.

Urinary 1H-NMR Metabolomics in the First Week of Life Can Anticipate BPD Diagnosis

Despite the advancements in medical knowledge and technology, the etiopathogenesis of bronchopulmonary dysplasia (BPD) is not yet fully understood although oxidative stress seems to play a role, leading to a very demanding management of these patients by the neonatologist. In this context, metabolomics can be useful in understanding, diagnosing, and treating this illness since it is one of the newest omics science that analyzes the metabolome of an individual through the investigation of biological fluids such as urine and blood. In this study, 18 patients admitted to the Neonatal Intensive Care Unit of the Cagliari University Hospital were enrolled. Among them, 11 patients represented the control group and 7 patients subsequently developed BPD. A sample of urine was collected from each patient at 7 days of life and analyzed through ¹H-NMR coupled with multivariate statistical analysis. The discriminant metabolites between the 2 groups noted were alanine, betaine, trimethylamine-N-oxide, lactate, and glycine. Utilizing metabolomics, it was possible to detect the urinary metabolomics fingerprint of neonates in the first week of life who subsequently developed BPD. Future studies are needed to confirm these promising results suggesting a possible role of microbiota and oxidative stress, and to apply this technology in clinical practice.

Differential effects of graphene materials on the metabolism and function of human skin cells

Graphene-related materials (GRMs) such as graphene oxide (GO) and few-layer graphene (FLG) are used in multiple biomedical applications; however, there is still insufficient information available regarding their interactions with the main biological barriers such as skin. In this study, we explored the effects of GO and FLG on HaCaTs human skin keratinocytes, using NMR-based metabolomics and fluorescence microscopy to evaluate the global impact of each GRM on cell fate and damage. GO and FLG at low concentrations (5 μg mL-1) induced a differential remodeling of the metabolome, preceded by an increase in the level of radical oxygen species (ROS) and free cytosolic Ca2+. These changes are linked to a concentration-dependent increase in cell death by triggering apoptosis and necrosis, the latter being predominant at higher concentrations of the nanostructures. In addition, both compounds reduce the ability of HaCaT cells to heal wounds. Our results demonstrate that the GO and FLG used in this study, which mainly differ in their oxidation state, slightly trigger differential effects on HaCaTs cells, but with evident outcomes at the cellular and molecular levels. Their behavior as pro-apoptotic/necrotic substances and their ability to inhibit cell migration, even at low doses, should be considered in the development of future applications.

NMR-based metabolomics studies of human prostate cancer tissue

INTRODUCTION

Prostate cancer (PCa) is one of the most prevalent cancers in men worldwide. Serum prostate-specific antigen (PSA) remains the most used biomarker in the detection and management of patients with PCa, in spite of the problems related with its low specificity, false positive rate and overdiagnosis. Furthermore, PSA is unable to discriminate indolent from aggressive PCa, which can lead to overtreatment. Early diagnosed and treated PCa can have a good prognosis and is potentially curable. Therefore, the discovery of new biomarkers able to detect clinically significant aggressive PCa is urgently needed.

METHODS

This revision was based on an electronic literature search, using Pubmed, with Nuclear Magnetic Resonance (NMR), tissue and prostate cancer as keywords. All metabolomic studies performed in PCa tissues by NMR spectroscopy, from 2007 until March 2018, were included in this review.

RESULTS

In the context of cancer, metabolomics allows the analysis of the entire metabolic profile of cancer cells. Several metabolic alterations occur in cancer cells to sustain their abnormal rates of proliferation. NMR proved to be a suitable methodology for the evaluation of these metabolic alterations in PCa tissues, allowing to unveil alterations in citrate, spermine, choline, choline-related compounds, lactate, alanine and glutamate.

CONCLUSION

The study of the metabolic alterations associated with PCa progression, accomplished by the analysis of PCa tissue by NMR, offers a promising approach for elucidating biochemical pathways affected by PCa and also for discovering new clinical biomarkers. The main metabolomic alterations associated with PCa development and promising biomarker metabolites for diagnosis of PCa were outlined.

Metabolomics in Plant Priming Research: The Way Forward?

A new era of plant biochemistry at the systems level is emerging, providing detailed descriptions of biochemical phenomena at the cellular and organismal level. This new era is marked by the advent of metabolomics—the qualitative and quantitative investigation of the entire metabolome (in a dynamic equilibrium) of a biological system. This field has developed as an indispensable methodological approach to study cellular biochemistry at a global level. For protection and survival in a constantly-changing environment, plants rely on a complex and multi-layered innate immune system. This involves surveillance of ‘self’ and ‘non-self,’ molecule-based systemic signalling and metabolic adaptations involving primary and secondary metabolites as well as epigenetic modulation mechanisms. Establishment of a pre-conditioned or primed state can sensitise or enhance aspects of innate immunity for faster and stronger responses. Comprehensive elucidation of the molecular and biochemical processes associated with the phenotypic defence state is vital for a better understanding of the molecular mechanisms that define the metabolism of plant–pathogen interactions. Such insights are essential for translational research and applications. Thus, this review highlights the prospects of metabolomics and addresses current challenges that hinder the realisation of the full potential of the field. Such limitations include partial coverage of the metabolome and maximising the value of metabolomics data (extraction of information and interpretation). Furthermore, the review points out key features that characterise both the plant innate immune system and enhancement of the latter, thus underlining insights from metabolomic studies in plant priming. Future perspectives in this inspiring area are included, with the aim of stimulating further studies leading to a better understanding of plant immunity at the metabolome level.

Characterization and toxicity of nanoscale fragments in wastewater treatment plant effluent

Much attention has been paid to extracting and isolating specific and well-known nanoparticles (especially for engineered nanomaterials) from complex environmental matrices. However, such research may not provide global information on actual contamination because nanoscale fragments exist as mixtures of various elements and matrices in the real environment. The present work first isolated and characterized nanoscale fragments in effluents from municipal wastewater treatment plants (WWTPs). The nanoscale fragments were found to be composed of 70-85% carbon and low amounts of oxygen, heavy metals and other elements and exhibited nanosheet topographies (approximately 0.87-1.31 nm thickness and 68-187 nm lateral length). Because the isolated nanoscale fragments were mixtures rather than one specific type of nanoparticle, they were present at high concentrations ranging from 0.07 to 0.55 mg/L. It was also found that the accumulation of nanoscale fragments in rice reached 0.59 mg/g under exposure to environmentally relevant concentrations, leading to marked phytotoxicity (e.g., ultrastructural damage to chloroplasts and mitochondria). Metabolic analysis revealed the toxicological mechanisms to be related to disorders of carbohydrate, amino acid and fatty acid metabolism. This study is the first to characterize the properties and analyze the toxicity of nanoscale fragments in the effluents of WWTPs. Given that WWTP effluents containing nanoscale fragments are continuously discharged to the soil, surface water and seas, nanoscale fragment materials deserve considerable attention in future work compared with the few widely studied engineered nanoparticles.

In-Vivo NMR Spectroscopy: A Powerful and Complimentary Tool for Understanding Environmental Toxicity

Part review, part perspective, this article examines the applications and potential of in-vivo Nuclear Magnetic Resonance (NMR) for understanding environmental toxicity. In-vivo NMR can be applied in high field NMR spectrometers using either magic angle spinning based approaches, or flow systems. Solution-state NMR in combination with a flow system provides a low stress approach to monitor dissolved metabolites, while magic angle spinning NMR allows the detection of all components (solutions, gels and solids), albeit with additional stress caused by the rapid sample spinning. With in-vivo NMR it is possible to use the same organisms for control and exposure studies (controls are the same organisms prior to exposure inside the NMR). As such individual variability can be reduced while continual data collection over time provides the temporal resolution required to discern complex interconnected response pathways. When multidimensional NMR is combined with isotopic labelling, a wide range of metabolites can be identified in-vivo providing a unique window into the living metabolome that is highly complementary to more traditional metabolomics studies employing extracts, tissues, or biofluids.

Functional Genomics Approaches to Studying Symbioses between Legumes and Nitrogen-Fixing Rhizobia

Biological nitrogen fixation gives legumes a pronounced growth advantage in nitrogen-deprived soils and is of considerable ecological and economic interest. In exchange for reduced atmospheric nitrogen, typically given to the plant in the form of amides or ureides, the legume provides nitrogen-fixing rhizobia with nutrients and highly specialised root structures called nodules. To elucidate the molecular basis underlying physiological adaptations on a genome-wide scale, functional genomics approaches, such as transcriptomics, proteomics, and metabolomics, have been used. This review presents an overview of the different functional genomics approaches that have been performed on rhizobial symbiosis, with a focus on studies investigating the molecular mechanisms used by the bacterial partner to interact with the legume. While rhizobia belonging to the alpha-proteobacterial group (alpha-rhizobia) have been well studied, few studies to date have investigated this process in beta-proteobacteria (beta-rhizobia).

Reprogramming the metabolome rescues retinal degeneration

Metabolomics studies in the context of ophthalmology have largely focused on identifying metabolite concentrations that characterize specific retinal diseases. Studies involving mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy have shown that individuals suffering from retinal diseases exhibit metabolic profiles that markedly differ from those of control individuals, supporting the notion that metabolites may serve as easily identifiable biomarkers for specific conditions. An emerging branch of metabolomics resulting from biomarker studies, however, involves the study of retinal metabolic dysfunction as causes of degeneration. Recent publications have identified a number of metabolic processes-including but not limited to glucose and oxygen metabolism-that, when perturbed, play a role in the degeneration of photoreceptor cells. As a result, such studies have led to further research elucidating methods for prolonging photoreceptor survival in an effort to halt degeneration in its early stages. This review will explore the ways in which metabolomics has deepened our understanding of the causes of retinal degeneration and discuss how metabolomics can be used to prevent retinal degeneration from progressing to its later disease stages.

Advances in metabolome information retrieval: turning chemistry into biology. Part I: analytical chemistry of the metabolome

Metabolites are small molecules produced by enzymatic reactions in a given organism. Metabolomics or metabolic phenotyping is a well-established omics aimed at comprehensively assessing metabolites in biological systems. These comprehensive analyses use analytical platforms, mainly nuclear magnetic resonance spectroscopy and mass spectrometry, along with associated separation methods to gather qualitative and quantitative data. Metabolomics holistically evaluates biological systems in an unbiased, data-driven approach that may ultimately support generation of hypotheses. The approach inherently allows the molecular characterization of a biological sample with regard to both internal (genetics) and environmental (exosome, microbiome) influences. Metabolomics workflows are based on whether the investigator knows a priori what kind of metabolites to assess. Thus, a targeted metabolomics approach is defined as a quantitative analysis (absolute concentrations are determined) or a semiquantitative analysis (relative intensities are determined) of a set of metabolites that are possibly linked to common chemical classes or a selected metabolic pathway. An untargeted metabolomics approach is a semiquantitative analysis of the largest possible number of metabolites contained in a biological sample. This is part I of a review intending to give an overview of the state of the art of major metabolic phenotyping technologies. Furthermore, their inherent analytical advantages and limits regarding experimental design, sample handling, standardization and workflow challenges are discussed.

NMR-based metabolomics of biofluids in cancer

This review describes the current status of NMR-based metabolomics of biofluids with respect to cancer risk assessment, detection, disease characterization, prognosis, and treatment monitoring. While the metabolism of cancer cells is altered compared with that of non-proliferating cells, the metabolome of blood and urine reflects the entire organism. We conclude that many studies show impressive associations between biofluid metabolomics and cancer progression, but translation to clinical practice is currently hindered by lack of validation, difficulties in biological interpretation, and non-standardized analytical procedures.