Sum of the parts: mass spectrometry-based metabolomics

High-throughput MALDI-MSI metabolite analysis of plant tissue microarrays

A novel metabolomics analysis technique, termed matrix-assisted laser desorption/ionization mass spectrometry imaging-based plant tissue microarray (MALDI-MSI-PTMA), was successfully developed for high-throughput metabolite detection and imaging from plant tissues. This technique completely overcomes the disadvantage that metabolites cannot be accessible on an intact plant tissue due to the limitations of the special structures of plant cells (e.g. epicuticular wax, cuticle and cell wall) through homogenization of plant tissues, preparation of PTMA moulds and matrix spraying of PTMA sections. Our study shows several properties of MALDI-MSI-PTMA, including no need of sample separation and enrichment, high-throughput metabolite detection and imaging (>1000 samples per day), high-stability mass spectrometry data acquisition and imaging reconstruction and high reproducibility of data. This novel technique was successfully used to quickly evaluate the effects of two plant growth regulator treatments (i.e. 6-benzylaminopurine and N-phenyl-N'-1,2,3-thiadiazol-5-ylurea) on endogenous metabolite expression in plant tissue culture specimens of Dracocephalum rupestre Hance (D. rupestre). Intra-day and inter-day evaluations indicated that the metabolite data detected on PTMA sections had good reproducibility and stability. A total of 312 metabolite ion signals in leaves tissues of D. rupestre were detected, of which 228 metabolite ion signals were identified, they were composed of 122 primary metabolites, 90 secondary metabolites and 16 identified metabolites of unknown classification. The results demonstrated the advantages of MALDI-MSI-PTMA technique for enhancing the overall detection ability of metabolites in plant tissues, indicating that MALDI-MSI-PTMA has the potential to become a powerful routine practice for high-throughput metabolite study in plant science.

Loss of CD4+ T cell-intrinsic arginase 1 accelerates Th1 response kinetics and reduces lung pathology during influenza infection

Arginase 1 (Arg1), the enzyme catalyzing the conversion of arginine to ornithine, is a hallmark of IL-10-producing immunoregulatory M2 macrophages. However, its expression in T cells is disputed. Here, we demonstrate that induction of Arg1 expression is a key feature of lung CD4+ T cells during mouse in vivo influenza infection. Conditional ablation of Arg1 in CD4+ T cells accelerated both virus-specific T helper 1 (Th1) effector responses and its resolution, resulting in efficient viral clearance and reduced lung pathology. Using unbiased transcriptomics and metabolomics, we found that Arg1-deficiency was distinct from Arg2-deficiency and caused altered glutamine metabolism. Rebalancing this perturbed glutamine flux normalized the cellular Th1 response. CD4+ T cells from rare ARG1-deficient patients or CRISPR-Cas9-mediated ARG1-deletion in healthy donor cells phenocopied the murine cellular phenotype. Collectively, CD4+ T cell-intrinsic Arg1 functions as an unexpected rheostat regulating the kinetics of the mammalian Th1 lifecycle with implications for Th1-associated tissue pathologies.

Mitochondrial proteostasis mediated by CRL5 Ozz and Alix maintains skeletal muscle function

High energy-demanding tissues, such as skeletal muscle, require mitochondrial proteostasis to function properly. Two quality-control mechanisms, the ubiquitin proteasome system (UPS) and the release of mitochondria-derived vesicles, safeguard mitochondrial proteostasis. However, whether these processes interact is unknown. Here we show that the E3 ligase CRL5 ^Ozz , a member of the UPS, and its substrate Alix control the mitochondrial concentration of Slc25A4, a solute carrier that is essential for ATP production. The mitochondria in Ozz ^-/- or Alix ^-/- skeletal muscle share overt morphologic alterations (they are supernumerary, swollen, and dysmorphic) and have abnormal metabolomic profiles. We found that CRL5 ^Ozz ubiquitinates Slc25A4 and promotes its proteasomal degradation, while Alix facilitates SLC25A4 loading into exosomes destined for lysosomal destruction. The loss of Ozz or Alix offsets steady-state levels of Slc25A4, which disturbs mitochondrial metabolism and alters muscle fiber composition. These findings reveal hitherto unknown regulatory functions of Ozz and Alix in mitochondrial proteostasis.

An efficient LC-MS method for isomer separation and detection of sugars, phosphorylated sugars, and organic acids

Assessing central carbon metabolism in plants can be challenging due to the dynamic range in pool sizes, with low levels of important phosphorylated sugars relative to more abundant sugars and organic acids. Here, we report a sensitive liquid chromatography-mass spectrometry method for analysing central metabolites on a hybrid column, where both anion-exchange and hydrophilic interaction chromatography (HILIC) ligands are embedded in the stationary phase. The liquid chromatography method was developed for enhanced selectivity of 27 central metabolites in a single run with sensitivity at femtomole levels observed for most phosphorylated sugars. The method resolved phosphorylated hexose, pentose, and triose isomers that are otherwise challenging. Compared with a standard HILIC approach, these metabolites had improved peak areas using our approach due to ion enhancement or low ion suppression in the biological sample matrix. The approach was applied to investigate metabolism in high lipid-producing tobacco leaves that exhibited increased levels of acetyl-CoA, a precursor for oil biosynthesis. The application of the method to isotopologue detection and quantification was considered through evaluating 13C-labeled seeds from Camelina sativa. The method provides a means to analyse intermediates more comprehensively in central metabolism of plant tissues.

Recent development of nanoparticle-assisted metabolites analysis with mass spectrometry

Metabolomics systematically studies the changes of metabolites in biological systems in the temporal or spatial dimensions. It is a challenging task for comprehensive analysis of metabolomics because of diverse physicochemical properties and wide concentration distribution of metabolites. Used as enrichment sorbents, chemoselective probes, chromatographic stationary phases, MS ionization matrix, nanomaterials play excellent roles in improving the selectivity, separation performance, detection sensitivity and identification efficiency of metabolites when mass spectrometry is employed as the detection technique. This review summarized the recent development of nanoparticle-assisted metabolites analysis in terms of assisting the pretreatment of biological samples, improving the separation performance and enhancing the MALDI-MS detection.

Supercritical fluid chromatography - Mass spectrometry in metabolomics: Past, present, and future perspectives

Metabolomics, which consists of the comprehensive analysis of metabolites within a biological system, has been playing a growing role in the implementation of personalized medicine in modern healthcare. A wide range of analytical approaches are used in metabolomics, notably mass spectrometry (MS) combined to liquid chromatography (LC), gas chromatography (GC), or capillary electrophoresis (CE). However, none of these methods enable a comprehensive analysis of the metabolome, due to its extreme complexity and the large differences in physico-chemical properties between metabolite classes. In this context, supercritical fluid chromatography (SFC) represents a promising alternative approach to improve the metabolome coverage, while further increasing the analysis throughput. SFC, which uses supercritical CO₂ as mobile phase, leads to numerous advantages such as improved kinetic performance and lower environmental impact. This chromatographic technique has gained a significant interest since the introduction of advanced instrumentation, together with the introduction of dedicated interfaces for hyphenating SFC to MS. Moreover, new developments in SFC column chemistry (including sub-2 µm particles), as well as the use of large amounts of organic modifiers and additives in the CO₂-based mobile phase, significantly extended the application range of SFC, enabling the simultaneous analysis of a large diversity of metabolites. Over the last years, several applications have been reported in metabolomics using SFC-MS - from lipophilic compounds, such as steroids and other lipids, to highly polar compounds, such as carbohydrates, amino acids, or nucleosides. With all these advantages, SFC-MS is promised to a bright future in the field of metabolomics.

Dose-Response Metabolomics To Understand Biochemical Mechanisms and Off-Target Drug Effects with the TOXcms Software

Small-molecule drugs and toxicants commonly interact with more than a single protein target, each of which may have unique effects on cellular phenotype. Although untargeted metabolomics is often applied to understand the mode of action of these chemicals, simple pairwise comparisons of treated and untreated samples are insufficient to resolve the effects of disrupting two or more independent protein targets. Here, we introduce a workflow for dose-response metabolomics to evaluate chemicals that potentially affect multiple proteins with different potencies. Our approach relies on treating samples with various concentrations of compound prior to analysis with mass spectrometry-based metabolomics. Data are then processed with software we developed called TOXcms, which statistically evaluates dose-response trends for each metabolomic signal according to user-defined tolerances and subsequently groups those that follow the same pattern. Although TOXcms was built upon the XCMS framework, it is compatible with any metabolomic data-processing software. Additionally, to enable correlation of dose responses beyond those that can be measured by metabolomics, TOXcms also accepts data from respirometry, cell death assays, other omic platforms, etc. In this work, we primarily focus on applying dose-response metabolomics to find off-target effects of drugs. Using metformin and etomoxir as examples, we demonstrate that each group of dose-response patterns identified by TOXcms signifies a metabolic response to a different protein target with a unique drug binding affinity. TOXcms is freely available on our laboratory website at http://pattilab.wustl.edu/software/toxcms .

Systems-level analysis of isotopic labeling in untargeted metabolomic data by X13CMS

Identification of previously unreported metabolites (so-called 'unknowns') in untargeted metabolomic data has become an increasingly active area of research. Considerably less attention, however, has been dedicated to identifying unknown metabolic pathways. Yet, for each unknown metabolite structure, there is potentially a yet-to-be-discovered chemical transformation. Elucidating these biochemical connections is essential to advancing our knowledge of cellular metabolism and can be achieved by tracking an isotopically labeled precursor to an unexpected product. In addition to their role in mapping metabolic fates, isotopic labels also provide critical insight into pathway dynamics (i.e., metabolic fluxes) that cannot be obtained from conventional label-free metabolomic analyses. When labeling is compared quantitatively between conditions, for example, isotopic tracers can enable relative pathway activities to be inferred. To discover unexpected chemical transformations or unanticipated differences in metabolic pathway activities, we have developed X13CMS, a platform for analyzing liquid chromatography/mass spectrometry (LC/MS) data at the systems level. After providing cells, animals, or patients with an isotopically enriched metabolite (e.g., 13C, 15N, or 2H), X13CMS identifies compounds that have incorporated the isotopic tracer and reports the extent of labeling for each. The analysis can be performed with a single condition, or isotopic fates can be compared between multiple conditions. The choice of which metabolite to enrich and which isotopic label to use is highly context dependent, but 13C-glucose and 13C-glutamine are often applied because they feed a large number of metabolic pathways. X13CMS is freely available.

Aqueous Humor Analysis Identifies Higher Branched Chain Amino Acid Metabolism as a Marker for Human Leukocyte Antigen-B27 Acute Anterior Uveitis and Disease Activity

PURPOSE

Human leukocyte antigen-B27 (HLA-B27)-positive acute anterior uveitis (AAU) has a higher recurrence rate and shows more anterior chamber cell infiltration compared with HLA-B27-negative patients, suggesting distinct etiologies of these clinically overlapping conditions. To advance our understanding of the biology of AAU, we characterized the metabolic profile of aqueous humor (AqH) of patients with HLA-B27-associated AAU (B27-AAU) and noninfectious idiopathic AAU (idiopathic AAU).

DESIGN

Experimental laboratory study.

METHODS

AqH samples from 2 independent cohorts totaling 30 patients with B27-AAU, 16 patients with idiopathic AAU, and 20 patients with cataracts underwent 2 individual rounds of direct infusion mass spectrometry. Features predicted by direct infusion mass spectrometry that facilitated maximum separation between the disease groups in regression models were validated by liquid chromatography/tandem mass spectrometry-based quantification with appropriate standards.

RESULTS

Partial least square-discriminant analysis revealed metabolite profiles that were able to separate patients with B27-AAU from those with iodiopathic AAU. Pathway enrichment analysis, based on metabolites on which separation of the groups in the partial least square-discriminant analysis model was based, demonstrated the involvement of branched-chain amino acid biosynthesis, ascorbate and aldarate metabolism, the tricarboxylic acid cycle, and glycolysis-diverting pathways (eg, serine biosynthesis) across all investigated cohorts. Notably, the metabolite ketoleucine was elevated in B27-AAU across all 3 runs and moderately-but robustly-correlated with anterior chamber cell count (correlation coefficient range 0.41-0.81).

CONCLUSIONS

These results illustrate metabolic heterogeneity between HLA-B27-positive and HLA-B27-negative AAU, including an increase of branched-chain amino acid biosynthesis, that reflects disease activity in AAU.

A Protocol to Compare Methods for Untargeted Metabolomics

There are thousands of published methods for profiling metabolites with liquid chromatography/mass spectrometry (LC/MS). While many have been evaluated and optimized for a small number of select metabolites, very few have been assessed on the basis of global metabolite coverage. Thus, when performing untargeted metabolomics, researchers often question which combination of extraction techniques, chromatographic separations, and mass spectrometers is best for global profiling. Method comparisons are complicated because thousands of LC/MS signals (so-called features) in a typical untargeted metabolomic experiment cannot be readily identified with current resources. It is therefore challenging to distinguish methods that increase signal number due to improved metabolite coverage from methods that increase signal number due to contamination and artifacts. Here, we present the credentialing protocol to remove the latter from untargeted metabolomic datasets without having to identify metabolite structures. This protocol can be used to compare or optimize methods pertaining to any step of the untargeted metabolomic workflow (e.g., extraction, chromatography, mass spectrometer, informatic software, etc.).

Ratiometric quantitation of thiol metabolites using non-isotopic mass tags

Ratiometric quantitation is used in mass spectrometry to account for variations in ionization efficiencies due to heterogenous sample matrixes. Isotopes are most commonly used to achieve ratiometric quantitation because of their ability to co-elute chromatographically with each other and to have similar ionization efficiencies. In the work presented here, a new non-isotopic quantitative tagging approach is presented which allows chromatographic co-elution and similar ionization efficiencies. Using two variations of maleimide tags, t-butyl and cyclohexyl maleimide, thiols are quantified with a high degree of linearity up to five-fold concentration differences. Because these two tags have similar hydrophobcities, they elute simultaneously which allows them to be used for ratiometric quantitation. Beyond the five-fold linear range, signal compression is observed. This technique was able to quantify thiol changes in both in vitro pharmacological treatments as well as in vivo diabetic tissue.

Mass spectrometry in untargeted liquid chromatography/mass spectrometry metabolomics: Electrospray ionisation parameters and global coverage of the metabolome

RATIONALE

Liquid chromatography coupled to mass spectrometry (LC/MS) is a dominant analytical platform in metabolomics, because of the high sensitivity and resolution, thus enabling large-scale coverage of metabolomes. Correspondingly, electrospray ionisation (ESI) is the favoured ionisation method in untargeted LC/MS metabolomics given the ability to produce large numbers of ions. In the workflow of LC/ESI-MS metabolomics, maximising the ionisation efficiency over a wide mass range is inevitably an essential and determining step, subsequently defining the extent of coverage of the metabolome under investigation. Thus in this study, electronic factors related to the functioning of the ESI source, namely the capillary and sample cone voltages, were explored to investigate the influence on the data acquired in metabolomic investigations.

METHODS

Hydromethanolic samples from an untargeted study (sorghum plants responding dynamically to fungal infection) were analysed on a high-resolution/definition LC/ESI-MS system. Here the capillary and sample cone voltages of the ZSpray™ ESI source were varied between 1.5-3.0 kV and 10.0-40.0 V, respectively. The acquired data were processed with MarkerLynx™ software and analysed using central composite design response surface methodology and chemometric approaches (principal component analysis and orthogonal projection latent structures-discriminant analysis).

RESULTS

The results evidently demonstrate that both capillary and sampling cone voltages not only significantly influence the recorded MS signals with regard to the number and abundance of features, but also the overall structure of the collected data. This consequently impacts on the information extracted from the data and thus affects coverage of the metabolome.

CONCLUSIONS

The observations postulate in that, untargeted LC/MS metabolomics, 'what you see is what you ionise'. Although there is convergence of collected data under different ESI conditions, the nuances observed indicate that the exploration of different ion source settings could be the best trade-off in expanding and maximising the metabolome coverage in untargeted metabolomic experiments.

Systems-Level Annotation of a Metabolomics Data Set Reduces 25 000 Features to Fewer than 1000 Unique Metabolites

When using liquid chromatography/mass spectrometry (LC/MS) to perform untargeted metabolomics, it is now routine to detect tens of thousands of features from biological samples. Poor understanding of the data, however, has complicated interpretation and masked the number of unique metabolites actually being measured in an experiment. Here we place an upper bound on the number of unique metabolites detected in Escherichia coli samples analyzed with one untargeted metabolomics method. We first group multiple features arising from the same analyte, which we call "degenerate features", using a context-driven annotation approach. Surprisingly, this analysis revealed thousands of previously unreported degeneracies that reduced the number of unique analytes to ∼2961. We then applied an orthogonal approach to remove nonbiological features from the data using the 13C-based credentialing technology. This further reduced the number of unique analytes to less than 1000. Our 90% reduction in data is 5-fold greater than previously published studies. On the basis of the results, we propose an alternative approach to untargeted metabolomics that relies on thoroughly annotated reference data sets. To this end, we introduce the creDBle database ( http://creDBle.wustl.edu ), which contains accurate mass, retention time, and MS/MS fragmentation data as well as annotations of all credentialed features.

Metabolite Measurement: Pitfalls to Avoid and Practices to Follow

Metabolites are the small biological molecules involved in energy conversion and biosynthesis. Studying metabolism is inherently challenging due to metabolites' reactivity, structural diversity, and broad concentration range. Herein, we review the common pitfalls encountered in metabolomics and provide concrete guidelines for obtaining accurate metabolite measurements, focusing on water-soluble primary metabolites. We show how seemingly straightforward sample preparation methods can introduce systematic errors (e.g., owing to interconversion among metabolites) and how proper selection of quenching solvent (e.g., acidic acetonitrile:methanol:water) can mitigate such problems. We discuss the specific strengths, pitfalls, and best practices for each common analytical platform: liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS), nuclear magnetic resonance (NMR), and enzyme assays. Together this information provides a pragmatic knowledge base for carrying out biologically informative metabolite measurements.

Toward biotechnology in space: High-throughput instruments for in situ biological research beyond Earth

Space biotechnology is a nascent field aimed at applying tools of modern biology to advance our goals in space exploration. These advances rely on our ability to exploit in situ high throughput techniques for amplification and sequencing DNA, and measuring levels of RNA transcripts, proteins and metabolites in a cell. These techniques, collectively known as "omics" techniques have already revolutionized terrestrial biology. A number of on-going efforts are aimed at developing instruments to carry out "omics" research in space, in particular on board the International Space Station and small satellites. For space applications these instruments require substantial and creative reengineering that includes automation, miniaturization and ensuring that the device is resistant to conditions in space and works independently of the direction of the gravity vector. Different paths taken to meet these requirements for different "omics" instruments are the subjects of this review. The advantages and disadvantages of these instruments and technological solutions and their level of readiness for deployment in space are discussed. Considering that effects of space environments on terrestrial organisms appear to be global, it is argued that high throughput instruments are essential to advance (1) biomedical and physiological studies to control and reduce space-related stressors on living systems, (2) application of biology to life support and in situ resource utilization, (3) planetary protection, and (4) basic research about the limits on life in space. It is also argued that carrying out measurements in situ provides considerable advantages over the traditional space biology paradigm that relies on post-flight data analysis.

Gut Microbiota Profiling: Metabolomics Based Approach to Unravel Compounds Affecting Human Health

The gut microbiota is composed of a huge number of different bacteria, that produce a large amount of compounds playing a key role in microbe selection and in the construction of a metabolic signaling network. The microbial activities are affected by environmental stimuli leading to the generation of a wide number of compounds, that influence the host metabolome and human health. Indeed, metabolite profiles related to the gut microbiota can offer deep insights on the impact of lifestyle and dietary factors on chronic and acute diseases. Metagenomics, metaproteomics and metabolomics are some of the meta-omics approaches to study the modulation of the gut microbiota. Metabolomic research applied to biofluids allows to: define the metabolic profile; identify and quantify classes and compounds of interest; characterize small molecules produced by intestinal microbes; and define the biochemical pathways of metabolites. Mass spectrometry and nuclear magnetic resonance spectroscopy are the principal technologies applied to metabolomics in terms of coverage, sensitivity and quantification. Moreover, the use of biostatistics and mathematical approaches coupled with metabolomics play a key role in the extraction of biologically meaningful information from wide datasets. Metabolomic studies in gut microbiota-related research have increased, focusing on the generation of novel biomarkers, which could lead to the development of mechanistic hypotheses potentially applicable to the development of nutritional and personalized therapies.

Gut Microbiota Profiling: Metabolomics Based Approach to Unravel Compounds Affecting Human Health

The gut microbiota is composed of a huge number of different bacteria, that produce a large amount of compounds playing a key role in microbe selection and in the construction of a metabolic signaling network. The microbial activities are affected by environmental stimuli leading to the generation of a wide number of compounds, that influence the host metabolome and human health. Indeed, metabolite profiles related to the gut microbiota can offer deep insights on the impact of lifestyle and dietary factors on chronic and acute diseases. Metagenomics, metaproteomics and metabolomics are some of the meta-omics approaches to study the modulation of the gut microbiota. Metabolomic research applied to biofluids allows to: define the metabolic profile; identify and quantify classes and compounds of interest; characterize small molecules produced by intestinal microbes; and define the biochemical pathways of metabolites. Mass spectrometry and nuclear magnetic resonance spectroscopy are the principal technologies applied to metabolomics in terms of coverage, sensitivity and quantification. Moreover, the use of biostatistics and mathematical approaches coupled with metabolomics play a key role in the extraction of biologically meaningful information from wide datasets. Metabolomic studies in gut microbiota-related research have increased, focusing on the generation of novel biomarkers, which could lead to the development of mechanistic hypotheses potentially applicable to the development of nutritional and personalized therapies.

Gut Microbiota Profiling: Metabolomics Based Approach to Unravel Compounds Affecting Human Health

The gut microbiota is composed of a huge number of different bacteria, that produce a large amount of compounds playing a key role in microbe selection and in the construction of a metabolic signaling network. The microbial activities are affected by environmental stimuli leading to the generation of a wide number of compounds, that influence the host metabolome and human health. Indeed, metabolite profiles related to the gut microbiota can offer deep insights on the impact of lifestyle and dietary factors on chronic and acute diseases. Metagenomics, metaproteomics and metabolomics are some of the meta-omics approaches to study the modulation of the gut microbiota. Metabolomic research applied to biofluids allows to: define the metabolic profile; identify and quantify classes and compounds of interest; characterize small molecules produced by intestinal microbes; and define the biochemical pathways of metabolites. Mass spectrometry and nuclear magnetic resonance spectroscopy are the principal technologies applied to metabolomics in terms of coverage, sensitivity and quantification. Moreover, the use of biostatistics and mathematical approaches coupled with metabolomics play a key role in the extraction of biologically meaningful information from wide datasets. Metabolomic studies in gut microbiota-related research have increased, focusing on the generation of novel biomarkers, which could lead to the development of mechanistic hypotheses potentially applicable to the development of nutritional and personalized therapies.

Metabolomics and Its Application to Acute Lung Diseases

Metabolomics is a rapidly expanding field of systems biology that is gaining significant attention in many areas of biomedical research. Also known as metabonomics, it comprises the analysis of all small molecules or metabolites that are present within an organism or a specific compartment of the body. Metabolite detection and quantification provide a valuable addition to genomics and proteomics and give unique insights into metabolic changes that occur in tangent to alterations in gene and protein activity that are associated with disease. As a novel approach to understanding disease, metabolomics provides a "snapshot" in time of all metabolites present in a biological sample such as whole blood, plasma, serum, urine, and many other specimens that may be obtained from either patients or experimental models. In this article, we review the burgeoning field of metabolomics in its application to acute lung diseases, specifically pneumonia and acute respiratory disease syndrome (ARDS). We also discuss the potential applications of metabolomics for monitoring exposure to aerosolized environmental toxins. Recent reports have suggested that metabolomics analysis using nuclear magnetic resonance (NMR) and mass spectrometry (MS) approaches may provide clinicians with the opportunity to identify new biomarkers that may predict progression to more severe disease, such as sepsis, which kills many patients each year. In addition, metabolomics may provide more detailed phenotyping of patient heterogeneity, which is needed to achieve the goal of precision medicine. However, although several experimental and clinical metabolomics studies have been conducted assessing the application of the science to acute lung diseases, only incremental progress has been made. Specifically, little is known about the metabolic phenotypes of these illnesses. These data are needed to substantiate metabolomics biomarker credentials so that clinicians can employ them for clinical decision-making and investigators can use them to design clinical trials.

Metabolomics in diabetes, a review

Metabolomics is a promising approach for the identification of chemical compounds that serve for early detection, diagnosis, prediction of therapeutic response and prognosis of disease. Moreover, metabolomics has shown to increase the diagnostic threshold and prediction of type 2 diabetes. Evidence suggests that branched-chain amino acids, acylcarnitines and aromatic amino acids may play an early role on insulin resistance, exposing defects on amino acid metabolism, β-oxidation, and tricarboxylic acid cycle. This review aims to provide a panoramic view of the metabolic shifts that antecede or follow type 2 diabetes. Key messages BCAAs, AAAs and acylcarnitines are strongly associated with early insulin resistance. Diabetes risk prediction has been improved when adding metabolomic markers of dysglycemia to standard clinical and biochemical factors.

A mass spectrometric insight into the origins of benign gynecological disorders

Applications of mass spectrometry (MS) are rapidly expanding and encompass molecular and cellular biology. MS aids in the analysis of in vivo global molecular alterations, identifying potential biomarkers which may improve diagnosis and treatment of various pathologies. MS has added new dimensionality to medical research. Pioneering gynecologists now study molecular mechanisms underlying female reproductive pathology with MS-based tools. Although benign gynecologic disorders including endometriosis, adenomyosis, leiomyoma, and polycystic ovarian syndrome (PCOS) carry low mortality rates, they cause significant physical, mental, and social detriments. Additionally, some benign disorders are unfortunately associated with malignancies. MS-based technology can detect malignant changes in formerly benign proteomes and metabolomes with distinct advantages of speed, sensitivity, and specificity. We present the use of MS in proteomics and metabolomics, and summarize the current understanding of the molecular pathways concerning female reproductive anatomy. Highlight discoveries of novel protein and metabolite biomarkers via MS-based technology, we underscore the clinical application of these techniques in the diagnosis and management of benign gynecological disorders. © 2015 Wiley Periodicals, Inc. Mass Spec Rev 36:450-470, 2017.

Omics-Based Biomarkers: Application of Metabolomics in Neuropsychiatric Disorders

One of the major concerns of modern society is to identify putative biomarkers that serve as a valuable early diagnostic tool to identify a subset of patients with increased risk to develop neuropsychiatric disorders. Biomarker identification in neuropsychiatric disorders is proposed to offer a number of important benefits to patient well-being, including prediction of forthcoming disease, diagnostic precision, and a level of disease description that would guide treatment choice. Nowadays, the metabolomics approach has unlocked new possibilities in diagnostics of devastating disorders like neuropsychiatric disorders. Metabolomics-based technologies have the potential to map early biochemical changes in disease and hence provide an opportunity to develop predictive biomarkers that can be used as indicators of pathological abnormalities prior to development of clinical symptoms of neuropsychiatric disorders. This review highlights different -omics strategies for biomarker discovery in neuropsychiatric disorders. We also highlight initial outcomes from metabolomics studies in psychiatric disorders such as schizophrenia, bipolar disorder, and addictive disorders. This review will also present issues and challenges regarding the implementation of the metabolomics approach as a routine diagnostic tool in the clinical laboratory in context with neuropsychiatric disorders.

Translational metabolomics in cancer research

Over the last decade there has been a bottleneck in the introduction of new validated cancer metabolic biomarkers into clinical practice. Unfortunately, there are no biomarkers with adequate sensitivity for the early detection of cancer, and there remain a reliance on cancer antigens for monitoring treatment. The need for new diagnostics has led to the exploration of untargeted metabolomics for discovery of early biomarkers of specific cancers and targeted metabolomics to elucidate mechanistic aspects of tumor progression. The successful translation of such strategies to the treatment of cancer would allow earlier intervention to improve survival. We have reviewed the methodology that is being used to achieve these goals together with recent advances in implementing translational metabolomics in cancer.

Neutral and Phospholipids of the Myxococcus xanthus Lipodome during Fruiting Body Formation and Germination

Myxobacteria are well-known for their complex life cycle, including the formation of spore-filled fruiting bodies. The model organism Myxococcus xanthus exhibits a highly complex composition of neutral and phospholipids, including triacylglycerols (TAGs), diacylglycerols (DAGs), phosphatidylethanolamines (PEs), phosphatidylglycerols (PGs), cardiolipins (CLs), and sphingolipids, including ceramides (Cers) and ceramide phosphoinositols (Cer-PIs). In addition, ether lipids have been shown to be involved in development and signaling. In this work, we describe the lipid profile of M. xanthus during its entire life cycle, including spore germination. PEs, representing one of the major components of the bacterial membrane, decreased by about 85% during development from vegetative rods to round myxospores, while TAGs first accumulated up to 2-fold before they declined 48 h after the induction of sporulation. Presumably, membrane lipids are incorporated into TAG-containing lipid bodies, serving as an intermediary energy source for myxospore formation. The ceramides Cer(d-19:0/iso-17:0) and Cer(d-19:0/16:0) accumulated 6-fold and 3-fold, respectively, after 24 h of development, identifying them to be novel putative biomarkers for M. xanthus sporulation. The most abundant ether lipid, 1-iso-15:0-alkyl-2,3-di-iso-15:0-acyl glycerol (TG1), exhibited a lipid profile different from that of all TAGs during sporulation, reinforcing its signaling character. The absence of all these lipid profile changes in mutants during development supports the importance of lipids in myxobacterial development. During germination of myxospores, only the de novo biosynthesis of new cell membrane fatty acids was observed. The unexpected accumulation of TAGs also during germination might indicate a function of TAGs as intermediary storage lipids during this part of the life cycle as well.

Metabolomic Analysis of Oxidative and Glycolytic Skeletal Muscles by Matrix-Assisted Laser Desorption/IonizationMass Spectrometric Imaging (MALDI MSI)

Skeletal muscles are composed of heterogeneous muscle fibers that have different physiological, morphological, biochemical, and histological characteristics. In this work, skeletal muscles extensor digitorum longus, soleus, and whole gastrocnemius were analyzed by matrix-assisted laser desorption/ionization mass spectrometry to characterize small molecule metabolites of oxidative and glycolytic muscle fiber types as well as to visualize biomarker localization. Multivariate data analysis such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were performed to extract significant features. Different metabolic fingerprints were observed from oxidative and glycolytic fibers. Higher abundances of biomolecules such as antioxidant anserine as well as acylcarnitines were observed in the glycolytic fibers, whereas taurine and some nucleotides were found to be localized in the oxidative fibers.

Correction of precursor and product ion relative abundances in order to standardize CID spectra and improve Ecom50 accuracy for non-targeted metabolomics

Quantitative biases in the abundance of precursor and product ions due to mass discrimination in RF-only ion guides results in inaccurate collision induced dissociation (CID) spectra. We evaluated the effects of collision cell RF voltage and collision energy on CID spectra using ten singly protonated compounds (46-854 Da) in an orthogonal acceleration time-of-flight mass spectrometer. The relative ion transfer efficiency, i.e. the relative amount of ions transferred through the ion guide at any particular RF voltage was shown to be dependent on the ion's m/z. We developed an algorithm to correct for the mass discriminating effects of RF voltage on CID spectra. The algorithm was tested for both precursor and product ions at multiple RF voltages and collision energies in order to ensure reliability. Our results suggest that compounds that generate major product ions with m/z values <150 have peak intensities that deviate substantially from their actual abundance. This has implications for small molecule metabolomics research, particularly for studies that rely on CID spectra matching methods for structure identification.

Cancer metabolomics in basic science perspective

As metabolomics investigates metabolic pathways with the focus on metabolites, it is a suitable approach to address the complex metabolic alteration in cancer. In addition, metabolic profiles are affected by environmental and post-natal changes, and therefore, directly measuring many metabolites may provide epigenetically relevant information in cancer. Despite much development in our understanding of cancer metabolism, focus is often directed to signaling or metabolic proteins that modulate the metabolite levels. In this review, we discuss the "metabolite-oriented view" on cancer metabolism. We cover how metabolomics research contributed to our current insights into the basic mechanism of metabolic alterations leading to cancer. Then, we discuss specific metabolites and related enzymatic pathways directly related with tumorigenesis. We particularly pay attention to how metabolites regulate signaling proteins and metabolic enzymes ultimately leading to cancer phenotypes. Finally, we address future prospects and challenges of metabolomics in cancer research.

Avoiding misannotation of in-source fragmentation products as cellular metabolites in liquid chromatography-mass spectrometry-based metabolomics

Liquid chromatography-mass spectrometry (LC-MS) technology allows for rapid quantitation of cellular metabolites, with metabolites identified by mass spectrometry and chromatographic retention time. Recently, with the development of rapid scanning high-resolution high accuracy mass spectrometers and the desire for high throughput screening, minimal or no chromatographic separation has become increasingly popular. When analyzing complex cellular extracts, however, the lack of chromatographic separation could potentially result in misannotation of structurally related metabolites. Here, we show that, even using electrospray ionization, a soft ionization method, in-source fragmentation generates unwanted byproducts of identical mass to common metabolites. For example, nucleotide-triphosphates generate nucleotide-diphosphates, and hexose-phosphates generate triose-phosphates. We evaluated yeast intracellular metabolite extracts and found more than 20 cases of in-source fragments that mimic common metabolites. Accordingly, chromatographic separation is required for accurate quantitation of many common cellular metabolites.

Proteomics, metabolomics, and protein interactomics in the characterization of the molecular features of major depressive disorder

Omics technologies emerged as complementary strategies to genomics in the attempt to understand human illnesses. In general, proteomics technologies emerged earlier than those of metabolomics for major depressive disorder (MDD) research, but both are driven by the identification of proteins and/or metabolites that can delineate a comprehensive characterization of MDD's molecular mechanisms, as well as lead to the identification of biomarker candidates of all types—prognosis, diagnosis, treatment, and patient stratification. Also, one can explore protein and metabolite interactomes in order to pinpoint additional molecules associated with the disease that had not been picked up initially. Here, results and methodological aspects of MDD research using proteomics, metabolomics, and protein interactomics are reviewed, focusing on human samples.

Metabolomic Analysis Provides Insights on Paraquat-Induced Parkinson-Like Symptoms in Drosophila melanogaster

Paraquat (PQ) exposure causes degeneration of the dopaminergic neurons in an exposed organism while altered metabolism has a role in various neurodegenerative disorders. Therefore, the study presented here was conceived to depict the role of altered metabolism in PQ-induced Parkinson-like symptoms and to explore Drosophila as a potential model organism for such studies. Metabolic profile was generated in control and in flies that were fed PQ (5, 10, and 20 mM) in the diet for 12 and 24 h concurrent with assessment of indices of oxidative stress, dopaminergic neurodegeneration, and behavioral alteration. PQ was found to significantly alter 24 metabolites belonging to different biological pathways along with significant alterations in the above indices. In addition, PQ attenuated brain dopamine content in the exposed organism. The study demonstrates that PQ-induced alteration in the metabolites leads to oxidative stress and neurodegeneration in the exposed organism along with movement disorder, a phenotype typical of Parkinson-like symptoms. The study is relevant in the context of Drosophila and humans because similar alteration in the metabolic pathways has been observed in both PQ-exposed Drosophila and in postmortem samples of patients with Parkinsonism. Furthermore, this study provides advocacy towards the applicability of Drosophila as an alternate model organism for pre-screening of environmental chemicals for their neurodegenerative potential with altered metabolism.

Analysis of the lipidome of xenografts using MALDI-IMS and UHPLC-ESI-QTOF

Human tumor xenografts in immunodeficient mice are a very popular model to study the development of cancer and to test new drug candidates. Among the parameters analyzed are the variations in the lipid composition, as they are good indicators of changes in the cellular metabolism. Here, we present a study on the distribution of lipids in xenografts of NCI-H1975 human lung cancer cells, using MALDI imaging mass spectrometry and UHPLC-ESI-QTOF. The identification of lipids directly from the tissue by MALDI was aided by the comparison with identification using ESI ionization in lipid extracts from the same xenografts. Lipids belonging to PCs, PIs, SMs, DAG, TAG, PS, PA, and PG classes were identified and their distribution over the xenograft was determined. Three areas were identified in the xenograft, corresponding to cells in different metabolic stages and to a layer of adipose tissue that covers the xenograft.

After the feature presentation: technologies bridging untargeted metabolomics and biology

Liquid chromatography/mass spectrometry-based untargeted metabolomics is now an established experimental approach that is being broadly applied by many laboratories worldwide. Interpreting untargeted metabolomic data, however, remains a challenge and limits the translation of results into biologically relevant conclusions. Here we review emerging technologies that can be applied after untargeted profiling to extend biological interpretation of metabolomic data. These technologies include advances in bioinformatic software that enable identification of isotopes and adducts, comprehensive pathway mapping, deconvolution of MS(2) data, and tracking of isotopically labeled compounds. There are also opportunities to gain additional biological insight by complementing the metabolomic analysis of homogenized samples with recently developed technologies for metabolite imaging of intact tissues. To maximize the value of these emerging technologies, a unified workflow is discussed that builds on the traditional untargeted metabolomic pipeline. Particularly when integrated together, the combination of the advances highlighted in this review helps transform lists of masses and fold changes characteristic of untargeted profiling results into structures, absolute concentrations, pathway fluxes, and localization patterns that are typically needed to understand biology.

Recent developments in liquid-phase separation techniques for metabolomics

Metabolomics is the comprehensive analysis of low molecular weight compounds in biological samples such as cells, body fluids and tissues. Comprehensive profiling of metabolites in complex sample matrices with the current analytical toolbox remains a huge challenge. Over the past few years, liquid chromatography–mass spectrometry (LC–MS) and capillary electrophoresis–mass spectrometry (CE–MS) have emerged as powerful complementary analytical techniques in the field of metabolomics. This Review provides an update of the most recent developments in LC–MS and CE–MS for metabolomics. Concerning LC–MS, attention is paid to developments in column technology and miniaturized systems, while strategies are discussed to improve the reproducibility and the concentration sensitivity of CE–MS for metabolomics studies. Novel interfacing techniques for coupling CE to MS are also considered. Representative examples illustrate the potential of the recent developments in LC–MS and CE–MS for metabolomics. Finally, some conclusions and perspectives are provided.

An untargeted metabolomic workflow to improve structural characterization of metabolites

Mass spectrometry-based metabolomics relies on MS(2) data for structural characterization of metabolites. To obtain the high-quality MS(2) data necessary to support metabolite identifications, ions of interest must be purely isolated for fragmentation. Here, we show that metabolomic MS(2) data are frequently characterized by contaminating ions that prevent structural identification. Although using narrow-isolation windows can minimize contaminating MS(2) fragments, even narrow windows are not always selective enough, and they can complicate data analysis by removing isotopic patterns from MS(2) spectra. Moreover, narrow windows can significantly reduce sensitivity. In this work, we introduce a novel, two-part approach for performing metabolomic identifications that addresses these issues. First, we collect MS(2) scans with less stringent isolation settings to obtain improved sensitivity at the expense of specificity. Then, by evaluating MS(2) fragment intensities as a function of retention time and precursor mass targeted for MS(2) analysis, we obtain deconvolved MS(2) spectra that are consistent with pure standards and can therefore be used for metabolite identification. The value of our approach is highlighted with metabolic extracts from brain, liver, astrocytes, as well as nerve tissue, and performance is evaluated by using pure metabolite standards in combination with simulations based on raw MS(2) data from the METLIN metabolite database. A R package implementing the algorithms used in our workflow is available on our laboratory website ( http://pattilab.wustl.edu/decoms2.php ).

Histone deacetylases in herpesvirus replication and virus-stimulated host defense

Emerging evidence highlights a critical role for protein acetylation during herpesvirus infection. As prominent modulators of protein acetylation, histone deacetylases (HDACs) are essential transcriptional and epigenetic regulators. Not surprisingly, viruses have evolved a wide array of mechanisms to subvert HDAC functions. Here, we review the mechanisms underlying HDAC regulation during herpesvirus infection. We next discuss the roles of acetylation in host defense against herpesvirus infection. Finally, we provide a perspective on the contribution of current mass spectrometry-based “omic” technologies to infectious disease research, offering a systems biology view of infection.