Lipid kinase PIK3C3 maintains healthy brown and white adipose tissues to prevent metabolic diseases.

Adequate mass and function of adipose tissues (ATs) play essential roles in preventing metabolic perturbations. The pathological reduction of ATs in lipodystrophy leads to an array of metabolic diseases. Understanding the underlying mechanisms may benefit the development of effective therapies. Several cellular processes, including autophagy and vesicle trafficking, function collectively to maintain AT homeostasis. Here, we investigated the impact of adipocyte-specific deletion of the lipid kinase phosphatidylinositol 3-kinase catalytic subunit type 3 (PIK3C3) on AT homeostasis and systemic metabolism in mice. We report that PIK3C3 functions in all ATs and that its absence disturbs adipocyte autophagy and hinders adipocyte differentiation, survival, and function with differential effects on brown and white ATs. These abnormalities cause loss of white ATs, whitening followed by loss of brown ATs, and impaired "browning" of white ATs. Consequently, mice exhibit compromised thermogenic capacity and develop dyslipidemia, hepatic steatosis, insulin resistance, and type 2 diabetes. While these effects of PIK3C3 largely contrast previous findings with the autophagy-related (ATG) protein ATG7 in adipocytes, mice with a combined deficiency in both factors reveal a dominant role of the PIK3C3-deficient phenotype. We have also found that dietary lipid excess exacerbates AT pathologies caused by PIK3C3 deficiency. Surprisingly, glucose tolerance is spared in adipocyte-specific PIK3C3-deficient mice, a phenotype that is more evident during dietary lipid excess. These findings reveal a crucial yet complex role for PIK3C3 in ATs, with potential therapeutic implications.

Metabolomic Signatures Differentiate Immune Responses in Avian Influenza Vaccine Recipients.

BACKGROUND: Avian influenza viruses pose significant risk to human health. Vaccines targeting the hemagglutinin of these viruses are poorly immunogenic without the use of adjuvants. METHODS: Twenty healthy men and women (18-49 years of age) were randomized to receive two doses of inactivated influenza A/H5N1 vaccine alone (IIV) or with AS03 adjuvant (IIV-AS03) one month apart. Urine and serum samples were collected on day 0 and on days 1, 3, and 7 following first vaccination and subjected to metabolomics analyses to identify metabolites, metabolic pathways, and metabolite clusters associated with immunization. RESULTS: Seventy-three differentially abundant (DA) serum and 88 urine metabolites were identified for any post-vaccination day comparison. Pathway analysis revealed enrichment of tryptophan, tyrosine and nicotinate metabolism in urine and serum among IIV-AS03 recipients. Increased urine abundance of 4-vinylphenol sulfate on Day 1 was associated with serologic response based on hemagglutination inhibition responses. In addition, 9 DA urine metabolites were identified in participants with malaise compared to those without. CONCLUSIONS: Our findings suggest that tryptophan, tyrosine, and nicotinate metabolism are upregulated among IIV-AS03 recipients compared with IIV alone. Metabolites within these pathways may serve as measures of immunogenicity and may provide mechanistic insights for adjuvanted vaccines.

Current Practices in LC-MS Untargeted Metabolomics: A Scoping Review on the Use of Pooled Quality Control Samples.

Untargeted metabolomics is an analytical approach with numerous applications serving as an effective metabolic phenotyping platform to characterize small molecules within a biological system. Data quality can be challenging to evaluate and demonstrate in metabolomics experiments. This has driven the use of pooled quality control (QC) samples for monitoring and, if necessary, correcting for analytical variance introduced during sample preparation and data acquisition stages. Described herein is a scoping literature review detailing the use of pooled QC samples in published untargeted liquid chromatography-mass spectrometry (LC-MS) based metabolomics studies. A literature query was performed, the list of papers was filtered, and suitable articles were randomly sampled. In total, 109 papers were each reviewed by at least five reviewers, answering predefined questions surrounding the use of pooled quality control samples. The results of the review indicate that use of pooled QC samples has been relatively widely adopted by the metabolomics community and that it is used at a similar frequency across biological taxa and sample types in both small- and large-scale studies. However, while many studies generated and analyzed pooled QC samples, relatively few reported the use of pooled QC samples to improve data quality. This demonstrates a clear opportunity for the field to more frequently utilize pooled QC samples for quality reporting, feature filtering, analytical drift correction, and metabolite annotation. Additionally, our survey approach enabled us to assess the ambiguity in the reporting of the methods used to describe the generation and use of pooled QC samples. This analysis indicates that many details of the QC framework are missing or unclear, limiting the reader's ability to determine which QC steps have been taken. Collectively, these results capture the current state of pooled QC sample usage and highlight existing strengths and deficiencies as they are applied in untargeted LC-MS metabolomics.

Improving confidence in lipidomic annotations by incorporating empirical ion mobility regression analysis and chemical class prediction.

MOTIVATION: Mass spectrometry-based untargeted lipidomics aims to globally characterize the lipids and lipid-like molecules in biological systems. Ion mobility increases coverage and confidence by offering an additional dimension of separation and a highly reproducible metric for feature annotation, the collision cross-section (CCS). RESULTS: We present a data processing workflow to increase confidence in molecular class annotations based on CCS values. This approach uses class-specific regression models built from a standardized CCS repository (the Unified CCS Compendium) in a parallel scheme that combines a new annotation filtering approach with a machine learning class prediction strategy. In a proof-of-concept study using murine brain lipid extracts, 883 lipids were assigned higher confidence identifications using the filtering approach, which reduced the tentative candidate lists by over 50% on average. An additional 192 unannotated compounds were assigned a predicted chemical class. AVAILABILITY AND IMPLEMENTATION: All relevant source code is available at https://github.com/McLeanResearchGroup/CCS-filter. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The acyl chains of phosphoinositide PIP3 alter the structure and function of nuclear receptor steroidogenic factor-1.

Nuclear receptors are transcription factors that bind lipids, an event that induces a structural conformation of the receptor that favors interaction with transcriptional coactivators. The nuclear receptor steroidogenic factor-1 (SF-1, NR5A1) binds the signaling phosphoinositides PI(4,5)P2 (PIP2) and PI(3,4,5)P3 (PIP3), and our previous crystal structures showed how the phosphoinositide headgroups regulate SF-1 function. However, what role the acyl chains play in regulating SF-1 structure remains unaddressed. Here, we used X-ray crystallography with in vitro binding and functional assays to examine how the acyl chains of PIP3 regulate human SF-1 ligand-binding domain structure and function. Altering acyl chain length and unsaturation regulates apparent binding of all tested phosphoinositides to SF-1. Mass spectrometry-based lipidomics data suggest C16 and C18 phospholipids preferentially associate with SF-1 expressed ectopically in bacteria. We then solved the 2.5 Å crystal structure of SF-1 bound to dioleoyl PIP3(18:1/18:1) to compare it with a matched structure of SF-1 bound to dipalmitoyl PIP3(16:0/16:0). The dioleoyl-bound structure was severely disordered in a specific SF-1 region associated with pathogenic human polymorphisms and within the coactivator-binding region critical for SF-1 function while inducing increased sensitivity to protease digestion in solution. Validating these structural observations, in vitro functional studies showed dioleoyl PIP3 induced 6-fold poorer affinity of a peroxisome proliferator-activated receptor gamma coactivator 1-alpha coactivator peptide for SF-1 compared with dipalmitoyl PIP3. Together, these data suggest the chemical nature of the phosphoinositide acyl chains controls the ordered state of specific, clinically important structural regions in SF-1, regulating SF-1 function in vitro.

Targeted and Untargeted Mass Spectrometry Reveals the Impact of High-Fat Diet on Peripheral Amino Acid Regulation in a Mouse Model of Alzheimer's Disease.

Recent research regarding amino acid metabolism has shown that there may be a link between obesity and Alzheimer's disease (AD). This work reports a metabolomics study using targeted and untargeted mass spectrometry-based metabolomic strategies to investigate this link. Targeted hydrophilic interaction liquid chromatography-triple quadrupole mass spectrometry and untargeted reversed-phase liquid chromatography-high resolution tandem mass spectrometry assays were developed to analyze the metabolic changes that occur in AD and obesity. APPSwe/PS1ΔE9 (APP/PSEN1) transgenic mice (to represent familial or early-onset AD) and wild-type littermate controls were fed either a high-fat diet (HFD, 60% kcal from lard) or a low-fat diet (LFD, 10% kcal from lard) from 2 months of age or a reversal diet (HFD, followed by LFD from 9.5 months). For targeted analyses, we applied the guidelines outlined in the Clinical and Laboratory Standards Institute (CLSI) LC-MS C62-A document and the U.S. Food and Drug Administration (FDA) bioanalytical method validation guidance for industry to evaluate the figures of merit of the assays. Our targeted and untargeted metabolomics results suggest that numerous peripheral pathways, specifically amino acid metabolism and fatty acid metabolism, were significantly affected by AD and diet. Multiple amino acids (including alanine, glutamic acid, leucine, isoleucine, and phenylalanine), carnitines, and members of the fatty acid oxidation pathway were significantly increased in APP/PSEN1 mice on HFD compared to those on LFD. More substantial effects and changes were observed in the APP/PSEN1 mice than in the WT mice, suggesting that they were more sensitive to an HFD. These dysregulated peripheral pathways include numerous amino acid pathways and fatty acid beta oxidation and suggest that obesity combined with AD further enhances cognitive impairment, possibly through aggravated mitochondrial dysfunction. Furthermore, partial reversibility of many altered pathways was observed, which highlights that diet change can mitigate the metabolic effects of AD. The same trends in individual amino acids were observed in both strategies, highlighting the biological validity of the results.

Targeted Strategy to Analyze Antiepileptic Drugs in Human Serum by LC-MS/MS and LC-Ion Mobility-MS.

Routine small-molecule analysis is challenging owing to the need for high selectivity and/or low limits of quantification. This work reports a liquid chromatography-tandem mass spectrometry (LC-MS/MS) method to quantify 14 antiepileptic drugs (AEDs) in human serum. For the optimized LC-MS/MS method described herein, we applied the guidelines outlined in the Clinical and Laboratory Standards Institute (CLSI) LC-MS C62-A document and the U.S. Food and Drug Administration (FDA) Bioanalytical Method Validation Guidance for Industry to evaluate the quality of the assay. In these studies, AED linearity, analyte recovery, matrix effects, precision, and accuracy were assessed. Using liquid chromatography-drift tube ion mobility-mass spectrometry (LC-DTIM-MS), a qualitative method was also used to increase confidence in AED identification using accurate mass and collision cross section (CCS) measurements. The LC-DTIM-MS method was also used to assess the ability of drift tube CCS measurements to aid in the separation and identification of AED structural isomers and other AEDs. These data show that another dimension of information, namely CCS measurements, provides an orthogonal dimension of structural information needed for AED analysis. Multiplexed AED measurements using LC-MS/MS and LC-DTIM-MS have the potential to enable better optimization of dosing owing to the high precision capabilities available in these types of analytical studies. Taken together, these data also show the ability to increase confidence in small-molecule identification and quantification using these analytical technologies.

Evaluating a targeted multiple reaction monitoring approach to global untargeted lipidomic analyses of human plasma.

RATIONALE: The Lipidyzer platform was recently updated on a SCIEX QTRAP 6500+ mass spectrometer and offers a targeted lipidomics assay including 1150 different lipids. We evaluated this targeted approach using human plasma samples and compared the results against a global untargeted lipidomics method using a high-resolution Q Exactive HF Orbitrap mass spectrometer. METHODS: Lipids from human plasma samples (N = 5) were extracted using a modified Bligh-Dyer approach. A global untargeted analysis was performed using a Thermo Orbitrap Q Exactive HF mass spectrometer, followed by data analysis using Progenesis QI software. Multiple reaction monitoring (MRM)-based targeted analysis was performed using a QTRAP 6500+ mass spectrometer, followed by data analysis using SCIEX OS software. The samples were injected on three separate days to assess reproducibility for both approaches. RESULTS: Overall, 465 lipids were identified from 11 lipid classes in both approaches, of which 159 were similar between the methods, 168 lipids were unique to the MRM approach, and 138 lipids were unique to the untargeted approach. Phosphatidylcholine and phosphatidylethanolamine species were the most commonly identified using the untargeted approach, while triacylglycerol species were the most commonly identified using the targeted MRM approach. The targeted MRM approach had more consistent relative abundances across the three days than the untargeted approach. Overall, the coefficient of variation for inter-day comparisons across all lipid classes was ∼ 23% for the untargeted approach and ∼ 9% for the targeted MRM approach. CONCLUSIONS: The targeted MRM approach identified similar numbers of lipids to a conventional untargeted approach, but had better representation of 11 lipid classes commonly identified by both approaches. Based on the separation methods employed, the conventional untargeted approach could better detect phosphatidylcholine and sphingomyelin lipid classes. The targeted MRM approach had lower inter-day variability than the untargeted approach when tested using a small group of plasma samples. These studies highlight the advantages in using targeted MRM approaches for human plasma lipidomics analysis.

Utilizing Untargeted Ion Mobility-Mass Spectrometry To Profile Changes in the Gut Metabolome Following Biliary Diversion Surgery.

Obesity and obesity-related disorders are a global epidemic affecting over 10% of the world's population. Treatment of these diseases has become increasingly challenging and expensive. The most effective and durable treatment for Class III obesity (body mass index ≥35 kg/m2) is bariatric surgery, namely, Roux-en-Y gastric bypass (RYGB) and vertical sleeve gastrectomy. These procedures are associated with increased circulating bile acids, molecules that not only facilitate intestinal fat absorption but are also potent hormones regulating numerous metabolic pathways. We recently reported on a novel surgical procedure in mice, termed distal gallbladder bile diversion to the ileum (GB-ILdist), that emulates the altered bile flow after RYGB without other manipulations of gastrointestinal anatomy. GB-ILdist improves oral glucose tolerance in mice made obese with high-fat diet. This is accompanied by fat malabsorption and weight loss, which complicates studying the role of elevated circulating bile acids in metabolic control. A less aggressive surgery in which the gallbladder bile is diverted to the proximal ileum, termed GB-ILprox, also improves glucose control but is not accompanied by fat malabsorption. To better understand the differential effects achieved by these bile diversion procedures, an untargeted ultraperformance liquid chromatography-ion mobility-mass spectrometry (UPLC-IM-MS) method was optimized for fecal samples derived from mice that have undergone bile diversion surgery. Utilizing the UPLC-IM-MS method, we were able to identify dysregulation of bile acids, short-chain fatty acids, and cholesterol derivatives that contribute to the differential metabolism resulting from these surgeries.

In Utero Exposure to Histological Chorioamnionitis Primes the Exometabolomic Profiles of Preterm CD4+ T Lymphocytes.

Histological chorioamnionitis (HCA) is an intrauterine inflammatory condition that increases the risk for preterm birth, death, and disability because of persistent systemic and localized inflammation. The immunological mechanisms sustaining this response in the preterm newborn remain unclear. We sought to determine the consequences of HCA exposure on the fetal CD4+ T lymphocyte exometabolome. We cultured naive CD4+ T lymphocytes from HCA-positive and -negative preterm infants matched for gestational age, sex, race, prenatal steroid exposure, and delivery mode. We collected conditioned media samples before and after a 6-h in vitro activation of naive CD4+ T lymphocytes with soluble staphylococcal enterotoxin B and anti-CD28. We analyzed samples by ultraperformance liquid chromatography ion mobility-mass spectrometry. We determined the impact of HCA on the CD4+ T lymphocyte exometabolome and identified potential biomarker metabolites by multivariate statistical analyses. We discovered that: 1) CD4+ T lymphocytes exposed to HCA exhibit divergent exometabolomic profiles in both naive and activated states; 2) ∼30% of detected metabolites differentially expressed in response to activation were unique to HCA-positive CD4+ T lymphocytes; 3) metabolic pathways associated with glutathione detoxification and tryptophan degradation were altered in HCA-positive CD4+ T lymphocytes; and 4) flow cytometry and cytokine analyses suggested a bias toward a TH1-biased immune response in HCA-positive samples. HCA exposure primes the neonatal adaptive immune processes by inducing changes to the exometabolomic profile of fetal CD4+ T lymphocytes. These exometabolomic changes may link HCA exposure to TH1 polarization of the neonatal adaptive immune response.

An Integrated, High-Throughput Strategy for Multiomic Systems Level Analysis.

Proteomics, metabolomics, and transcriptomics generate comprehensive data sets, and current biocomputational capabilities allow their efficient integration for systems biology analysis. Published multiomics studies cover methodological advances as well as applications to biological questions. However, few studies have focused on the development of a high-throughput, unified sample preparation approach to complement high-throughput omic analytics. This report details the automation, benchmarking, and application of a strategy for transcriptomic, proteomic, and metabolomic analyses from a common sample. The approach, sample preparation for multi-omics technologies (SPOT), provides equivalent performance to typical individual omic preparation methods but greatly enhances throughput and minimizes the resources required for multiomic experiments. SPOT was applied to a multiomics time course experiment for zinc-treated HL-60 cells. The data reveal Zn effects on NRF2 antioxidant and NFkappaB signaling. High-throughput approaches such as these are critical for the acquisition of temporally resolved, multicondition, large multiomic data sets such as those necessary to assess complex clinical and biological concerns. Ultimately, this type of approach will provide an expanded understanding of challenging scientific questions across many fields.

Untargeted Molecular Discovery in Primary Metabolism: Collision Cross Section as a Molecular Descriptor in Ion Mobility-Mass Spectrometry.

In this work, we established a collision cross section (CCS) library of primary metabolites based on analytical standards in the Mass Spectrometry Metabolite Library of Standards (MSMLS) using a commercially available ion mobility-mass spectrometer (IM-MS). From the 554 unique compounds in the MSMLS plate library, we obtained a total of 1246 CCS measurements over a wide range of biochemical classes and adduct types. Resulting data analysis demonstrated that the curated CCS library provides broad molecular coverage of metabolic pathways and highlights intrinsic mass-mobility relationships for specific metabolite superclasses. The separation and characterization of isomeric metabolites were assessed, and all molecular species contained within the plate library, including isomers, were critically evaluated to determine the analytical separation efficiency in both the mass ( m/ z) and mobility (CCS/ΔCCS) dimension required for untargeted metabolomic analyses. To further demonstrate the analytical utility of CCS as an additional molecular descriptor, a well-characterized biological sample of human plasma serum (NIST SRM 1950) was examined by LC-IM-MS and used to provide a detailed isomeric analysis of carbohydrate constituents by ion mobility.

Automated flow injection method for the high precision determination of drift tube ion mobility collision cross sections.

The field of ion mobility-based omics studies requires high-quality collision cross section (CCS) libraries to effectively utilize CCS as a molecular descriptor. Absolute CCS values with the highest precision are obtained on drift tube instruments by measuring the drift time of ions at multiple drift voltages, commonly referred to as a 'stepped field' experiment. However, generating large scale absolute CCS libraries from drift tube instruments is time consuming due to the current lack of high-throughput methods. This communication reports a fully automated stepped-field method to acquire absolute CCS on commercially available equipment. Using a drift tube ion mobility-mass spectrometer (DTIM-MS) coupled to a minimally modified liquid chromatography (LC) system, CCS values can be measured online with a carefully timed flow injection analysis (FIA) experiment. Results demonstrate that the FIA stepped-field method yields CCS values which are of high analytical precision (<0.4% relative standard deviation, RSD) and accuracy (≤0.4% difference) comparable to CCS values obtained using traditional direct-infusion stepped-field experiments. This high-throughput CCS method consumes very little sample volume (20 µL) and will expedite the generation of large-scale CCS libraries to support molecular identification within global untargeted studies.

Integrated, High-Throughput, Multiomics Platform Enables Data-Driven Construction of Cellular Responses and Reveals Global Drug Mechanisms of Action.

An understanding of how cells respond to perturbation is essential for biological applications; however, most approaches for profiling cellular response are limited in scope to pre-established targets. Global analysis of molecular mechanism will advance our understanding of the complex networks constituting cellular perturbation and lead to advancements in areas, such as infectious disease pathogenesis, developmental biology, pathophysiology, pharmacology, and toxicology. We have developed a high-throughput multiomics platform for comprehensive, de novo characterization of cellular mechanisms of action. Platform validation using cisplatin as a test compound demonstrates quantification of over 10 000 unique, significant molecular changes in less than 30 days. These data provide excellent coverage of known cisplatin-induced molecular changes and previously unrecognized insights into cisplatin resistance. This proof-of-principle study demonstrates the value of this platform as a resource to understand complex cellular responses in a high-throughput manner.

Metabolic consequences of inflammatory disruption of the blood-brain barrier in an organ-on-chip model of the human neurovascular unit.

BACKGROUND: Understanding blood-brain barrier responses to inflammatory stimulation (such as lipopolysaccharide mimicking a systemic infection or a cytokine cocktail that could be the result of local or systemic inflammation) is essential to understanding the effect of inflammatory stimulation on the brain. It is through the filter of the blood-brain barrier that the brain responds to outside influences, and the blood-brain barrier is a critical point of failure in neuroinflammation. It is important to note that this interaction is not a static response, but one that evolves over time. While current models have provided invaluable information regarding the interaction between cytokine stimulation, the blood-brain barrier, and the brain, these approaches-whether in vivo or in vitro-have often been only snapshots of this complex web of interactions. METHODS: We utilize new advances in microfluidics, organs-on-chips, and metabolomics to examine the complex relationship of inflammation and its effects on blood-brain barrier function ex vivo and the metabolic consequences of these responses and repair mechanisms. In this study, we pair a novel dual-chamber, organ-on-chip microfluidic device, the NeuroVascular Unit, with small-volume cytokine detection and mass spectrometry analysis to investigate how the blood-brain barrier responds to two different but overlapping drivers of neuroinflammation, lipopolysaccharide and a cytokine cocktail of IL-1ß, TNF-α, and MCP1,2. RESULTS: In this study, we show that (1) during initial exposure to lipopolysaccharide, the blood-brain barrier is compromised as expected, with increased diffusion and reduced presence of tight junctions, but that over time, the barrier is capable of at least partial recovery; (2) a cytokine cocktail also contributes to a loss of barrier function; (3) from this time-dependent cytokine activation, metabolic signature profiles can be obtained for both the brain and vascular sides of the blood-brain barrier model; and (4) collectively, we can use metabolite analysis to identify critical pathways in inflammatory response. CONCLUSIONS: Taken together, these findings present new data that allow us to study the initial effects of inflammatory stimulation on blood-brain barrier disruption, cytokine activation, and metabolic pathway changes that drive the response and recovery of the barrier during continued inflammatory exposure.

Systems-Wide High-Dimensional Data Acquisition and Informatics Using Structural Mass Spectrometry Strategies.

BACKGROUND: Untargeted multiomics data sets are obtained for samples in systems, synthetic, and chemical biology by integrating chromatographic separations with ion mobility-mass spectrometry (IM-MS) analysis. The data sets are interrogated using bioinformatics strategies to organize the data for identification prioritization. CONTENT: The use of big data approaches for data mining of massive data sets in systems-wide analyses is presented. Untargeted biological data across multiomics dimensions are obtained using a variety of chromatography strategies with structural MS. Separation timescales for different techniques and the resulting data deluge when combined with IM-MS are presented. Data mining self-organizing map strategies are used to rapidly filter the data, highlighting those features describing uniqueness to the query. Examples are provided in longitudinal analyses in synthetic biology and human liver exposure to acetaminophen, and in chemical biology for natural product discovery from bacterial biomes. CONCLUSIONS: Matching the separation timescales of different forms of chromatography with IM-MS provides sufficient multiomics selectivity to perform untargeted systems-wide analyses. New data mining strategies provide a means for rapidly interrogating these data sets for feature prioritization and discovery in a range of applications in systems, synthetic, and chemical biology.

Wavelet-based peak detection and a new charge inference procedure for MS/MS implemented in ProteoWizard's msConvert.

We report the implementation of high-quality signal processing algorithms into ProteoWizard, an efficient, open-source software package designed for analyzing proteomics tandem mass spectrometry data. Specifically, a new wavelet-based peak-picker (CantWaiT) and a precursor charge determination algorithm (Turbocharger) have been implemented. These additions into ProteoWizard provide universal tools that are independent of vendor platform for tandem mass spectrometry analyses and have particular utility for intralaboratory studies requiring the advantages of different platforms convergent on a particular workflow or for interlaboratory investigations spanning multiple platforms. We compared results from these tools to those obtained using vendor and commercial software, finding that in all cases our algorithms resulted in a comparable number of identified peptides for simple and complex samples measured on Waters, Agilent, and AB SCIEX quadrupole time-of-flight and Thermo Q-Exactive mass spectrometers. The mass accuracy of matched precursor ions also compared favorably with vendor and commercial tools. Additionally, typical analysis runtimes (∼1-100 ms per MS/MS spectrum) were short enough to enable the practical use of these high-quality signal processing tools for large clinical and research data sets.

Phenotypic mapping of metabolic profiles using self-organizing maps of high-dimensional mass spectrometry data.

A metabolic system is composed of inherently interconnected metabolic precursors, intermediates, and products. The analysis of untargeted metabolomics data has conventionally been performed through the use of comparative statistics or multivariate statistical analysis-based approaches; however, each falls short in representing the related nature of metabolic perturbations. Herein, we describe a complementary method for the analysis of large metabolite inventories using a data-driven approach based upon a self-organizing map algorithm. This workflow allows for the unsupervised clustering, and subsequent prioritization of, correlated features through Gestalt comparisons of metabolic heat maps. We describe this methodology in detail, including a comparison to conventional metabolomics approaches, and demonstrate the application of this method to the analysis of the metabolic repercussions of prolonged cocaine exposure in rat sera profiles.

Metabolic consequences of interleukin-6 challenge in developing neurons and astroglia.

BACKGROUND: Maternal immune activation and subsequent interleukin-6 (IL-6) induction disrupt normal brain development and predispose the offspring to developing autism and schizophrenia. While several proteins have been identified as having some link to these developmental disorders, their prevalence is still small and their causative role, if any, is not well understood. However, understanding the metabolic consequences of environmental predisposing factors could shed light on disorders such as autism and schizophrenia. METHODS: To gain a better understanding of the metabolic consequences of IL-6 exposure on developing central nervous system (CNS) cells, we separately exposed developing neuron and astroglia cultures to IL-6 for 2 hours while collecting effluent from our gravity-fed microfluidic chambers. By coupling microfluidic technologies to ultra-performance liquid chromatography-ion mobility-mass spectrometry (UPLC-IM-MS), we were able to characterize the metabolic response of these CNS cells to a narrow window of IL-6 exposure. RESULTS: Our results revealed that 1) the use of this technology, due to its superb media volume:cell volume ratio, is ideally suited for analysis of cell-type-specific exometabolome signatures; 2) developing neurons have low secretory activity at baseline, while astroglia show strong metabolic activity; 3) both neurons and astroglia respond to IL-6 exposure in a cell type-specific fashion; 4) the astroglial response to IL-6 stimulation is predominantly characterized by increased levels of metabolites, while neurons mostly depress their metabolic activity; and 5) disturbances in glycerophospholipid metabolism and tryptophan/kynurenine metabolite secretion are two putative mechanisms by which IL-6 affects the developing nervous system. CONCLUSIONS: Our findings are potentially critical for understanding the mechanism by which IL-6 disrupts brain function, and they provide information about the molecular cascade that links maternal immune activation to developmental brain disorders.

Alterations in Cerebrospinal Fluid Urea Occur in Late Manifest Huntington's Disease.

Background: Huntington's disease (HD) is a neurodegenerative disorder caused by expanded cytosine-adenine-guanine (CAG) repeats in the Huntingtin gene, resulting in the production of mutant huntingtin proteins (mHTT). Previous research has identified urea as a key metabolite elevated in HD animal models and postmortem tissues of HD patients. However, the relationship between disease course and urea elevations, along with the molecular mechanisms responsible for these disturbances remain unknown. Objective: To better understand the molecular disturbances and timing of urea cycle metabolism across different stages in HD. Methods: We completed a global metabolomic profile of cerebrospinal fluid (CSF) from individuals who were at several stages of disease: pre-manifest (PRE), manifest (MAN), and late manifest (LATE) HD participants, and compared to controls. Results: Approximately 500 metabolites were significantly altered in PRE participants compared to controls, although no significant differences in CSF urea or urea metabolites were observed. CSF urea was significantly elevated in LATE participants only. There were no changes in the urea metabolites citrulline, ornithine, and arginine. Conclusions: Overall, our study confirms that CSF elevations occur late in the HD course, and these changes may reflect accumulating deficits in cellular energy metabolism.