Offline Two-Dimensional Liquid Chromatography-Mass Spectrometry for Deep Annotation of the Fecal Metabolome Following Fecal Microbiota Transplantation.

Biological interpretation of untargeted LC-MS-based metabolomics data depends on accurate compound identification, but current techniques fall short of identifying most features that can be detected. The human fecal metabolome is complex, variable, incompletely annotated, and serves as an ideal matrix to evaluate novel compound identification methods. We devised an experimental strategy for compound annotation using multidimensional chromatography and semiautomated feature alignment and applied these methods to study the fecal metabolome in the context of fecal microbiota transplantation (FMT) for recurrent C. difficile infection. Pooled fecal samples were fractionated using semipreparative liquid chromatography and analyzed by an orthogonal LC-MS/MS method. The resulting spectra were searched against commercial, public, and local spectral libraries, and annotations were vetted using retention time alignment and prediction. Multidimensional chromatography yielded more than a 2-fold improvement in identified compounds compared to conventional LC-MS/MS and successfully identified several rare and previously unreported compounds, including novel fatty-acid conjugated bile acid species. Using an automated software-based feature alignment strategy, most metabolites identified by the new approach could be matched to features that were detected but not identified in single-dimensional LC-MS/MS data. Overall, our approach represents a powerful strategy to enhance compound identification and biological insight from untargeted metabolomics data.

Nutrient-dependent regulation of ß-cell proinsulin content.

Insulin is made from proinsulin, but the extent to which fasting/feeding controls the homeostatically regulated proinsulin pool in pancreatic ß-cells remains largely unknown. Here, we first examined ß-cell lines (INS1E and Min6, which proliferate slowly and are routinely fed fresh medium every 2-3 days) and found that the proinsulin pool size responds to each feeding within 1 to 2 h, affected both by the quantity of fresh nutrients and the frequency with which they are provided. We observed no effect of nutrient feeding on the overall rate of proinsulin turnover as quantified from cycloheximide-chase experiments. We show that nutrient feeding is primarily linked to rapid dephosphorylation of translation initiation factor eIF2α, presaging increased proinsulin levels (and thereafter, insulin levels), followed by its rephosphorylation during the ensuing hours that correspond to a fall in proinsulin levels. The decline of proinsulin levels is blunted by the integrated stress response inhibitor, ISRIB, or by inhibition of eIF2α rephosphorylation with a general control nonderepressible 2 (not PERK) kinase inhibitor. In addition, we demonstrate that amino acids contribute importantly to the proinsulin pool; mass spectrometry shows that ß-cells avidly consume extracellular glutamine, serine, and cysteine. Finally, we show that in both rodent and human pancreatic islets, fresh nutrient availability dynamically increases preproinsulin, which can be quantified without pulse-labeling. Thus, the proinsulin available for insulin biosynthesis is rhythmically controlled by fasting/feeding cycles.

The molecular and metabolic program by which white adipocytes adapt to cool physiologic temperatures.

Although visceral adipocytes located within the body's central core are maintained at approximately 37°C, adipocytes within bone marrow, subcutaneous, and dermal depots are found primarily within the peripheral shell and generally exist at cooler temperatures. Responses of brown and beige/brite adipocytes to cold stress are well studied; however, comparatively little is known about mechanisms by which white adipocytes adapt to temperatures below 37°C. Here, we report that adaptation of cultured adipocytes to 31°C, the temperature at which distal marrow adipose tissues and subcutaneous adipose tissues often reside, increases anabolic and catabolic lipid metabolism, and elevates oxygen consumption. Cool adipocytes rely less on glucose and more on pyruvate, glutamine, and, especially, fatty acids as energy sources. Exposure of cultured adipocytes and gluteal white adipose tissue (WAT) to cool temperatures activates a shared program of gene expression. Cool temperatures induce stearoyl-CoA desaturase-1 (SCD1) expression and monounsaturated lipid levels in cultured adipocytes and distal bone marrow adipose tissues (BMATs), and SCD1 activity is required for acquisition of maximal oxygen consumption at 31°C.

Compound Identification Strategies in Mass Spectrometry-Based Metabolomics and Pharmacometabolomics.

The metabolome is composed of a vast array of molecules, including endogenous metabolites and lipids, diet- and microbiome-derived substances, pharmaceuticals and supplements, and exposome chemicals. Correct identification of compounds from this diversity of classes is essential to derive biologically relevant insights from metabolomics data. In this chapter, we aim to provide a practical overview of compound identification strategies for mass spectrometry-based metabolomics, with a particular eye toward pharmacologically-relevant studies. First, we describe routine compound identification strategies applicable to targeted metabolomics. Next, we discuss both experimental (data acquisition-focused) and computational (software-focused) strategies used to identify unknown compounds in untargeted metabolomics data. We then discuss the importance of, and methods for, assessing and reporting the level of confidence of compound identifications. Throughout the chapter, we discuss how these steps can be implemented using today's technology, but also highlight research underway to further improve accuracy and certainty of compound identification. For readers interested in interpreting metabolomics data already collected, this chapter will supply important context regarding the origin of the metabolite names assigned to features in the data and help them assess the certainty of the identifications. For those planning new data acquisition, the chapter supplies guidance for designing experiments and selecting analysis methods to enable accurate compound identification, and it will point the reader toward best-practice data analysis and reporting strategies to allow sound biological and pharmacological interpretation.

metabCombiner: Paired Untargeted LC-HRMS Metabolomics Feature Matching and Concatenation of Disparately Acquired Data Sets.

LC-HRMS experiments detect thousands of compounds, with only a small fraction of them identified in most studies. Traditional data processing pipelines contain an alignment step to assemble the measurements of overlapping features across samples into a unified table. However, data sets acquired under nonidentical conditions are not amenable to this process, mostly due to significant alterations in chromatographic retention times. Alignment of features between disparately acquired LC-MS metabolomics data could aid collaborative compound identification efforts and enable meta-analyses of expanded data sets. Here, we describe metabCombiner, a new computational pipeline for matching known and unknown features in a pair of untargeted LC-MS data sets and concatenating their abundances into a combined table of intersecting feature measurements. metabCombiner groups features by mass-to-charge (m/z) values to generate a search space of possible feature pair alignments, fits a spline through a set of selected retention time ordered pairs, and ranks alignments by m/z, mapped retention time, and relative abundance similarity. We evaluated this workflow on a pair of plasma metabolomics data sets acquired with different gradient elution methods, achieving a mean absolute retention time prediction error of roughly 0.06 min and a weighted per-compound matching accuracy of approximately 90%. We further demonstrate the utility of this method by comprehensively mapping features in urine and muscle metabolomics data sets acquired from different laboratories. metabCombiner has the potential to bridge the gap between otherwise incompatible metabolomics data sets and is available as an R package at https://github.com/hhabra/metabCombiner and Bioconductor.

Modifying Chromatography Conditions for Improved Unknown Feature Identification in Untargeted Metabolomics.

Untargeted metabolomics is an essential component of systems biology research, but it is plagued by a high proportion of detectable features not identified with a chemical structure. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) experiments produce spectra that can be searched against databases to help identify or classify these unknowns, but many features do not generate spectra of sufficient quality to enable successful annotation. Here, we explore alterations to gradient length, mass loading, and rolling precursor ion exclusion parameters for reversed phase liquid chromatography (RPLC) and hydrophilic interaction liquid chromatography (HILIC) that improve compound identification performance for human plasma samples. A manual review of spectral matches from the HILIC data set was used to determine reasonable thresholds for search score and other metrics to enable semi-automated MS/MS data analysis. Compared to typical LC-MS/MS conditions, methods adapted for compound identification increased the total number of unique metabolites that could be matched to a spectral database from 214 to 2052. Following data alignment, 68.0% of newly identified features from the modified conditions could be detected and quantitated using a routine 20-min LC-MS run. Finally, a localized machine learning model was developed to classify the remaining unknowns and select a subset that shared spectral characteristics with successfully identified features. A total of 576 and 749 unidentified features in the HILIC and RPLC data sets were classified by the model as high-priority unknowns or higher-importance targets for follow-up analysis. Overall, our study presents a simple strategy to more deeply annotate untargeted metabolomics data for a modest additional investment of time and sample.

Untargeted Metabolomics Differentiates l-Carnitine Treated Septic Shock 1-Year Survivors and Nonsurvivors.

l-Carnitine is a candidate therapeutic for the treatment of septic shock, a condition that carries a ≥40% mortality. Responsiveness to l-carnitine may hinge on unique metabolic profiles that are not evident from the clinical phenotype. To define these profiles, we performed an untargeted metabolomic analysis of serum from 21 male sepsis patients enrolled in a placebo-controlled l-carnitine clinical trial. Although treatment with l-carnitine is known to induce changes in the sepsis metabolome, we found a distinct set of metabolites that differentiated 1-year survivors from nonsurvivors. Following feature alignment, we employed a new and innovative data reduction strategy followed by false discovery correction, and identified 63 metabolites that differentiated carnitine-treated 1-year survivors versus nonsurvivors. Following identification by MS/MS and database search, several metabolite markers of vascular inflammation were determined to be prominently elevated in the carnitine-treated nonsurvivor cohort, including fibrinopeptide A, allysine, and histamine. While preliminary, these results corroborate that metabolic profiles may be useful to differentiate l-carnitine treatment responsiveness. Furthermore, these data show that the metabolic signature of l-carnitine-treated nonsurvivors is associated with a severity of illness (e.g., vascular inflammation) that is not routinely clinically detected.

Dimethyl Itaconate Is Not Metabolized into Itaconate Intracellularly.

Itaconic acid is an important metabolite produced by macrophages after stimulation with LPS. The role of itaconate in the inflammatory cascade is unclear. Here we used [13C]itaconate and dimethyl [13C]itaconate (DMI) to probe itaconate metabolism, and find that [13C]DMI is not metabolized to itaconate. [13C]Itaconate in the cell culture medium leads to elevated intracellular levels of unlabeled succinate, with no evidence of intracellular uptake. The goal of this study is to encourage the development of effective pro-drug strategies to increase the intracellular levels of itaconate, which will enable more conclusive analysis of its action on macrophages and other cell and tissue types.

Sparse network modeling and metscape-based visualization methods for the analysis of large-scale metabolomics data.

MOTIVATION: Recent technological advances in mass spectrometry, development of richer mass spectral libraries and data processing tools have enabled large scale metabolic profiling. Biological interpretation of metabolomics studies heavily relies on knowledge-based tools that contain information about metabolic pathways. Incomplete coverage of different areas of metabolism and lack of information about non-canonical connections between metabolites limits the scope of applications of such tools. Furthermore, the presence of a large number of unknown features, which cannot be readily identified, but nonetheless can represent bona fide compounds, also considerably complicates biological interpretation of the data. RESULTS: Leveraging recent developments in the statistical analysis of high-dimensional data, we developed a new Debiased Sparse Partial Correlation algorithm (DSPC) for estimating partial correlation networks and implemented it as a Java-based CorrelationCalculator program. We also introduce a new version of our previously developed tool Metscape that enables building and visualization of correlation networks. We demonstrate the utility of these tools by constructing biologically relevant networks and in aiding identification of unknown compounds. AVAILABILITY AND IMPLEMENTATION: http://metscape.med.umich.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

RhoC GTPase Is a Potent Regulator of Glutamine Metabolism and N-Acetylaspartate Production in Inflammatory Breast Cancer Cells.

Inflammatory breast cancer (IBC) is an extremely lethal cancer that rapidly metastasizes. Although the molecular attributes of IBC have been described, little is known about the underlying metabolic features of the disease. Using a variety of metabolic assays, including (13)C tracer experiments, we found that SUM149 cells, the primary in vitro model of IBC, exhibit metabolic abnormalities that distinguish them from other breast cancer cells, including elevated levels of N-acetylaspartate, a metabolite primarily associated with neuronal disorders and gliomas. Here we provide the first evidence of N-acetylaspartate in breast cancer. We also report that the oncogene RhoC, a driver of metastatic potential, modulates glutamine and N-acetylaspartate metabolism in IBC cells in vitro, revealing a novel role for RhoC as a regulator of tumor cell metabolism that extends beyond its well known role in cytoskeletal rearrangement.

Monitoring cell secretions on microfluidic chips using solid-phase extraction with mass spectrometry.

Microfluidics is an enabling technology for both cell biology and chemical analysis. We combine these attributes with a microfluidic device for on-line solid-phase extraction (SPE) and mass spectrometry (MS) analysis of secreted metabolites from living cells in culture on the chip. The device was constructed with polydimethylsiloxane (PDMS) and contains a reversibly sealed chamber for perfusing cells. A multilayer design allowed a series of valves to control an on-chip 7.5 µL injection loop downstream of the cell chamber with operation similar to a six-port valve. The valve collects sample and then diverts it to a packed SPE bed that was connected in-line to treat samples prior to MS analysis. The valve allows samples to be collected and injected onto the SPE bed while preventing exposure of cells to added back pressure from the SPE bed and organic solvents needed to elute collected chemicals. Here, cultured murine 3T3-L1 adipocytes were loaded into the cell chamber and non-esterified fatty acids (NEFAs) that were secreted by the cells were monitored by SPE-MS at 30 min intervals. The limit of detection for a palmitoleic acid standard was 1.4 µM. Due to the multiplexed detection capabilities of MS, a variety of NEFAs were detected. Upon stimulation with isoproterenol and forskolin, secretion of select NEFAs was elevated an average of 1.5-fold compared to basal levels. Despite the 30-min delay between sample injections, this device is a step towards a miniaturized system that allows automated monitoring and identification of a variety of molecules in the extracellular environment.

Increased glucose metabolism and glycerolipid formation by fatty acids and GPR40 receptor signaling underlies the fatty acid potentiation of insulin secretion.

Acute fatty acid (FA) exposure potentiates glucose-stimulated insulin secretion in ß cells through metabolic and receptor-mediated effects. We assessed the effect of fatty acids on the dynamics of the metabolome in INS-1 cells following exposure to [U-(13)C]glucose to assess flux through metabolic pathways. Metabolite profiling showed a fatty acid-induced increase in long chain acyl-CoAs that were rapidly esterified with glucose-derived glycerol-3-phosphate to form lysophosphatidic acid, mono- and diacylglycerols, and other glycerolipids, some implicated in augmenting insulin secretion. Glucose utilization and glycolytic flux increased, along with a reduction in the NADH/NAD(+) ratio, presumably by an increase in conversion of dihydroxyacetone phosphate to glycerol-3-phosphate. The fatty acid-induced increase in glycolysis also resulted in increases in tricarboxylic cycle flux and oxygen consumption. Inhibition of fatty acid activation of FFAR1/GPR40 by an antagonist decreased glycerolipid formation, attenuated fatty acid increases in glucose oxidation, and increased mitochondrial FA flux, as evidenced by increased acylcarnitine levels. Conversely, FFAR1/GPR40 activation in the presence of low FA increased flux into glycerolipids and enhanced glucose oxidation. These results suggest that, by remodeling glucose and lipid metabolism, fatty acid significantly increases the formation of both lipid- and TCA cycle-derived intermediates that augment insulin secretion, increasing our understanding of mechanisms underlying ß cell insulin secretion.

Untargeted LC-MS metabolomics of bronchoalveolar lavage fluid differentiates acute respiratory distress syndrome from health.

Acute respiratory distress syndrome (ARDS) remains a significant hazard to human health and is clinically challenging because there are no prognostic biomarkers and no effective pharmacotherapy. The lung compartment metabolome may detail the status of the local environment that could be useful in ARDS biomarker discovery and the identification of drug target opportunities. However, neither the utility of bronchoalveolar lavage fluid (BALF) as a biofluid for metabolomics nor the optimal analytical platform for metabolite identification is established. To address this, we undertook a study to compare metabolites in BALF samples from patients with ARDS and healthy controls using a newly developed liquid chromatography (LC)-mass spectroscopy (MS) platform for untargeted metabolomics. Following initial testing of three different high-performance liquid chromatography (HPLC) columns, we determined that reversed phase (RP)-LC and hydrophilic interaction chromatography (HILIC) were the most informative chromatographic methods because they yielded the most and highest quality data. Following confirmation of metabolite identification, statistical analysis resulted in 37 differentiating metabolites in the BALF of ARDS compared with health across both analytical platforms. Pathway analysis revealed networks associated with amino acid metabolism, glycolysis and gluconeogenesis, fatty acid biosynthesis, phospholipids, and purine metabolism in the ARDS BALF. The complementary analytical platforms of RPLC and HILIC-LC generated informative, insightful metabolomics data of the ARDS lung environment.

Artificial sweeteners stimulate adipogenesis and suppress lipolysis independently of sweet taste receptors.

G protein-coupled receptors mediate responses to a myriad of ligands, some of which regulate adipocyte differentiation and metabolism. The sweet taste receptors T1R2 and T1R3 are G protein-coupled receptors that function as carbohydrate sensors in taste buds, gut, and pancreas. Here we report that sweet taste receptors T1R2 and T1R3 are expressed throughout adipogenesis and in adipose tissues. Treatment of mouse and human precursor cells with artificial sweeteners, saccharin and acesulfame potassium, enhanced adipogenesis. Saccharin treatment of 3T3-L1 cells and primary mesenchymal stem cells rapidly stimulated phosphorylation of Akt and downstream targets with functions in adipogenesis such as cAMP-response element-binding protein and FOXO1; however, increased expression of peroxisome proliferator-activated receptor γ and CCAAT/enhancer-binding protein α was not observed until relatively late in differentiation. Saccharin-stimulated Akt phosphorylation at Thr-308 occurred within 5 min, was phosphatidylinositol 3-kinase-dependent, and occurred in the presence of high concentrations of insulin and dexamethasone; phosphorylation of Ser-473 occurred more gradually. Surprisingly, neither saccharin-stimulated adipogenesis nor Thr-308 phosphorylation was dependent on expression of T1R2 and/or T1R3, although Ser-473 phosphorylation was impaired in T1R2/T1R3 double knock-out precursors. In mature adipocytes, artificial sweetener treatment suppressed lipolysis even in the presence of forskolin, and lipolytic responses were correlated with phosphorylation of hormone-sensitive lipase. Suppression of lipolysis by saccharin in adipocytes was also independent of T1R2 and T1R3. These results suggest that some artificial sweeteners have previously uncharacterized metabolic effects on adipocyte differentiation and metabolism and that effects of artificial sweeteners on adipose tissue biology may be largely independent of the classical sweet taste receptors, T1R2 and T1R3.

Sex modulates the diet-induced changes to the plasma lipidome in a rat model of cardiorespiratory fitness.

OBJECTIVE: Individuals with higher intrinsic cardiorespiratory fitness (CRF) experience decreased rates of cardiometabolic disease and mortality, and high CRF is associated with increased utilization of fatty acids (FAs) for energy. Studies suggest a complex relationship between CRF, diet, and sex with health outcomes, but this interaction is understudied. We hypothesized that FA utilization differences by fitness and sex could be detected in the plasma metabolome when rats or humans were fed a high carbohydrate (HC) or high fat (HF) diet. METHODS: Male and female rats selectively bred for low (LCR) and high (HCR) CRF were fed a chow diet or a sucrose-free HF (45 % fat) or HC (10 % fat) diet. Plasma samples were collected at days 0, 3, and 14. Human plasma data was collected from male and female participants who were randomized into a HC or HF diet for 21 days. Samples were analyzed using liquid chromatography-mass spectrometry and regression statistics were used to quantify the effect of diet, CRF, and sex on the lipidome. RESULTS: In rats, the baseline lipidome is more significantly influenced by sex than by CRF, especially as elevated diglycerides, triglycerides, phosphatidylcholines, and lysophosphatidylcholines in males. A dynamic response to diet was observed 3 days after diet, but after 14 days of either diet, the lipidome was modulated by sex with a larger effect size than by diet. Data from the human study also suggests a sex-dependent response to diet with opposite directionality of affect compared to rats, highlighting species-dependent responses to dietary intervention.

metabCombiner 2.0: Disparate Multi-Dataset Feature Alignment for LC-MS Metabolomics.

Liquid chromatography-high-resolution mass spectrometry (LC-HRMS), as applied to untargeted metabolomics, enables the simultaneous detection of thousands of small molecules, generating complex datasets. Alignment is a crucial step in data processing pipelines, whereby LC-MS features derived from common ions are assembled into a unified matrix amenable to further analysis. Variability in the analytical factors that influence liquid chromatography separations complicates data alignment. This is prominent when aligning data acquired in different laboratories, generated using non-identical instruments, or between batches from large-scale studies. Previously, we developed metabCombiner for aligning disparately acquired LC-MS metabolomics datasets. Here, we report significant upgrades to metabCombiner that enable the stepwise alignment of multiple untargeted LC-MS metabolomics datasets, facilitating inter-laboratory reproducibility studies. To accomplish this, a "primary" feature list is used as a template for matching compounds in "target" feature lists. We demonstrate this workflow by aligning four lipidomics datasets from core laboratories generated using each institution's in-house LC-MS instrumentation and methods. We also introduce batchCombine, an application of the metabCombiner framework for aligning experiments composed of multiple batches. metabCombiner is available as an R package on Github and Bioconductor, along with a new online version implemented as an R Shiny App.

Offline Two-dimensional Liquid Chromatography-Mass Spectrometry for Deep Annotation of the Fecal Metabolome.

Compound identification is an essential task in the workflow of untargeted metabolomics since the interpretation of the data in a biological context depends on the correct assignment of chemical identities to the features it contains. Current techniques fall short of identifying all or even most observable features in untargeted metabolomics data, even after rigorous data cleaning approaches to remove degenerate features are applied. Hence, new strategies are required to annotate the metabolome more deeply and accurately. The human fecal metabolome, which is the focus of substantial biomedical interest, is a more complex, more variable, yet lesser-investigated sample matrix compared to widely studied sample types like human plasma. This manuscript describes a novel experimental strategy using multidimensional chromatography to facilitate compound identification in untargeted metabolomics. Pooled fecal metabolite extract samples were fractionated using offline semi-preparative liquid chromatography. The resulting fractions were analyzed by an orthogonal LC-MS/MS method, and the data were searched against commercial, public, and local spectral libraries. Multidimensional chromatography yielded more than a 3-fold improvement in identified compounds compared to the typical single-dimensional LC-MS/MS approach and successfully identified several rare and novel compounds, including atypical conjugated bile acid species. Most features identified by the new approach could be matched to features that were detectable but not identifiable in the original single-dimension LC-MS data. Overall, our approach represents a powerful strategy for deeper annotation of the metabolome that can be implemented with commercially-available instrumentation, and should apply to any dataset requiring deeper annotation of the metabolome.

Sustained Perturbation of Metabolism and Metabolic Subphenotypes Are Associated With Mortality and Protein Markers of the Host Response.

Perturbed host metabolism is increasingly recognized as a pillar of sepsis pathogenesis, yet the dynamic alterations in metabolism and its relationship to other components of the host response remain incompletely understood. We sought to identify the early host-metabolic response in patients with septic shock and to explore biophysiological phenotyping and differences in clinical outcomes among metabolic subgroups. DESIGN: We measured serum metabolites and proteins reflective of the host-immune and endothelial response in patients with septic shock. SETTING: We considered patients from the placebo arm of a completed phase II, randomized controlled trial conducted at 16 U.S. medical centers. Serum was collected at baseline (within 24 hr of the identification of septic shock), 24-hour, and 48-hour postenrollment. Linear mixed models were built to assess the early trajectory of protein analytes and metabolites stratified by 28-day mortality status. Unsupervised clustering of baseline metabolomics data was conducted to identify subgroups of patients. PATIENTS: Patients with vasopressor-dependent septic shock and moderate organ dysfunction that were enrolled in the placebo arm of a clinical trial. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Fifty-one metabolites and 10 protein analytes were measured longitudinally in 72 patients with septic shock. In the 30 patients (41.7%) who died prior to 28 days, systemic concentrations of acylcarnitines and interleukin (IL)-8 were elevated at baseline and persisted at T24 and T48 throughout early resuscitation. Concentrations of pyruvate, IL-6, tumor necrosis factor-α, and angiopoietin-2 decreased at a slower rate in patients who died. Two groups emerged from clustering of baseline metabolites. Group 1 was characterized by higher levels of acylcarnitines, greater organ dysfunction at baseline and postresuscitation (p < 0.05), and greater mortality over 1 year (p < 0.001). CONCLUSIONS: Among patients with septic shock, nonsurvivors exhibited a more profound and persistent dysregulation in protein analytes attributable to neutrophil activation and disruption of mitochondrial-related metabolism than survivors.