Comprehensive Metabolomic Analysis of Human Heart Tissue Enabled by Parallel Metabolite Extraction and High-Resolution Mass Spectrometry.

The heart contracts incessantly and requires a constant supply of energy, utilizing numerous metabolic substrates, such as fatty acids, carbohydrates, lipids, and amino acids, to supply its high energy demands. Therefore, a comprehensive analysis of various metabolites is urgently needed for understanding cardiac metabolism; however, complete metabolome analyses remain challenging due to the broad range of metabolite polarities, which makes extraction and detection difficult. Herein, we implemented parallel metabolite extractions and high-resolution mass spectrometry (MS)-based methods to obtain a comprehensive analysis of the human heart metabolome. To capture the diverse range of metabolite polarities, we first performed six parallel liquid-liquid extractions (three monophasic, two biphasic, and one triphasic) of healthy human donor heart tissue. Next, we utilized two complementary MS platforms for metabolite detection: direct-infusion ultrahigh-resolution Fourier-transform ion cyclotron resonance (DI-FTICR) and high-resolution liquid chromatography quadrupole time-of-flight tandem MS (LC-Q-TOF-MS/MS). Using DI-FTICR MS, 9644 metabolic features were detected where 7156 were assigned a molecular formula and 1107 were annotated by accurate mass assignment. Using LC-Q-TOF-MS/MS, 21,428 metabolic features were detected where 285 metabolites were identified based on fragmentation matching against publicly available libraries. Collectively, 1340 heart metabolites were identified in this study, which span a wide range of polarities including polar (benzenoids, carbohydrates, and nucleosides) as well as nonpolar (phosphatidylcholines, acylcarnitines, and fatty acids) compounds. The results from this study will provide critical knowledge regarding the selection of appropriate extraction and MS detection methods for the analysis of the diverse classes of human heart metabolites.

Comprehensive Metabolomic Analysis of Human Heart Tissue Enabled by Parallel Metabolite Extraction and High-Resolution Mass Spectrometry.

The heart contracts incessantly and requires a constant supply of energy, utilizing numerous metabolic substrates such as fatty acids, carbohydrates, lipids, and amino acids to supply its high energy demands. Therefore, a comprehensive analysis of various metabolites is urgently needed for understanding cardiac metabolism; however, complete metabolome analyses remain challenging due to the broad range of metabolite polarities which makes extraction and detection difficult. Herein, we implemented parallel metabolite extractions and high-resolution mass spectrometry (MS)-based methods to obtain a comprehensive analysis of the human heart metabolome. To capture the diverse range of metabolite polarities, we first performed six parallel liquid-liquid extractions (three monophasic, two biphasic, and one triphasic extractions) of healthy human donor heart tissue. Next, we utilized two complementary MS platforms for metabolite detection - direct-infusion ultrahigh-resolution Fourier-transform ion cyclotron resonance (DI-FTICR) and high-resolution liquid chromatography quadrupole time-of-flight tandem MS (LC-Q-TOF MS/MS). Using DI-FTICR MS, 9,521 metabolic features were detected where 7,699 were assigned a chemical formula and 1,756 were assigned an annotated by accurate mass assignment. Using LC-Q-TOF MS/MS, 21,428 metabolic features were detected where 626 metabolites were identified based on fragmentation matching against publicly available libraries. Collectively, 2276 heart metabolites were identified in this study which span a wide range of polarities including polar (benzenoids, alkaloids and derivatives and nucleosides) as well as non-polar (phosphatidylcholines, acylcarnitines, and fatty acids) compounds. The results of this study will provide critical knowledge regarding the selection of appropriate extraction and MS detection methods for the analysis of the diverse classes of human heart metabolites.

Metformin Monotherapy Alters the Human Plasma Lipidome Independent of Clinical Markers of Glycemic Control and Cardiovascular Disease Risk in a Type 2 Diabetes Clinical Cohort.

Type 2 diabetes (T2D) is a rising pandemic worldwide. Diet and lifestyle changes are typically the first intervention for T2D. When this intervention fails, the biguanide metformin is the most common pharmaceutical therapy. Yet its full mechanisms of action remain unknown. In this work, we applied an ultrahigh resolution, mass spectrometry-based platform for untargeted plasma metabolomics to human plasma samples from a case-control observational study of nondiabetic and well-controlled T2D subjects, the latter treated conservatively with metformin or diet and lifestyle changes only. No statistically significant differences existed in baseline demographic parameters, glucose control, or clinical markers of cardiovascular disease risk between the two T2D groups, which we hypothesized would allow the identification of circulating metabolites independently associated with treatment modality. Over 3000 blank-reduced metabolic features were detected, with the majority of annotated features being lipids or lipid-like molecules. Altered abundance of multiple fatty acids and phospholipids were found in T2D subjects treated with diet and lifestyle changes as compared with nondiabetic subjects, changes that were often reversed by metformin. Our findings provide direct evidence that metformin monotherapy alters the human plasma lipidome independent of T2D disease control and support a potential cardioprotective effect of metformin worthy of future study. SIGNIFICANCE STATEMENT: This work provides important new information on the systemic effects of metformin in type 2 diabetic subjects. We observed significant changes in the plasma lipidome with metformin therapy, with metabolite classes previously associated with cardiovascular disease risk significantly reduced as compared to diet and lifestyle changes. While cardiovascular disease risk was not a primary outcome of our study, our results provide a jumping-off point for future work into the cardioprotective effects of metformin, even in well-controlled type 2 diabetes.

Multiomics Method Enabled by Sequential Metabolomics and Proteomics for Human Pluripotent Stem-Cell-Derived Cardiomyocytes.

Human pluripotent stem-cell-derived cardiomyocytes (hPSC-CMs) show immense promise for patient-specific disease modeling, cardiotoxicity screening, and regenerative therapy development. However, thus far, hPSC-CMs in culture have not recapitulated the structural or functional properties of adult CMs in vivo. To gain global insight into hPSC-CM biology, we established a multiomics method for analyzing the hPSC-CM metabolome and proteome from the same cell culture, creating multidimensional profiles of hPSC-CMs. Specifically, we developed a sequential extraction to capture metabolites and proteins from the same hPSC-CM monolayer cultures and analyzed these extracts using high-resolution mass spectrometry. Using this method, we annotated 205 metabolites/lipids and 4319 proteins from 106 cells with high reproducibility. We further integrated the proteome and metabolome measurements to create network profiles of molecular phenotypes for hPSC-CMs. Out of 310 pathways identified using metabolomics and proteomics, 40 pathways were considered significantly overrepresented (false-discovery-rate-corrected p ≤ 0.05). Highly populated pathways included those involved in protein synthesis (ribosome, spliceosome), ATP generation (oxidative phosphorylation), and cardiac muscle contraction. This multiomics method achieves a deep coverage of metabolites and proteins, creating a multidimensional view of the hPSC-CM phenotype, which provides a strong technological foundation to advance the understanding of hPSC-CM biology. Raw data are available in the MassIVE repository with identifier MSV000088010.

Human Islet Expression Levels of Prostaglandin E2 Synthetic Enzymes, But Not Prostaglandin EP3 Receptor, Are Positively Correlated with Markers of ß-Cell Function and Mass in Nondiabetic Obesity.

Elevated islet production of prostaglandin E2 (PGE2), an arachidonic acid metabolite, and expression of prostaglandin E2 receptor subtype EP3 (EP3) are well-known contributors to the ß-cell dysfunction of type 2 diabetes (T2D). Yet, many of the same pathophysiological conditions exist in obesity, and little is known about how the PGE2 production and signaling pathway influences nondiabetic ß-cell function. In this work, plasma arachidonic acid and PGE2 metabolite levels were quantified in a cohort of nondiabetic and T2D human subjects to identify their relationship with glycemic control, obesity, and systemic inflammation. In order to link these findings to processes happening at the islet level, cadaveric human islets were subject to gene expression and functional assays. Interleukin-6 (IL-6) and cyclooxygenase-2 (COX-2) mRNA levels, but not those of EP3, positively correlated with donor body mass index (BMI). IL-6 expression also strongly correlated with the expression of COX-2 and other PGE2 synthetic pathway genes. Insulin secretion assays using an EP3-specific antagonist confirmed functionally relevant upregulation of PGE2 production. Yet, islets from obese donors were not dysfunctional, secreting just as much insulin in basal and stimulatory conditions as those from nonobese donors as a percent of content. Islet insulin content, on the other hand, was increased with both donor BMI and islet COX-2 expression, while EP3 expression was unaffected. We conclude that upregulated islet PGE2 production may be part of the ß-cell adaption response to obesity and insulin resistance that only becomes dysfunctional when both ligand and receptor are highly expressed in T2D.

Extraction optimization for combined metabolomics, peptidomics, and proteomics analysis of gut microbiota samples.

Multiomic studies are increasingly performed to gain a deeper understanding of molecular processes occurring in a biological system, such as the complex microbial communities (i.e., microbiota) that reside the distal gut. While a combination of metabolomics and proteomics is more commonly used, multiomics studies including peptidomcis characterization are less frequently undertaken. Here, we investigated three different extraction methods, chosen for their previous use in extracting metabolites, peptides, and proteins, and compared their ability to perform metabolomic, peptidomic, and proteomic analysis of mouse cecum content. The methanol/chloroform/water extraction performed the best for metabolomic and peptidomic analysis as it detected the largest number of small molecules and identified the largest number of peptides, but the acidified methanol extraction performed best for proteomics analysis as it had the highest number of protein identifications. The methanol/chloroform/water extraction was further analyzed by identifying metabolites with tandem mass spectrometry (MS/MS) analysis and by gene ontology analysis for the peptide and protein results to provide a multiomics analysis of the gut microbiota.

Ultrahigh-Resolution Mass Spectrometry-Based Platform for Plasma Metabolomics Applied to Type 2 Diabetes Research.

Metabolomics-the endpoint of the omics cascade-is increasingly recognized as a preferred method for understanding the ultimate responses of biological systems to stress. Flow injection electrospray (FIE) mass spectrometry (MS) has advantages for untargeted metabolic fingerprinting due to its simplicity and capability for high-throughput screening but requires a high-resolution mass spectrometer to resolve metabolite features. In this study, we developed and validated a high-throughput and highly reproducible metabolomics platform integrating FIE with ultrahigh-resolution Fourier transform ion cyclotron resonance (FTICR) MS for analysis of both polar and nonpolar metabolite features from plasma samples. FIE-FTICR MS enables high-throughput detection of hundreds of metabolite features in a single mass spectrum without a front-end separation step. Using plasma samples from genetically identical obese mice with or without type 2 diabetes (T2D), we validated the intra and intersample reproducibility of our method and its robustness for simultaneously detecting alterations in both polar and nonpolar metabolite features. Only 5 min is needed to acquire an ultra-high resolution mass spectrum in either a positive or negative ionization mode. Approximately 1000 metabolic features were reproducibly detected and annotated in each mouse plasma group. For significantly altered and highly abundant metabolite features, targeted tandem MS (MS/MS) analyses can be applied to confirm their identity. With this integrated platform, we successfully detected over 300 statistically significant metabolic features in T2D mouse plasma as compared to controls and identified new T2D biomarker candidates. This FIE-FTICR MS-based method is of high throughput and highly reproducible with great promise for metabolomics studies toward a better understanding and diagnosis of human diseases.

Functional Microbiomics Reveals Alterations of the Gut Microbiome and Host Co-Metabolism in Patients With Alcoholic Hepatitis.

Alcohol-related liver disease is a major public health burden, and the gut microbiota is an important contributor to disease pathogenesis. The aim of the present study is to characterize functional alterations of the gut microbiota and test their performance for short-term mortality prediction in patients with alcoholic hepatitis. We integrated shotgun metagenomics with untargeted metabolomics to investigate functional alterations of the gut microbiota and host co-metabolism in a multicenter cohort of patients with alcoholic hepatitis. Profound changes were found in the gut microbial composition, functional metagenome, serum, and fecal metabolomes in patients with alcoholic hepatitis compared with nonalcoholic controls. We demonstrate that in comparison with single omics alone, the performance to predict 30-day mortality was improved when combining microbial pathways with respective serum metabolites in patients with alcoholic hepatitis. The area under the receiver operating curve was higher than 0.85 for the tryptophan, isoleucine, and methionine pathways as predictors for 30-day mortality, but achieved 0.989 for using the urea cycle pathway in combination with serum urea, with a bias-corrected prediction error of 0.083 when using leave-one-out cross validation. Conclusion: Our study reveals changes in key microbial metabolic pathways associated with disease severity that predict short-term mortality in our cohort of patients with alcoholic hepatitis.

A Comprehensive Plasma Metabolomics Dataset for a Cohort of Mouse Knockouts within the International Mouse Phenotyping Consortium.

Mouse knockouts facilitate the study ofgene functions. Often, multiple abnormal phenotypes are induced when a gene is inactivated. The International Mouse Phenotyping Consortium (IMPC) has generated thousands of mouse knockouts and catalogued their phenotype data. We have acquired metabolomics data from 220 plasma samples from 30 unique mouse gene knockouts and corresponding wildtype mice from the IMPC. To acquire comprehensive metabolomics data, we have used liquid chromatography (LC) combined with mass spectrometry (MS) for detecting polar and lipophilic compounds in an untargeted approach. We have also used targeted methods to measure bile acids, steroids and oxylipins. In addition, we have used gas chromatography GC-TOFMS for measuring primary metabolites. The metabolomics dataset reports 832 unique structurally identified metabolites from 124 chemical classes as determined by ChemRICH software. The GCMS and LCMS raw data files, intermediate and finalized data matrices, R-Scripts, annotation databases, and extracted ion chromatograms are provided in this data descriptor. The dataset can be used for subsequent studies to link genetic variants with molecular mechanisms and phenotypes.

Primed mesenchymal stem cells package exosomes with metabolites associated with immunomodulation.

Mesenchymal stem cell (MSC) based therapies are currently being evaluated as a putative therapeutic in numerous human clinical trials. Recent reports have established that exosomes mediate much of the therapeutic properties of MSCs. Exosomes are nanovesicles which mediate intercellular communication, transmitting signals between cells which regulate a diverse range of biological processes. MSC-derived exosomes are packaged with numerous types of proteins and RNAs, however, their metabolomic and lipidomic profiles to date have not been well characterized. We previously reported that MSCs, in response to priming culture conditions that mimic the in vivo microenvironmental niche, substantially modulate cellular signaling and significantly increase the secretion of exosomes. Here we report that MSCs exposed to such priming conditions undergo glycolytic reprogramming, which homogenizes MSCs' metabolomic profile. In addition, we establish that exosomes derive from primed MSCs are packaged with numerous metabolites that have been directly associated with immunomodulation, including M2 macrophage polarization and regulatory T lymphocyte induction.

Structure Annotation of All Mass Spectra in Untargeted Metabolomics.

Urine metabolites are used in many clinical and biomedical studies but usually only for a few classic compounds. Metabolomics detects vastly more metabolic signals that may be used to precisely define the health status of individuals. However, many compounds remain unidentified, hampering biochemical conclusions. Here, we annotate all metabolites detected by two untargeted metabolomic assays, hydrophilic interaction chromatography (HILIC)-Q Exactive HF mass spectrometry and charged surface hybrid (CSH)-Q Exactive HF mass spectrometry. Over 9,000 unique metabolite signals were detected, of which 42% triggered MS/MS fragmentations in data-dependent mode. On the highest Metabolomics Standards Initiative (MSI) confidence level 1, we identified 175 compounds using authentic standards with precursor mass, retention time, and MS/MS matching. An additional 578 compounds were annotated by precursor accurate mass and MS/MS matching alone, MSI level 2, including a novel library specifically geared at acylcarnitines (CarniBlast). The rest of the metabolome is usually left unannotated. To fill this gap, we used the in silico fragmentation tool CSI:FingerID and the new NIST hybrid search to annotate all further compounds (MSI level 3). Testing the top-ranked metabolites in CSI:Finger ID annotations yielded 40% accuracy when applied to the MSI level 1 identified compounds. We classified all MSI level 3 annotations by the NIST hybrid search using the ClassyFire ontology into 21 superclasses that were further distinguished into 184 chemical classes. ClassyFire annotations showed that the previously unannotated urine metabolome consists of 28% derivatives of organic acids, 16% heterocyclics, and 16% lipids as major classes.

An Isoprene Lipid-Binding Protein Promotes Eukaryotic Coenzyme Q Biosynthesis.

The biosynthesis of coenzyme Q presents a paradigm for how cells surmount hydrophobic barriers in lipid biology. In eukaryotes, CoQ precursors-among nature's most hydrophobic molecules-must somehow be presented to a series of enzymes peripherally associated with the mitochondrial inner membrane. Here, we reveal that this process relies on custom lipid-binding properties of COQ9. We show that COQ9 repurposes the bacterial TetR fold to bind aromatic isoprenes with high specificity, including CoQ intermediates that likely reside entirely within the bilayer. We reveal a process by which COQ9 associates with cardiolipin-rich membranes and warps the membrane surface to access this cargo. Finally, we identify a molecular interface between COQ9 and the hydroxylase COQ7, motivating a model whereby COQ9 presents intermediates directly to CoQ enzymes. Overall, our results provide a mechanism for how a lipid-binding protein might access, select, and deliver specific cargo from a membrane to promote biosynthesis.

Effects of Gut Bacteria Depletion and High-Na+ and Low-K+ Intake on Circulating Levels of Biogenic Amines.

SCOPE: High-sodium and low-potassium (HNaLK) content in Western diets increases the risk of hypertension and cardiovascular disease (CVD). It is investigated if the dietary minerals interact with gut bacteria to modulate circulating levels of biogenic amines, which are implicated in various pathologies, including hypertension and CVD. METHODS AND RESULTS: Using a metabolomic approach to target biogenic amines, the effects of gut bacteria depletion and HNaLK intake on circulating levels of biogenic amines in rats are examined. Forty-five metabolites whose plasma levels are significantly altered by gut bacteria depletion (p < 0.05) are found, indicating their regulation by gut bacteria. Many of them are not previously linked to gut bacteria; therefore, these data provide novel insights into physiological or pathological roles of gut bacteria. A number of plasma metabolites that are altered both by gut bacteria and HNaLK intake are also found, suggesting possible interactions of the diet and gut bacteria in the modulation of these metabolites. The diet effects are observed with significant changes in the gut bacterial taxa Porphyromonadaceae and Prevotellaceae (p < 0.05). CONCLUSION: The dietary minerals may regulate abundances of certain gut bacteria to alter circulating levels of biogenic amines, which may be linked to host physiology or pathology.

Generation and quality control of lipidomics data for the alzheimer's disease neuroimaging initiative cohort.

Alzheimer's disease (AD) is a major public health priority with a large socioeconomic burden and complex etiology. The Alzheimer Disease Metabolomics Consortium (ADMC) and the Alzheimer Disease Neuroimaging Initiative (ADNI) aim to gain new biological insights in the disease etiology. We report here an untargeted lipidomics of serum specimens of 806 subjects within the ADNI1 cohort (188 AD, 392 mild cognitive impairment and 226 cognitively normal subjects) along with 83 quality control samples. Lipids were detected and measured using an ultra-high-performance liquid chromatography quadruple/time-of-flight mass spectrometry (UHPLC-QTOF MS) instrument operated in both negative and positive electrospray ionization modes. The dataset includes a total 513 unique lipid species out of which 341 are known lipids. For over 95% of the detected lipids, a relative standard deviation of better than 20% was achieved in the quality control samples, indicating high technical reproducibility. Association modeling of this dataset and available clinical, metabolomics and drug-use data will provide novel insights into the AD etiology. These datasets are available at the ADNI repository at http://adni.loni.usc.edu/.

Metabolomics Reveals the Molecular Mechanisms of Copper Induced Cucumber Leaf ( Cucumis sativus) Senescence.

Excess copper may disturb plant photosynthesis and induce leaf senescence. The underlying toxicity mechanism is not well understood. Here, 3-week-old cucumber plants were foliar exposed to different copper concentrations (10, 100, and 500 mg/L) for a final dose of 0.21, 2.1, and 10 mg/plant, using CuSO4 as the Cu ion source for 7 days, three times per day. Metabolomics quantified 149 primary and 79 secondary metabolites. A number of intermediates of the tricarboxylic acid (TCA) cycle were significantly down-regulated 1.4-2.4 fold, indicating a perturbed carbohydrate metabolism. Ascorbate and aldarate metabolism and shikimate-phenylpropanoid biosynthesis (antioxidant and defense related pathways) were perturbed by excess copper. These metabolic responses occur even at the lowest copper dose considered although no phenotype changes were observed at this dose. High copper dose resulted in a 2-fold increase in phytol, a degradation product of chlorophyll. Polyphenol metabolomics revealed that some flavonoids were down-regulated, while the nonflavonoid 4-hydroxycinnamic acid and trans-2-hydroxycinnamic acid were significantly up-regulated 4- and 26-fold compared to the control. This study enhances current understanding of copper toxicity to plants and demonstrates that metabolomics profiling provides a more comprehensive view of plant responses to stressors, which can be applied to other plant species and contaminants.

Mass Spectral Feature List Optimizer (MS-FLO): A Tool To Minimize False Positive Peak Reports in Untargeted Liquid Chromatography-Mass Spectroscopy (LC-MS) Data Processing.

Untargeted metabolomics by liquid chromatography-mass spectrometry generates data-rich chromatograms in the form of m/z-retention time features. Managing such datasets is a bottleneck. Many popular data processing tools, including XCMS-online and MZmine2, yield numerous false-positive peak detections. Flagging and removing such false peaks manually is a time-consuming task and prone to human error. We present a web application, Mass Spectral Feature List Optimizer (MS-FLO), to improve the quality of feature lists after initial processing to expedite the process of data curation. The tool utilizes retention time alignments, accurate mass tolerances, Pearson's correlation analysis, and peak height similarity to identify ion adducts, duplicate peak reports, and isotopic features of the main monoisotopic metabolites. Removing such erroneous peaks reduces the overall number of metabolites in data reports and improves the quality of subsequent statistical investigations. To demonstrate the effectiveness of MS-FLO, we processed 28 biological studies and uploaded raw and results data to the Metabolomics Workbench website ( www.metabolomicsworkbench.org ), encompassing 1481 chromatograms produced by two different data processing programs used in-house (MZmine2 and later MS-DIAL). Post-processing of datasets with MS-FLO yielded a 7.8% automated reduction of total peak features and flagged an additional 7.9% of features, per dataset, for review by the user. When manually curated, 87% of these additional flagged features were verified false positives. MS-FLO is an open source web application that is freely available for use at http://msflo.fiehnlab.ucdavis.edu .