Multivariate Analysis in Metabolomics

A Tale of Two Biomarkers: Untargeted 1H NMR Metabolomic Fingerprinting of BHBA and NEFA in Early Lactation Dairy Cows

Disorders of energy metabolism, which can result from a failure to adapt to the period of negative energy balance immediately after calving, have significant negative effects on the health, welfare and profitability of dairy cows. The most common biomarkers of energy balance in dairy cows are β-hydroxybutyrate (BHBA) and non-esterified fatty acids (NEFA). While elevated concentrations of these biomarkers are associated with similar negative health and production outcomes, the phenotypic and genetic correlations between them are weak. In this study, we used an untargeted ¹H NMR metabolomics approach to investigate the serum metabolomic fingerprints of BHBA and NEFA. Serum samples were collected from 298 cows in early lactation (calibration dataset N = 248, validation N = 50). Metabolomic fingerprinting was done by regressing ¹H NMR spectra against BHBA and NEFA concentrations (determined using colorimetric assays) using orthogonal partial least squares regression. Prediction accuracies were high for BHBA models, and moderately high for NEFA models (R² of external validation of 0.88 and 0.75, respectively). We identified 16 metabolites that were significantly (variable importance of projection score > 1) correlated with the concentration of one or both biomarkers. These metabolites were primarily intermediates of energy, phospholipid, and/or methyl donor metabolism. Of the significant metabolites identified; (1) two (acetate and creatine) were positively correlated with BHBA but negatively correlated with NEFA, (2) nine had similar associations with both BHBA and NEFA, (3) two were correlated with only BHBA concentration, and (4) three were only correlated with NEFA concentration. Overall, our results suggest that BHBA and NEFA are indicative of similar metabolic states in clinically healthy animals, but that several significant metabolic differences exist that help to explain the weak correlations between them. We also identified several metabolites that may be useful intermediate phenotypes in genomic selection for improved metabolic health.

Serum metabolites associate with physical performance among middle-aged adults: Evidence from the Bogalusa Heart Study

Age-related declines in physical performance predict cognitive impairment, disability, chronic disease exacerbation, and mortality. We conducted a metabolome-wide association study of physical performance among Bogalusa Heart Study participants. Bonferroni corrected multivariate-adjusted linear regression was employed to examine cross-sectional associations between single metabolites and baseline gait speed (N=1,227) and grip strength (N=1,164). In a sub-sample of participants with repeated assessments of gait speed (N=282) and grip strength (N=201), significant metabolites from the cross-sectional analyses were tested for association with change in physical performance over 2.9 years of follow-up. Thirty-five and seven metabolites associated with baseline gait speed and grip strength respectively, including six metabolites that associated with both phenotypes. Three metabolites associated with preservation or improvement in gait speed over follow-up, including: sphingomyelin (40:2) (P=2.6×10^-4) and behenoyl sphingomyelin (d18:1/22:0) and ergothioneine (both P<0.05). Seven metabolites associated with declines in gait speed, including: 1-carboxyethylphenylalanine (P=8.8×10^-5), and N-acetylaspartate, N-formylmethionine, S-adenosylhomocysteine, N-acetylneuraminate, N2,N2-dimethylguanosine, and gamma-glutamylphenylalanine (all P<0.05). Two metabolite modules reflecting sphingolipid and bile acid metabolism associated with physical performance (minimum P=7.6×10^-4). These results add to the accumulating evidence suggesting an important role of the human metabolome in physical performance and specifically implicate lipid, nucleotide, and amino acid metabolism in early physical performance decline.

1H HRMAS-NMR based metabolic fingerprints for discrimination of cheeses based on sensory qualities

In this study, the ¹H HRMAS-NMR (High-resolution Magic Angle Spinning-Nuclear Magnetic Resonance) spectra of 52 cheese samples obtained from the South Korean dairy farms were evaluated for their metabolic profiling and intensities associating with the sensory qualities. The NMR profiles displayed a broad range of compounds comprising amino acids, carbohydrates, organic acids, and phospholipids. Afterwards, the cheese samples were categorized into three groups (more likeness - G1, moderate likeness - G2, less likeness - G3), in relating to their sensory scores. The NMR data of the samples were later investigated through multivariate statistical tools to define the variations in metabolic fingerprints of every cheese sample and their intensities hailing in individual sensory groups. The unsupervised PCA employing all cheese samples unveiled the uniqueness in metabolite profiles of the brown and cheddar type cheeses (outliers). Moreover, Gouda and other types of cheeses displayed samples positioning in respective of their metabolite profiles. The pairwise comparison of sensory groups in the supervised models perceived better separation in OPLS-DA than PLS-DA. The corresponding VIP (PLS-DA) and loading (OPLS-DA) plots revealed amino acids and organic acids (lactate, citrate) as significant variables. The discrimination of G 1 Gouda type of cheeses against G 2 and G 3 was highly associated with their citrate levels. Further investigation using heatmaps displayed clear differentiation between each sensory group in terms of the levels of amino acids, lactate, citrate, phospholipids, and glycerol, conveying these variations are likely due to proteolytic and metabolic processes in cheese ripening. This study concluded that ¹H HRMAS-NMR metabolite profile of the Korean cheeses is consistence with their sensory qualities. Further, the candidate metabolites identified in this study confers their potential application as biomarkers in cheese industries for faster and effective validation of sensory characteristics.

Label-free plasma proteomics identifies haptoglobin-related protein as candidate marker of idiopathic pulmonary fibrosis and dysregulation of complement and oxidative pathways

Idiopathic pulmonary fibrosis (IPF) is a lung parenchymal disease of unknown cause usually occurring in older adults. It is a chronic and progressive condition with poor prognosis and diagnosis is largely clinical. Currently, there exist few biomarkers that can predict patient outcome or response to therapies. Together with lack of markers, the need for novel markers for the detection and monitoring of IPF, is paramount. We have performed label-free plasma proteomics of thirty six individuals, 17 of which had confirmed IPF. Proteomics data was analyzed by volcano plot, hierarchical clustering, Partial-least square discriminant analysis (PLS-DA) and Ingenuity pathway analysis. Univariate and multivariate statistical analysis overlap identified haptoglobin-related protein as a possible marker of IPF when compared to control samples (Area under the curve 0.851, ROC-analysis). LXR/RXR activation and complement activation pathways were enriched in t-test significant proteins and oxidative regulators, complement proteins and protease inhibitors were enriched in PLS-DA significant proteins. Our pilot study points towards aberrations in complement activation and oxidative damage in IPF patients and provides haptoglobin-related protein as a new candidate biomarker of IPF.

Serum metabolites associate with lipid phenotypes among Bogalusa Heart Study participants

BACKGROUND AND AIMS

Dyslipidemia has been identified as a major risk factor for cardiovascular disease. We aimed to identify metabolites and metabolite modules showing novel association with lipids among Bogalusa Heart Study (BHS) participants using untargeted metabolomics.

METHODS AND RESULTS

Untargeted ultrahigh performance liquid chromatography-tandem mass spectroscopy was used to quantify serum metabolites of 1 243 BHS participants (816 whites and 427 African-Americans). The association of single metabolites with lipids was assessed using multiple linear regression models to adjust for covariables. Weighted correlation network analysis was utilized to identify modules of co-abundant metabolites and examine their covariable adjusted correlations with lipids. All analyses were conducted according to race and using Bonferroni-corrected α-thresholds to determine statistical significance. Thirteen metabolites with known biochemical identities showing novel association achieved Bonferroni-significance, p < 1.04 × 10^-5, and showed consistent effect directions in both whites and African-Americans. Twelve were from lipid sub-pathways including fatty acid metabolism (arachidonoylcholine, dihomo-linolenoyl-choline, docosahexaenoylcholine, linoleoylcholine, oleoylcholine, palmitoylcholine, and stearoylcholine), monohydroxy fatty acids (2-hydroxybehenate, 2-hydroxypalmitate, and 2-hydroxystearate), and lysoplasmalogens [1-(1-enyl-oleoyl)-GPE (P-18:1) and 1-(1-enyl-stearoyl)-GPE (P-18:0)]. The gamma-glutamylglutamine, peptide from the gamma-glutamyl amino acid sub-pathway, were also identified. In addition, four metabolite modules achieved Bonferroni-significance, p < 1.39 × 10^-3, in both whites and African-Americans. These four modules were largely comprised of metabolites from lipid sub-pathways, with one module comprised of metabolites which were not identified in the single metabolite analyses.

CONCLUSION

The current study identified 13 metabolites and 4 metabolite modules showing novel association with lipids, providing new insights into the physiological mechanisms regulating lipid levels.

The role of changing temperature in microbial metabolic processes during permafrost thaw

Approximately one fourth of the Earth's Northern Hemisphere is underlain by permafrost, earth materials (soil, organic matter, or bedrock), that has been continuously frozen for at least two consecutive years. Numerous studies point to evidence of accelerated climate warming in the Arctic and sub-Arctic where permafrost is located. Changes to permafrost biochemical processes may critically impact ecosystem processes at the landscape scale. Here, we sought to understand how the permafrost metabolome responds to thaw and how this response differs based on location (i.e. chronosequence of permafrost formation constituting diverse permafrost types). We analyzed metabolites from microbial cells originating from Alaskan permafrost. Overall, permafrost thaw induced a shift in microbial metabolic processes. Of note were the dissimilarities in biochemical structure between frozen and thawed samples. The thawed permafrost metabolomes from different locations were highly similar. In the intact permafrost, several metabolites with antagonist properties were identified, illustrating the competitive survival strategy required to survive a frozen state. Interestingly, the intensity of these antagonistic metabolites decreased with warmer temperature, indicating a shift in ecological strategies in thawed permafrost. These findings illustrate the impact of change in temperature and spatial variability as permafrost undergoes thaw, knowledge that will become crucial for predicting permafrost biogeochemical dynamics as the Arctic and Antarctic landscapes continue to warm.

Toxicometabolomics of the new psychoactive substances α-PBP and α-PEP studied in HepaRG cell incubates by means of untargeted metabolomics revealed unexpected amino acid adducts

Toxicometabolomics, essentially applying metabolomics to toxicology of endogenous compounds such as drugs of abuse or new psychoactive substances (NPS), can be investigated by using different in vitro models and dedicated metabolomics techniques to enhance the number of relevant findings. The present study aimed to study the toxicometabolomics of the two NPS α-pyrrolidinobutiophenone (1-phenyl-2-(pyrrolidin-1-yl)butan-1-one, α-PBP) and α-pyrrolidinoheptaphenone (1-phenyl-2-(pyrrolidin-1-yl)heptan-1-one, α-PEP, PV8) in HepaRG cell line incubates. Evaluation was performed using reversed-phase and normal-phase liquid chromatography coupled with high-resolution mass spectrometry in positive and negative ionization mode, respectively, to analyze cells and cell media. Statistical evaluation was performed using one-way ANOVA, principal component discriminant function analysis, as well as hierarchical clustering. In general, the analysis of cells did not mainly reveal any features, but the parent compounds of the drugs of abuse. For α-PBP an increase in N-methylnicotinamide was found, which may indicate hepatotoxic potential of the substance. After analysis of cell media, significant features led to the identification of several metabolites of both compounds. Amino acid adducts with glycine and alanine were found, and these have not been described in any study before and are likely to appear in vivo. Additionally, significant changes in the metabolism of cholesterol were revealed after incubation with α-PEP. In summary, the application of metabolomics techniques after HepaRG cells exposure to NPS did not lead to an increased number of identified drug metabolites compared to previously published studies, but gave a wider perspective on the physiological effect of the investigated compounds on human liver cells.

High abundance of Ralstonia solanacearum changed tomato rhizosphere microbiome and metabolome

BACKGROUND

Rhizosphere microbiome is dynamic and influenced by environment factors surrounded including pathogen invasion. We studied the effects of Ralstonia solanacearum pathogen abundance on rhizosphere microbiome and metabolome by using high throughput sequencing and GC-MS technology.

RESULTS

There is significant difference between two rhizosphere bacterial communities of higher or lower pathogen abundance, and this difference of microbiomes was significant even ignoring the existence of pathogen. Higher pathogen abundance decreased the alpha diversity of rhizosphere bacterial community as well as connections in co-occurrence networks. Several bacterial groups such as Bacillus and Chitinophaga were negatively related to the pathogen abundance. The GC-MS analysis revealed significantly different metabolomes in two groups of rhizosphere soils, i.e., the rhizosphere soil of lower harbored more sugars such as fructose, sucrose and melibiose than that in high pathogen abundance.

CONCLUSIONS

The dissimilar metabolomes in two rhizosphere soils likely explained the difference of bacterial communities with Mantel test. Bacillus and Chitinophaga as well as sugar compounds negatively correlated with high abundance of pathogen indicated their potential biocontrol ability.

Metabolism of JQ1, an inhibitor of bromodomain and extra terminal bromodomain proteins, in human and mouse liver microsomes†

JQ1 is a small-molecule inhibitor of the bromodomain and extra terminal (BET) protein family that potently inhibits the bromodomain testis-specific protein (BRDT), which is essential for spermatogenesis. JQ1 treatment produces a reversible contraceptive effect by targeting the activity of BRDT in mouse male germ cells, validating BRDT as a male contraceptive target. Although JQ1 possesses favourable physical properties, it exhibits a short half-life. Because the details of xenobiotic metabolism play important roles in the optimization of drug candidates and in determining the role of metabolism in drug efficacy, we investigated the metabolism of JQ1 in human and mouse liver microsomes. We present the first comprehensive view of JQ1 metabolism in liver microsomes, distinguishing nine JQ1 metabolites, including three monohydroxylated, one de-tert-butylated, two dihydroxylated, one monohydroxylated/dehydrogenated, one monohydroxylated-de-tert-butylated and one dihydroxylated/dehydrogenated variant of JQ1. The dominant metabolite (M1) in both human and mouse liver microsomes is monohydroxylated on the fused three-ring core. Using recombinant cytochrome P450 (CYP) enzymes, chemical inhibitors and the liver S9 fraction of Cyp3a-null mice, we identify enzymes that contribute to the formation of these metabolites. Cytochrome P450 family 3 subfamily A member 4 (CYP3A4) is the main contributor to the production of JQ1 metabolites in vitro, and the CYP3A4/5 inhibitor ketoconazole strongly inhibits JQ1 metabolism in both human and mouse liver microsomes. Our findings suggest that JQ1 half-life and efficacy might be improved in vivo by co-administration of a selective CYP inhibitor, thereby impacting the use of JQ1 as a probe for BRDT activity in spermatogenesis and as a probe or therapeutic in other systems.

Modulation of Plasma Membrane Composition and Microdomain Organization Impairs Heat Shock Protein Expression in B16-F10 Mouse Melanoma Cells

The heat shock response (HSR) regulates induction of stress/heat shock proteins (HSPs) to preserve proteostasis during cellular stress. Earlier, our group established that the plasma membrane (PM) acts as a sensor and regulator of HSR through changes in its microdomain organization. PM microdomains such as lipid rafts, dynamic nanoscale assemblies enriched in cholesterol and sphingomyelin, and caveolae, cholesterol-rich PM invaginations, constitute clustering platforms for proteins functional in signaling cascades. Here, we aimed to compare the effect of cyclodextrin (MβCD)- and nystatin-induced cholesterol modulations on stress-activated expression of the representative HSPs, HSP70, and HSP25 in mouse B16-F10 melanoma cells. Depletion of cholesterol levels with MβCD impaired the heat-inducibility of both HSP70 and HSP25. Sequestration of cholesterol with nystatin impaired the heat-inducibility of HSP25 but not of HSP70. Imaging fluorescent correlation spectroscopy marked a modulated lateral diffusion constant of fluorescently labelled cholesterol in PM during cholesterol deprived conditions. Lipidomics analysis upon MβCD treatment revealed, next to cholesterol reductions, decreased lysophosphatidylcholine and phosphatidic acid levels. These data not only highlight the involvement of PM integrity in HSR but also suggest that altered dynamics of specific cholesterol pools could represent a mechanism to fine tune HSP expression.

Untargeted Metabolomics and Steroid Signatures in Urine of Male Pattern Baldness Patients after Finasteride Treatment for a Year

Male pattern baldness (MPB) has been associated with dihydrotestosterone (DHT) expression. Finasteride treats MPB by inhibiting 5-alpha reductase and blocking DHT production. In this study, we aimed to identify metabolic differences in urinary metabolomics profiles between MPB patients after a one-year treatment with finasteride and healthy controls. Untargeted and targeted metabolomics profiling was performed using liquid chromatography-mass spectrometry (LC-MS). We hypothesized that there would be changes in overall metabolite concentrations, especially steroids, in the urine of hair loss patients treated with finasteride and normal subjects. Untargeted analysis indicated differences in steroid hormone biosynthesis. Therefore, we conducted targeted profiling for steroid hormone biosynthesis to identify potential biomarkers, especially androgens and estrogens. Our study confirmed the differences in the concentration of urinary androgens and estrogens between healthy controls and MPB patients. Moreover, the effect of finasteride was confirmed by the DHT/T ratio in urine samples of MPB patients. Our metabolomics approach provided insight into the physiological alterations in MPB patients who have been treated with finasteride for a year and provided evidence for the association of finasteride and estrogen levels. Through a targeted approach, our results suggest that urinary estrogens must be studied in relation to MPB and post-finasteride syndrome.

Dietary Supplementation With Magnolia Bark Extract Alters Chicken Intestinal Metabolite Levels

Magnolia bark extract administered as a dietary supplement to poultry confers a performance and health benefit, but the mechanisms are unknown. Here, a metabolomics approach was used to identify changes in intestinal metabolite levels in chickens fed an unsupplemented diet or a diet supplemented with magnolia bark extract. Total body weight gains of chickens fed magnolia bark-supplemented diets were increased 2% (from 861 to 878 g/chicken), compared with chickens fed an unsupplemented diet. Compared with unsupplemented controls, the levels of 278 intestinal biochemicals (metabolites) were altered (165 increased, 113 decreased) in chickens given the magnolia-supplemented diet. Data for biochemicals of intestinal contents of chickens fed the unsupplemented diet clustered on the left side of the PCA score plot, while those of the magnolia-supplemented diet were separated and clustered on the right side. The biochemicals included changes in the levels of amino acids, fatty acids, peptides, and nucleosides, which provided a distinctive biochemical signature unique to the magnolia-supplemented group, compared with the unsupplemented group. These results provide the foundation for future studies to identify naturally-produced biochemicals that might be used to improve poultry growth performance.

lipidr: A Software Tool for Data Mining and Analysis of Lipidomics Datasets

The rapid evolution of mass spectrometry (MS)-based lipidomics has enabled the simultaneous measurement of numerous lipid classes. With lipidomics datasets becoming increasingly available, lipidomic-focused software tools are required to facilitate data analysis as well as mining of public datasets, integrating lipidomics-unique molecular information such as lipid class, chain length, and unsaturation. To address this need, we developed lipidr, an open-source R/Bioconductor package for data mining and analysis of lipidomics datasets. lipidr implements a comprehensive lipidomic-focused analysis workflow for targeted and untargeted lipidomics. lipidr imports numerical matrices, Skyline exports, and Metabolomics Workbench files directly into R, automatically inferring lipid class and chain information from lipid names. Through integration with the Metabolomics Workbench API, users can search, download, and reanalyze public lipidomics datasets seamlessly. lipidr allows thorough data inspection, normalization, and uni- and multivariate analyses, displaying results as interactive visualizations. To enable interpretation of lipid class, chain length, and total unsaturation data, we also developed and implemented a novel lipid set enrichment analysis. A companion online guide with two live example datasets is presented at https://www.lipidr.org/. We expect that the ease of use and innovative features of lipidr will allow the lipidomics research community to gain novel detailed insights from lipidomics data.

Metabolomic-guided discovery of cyclic nonribosomal peptides from Xylaria ellisii sp. nov., a leaf and stem endophyte of Vaccinium angustifolium

Fungal endophytes are sources of novel bioactive compounds but relatively few agriculturally important fruiting plants harboring endophytes have been carefully studied. Previously, we identified a griseofulvin-producing Xylaria species isolated from Vaccinium angustifolium, V. corymbosum, and Pinus strobus. Morphological and genomic analysis determined that it was a new species, described here as Xylaria ellisii. Untargeted high-resolution LC-MS metabolomic analysis of the extracted filtrates and mycelium from 15 blueberry isolates of this endophyte revealed differences in their metabolite profiles. Toxicity screening of the extracts showed that bioactivity was not linked to production of griseofulvin, indicating this species was making additional bioactive compounds. Multivariate statistical analysis of LC-MS data was used to identify key outlier features in the spectra. This allowed potentially new compounds to be targeted for isolation and characterization. This approach resulted in the discovery of eight new proline-containing cyclic nonribosomal peptides, which we have given the trivial names ellisiiamides A-H. Three of these peptides were purified and their structures elucidated by one and two-dimensional nuclear magnetic resonance spectroscopy (1D and 2D NMR) and high-resolution tandem mass spectrometry (HRMS/MS) analysis. The remaining five new compounds were identified and annotated by high-resolution mass spectrometry. Ellisiiamide A demonstrated Gram-negative activity against Escherichia coli BW25113, which is the first reported for this scaffold. Additionally, several known natural products including griseofulvin, dechlorogriseofulvin, epoxy/cytochalasin D, zygosporin E, hirsutatin A, cyclic pentapeptides #1–2 and xylariotide A were also characterized from this species.

Chemical Fingerprinting of Olive Oils by Electrospray Ionization-Differential Mobility Analysis-Mass Spectrometry: A New Alternative to Food Authenticity Testing

Recently, the olive oil industry has been the subject of harsh criticism for false labeling and even adulterating olive oils. This situation in which both the industry and the population are affected leads to an urgent need to increase controls to avoid fraudulent activities around this precious product. The aim of this work is to propose a new analytical platform by coupling electrospray ionization (ESI), differential mobility analysis (DMA), and mass spectrometry (MS) for the analysis of olive oils based on the information obtained from the chemical fingerprint (nontargeted analyses). Regarding the sample preparation, two approaches were proposed: (i) sample dilution and (ii) liquid-liquid extraction (LLE). To demonstrate the feasibility of the method, 30 olive oil samples in 3 different categories were analyzed, using 21 of them to elaborate the classification model and the remaining 9 to test it (blind samples). To develop the prediction model, principal component analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLS-DA) were used. The overall success rate of the classification to differentiate among extra virgin olive oil (EVOO), virgin olive oil (VOO), and lampante olive oil (LOO) was 89% for the LLE samples and 67% for the diluted samples. However, combining both methods, the ability to differentiate EVOO from lower-quality oils (VOO and LOO) and the edible oils (EVOO and VOO) from nonedible oil (LOO) was 100%. The results show that ESI-DMA-MS can become an effective tool for the olive oil sector.

Differences in the serum metabolome and lipidome identify potential biomarkers for seronegative rheumatoid arthritis versus psoriatic arthritis

Objectives

The differential diagnosis of seronegative rheumatoid arthritis (negRA) and psoriasis arthritis (PsA) is often difficult due to the similarity of symptoms and the unavailability of reliable clinical markers. Since chronic inflammation induces major changes in the serum metabolome and lipidome, we tested whether differences in serum metabolites and lipids could aid in improving the differential diagnosis of these diseases.

Methods

Sera from negRA and PsA patients with established diagnosis were collected to build a biomarker-discovery cohort and a blinded validation cohort. Samples were analysed by proton nuclear magnetic resonance. Metabolite concentrations were calculated from the spectra and used to select the variables to build a multivariate diagnostic model.

Results

Univariate analysis demonstrated differences in serological concentrations of amino acids: alanine, threonine, leucine, phenylalanine and valine; organic compounds: acetate, creatine, lactate and choline; and lipid ratios L3/L1, L5/L1 and L6/L1, but yielded area under the curve (AUC) values lower than 70%, indicating poor specificity and sensitivity. A multivariate diagnostic model that included age, gender, the concentrations of alanine, succinate and creatine phosphate and the lipid ratios L2/L1, L5/L1 and L6/L1 improved the sensitivity and specificity of the diagnosis with an AUC of 84.5%. Using this biomarker model, 71% of patients from a blinded validation cohort were correctly classified.

Conclusions

PsA and negRA have distinct serum metabolomic and lipidomic signatures that can be used as biomarkers to discriminate between them. After validation in larger multiethnic cohorts this diagnostic model may become a valuable tool for a definite diagnosis of negRA or PsA patients.

Novel Findings From a Metabolomics Study of Left Ventricular Diastolic Function: The Bogalusa Heart Study

Background

Diastolic dysfunction is one important causal factor for heart failure with preserved ejection fraction, yet the metabolic signature associated with this subclinical phenotype remains unknown.

Methods and Results

Ultra‐high‐performance liquid chromatography–tandem mass spectroscopy was used to conduct untargeted metabolomic analysis of fasting serum samples in 1050 white and black participants of the BHS (Bogalusa Heart Study). After quality control, 1202 metabolites were individually tested for association with 5 echocardiographic measures of left ventricular diastolic function using multivariable‐adjusted linear regression. Measures of left ventricular diastolic function included the ratio of peak early filling velocity to peak late filling velocity, ratio of peak early filling velocity to mitral annular velocity, deceleration time, isovolumic relaxation time, and left atrial maximum volume index (LAVI). Analyses adjusted for multiple cardiovascular disease risk factors and used Bonferroni‐corrected alpha thresholds. Eight metabolites robustly associated with left ventricular diastolic function in the overall population and demonstrated consistent associations in white and black study participants. N‐formylmethionine (B=0.05; P=1.50×10⁻⁷); 1‐methylhistidine (B=0.05; P=1.60×10⁻⁷); formiminoglutamate (B=0.07; P=5.60×10⁻⁷); N2, N5‐diacetylornithine (B=0.05; P=1.30×10⁻⁷); N‐trimethyl 5‐aminovalerate (B=0.04; P=5.10×10⁻⁶); 5‐methylthioadenosine (B=0.04; P=1.40×10⁻⁵); and methionine sulfoxide (B=0.04; P=3.80×10⁻⁶) were significantly associated with the natural log of the ratio of peak early filling velocity to mitral annular velocity. Butyrylcarnitine (B=3.18; P=2.10×10⁻⁶) was significantly associated with isovolumic relaxation time.

Conclusions

The current study identified novel findings of metabolite associations with left ventricular diastolic function, suggesting that the serum metabolome, and its underlying biological pathways, may be implicated in heart failure with preserved ejection fraction pathogenesis.

Improved workflow for mass spectrometry-based metabolomics analysis of the heart

MS-based metabolomics methods are powerful techniques to map the complex and interconnected metabolic pathways of the heart; however, normalization of metabolite abundance to sample input in heart tissues remains a technical challenge. Herein, we describe an improved GC-MS-based metabolomics workflow that uses insoluble protein-derived glutamate for the normalization of metabolites within each sample and includes normalization to protein-derived amino acids to reduce biological variation and detect small metabolic changes. Moreover, glycogen is measured within the metabolomics workflow. We applied this workflow to study heart metabolism by first comparing two different methods of heart removal: the Langendorff heart method (reverse aortic perfusion) and in situ freezing of mouse heart with a modified tissue freeze-clamp approach. We then used the in situ freezing method to study the effects of acute β-adrenergic receptor stimulation (through isoproterenol (ISO) treatment) on heart metabolism. Using our workflow and within minutes, ISO reduced the levels of metabolites involved in glycogen metabolism, glycolysis, and the Krebs cycle, but the levels of pentose phosphate pathway metabolites and of many free amino acids remained unchanged. This observation was coupled to a 6-fold increase in phosphorylated adenosine nucleotide abundance. These results support the notion that ISO acutely accelerates oxidative metabolism of glucose to meet the ATP demand required to support increased heart rate and cardiac output. In summary, our MS-based metabolomics workflow enables improved quantification of cardiac metabolites and may also be compatible with other methods such as LC or capillary electrophoresis.

Dexamethasone-Induced Perturbations in Tissue Metabolomics Revealed by Chemical Isotope Labeling LC-MS analysis

Dexamethasone (Dex) is a synthetic glucocorticoid (GC) drug commonly used clinically for the treatment of several inflammatory and immune-mediated diseases. Despite its broad range of indications, the long-term use of Dex is known to be associated with specific abnormalities in several tissues and organs. In this study, the metabolomic effects on five different organs induced by the chronic administration of Dex in the Sprague–Dawley rat model were investigated using the chemical isotope labeling liquid chromatography-mass spectrometry (CIL LC-MS) platform, which targets the amine/phenol submetabolomes. Compared to controls, a prolonged intake of Dex resulted in significant perturbations in the levels of 492, 442, 300, 186, and 105 metabolites in the brain, skeletal muscle, liver, kidney, and heart tissues, respectively. The positively identified metabolites were mapped to diverse molecular pathways in different organs. In the brain, perturbations in protein biosynthesis, amino acid metabolism, and monoamine neurotransmitter synthesis were identified, while in the heart, pyrimidine metabolism and branched amino acid biosynthesis were the most significantly impaired pathways. In the kidney, several amino acid pathways were dysregulated, which reflected impairments in several biological functions, including gluconeogenesis and ureagenesis. Beta-alanine metabolism and uridine homeostasis were profoundly affected in liver tissues, whereas alterations of glutathione, arginine, glutamine, and nitrogen metabolism pointed to the modulation of muscle metabolism and disturbances in energy production and muscle mass in skeletal muscle. The differential expression of multiple dipeptides was most significant in the liver (down-regulated), brain (up-regulation), and kidney tissues, but not in the heart or skeletal muscle tissues. The identification of clinically relevant pathways provides holistic insights into the tissue molecular responses induced by Dex and understanding of the underlying mechanisms associated with their side effects. Our data suggest a potential role for glutathione supplementation and dipeptide modulators as novel therapeutic interventions to mitigate the side effects induced by Dex therapy.

Systematic impacts of chronic unpredictable mild stress on metabolomics in rats

Depression is the most common disabling psychiatric disease, with a high prevalence and mortality. Chronic unpredictable mild stress (CUMS) is a well-accepted method used to mimic clinical depression. Recent evidence has consistently suggested that the cumulative effects of CUMS could lead to allostatic overload in the body, thereby inducing systemic disorders; however, there are no previous systematic metabonomics studies on the main stress-targeted tissues associated with depression. A non-targeted gas chromatography–mass spectrometry (GC–MS) approach was used to identify metabolic biomarkers in the main stress-targeted tissues (serum, heart, liver, brain, and kidney) in a CUMS model of depression. Male Sprague–Dawley rats were randomly allocated to the CUMS group (n = 8) or a control group (n = 8). Multivariate analysis was performed to identify the metabolites that were differentially expressed between the two groups. There were 10, 10, 9, 4, and 7 differentially expressed metabolites in the serum, heart, liver, brain and kidney tissues, respectively, between the control and CUMS groups. These were linked to nine different pathways related to the metabolism of amino acids, lipids, and energy. In summary, we provided a comprehensive understanding of metabolic alterations in the main stress-targeted tissues, helping to understand the potential mechanisms underlying depression.

Development of an LC-MS multivariate nontargeted methodology for differential analysis of the peptide profile of Asian hornet venom (Vespa velutina nigrithorax): application to the investigation of the impact of collection period variation

Insect venom is a highly complex mixture of bioactive compounds, containing proteins, peptides, and small molecules. Environmental factors can alter the venom composition and lead to intraspecific variation in its bioactivity properties. The investigation of discriminating compounds caused by variation impacts can be a key to manage sampling and explore the bioactive compounds. The present study reports the development of a peptidomic methodology based on UHPLC-ESI-QTOF-HRMS analysis followed by a nontargeted multivariate analysis to reveal the profile variance of Vespa velutina venom collected in different conditions. The reliability of the approach was enhanced by optimizing certain XCMS data processing parameters and determining the sample peak threshold to eliminate the interfering features. This approach demonstrated a good repeatability and a criterion coefficient of variation (CV) > 30% was set for deleting nonrepeatable features from the matrix. The methodology was then applied to investigate the impact of collection period variation. PCA and PLS-DA models were used and validated by cross-validation and permutation tests. A slight discrimination was found between winter and summer hornet venom in two successive years with 10 common discriminating compounds. Graphical abstract.

Using MetaboAnalyst 4.0 for Metabolomics Data Analysis, Interpretation, and Integration with Other Omics Data

MetaboAnalyst ( www.metaboanalyst.ca ) is an easy-to-use, comprehensive web-based tool, freely available for metabolomics data processing, statistical analysis, functional interpretation, as well as integration with other omics data. This chapter first provides an introductory overview to the current MetaboAnalyst (version 4.0) with regards to its underlying design concepts and user interface structure. Subsequent sections describe three common metabolomics data analysis workflows covering targeted metabolomics, untargeted metabolomics, and multi-omics data integration.

Severity-Dependent Profile of the Metabolome in Hypospadias

Background & Objective: Hypospadias, characterized by the displacement of the opening of the urethra at any point in the medial-ventral side of the penis, is classified upon severity as mild (Type I) and severe (Type II and Type III) hypospadias. Hypospadias' etiology is idiopathic in the majority of cases, and underlying causes seem of multifactorial origin. Studies regarding genetic variants support this notion. It is unknown whether downstream gene products fit this profile. This study evaluated the metabolome of hypospadias by using the emerging technology of metabolomics in the search for distinct cellular processes associated with hypospadias' etiology according to the severity of this congenital urogenital condition. Methods: Foreskin samples were collected during urethroplasty from boys with Type I, II, and III hypospadias or undergoing elective circumcision (N = 28) between 5 and 28 months of age. Samples were processed and submitted to gas chromatography-mass spectrometry (GC/MS). MetaboloAnalyst (http://www.metaboanalyst.ca/) online platform was used for bioinformatic analyses. Results: Thirty-five metabolites across experimental groups were identified by GC/MS. Principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) showed that the metabolome of Type II and Type III hypospadias patients differs from the metabolome of Type I hypospadias and control patients. Of those 35, 10 amino acids were found in significantly low concentrations in severe hypospadias: aspartate, glutamate, glycine, isoleucine, leucine, lysine, methionine, phenylalanine, proline, and tyrosine. A high concentration of the amino acid lysine was detected in mild hypospadias. Conclusions: The observed downregulation of specific amino acids in severe hypospadias provides alternative routes for future research aiming to identify disrupted networks and pathways while considering the severity of hypospadias.

Metabolic Profiling in Human Fibroblasts Enables Subtype Clustering in Glycogen Storage Disease

Glycogen storage disease subtypes I and III (GSD I and GSD III) are monogenic inherited disorders of metabolism that disrupt glycogen metabolism. Unavailability of glucose in GSD I and induction of gluconeogenesis in GSD III modify energy sources and possibly, mitochondrial function. Abnormal mitochondrial structure and function were described in mice with GSD Ia, yet significantly less research is available in human cells and ketotic forms of the disease. We hypothesized that impaired glycogen storage results in distinct metabolic phenotypes in the extra- and intracellular compartments that may contribute to pathogenesis. Herein, we examined mitochondrial organization in live cells by spinning-disk confocal microscopy and profiled extra- and intracellular metabolites by targeted LC-MS/MS in cultured fibroblasts from healthy controls and from patients with GSD Ia, GSD Ib, and GSD III. Results from live imaging revealed that mitochondrial content and network morphology of GSD cells are comparable to that of healthy controls. Likewise, healthy controls and GSD cells exhibited comparable basal oxygen consumption rates. Targeted metabolomics followed by principal component analysis (PCA) and hierarchical clustering (HC) uncovered metabolically distinct poises of healthy controls and GSD subtypes. Assessment of individual metabolites recapitulated dysfunctional energy production (glycolysis, Krebs cycle, succinate), reduced creatinine export in GSD Ia and GSD III, and reduced antioxidant defense of the cysteine and glutathione systems. Our study serves as proof-of-concept that extra- and intracellular metabolite profiles distinguish glycogen storage disease subtypes from healthy controls. We posit that metabolite profiles provide hints to disease mechanisms as well as to nutritional and pharmacological elements that may optimize current treatment strategies.

Towards early monitoring of chemotherapy-induced drug resistance based on single cell metabolomics: Combining single-probe mass spectrometry with machine learning

Despite the presence of methods evaluating drug resistance during chemotherapies, techniques, which allow for monitoring the degree of drug resistance in early chemotherapeutic stage from single cells in their native microenvironment, are still absent. Herein, we report an analytical approach that combines single cell mass spectrometry (SCMS) based metabolomics with machine learning (ML) models to address the existing challenges. Metabolomic profiles of live cancer cells (HCT-116) with different levels (i.e., no, low, and high) of chemotherapy-induced drug resistance were measured using the Single-probe SCMS technique. A series of ML models, including random forest (RF), artificial neural network (ANN), and penalized logistic regression (LR), were constructed to predict the degrees of drug resistance of individual cells. A systematic comparison of performance was conducted among multiple models, and the method validation was carried out experimentally. Our results indicate that these ML models, especially the RF model constructed on the obtained SCMS datasets, can rapidly and accurately predict different degrees of drug resistance of live single cells. With such rapid and reliable assessment of drug resistance demonstrated at the single cell level, our method can be potentially employed to evaluate chemotherapeutic efficacy in the clinic.

In vitro metabolomic footprint of the Echinococcus multilocularis metacestode

Alveolar echinococcosis (AE) is a zoonotic disease that is deadly if left untreated. AE is caused by the larval metacestode stage of the cestode Echinococcus multilocularis. Better knowledge on the host-parasite interface could yield novel targets for improvement of the treatment against AE. We analyzed culture media incubated with in vitro grown E. multilocularis metacestodes by 1H nuclear magnetic resonance spectroscopy to identify the unknown metabolic footprint of the parasite. Moreover, we quantitatively analyzed all amino acids, acetate, glucose, lactate, and succinate in time-course experiments using liquid chromatography and enzymatic assays. The E. multilocularis metacestodes consumed glucose and, surprisingly, threonine and produced succinate, acetate, and alanine as major fermentation products. The metabolic composition of vesicle fluid (VF) from in vitro grown E. multilocularis metacestodes was different from parasite-incubated culture medium with respect to the abundance, but not the spectrum, of metabolites, and some metabolites, in particular amino acids, accumulated in the VF. Overall, this study presents the first characterization of the in vitro metabolic footprint of E. multilocularis metacestodes and VF composition, and it provides the basis for analyses of potentially targetable pathways for future drug development.

From Samples to Insights into Metabolism: Uncovering Biologically Relevant Information in LC-HRMS Metabolomics Data

Untargeted metabolomics (including lipidomics) is a holistic approach to biomarker discovery and mechanistic insights into disease onset and progression, and response to intervention. Each step of the analytical and statistical pipeline is crucial for the generation of high-quality, robust data. Metabolite identification remains the bottleneck in these studies; therefore, confidence in the data produced is paramount in order to maximize the biological output. Here, we outline the key steps of the metabolomics workflow and provide details on important parameters and considerations. Studies should be designed carefully to ensure appropriate statistical power and adequate controls. Subsequent sample handling and preparation should avoid the introduction of bias, which can significantly affect downstream data interpretation. It is not possible to cover the entire metabolome with a single platform; therefore, the analytical platform should reflect the biological sample under investigation and the question(s) under consideration. The large, complex datasets produced need to be pre-processed in order to extract meaningful information. Finally, the most time-consuming steps are metabolite identification, as well as metabolic pathway and network analysis. Here we discuss some widely used tools and the pitfalls of each step of the workflow, with the ultimate aim of guiding the reader towards the most efficient pipeline for their metabolomics studies.

Metabolomics of Small Intestine Neuroendocrine Tumors and Related Hepatic Metastases

To assess the metabolomic fingerprint of small intestine neuroendocrine tumors (SI-NETs) and related hepatic metastases, and to investigate the influence of the hepatic environment on SI-NETs metabolome. Ninety-four tissue samples, including 46 SI-NETs, 18 hepatic NET metastases and 30 normal SI and liver samples, were analyzed using 1H-magic angle spinning (HRMAS) NMR nuclear magnetic resonance (NMR) spectroscopy. Twenty-seven metabolites were identified and quantified. Differences between primary NETs vs. normal SI and primary NETs vs. hepatic metastases, were assessed. Network analysis was performed according to several clinical and pathological features. Succinate, glutathion, taurine, myoinositol and glycerophosphocholine characterized NETs. Normal SI specimens showed higher levels of alanine, creatine, ethanolamine and aspartate. PLS-DA revealed a continuum-like distribution among normal SI, G1-SI-NETs and G2-SI-NETs. The G2-SI-NET distribution was closer and clearly separated from normal SI tissue. Lower concentration of glucose, serine and glycine, and increased levels of choline-containing compounds, taurine, lactate and alanine, were found in SI-NETs with more aggressive tumors. Higher abundance of acetate, succinate, choline, phosphocholine, taurine, lactate and aspartate discriminated liver metastases from normal hepatic parenchyma. Higher levels of alanine, ethanolamine, glycerophosphocholine and glucose was found in hepatic metastases than in primary SI-NETs. The present work gives for the first time a snapshot of the metabolomic characteristics of SI-NETs, suggesting the existence of complex metabolic reality, maybe characteristic of different tumor evolution.

Investigation of the metabolic difference between ST-elevated myocardial infarction and non-ST-elevated myocardial infarction via LC/Q-TOF/MS/MS

Acute coronary syndrome (ACS) is a clinical condition caused by a disturbance in myocardial blood flow. ACS can be basically divided into two forms: ST elevation myocardial infarction (STEMI) due to complete occlusion of the coronary artery and non-ST elevation myocardial infarction (NSTEMI) due to partial occlusion of the coronary artery. In this study, we aimed to monitor the metabolite profile of STEMI and NSTEMI patients and compare the results via untargeted metabolomics approach. Serum samples were collected from STEMI and NSTEMI patients, and each group consists of 20 participants. Extraction was achieved by acetonitrile, and chromatographic separation was performed by LC/Q-TOF/MS/MS accompanied with dual AJS ESI positive ion mode. METLIN, MATLAB 2017a-PLS Toolbox7.2, and Human Metabolome Database were utilized for bioinformatics evaluation of obtained findings. In our results, 203 m/z ratio was detected and 163 m/z ratio passed the significance criteria (fold analysis > 1.5 and p < 0.05). Twenty-five metabolites including BCAAs, LysoPC species, lactic acid, succinate, malonic acid, maleic acid, butyric acid, carnitine, and betaine were identified. In conclusion, new biomarker candidates were identified to differentiate the diagnosis of STEMI and NSTEMI. Identified metabolites are indicative of alterations in oxidative stress, hypoxia, TCA cycle, and amino acid metabolism.

Chemical and Antifungal Variability of Several Accessions of Azadirachta indica A. Juss. from Six Locations Across the Colombian Caribbean Coast: Identification of Antifungal Azadirone Limonoids

Plant materials (i.e., leaves, fruits, and seeds) from 40 trees of Azadirachta indica A. Juss. were collected from six different locations across the Colombian Caribbean coast. Eighty-four ethanolic extracts were prepared and the total limonoid contents (TLiC) and the antifungal activity against Fusarium oxysporum conidia were measured. Their chemical profiles were also recorded via liquid chromatography-electrospray ionization interface-mass spectrometry (LC-ESI-MS) analysis and the top-ranked features were then annotated after supervised multivariate statistics. Inter-location chemical variability within sample set was assessed by sparse partial least squares discriminant analysis (sPLS-DA) and the chemical profiles and biological activity datasets were integrated through single-Y orthogonal partial least squares (OPLS) to identify antifungal bioactives in test extracts. The TLiC and antifungal activity (IC₅₀ values) of the A. indica-derived extracts were found to be ranging from 4.5 to 48.5 mg limonin equivalent per g dry extract and 0.08-44.8 μg/mL, respectively. The presence/abundance of particular limonoids between collected samples influenced the variability among locations. In addition, the integration of chemical and antifungal activity datasets showed five features as markers probably contributing to the bioactivity, annotated as compounds with an azadirone-like moiety. To validate the information provided by the single-Y OPLS model, a high performance liquid chromatography (HPLC)-based microfractionation was then carried out on an active extract. The combined plot of chromatographic profile and microfraction bioactivity also evidenced five signals possessing the highest antifungal activity. The most active limonoid was identified as nimonol 1. Hence, this untargeted metabolite profiling was considered as a convenient tool for identifying metabolites as inter-location markers as well as antifungals against Fusarium oxysporum.

Metabolomics technology and bioinformatics for precision medicine

Precision medicine is rapidly emerging as a strategy to tailor medical treatment to a small group or even individual patients based on their genetics, environment and lifestyle. Precision medicine relies heavily on developments in systems biology and omics disciplines, including metabolomics. Combination of metabolomics with sophisticated bioinformatics analysis and mathematical modeling has an extreme power to provide a metabolic snapshot of the patient over the course of disease and treatment or classifying patients into subpopulations and subgroups requiring individual medical treatment. Although a powerful approach, metabolomics have certain limitations in technology and bioinformatics. We will review various aspects of metabolomics technology and bioinformatics, from data generation, bioinformatics analysis, data fusion and mathematical modeling to data management, in the context of precision medicine.

A Metabolomic Approach for the In Vivo Study of Gold Nanospheres and Nanostars after a Single-Dose Intravenous Administration to Wistar Rats

Gold nanoparticles (AuNPs) are promising nanoplatforms for drug therapy, diagnostic and imaging. However, biological comparison studies for different types of AuNPs fail in consistency due to the lack of sensitive methods to detect subtle differences in the expression of toxicity. Therefore, innovative and sensitive approaches such as metabolomics are much needed to discriminate toxicity, specially at low doses. The current work aims to compare the in vivo toxicological effects of gold nanospheres versus gold nanostars (of similar ~40 nm diameter and coated with 11-mercaptoundecanoic acid) 24 h after an intravenous administration of a single dose (1.33 × 10¹¹ AuNPs/kg) to Wistar rats. The biodistribution of both types of AuNPs was determined by graphite furnace atomic absorption spectroscopy. The metabolic effects of the AuNPs on their main target organ, the liver, were analyzed using a GC-MS-based metabolomic approach. Conventional toxicological endpoints, including the levels of ATP and reduced and oxidized glutathione, were also investigated. The results show that AuNPs preferentially accumulate in the liver and, to a lesser extent, in the spleen and lungs. In other organs (kidney, heart, brain), Au content was below the limit of quantification. Reduced glutathione levels increased for both nanospheres and nanostars in the liver, but ATP levels were unaltered. Multivariate analysis showed a good discrimination between the two types of AuNPs (sphere- versus star-shaped nanoparticles) and compared to control group. The metabolic pathways involved in the discrimination were associated with the metabolism of fatty acids, pyrimidine and purine, arachidonic acid, biotin, glycine and synthesis of amino acids. In conclusion, the biodistribution, toxicological, and metabolic profiles of gold nanospheres and gold nanostars were described. Metabolomics proved to be a very useful tool for the comparative study of different types of AuNPs and raised awareness about the pathways associated to their distinct biological effects.

Correlations between the metabolic profile and 18F-FDG-Positron Emission Tomography-Computed Tomography parameters reveal the complexity of the metabolic reprogramming within lung cancer patients

Several studies have demonstrated that the metabolite composition of plasma may indicate the presence of lung cancer. The metabolism of cancer is characterized by an enhanced glucose uptake and glycolysis which is exploited by ¹⁸F-FDG positron emission tomography (PET) in the work-up and management of cancer. This study aims to explore relationships between ¹H-NMR spectroscopy derived plasma metabolite concentrations and the uptake of labeled glucose (¹⁸F-FDG) in lung cancer tissue. PET parameters of interest are standard maximal uptake values (SUV_max), total body metabolic active tumor volumes (MATV_WTB) and total body total lesion glycolysis (TLG_WTB) values. Patients with high values of these parameters have higher plasma concentrations of N-acetylated glycoproteins which suggest an upregulation of the hexosamines biosynthesis. High MATV_WTB and TLG_WTB values are associated with higher concentrations of glucose, glycerol, N-acetylated glycoproteins, threonine, aspartate and valine and lower levels of sphingomyelins and phosphatidylcholines appearing at the surface of lipoproteins. These higher concentrations of glucose and non-carbohydrate glucose precursors such as amino acids and glycerol suggests involvement of the gluconeogenesis pathway. The lower plasma concentration of those phospholipids points to a higher need for membrane synthesis. Our results indicate that the metabolic reprogramming in cancer is more complex than the initially described Warburg effect.

Metabolomic studies of Pseudomonas aeruginosa

Pseudomonas aeruginosa is a common, Gram-negative environmental organism. It can be a significant pathogenic factor of severe infections in humans, especially in cystic fibrosis patients. Due to its natural resistance to antibiotics and the ability to form biofilms, infection with this pathogen can cause severe therapeutic problems. In recent years, metabolomic studies of P. aeruginosa have been performed. Therefore, in this review, we discussed recent achievements in the use of metabolomics methods in bacterial identification, differentiation, the interconnection between genome and metabolome, the influence of external factors on the bacterial metabolome and identification of new metabolites produced by P. aeruginosa. All of these studies may provide valuable information about metabolic pathways leading to an understanding of the adaptations of bacterial strains to a host environment, which can lead to new drug development and/or elaboration of new treatment and diagnostics strategies for Pseudomonas.

Tumor cell sensitivity to vemurafenib can be predicted from protein expression in a BRAF-V600E basket trial setting

BACKGROUND

Genetics-based basket trials have emerged to test targeted therapeutics across multiple cancer types. However, while vemurafenib is FDA-approved for BRAF-V600E melanomas, the non-melanoma basket trial was unsuccessful, suggesting mutation status is insufficient to predict response. We hypothesized that proteomic data would complement mutation status to identify vemurafenib-sensitive tumors and effective co-treatments for BRAF-V600E tumors with inherent resistance.

METHODS

Reverse Phase Proteomic Array (RPPA, MD Anderson Cell Lines Project), RNAseq (Cancer Cell Line Encyclopedia) and vemurafenib sensitivity (Cancer Therapeutic Response Portal) data for BRAF-V600E cancer cell lines were curated. Linear and nonlinear regression models using RPPA protein or RNAseq were evaluated and compared based on their ability to predict BRAF-V600E cell line sensitivity (area under the dose response curve). Accuracies of all models were evaluated using hold-out testing. CausalPath software was used to identify protein-protein interaction networks that could explain differential protein expression in resistant cells. Human examination of features employed by the model, the identified protein interaction networks, and model simulation suggested anti-ErbB co-therapy would counter intrinsic resistance to vemurafenib. To validate this potential co-therapy, cell lines were treated with vemurafenib and dacomitinib (a pan-ErbB inhibitor) and the number of viable cells was measured.

RESULTS

Orthogonal partial least squares (O-PLS) predicted vemurafenib sensitivity with greater accuracy in both melanoma and non-melanoma BRAF-V600E cell lines than other leading machine learning methods, specifically Random Forests, Support Vector Regression (linear and quadratic kernels) and LASSO-penalized regression. Additionally, use of transcriptomic in place of proteomic data weakened model performance. Model analysis revealed that resistant lines had elevated expression and activation of ErbB receptors, suggesting ErbB inhibition could improve vemurafenib response. As predicted, experimental evaluation of vemurafenib plus dacomitinb demonstrated improved efficacy relative to monotherapies.

CONCLUSIONS

Combined, our results support that inclusion of proteomics can predict drug response and identify co-therapies in a basket setting.

NMR-based metabolic profiling and comparison of Japanese persimmon cultivars

Persimmons are a traditional, autumnal, and healthy fruit commonly consumed in Japan and East Asia based on the saying, "a persimmon a day keeps the doctor away." The differences in metabolites among five major Japanese persimmon cultivars were investigated using a nuclear magnetic resonance (NMR)-based metabolomics approach. By using a broadband water suppression enhanced through T₁ effects (WET) method for the sensitive detection of minor metabolites, better discrimination among cultivars and more informative details regarding their metabolic differences have been achieved compared to those achieved in conventional ¹H NMR sequences. Among the nonastringent cultivars analyzed, the Taishu cultivar has the highest abundance of amino acids. The Matsumotowase-Fuyu cultivar contains ethyl-β-glycosides as characteristic components, which may relate to fruit softening. Citric acid concentration is higher in Maekawa Jiro than in other nonastringent cultivars. Among the two astringent cultivars analyzed, ethanol was significantly higher in Hiratanenashi than in Yotsumizo, which indicates different reactivity during deastringency treatments. The present study proposes an efficient and relatively quantitative metabolomics approach based on broadband WET NMR spectra.

Cytosolic 10-formyltetrahydrofolate dehydrogenase regulates glycine metabolism in mouse liver

ALDH1L1 (10-formyltetrahydrofolate dehydrogenase), an enzyme of folate metabolism highly expressed in liver, metabolizes 10-formyltetrahydrofolate to produce tetrahydrofolate (THF). This reaction might have a regulatory function towards reduced folate pools, de novo purine biosynthesis, and the flux of folate-bound methyl groups. To understand the role of the enzyme in cellular metabolism, Aldh1l1^−/− mice were generated using an ES cell clone (C57BL/6N background) from KOMP repository. Though Aldh1l1^−/− mice were viable and did not have an apparent phenotype, metabolomic analysis indicated that they had metabolic signs of folate deficiency. Specifically, the intermediate of the histidine degradation pathway and a marker of folate deficiency, formiminoglutamate, was increased more than 15-fold in livers of Aldh1l1^−/− mice. At the same time, blood folate levels were not changed and the total folate pool in the liver was decreased by only 20%. A two-fold decrease in glycine and a strong drop in glycine conjugates, a likely result of glycine shortage, were also observed in Aldh1l1^−/− mice. Our study indicates that in the absence of ALDH1L1 enzyme, 10-formyl-THF cannot be efficiently metabolized in the liver. This leads to the decrease in THF causing reduced generation of glycine from serine and impaired histidine degradation, two pathways strictly dependent on THF.

Metabolomic Analysis of Platelets of Patients With Aspirin Non-Response

Background: Aspirin is the most commonly used antiplatelet agent for the prevention of cardiovascular diseases. However, a certain proportion of patients do not respond to aspirin therapy. The mechanisms of aspirin non-response remain unknown. The unique metabolomes in platelets of patients with coronary artery disease (CAD) with aspirin non-response may be one of the causes of aspirin resistance.

Materials and Methods: We enrolled 29 patients with CAD who were aspirin non-responders, defined as a study subject who were taking aspirin with a platelet aggregation time less than 193 s by PFA-100, and 31 age- and sex-matched patients with CAD who were responders. All subjects had been taking 100 mg of aspirin per day for more than 1 month. Hydrophilic metabolites from the platelet samples were extracted and analyzed by nuclear magnetic resonance (NMR). Both 1D ¹H and 2D J-resolved NMR spectra were obtained followed by spectral processing and multivariate statistical analysis, such as partial least squares discriminant analysis (PLS-DA).

Results: Eleven metabolites were identified. The PLS-DA model could not distinguish aspirin non-responders from responders. Those with low serum glycine level had significantly shorter platelet aggregation time (mean, 175.0 s) compared with those with high serum glycine level (259.5 s). However, this association became non-significant after correction for multiple tests.

Conclusions: The hydrophilic metabolic profile of platelets was not different between aspirin non-responders and responders. An association between lower glycine levels and higher platelet activity in patients younger than 65 years suggests an important role of glycine in the pathophysiology of aspirin non-response.

Untargeted Metabolomics for Autism Spectrum Disorders: Current Status and Future Directions

Autism spectrum disorders (ASDs) are a group of neurodevelopment disorders characterized by childhood onset deficits in social communication and interaction. Although the exact etiology of most cases of ASDs is unknown, a portion has been proposed to be associated with various metabolic abnormalities including mitochondrial dysfunction, disorders of cholesterol metabolism, and folate abnormalities. Targeted biochemical testing like plasma amino acid and acylcarnitine profiles have demonstrated limited utility in helping to diagnose and manage such patients. Untargeted metabolomics has emerged, however, as a promising tool in screening for underlying biochemical abnormalities and managing treatment and as a means of investigating possible novel biomarkers for the disorder. Here, we review the principles and methodology behind untargeted metabolomics, recent pilot studies utilizing this technology, and areas in which it may be integrated into the care of children with this disorder in the future.

Near-roadway air pollution exposure and altered fatty acid oxidation among adolescents and young adults - The interplay with obesity

BACKGROUND

Air pollution exposure has been shown to increase the risk of obesity and metabolic dysfunction in animal models and human studies. However, the metabolic pathways altered by air pollution exposure are unclear, especially in adolescents and young adults who are at a critical period in the development of cardio-metabolic diseases.

OBJECTIVES

The aim of this study was to examine the associations between air pollution exposure and indices of fatty acid and amino acid metabolism.

METHODS

A total of 173 young adults (18-23 years) from eight Children's Health Study (CHS) Southern California communities were examined from 2014 to 2018. Near-roadway air pollution (NRAP) exposure (freeway and non-freeway) and regional air pollution exposure (nitrogen dioxide, ozone and particulate matter) during one year before the study visit were estimated based on participants' residential addresses. Serum concentrations of 64 targeted metabolites including amino acids, acylcarnitines, non-esterified fatty acid (NEFA) and glycerol were measured in fasting serum samples. Principal component analysis of metabolites was performed to identify metabolite clusters that represent key metabolic pathways. Mixed effects models were used to analyze the associations of air pollution exposure with metabolomic principal component (PC) scores and individual metabolite concentrations adjusting for potential confounders.

RESULTS

Higher lagged one-year averaged non-freeway NRAP exposure was associated with higher concentrations of NEFA oxidation byproducts and higher NEFA-related PC score (all p's ≤ 0.038). The effect sizes were larger among obese individuals (interaction p = 0.047). Among females, higher freeway NRAP exposure was also associated with a higher NEFA-related PC score (p = 0.042). Among all participants, higher freeway NRAP exposure was associated with a lower PC score for lower concentrations of short- and median-chain acylcarnitines (p = 0.044).

CONCLUSIONS

Results of this study indicate that NRAP exposure is associated with altered fatty acid metabolism, which could contribute to the metabolic perturbation in obese youth.

Metabolomics combined with network pharmacology exploration reveals the modulatory properties of Astragali Radix extract in the treatment of liver fibrosis

Background

Astragali Radix (AR) is widely-used for improving liver fibrosis, but, the mechanism of action has not been systematically explained. This study aims to investigate the mechanism of AR intervention in liver fibrosis based on comprehensive metabolomics combined with network pharmacology approach.

Materials and methods

UPLC–Q-TOF/MS based metabolomics technique was used to explore the specific metabolites and possible pathways of AR affecting the pathological process of liver fibrosis. Network pharmacology analysis was introduced to explore the key targets of AR regarding the mechanisms on liver fibrosis.

Results

AR significantly reduced the levels of ALT, AST and AKP in serum, and improved pathological characteristics. Metabolomics analysis showed that the therapeutic effect of AR was mainly related to the regulation of nine metabolites, including sphingosine, 6-keto-prostaglandin F1a, LysoPC (O-18:0), 3-dehydrosphinganine, 5,6-epoxy-8,11,14-eicosatrienoic acid, leukotriene C4, taurochenodesoxycholic acid, LysoPC (18:1 (9Z)) and 2-acetyl-1-alkyl-sn-glycero-3-phosphocholine. Pathway analysis indicated that the treatment of AR on liver fibrosis was related to arachidonic acid metabolism, ether lipid metabolism, sphingolipid metabolism, glycerophospholipid metabolism and primary bile acid biosynthesis. Validation of the key targets by network pharmacology analysis of potential metabolic markers showed that AR significantly down-regulated the expression of CYP1B1 and up-regulated the expression of CYP1A2 and PCYT1A.

Conclusion

Metabolomics combined with network pharmacology was used for the first time to clarify that the treatment of AR on liver fibrosis, which is related to the regulation of arachidonic acid metabolism and ether lipid metabolism by modulating the expression of CYP1A2, CYP1B1 and PCYT1A. And the integrated approach can provide new strategies and ideas for the study of molecular mechanisms of traditional Chinese medicines in the treatment of liver fibrosis.

Evaluation of Multivariate Classification Models for Analyzing NMR Metabolomics Data

Analytical techniques such as NMR and mass spectrometry can generate large metabolomics data sets containing thousands of spectral features derived from numerous biological observations. Multivariate data analysis is routinely used to uncover the underlying biological information contained within these large metabolomics data sets. This is typically accomplished by classifying the observations into groups (e.g., control versus treated) and by identifying associated discriminating features. There are a variety of classification models to select from, which include some well-established techniques (e.g., principal component analysis [PCA], orthogonal projection to latent structure [OPLS], or partial least-squares projection to latent structures [PLS]) and newly emerging machine learning algorithms (e.g., support vector machines or random forests). However, it is unclear which classification model, if any, is an optimal choice for the analysis of metabolomics data. Herein, we present a comprehensive evaluation of five common classification models routinely employed in the metabolomics field and that are also currently available in our MVAPACK metabolomics software package. Simulated and experimental NMR data sets with various levels of group separation were used to evaluate each model. Model performance was assessed by classification accuracy rate, by the area under a receiver operating characteristic (AUROC) curve, and by the identification of true discriminating features. Our findings suggest that the five classification models perform equally well with robust data sets. Only when the models are stressed with subtle data set differences does OPLS emerge as the best-performing model. OPLS maintained a high-prediction accuracy rate and a large area under the ROC curve while yielding loadings closest to the true loadings with limited group separations.

Evaluation of In Silico Multifeature Libraries for Providing Evidence for the Presence of Small Molecules in Synthetic Blinded Samples

The current gold standard for unambiguous molecular identification in metabolomics analysis is comparing two or more orthogonal properties from the analysis of authentic reference materials (standards) to experimental data acquired in the same laboratory with the same analytical methods. This represents a significant limitation for comprehensive chemical identification of small molecules in complex samples. The process is time consuming and costly, and the majority of molecules are not yet represented by standards. Thus, there is a need to assemble evidence for the presence of small molecules in complex samples through the use of libraries containing calculated chemical properties. To address this need, we developed a Multi-Attribute Matching Engine (MAME) and a library derived in part from our in silico chemical library engine (ISiCLE). Here, we describe an initial evaluation of these methods in a blinded analysis of synthetic chemical mixtures as part of the U.S. Environmental Protection Agency's (EPA) Non-Targeted Analysis Collaborative Trial (ENTACT, Phase 1). For molecules in all mixtures, the initial blinded false negative rate (FNR), false discovery rate (FDR), and accuracy were 57%, 77%, and 91%, respectively. For high evidence scores, the FDR was 35%. After unblinding of the sample compositions, we optimized the scoring parameters to better exploit the available evidence and increased the accuracy for molecules suspected as present. The final FNR, FDR, and accuracy were 67%, 53%, and 96%, respectively. For high evidence scores, the FDR was 10%. This study demonstrates that multiattribute matching methods in conjunction with in silico libraries may one day enable reduced reliance on experimentally derived libraries for building evidence for the presence of molecules in complex samples.

Evaluation of sodium deoxycholate as solubilization buffer for oil palm proteomics analysis

Protein solubility is a critical prerequisite to any proteomics analysis. Combination of urea/thiourea and 3-[(3-cholamidopropyl)dimethylammonio]-1-propanesulfonate (CHAPS) have been routinely used to enhance protein solubilization for oil palm proteomics studies in recent years. The goals of these proteomics analysis are essentially to complement the knowledge regarding the regulation networks and mechanisms of the oil palm fatty acid biosynthesis. Through omics integration, the information is able to build a regulatory model to support efforts in improving the economic value and sustainability of palm oil in the global oil and vegetable market. Our study evaluated the utilization of sodium deoxycholate as an alternative solubilization buffer/additive to urea/thiourea and CHAPS. Efficiency of urea/thiourea/CHAPS, urea/CHAPS, urea/sodium deoxycholate and sodium deoxycholate buffers in solubilizing the oil palm (Elaeis guineensis var. Tenera) mesocarp proteins were compared. Based on the protein yields and electrophoretic profile, combination of urea/thiourea/CHAPS were shown to remain a better solubilization buffer and additive, but the differences with sodium deoxycholate buffer was insignificant. A deeper mass spectrometric and statistical analyses on the identified proteins and peptides from all the evaluated solubilization buffers revealed that sodium deoxycholate had increased the number of identified proteins from oil palm mesocarps, enriched their gene ontologies and reduced the number of carbamylated lysine residues by more than 67.0%, compared to urea/thiourea/CHAPS buffer. Although only 62.0% of the total identified proteins were shared between the urea/thiourea/CHAPS and sodium deoxycholate buffers, the importance of the remaining 38.0% proteins depends on the applications. The only observed limitations to the application of sodium deoxycholate in protein solubilization were the interference with protein quantitation and but it could be easily rectified through a 4-fold dilution. All the proteomics data are available via ProteomeXchange with identifier PXD013255. In conclusion, sodium deoxycholate is applicable in the solubilization of proteins extracted from oil palm mesocarps with higher efficiency compared to urea/thiourea/CHAPS buffer. The sodium deoxycholate buffer is more favorable for proteomics analysis due to its proven advantages over urea/thiourea/CHAPS buffer.

Integrating a generalized data analysis workflow with the Single-probe mass spectrometry experiment for single cell metabolomics

We conducted single cell metabolomics studies of live cancer cells through online single cell mass spectrometry (SCMS) experiments combined with a generalized comprehensive data analysis workflow. The SCMS experiments were carried out using the Single-probe device coupled with a mass spectrometer to measure molecular profiles of cells in response to two mitotic inhibitors, taxol and vinblastine, under a series of treatment conditions. SCMS metabolomic data were analyzed using a comprehensive approach, including data pre-treatment, visualization, statistical analysis, machine learning, and pathway enrichment analysis. For comparative studies, traditional liquid chromatography-MS (LC-MS) experiments were conducted using lysates prepared from bulk cell samples. Metabolomic profiles of single cells were visualized through Partial Least Square-Discriminant Analysis (PLS-DA), and the phenotypic biomarkers associated with emerging phenotypes induced by drug treatment were discovered and compared through a series of rigorous statistical analysis. Species of interest were further identified at both the single cell and population levels. In addition, four biological pathways potentially involved in the drug treatment were determined through pathway enrichment analysis. Our work demonstrated the capability of comprehensive pipeline studies of single cell metabolomics. This method can be potentially applied to broader types of SCMS datasets for future pharmaceutical and chemotherapeutic research.