Determination of total concentration of chemically labeled metabolites as a means of metabolome sample normalization and sample loading optimization in mass spectrometry-based metabolomics

Challenges and recent advances in quantitative mass spectrometry-based metabolomics

The field of metabolomics has gained tremendous interest in recent years. Whether the goal is to discover biomarkers related to certain pathologies or to better understand the impact of a drug or contaminant, numerous studies have demonstrated how crucial it is to understand variations in metabolism. Detailed knowledge of metabolic variabilities can lead to more effective treatments, as well as faster or less invasive diagnostics. Exploratory approaches are often employed in metabolomics, using relative quantitation to look at perturbations between groups of samples. Most metabolomics studies have been based on metabolite profiling using relative quantitation, with very few studies using an approach for absolute quantitation. Using accurate quantitation facilitates the comparison between different studies, as well as enabling longitudinal studies. In this review, we discuss the most widely used techniques for quantitative metabolomics using mass spectrometry (MS). Various aspects will be addressed, such as the use of external and/or internal standards, derivatization techniques, in vivo isotopic labelling, or quantitative MS imaging. The principles, as well as the associated limitations and challenges, will be described for each approach.

Analyses of plasma metabolites using a high performance four-channel CIL LC-MS method and identification of metabolites associated with enteric methane emissions in beef cattle

Reducing enteric methane (one greenhouse gas) emissions from beef cattle not only can be beneficial in reducing global warming, but also improve efficiency of nutrient utilization in the production system. However, direct measurement of enteric methane emissions on individual cattle is difficult and expensive. The objective of this study was to detect plasma metabolites that are associated with enteric methane emissions in beef cattle. Average enteric methane emissions (CH4) per day (AVG_DAILYCH4) for each individual cattle were measured using the GreenFeed emission monitoring (GEM) unit system, and beef cattle with divergent AVG_DAILYCH4 from Angus (n = 10 for the low CH4 group and 9 for the high CH4 group), Charolais (n = 10 for low and 10 for = high), and Kinsella Composite (n = 10 for low and 10 for high) populations were used for plasma metabolite quantification and metabolite-CH4 association analyses. Blood samples of these cattle were collected near the end of the GEM system tests and a high performance four-channel chemical isotope labeling (CIL) liquid chromatography (LC) mass spectrometer (MS) method was applied to identify and quantify concentrations of metabolites. The four-channel CIL LC-MS method detected 4235 metabolites, of which 1105 were found to be significantly associated with AVG_DAILYCH4 by a t-test, while 1305 were significantly associated with AVG_DAILYCH4 by a regression analysis at p<0.05. Both the results of the t-test and regression analysis revealed that metabolites that were associated with enteric methane emissions in beef cattle were largely breed-specific whereas 4.29% to 6.39% CH4 associated metabolites were common across the three breed populations and 11.07% to 19.08% were common between two breed populations. Pathway analyses of the CH4 associated metabolites identified top enriched molecular processes for each breed population, including arginine and proline metabolism, arginine biosynthesis, butanoate metabolism, and glutathione metabolism for Angus; beta-alanine metabolism, pyruvate metabolism, glycolysis / gluconeogenesis, and citrate cycle (TCA cycle) for Charolais; phenylalanine, tyrosine and tryptophan biosynthesis, phenylalanine metabolism, arginine biosynthesis, and arginine and proline metabolism for Kinsella Composite. The detected CH4 associated metabolites and enriched molecular processes will help understand biological mechanisms of enteric methane emissions in beef cattle. The detected CH4 associated plasma metabolites will also provide valuable resources to further characterize the metabolites and verify their utility as biomarkers for selection of cattle with reduced methane emissions.

HDPairFinder: A data processing platform for hydrogen/deuterium isotopic labeling-based nontargeted analysis of trace-level amino-containing chemicals in environmental water

The combination of hydrogen/deuterium (H/D) formaldehyde-based isotopic methyl labeling with solid-phase extraction and high-performance liquid chromatography-high resolution mass spectrometry (HPLC-HRMS) is a powerful analytical solution for nontargeted analysis of trace-level amino-containing chemicals in water samples. Given the huge amount of chemical information generated in HPLC-HRMS analysis, identifying all possible H/D-labeled amino chemicals presents a significant challenge in data processing. To address this, we designed a streamlined data processing pipeline that can automatically extract H/D-labeled amino chemicals from the raw HPLC-HRMS data with high accuracy and efficiency. First, we developed a cross-correlation algorithm to correct the retention time shift resulting from deuterium isotopic effects, which enables reliable pairing of H- and D-labeled peaks. Second, we implemented several bioinformatic solutions to remove false chemical features generated by in-source fragmentation, salt adduction, and natural 13C isotopes. Third, we used a data mining strategy to construct the AMINES library that consists of over 38,000 structure-disjointed primary and secondary amines to facilitate putative compound annotation. Finally, we integrated these modules into a freely available R program, HDPairFinder.R. The rationale of each module was justified and its performance tested using experimental H/D-labeled chemical standards and authentic water samples. We further demonstrated the application of HDPairFinder to effectively extract N-containing contaminants, thus enabling the monitoring of changes of primary and secondary N-compounds in authentic water samples. HDPairFinder is a reliable bioinformatic tool for rapid processing of H/D isotopic methyl labeling-based nontargeted analysis of water samples, and will facilitate a better understanding of N-containing chemical compounds in water.

Citrullinemia type II accompanied by mental derangement combined with multidrug resistance 3 decrease, case report

Background

Here we report a rare case of citrullinemia type II (CTLN2) accompanied by mental derangement with a deficiency of multidrug resistance 3 (MDR3) in the liver.

Case presentation

The clinical data of a 17-year-old girl were collected. Liver puncture was performed, and hepatic expression of MDR3 was determined by immunohistochemistry. Serum amino acids of the patient and her parents wwere determined by a chemical isotope labeling liquid chromatography-mass spectrometry (CIL LC-MS). Genetic mutations of ABCB4 and SLC25A13 were screened by whole-exome sequencing. Immunohistochemical analysis showed a remarkably lower expression of MDR3. Mutation in ABCB4 gene was not found and whole-exome sequencing revealed the SLC25A13 mutation 852-855 del. Elevated serum levels of citrulline, homocitrulline, and homoarginine in the patient and her mother were found.

Conclusions

We reported a rare case of CTLN2 combined with MDR3 deficiency, without mutation of ABCB4. The link between MDR3 down-expression and CTLN2 warrants further investigation. Meanwhile, clinicians need to further rule out the possibility of CTLN2 if MDR3 decreases in adolescent patients with mental disorders and abnormal liver function.

Comprehensive investigation on volatile and non-volatile metabolites in low-salt doubanjiang with different fermentation methods

Doubanjiang is a well-known fermented condiment in China, but the high-salt concentration in its traditional manufacture process greatly lengthens the fermentation time, and leads to potential health risks. Here, the effects of salt reduction and co-inoculated starters (Tetragenococcus halophilus and Zygosaccharomyces rouxii) on the volatile metabolites (VMs) and non-volatile metabolites (NVMs) of doubanjiang were investigated using metabolomics technology and chemometrics analysis. Results showed that 75 VMs were identified, and 12 of them had significant aroma contribution (ROVAs ≥ 1). In addition, 106 NVMs were defined as significantly different metabolites (p < 0.05; VIP ≥ 1). Salt reduction could significantly increase the concentrations of VMs, but this strategy also promoted some undesirable odors like 2-phetylfuran and hexanoic acid, which could be totally suppressed by inoculation of starter. Moreover, the two starters improved amino acid, ester, and acid metabolites. This study provides a deeper insight into the development of low-salt fermented foods.

Development of a High-Coverage Quantitative Metabolome Analysis Method Using Four-Channel Chemical Isotope Labeling LC-MS for Analyzing High-Salt Fermented Food

Metabolome analysis of high-salt fermented food can be an analytical challenge, as the salts can interfere with the sample processing and analysis. In this work, we describe a four-channel chemical isotope labeling (CIL) LC-MS approach for a comprehensive metabolome analysis of high-salt fermented food. The workflow includes metabolite extraction, chemical labeling of metabolites using dansyl chloride, dansylhydrazine, or p-dimethylaminophenacyl bromide reagents to enhance separation and ionization, LC-UV measurement of the total concentration of dansyl-labeled metabolites in each sample for sample normalization, mixing of ¹³C- and ¹²C-reagent-labeled samples, high-resolution LC-MS analysis, and data processing. Metabolome analysis of fermented foods, including fermented red pepper (FRP) sauce, soy sauce, and sufu (a fermented soybean food), showed unprecedented high metabolic coverage. Metabolome comparison of FRP, soy sauce, and sufu, as well as soy sauce and sufu, indicated great diversity of metabolite types and abundances in these foods. In addition, we analyzed two groups of samples of the same type, FRP with 10% (w/w) and 15% (w/w) salt contents, and detected large variations in multiple categories of metabolites belonging to a number of different metabolic pathways. We envisage that this CIL LC-MS approach can be generally used for metabolomic studies of high-salt fermented food. CIL LC-MS allows high-coverage identification and quantification that could not be done using other methods.

High-Coverage Quantitative Metabolomics of Human Urine: Effects of Freeze-Thaw Cycles on the Urine Metabolome and Biomarker Discovery

Urine sample storage after collection at ultra-low-temperature (e.g., -80 °C) is normally required for comparative metabolome analysis of many samples, and therefore, freeze-thaw cycles (FTCs) are unavoidable. However, the reported effects of FTCs on the urine metabolome are controversial. Moreover, there is no report on the study of how urine FTCs affect biomarker discovery. Herein, we present our study of the FTC effects on the urine metabolome and biomarker discovery using a high-coverage quantitative metabolomics platform. Our study involved two centers located in Hangzhou, China, and Edmonton, Canada, to perform metabolome analysis of two separate cohorts of urine samples. The same workflow of sample preparation and dansylation isotope labeling LC-MS was used for in-depth analysis of the amine/phenol submetabolome. The analysis of 320 samples from the Hangzhou cohort consisting of 80 healthy subjects with each urine being subjected to four FTCs resulted in relative quantification of 3682 metabolites with 3307 identified or mass-matched. The analysis of 176 samples from the Edmonton cohort of 44 subjects with four FTCs quantified 3516 metabolites with 3166 identified or mass-matched. Multivariate and univariate analyses indicated that significant variations (fold change ≥ 1.5 with q-value ≤ 0.05) from FTCs were only observed in a very small fraction of the metabolites (<0.3%). Moreover, various metabolites did not show a consistent pattern of concentration changes from one to four FTCs, allowing the use of two separate cohorts of samples to remove these randomly changed metabolites. Three metabolite biomarkers for separating males and females were discovered, and FTC did not influence their discovery.

MAFFIN: Metabolomics Sample Normalization Using Maximal Density Fold Change with High-Quality Metabolic Features and Corrected Signal Intensities

MOTIVATION

Post-acquisition sample normalization is a critical step in comparative metabolomics to remove the variation introduced by sample amount or concentration difference. Previously reported approaches are either specific to one sample type or built on strong assumptions on data structure, which are limited to certain levels. This encouraged us to develop MAFFIN, an accurate and robust post-acquisition sample normalization workflow that works universally for metabolomics data collected on mass spectrometry (MS) platforms.

RESULTS

MAFFIN calculates normalization factors using maximal density fold change (MDFC) computed by a kernel density-based approach. Using both simulated data and 20 metabolomics data sets, we showcased that MDFC outperforms four commonly used normalization methods in terms of reducing the intragroup variation among samples. Two essential steps, overlooked in conventional methods, were also examined and incorporated into MAFFIN. (1) MAFFIN uses multiple orthogonal criteria to select high-quality features for normalization factor calculation, which minimizes the bias caused by abiotic features or metabolites with poor quantitative performance. (2) MAFFIN corrects the MS signal intensities of high-quality features using serial quality control (QC) samples, which guarantees the accuracy of fold change calculations. MAFFIN was applied to a human saliva metabolomics study and led to better data separation in principal component analysis (PCA) and more confirmed significantly altered metabolites.

AVAILABILITY AND IMPLEMENTATION

The MAFFIN algorithm was implemented in an R package named MAFFIN. Package installation, user instruction, and demo data are available at https://github.com/HuanLab/MAFFIN.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Evaluating and minimizing batch effects in metabolomics

Determining metabolomic differences among samples of different phenotypes is a critical component of metabolomics research. With the rapid advances in analytical tools such as ultrahigh-resolution chromatography and mass spectrometry, an increasing number of metabolites can now be profiled with high quantification accuracy. The increased detectability and accuracy raise the level of stringiness required to reduce or control any experimental artifacts that can interfere with the measurement of phenotype-related metabolome changes. One of the artifacts is the batch effect that can be caused by multiple sources. In this review, we discuss the origins of batch effects, approaches to detect interbatch variations, and methods to correct unwanted data variability due to batch effects. We recognize that minimizing batch effects is currently an active research area, yet a very challenging task from both experimental and data processing perspectives. Thus, we try to be critical in describing the performance of a reported method with the hope of stimulating further studies for improving existing methods or developing new methods.

In-depth characterisation of the urine metabolome in cats with and without urinary tract diseases

INTRODUCTION

Our understanding of the urine metabolome and its association with urinary tract disease is limited in cats.

OBJECTIVES

We conducted a case-control study to characterise the feline urine metabolome, investigate its association with chronic kidney disease (CKD) and feline idiopathic cystitis (FIC), and assess its compositional relationship with the urine microbiome.

METHODS

The urine metabolome of 45 owned cats, including 23 controls, 16 CKD, and 6 FIC cases, was characterised by an untargeted metabolomics approach using high-performance chemical isotope labelling liquid chromatography-mass spectrometry.

RESULTS

We detected 9411 unique compounds in the urine of controls and cases and identified 1037 metabolites with high confidence. Amino acids, peptides, and analogues dominated these metabolites (32.2%), followed by carbonyl compounds (7.1%) and carbohydrates (6.5%). Seven controls from one household showed a significant level of metabolome clustering, with a distinct separation from controls from other households (p value < 0.001). Owner surveys revealed that this cluster of cats was fed dry food only, whereas all but one other control had wet food in their diet. Accordingly, the diet type was significantly associated with the urine metabolome composition in our multivariate model (p value = 0.001). Metabolites significantly altered in this cluster included taurine, an essential amino acid in cats. Urine metabolome profiles were not significantly different in CKD and FIC cases compared with controls, and no significant compositional relationship was detected between the urine metabolome and microbiome.

CONCLUSION

Our study reveals in-depth diversity of the feline urine metabolome composition, and suggests that it can vary considerably depending on environmental factors.

Development of a method for dansylation of metabolites using organic solvent-compatible buffer systems for amine/phenol submetabolome analysis

Metabolomics, which serves as a readout of biological processes and diseases monitoring, is an informative research area for disease biomarker discovery and systems biology studies. In particular, reversed-phase liquid chromatography-mass spectrometry (RPLC-MS) has become a powerful and popular tool for metabolomics analysis, enabling the detection of most metabolites. Very polar and ionic metabolites, however, are less easily detected because of their poor retention in RP columns. Dansylation of metabolites simplifies the sub-metabolome analysis by reducing its complexity and increasing both hydrophobicity and ionization ability. However, the various metabolite concentrations in clinical samples have a wide dynamic range with highly individual variation in total metabolite amount, such as in saliva. The bicarbonate buffer typically used in dansylation labeling reactions induces solvent stratification, resulting in poor reproducibility, selective sample loss and an increase in false-determined metabolite peaks. In this study, we optimized the dansylation protocol for samples with wide concentration range of metabolites, utilizing diisopropylethylamine (DIPEA) or tri-ethylamine (TEA) in place of bicarbonate buffer, and presented the results of a systemic investigation of the influences of individual processes involved on the overall performance of the protocol. In addition to achieving high reproducibility, substitution of DIPEA or TEA buffer resulted in similar labeling efficiency of most metabolites and more efficient labeling of some metabolites with a higher pK_a. With this improvement, compounds that are only present in samples in trace amounts can be detected, and more comprehensive metabolomics profiles can be acquired for biomarker discovery or pathway analysis, making it possible to analyze clinical samples with limited amounts of metabolites.

Alteration of the Canine Metabolome After a 3-Week Supplementation of Cannabidiol (CBD) Containing Treats: An Exploratory Study of Healthy Animals

Despite the increased interest and widespread use of cannabidiol (CBD) in humans and companion animals, much remains to be learned about its effects on health and physiology. Metabolomics is a useful tool to evaluate changes in the health status of animals and to analyze metabolic alterations caused by diet, disease, or other factors. Thus, the purpose of this investigation was to evaluate the impact of CBD supplementation on the canine plasma metabolome. Sixteen dogs (18.2 ± 3.4 kg BW) were utilized in a completely randomized design with treatments consisting of control and 4.5 mg CBD/kg BW/d. After 21 d of treatment, blood was collected ~2 h after treat consumption. Plasma collected from samples was analyzed using CIL/LC-MS-based untargeted metabolomics to analyze amine/phenol- and carbonyl-containing metabolites. Metabolites that differed - fold change (FC) ≥ 1.2 or ≤ 0.83 and false discovery ratio (FDR) ≤ 0.05 - between the two treatments were identified using a volcano plot. Biomarker analysis based on receiver operating characteristic (ROC) curves was performed to identify biomarker candidates (area under ROC ≥ 0.90) of the effects of CBD supplementation. Volcano plot analysis revealed that 32 amine/phenol-containing metabolites and five carbonyl-containing metabolites were differentially altered (FC ≥ 1.2 or ≤ 0.83, FDR ≤ 0.05) by CBD; these metabolites are involved in the metabolism of amino acids, glucose, vitamins, nucleotides, and hydroxycinnamic acid derivatives. Biomarker analysis identified 24 amine/phenol-containing metabolites and 1 carbonyl-containing metabolite as candidate biomarkers of the effects of CBD (area under ROC ≥ 0.90; P < 0.01). Results of this study indicate that 3 weeks of 4.5 mg CBD/kg BW/d supplementation altered the canine metabolome. Additional work is warranted to investigate the physiological relevance of these changes.

Comprehensive Serum Lipidomics for Detecting Incipient Dementia in Parkinson's Disease

While a number of methods are available for analyzing lipids, unbiased untargeted lipidomics with high coverage remains a challenge. In this work, we report a study of isotope-standard-assisted liquid chromatography mass spectrometry lipidomics of serum for biomarker discovery. We focus on Parkinson's disease (PD), a neurodegenerative disorder that often progresses to dementia. Currently, the diagnosis of PD is purely clinical and there is limited ability to predict which PD patients will transition to dementia, hampering early interventions. We studied serum samples from healthy controls and PD patients with no clinical signs of dementia. A follow-up 3 years later revealed that a subset of PD patients had transitioned to dementia. Using the baseline samples, we constructed two biomarker panels to differentiate (1) PD patients from healthy controls and (2) PD patients that remained cognitively stable from PD patients with incipient dementia (diagnosed 3 years after sample collection). The proposed biomarker panels displayed excellent performance and may be useful for detecting prodromal PD dementia, allowing early interventions and prevention efforts. The biochemistry of significantly changed lipids is also discussed within the current knowledge of neurological pathologies. Our results are promising and future work using a larger cohort of samples is warranted.

Computational Variation: An Underinvestigated Quantitative Variability Caused by Automated Data Processing in Untargeted Metabolomics

Computational tools are commonly used in untargeted metabolomics to automatically extract metabolic features from liquid chromatography-mass spectrometry (LC-MS) raw data. However, due to the incapability of software to accurately determine chromatographic peak heights/areas for features with poor chromatographic peak shape, automated data processing in untargeted metabolomics faces additional quantitative variation (i.e., computational variation) besides the well-recognized analytical and biological variations. In this work, using multiple biological samples, we investigated how experimental factors, including sample concentrations, LC separation columns, and data processing programs, contribute to computational variation. For example, we found that the peak height (PH)-based quantification is more precise when MS-DIAL was used for data processing. We further systematically compared the different patterns of computational variation between PH- and peak area (PA)-based quantitative measurements. Our results suggest that the magnitude of computational variation is highly consistent at a given concentration. Hence, we proposed a quality control (QC) sample-based correction workflow to minimize computational variation by automatically selecting PH or PA-based measurement for each intensity value. This bioinformatic solution was demonstrated in a metabolomic comparison of leukemia patients before and after chemotherapy. Our novel workflow can be effectively applied on 652 out of 915 metabolic features, and over 31% (206 out of 652) of corrected features showed distinctly changed statistical significance. Overall, this work highlights computational variation, a considerable but underinvestigated quantitative variability in omics-scale quantitative analyses. In addition, the proposed bioinformatic solution can minimize computational variation, thus providing a more confident statistical comparison among biological groups in quantitative metabolomics.

High-Coverage Metabolome Analysis Reveals Significant Diet Effects of Autoclaved and Irradiated Feed on Mouse Fecal and Urine Metabolomics

SCOPE

Using metabolomics to study the relations of nutrition and health requires stringent control of the experimental conditions used in an animal model. This work investigates the diet effects of autoclaved and irradiated feed on mouse urine and fecal metabolomics.

METHODS AND RESULTS

C57BL/6 mice are fed normal-irradiation sterilized diet (n = 9), autoclave sterilized diet (n = 9), and high-irradiation sterilized diet (n = 9) for 4 weeks. Differential chemical isotope labeling liquid chromatography mass spectrometry is used to quantify the metabolome variations of urine and feces collected at five time points. Significant differences are observed in urine or fecal metabolomes of mice fed autoclaved diet versus mice fed high-irradiation diet or fed normal-irradiation diet, while the differences are small between the mice fed normal-irradiation and high-irradiation diet. Correlation studies of metabolite changes of diet- and aging-related biomarkers indicate a large overlap of significantly affected metabolites by the two factors.

CONCLUSIONS

Diet can be a confounding factor that needs to be carefully considered when a metabolomics study is designed and metabolomic results of a mouse model of nutritional or other biological study are interpreted. Using the same sterilized diet for a given metabolomics project is essential to control the diet effect.

Normalization of Samples of Limited Amounts in Quantitative Metabolomics Using Liquid Chromatography Fluorescence Detection with Dansyl Labeling of Metabolites

Quantitative metabolomics requires the analysis of the same or a very similar amount of samples in order to accurately determine the concentration differences of individual metabolites in comparative samples. Ideally, the total amount or concentration of metabolites in each sample is measured to normalize all the analyzed samples. In this work, we describe a very sensitive method to measure a subclass of metabolites as a surrogate quantifier for normalization of samples with limited amounts. This method starts with low-volume dansyl labeling of all metabolites containing a primary/secondary amine or phenol group in a sample to produce a final solution of 21 μL. The dansyl-labeled metabolites generate fluorescence signals at 520 nm with photoexcitation at 250 nm. To remove the interference of dansyl hydroxyl products (Dns-OH) formed from the labeling reagents used, a fast-gradient liquid chromatography separation is used to elute Dns-OH using aqueous solution, followed by organic solvent elution to produce a chromatographic peak of labeled metabolites, giving a measurement throughput of 6 min per sample. The integrated fluorescence signals of the peak are found to be related to the injection amount of the dansyl-labeled metabolites. A calibration curve using mixtures of dansyl-labeled amino acids is used to determine the total concentration of labeled metabolites in a sample. This concentration is used for normalization of samples in the range from 2 to 120 μM in 21 μL with only 1 μL consumed for fluorescence quantification (i.e., 2-120 pmol). We demonstrate the application of this sensitive sample normalization method in comparative metabolome analysis of human cancer cells, MCF-7 cells, treated with and without resveratrol, using a starting material of as low as 500 cells.

Obesity Connected Metabolic Changes in Type 2 Diabetic Patients Treated With Metformin

Metformin is widely used in the treatment of Type 2 Diabetes Mellitus (T2DM). However, it is known to have beneficial effects in many other conditions, including obesity and cancer. In this study, we aimed to investigate the metabolic effect of metformin in T2DM and its impact on obesity. A mass spectrometry (MS)-based metabolomics approach was used to analyze samples from two cohorts, including healthy lean and obese control, and lean as well as obese T2DM patients on metformin regimen in the last 6 months. The results show a clear group separation and sample clustering between the study groups due to both T2DM and metformin administration. Seventy-one metabolites were dysregulated in diabetic obese patients (30 up-regulated and 41 down-regulated), and their levels were unchanged with metformin administration. However, 30 metabolites were dysregulated (21 were up-regulated and 9 were down-regulated) and then restored to obese control levels by metformin administration in obese diabetic patients. Furthermore, in obese diabetic patients, the level of 10 metabolites was dysregulated only after metformin administration. Most of these dysregulated metabolites were dipeptides, aliphatic amino acids, nucleic acid derivatives, and urea cycle components. The metabolic pattern of 62 metabolites was persistent, and their levels were affected by neither T2DM nor metformin in obesity. Interestingly, 9 metabolites were significantly dysregulated between lean and obese cohorts due to T2DM and metformin regardless of the obesity status. These include arginine, citrulline, guanidoacetic acid, proline, alanine, taurine, 5-hydroxyindoleacetic acid, and 5-hydroxymethyluracil. Understanding the metabolic alterations taking place upon metformin treatment would shed light on possible molecular targets of metformin, especially in conditions like T2DM and obesity.

High-coverage quantitative liver metabolomics using perfused and non-perfused liver tissues

Comprehensive analysis of the liver metabolome can be very useful for discovering disease biomarkers and studying diseases, especially liver-related diseases. However, the presence of a relatively large amount of blood in liver tissue may have a profound effect on liver tissue metabolome analysis. We designed a study to address this issue in order to develop a liver metabolomics workflow based on high-coverage quantitative metabolome analysis using differential chemical isotope labeling (CIL) LC-MS. In the first set of experiments, we compared the metabolomes of mouse serum, non-perfused liver, and perfused liver without and with varying amounts of blood added. We found that there was a significant metabolome difference between the perfused liver and non-perfused liver. To illustrate the effects of perfusion conditions on tissue metabolome analysis, we analyzed the mouse livers that were subjected to perfusion under two different conditions. We found that ice-cold temperature perfusion led to less change of the liver metabolome, compared to room temperature perfusion; however, there was still a significant metabolome difference between the ice-cold-perfused liver and the non-perfused liver. Finally, we applied the method to a chemical (carbon tetrachloride) exposure liver injury model to examine the effects of blood in liver on the detection of significantly changed metabolites in two comparative groups of mice. Using multivariate and univariate analyses of the serum and liver metabolomes of control and diseased mice, we detected many unique significant metabolites in serum as well as in liver. This work demonstrates that perfusion can alter the liver metabolome significantly. Therefore, we recommend the use of non-perfused liver for high-coverage liver metabolomics.

Distinctive metabolic profiles between Cystic Fibrosis mutational subclasses and lung function

INTRODUCTION

Cystic fibrosis (CF) is a lethal multisystemic disease of a monogenic origin with numerous mutations. Functional defects in the cystic fibrosis transmembrane conductance receptor (CFTR) protein based on these mutations are categorised into distinct classes having different clinical presentations and disease severity.

OBJECTIVES

The present study aimed to create a comprehensive metabolomic profile of altered metabolites in patients with CF, among different classes and in relation to lung function.

METHODS

A chemical isotope labeling liquid chromatography-mass spectrometry metabolomics was used to study the serum metabolic profiles of young and adult CF (n = 39) patients and healthy controls (n = 30). Comparisons were made at three levels, CF vs. controls, among mutational classes of CF, between CF class III and IV, and correlated the lung function findings.

RESULTS

A distinctive metabolic profile was observed in the three analyses. 78, 20, and 13 significantly differentially dysregulated metabolites were identified in the patients with CF, among the different classes and between class III and IV, respectively. The significantly identified metabolites included amino acids, di-, and tri-peptides, glutathione, glutamine, glutamate, and arginine metabolism. The top significant metabolites include 1-Aminopropan-2-ol, ophthalmate, serotonin, cystathionine, and gamma-glutamylglutamic acid. Lung function represented by an above-average FEV1% level was associated with decreased glutamic acid and increased guanosine levels.

CONCLUSION

Metabolomic profiling identified alterations in different amino acids and dipeptides, involved in regulating glutathione metabolism. Two metabolites, 3,4-dihydroxymandelate-3-O-sulfate and 5-Aminopentanoic acid, were identified in common between the three anlayses and may represent as highly sensitive biomarkers for CF.

Effects of Multi-Species Direct-Fed Microbial Products on Ruminal Metatranscriptome and Carboxyl-Metabolome of Beef Steers

We examined the effects of two direct-fed microbial (DFM) products containing multiple microbial species and their fermentation products on ruminal metatranscriptome and carboxyl-metabolome of beef steers. Nine ruminally-cannulated Holstein steers were assigned to 3 treatments arranged in a 3 × 3 Latin square design with three 21-d periods. Dietary treatments were (1) Control (CON; basal diet without additive), (2) Commence (PROB; basal diet plus 19 g/d of Commence), and (3) RX3 (SYNB; basal diet plus 28 g/d of RX3). Commence and RX3 are both S. cerevisiae-based DFM products containing several microbial species and their fermentation products. Mixed ruminal contents collected multiple times after feeding on day 21 were used for metatranscriptome and carboxyl-metabolome analysis. Partial least squares discriminant analysis revealed a distinct transcriptionally active taxonomy profiles between CON and each of the PROB and SYNB samples. Compared to CON, the steers fed supplemental PROB had 3 differential (LDA ≥ 2.0; p ≤ 0.05) transcriptionally active taxa, none of which were at the species level, and those fed SYNB had eight differential (LDA > 2.0, p ≤ 0.05) transcriptionally active taxa, but there was no difference (p > 0.05) between PROB and SYNB. No functional microbial genes were differentially expressed among the treatments. Compared with CON, 3 metabolites (hydroxylpropionic acid and 2 isomers of propionic acid) were increased (FC ≥ 1.2, FDR ≤ 0.05), whereas 15 metabolites, including succinic acid and fatty acid peroxidation and amino acid degradation products were reduced (FC ≤ 0.83, FDR ≤ 0.05) by supplemental PROB. Compared with CON, 2 metabolites (2 isomers of propionic acid) were increased (FC ≥ 1.2, FDR ≤ 0.05), whereas 2 metabolites (succinic acid and pimelate) were reduced (FC ≤ 0.83, FDR ≤ 0.05) by supplemental SYNB. Compared to SYNB, supplemental PROB reduced (FC ≤ 0.83, FDR ≤ 0.05) the relative abundance of four fatty acid peroxidation products in the rumen. This study demonstrated that dietary supplementation with either PROB or SYNB altered the ruminal fermentation pattern. In addition, supplemental PROB reduced concentrations of metabolic products of fatty acid peroxidation and amino acid degradation. Future studies are needed to evaluate the significance of these alterations to ruminal fatty acid and amino acid metabolisms, and their influence on beef cattle performance.

STRATEGIES AND CHALLENGES IN METHOD DEVELOPMENT AND VALIDATION FOR THE ABSOLUTE QUANTIFICATION OF ENDOGENOUS BIOMARKER METABOLITES USING LIQUID CHROMATOGRAPHY-TANDEM MASS SPECTROMETRY

Metabolomics is a dynamically evolving field, with a major application in identifying biomarkers for drug development and personalized medicine. Numerous metabolomic studies have identified endogenous metabolites that, in principle, are eligible for translation to clinical practice. However, few metabolomic-derived biomarker candidates have been qualified by regulatory bodies for clinical applications. Such interruption in the biomarker qualification process can be largely attributed to various reasons including inappropriate study design and inadequate data to support the clinical utility of the biomarkers. In addition, the lack of robust assays for the routine quantification of candidate biomarkers has been suggested as a potential bottleneck in the biomarker qualification process. In fact, the nature of the endogenous metabolites precludes the application of the current validation guidelines for bioanalytical methods. As a result, there have been individual efforts in modifying existing guidelines and/or developing alternative approaches to facilitate method validation. In this review, three main challenges for method development and validation for endogenous metabolites are discussed, namely matrix effects evaluation, alternative analyte-free matrices, and the choice of internal standards (ISs). Some studies have modified the equations described by the European Medicines Agency for the evaluation of matrix effects. However, alternative strategies were also described; for instance, calibration curves can be generated in solvents and in biological samples and the slopes can be compared through ratios, relative standard deviation, or a modified Stufour suggested approaches while quantifying mainly endogenous metabolitesdent t-test. ISs, on the contrary, are diverse; in which seven different possible types, used in metabolomics-based studies, were identified in the literature. Each type has its advantages and limitations; however, isotope-labeled ISs and ISs created through isotope derivatization show superior performance. Finally, alternative matrices have been described and tested during method development and validation for the quantification of endogenous entities. These alternatives are discussed in detail, highlighting their advantages and shortcomings. The goal of this review is to compare, apprise, and debate current knowledge and practices in order to aid researchers and clinical scientists in developing robust assays needed during the qualification process of candidate metabolite biomarkers. © 2019 John Wiley & Sons Ltd. Mass Spec Rev.

Plasma Carboxyl-Metabolome Is Associated with Average Daily Gain Divergence in Beef Steers

We applied an untargeted metabolomics technique to analyze the plasma carboxyl-metabolome of beef steers with divergent average daily gain (ADG). Forty-eight newly weaned Angus crossbred beef steers were fed the same total mixed ration ad libitum for 42 days. On day 42, the steers were divided into two groups of lowest (LF: n = 8) and highest ADG (HF: n = 8), and blood samples were obtained from the two groups for plasma preparation. Relative quantification of carboxylic-acid-containing metabolites in the plasma samples was determined using a metabolomics technique based on chemical isotope labeling liquid chromatography mass spectrometry. Metabolites that differed (fold change (FC) ≥ 1.2 or ≤ 0.83 and FDR ≤ 0.05) between LF and HF were identified using a volcano plot. Metabolite set enrichment analysis (MSEA) of the differential metabolites was done to determine the metabolic pathways or enzymes that were potentially altered. In total, 328 metabolites were identified. Volcano plot analysis revealed 43 differentially abundant metabolites; several short chain fatty acids and ketone bodies had greater abundance in HF steers. Conversely, several long chain fatty acids were greater in LF steers. Five enzymatic pathways, such as fatty acyl CoA elongation and fatty-acid CoA ligase were altered based on MSEA. This study demonstrated that beef steers with divergent ADG had altered plasma carboxyl-metabolome, which is possibly caused by altered abundances and/or activities of enzymes involved in fatty acid oxidation and biosynthesis in the liver.

Distinctive Metabolomics Patterns Associated With Insulin Resistance and Type 2 Diabetes Mellitus

Obesity is associated with an increased risk of insulin resistance (IR) and type 2 diabetes mellitus (T2DM) which is a multi-factorial disease associated with a dysregulated metabolism and can be prevented in pre-diabetic individuals with impaired glucose tolerance. A metabolomic approach emphasizing metabolic pathways is critical to our understanding of this heterogeneous disease. This study aimed to characterize the serum metabolomic fingerprint and multi-metabolite signatures associated with IR and T2DM. Here, we have used untargeted high-performance chemical isotope labeling (CIL) liquid chromatography-mass spectrometry (LC-MS) to identify candidate biomarkers of IR and T2DM in sera from 30 adults of normal weight, 26 obese adults, and 16 adults newly diagnosed with T2DM. Among the 3633 peak pairs detected, 62% were either identified or matched. A group of 78 metabolites were up-regulated and 111 metabolites were down-regulated comparing obese to lean group while 459 metabolites were up-regulated and 166 metabolites were down-regulated comparing T2DM to obese groups. Several metabolites were identified as IR potential biomarkers, including amino acids (Asn, Gln, and His), methionine (Met) sulfoxide, 2-methyl-3-hydroxy-5-formylpyridine-4-carboxylate, serotonin, L-2-amino-3-oxobutanoic acid, and 4,6-dihydroxyquinoline. T2DM was associated with dysregulation of 42 metabolites, including amino acids, amino acids metabolites, and dipeptides. In conclusion, these pilot data have identified IR and T2DM metabolomics panels as potential novel biomarkers of IR and identified metabolites associated with T2DM, with possible diagnostic and therapeutic applications. Further studies to confirm these associations in prospective cohorts are warranted.

Comparative effects of two multispecies direct-fed microbial products on energy status, nutrient digestibility, and ruminal fermentation, bacterial community, and metabolome of beef steers

We examined the effects of two direct-fed microbials (DFM) containing multiple microbial species and their fermentation products on energy status, nutrient digestibility, and ruminal fermentation, bacterial community, and metabolome of beef steers. Nine ruminally cannulated Holstein steers (mean ± SD body weight: 243 ± 12.4 kg) were assigned to three treatments arranged in a triplicated 3 × 3 Latin square design with three 21-d periods. Dietary treatments were 1) control (CON; basal diet), 2) Commence (PROB; basal diet plus 19 g/d of Commence), and 3) RX3 (SYNB; basal diet plus 28 g/d of RX3). Commence and RX3 are both multispecies DFM products. From day 16 to 20 of each period, feed and fecal samples were collected daily to determine the apparent total tract digestibilities of nutrients using indigestible neutral detergent fiber method. On day 21 of each period, blood samples were collected for analysis of plasma glucose and nonesterified fatty acid. Ruminal contents were collected at approximately 1, 3, 6, 9, 12, and 18 h after feeding on day 21 for analysis of volatile fatty acids (VFA), lactate, ammonia-N concentrations, bacterial community, and metabolome profile. Total tract digestibilities of nutrients did not differ (P > 0.05) among treatments. Compared with CON, steers fed either supplemental PROB or SYNB had greater (P = 0.04) plasma glucose concentrations. Compared with CON, total ruminal VFA, propionate, isovalerate, and valerate concentrations increased (P ≤ 0.05) or tended to increase (P ≤ 0.10) with either supplemental PROB or SYNB, but were not different (P > 0.05) between PROB and SYNB. Compared with CON, PROB reduced (P ≤ 0.05) the relative abundance of Prevotella 1 and Prevotellaceae UCG-001 but increased (P ≤ 0.05) the relative abundance of Rikenellaceae RC9, Succinivibrionaceae UCG-001, Succiniclasticum, and Ruminococcaceae UCG-002. Supplemental SYNB decreased (P ≤ 0.05) the relative abundance of Prevotella 1 and Prevotellaceae UCG-001 but increased (P ≤ 0.05) the relative abundance of Prevotella 7, Succinivibrio, Succiniclasticum, and Ruminococcaceae UCG-014. Compared with CON, metabolome analysis revealed that some amino acids were increased (P ≤ 0.05) in steers fed PROB. This study demonstrated that, compared with CON, supplementation of either PROB or SYNB altered the ruminal bacterial community and metabolome differently; however, their effects on the ruminal VFA profile and energy status of the steers were not different from each other.

Comprehensive Lipidomic and Metabolomic Analysis for Studying Metabolic Changes in Lung Tissue Induced by a Vaccine against Respiratory Syncytial Virus

Respiratory syncytial virus (RSV) is the leading cause of acute lower respiratory infections in young children. Although the disease may be severe in immunocompromised, young, and elderly people, there is currently no approved vaccine. We previously reported the development and immunological assessment of a novel intranasal vaccine formulation consisting of a truncated version of the RSV fusion protein (ΔF) combined with a three-component adjuvant (TriAdj). Now, we aim to investigate the mechanism of action of the ΔF/TriAdj formulation by searching for metabolic alterations caused by intranasal immunization and the RSV challenge. We carried out untargeted lipidomics and submetabolome profiling (carboxylic acids and amine/phenol-containing metabolites) of lung tissue from ΔF/TriAdj-immunized and nonimmunized, RSV-challenged mice. We observed significant changes of lipids involved in the lung surfactant layer for the nonimmunized animals compared to healthy controls but not for the immunized mice. Metabolic pathways involving the synthesis and regulation of amino acids and unsaturated fatty acids were also modulated by immunization and the RSV challenge. This study illustrates that lipidomic and metabolomic profiling could provide a more comprehensive understanding of the immunological and metabolic alterations caused by RSV and the modulation effected by the ΔF/TriAdj formulation.

High tolerance to instrument drifts by differential chemical isotope labeling LC-MS: A case study of the effect of LC leak in long-term sample runs on quantitative metabolome analysis

Metabolomics study of a biological system often involves the analysis of many comparative samples over a period of several days or weeks. This process of long-term sample runs can encounter unexpected instrument drifts such as small leaks in liquid chromatography-mass spectrometry (LC-MS), degradation of column performance, and MS signal intensity change. A robust analytical method should ideally tolerate these instrumental drifts as much as possible. In this work, we report a case study to demonstrate the high tolerance of differential chemical isotope labeling (CIL) LC-MS method for quantitative metabolome analysis. In a study of using a rat model to examine the metabolome changes during rheumatoid arthritis (RA) disease development and treatment, over 468 samples were analyzed over a period of 15 days in three batches. During the sample runs, a small leak in LC was discovered after a batch of analyses was completed. Reanalysis of these samples was not an option as sample amounts were limited. To overcome the problem caused by the small leak, we applied a method of retention time correction to the LC-MS data to align peak pairs from different runs with different degrees of leak, followed by peak ratio calculation and analysis. Herein, we illustrate that using 12 C-/13 C-peak pair intensity values in CIL LC-MS as a measurement of concentration changes in different samples could tolerate the signal drifts, while using the absolute intensity values (ie, 12 C-peak as in conventional LC-MS) was not as reliable. We hope that the case study illustrated and the method of overcoming the small-leak-caused signal drifts can be helpful to others who may encounter this kind of situation in long-term sample runs.

Average daily gain divergence in beef steers is associated with altered plasma metabolome and whole blood immune-related gene expression

We evaluated the plasma amine/phenol- and carbonyl-metabolome and whole-blood immune gene expression profiles in beef steers with divergent average daily gain (ADG). Forty-eight Angus crossbred beef steers (21 days postweaning; 210 ± 8.5 kg of body weight) were fed the same total mixed ration ad libitum for 42 days with free access to water. After 42 days of feeding, the steers were divided into two groups of lowest (LF: n = 8) and highest ADG (HF: n = 8). Blood samples were taken from all steers. The blood samples from LF and HF steers were used for further analysis. A subsample of the whole blood was immediately transferred into RNA-protect tubes for RNA extraction and messenger RNA expressions of 84 genes involved in innate and adaptive immune responses. Another subsample of the whole blood was immediately centrifuged to harvest the plasma for subsequent metabolome analysis. The average daily dry matter intake of the steers in LF and HF was 6.08 kg ± 0.57 and 6.04 kg ± 0.42, respectively, and was similar between the two groups (P = 0.72). The ADG (1.09 kg ± 0.13) of LF was lower (P = 0.01) than that of HF (1.63 kg ± 0.20). The expressions of 10 immune-related genes were upregulated (FC ≥ 1.2; P ≤ 0.05) in HF steers; these genes were involved in viral pathogen recognition and eradication, defense against intracellular and extracellular pathogens and parasites, and immune response homeostasis. A total number of 42 carbonyl-containing metabolites and 229 amine/phenol-containing metabolites were identified in the plasma samples of both groups. No alteration in carbonyl-metabolome was detected. Ten metabolites with immunomodulatory, anti-inflammatory, and reactive oxygen-scavenging properties were greater (FDR ≤ 0.05) in HF steers, whereas eight metabolites including arginine, phenylalanine, guanidoacetic acid, and aspartyl-threonine were greater in LF steers. This study demonstrated that beef steers with divergent ADG had altered plasma amine/phenol metabolome and immune-related gene expressions in the blood. Notably, plasma metabolites and immune-related genes of great health benefits were greater in steers with high ADG.

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.

Characterizing the effects of hypoxia on the metabolic profiles of mesenchymal stromal cells derived from three tissue sources using chemical isotope labeling liquid chromatography-mass spectrometry

Microenvironmental factors such as oxygen concentration mediate key effects on the biology of mesenchymal stromal cells (MSCs). Herein, we performed an in-depth characterization of the metabolic behavior of MSCs derived from the placenta, umbilical cord, and adipose tissue (termed hPMSCs, UC-MSCs, and AD-MSCs, respectively) at physiological (hypoxic; 5% oxygen [O₂]) and standardized (normoxic; 21% O₂) O₂ concentrations using chemical isotope labeling liquid chromatography-mass spectrometry. ¹²C- and ¹³C-isotope dansylation (Dns) labeling was used to analyze the amine/phenol submetabolome, and 2574 peak pairs or metabolites were detected and quantified, from which 52 metabolites were positively identified using a library of 275 Dns-metabolite standards; 2189 metabolites were putatively identified. Next, we identified six metabolites using the Dns library, as well as 14 hypoxic biomarkers from the human metabolome database out of 96 altered metabolites. Ultimately, metabolic pathway analyses were performed to evaluate the associated pathways. Based on pathways identified using the Kyoto Encyclopedia of Genes and Genomes, we identified significant changes in the metabolic profiles of MSCs in response to different O₂ concentrations. These results collectively suggest that O₂ concentration has the strongest influence on hPMSCs metabolic characteristics, and that 5% O₂ promotes arginine and proline metabolism in hPMSCs and UC-MSCs but decreases gluconeogenesis (alanine-glucose) rates in hPMSCs and AD-MSCs. These changes indicate that MSCs derived from different sources exhibit distinct metabolic profiles.

Effects of a blend of Saccharomyces cerevisiae-based direct-fed microbial and fermentation products in the diet of newly weaned beef steers: growth performance, whole-blood immune gene expression, serum biochemistry, and plasma metabolome1

We examined the effects of dietary supplementation of a Saccharomyces cerevisiae-based direct-fed microbial (DFM) on the growth performance, whole-blood immune gene expression, serum biochemistry, and plasma metabolome of newly weaned beef steers during a 42 d receiving period. Forty newly weaned Angus crossbred steers (7 d post-weaning; 210 ± 12 kg of BW; 180 ± 17 d of age) from a single source were stratified by BW and randomly assigned to 1 of 2 treatments: basal diet with no additive (CON; n = 20) or a basal diet top-dressed with 19 g of the DFM (PROB; n = 20). Daily DMI and weekly body weights were measured to calculate average daily gain (ADG) and feed efficiency (FE). Expression of 84 immune-related genes was analyzed on blood samples collected on days 21 and 42. Serum biochemical parameters and plasma metabolome were analyzed on days 0, 21, and 42. On day 40, fecal grab samples were collected for pH measurement. Compared with CON, dietary supplementation of PROB increased final body weight (P = 0.01) and ADG (1.42 vs. 1.23 kg; P = 0.04) over the 42 d feeding trial. There was a tendency for improved FE with PROB supplementation (P = 0.10). No treatment effect (P = 0.24) on DMI was observed. Supplementation with PROB increased (P ≤ 0.05) the concentrations of serum calcium, total protein, and albumin. Compared with CON, dietary supplementation with PROB increased (P ≤ 0.05) the expression of some immune-related genes involved in detecting pathogen-associated molecular patterns (such as TLR1, TLR2, and TLR6), T-cell differentiation (such as STAT6, ICAM1, RORC, TBX21, and CXCR3) and others such as TNF and CASP1, on day 21 and/or day 42. Conversely, IL-8 was upregulated (P = 0.01) in beef steers fed CON diet on day 21. Plasma untargeted plasma metabolome analysis revealed an increase (P ≤ 0.05) in the concentration of metabolites, 5-methylcytosine and indoleacrylic acid involved in protecting the animals against inflammation in steers fed PROB diet. There was a tendency for lower fecal pH in steers fed PROB diet (P = 0.08), a possible indication of increased hindgut fermentation. This study demonstrated that supplementation of PROB diet improved the performance, nutritional status, and health of newly weaned beef steers during a 42 d receiving period.

Metabolomics Analyses of Saliva Detect Novel Biomarkers of Alzheimer's Disease

Using a non-invasive biofluid (saliva), we apply a powerful metabolomics workflow for unbiased biomarker discovery in Alzheimer's disease (AD). We profile and differentiate Cognitively Normal (CN), Mild Cognitive Impairment (MCI), and AD groups. The workflow involves differential chemical isotope labeling liquid chromatography mass spectrometry using dansylation derivatization for in-depth profiling of the amine/phenol submetabolome. The total sample (N = 109) was divided in to the Discovery Phase (DP) (n = 82; 35 CN, 25 MCI, 22 AD) and a provisional Validation Phase (VP) (n = 27; 10 CN, 10 MCI, 7 AD). In DP we detected 6,230 metabolites. Pairwise analyses confirmed biomarkers for AD versus CN (63), AD versus MCI (47), and MCI versus CN (2). We then determined the top discriminating biomarkers and diagnostic panels. A 3-metabolite panel distinguished AD from CN and MCI (DP and VP: Area Under the Curve [AUC] = 1.000). The MCI and CN groups were best discriminated with a 2-metabolite panel (DP: AUC = 0.779; VP: AUC = 0.889). In addition, using positively confirmed metabolites, we were able to distinguish AD from CN and MCI with good diagnostic performance (AUC > 0.8). Saliva is a promising biofluid for both unbiased and targeted AD biomarker discovery and mechanism detection. Given its wide availability and convenient accessibility, saliva is a biofluid that can promote diversification of global AD biomarker research.

Metabolomic and Immunological Profiling of Respiratory Syncytial Virus Infection after Intranasal Immunization with a Subunit Vaccine Candidate

Respiratory syncytial virus (RSV) is a significant cause of mortality and morbidity in infants, the elderly, immunocompromised individuals, and patients with congenital heart diseases. Despite extensive efforts, a vaccine against RSV is still not available. We have previously reported the development of a subunit vaccine (ΔF/TriAdj) composed of a truncated version of the fusion protein (ΔF) and a polymer-based combination adjuvant (TriAdj). We compared inflammatory responses of ΔF/TriAdj-vaccinated and unvaccinated mice following intranasal challenge with RSV. Rapid and early inflammatory responses were observed in lung samples from both groups but modulated in the vaccinated group 7 days after the viral challenge. The underlying mechanism of action of ΔF/TriAdj was further studied through LC-MS-based metabolomic profiling by using ¹²C- or ¹³C-dansyl labeling for the amine/phenol submetabolome. RSV infection predominantly affected the amino acid biosynthesis pathways and urea cycle, whereas ΔF/TriAdj modulated the concentrations of almost all of the altered metabolites. Tryptophan metabolites were significantly affected, including indole, l-kynurenine, xanthurenic acid, serotonin, 5-hydroxyindoleacetic acid, and 6-hydroxymelatonin. The results from the present study provide further mechanistic insights into the mode of action of this RSV vaccine candidate and have important implications in the design of metabolic therapeutic interventions.

Chemical isotope labeling liquid chromatography mass spectrometry for investigating acute dietary effects of cow milk consumption on human urine metabolome

Biomarker discovery has been increasingly important in the field of metabolomics for the detection and understanding of diseases. Of the many biofluids available for metabolomics, urine is a preferred option as it is non-invasive to collect and contains a wide range of metabolites reflective of the health status of the testing individual. However, urine also contains many exogenous metabolites which are introduced through various sources such as diet. This complicates the data interpretation when searching the metabolome for disease-related endogenous metabolites. Since diet is difficult to control, this work aims to study the acute effects of diet (particularly cow milk) consumption on the human urine amine/phenol submetabolome by utilizing differential chemical isotope labeling (CIL) liquid chromatography mass spectrometry (LC-MS). LC-MS analysis of 62 urine samples collected before and after (1 hour and 2 hours) milk intake resulted in the detection of 4985 metabolites with an average of 3815 ± 206 (n = 62) detected per sample. The work aims to differentiate the exogenous “food” metabolites from the endogenous metabolite pool and to determine any dietary effects from milk intake on the human urine metabolome.

Bretschneider solution-induced alterations in the urine metabolome in cardiac surgery patients

The development of Bretschneider’s histidine-tryptophan-ketoglutarate (HTK) cardioplegia solution represented a major advancement in cardiac surgery, offering significant myocardial protection. However, metabolic changes induced by this additive in the whole body have not been systematically investigated. Using an untargeted mass spectrometry-based method to deeply explore the urine metabolome, we sought to provide a holistic and systematic view of metabolic perturbations occurred in patients receiving HTK. Prospective urine samples were collected from 100 patients who had undergone cardiac surgery, and metabolomic changes were profiled using a high-performance chemical isotope labeling liquid chromatography-mass spectrometry (LC-MS) method. A total of 14,642 peak pairs or metabolites were quantified using differential ¹³C-/¹²C-dansyl labeling LC-MS, which targets the amine/phenol submetabolome from urine specimens. We identified 223 metabolites that showed significant concentration change (fold change > 5) and assembled several potential metabolic pathway maps derived from these dysregulated metabolites. Our data indicated upregulated histidine metabolism with subsequently increased glutamine/glutamate metabolism, altered purine and pyrimidine metabolism, and enhanced vitamin B₆ metabolism in patients receiving HTK. Our findings provide solid evidence that HTK solution causes significant perturbations in several metabolic pathways and establish a basis for further study of key mechanisms underlying its organ-protective or potential harmful effects.

Comparative analysis of creatinine and osmolality as urine normalization strategies in targeted metabolomics for the differential diagnosis of asthma and COPD

INTRODUCTION

Urine is an ideal matrix for metabolomics investigation due to its non-invasive nature of collection and its rich metabolite content. Despite the advancements in mass spectrometry and ¹H-NMR platforms in urine metabolomics, the statistical analysis of the generated data is challenged with the need to adjust for the hydration status of the person. Normalization to creatinine or osmolality values are the most adopted strategies, however, each technique has its challenges that can hinder its wider application. We have been developing targeted urine metabolomic methods to differentiate two important respiratory diseases, namely asthma and chronic obstructive pulmonary disease (COPD).

OBJECTIVE

To assess whether the statistical model of separation of diseases using targeted metabolomic data would be improved by normalization to osmolality instead of creatinine.

METHODS

The concentration of 32 metabolites was previously measured by two liquid chromatography-tandem mass spectrometry methods in 51 human urine samples with either asthma (n = 25) or COPD (n = 26). The data was normalized to creatinine or osmolality. Statistical analysis of the normalized values in each disease was performed using partial least square discriminant analysis (PLS-DA). Models of separation of diseases were compared.

RESULTS

We found that normalization to creatinine or osmolality did not significantly change the PLS-DA models of separation (R²Q² = 0.919, 0.705 vs R²Q² = 0.929, 0.671, respectively). The metabolites of importance in the models remained similar for both normalization methods.

CONCLUSION

Our findings suggest that targeted urine metabolomic data can be normalized for hydration using creatinine or osmolality with no significant impact on the diagnostic accuracy of the model.

Baseline urine metabolic phenotype in patients with severe alcoholic hepatitis and its association with outcome

Severe alcoholic hepatitis (SAH) has a high mortality rate, and corticosteroid therapy is effective in 60% patients. This study aimed to investigate a baseline metabolic phenotype that could help stratify patients not likely to respond to steroid therapy and to have an unfavorable outcome. Baseline urine metabolome was studied in patients with SAH using ultra‐high performance liquid chromatography and high‐resolution mass spectrometry. Patients were categorized as responders (Rs, n = 52) and nonresponders (NRs, n = 8) at day 7 according to the Lille score. Multivariate projection analysis identified metabolites in the discovery cohort (n = 60) and assessed these in a validation cohort of 80 patients (60 Rs, 20 NRs). A total of 212 features were annotated by using metabolomic/biochemical/spectral databases for metabolite identification. After a stringent selection procedure, a total of nine urinary metabolites linked to mitochondrial functions significantly discriminated nonresponders, most importantly by increased acetyl‐L‐carnitine (12‐fold), octanoylcarnitine (4‐fold), decanoylcarnitine (4‐fold), and alpha‐ketoglutaric acid (2‐fold) levels. Additionally, urinary acetyl‐L‐carnitine and 3‐hydroxysebasic acid discriminated nonsurvivors (P < 0.01). These urinary metabolites significantly correlated to severity indices and mortality (r > 0.3; P < 0.01) and were associated with nonresponse (odds ratio >3.0; P < 0.001). In the validation cohort, baseline urinary acetyl‐L‐carnitine documented an area under the receiver operating curve of 0.96 (0.85‐0.99) for nonresponse prediction and a hazard ratio of 3.5 (1.5‐8.3) for the prediction of mortality in patients with SAH. Acetyl‐L‐carnitine at a level of >2,500 ng/mL reliably segregated survivors from nonsurvivors (P < 0.01, log‐rank test) in our study cohort. Conclusion: Urinary metabolome signatures related to mitochondrial functions can predict pretherapy steroid response and disease outcome in patients with SAH. (Hepatology Communications 2018;2:628‐643)

Chemical Isotope Labeling LC-MS for Human Blood Metabolome Analysis

Blood is a widely used biofluid in discovery metabolomic research to search for clinical metabolite biomarkers of diseases. Analyzing the entire human blood metabolome is a major analytical challenge, as blood, after being processed into serum or plasma, contains thousands of metabolites with diverse chemical and physical properties as well as a wide range of concentrations. We describe an enabling method based on high-performance chemical isotope labeling (CIL) liquid chromatography-mass spectrometry (LC-MS) for in-depth quantification of the metabolomic differences in comparative blood samples with high accuracy and precision.

Metabolomic analysis of oxidative stress: Superoxide dismutase mutation and paraquat induced stress in Drosophila melanogaster

Oxidative stress results in substantial biochemical and physiological perturbations in essentially all organisms. To determine the broad metabolic effects of oxidative stress, we have quantified the response in Drosophila melanogaster to both genetically and environmentally derived oxidative stress. Flies were challenged with loss of Superoxide dismutase activity or chronic or acute exposure to the oxidizing chemical paraquat. Metabolic changes were then quantified using a recently developed chemical isotope labeling (CIL) liquid chromatography - mass spectrometry (LC-MS) platform that targets the carboxylic acid and amine/phenol submetabolomes with high metabolic coverage. We discovered wide spread changes in both submetabolomes in response to all three types of stresses including: changes to the urea cycle, tryptophan metabolism, porphyrin metabolism, as well as a series of metabolic pathways involved in glutathione synthesis. Strikingly, while there are commonalities across the conditions, all three resulted in different metabolomic responses, with the greatest difference between the genetic and environmental responses. Genetic oxidative stress resulted in substantially more widespread effects, both in terms of the percent of the metabolome altered, and the magnitude of changes in individual metabolites. Chronic and acute environmental stress resulted in more similar responses although both were distinct from genetic stress. Overall, these results indicate that the metabolomic response to oxidative stress is complex, reaching across multiple metabolic pathways, with some shared features but with more features unique to different, specific stressors.

Profiling novel metabolic biomarkers for Parkinson's disease using in-depth metabolomic analysis

OBJECTIVE

To profile the amine/phenol submetabolome to determine potential metabolite biomarkers associated with Parkinson's disease (PD) and PD with incipient dementia.

METHODS

At baseline of a 3-wave (18-month intervals) longitudinal study, serum samples were collected from 42 healthy controls and 43 PD patients. By wave 3 (year 3), 16 PD patients were diagnosed with dementia and were classified as PD with incipient dementia at baseline. Metabolomic profiling using dansylation isotope labeling liquid chromatography mass spectrometry was conducted to compare controls with the full PD, PD with no dementia, and PD with incipient dementia groups.

RESULTS

Metabolomic analyses detected 719 common metabolites in 80% of the samples. Some were significantly altered in pairwise comparison of different groups (fold change of >1.2 or <0.83 with q < 0.05). We discriminated PD and controls by using a 5-metabolite panel, vanillic acid, 3-hydroxykynurenine, isoleucyl-alanine, 5-acetylamino-6-amino-3-methyluracil, and theophylline. The receiver operating characteristic curve produced an area-under-the-curve value of 0.955 with 87.5% sensitivity and 93.0% specificity. In comparing PD with no dementia with PD with incipient dementia, we used an 8-metabolite panel, His-Asn-Asp-Ser, 3,4-dihydroxyphenylacetone, desaminotyrosine, hydroxy-isoleucine, alanyl-alanine, putrescine [-2H], purine [+O] and its riboside. This produced an area-under-the-curve value of 0.862 with 80.0% sensitivity and 77.0% specificity.

CONCLUSIONS

The significantly altered metabolites can be used to differentiate (1) PD patients from healthy controls with high accuracy and (2) the stable PD with no dementia group from those with incipient dementia. Following further validation in larger cohorts, these metabolites could be used for both discrimination and establishing prognosis in PD. © 2017 International Parkinson and Movement Disorder Society.

Overcoming Sample Matrix Effect in Quantitative Blood Metabolomics Using Chemical Isotope Labeling Liquid Chromatography Mass Spectrometry

Blood is widely used for discovery metabolomics to search for disease biomarkers. However, blood sample matrix can have a profound effect on metabolome analysis, which can impose an undesirable restriction on the type of blood collection tubes that can be used for blood metabolomics. We investigated the effect of blood sample matrix on metabolome analysis using a high-coverage and quantitative metabolome profiling technique based on differential chemical isotope labeling (CIL) LC-MS. We used ¹²C-/¹³C-dansylation LC-MS to perform relative quantification of the amine/phenol submetabolomes of four types of samples (i.e., serum, EDTA plasma, heparin plasma, and citrate plasma) collected from healthy individuals and compare their metabolomic results. From the analysis of 80 plasma and serum samples in experimental triplicate, we detected a total of 3651 metabolites with an average of 1818 metabolites per run (n = 240). The number of metabolites detected and the precision and accuracy of relative quantification were found to be independent of the sample type. Within each sample type, the metabolome data set could reveal biological variation (e.g., sex separation). Although the relative concentrations of some individual metabolites might be different in the four types of samples, for sex separation, all 66 significant metabolites with larger fold-changes (FC ≥ 2 and p < 0.05) found in at least one sample type could be found in the other types of samples with similar or somewhat reduced, but still significant, fold-changes. Our results indicate that CIL LC-MS could overcome the sample matrix effect, thereby greatly broadening the scope of blood metabolomics; any blood samples properly collected in routine clinical settings, including those in biobanks originally used for other purposes, can potentially be used for discovery metabolomics.

Nonocclusive Sweat Collection Combined with Chemical Isotope Labeling LC-MS for Human Sweat Metabolomics and Mapping the Sweat Metabolomes at Different Skin Locations

Human sweat is an excellent biofluid candidate for metabolomics due to its noninvasive sample collection and relatively simple matrix. We report a simple and inexpensive method for sweat collection over a defined period (e.g., 24 h) based on the use of a nonocclusive style sweat patch adhered to a skin. This method was combined with differential chemical isotope labeling (CIL) LC-MS for mapping the metabolome profiles of sweat samples collected from skins of the left forearm, lower back, and neck of 20 healthy volunteers. Three 24-h sweat samples were collected at three different days from each subject for examining day-to-day metabolome variations. A total of 342 LC-MS runs were carried out (two runs were discarded due to instrumental issue), resulting in the detection and relative quantification of 3140 sweat metabolites with 84 metabolites identified and 2716 metabolites mass-matched to metabolome databases. Multivariate and univariate analyses of the metabolome data revealed a location-dependence characteristic of the sweat metabolome, offering a possibility of mapping the sweat metabolic differences according to skin locations. Significant differences in male and female sweat metabolomes could be detected, demonstrating the possibility of using the sweat metabolome to reveal biological variations among different comparative groups. Thus, the combination of noninvasive sweat collection and CIL LC-MS is a robust analytical tool for sweat metabolomics with potential applications including daily monitoring of the sweat metabolome as health indicators, discovering sweat-based disease biomarkers, and metabolomic mapping of sweat collected from different areas of skin with and without injuries or diseases.