Quantitative Metabolome Analysis Based on Chromatographic Peak Reconstruction in Chemical Isotope Labeling Liquid Chromatography Mass Spectrometry

A practical introduction to holo-omics

Holo-omics refers to the joint study of non-targeted molecular data layers from host-microbiota systems or holobionts, which is increasingly employed to disentangle the complex interactions between the elements that compose them. We navigate through the generation, analysis, and integration of omics data, focusing on the commonalities and main differences to generate and analyze the various types of omics, with a special focus on optimizing data generation and integration. We advocate for careful generation and distillation of data, followed by independent exploration and analyses of the single omic layers to obtain a better understanding of the study system, before the integration of multiple omic layers in a final model is attempted. We highlight critical decision points to achieve this aim and flag the main challenges to address complex biological questions regarding the integrative study of host-microbiota relationships.

Salivary metabolomic identification of biomarker candidates for oral melanoma and oral squamous cell carcinoma in dogs

BACKGROUND

Oral melanoma (OM) and oral squamous cell carcinoma (OSCC) are frequently diagnosed in dogs, presenting a challenge in distinguishing them from benign oral tumors (BN). Salivary metabolomic biomarkers offer a practical solution because of saliva's direct contact with tumors and the noninvasive nature of collection.

OBJECTIVE

Assess the diversity and abundance of the salivary metabolome in dogs with BN, OM, and OSCC using amine/phenol submetabolome analysis and high-performance chemical isotope labeling liquid chromatography-mass spectrometry (CIL LC-MS).

ANIMALS

Study included 11 BN, 24 OM, 10 OSCC, and 20 healthy control dogs.

METHODS

Case-control cross-sectional study was conducted to assess salivary submetabolic profiles in dogs with BN, OM, and OSCC and healthy dogs. Samples were labeled with 12C-dansyl chloride and analyzed using CIL LC-MS targeted to amine- and phenol-containing metabolites for amine/phenol submetabolome analysis.

RESULTS

Distinct clusters and significant differences in metabolite concentrations were observed among the oral cancer, BN, and control groups. A total of 154 and 66 metabolites showed significantly altered concentrations, particularly in OM and OSCC, respectively, when compared with BN (Padj < .05). Potential metabolic biomarkers were identified for each cancer, including decreased concentrations of seryl-arginine and sarcosine in OSCC. Moreover, high-confidence putative metabolites were identified, including an increase in tryptophyl-threonine and a decrease in 1,2-dihydroxynapthalene-6-sulfonic acid and hydroxyprolyl-hydroxyproline for OM.

CONCLUSIONS AND CLINICAL IMPORTANCE

We identified high coverage of the amine/phenol submetabolome, including seryl-arginine, and sarcosine, in OSCC. Our findings emphasize the potential of these biomarkers for distinguishing between oral OSCC and BN in dogs.

Effects of direct-fed microbial supplement on ruminal and plasma metabolome of early-lactation dairy cows: Untargeted metabolomics approach

We examined the effects of 2 multispecies direct-fed microbial (DFM) supplements on ruminal and plasma metabolome of early-lactation dairy cows using a high-coverage untargeted metabolomics approach. A total of 45 multiparous Holstein cows (41 ± 7 DIM) were enrolled for the 14-d pre-experimental and 91-d experimental period and were a subset from a lactation performance study, which used 114 cows. Cows were blocked using pre-experimental energy-corrected milk yield and randomly assigned within each block to 1 of 3 treatments: (1) corn silage-based diet with no DFM supplement (control; CON), (2) basal diet top-dressed with a mixture of Lactobacillus animalis and Propionibacterium freudenreichii at 3 × 109 cfu/d (PRO-A), or (3) basal diet top-dressed with a mixture of L. animalis, P. freudenreichii, Bacillus subtilis, and Bacillus licheniformis at 11.8 × 109 cfu/d (PRO-B). The basal diet was fed ad libitum daily as a TMR at 0600 and 1200 h for a duration of 91 d. Rumen fluid and blood samples were taken on d -3, 28, 49, 70, and 91 and immediately stored at -80°C. Before analysis, ruminal and plasma samples from d 28, 49, 70, and 91 were composited. An in-depth, untargeted metabolome profile of the composite rumen and plasma samples and the d -3 samples was developed by using a chemical isotope labeling/liquid chromatography-mass spectrometry (LC-MS)-based technique. Differentially abundant metabolites (taking into account fold change [FC] values and false discovery rates [FDR]) were identified with a volcano plot. In the rumen, compared with the CON diet, supplemental PRO-A increased (FC ≥1.2; FDR ≤0.05) the relative concentrations of 9 metabolites, including 2-hydroxy-2,4-pentadienoic acid, glutaric acid, quinolinic acid, and shikimic acid, and PRO-B increased relative concentrations of 16 metabolites, including 2-hydroxy-2,4-pentadienoic acid, glutaric acid, 16-hydroxypalmitic acid, and 2 propionate precursors (succinic and methylsuccinic acids). Relative to PRO-A, supplemental PRO-B increased (FC ≥1.2; FDR ≤0.05) relative rumen concentrations of 3 metabolites, 16-hydroxypalmitic acid, indole-3-carboxylic acid, and 5-aminopentanoic acid, but reduced relative rumen concentrations of 13 metabolites, including carnitine, threonic acid, and shikimic acid. Compared with the CON diet, relative concentrations of 13 plasma metabolites, including myxochelin A and glyceraldehyde, were increased (FC ≥1.2; FDR ≤0.05) by PRO-A supplementation, whereas those of 9 plasma metabolites, including 4-(2-aminophenyl)-2,4-dioxobutanoic acid, N-acetylornithine, and S-norlaudanosolin, were reduced (FC ≤0.83; FDR ≤0.05). Supplemental PRO-B increased (FC ≥1.2; FDR ≤0.05) relative concentrations of 9 plasma metabolites, including trans-o-hydroxybenzylidenepyruvic acid and 3-methylsalicylaldehyde, and reduced relative concentrations of 4 plasma metabolites, including β-ethynylserine and kynurenine. Pathway analysis of the differentially abundant metabolites in both rumen and plasma revealed that these metabolites are involved in AA and fatty acid metabolism and have antimicrobial and immune-stimulating properties. The results of this study demonstrated that dietary supplementation with either PRO-A or PRO-B altered the plasma and ruminal metabolome. Notably, ruminal and plasma metabolites involved in the metabolism of AA and fatty acids and those with immunomodulatory properties were altered by either or both of the 2 microbial additives.

Unraveling the potential of segment scan mass spectral acquisition for chemical isotope labeling LC-MS-based metabolome analysis: Performance assessment across different types of biological samples

BACKGROUND

Chemical isotope labeling (CIL) LC-MS is a powerful tool for metabolome analysis with high metabolomic coverage and quantification accuracy. In CIL LC-MS, the overall metabolite detection efficiency using Orbitrap MS can be further improved by employing a segment scan method where the full m/z range is divided into multiple segments for spectral acquisition with a significant increase in the in-spectrum dynamic range. Considering the metabolic complexity in different types of biological samples (e.g., feces, urine, serum/plasma, cell/tissue extracts, saliva, etc.), we report the development and evaluation of the segment scan method for metabolome analysis of different sample types.

RESULTS

It was found that sample complexity significantly influenced the performance of the segment scan method. In metabolically complex samples such as feces and urine, the method yielded a substantial increase (up to 94 %) in detected peak pairs or metabolites, compared to conventional full scan. Conversely, less complex samples like saliva exhibited more modest gains (approximately 25 %). Based on the observations, a 120-m/z segment scan method was determined as a routine approach for CIL LC-Orbitrap-MS-based metabolomics with good compatibility with different types of biological samples. For this method, a further investigation on relative quantification accuracy was done. The peak area ratios of 12C-/13-labeled metabolites were slightly reduced with 72%-84 % of peak pairs falling within the ±25 % range of the anticipated peak ratio of 1.0 among different samples, as opposed to 81%-90 % in the full scan, which was attributed to the inclusion of more low-abundance peak pairs within the narrow MS segments. However, the overall peak ratio measurement precision was not significantly affected by the segment scan.

SIGNIFICANCE AND NOVELTY

The segment scan method was found to be useful for CIL LC-Orbitrap-MS-based metabolome analysis of different types of samples with significant improvement in metabolite detectability (25-94 % increase), compared to the conventional full scan method.

Evaluating LC-HRMS metabolomics data processing software using FAIR principles for research software

BACKGROUND

Liquid chromatography-high resolution mass spectrometry (LC-HRMS) is a popular approach for metabolomics data acquisition and requires many data processing software tools. The FAIR Principles - Findability, Accessibility, Interoperability, and Reusability - were proposed to promote open science and reusable data management, and to maximize the benefit obtained from contemporary and formal scholarly digital publishing. More recently, the FAIR principles were extended to include Research Software (FAIR4RS).

AIM OF REVIEW

This study facilitates open science in metabolomics by providing an implementation solution for adopting FAIR4RS in the LC-HRMS metabolomics data processing software. We believe our evaluation guidelines and results can help improve the FAIRness of research software.

KEY SCIENTIFIC CONCEPTS OF REVIEW

We evaluated 124 LC-HRMS metabolomics data processing software obtained from a systematic review and selected 61 software for detailed evaluation using FAIR4RS-related criteria, which were extracted from the literature along with internal discussions. We assigned each criterion one or more FAIR4RS categories through discussion. The minimum, median, and maximum percentages of criteria fulfillment of software were 21.6%, 47.7%, and 71.8%. Statistical analysis revealed no significant improvement in FAIRness over time. We identified four criteria covering multiple FAIR4RS categories but had a low %fulfillment: (1) No software had semantic annotation of key information; (2) only 6.3% of evaluated software were registered to Zenodo and received DOIs; (3) only 14.5% of selected software had official software containerization or virtual machine; (4) only 16.7% of evaluated software had a fully documented functions in code. According to the results, we discussed improvement strategies and future directions.

Segment Scan Mass Spectral Acquisition for Increasing the Metabolite Detectability in Chemical Isotope Labeling Liquid Chromatography-Mass Spectrometry Metabolome Analysis

We report a segmented spectrum scan method using Orbitrap MS in chemical isotope labeling (CIL) liquid chromatography-mass spectrometry (LC-MS) for improving the metabolite detection efficiency. In this method, the full m/z range is divided into multiple segments with the scanning of each segment to produce multiple narrow-range spectra during the LC data acquisition. These segmented spectra are separately processed to extract the peak pair information with each peak pair arising from a differentially labeled metabolite in the analysis of a mixture of ¹³C and ¹²C reagent-labeled samples. The sublists of peak pairs are merged to form the final peak pair list from the LC-MS run. Various experimental conditions, including automatic gain control (AGC) values, mass resolutions, segment m/z widths, number of segments, and total data acquisition time in the LC run, were examined to arrive at an optimal setting in the segment scan for increasing the number of detectable metabolites while maintaining the same analysis time as in the full scan. The optimal method used a segment width of 120 m/z with 60k resolution for a 16 min CIL LC-MS run. Using dansyl-labeled human urine samples as an example, we demonstrated that this method could detect 5867 peak pairs or metabolites (not features), compared to 3765 peak pairs detectable in a full scan, representing a 56% gain. Out of 5867 peak pairs, 5575 (95.0%) could be identified or mass-matched. The relative quantification accuracy was slightly reduced (81% peak pairs were within ±25% of the expected peak ratio of 1.0 in full, compared to 87% in the full scan) due to the inclusion of more low-abundance peak pairs in the segment scan. The peak ratio measurement precision was not significantly affected by the segment scan. We also showed the increase of the peak pair number detectable from 3843 in the full scan to 7273 (89% gain) using the Orbitrap operated at 120k resolution with a 60 m/z segment width when multiple repeat sample injections were used. Thus, segment scan Orbitrap MS is an enabling method for detecting coeluting metabolites in CIL LC-MS for increasing the metabolomic coverage.

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.

Chemical Group-Based Metabolome Analysis Identifies Candidate Plasma Biomarkers Associated With Residual Feed Intake in Beef Steers

We applied chemical group-based metabolomics to identify blood metabolic signatures associated with residual feed intake in beef cattle. A group of 56 crossbred growing beef steers (average BW = 261.3 ± 18.5 kg) were adapted to a high-forage total mixed ration in a confinement dry lot equipped with GrowSafe intake nodes for period of 49 d to determine their residual feed intake classification (RFI). After RFI determination, weekly blood samples were collected three times from beef steers with the lowest RFI [most efficient (HFE); n = 8] and highest RFI and least-efficient [least efficient (LFE); n = 8]. Plasma was prepared by centrifugation and composited for each steer. Metabolome analysis was conducted using a chemical isotope labeling (CIL)/liquid chromatography–mass spectrometry, which permitted the analysis of metabolites containing amine/phenol-, carboxylic acid-, and carbonyl-chemical groups, which are metabolites associated with metabolisms of amino acids, fatty acids, and carbohydrates, respectively. A total number of 495 amine/phenol-containing metabolites were detected and identified; pathway analysis of all these metabolites showed that arginine biosynthesis and histidine metabolism were enriched (P &lt; 0.10) in HFE, relative to LFE steers. Biomarker analyses of the amine/phenol-metabolites identified methionine, 5-aminopentanoic acid, 2-aminohexanedioic acid, and 4-chlorolysine as candidate biomarkers of RFI [false discovery rate ≤ 0.05; Area Under the Curve (AUC) &gt; 0.90]. A total of 118 and 330 metabolites containing carbonyl- and carboxylic acid-chemical groups, respectively were detected and identified; no metabolic pathways associated with these metabolites were altered and only one candidate biomarker (methionine sulfoxide) was identified. These results identified five candidate metabolite biomarkers of RFI in beef cattle which are mostly associated with amino acid metabolism. Further validation using a larger cohort of beef cattle of different genetic pedigree is required to confirm these findings.

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.

Metabolic profile of irradiated whole blood by chemical isotope-labeling liquid chromatography-mass spectrometry

Irradiated blood is a new type of blood product used to prevent transfusion-associated graft-versus-host disease. However, the effects of irradiation on the metabolism of plasma, red blood cells (RBCs), and peripheral blood mononuclear cells (PBMCs) are largely unknown. We developed a workflow for testing metabolic changes in whole blood to determine the impact of irradiation by chemical isotope labeling liquid chromatography-mass spectrometry (CIL LC-MS). Blood parameters, PBMC proliferation and apoptosis were examined before and after irradiation. Next, the amine/phenol metabolites in the blood components were assayed by ¹²C- and¹³C-dansylation labeling LC-MS. We identified 1654, 1730, and 1666 peak pairs in plasma, RBCs, and PBMCs, respectively. We screened out 367, 177, and 219 significant metabolites in plasma, RBCs, and PBMCs, respectively, by principle component analyses, volcano plots, and Venn plots. Metabolic pathway analyses showed that irradiation modulated taurine and hypotaurine metabolism in plasma and purine metabolism in RBCs and PBMCs. Changes in potential biomarkers, including an increase in hypoxanthine level and a decrease in adenine level, may be related to the dysfunction of DNA synthesis in PBMCs. The decreased AMP level in RBCs may interfere with RBC storage lesions. Our research provides a more comprehensive perspective on blood metabolism associated with irradiation.

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 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.

Effects of a blend of mannan and glucan on growth performance, apparent nutrient digestibility, energy status, and whole-blood immune gene expression of beef steers during a 42-d receiving period

We examined the effects of dietary supplementation of a blend of mannan and glucan on the growth performance, energy status, and whole-blood immune gene expression of newly weaned beef steers during a 42-d receiving period. Forty-eight newly weaned Angus crossbred steers (2-d post-weaning; 199 ± 13 kg of initial body weight [BW]) from a single source were stratified by BW and randomly assigned to one of the two treatments: basal diet with no additive (CON; n = 24) or a basal diet top-dressed with 5 g of a blend of mannan and glucan (MANGLU; n = 24). Average daily gain (ADG) and feed efficiency (FE) from days 1 to 14, 15 to 42, and 1 to 42 were calculated from daily dry matter intake (DMI) and weekly BW. Blood samples were collected on days 0, 14, and 42 for measurement of plasma glucose and nonesterified fatty acids (NEFA). Blood samples collected on days 14 and 42 were composited for each steer for untargeted carbonyl-metabolome analysis (measurement of carbonyl-containing metabolites). Expression of 84 immune-related genes was analyzed on blood samples collected on day 42. Beginning on days 37 to 42, total mixed ration, refusals, and fecal samples were collected once daily to determine apparent total tract digestibility of DM, CP, NDF, and ADF using indigestible NDF as an internal marker. Over the 42-d feeding trial, supplemental MANGLU tended to increase final BW (P = 0.07) and ADG (P = 0.06). Compared to CON, beef steers fed supplemental MANGLU had greater (P = 0.01) DMI during the first 14 d, greater DM digestibility (P = 0.03), and tended to have greater NDF digestibility (P = 0.09). No treatment effects (P > 0.10) on plasma glucose and NEFA on days 14 and 42 were detected; however, carbonyl-metabolome analysis revealed increased (FDR ≤ 0.05) plasma concentrations of galactose and glyceraldehydes, and altered (FDR ≤ 0.05) concentrations of some microbiome-derived metabolites in beef steers fed MANGLU. Compared with CON, MANGLU increased (P ≤ 0.05) the expression of five immune-related genes involved in recognition of and mounting immune defense against microbial pathogens. In conclusion, the results of this study demonstrated that supplemental MANGLU enhances beef cattle immunocompetence and productivity during feedlot receiving period.

Novel plasma metabolite markers of attention-deficit/hyperactivity disorder identified using high-performance chemical isotope labelling-based liquid chromatography-mass spectrometry

OBJECTIVES

Metabolites are the intermediate and final products of biological processes and ultimately reflect the responses of these processes to genetic regulation and environmental perturbations, including those involved in attention deficit/hyperactivity disorder (ADHD).

METHODS

We identified a quantitative profile of plasma metabolites in 58 ADHD patients (mean age 9.0 years, 77.6% males) and 38 healthy control subjects (mean age 10.2 years, 55.3% males) using the high-performance chemical isotope labelling (CIL)-based liquid chromatography-mass spectrometry (LC-MS). Using a volcano plot and orthogonal projections to latent structure-discriminant analysis (OPLS-DA), we determined nine metabolites with differentially expressed levels in ADHD plasma samples.

RESULTS

Compared to the control group, the plasma levels of guanosine, O-phosphoethanolamine, phenyl-leucine, hypoxanthine, 4-aminohippuric acid, 5-hydroxylysine, and L-cystine appeared increased in the ADHD patients, whilegentisic acid and tryptophyl-phenylalanine were down-regulated in the patients with ADHD. We found that the plasma levels of all nine metabolites were able to discriminate the ADHD group from the control group. Levels of O-phosphoethanolamine, 4-aminohippuric acid, 5-hydroxylysine, L-cystine, tryptophyl-phenylalanine, and gentisic acid were significantly correlated with clinical ADHD symptoms.

CONCLUSIONS

This study is the first to use the CIL-based LC-MS to profile ADHD plasma metabolites, and we identified nine novel metabolite biomarkers of ADHD.

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.

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.

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.

Metabolomic profiling of parapneumonic effusion reveals a regulatory role of dipeptides in interleukin-8 production in neutrophil-like cells

Bacterial pneumonia is a lethal condition, and approximately 40% of bacterial pneumonia patients experience parapneumonic effusion (PPE). Based on the severity of inflammation, PPEs can be categorized as early-stage uncomplicated PPE (UPPE), advanced-stage complicated PPE (CPPE) and, most seriously, thoracic empyema. Appropriate antibiotic treatment at the early stage of PPE can prevent PPE progression and reduce mortality, indicating that understanding PPE generation and components can help researchers develop corresponding treatment strategies for PPE. To this end, metabolomes of 73 PPE (38 UPPE and 35 CPPE samples) and 30 malignant pleural effusion (MPE) samples were profiled with differential ¹²C₂-/¹³C₂-isotope dansylation labeling-based mass spectrometry. We found that PPE is characterized by elevated levels of dipeptides, especially for PPEs at advanced stages. Furthermore, with integrated proteomic and transcriptomic analyses of PPEs, the levels of dipeptides were strongly associated with the production of interleukin-8 (IL-8), an inflammation-associated cytokine. The production of IL-8 indeed increased upon the treatment of HL-60-derived neutrophilic cells with dipeptides, Gly-Val and Gly-Tyr. Our findings help to elucidate the metabolic perturbations present in PPE and indicate for the first time that dipeptides may be involved in the immune regulation observed during PPE progression.

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.

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.

Large-scale targeted metabolomics method for metabolite profiling of human samples

Targeted metabolomics has significant advantages for quantification but suffers from reduced metabolite coverage. In this study, we developed a large-scale targeted metabolomics method and expanded its applicability to various human samples. This approach initially involved unbiased identification of metabolites in human cells, tissues and body fluids using ultra high-performance liquid chromatography (UHPLC) coupled to high-resolution Orbitrap mass spectrometry (MS). Targeted metabolomics method was established with utility of UHPLC-triple quadrupole MS, which enables targeted profiling of over 400 biologically important metabolites (e.g., amino acids, sugars, nucleotides, dipeptides, coenzymes, and fatty acids), covering 92 metabolic pathways (e.g., Krebs cycle, glycolysis, amino acids metabolism, ammonia recycling, and one-carbon metabolism). The present method displayed better sensitivity, repeatability and linearity than the Orbitrap MS-based untargeted metabolomics approach and demonstrated excellent performance in lung cancer biomarker discovery, in which 107 differential metabolites were able to discriminate between carcinoma and adjacent normal tissues, implicating the Warburg effect, alteration of redox state, and nucleotide metabolism of lung cancer. This new method is flexible and expandable and offers many advantages for metabolomics analysis, such as wide metabolite coverage, good repeatability and linearity and excellent capability in biomarker discovery, making it useful for both basic and clinical metabolic research.

Effects of a blend of Saccharomyces cerevisiae-based direct-fed microbial and fermentation products on plasma carbonyl-metabolome and fecal bacterial community of beef steers

BACKGROUND

Previous studies have evaluated the metabolic status of animals fed direct-fed microbial (DFM) using enzyme-based assays which are time-consuming and limited to a few metabolites. In addition, little emphasis has been placed on investigating the effects of DFM on hindgut microbiota. We examined the effects of dietary supplementation of a blend of Saccharomyces cerevisiae-based DFM and fermentation products on the plasma concentrations of carbonyl-containing metabolites via a metabolomics approach, and fecal bacterial community, via 16S rRNA gene sequencing, of beef steers during a 42-day receiving period. Forty newly weaned steers were randomly assigned to receive a basal diet with no additive (CON; n = 20) or a basal diet supplemented with 19 g of Commence™ (PROB; n = 20) for a 42-day period. Commence™ (PMI, Arden Hills, MN) is a blend of 6.2 × 1011 cfu/g of S. cerevisiae, 3.5 × 1010 cfu/g of a mixture of Enterococcus lactis, Bacillus subtilis, Enterococcus faecium, and Lactobacillus casei, and the fermentation products of these aforementioned microorganisms and those of Aspergillus oryzae and Aspergillus niger. On d 0 and 40, rectal fecal samples were collected randomly from 10 steers from each treatment group. On d 42, blood was collected for plasma preparation.

RESULTS

A total number of 812 plasma metabolites were detected. Up to 305 metabolites [fold change (FC) ≥ 1.5, FDR ≤ 0.01] including glucose, hippuric acid, and 5-hydroxykynurenamine were increased by PROB supplementation, whereas 199 metabolites (FC ≤ 0.63, FDR ≤ 0.01) including acetoacetate were reduced. Supplementation of PROB increased (P ≤ 0.05) the relative abundance of Prevotellaceae UCG-003, Megasphaera, Dorea, Acetitomaculum, and Blautia. In contrast, the relative abundance of Elusimicrobium, Moheibacter, Stenotrophomonas, Comamonas, and uncultured bacterium belonging to family p-2534-18B5 gut group (phylum Bacteroidetes) were reduced (P ≤ 0.05).

CONCLUSIONS

The results of this study demonstrated that supplementation of PROB altered both the plasma carbonyl metabolome towards increased glucose concentration suggesting an improved energy status, and fecal bacterial community, suggesting an increased hindgut fermentation of the beef steers.

Fungal laccase-mediated humification of estrogens in aquatic ecosystems

Estrogens are a category of non-degradable organic pollutants prevalent in aquatic environments with reported health risks in human and wildlife reproduction. A biotechnological approach is proposed for utilizing fungal laccase-mediated humification reactions (L-MHRs) to remove estrogens from water. Through a reactive radical-mediated C-C, C-O-C, or C-N-C covalent coupling mechanism, multifarious complex polymeric structures are generated having limited solubilities, which significantly reduces their estrogenic activity and ecotoxicity. This review highlights the available literature associated with the self/cross-coupling mechanism of fungal L-MHRs in catalyzing the single-electron oxidation of estrogens and humic acid (HA). Advances in identifying unknown estrogen-HA cross-coupling products using high-resolution mass spectrometry combined with ¹³C-isotope labeling and ¹³C NMR may provide key research directions beneficial to aquatic ecological restoration measures.

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.

Mass Accuracy Check Using Common Background Peaks for Improving Metabolome Data Quality in Chemical Isotope Labeling LC-MS

Chemical isotope labeling (CIL) LC-MS is a highly sensitive and quantitative method for metabolome analysis. Because of a large number of peaks detectable in a sample and the need of running many samples in a metabolomics project, any significant change in mass measurement accuracy during the whole period of running samples can adversely affect the downstream peak alignment and quantitative analysis. Herein, we report a rapid method to check the mass accuracy of individual spectra in each CIL LC-MS run in order to flag up any run containing spectra with accuracy drift that falls outside the expected error. The flagged run may be re-run or discarded before merging with other runs for peak alignment and analysis. This method is based on the observation that some background signals are commonly detected in almost all spectra collected in CIL LC-MS runs. A mass accuracy check (MAC) software program has been developed to first find the common background mass peaks and then use them as mass references to calculate any mass shifts over the course of multiple sample runs. Using a metabolome dataset of 324 human cerebrospinal fluid (CSF) samples and 35 quality control (QC) samples produced by CIL LC-MS, we show that this accuracy check method can streamline the initial raw data processing for downstream analysis in metabolomics.

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.

MSC-triggered metabolomic alterations in liver-resident immune cells isolated from CCl4-induced mouse ALI model

Clinical trials testing mesenchymal stem cell (MSC) as a cellular remedy for acute liver injury (ALI) are underway, but its underlying mechanism has not been thoroughly scrutinized. We highlight that the metabolomic profile of the liver-resident immune cells is significantly altered after MSC administration; its potential correlation with ALI remission is discussed in this study. C57BL/6 mice are randomly divided into three groups: the sham group, MSC-treated ALI group and PBS-treated ALI group; acute liver injury is induced by intraperitoneal injection of carbon tetrachloride. A high-performance chemical isotope labeling liquid chromatography-mass spectrometry (CIL LC-MS) is exploited to profile amine, phenol and carbonyl submetabolome of the liver-resident immune cells in different treatments. 4295 peak pairs are quantified and 2461 peak pairs are further identified in zero-reaction and one-reaction libraries. Clear separation of the three groups is observed in the global PCA and OPLS-DA analyses. We identified 256 metabolites to be candidate biomarkers for ALI-activated immunity and 114 metabolites to be candidate biomarkers for MSC-modulated immunity. Ariginine, aspartate and glutamate metabolism are most affected in both cases, with eight significantly regulated metabolites as joints (glutamic-gamma-semialdehyde, aspartate acid, glutamate acid, gamma-Aminobutyric acidorinithine, 2-keto-glutaramic acid, N-acetylornithine, citrulline and ornithine). These findings shed new light on the therapeutic benefit of immune modulation during ALI rescue. It needs to be further investigated whether exogenous supply of certain metabolites will exert a profound impact on the metabolic network, crosstalking with immune responses and modulating ALI prognosis.

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.

Alzheimer's Biomarkers From Multiple Modalities Selectively Discriminate Clinical Status: Relative Importance of Salivary Metabolomics Panels, Genetic, Lifestyle, Cognitive, Functional Health and Demographic Risk Markers

Background: Among the neurodegenerative diseases of aging, sporadic Alzheimer’s disease (AD) is the most prevalent and perhaps the most feared. With virtually no success at finding pharmaceutical therapeutics for altering progressive AD after diagnosis, research attention is increasingly directed at discovering biological and other markers that detect AD risk in the long asymptomatic phase. Both early detection and precision preclinical intervention require systematic investigation of multiple modalities and combinations of AD-related biomarkers and risk factors. We extend recent unbiased metabolomics research that produced a set of metabolite biomarker panels tailored to the discrimination of cognitively normal (CN), cognitively impaired and AD patients. Specifically, we compare the prediction importance of these panels with five other sets of modifiable and non-modifiable AD risk factors (genetic, lifestyle, cognitive, functional health and bio-demographic) in three clinical groups.

Method: The three groups were: CN (n = 35), mild cognitive impairment (MCI; n = 25), and AD (n = 22). In a series of three pairwise comparisons, we used machine learning technology random forest analysis (RFA) to test relative predictive importance of up to 19 risk biomarkers from the six AD risk domains.

Results: The three RFA multimodal prediction analyses produced significant discriminating risk factors. First, discriminating AD from CN was the AD metabolite panel and two cognitive markers. Second, discriminating AD from MCI was the AD/MCI metabolite panel and two cognitive markers. Third, discriminating MCI from CN was the MCI metabolite panel and seven markers from four other risk modalities: genetic, lifestyle, cognition and functional health.

Conclusions: Salivary metabolomics biomarker panels, supplemented by other risk markers, were robust predictors of: (1) clinical differences in impairment and dementia and even; (2) subtle differences between CN and MCI. For the latter, the metabolite panel was supplemented by biomarkers that were both modifiable (e.g., functional) and non-modifiable (e.g., genetic). Comparing, integrating and identifying important multi-modal predictors may lead to novel combinations of complex risk profiles potentially indicative of neuropathological changes in asymptomatic or preclinical AD.

Comprehensive Screening and Identification of Fatty Acid Esters of Hydroxy Fatty Acids in Plant Tissues by Chemical Isotope Labeling-Assisted Liquid Chromatography-Mass Spectrometry

Fatty acid esters of hydroxy fatty acids (FAHFAs) are a new class of lipid mediators with promising anti-diabetic and anti-inflammatory properties. Comprehensive screening and identification of FAHFAs in biological samples would be beneficial to the discovery of new FAHFAs and enable greater understanding of their biological functions. Here, we report the comprehensive screening of FAHFAs in rice and  Arabidopsis thaliana by chemical isotope labeling-assisted liquid chromatography-mass spectrometry (CIL-LC-MS). Multiple reaction monitoring (MRM) was used for screening of FAHFAs. With the proposed method, we detected 49 potential FAHFA families, including 262 regioisomers, in tissues of rice and Arabidopsis thaliana, which greatly extends our knowledge of known FAHFAs. In addition, we proposed a strategy to identify FAHFA regioisomers based on their retention on a reversed-phase LC column. Using the proposed identification strategy, we identified 71 regioisomers from 11 FAHFA families based on commercial standards and characteristic chromatographic retention behaviors. The screening technique could allow for the discovery of new FAHFAs in biological samples. The new FAHFAs identified in this work will contribute to the in-depth study of the functions of FAHFAs.

Impact of Oxygen Concentration on Metabolic Profile of Human Placenta-Derived Mesenchymal Stem Cells As Determined by Chemical Isotope Labeling LC-MS

The placenta resides in a physiologically low oxygen microenvironment of the body. Hypoxia induces a wide range of stem cell cellular activities. Here, we report a workflow for exploring the role of physiological (hypoxic, 5% oxygen) and original cell culture (normoxic, 21% oxygen) oxygen concentrations in regulating the metabolic status of human placenta-derived mesenchymal stem cells (hPMSCs). The general biological characteristics of hPMSCs were assessed via a variety of approaches such as cell counts, flow cytometry and differentiation study. A sensitive ¹³C/¹²C-dansyl labeling liquid chromatography-mass spectrometry (LC-MS) method targeting the amine/phenol submetabolome was used for metabolic profiling of the cell and corresponding culture supernatant. Multivariate and univariate statistical analyses were used to analyze the metabolomics data. hPMSCs cultured in hypoxia display smaller size, higher proliferation, greater differentiation ability and no difference in immunophenotype. Overall, 2987 and 2860 peak pairs or metabolites were detected and quantified in hPMSCs and culture supernatant, respectively. Approximately 86.0% of cellular metabolites and 84.3% of culture supernatant peak pairs were identified using a dansyl standard library or matched to metabolite structures using accurate mass search against human metabolome libraries. The orthogonal partial least-squares discriminant analysis (OPLS-DA) showed a clear separation between the hypoxic group and the normoxic group. Ten metabolites from cells and six metabolites from culture supernatant were identified as potential biomarkers of hypoxia. This study demonstrated that chemical isotope labeling LC-MS can be used to reveal the role of oxygen in the regulation of hPMSC metabolism, whereby physiological oxygen concentrations may promote arginine and proline metabolism, pantothenate and coenzyme A (CoA) biosynthesis, and alanine, aspartate and glutamate metabolism.

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.

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.

Development of High-Performance Chemical Isotope Labeling LC-MS for Profiling the Carbonyl Submetabolome

Metabolites containing a carbonyl group represent several important classes of molecules including various forms of ketones and aldehydes such as steroids and sugars. We report a high-performance chemical isotope labeling (CIL) LC-MS method for profiling the carbonyl submetabolome with high coverage and high accuracy and precision of relative quantification. This method is based on the use of dansylhydrazine (DnsHz) labeling of carbonyl metabolites to change their chemical and physical properties to such an extent that the labeled metabolites can be efficiently separated by reversed phase LC and ionized by electrospray ionization MS. In the analysis of six standards representing different carbonyl classes, acetaldehyde could be ionized only after labeling and MS signals were significantly increased for other 5 standards with an enhancement factor ranging from ∼15-fold for androsterone to ∼940-fold for 2-butanone. Differential ¹²C- and ¹³C-DnsHz labeling was developed for quantifying metabolic differences in comparative samples where individual samples were separately labeled with ¹²C-labeling and spiked with a ¹³C-labeled pooled sample, followed by LC-MS analysis, peak pair picking, and peak intensity ratio measurement. In the replicate analysis of a 1:1 ¹²C-/¹³C-labeled human urine mixture (n = 6), an average of 2030 ± 39 pairs per run were detected with 1737 pairs in common, indicating the possibility of detecting a large number of carbonyl metabolites as well as high reproducibility of peak pair detection. The average RSD of the peak pair ratios was 7.6%, and 95.6% of the pairs had a RSD value of less than 20%, demonstrating high precision for peak ratio measurement. In addition, the ratios of most peak pairs were close to the expected value of 1.0 (e.g., 95.5% of them had ratios of between 0.67 and 1.5), showing the high accuracy of the method. For metabolite identification, a library of DnsHz-labeled standards was constructed, including 78 carbonyl metabolites with each containing MS, retention time (RT), and MS/MS information. This library and an online search program for labeled carbonyl metabolite identification based on MS, RT, and MS/MS matches have been implemented in a freely available Website, www.mycompoundid.org . Using this library, out of the 1737 peak pairs detected in urine, 33 metabolites were positively identified. In addition, 1333 peak pairs could be matched to the metabolome databases with most of them belonging to the carbonyl metabolites. These results show that ¹²C-/¹³C-DnsHz labeling LC-MS is a useful tool for profiling the carbonyl submetabolome of complex samples with high coverage.

Development of Chemical Isotope Labeling LC-MS for Milk Metabolomics: Comprehensive and Quantitative Profiling of the Amine/Phenol Submetabolome

Milk is a complex sample containing a variety of proteins, lipids, and metabolites. Studying the milk metabolome represents an important application of metabolomics in the general area of nutritional research. However, comprehensive and quantitative analysis of milk metabolites is a challenging task due to the wide range of variations in chemical/physical properties and concentrations of these metabolites. We report an analytical workflow for in-depth profiling of the milk metabolome based on chemical isotope labeling (CIL) and liquid chromatography mass spectrometry (LC-MS) with a focus of using dansylation labeling to target the amine/phenol submetabolome. An optimal sample preparation method, including the use of methanol at a 3:1 ratio of solvent to milk for protein precipitation and dichloromethane for lipid removal, was developed to detect and quantify as many metabolites as possible. This workflow was found to be generally applicable to profile milk metabolomes of different species (cow, goat, and human) and types. Results from experimental replicate analysis (n = 5) of 1:1, 2:1, and 1:2 ¹²C-/¹³C-labeled cow milk samples showed that 95.7%, 94.3%, and 93.2% of peak pairs, respectively, had ratio values within ±50% accuracy range and 90.7%, 92.6%, and 90.8% peak pairs had RSD values of less than 20%. In the metabolomic analysis of 36 samples from different categories of cow milk (brands, batches, and fat percentages) with experimental triplicates, a total of 7104 peak pairs or metabolites could be detected with an average of 4573 ± 505 (n = 108) pairs detected per LC-MS run. Among them, 3820 peak pairs were commonly detected in over 80% of the samples with 70 metabolites positively identified by mass and retention time matches to the dansyl standard library and 2988 pairs with their masses matched to the human metabolome libraries. This unprecedentedly high coverage of the amine/phenol submetabolome illustrates the complexity of the milk metabolome. Since milk and milk products are consumed in large quantities on a daily basis, the intake of these milk metabolites even at low concentrations can be cumulatively high. The high-coverage analysis of the milk metabolome using CIL LC-MS should be very useful in future research involving the study of the effects of these metabolites on human health. It should also be useful in the dairy industry in areas such as improving milk production, developing new processing technologies, developing improved nutritional products, quality control, and milk product authentication.

Chemical Isotope Labeling LC-MS for Monitoring Disease Progression and Treatment in Animal Models: Plasma Metabolomics Study of Osteoarthritis Rat Model

We report a chemical isotope labeling (CIL) liquid chromatography mass spectrometry (LC-MS) method generally applicable for tracking metabolomic changes from samples collected in an animal model for studying disease development and treatment. A rat model of surgically induced osteoarthritis (OA) was used as an example to illustrate the workflow and technical performance. Experimental duplicate analyses of 234 plasma samples were carried out using dansylation labeling LC-MS targeting the amine/phenol submetabolome. These samples composed of 39 groups (6 rats per group) were collected at multiple time points with sham operation, OA control group, and OA rats with treatment, separately, using glucosamine/Celecoxib and three traditional Chinese medicines (Epimedii folium, Chuanxiong Rhizoma and Bushen-Huoxue). In total, 3893 metabolites could be detected and 2923 of them were consistently detected in more than 50% of the runs. This high-coverage submetabolome dataset could be used to track OA progression and treatment. Many differentiating metabolites were found and 11 metabolites including 2-aminoadipic acid, saccharopine and GABA were selected as potential biomarkers of OA progression and OA treatment. This study illustrates that CIL LC-MS is a very useful technique for monitoring incremental metabolomic changes with high coverage and accuracy for studying disease progression and treatment in animal models.