Differential 12C-/13C-Isotope Dansylation Labeling and Fast Liquid Chromatography/Mass Spectrometry for Absolute and Relative Quantification of the Metabolome

Differential effects of milk, yogurt, and cheese on energy homeostasis and brown adipose tissue phenotype in high-fat diet-induced obese mice

Aim: We hypothesized that milk, yogurt, and cheese have differential impacts on energy expenditure (EE) and obesity in mice fed a high-fat diet (HFD). Methods: C57BL/6 mice (n = 16 per group) were fed a HFD or a HFD supplemented with fat-free milk (MILK), fat-free plain yogurt (YOG), or reduced-fat cheddar cheese (CHE; 19 kcal% fat), each provided at 10% of the daily energy intake, for 8 weeks. EE was quantified using a metabolic chamber. Metabolic pathways related to BAT mitochondrial function and uncoupling protein 1 (UCP1) abundance were assessed. Serum lipidomic profiles were analyzed to identify potential mediators of the observed effects. Results: MILK supplementation lowered weight gain and fat accumulation and enhanced EE and BAT thermogenesis, perhaps via the SIRT1-AMPK-PGC1α axis in BAT. This led to elevated UCP1 abundance and enhanced the abundance of hormone-sensitive lipase (HSL). MILK also altered serum lipid species, indicating enhanced energy use, and promoted BAT thermogenesis and mitochondrial function pathways. YOG exhibited a similar pattern but a lower magnitude of effects than MILK on reducing weight gain and fat mass, increasing EE, and BAT thermogenic proteins, including AMPK-PGC1α-UCP1. Both MILK and YOG showed a relative increase in serum PC 15:0_15:0 and LPC 15:0. In contrast, CHE reduced weight gain and increased EE without impacting BAT thermogenesis proteins or serum lipid species. Conclusion: Our study showed that MILK, YOG, and CHE reduced weight gain in mice on a HFD by increasing EE. MILK and YOG also up-regulated BAT thermogenesis, while both additionally altered lipids involved in fat metabolism and inflammation. CHE did not affect BAT thermogenesis and lipid species compared to HFD.

Deep learning prediction of electrospray ionization tandem mass spectra of chemically derived molecules

Chemical derivatization is a powerful strategy to enhance sensitivity and selectivity of liquid chromatography-mass spectrometry for non-targeted analysis of chemicals in complex mixtures. However, it remains impossible to obtain large sets of reference spectra for chemically derived molecules (CDMs), representing a major barrier in real-world applications. Herein, we describe a deep learning approach that enables accurate prediction of electrospray ionization tandem mass spectra for CDMs (DeepCDM). DeepCDM is established by transfer learning from a generic spectrum predicting model using a small set of experimentally acquired tandem mass spectra of CDMs, which converts a generic model with low predictability for CDMs into a specialized model with high predictability. We demonstrate DeepCDM by predicting electrospray ionization tandem mass spectra of dansylated molecules. The success in establishing Dns-MS further enables the development of DnsBank, a dansylation-specialized in silico spectral library. DnsBank achieves significant increases of accurate annotation rates of dansylated molecules, facilitating discovery of new hazardous pollutants from an environmental study of leather industrial wastewater. DeepCDM is also highly versatile for other classes of CDMs. Therefore, we envision that DeepCDM will pave a way for high-throughput identification of CDMs in non-targeted analysis to dig unknowns with potential health impacts from emerging anthropogenic chemicals.

Serum metabolomics reveals the effectiveness of human placental mesenchymal stem cell therapy for Crohn's disease

Mesenchymal stem cell (MSC) therapy offers a promising cure for Crohn's disease (CD), however, its therapeutic effects vary significantly due to individual differences. Therefore, identifying easily detectable biomarkers is essential to assess the efficacy of MSC therapy. In this study, SAMP1/Yit mice were used as a model of CD, which develop spontaneous chronic ileitis, closely resembling the characteristics present in CD patients. Serum metabolic alterations during treatment were analyzed, through the application of differential 12C-/13C-dansylation labeling liquid chromatography-mass spectrometry. Based on the significant differences and time-varying trends of serum amine/phenol-containing metabolites abundance between the control group, the model group, and the treatment group, four serum biomarkers were ultimately screened for evaluating the efficacy of MSC treatment for CD, namely 4-hydroxyphenylpyruvate, 4-hydroxyphenylacetaldehyde, caffeate, and N-acetyltryptamine, whose abundances both increased in the serum of CD model mice and decreased after MSC treatment. These metabolic alterations were associated with tyrosine metabolism, which was validated by the dysregulation of related enzymes. The discovery of biomarkers may help to improve the targeting and effectiveness of treatment and provide innovative prospects for the clinical application of MSC for CD.

Plasma metabolome analysis for predicting antiviral treatment efficacy in chronic hepatitis B: diagnostic biomarkers and therapeutic insights

The early and accurate identification of predictive biomarkers for antiviral treatment efficacy remains a significant clinical challenge, particularly in the management of chronic hepatitis B (CHB). This study aimed to assess whether the plasma metabolome could reliably predict the success of antiviral therapy in CHB patients. We conducted a retrospective analysis on 56 treatment-naive CHB patients at the First Affiliated Hospital of Zhejiang University from December 2013 to March 2016. Patients who underwent a 48-week treatment regimen of entecavir (ETV) and interferon-alpha (IFN-α) were randomly assigned to either a discovery cohort (n=29) or a validation cohort (n=27). Based on the outcome of the treatment, patients were classified as HBeAg seroconversion group (High responders, Hrp) or the non-remission group (Low responder, Lrp). Our methodology involved an untargeted analysis of the amine/phenol and carboxylic acid submetabolomes in the CHB patients under treatment, utilizing chemical isotope labeling (CIL) techniques with liquid chromatography-mass spectrometry (LC-MS). Several metabolites were identified as having significant diagnostic potential for distinguishing Hrp from Lrp, with areas under the receiver operating characteristic curve (AUC) exceeding those typical clinical indicators. Notably, four metabolites, namely 2-methyl-3-ketovaleric acid, 2-ketohexanoic acid, 6-oxo-1,4,5,6-tetrahydronicotinic acid, and α-ketoisovaleric acid, demonstrated exceptionally high sensitivity and specificity in both cohorts, nearing 100%. In contrast, the clinical indicators, including HBcAb, log(HBsAg), and HBeAb, demonstrated lower and inconsistent sensitivity and specificity between the discovery and validation cohorts. Using HBcAb as a marker, the sensitivity was 87.5% with 76.9% specificity in the discovery cohort; however, the sensitivity dropped to 46.7% with 91.7% specificity in the validation cohort. Using log(HBsAg), the sensitivity was 84.6% with 69.2% specificity in the discovery cohort, compared to 85.7% sensitivity and 83.3% specificity in the validation cohort. For HBeAb, the separation of Hrp and Lrp had a sensitivity of 87.5% with 69.2% specificity in the discovery cohort, while the validation cohort showed 86.7% sensitivity and 91.7% specificity.

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.

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.

The Role of Coinhibitory Receptors in B Cell Dysregulation in SARS-CoV-2-Infected Individuals with Severe Disease

Severe SARS-CoV-2 infection is associated with significant immune dysregulation involving different immune cell subsets. In this study, when analyzing critically ill COVID-19 patients versus those with mild disease, we observed a significant reduction in total and memory B cell subsets but an increase in naive B cells. Moreover, B cells from COVID-19 patients displayed impaired effector functions, evidenced by diminished proliferative capacity, reduced cytokine, and Ab production. This functional impairment was accompanied by an increased apoptotic potential upon stimulation in B cells from severely ill COVID-19 patients. Our further studies revealed the expansion of B cells expressing coinhibitory molecules (PD-1, PD-L1, TIM-1, VISTA, CTLA-4, and Gal-9) in intensive care unit (ICU)-admitted patients but not in those with mild disease. The coinhibitory receptor expression was linked to altered IgA and IgG expression and increased the apoptotic capacity of B cells. Also, we found a reduced frequency of CD24hiCD38hi regulatory B cells with impaired IL-10 production. Our mechanistic studies revealed that the upregulation of PD-L1 was linked to elevated plasma IL-6 levels in COVID-19 patients. This implies a connection between the cytokine storm and altered B cell phenotype and function. Finally, our metabolomic analysis showed a significant reduction in tryptophan but elevation of kynurenine in ICU-admitted COVID-19 patients. We found that kynurenine promotes PD-L1 expression in B cells, correlating with increased IL-6R expression and STAT1/STAT3 activation. Our observations provide novel insights into the complex interplay of B cell dysregulation, implicating coinhibitory receptors, IL-6, and kynurenine in impaired B cell effector functions, potentially contributing to the pathogenesis of COVID-19.

High-sensitivity profiling of dipeptides in sauce-flavor Baijiu Daqu by chemical derivatization and ultrahigh-performance liquid chromatography coupled with high-resolution mass spectrometry

Dipeptides in sauce-flavor Baijiu Daqu are protein degradation products during the fermentation of Daqu, which are believed to play a crucial role in the flavor and quality of Baijiu. Herein, we integrated dansyl chloride derivatization with ultrahigh-performance liquid chromatography coupled with high-resolution mass spectrometry (UHPLC-HRMS) for comprehensively profiling dipeptides in Daqu. The derivatization efficiency was higher than 99.1 % for all 17 dipeptide standards under the optimized derivatization conditions. In total, 118 dipeptides were detected in Daqu. The method was validated and the analytical characteristics including the linearity (spanned across 2-4 orders of magnitude), precision (1.2-19.9 %), limit of detection (varied from 1.1 to 53.4 pmol/mL) and the stability (3.6-15.8 %) are satisfactory. The usefulness of the method was examined by studying the distribution characteristics of dipeptides in Daqu under different production conditions. The present method provides an effective and robust strategy for comprehensively analyzing dipeptide compounds in complex biological samples.

Urine Metabolic Profiling for Rapid Lung Cancer Screening: A Strategy Combining Rh-Doped SrTiO3-Assisted Laser Desorption/Ionization Mass Spectrometry and Machine Learning

Lung cancer ranks among the cancers with the highest global incidence rates and mortality. Swift and extensive screening is crucial for the early-stage diagnosis of lung cancer. Laser desorption/ionization mass spectrometry (LDI-MS) possesses clear advantages over traditional analytical methods for large-scale analysis due to its unique features, such as simple sample processing, rapid speed, and high-throughput performance. As n-type semiconductors, titanate-based perovskite materials can generate charge carriers under ultraviolet light irradiation, providing the capability for use as an LDI-MS substrate. In this study, we employ Rh-doped SrTiO3 (STO/Rh)-assisted LDI-MS combined with machine learning to establish a method for urine-based lung cancer screening. We directly analyzed urine metabolites from lung cancer patients (LCs), pneumonia patients (PNs), and healthy controls (HCs) without employing any pretreatment. Through the integration of machine learning, LCs are successfully distinguished from HCs and PNs, achieving impressive area under the curve (AUC) values of 0.940 for LCs vs HCs and 0.864 for LCs vs PNs. Furthermore, we identified 10 metabolites with significantly altered levels in LCs, leading to the discovery of related pathways through metabolic enrichment analysis. These results suggest the potential of this method for rapidly distinguishing LCs in clinical applications and promoting precision medicine.

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.

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.

Metabolomic and immune alterations in long COVID patients with chronic fatigue syndrome

Introduction

A group of SARS-CoV-2 infected individuals present lingering symptoms, defined as long COVID (LC), that may last months or years post the onset of acute disease. A portion of LC patients have symptoms similar to myalgic encephalomyelitis or chronic fatigue syndrome (ME/CFS), which results in a substantial reduction in their quality of life. A better understanding of the pathophysiology of LC, in particular, ME/CFS is urgently needed.

Methods

We identified and studied metabolites and soluble biomarkers in plasma from LC individuals mainly exhibiting ME/CFS compared to age-sex-matched recovered individuals (R) without LC, acute COVID-19 patients (A), and to SARS-CoV-2 unexposed healthy individuals (HC).

Results

Through these analyses, we identified alterations in several metabolomic pathways in LC vs other groups. Plasma metabolomics analysis showed that LC differed from the R and HC groups. Of note, the R group also exhibited a different metabolomic profile than HC. Moreover, we observed a significant elevation in the plasma pro-inflammatory biomarkers (e.g. IL-1α, IL-6, TNF-α, Flt-1, and sCD14) but the reduction in ATP in LC patients. Our results demonstrate that LC patients exhibit persistent metabolomic abnormalities 12 months after the acute COVID-19 disease. Of note, such metabolomic alterations can be observed in the R group 12 months after the acute disease. Hence, the metabolomic recovery period for infected individuals with SARS-CoV-2 might be long-lasting. In particular, we found a significant reduction in sarcosine and serine concentrations in LC patients, which was inversely correlated with depression, anxiety, and cognitive dysfunction scores.

Conclusion

Our study findings provide a comprehensive metabolomic knowledge base and other soluble biomarkers for a better understanding of the pathophysiology of LC and suggests sarcosine and serine supplementations might have potential therapeutic implications in LC patients. Finally, our study reveals that LC disproportionally affects females more than males, as evidenced by nearly 70% of our LC patients being female.

Effects of a liquid and dry Saccharomyces cerevisiae fermentation product feeding program on ruminal fermentation, total tract digestibility, and plasma metabolome of Holstein steers receiving a grain-based diet

The study aimed to determine the effects of a postbiotic feeding program consisting of liquid and dry Saccharomyces cerevisiae fermentation product (SCFP) on ruminal fermentation, digestibility, and plasma metabolome of Holstein steers receiving a grain-based diet. Eight Holstein steers (body weight, BW, 467 ± 13.9 kg) equipped with rumen cannulas were used in a crossover design study, with 21 d per period and a 7-d washout period in between periods. Steers were stratified by initial BW and assigned to 1 of 2 treatments. The treatments were 1) Control, basal finishing diet only (CON); 2) SCFP, 1-d feeding of liquid SCFP (infused into the rumen via the cannula at 11 mL/100 kg BW) followed by daily feeding of dry SCFP (12 g/d, top-dressed). Feed and spot fecal samples were collected during days 17 to 20 for determination of digestibility and fecal excretion of N, P, Cu, and Zn. Digestibility was measured using acid-insoluble ash as an internal marker. Blood samples were collected on day 21 before the morning feeding. Rumen fluid samples were collected on days 0, 1, 2, 3, 5, and 21 via rumen cannula. Results were analyzed with the GLIMMIX procedure of SAS 9.4 (SAS, 2023). Treatment did not affect dry matter intake (P = 0.15) and digestibility (P ≥ 0.62). The fecal output and absorption of Zn, Cu, P, and N were not affected (P > 0.22) by treatment. On day 1, the liquid SCFP supplementation tended to reduce (P = 0.07) ruminal VFA concentration and increased (P < 0.01) the molar proportion of valerate. Feeding SCFP tended to increase total ruminal VFA on day 5 (P = 0.08) and significantly increased total VFA on day 21 (P = 0.05). Ruminal NH3-N was reduced (P = 0.02) on day 21 by supplementing SCFP. Treatment did not affect the production of proinflammatory cytokines, interleukin (IL)-1β (P > 0.19), and IL-6 (P > 0.12) in the whole blood in response to various toll-like receptor stimulants in vitro. Feeding SCFP enriched (P ≤ 0.05) plasma metabolic pathways, including citric acid cycle, pyrimidine metabolism, glycolysis/gluconeogenesis, retinol metabolism, and inositol phosphate metabolism pathways. In summary, supplementing liquid SCFP with subsequent dry SCFP enhanced ruminal total VFA production and reduced NH3-N concentration in the rumen. Furthermore, feeding SCFP enriched several important pathways in lipid, protein, and glucose metabolism, which may improve feed efficiency of energy and protein in Holstein steers.

Dual strategy for 13C-Metabolic flux analysis of central carbon and energy metabolism in Mammalian cells based on LC-isoMRM-MS

Central carbon and energy metabolism are the most concerned metabolic pathways in 13C-Metabolic flux analysis (13C-MFA). However, some α-keto acids, ribonucleoside triphosphate (NTPs) and deoxyribonucleoside triphosphate (dNTPs) involved in central carbon and energy metabolism pathways were unstable or reactive, leading to inaccurate metabolic flux analysis. To achieve accurate 13C-MFA of central carbon and energy metabolism, we proposed a dual strategy for the detection of 101 metabolites in glucose metabolism pathways. N-Methylphenylethylamine (MPEA) was utilized for derivatization of 4 carboxyl (α-keto acids) and 8 phosphate metabolites (NTPs and dNTPs). After derivatization, the MPEA derivatives were investigated to be stable for 4 weeks under 4 °C and detected with high intensity in ∼104 cells. On the other hand, we analyzed an additional 89 metabolites in central carbon and energy metabolic pathways were directly analyzed by liquid chromatography tandem mass spectrometry (LC-MRM-MS). The limit of detection (LODs) of our method were as low as 0.05 ng/mL and the linear range was at least two orders of magnitude with determination coefficient (R2) > 0.9701. The relative standard divisions (RSDs) of intra- and inter-day of 95% metabolites were below 20%. In addition, the isotope list of 82 detected metabolites in central carbon and energy metabolism were generated according to isotopologues and isotopomers for each metabolite resulting from 13C incorporation. Accurate assessment of mass isotopomer distributions (MIDs) of intracellular 13C-labeled metabolites was achieved in [U-13C]-glucose cultured HepG2 cells by our dual strategy. Finally, we performed MID analysis of 101 metabolites in central carbon and energy metabolism. Overall, this dual method is reproducible and robust for application on 13C-MFA and has a great potential for studying clinical isotope labeled samples.

Recent advances in LC-MS-based metabolomics for clinical biomarker discovery

The employment of liquid chromatography-mass spectrometry (LC-MS) untargeted and targeted metabolomics has led to the discovery of novel biomarkers and improved the understanding of various disease mechanisms. Numerous strategies have been reported to expand the metabolite coverage in LC-MS-untargeted and targeted metabolomics. To improve the sensitivity of low-abundance or poor-ionized metabolites for reducing the amount of clinical sample, chemical derivatization methods are used to target different functional groups. Proper sample preparation is beneficial for reducing the matrix effect, maintaining the stability of the LC-MS system, and increasing the metabolite coverage. Machine learning has recently been integrated into the workflow of LC-MS metabolomics to accelerate metabolite identification and data-processing automation, and increase the accuracy of disease classification and clinical outcome prediction. Due to the rapidly growing utility of LC-MS metabolomics in discovering disease markers, this review will address the recent advances in the field and offer perspectives on various strategies for expanding metabolite coverage, chemical derivatization, sample preparation, clinical disease markers, and machining learning for disease modeling.

Reductive carboxylation epigenetically instructs T cell differentiation

Protective immunity against pathogens or cancer is mediated by the activation and clonal expansion of antigen-specific naive T cells into effector T cells. To sustain their rapid proliferation and effector functions, naive T cells switch their quiescent metabolism to an anabolic metabolism through increased levels of aerobic glycolysis, but also through mitochondrial metabolism and oxidative phosphorylation, generating energy and signalling molecules1-3. However, how that metabolic rewiring drives and defines the differentiation of T cells remains unclear. Here we show that proliferating effector CD8+ T cells reductively carboxylate glutamine through the mitochondrial enzyme isocitrate dehydrogenase 2 (IDH2). Notably, deletion of the gene encoding IDH2 does not impair the proliferation of T cells nor their effector function, but promotes the differentiation of memory CD8+ T cells. Accordingly, inhibiting IDH2 during ex vivo manufacturing of chimeric antigen receptor (CAR) T cells induces features of memory T cells and enhances antitumour activity in melanoma, leukaemia and multiple myeloma. Mechanistically, inhibition of IDH2 activates compensating metabolic pathways that cause a disequilibrium in metabolites regulating histone-modifying enzymes, and this maintains chromatin accessibility at genes that are required for the differentiation of memory T cells. These findings show that reductive carboxylation in CD8+ T cells is dispensable for their effector response and proliferation, but that it mainly produces a pattern of metabolites that epigenetically locks CD8+ T cells into a terminal effector differentiation program. Blocking this metabolic route allows the increased formation of memory T cells, which could be exploited to optimize the therapeutic efficacy of CAR T cells.

Intensity-dependent mass search for improving metabolite database matches in chemical isotope labeling LC-QTOF-MS-based metabolomics

Liquid chromatography mass spectrometry (LC-MS) has been increasingly used for metabolome analysis. One of the critical steps in the LC-MS metabolome analysis workflow is related to metabolite identification. Among the measured parameters, peak mass is commonly used to search against a database for potential metabolite matches. Higher accuracy mass measurement allows the use of a narrower mass tolerance window for mass search. While various types of mass analyzers can routinely measure a peak mass with an error of less than a few ppm, mass measurement accuracy is not uniform for peaks with different intensities, particularly for quadrupole time-of-flight (QTOF) MS. Herein we present a simple and convenient method to determine the relation between peak intensity and mass error in LC-QTOF-MS-based metabolome analysis, followed by intensity-dependent mass search (IDMS) of a database for metabolite matches. This method is based on running a series of sodium formate mass calibrants, as part of the standard operating procedure (SOP) in LC-MS data acquisition, and then curve-fitting the measured mass errors and peak intensities. We show that, in two different quadrupole time-of-flight (QTOF) mass analyzers, mass accuracy is generally reduced as peak intensity decreases, which is independent of m/z values in the range commonly used for metabolite detection (e.g., m/z < 1000). We demonstrate the improvement in metabolite matches using IDMS in the analyses of dansyl labeled standards and human urine samples. We have implemented the IDMS method in the freely available MCID database at www.mycompoundid.org, which is composed of 8021 known human endogenous metabolites and their predicted metabolic products (375,809 compounds from one metabolic reaction and 10,583,901 compounds from two reactions).

Machine learning liquid chromatography retention time prediction model augments the dansylation strategy for metabolite analysis of urine samples

Herein, a standalone software equipped with a graphic user interface (GUI) is developed to predict liquid chromatography mass spectrometry (LC-MS) retention times (RTs) of dansylated metabolites. Dansylation metabolomics strategy developed by Li et al. narrows down a vast chemical space of metabolites into the metabolites containing amines and phenolic hydroxyls. Combined with differential isotope labeling, e.g., 12C-reagent labeled individual samples spiked with a 13C-reagent labeled reference or pooled sample, LC-MS analysis of the dansylated samples enables accurate relative quantification of all labeled metabolites. Herein, the LC-RTs for dansylated metabolites are predicted using an artificial neural network (ANN) machine-learning model. For the ANN modeling, 315 dansylated urine metabolites obtained from the DnsID database are used. The ANN LC-RT prediction model was reliable, with a mean absolute deviation of 0.74 min for the 30 min LC run. In the RT model, a deviation of more than 2 min was observed in only 3.2% of the total 315 metabolites, while a deviation of 1.5 min or more was observed in 11% of the metabolites. Furthermore, it was found that the LC-RT prediction was also reliable even for metabolites containing both amine and phenolic functional groups that can undergo dansylation on either one of the two functional groups, resulting in the generation of two isomeric forms. This RT-prediction model is embedded into a user-friendly GUI and can be used for identifying nontargeted dansylated metabolites with unknown RTs, along with accurate mass measurements. Furthermore, it is demonstrated that the developed software can help identify metabolites from a urine sample of an anonymous healthy pregnant woman.

An Integrated Multi-OMICS Approach Highlights Elevated Non-Esterified Fatty Acids Impact BeWo Trophoblast Metabolism and Lipid Processing

Maternal obesity and gestational diabetes mellitus (GDM) are linked with impaired placental function and early onset of non-communicable cardiometabolic diseases in offspring. Previous studies have highlighted that the dietary non-esterified fatty acids (NEFAs) palmitate (PA) and oleate (OA), key dietary metabolites associated with maternal obesity and GDM, are potential modulators of placental lipid processing. Using the BeWo cell line model, the current study integrated transcriptomic (mRNA microarray), metabolomic, and lipidomic readouts to characterize the underlying impacts of exogenous PA and OA on placental villous trophoblast cell metabolism. Targeted gas chromatography and thin-layer chromatography highlighted that saturated and monounsaturated NEFAs differentially impact BeWo cell lipid profiles. Furthermore, cellular lipid profiles differed when exposed to single and multiple NEFA species. Additional multi-omic analyses suggested that PA exposure is associated with enrichment in β-oxidation pathways, while OA exposure is associated with enrichment in anti-inflammatory and antioxidant pathways. Overall, this study further demonstrated that dietary PA and OA are important regulators of placental lipid metabolism. Encouraging appropriate dietary advice and implementing dietary interventions to maintain appropriate placental function by limiting excessive exposure to saturated NEFAs remain crucial in managing at-risk obese and GDM pregnancies.

Quantitative, Targeted Analysis of Gut Microbiota Derived Metabolites Provides Novel Biomarkers of Early Diabetic Kidney Disease in Type 2 Diabetes Mellitus Patients

Diabetic kidney disease (DKD) is one of the most debilitating complications of type 2 diabetes mellitus (T2DM), as it progresses silently to end-stage renal disease (ESRD). The discovery of novel biomarkers of early DKD becomes acute, as its incidence is reaching catastrophic proportions. Our study aimed to quantify previously identified metabolites from serum and urine through untargeted ultra-high-performance liquid chromatography coupled with electrospray ionization-quadrupole-time of flight-mass spectrometry (UHPLC-QTOF-ESI+-MS) techniques, such as the following: arginine, dimethylarginine, hippuric acid, indoxyl sulfate, p-cresyl sulfate, L-acetylcarnitine, butenoylcarnitine and sorbitol. The study concept was based on the targeted analysis of selected metabolites, using the serum and urine of 20 healthy subjects and 90 T2DM patients with DKD in different stages (normoalbuminuria-uACR < 30 mg/g; microalbuminuria-uACR 30-300 mg/g; macroalbuminuria-uACR > 300 mg/g). The quantitative evaluation of metabolites was performed with pure standards, followed by the validation methods such as the limit of detection (LOD) and the limit of quantification (LOQ). The following metabolites from this study resulted as possible biomarkers of early DKD: in serum-arginine, dimethylarginine, hippuric acid, indoxyl sulfate, butenoylcarnitine and sorbitol and in urine-p-cresyl sulfate.

In-depth profiling of di(2-ethylhexyl) phthalate metabolic footprints in rats using click chemistry-mass spectrometry probes

Di(2-ethylhexyl) phthalate (DEHP), the most widely used plasticizers in the world, has been regarded as an endocrine disrupting chemical with serious adverse health outcomes. Accumulating evidence strongly suggests that the undesirable biological effects of DEHP are meditated by its metabolites rather than itself. However, the metabolic footprints of DEHP in vivo are still unclear. Here we developed a click chemistry-assisted mass spectrometry (CC-MS) strategy for in-depth profiling DEHP metabolites in rats. An alkyne-modified DEHP analogue (alkyne-DEHP) was synthesized as a tracer for in vivo tracing, and a pair of MS probes (4-azido-nphenylbenzamide, 4-ANPA, and its deuterated reagent d₅-4-ANPA) were prepared to specifically label the alkyne-DEHP metabolites, and prominently improve their detection sensitivity and selectivity. Using the CC-MS strategy, we successfully screened 247 alkyne-DEHP metabolites from rat urine, feces, and serum, including many unrevealed metabolites, such as oxidized phthalate diester metabolites and glucuronides of phthalate monoester metabolites. The discovery of new DEHP metabolites provides additional insights for understanding the metabolism of DEHP, which may be beneficial in exploring the mechanism underlying DEHP induced-toxicity in the future.

Hyphenation of Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) with separation methods: The art of compromises and the possible - A review

This review provides an overview of the online hyphenation of Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FT-ICR MS) with separation methods to date. The online coupling between separation techniques (gas and liquid chromatography, capillary electrophoresis) and FT-ICR MS essentially raises questions of compromise and is not look as straightforward as hyphenation with other analyzers (QTOF-MS for instance). FT-ICR MS requires time to reach its highest resolving power and accuracy in mass measurement capabilities whereas chromatographic and electrophoretic peaks are transient. In many applications, the strengths and the weaknesses of each technique are balanced by their hyphenation. Untargeted "Omics" (e.g. proteomics, metabolomics, petroleomics, …) is one of the main areas of application for FT-ICR MS hyphenated to online separation techniques because of the complexity of the sample. FT-ICR MS achieves the required high mass measurement accuracy to determine accurate molecular formulae and resolution for isobar distinction. Meanwhile separation techniques highlight isomers and reduce the ion suppression effects extending the dynamic range. Even if the implementation of FT-ICR MS hyphenated with online separation methods is a little trickier (the art of compromise), this review shows that it provides unparalleled results to the scientific community (the art of the possible), along with raising the issue of its future in the field with the relentless technological progress.

Metabolomics profiling distinctively identified end-stage renal disease patients from chronic kidney disease patients

Chronic kidney disease (CKD) is a serious public health problem characterized by progressive kidney function loss leading to end-stage renal disease (ESRD) that demands dialysis or kidney transplantation. Early detection can prevent or delay progression to ESRD. The study aimed to gain new insights into the perturbed biochemical reactions and to identify novel distinct biomarkers between ESRD and CKD. Serum samples of 32 patients with ESRD (n = 13) and CKD (n = 19) were analyzed using chemical isotope labeling liquid chromatography-mass spectrometry metabolomics approach. A total of 193 metabolites were significantly altered in ESRD compared to CKD and were mainly involved in aminoacyl-tRNA biosynthesis, branched-chain amino acid (BCAA) biosynthesis, taurine metabolism, and tryptophan metabolism. Three kynurenine derivatives, namely, 2-aminobenzoic acid, xanthurenic acid, and hydroxypicolinic acid were upregulated in ESRD compared to CKD due to the significant decrease in glomerular filtration rate with the progression of CKD to ESRD. N-Hydroxy-isoleucine, 2-aminobenzoic acid, and picolinic acid yielded AUC > 0.99 when analyzed using Receiver Operating Characteristic (ROC) analysis. Our findings suggest that inhibiting the kynurenine pathway might be a promising target to delay CKD progression and that metabolites with high discriminative ability might serve as potential prognostic biomarkers to monitor the progression of CKD to ESRD or used in combination with current markers to indicate the status of kidney damage better.

Quantification of Serum Metabolites in Early Colorectal Adenomas Using Isobaric Labeling Mass Spectrometry

A major challenge in reducing the death rate of colorectal cancer is to screen patients using low-invasive testing. A blood test shows a high compliance rate with reduced invasiveness. In this work, a multiplex isobaric tag labeling strategy coupled with mass spectrometry is adopted to relatively quantify primary and secondary amine-containing metabolites in serum for the discovery of metabolite level changes of colorectal cancer. Serum samples from patients at different risk statuses and colorectal cancer growth statuses are studied. Metabolite identification is based on accurate mass matching and/or retention time of labeled metabolite standards. We quantify 40 metabolites across all the serum samples, including 18 metabolites validated with standards. We find significantly decreased levels of threonine and asparagine in the patients with growing adenomas or high-risk adenomas (p < 0.05). Glutamine levels decrease in patients with adenomas of unknown growth status or high-risk adenomas. In contrast, arginine levels are elevated in patients with low-risk adenoma. Receiver operating characteristic analysis shows high sensitivity and specificity of these metabolites for detecting growing adenomas. Based on these results, we conclude that a few metabolites identified here might contribute to distinguishing colorectal patients with growing adenomas from normal individuals and patients with unknown growth status of adenomas.

Metabolic deuterium oxide (D2O) labeling in quantitative omics studies: A tutorial review

Mass spectrometry (MS) is an invaluable tool for sensitive detection and characterization of individual biomolecules in omics studies. MS combined with stable isotope labeling enables the accurate and precise determination of quantitative changes occurring in biological samples. Metabolic isotope labeling, wherein isotopes are introduced into biomolecules through biosynthetic metabolism, is one of the main labeling strategies. Among the precursors employed in metabolic isotope labeling, deuterium oxide (D₂O) is cost-effective and easy to implement in any biological systems. This tutorial review aims to explain the basic principle of D₂O labeling and its applications in omics research. D₂O labeling incorporates D into stable C-H bonds in various biomolecules, including nucleotides, proteins, lipids, and carbohydrates. Typically, D₂O labeling is performed at low enrichment of 1%-10% D₂O, which causes subtle changes in the isotopic distribution of a biomolecule, instead of the complete separation between labeled and unlabeled samples in a mass spectrum. D₂O labeling has been employed in various omics studies to determine the metabolic flux, turnover rate, and relative quantification. Moreover, the advantages and challenges of D₂O labeling and its future prospects in quantitative omics are discussed. The economy, versatility, and convenience of D₂O labeling will be beneficial for the long-term omics studies for higher organisms.

Non-Targeted Metabolomic Profiling Identifies Metabolites with Potential Antimicrobial Activity from an Anaerobic Bacterium Closely Related to Terrisporobacter Species

This work focused on the metabolomic profiling of the conditioned medium (FS03CM) produced by an anaerobic bacterium closely related to Terrisporobacter spp. to identify potential antimicrobial metabolites. The metabolome of the conditioned medium was profiled by two-channel Chemical Isotope Labelling (CIL) LC-MS. The detected metabolites were identified or matched by conducting a library search using different confidence levels. Forty-eight significantly changed metabolites were identified with high confidence after the growth of isolate FS03 in cooked meat glucose starch (CMGS) medium. Some of the secondary metabolites identified with known antimicrobial activities were 4-hydroxyphenyllactate, 3-hydroxyphenylacetic acid, acetic acid, isobutyric acid, valeric acid, and tryptamine. Our findings revealed the presence of different secondary metabolites with previously reported antimicrobial activities and suggested the capability of producing antimicrobial metabolites by the anaerobic bacterium FS03.

The metabolic and lipidomic profiling of the effects of tracheal occlusion in a rabbit model of congenital diaphragmatic hernia

PURPOSE

Fetal tracheal occlusion (TO) reverses the pulmonary hypoplasia associated with congenital diaphragmatic hernia (CDH), but its mechanism of action remains poorly understood. 'Omic' readouts capture metabolic and lipid processing function, which aid in understanding CDH and TO metabolic mechanisms.

METHODS

CDH was created in fetal rabbits at 23 days, TO at 28 days and lung collection at 31 days (Term ∼32 days). Lung-body weight ratio (LBWR) and mean terminal bronchiole density (MTBD) were determined. In a cohort, left and right lungs were collected, weighed, and samples homogenized, and extracts collected for non-targeted metabolomic and lipidomic profiling via LC-MS and LC-MS/MS, respectively.

RESULTS

LBWR was significantly lower in CDH while CDH + TO was similar to controls (p = 0.003). MTBD was significantly higher in CDH fetuses and restored to control and sham levels in CDH + TO (p < 0.001). CDH and CDH + TO resulted in significant differences in metabolome and lipidome profiles compared to sham controls. A significant number of altered metabolites and lipids between the controls and CDH groups and the CDH and CDH + TO fetuses were identified. Significant changes in the ubiquinone and other terpenoid-quinone biosynthesis pathway and the tyrosine metabolism pathway were observed in CDH + TO.

CONCLUSION

CDH + TO reverses pulmonary hypoplasia in the CDH rabbit, in association with a specific metabolic and lipid signature. A synergistic untargeted 'omics' approach provides a global signature for CDH and CDH + TO, highlighting cellular mechanisms among lipids and other metabolites, enabling comprehensive network analysis to identify critical metabolic drivers in disease pathology and recovery.

TYPE OF STUDY

Basic Science, Prospective.

LEVEL OF EVIDENCE

II.

The impact of hyperglycemia upon BeWo trophoblast cell metabolic function: A multi-OMICS and functional metabolic analysis

Pre-existing and gestationally-developed diabetes mellitus have been linked with impairments in placental villous trophoblast cell metabolic function, that are thought to underlie the development of metabolic diseases early in the lives of the exposed offspring. Previous research using placental cell lines and ex vivo trophoblast preparations have highlighted hyperglycemia is an important independent regulator of placental function. However, it is poorly understood if hyperglycemia directly influences aspects of placental metabolic function, including nutrient storage and mitochondrial respiration, that are altered in term diabetic placentae. The current study examined metabolic and mitochondrial function as well as nutrient storage in both undifferentiated cytotrophoblast and differentiated syncytiotrophoblast BeWo cells cultured under hyperglycemia conditions (25 mM glucose) for 72 hours to further characterize the direct impacts of placental hyperglycemic exposure. Hyperglycemic-exposed BeWo trophoblasts displayed increased glycogen and triglyceride nutrient stores, but real-time functional readouts of metabolic enzyme activity and mitochondrial respiratory activity were not altered. However, specific investigation into mitochondrial dynamics highlighted increased expression of markers associated with mitochondrial fission that could indicate high glucose-exposed trophoblasts are transitioning towards mitochondrial dysfunction. To further characterize the impacts of independent hyperglycemia, the current study subsequently utilized a multi-omics approach and evaluated the transcriptomic and metabolomic signatures of BeWo cytotrophoblasts. BeWo cytotrophoblasts exposed to hyperglycemia displayed increased mRNA expression of ACSL1, HSD11B2, RPS6KA5, and LAP3 and reduced mRNA expression of CYP2F1, and HK2, concomitant with increased levels of: lactate, malonate, and riboflavin metabolites. These changes highlighted important underlying alterations to glucose, glutathione, fatty acid, and glucocorticoid metabolism in BeWo trophoblasts exposed to hyperglycemia. Overall, these results demonstrate that hyperglycemia is an important independent regulator of key areas of placental metabolism, nutrient storage, and mitochondrial function, and these data continue to expand our knowledge on mechanisms governing the development of placental dysfunction.

Compound Identification Strategies in Mass Spectrometry-Based Metabolomics and Pharmacometabolomics

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

Au Nanoparticles (NPs) Decorated Co Doped ZnO Semiconductor (Co400-ZnO/Au) Nanocomposites for Novel SERS Substrates

Au nanoparticles were decorated on the surface of Co-doped ZnO with a certain ratio of Co²⁺/Co³⁺ to obtain a novel semiconductor-metal composite. The optimal substrate, designated as Co₄₀₀-ZnO/Au, is beneficial to the promotion of separation efficiency of electron and hole in a semiconductor excited under visible laser exposure, which the enhances localized surface plasmon resonance (LSPR) of the Au nanoparticles. As an interesting finding, during Co doping, quantum dots of ZnO are generated, which strengthen the strong semiconductor metal interaction (SSSMI) effect. Eventually, the synergistic effect effectively advances the surface enhancement Raman scattering (SERS) performance of Co₄₀₀-ZnO/Au composite. The enhancement mechanism is addressed in-depth by morphologic characterization, UV-visible, X-ray diffraction, photoluminescence, X-ray photoelectron spectroscopy, density functional theory, and finite difference time domain (FDTD) simulations. By using Co₄₀₀-ZnO/Au, SERS detection of Rhodamine 6G presents a limit of detection (LOD) of 1 × 10^-9 M. As a real application, the Co₄₀₀-ZnO/Au-based SERS method is utilized to inspect tyramine in beer and the detectable concentration of 1 × 10^-8 M is achieved. In this work, the doping strategy is expected to realize a quantum effect, triggering a SSSMI effect for developing promising SERS substrates in future.

Metabolomics Profiling of Nephrotic Syndrome towards Biomarker Discovery

Nephrotic syndrome (NS) is a kidney illness characterized by excessive proteinuria, hypoalbuminemia, edema, and hyperlipidemia, which may lead to kidney failure and necessitate renal transplantation. End-stage renal disease, cardiovascular issues, and mortality are much more common in those with NS. Therefore, the present study aimed to identify potential new biomarkers associated with the pathogenesis and diagnosis of NS. The liquid chromatography–mass spectrometry (LC–MS) metabolomics approach was applied to profile the metabolome of human serum of patients with NS. A total of 176 metabolites were significantly altered in NS compared to the control. Arginine, proline, and tryptophan metabolism; arginine, phenylalanine, tyrosine, and tryptophan biosynthesis were the most common metabolic pathways dysregulated in NS. Furthermore, alanyl-lysine and isoleucyl-threonine had the highest discrimination between NS and healthy groups. The candidate biomarkers may lead to understanding the possible metabolic alterations associated with NS and serve as potential diagnostic biomarkers.

Metabolic perturbations in human hepatocytes induced by bis (2-ethylhexyl)-2,3,4,5-tetrabromophthalate exposure: Insights from high-coverage quantitative metabolomics

Bis (2-ethylhexyl)-2,3,4,5-tetrabromophthalate (TBPH) is an extensively used novel brominated flame retardant that is present ubiquitously in the environment and in biota. However, there is inadequate data on its potential hepatotoxicity to humans. In this study, high-coverage quantitative metabolomics based on ¹²C-/¹³C-dansylation labeling LC-MS was performed for the first time to assess the metabolic perturbations and underlying mechanisms of TBPH on human hepatocytes. HepG2 cells were exposed to TBPH at dosages of 0.1,1,10 μM for 24 or 72 h. Overall, 1887 and 1364 amine/phenol-containing metabolites were relatively quantified in cells and culture supernatant. Our results revealed that exposure to 0.1 μM TBPH showed little adverse effects, whereas exposure to 10 μM TBPH for 24 h enhanced intracellular protein catabolism and disrupted energy and lipid homeostasis-related pathways such as histidine metabolism, pantothenate and CoA biosynthesis, alanine, aspartate and glutamate metabolism. Nevertheless, most of these perturbations returned to the same levels as controls after 72 h of exposure. Additionally, prolonged TBPH exposure increased oxidative stress, as reflected by marked disturbances in taurine metabolism. This study sensitively revealed the dysregulations of intracellular and extracellular metabolome induced by TBPH, providing a comprehensive understanding of metabolic responses of cells to novel brominated flame retardants.

Instrument-type effects on chemical isotope labeling LC-MS metabolome analysis: Quadrupole time-of-flight MS vs. Orbitrap MS

Chemical isotope labeling (CIL) LC-MS is a powerful tool for metabolome analysis with markedly improved metabolomic coverage and quantification accuracy over the conventional LC-MS technique. In addition, with differential isotope labeling, each labeled metabolite is detected as a peak pair in the mass spectra, offering the possibility of differentiating true metabolite peaks from the singlet noise or background peaks. In this study, we examined the effects of instrument type on the detectability of true metabolites with a focus on the comparison of quadrupole time-of-flight (QTOF) and Orbitrap mass spectrometers. Using the same ultra-high-performance liquid chromatography setup and optimized running conditions for QTOF and Orbitrap, we compared the total number of peak pairs detected and identified from the two instruments using human urine and serum as the test samples. Many common peak pairs were detected from the two instruments; however, there were a significant number of unique peak pairs detected in each type of instrument. By combining the datasets obtained using QTOF and Orbitrap, the total number of peak pairs detected could be significantly increased. We also examined the effect of mass resolving power on peak pair detection in Orbitrap (60,000 vs. 120,000 resolution). The observed differences in peak pair detectability were much less than those of QTOF vs. Orbitrap. However, the type of peak pairs detected using different resolutions could be somewhat different, offering the possibility of increasing the overall number of peak pairs by combining the two datasets obtained at two different resolutions. The results from this study clearly indicate that instrument type can have a profound effect on metabolite detection in CIL LC-MS. Therefore, comparison of metabolome data generated using different instruments needs to be carefully done. Moreover, future research (e.g., hardware modifications) is warranted to minimize the differences in order to generate more reproducible metabolome data from different types of instruments.

Analysis of endogenous metabolites using multifunctional derivatization and capillary RPLC-MS

Heterogeneity in metabolite structure and charge state complicates their analysis in electrospray mass spectrometry (ESI-MS). Complications such as diminished signal response and quantitation can be reduced by sequential dual-stage derivatization and capillary RP LC-ESI-MS analysis. Our sequential dual-stage chemical derivatization reacts analyte primary amine and hydroxyl groups with a linear acyl chloride head containing a tertiary amine moiety. Analyte carboxylate groups are then coupled to a linear amine tag with a tertiary amine moiety. This increase in the number of tags on analytes increases analyte proton affinity and hydrophobicity. We derivatized 250 metabolite standards which on average improved signal to noise by >44-fold, with an average limit of detection of 66 nM and R² of 0.98. This system detected 107 metabolites from 18 BAECs, 111 metabolites from human urine, and 153 from human serum based on retention time, exact mass, and MS/MS matches from a derivatized standard library. As a proof of concept, aortic endothelial cells were treated with epinephrine and analyzed by the dual-stage derivatization. We observed changes in 32 metabolites with many increases related to energy metabolism, specifically in the TCA cycle. A decrease in lactate levels and corresponding increase in pyruvate levels suggest that epinephrine causes a movement away from glycolytic reliance on energy and a shift towards the more efficient TCA respiration for increasing energy.

Determination of trace concentrations of simple phenols in ambient PM samples

Phenols are hazardous, but yet ubiquitous in the environment, including in atmospheric aerosols due to combustion emissions. There, phenols are subjected to secondary transformations, producing even more toxic nitrophenolic air pollutants. However, primary simple phenols, i.e. those containing only hydroxyl, methyl and methoxy substituents are not easy to detect. Trace concentrations, semi-volatile character and poorly ionizable functional groups prevent us from their determination by the most common analytical techniques, such as gas and liquid chromatography with mass spectrometric detection (GC/LC-MS). Here, we present a new derivatization method for MS/MS detection with positive ion electrospray ionization (+ESI-MS/MS) of simple phenols in atmospheric particulate matter (PM) extracts. The method is sensitive, selective, and robust, and requires no sample concentration step, which is critical due to the volatile character of the target analytes. After derivatization with dansyl chloride, phenol, catechol, cresols and guaiacol were detected in urban PM samples from Ljubljana, Slovenia. This method finally enables to study the abundance of primary phenols in atmospheric PM from different sources, which will improve understanding of secondary aerosol (trans)formation pathways and allow for more targeted mitigation strategies in respect to airborne phenolic pollutants.

Multi-OMICs analysis reveals metabolic and epigenetic changes associated with macrophage polarization

Macrophages (MФ) are an essential immune cell for defense and repair that travel to different tissues and adapt based on local stimuli. A critical factor that may govern their polarization is the crosstalk between metabolism and epigenetics. However, simultaneous measurements of metabolites, epigenetics, and proteins (phenotype) have been a major technical challenge. To address this, we have developed a novel triomics approach using mass spectrometry to comprehensively analyze metabolites, proteins, and histone modifications in a single sample. To demonstrate this technique, we investigated the metabolic-epigenetic-phenotype axis following polarization of human blood–derived monocytes into either ‘proinflammatory M1-’ or ‘anti-inflammatory M2-’ MФs. We report here a complex relationship between arginine, tryptophan, glucose, and the citric acid cycle metabolism, protein and histone post-translational modifications, and human macrophage polarization that was previously not described. Surprisingly, M1-MФs had globally reduced histone acetylation levels but high levels of acetylated amino acids. This suggests acetyl-CoA was diverted, in part, toward acetylated amino acids. Consistent with this, stable isotope tracing of glucose revealed reduced usage of acetyl-CoA for histone acetylation in M1-MФs. Furthermore, isotope tracing also revealed MФs uncoupled glycolysis from the tricarboxylic acid cycle, as evidenced by poor isotope enrichment of succinate. M2-MФs had high levels of kynurenine and serotonin, which are reported to have immune-suppressive effects. Kynurenine is upstream of de novo NAD⁺ metabolism that is a necessary cofactor for Sirtuin-type histone deacetylases. Taken together, we demonstrate a complex interplay between metabolism and epigenetics that may ultimately influence cell phenotype.

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.

The first plastic produced, but the latest studied in microplastics research: The assessment of leaching, ecotoxicity and bioadhesion of Bakelite microplastics

Bakelite, the first synthetic plastic, is a rather unexplored material in the field of ecotoxicology, despite its long production and use. The aim of this study was to investigate the ecotoxicity of Bakelite microplastics (before and after leaching) and its leachates on four aquatic organisms: the crustacean Daphnia magna, the plant Lemna minor, the bacterium Allivibrio fischeri and the alga Pseudokirchneriella subcapitata. Bakelite microplastics before and after leaching and leachates affected all organisms, but to varying degrees. Leachates showed increased ecotoxicity to Daphnia magna, while Pseudokirchneriella subcapitata was more affected by particles. For Lemna minor and Allivibrio fischeri, the effects of particles before leaching and leachate were comparable, while the negative effect of particles after leaching was minimal or not present. All leachates were analysed, and phenol and phenol-like compounds were the predominant organics found. In addition, bioadhesion of Bakelite microplastics to the surface of Daphnia magna and Lemna minor was confirmed, but the particles were mainly weakly adhered. Results of this study suggest that, in addition to the recently studied microplastics from consumer products (e.g. from polyethylene and polystyrene), microplastics from industrial plastics such as Bakelite may be of increasing concern, primarily due to leaching of toxic chemicals.

Isotope Ratio Outlier Analysis (IROA) for HPLC-TOFMS-Based Metabolomics of Human Urine

Metabolic fingerprinting by mass spectrometry aims at the comprehensive, semiquantitative analysis of metabolites. Isotope dilution, if successfully implemented, may provide a more reliable, relative quantification. Therefore, the 13C labeled yeast extract of the IROA TruQuant kit was added as an internal standard (IS) to human urine samples measured in full-scan mode on a high-performance liquid chromatography-time-of-flight mass spectrometer (HPLC-TOFMS) system. The isotope ratio approach enabled the analysis of 112 metabolites. The correlation with reference data did not improve significantly using 12C/13C ratios compared to absolute 12C peak areas. Moreover, using an intricate 13C-labeled standard increased the complexity of the mass spectra, which made correct signal annotation more challenging. On the positive side, the ratio approach helps to reduce batch effects, but it does not perform better than computational methods such as the "removebatcheffect" function in the R package Limma.

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.

Ultrasensitive Determination of Sugar Phosphates in Trace Samples by Stable Isotope Chemical Labeling Combined with RPLC-MS

Sugar phosphates are important metabolic intermediates in organisms and play a vital role in energy and central carbon metabolism. Profiling of sugar phosphates is of great significance but full of challenges due to their high structural similarity and low sensitivities in liquid chromatography (LC)-mass spectrometry (MS). In this study, we developed a novel stable isotope chemical labeling combined with the reversed-phase (RP)LC-MS method for ultrasensitive determination of sugar phosphates at the single-cell level. By chemical derivatization with 2-(diazo-methyl)-N-methyl-N-phenyl-benzamide (2-DMBA) and d₅-2-DMBA, sugar phosphate isomers can obtain better separation and identification, and the detection sensitivities of sugar phosphates increased by 3.5-147 folds. The obtained limits of detection of sugar phosphates ranged from 5 to 16 pg/mL. Using this method, we achieved ultrasensitive and accurate quantification of 12 sugar phosphates in different trace biological samples. Benefiting from the improved separation and detection sensitivity, we successfully quantified five sugar phosphates (d-glucose 1-phosphate, d-mannose 6-phosphate, d-fructose 6-phosphate, d-glucose 6-phosphate, and seduheptulose 7-phosphate) in a single protoplast of Arabidopsis thaliana.

Molecular Probes, Chemosensors, and Nanosensors for Optical Detection of Biorelevant Molecules and Ions in Aqueous Media and Biofluids

Synthetic molecular probes, chemosensors, and nanosensors used in combination with innovative assay protocols hold great potential for the development of robust, low-cost, and fast-responding sensors that are applicable in biofluids (urine, blood, and saliva). Particularly, the development of sensors for metabolites, neurotransmitters, drugs, and inorganic ions is highly desirable due to a lack of suitable biosensors. In addition, the monitoring and analysis of metabolic and signaling networks in cells and organisms by optical probes and chemosensors is becoming increasingly important in molecular biology and medicine. Thus, new perspectives for personalized diagnostics, theranostics, and biochemical/medical research will be unlocked when standing limitations of artificial binders and receptors are overcome. In this review, we survey synthetic sensing systems that have promising (future) application potential for the detection of small molecules, cations, and anions in aqueous media and biofluids. Special attention was given to sensing systems that provide a readily measurable optical signal through dynamic covalent chemistry, supramolecular host-guest interactions, or nanoparticles featuring plasmonic effects. This review shall also enable the reader to evaluate the current performance of molecular probes, chemosensors, and nanosensors in terms of sensitivity and selectivity with respect to practical requirement, and thereby inspiring new ideas for the development of further advanced systems.

Metabolomic Alterations in the Tear Fluids of Patients With Superior Limbic Keratoconjunctivitis

Purpose

Superior limbic keratoconjunctivitis (SLK) is a bilateral, chronic inflammatory disease that recurs for up to several years; however, the fundamental processes involved in its pathogenic mechanisms remain unknown. We aimed to investigate the metabolomic alterations in the tear fluids of patients with superior limbic keratoconjunctivitis (SLK) compared with those of healthy volunteers (Ctrl group).

Methods

We performed a cross-sectional study involving 42 subjects. Tear fluid was taken from one eye of 24 SLK patients (40.13 ± 14.55 years, 83.33% female) and 18 healthy volunteers (Ctrl, 39.89 ± 9.2 years, 72.22% female) using Schirmer strips. After the liquid extraction of tear metabolites, samples were infused into the QE HFX Orbitrap mass spectrometer in both positive and negative ion mode. Metabolites were quantitatively analyzed and matched with entries in the HMDB database. Metabolic differences between the SLK group and the control group were identified based on multivariate statistical analysis. Open database sources, including SMPDB and MetaboAnalyst, were used to identify metabolic pathways.

Results

Among 179 metabolites retained for annotation, 133 metabolites were finally identified, among which 50 were found to be significantly changed in SLK patients. Of these 50 metabolites, 31 metabolites significantly increased and 19 metabolites decreased in SLK patients. The altered metabolites are mainly involved in α linolenic acid and linoleic acid metabolism, ketone body metabolism, butyrate metabolism, mitochondrial electron transport chain, carnitine synthesis, and so on. The most significantly changed pathway was linoleic acid metabolism. To explore the utility of tear biomarkers, a model combining 9 metabolites (phenol, ethyl glucuronide, eicosapentaenoic acid, 12-keto-leukotriene B4, linoleic acid, hypoxanthine, triethanolamine, 1-nitrohexane, and terephthalic acid) was selected as a candidate biomarker.

Conclusion

The results reveal that SLK has a specific metabolomic profile, of which some key elements can serve as potential biomarkers of SLK for diagnostic and prognostic purposes. The findings of this study are novel and provide a basis for further investigations of the mechanism of SLK.

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.

Development of a novel analytical method for inflammation and immunity-related metabolites in serum based on liquid chromatography tandem mass spectrometry

Some metabolites have been found to play key roles in inflammation and immunity events that are associated with diseases such as cancer, diabetes and cytokine release syndrome. Characterization upon the inflammation and immunity-related metabolites (IIMs) will be helpful to the assessment of related pathological states. Although these metabolites have been partially reported in previous studies, the methods for specific measurement of them remain lacking. In the present study, a liquid chromatography - mass spectrometry based method was developed for the targeted analyses of 45 IIMs including amino acids, organic acids, phosphatidylcholines (PCs), polyunsaturated fatty acids and hormones selected based on the literature knowledge. Direct extraction with dansyl-chloride in acetonitrile was proved to be the most efficient and time-saving strategy, in which precipitation, extraction and derivatization were integrated. IIMs derivatized for 4 min and quenched for 2 min revealed the most comprehensive abundance. Based on the defined conditions, all the IIMs had a low limit of detection smaller than 1 ng/mL with the linear range greater than three orders of magnitude. The relative standard derivations of intra-day and inter-day precisions were ranged from 2.2% to 13.4% and 1.7% to 19.5%, respectively. The recovery rates and accuracy in low concentration were 98.9% ± 5.6% and 106.7% ± 11.6%, in medium concentration were 97.1% ± 6.8% and 106.9% ± 9.5%, and in high concentration were 98.4% ± 8.9% and 98.1% ± 8.1%, respectively. Matrix effect and stability were ranged from -37.8% to 35.6% and 2.9% to 14.2%, respectively. To show the usefulness of the method, serum IIMs in hepatitis B virus (HBV) infected patients and healthy subjects were determined and compared. Bile acids, lipoxygenase-mediated lipid mediators and non-enzymatic products showed global increases, whereas most of LysoPCs and cyclooxygenase-mediated prostaglandin D2 decreased in HBV serum samples. This study provided a robust approach for the characterization of IIMs.