Comprehensive Targeted Metabolomic Assay for Urine Analysis

Metabolomic identification of predictive and early biomarkers of cisplatin-induced acute kidney injury in adult head and neck cancer patients

AIM

Cisplatin causes acute kidney injury (AKI) in approximately one third of patients. Serum creatinine and urinary output are poor markers of cisplatin-induced AKI. Metabolomics was utilized to identify predictive or early diagnostic biomarkers of cisplatin-induced AKI.

METHODS

Thirty-one adult head and neck cancer patients receiving cisplatin (dose ≥70 mg/m2) were recruited for metabolomics analysis. Urine and serum samples were collected prior to cisplatin (pre), 24-48 h after cisplatin (24-48 h) and 5-14 days (post) after cisplatin. Based on serum creatinine concentrations measured at the post timepoint, 11/31 patients were classified with clinical AKI. Untargeted metabolomics was performed using liquid chromatography-mass spectrometry (LC-MS).

RESULTS

Metabolic discrimination was observed between "AKI" patients and "no AKI" patients at all timepoints. Urinary glycine, hippuric acid sulfate, 3-hydroxydecanedioc acid and suberate were significantly different between AKI patients and no AKI patients prior to cisplatin infusion. Urinary glycine and hippuric acid sulfate were lower (-2.22-fold and -8.85-fold), whereas 3-hydroxydecanedioc acid and suberate were higher (3.62-fold and 1.91-fold) in AKI patients relative to no AKI patients. Several urine and serum metabolites were found to be altered 24-48 h following cisplatin infusion, particularly metabolites involved with mitochondrial energetics.

CONCLUSIONS

We propose glycine, hippuric acid sulfate, 3-hydroxydecanedioc acid and suberate as predictive biomarkers of predisposition to cisplatin-induced AKI. Metabolites indicative of mitochondrial dysfunction may serve as early markers of subclinical AKI.

Functional assays reflective of cancer hallmarks in BT-549 cells are not impacted by media supplemented with exercise-trained plasma

Media supplemented with sera from acutely exercised men has been shown to have 'anti-cancer' effects on prostate and breast cancer cell lines. This study investigated whether media supplemented with plasma samples taken at rest (≥30 h since the most recent exercise session) from men who were endurance-trained (END), strength-trained (STR) or recreationally active controls (CON) impacted the results of four assays that mimic hallmarks of cancer (proliferation, migration, extracellular matrix invasion and anoikis resistance) in the BT-549 breast cancer cell line. Compared to control conditions of either serum-free media or fetal bovine serum as appropriate, BT-549 cells cultured with plasma-supplemented media regardless of group resulted in greater cell proliferation (∼20-50%) and cell migration (∼15-20%), and lower extracellular matrix invasion (∼10-20%) and anoikis resistance (∼15-20%). Supplementing media with plasma from END or STR did not impact any outcomes of these assays compared to plasma from CON. Media supplemented with human plasma can impact functional assays reflective of cancer hallmarks in BT-549 cells, but effects of exercise on proliferation, migration, extracellular matrix invasion and anoikis resistance were not evident in resting blood samples of individuals with a prior history of exercise training.

Therapeutic application of nicotinamide: As a potential target for inhibiting fibrotic scar formation following spinal cord injury

AIM

We aimed to confirm the inhibitory effect of nicotinamide on fibrotic scar formation following spinal cord injury in mice using functional metabolomics.

METHODS

We proposed a novel functional metabolomics strategy to establish correlations between gene expression changes and metabolic phenotypes using integrated multi-omics analysis. Through the integration of quantitative metabolites analysis and assessments of differential gene expression, we identified nicotinamide as a functional metabolite capable of inhibiting fibrotic scar formation and confirmed the effect in vivo using a mouse model of spinal cord injury. Furthermore, to mimic fibrosis models in vitro, primary mouse embryonic fibroblasts and spinal cord fibroblasts were stimulated by TGFβ, and the influence of nicotinamide on TGFβ-induced fibrosis-associated genes and its underlying mechanism were examined.

RESULTS

Administration of nicotinamide led to a reduction in fibrotic lesion area and promoted functional rehabilitation following spinal cord injury. Nicotinamide effectively downregulated the expression of fibrosis genes, including Col1α1, Vimentin, Col4α1, Col1α2, Fn1, and Acta2, by repressing the TGFβ/SMADs pathway.

CONCLUSION

Our functional metabolomics strategy identified nicotinamide as a metabolite with the potential to inhibit fibrotic scar formation following SCI by suppressing the TGFβ/SMADs signaling. This finding provides new therapeutic strategies and new ideas for clinical treatment.

Development of a point-of-care colorimetric metabolomic sensor platform

Metabolomics is the large-scale study of small molecule metabolites within a biological system. It has applications in measuring dietary intake, predicting heart disease risk, and diagnosing cancer. Metabolites are often measured using high-end analytical tools such as mass spectrometers or large spectrophotometers. However, due to their size, cost, and need for skilled operators, using such equipment at the bedside is not practical. To address this issue, we have developed a low-cost, portable, optical color sensor platform for metabolite detection. This platform includes LEDs, sensors, microcontrollers, a power source, and a Bluetooth chip enclosed within a 3D-printed light-tight case. We evaluated the color sensor's performance using both a range of dyed water samples as well as well-established colorimetric reactions for specific metabolite detection. The sensor accurately measured creatinine, L-carnitine, ascorbate, and succinate well within normal human urine levels with accuracy and sensitivity equal to or better than a standard laboratory spectrophotometer. Our color sensor offers a cost-effective, portable alternative for measuring metabolites via colorimetric assays, thereby enabling low-cost, point-of-care metabolite testing.

Olaris Global Panel (OGP): A Highly Accurate and Reproducible Triple Quadrupole Mass Spectrometry-Based Metabolomics Method for Clinical Biomarker Discovery

Mass spectrometry (MS)-based clinical metabolomics is very promising for the discovery of new biomarkers and diagnostics. However, poor data accuracy and reproducibility limit its true potential, especially when performing data analysis across multiple sample sets. While high-resolution mass spectrometry has gained considerable popularity for discovery metabolomics, triple quadrupole (QqQ) instruments offer several benefits for the measurement of known metabolites in clinical samples. These benefits include high sensitivity and a wide dynamic range. Here, we present the Olaris Global Panel (OGP), a HILIC LC-QqQ MS method for the comprehensive analysis of ~250 metabolites from all major metabolic pathways in clinical samples. For the development of this method, multiple HILIC columns and mobile phase conditions were compared, the robustness of the leading LC method assessed, and MS acquisition settings optimized for optimal data quality. Next, the effect of U-13C metabolite yeast extract spike-ins was assessed based on data accuracy and precision. The use of these U-13C-metabolites as internal standards improved the goodness of fit to a linear calibration curve from r2 < 0.75 for raw data to >0.90 for most metabolites across the entire clinical concentration range of urine samples. Median within-batch CVs for all metabolite ratios to internal standards were consistently lower than 7% and less than 10% across batches that were acquired over a six-month period. Finally, the robustness of the OGP method, and its ability to identify biomarkers, was confirmed using a large sample set.

Wide metabolite coverage LC-MS/MS assay for the diagnosis of inherited metabolic disorders in urine

OBJECTIVE

The laboratory diagnosis of inherited metabolic disorders (IMD) has undergone significant development in recent decades, mainly due to the use of mass spectrometry, which allows rapid multicomponent analysis of a wide range of metabolites. Combined with advanced software tools, the diagnosis becomes more efficient as a benefit for both physicians and patients.

METHODS

A hydrophilic interaction liquid chromatography coupled with tandem mass spectrometry assay for determination of urinary purines, pyrimidines, N-acylglycines, N-acetylated amino acids, sugars, sugar alcohols and other diagnostically important biomarkers was developed and validated. Evaluation of the results consisting of utilisation of robust scaling and advanced visualization tools is simple and even suitable for urgent requirements.

RESULTS

The developed method, covering 65 biomarkers, provides a comprehensive diagnostic platform for 51 IMD. For most analytes, linearity with R2 > 0.99, intra and inter-day accuracy between 80 and 120 % and precision lower than 20 % were achieved. Diagnostic workflow was evaluated on 47 patients and External Quality Assurance samples involving a total of 24 different IMD. Over seven years, more than 2300 urine samples from patients suspected for IMD have been routinely analysed.

CONCLUSIONS

This method offers the advantage of a broad coverage of intermediate metabolites of interest and therefore may be a potential alternative and simplification for clinical laboratories that use multiple methods for screening these markers.

Quantification of the immunometabolite protein modifications S-2-succinocysteine and 2,3-dicarboxypropylcysteine

The tricarboxylic acid (TCA) cycle metabolite fumarate nonenzymatically reacts with the amino acid cysteine to form S-(2-succino)cysteine (2SC), referred to as protein succination. The immunometabolite itaconate accumulates during lipopolysaccharide (LPS) stimulation of macrophages and microglia. Itaconate nonenzymatically reacts with cysteine residues to generate 2,3-dicarboxypropylcysteine (2,3-DCP), referred to as protein dicarboxypropylation. Since fumarate and itaconate levels dynamically change in activated immune cells, the levels of both 2SC and 2,3-DCP reflect the abundance of these metabolites and their capacity to modify protein thiols. We generated ethyl esters of 2SC and 2,3-DCP from protein hydrolysates and used stable isotope dilution mass spectrometry to determine the abundance of these in LPS-stimulated Highly Aggressively Proliferating Immortalized (HAPI) microglia. To quantify the stoichiometry of the succination and dicarboxypropylation, reduced cysteines were alkylated with iodoacetic acid to form S-carboxymethylcysteine (CMC), which was then esterified. Itaconate-derived 2,3-DCP, but not fumarate-derived 2SC, increased in LPS-treated HAPI microglia. Stoichiometric measurements demonstrated that 2,3-DCP increased from 1.57% to 9.07% of total cysteines upon LPS stimulation. This methodology to simultaneously distinguish and quantify both 2SC and 2,3-DCP will have broad applications in the physiology of metabolic diseases. In addition, we find that available anti-2SC antibodies also detect the structurally similar 2,3-DCP, therefore "succinate moiety" may better describe the antigen recognized.NEW & NOTEWORTHY Itaconate and fumarate have roles as immunometabolites modulating the macrophage response to inflammation. Both immunometabolites chemically modify protein cysteine residues to modulate the immune response. Itaconate and fumarate levels change dynamically, whereas their stable protein modifications can be quantified by mass spectrometry. This method distinguishes itaconate and fumarate-derived protein modifications and will allow researchers to quantify their contributions in isolated cell types and tissues across a range of metabolic diseases.

Urinary Metabolomic Differentiation of Infants Fed on Human Breastmilk and Formulated Milk

Human breastmilk is an invaluable nutritional and pharmacological resource with a highly diverse metabolite profile, which can directly affect the metabolism of infants. Application of metabolomics can discriminate the complex relationship between such nutrients and infant health. As the most common biological fluid in metabolomic study, infant urinary metabolomics may provide the physiological impacts of different nutritional resources, namely human breastmilk and formulated milk. In this study, we aimed to identify possible differences in the urine metabolome of 30 infants (1-14 days after birth) fed with breast milk (n = 15) or formulated milk (n = 15). From metabolomic analysis with gas chromatography-mass spectrometry, 163 metabolites from single mass spectrometry (GC-MS), and 383 metabolites from tandem mass spectrometry (GC-MS/MS) were confirmed in urinary samples. Various multivariate statistical analysis were performed to discriminate the differences originating from physiological/nutritional variables, including human breastmilk/formulate milk feeding, sex, and duration of feeding. Both unsupervised and supervised discriminant analyses indicated that feeding resources (human breastmilk/formulated milk) gave marginal but significant differences in urinary metabolomes, while other factors (sex, duration of feeding) did not show notable discrimination between groups. According to the biomarker analyses, several organic acid and amino acids showed statistically significant differences between different feeding resources, such as 2-hydroxyhippurate.

From dysbiosis to neuropathologies: Toxic effects of glyphosate in zebrafish

Glyphosate, a globally prevalent herbicide known for its selective inhibition of the shikimate pathway in plants, is now implicated in physiological effects on humans and animals, probably due to its impacts in their gut microbiomes which possess the shikimate pathway. In this study, we investigate the effects of environmentally relevant concentrations of glyphosate on the gut microbiota, neurotransmitter levels, and anxiety in zebrafish. Our findings demonstrate that glyphosate exposure leads to dysbiosis in the zebrafish gut, alterations in central and peripheral serotonin levels, increased dopamine levels in the brain, and notable changes in anxiety and social behavior. While the dysbiosis can be attributed to glyphosate's antimicrobial properties, the observed effects on neurotransmitter levels leading to the reported induction of oxidative stress in the brain indicate a novel and significant mode of action for glyphosate, namely the impairment of the microbiome-gut-axis. While further investigations are necessary to determine the relevance of this mechanism in humans, our findings shed light on the potential explanation for the contradictory reports on the safety of glyphosate for consumers.

Smartphone-Based Fluorescent Profiling of Quaternary MicroRNAs in Urine for Rapid Diagnosis of Urological Cancers Using a Multiplexed Isothermal Exponential Amplification Reaction

Urological cancers such as bladder or prostate cancer represent one of the most malignant tumors that accounts for an extremely high mortality. However, conventionally standard diagnostics for urological cancers are hardly available in low-resource settings. We developed herein a hand-held fluorescent imaging platform by integrating a multiplexed isothermal exponential amplification reaction (EXPAR) with a microgel-enriched methodology for sensitive profiling of quaternary microRNAs (miRNAs) in urine and quick diagnosis of urological cancers at the early stage. The target miRNA mixtures in the urine underwent four parallel EXPARs without cross-reactivity, followed by surface concentration and hybridization by the encoded polyacrylamide microgels. This mix-and-read strategy allowed for one-pot analysis of several key miRNAs simultaneously and provided 5-fold enhancement in fluorescent detection sensitivities compared to the individual EXPAR-based assays. Four urinary miRNAs (let-7a, miRNA-155, -223, and -143) could be quantitatively determined in a wide linear range from 50 fM to 30 nM, with the limits of detection at femtomolar levels. Using a smartphone-based imaging microreader, healthy and cancerous cohorts with prostate, bladder, and renal cell cancers could be discriminated in 30 min with the accuracy >83% using linear discriminant analysis. The developed detection platform has proven to be a portable, noninvasive, and useful complement to the toolbox for miRNA-based liquid biopsies, which holds immense potential and advantage for regular and large-scale applications in early cancer diagnosis.

Rapid and efficient LC-MS/MS diagnosis of inherited metabolic disorders: a semi-automated workflow for analysis of organic acids, acylglycines, and acylcarnitines in urine

OBJECTIVES

The analysis of organic acids in urine is an important part of the diagnosis of inherited metabolic disorders (IMDs), for which gas chromatography coupled with mass spectrometry is still predominantly used.

METHODS

Ultra-performance liquid chromatography-tandem mass spectrometry (LC-MS/MS) assay for urinary organic acids, acylcarnitines and acylglycines was developed and validated. Sample preparation consists only of dilution and the addition of internal standards. Raw data processing is quick and easy using selective scheduled multiple reaction monitoring mode. A robust standardised value calculation as a data transformation together with advanced automatic visualisation tools are applied for easy evaluation of complex data.

RESULTS

The developed method covers 146 biomarkers consisting of organic acids (n=99), acylglycines (n=15) and acylcarnitines (n=32) including all clinically important isomeric compounds present. Linearity with r2>0.98 for 118 analytes, inter-day accuracy between 80 and 120 % and imprecision under 15 % for 120 analytes were achieved. Over 2 years, more than 800 urine samples from children tested for IMDs were analysed. The workflow was evaluated on 93 patient samples and ERNDIM External Quality Assurance samples involving a total of 34 different IMDs.

CONCLUSIONS

The established LC-MS/MS workflow offers a comprehensive analysis of a wide range of organic acids, acylcarnitines and acylglycines in urine to perform effective, rapid and sensitive semi-automated diagnosis of more than 80 IMDs.

A large-scale, targeted metabolomics method for the analysis and quantification of metabolites in human plasma via liquid chromatography-mass spectrometry

Metabolomics is the study of small molecules, primarily metabolites, that are produced during metabolic processes. Analysis of the composition of an organism's metabolome can yield useful information about an individual's health status at any given time. In recent years, the development of large-scale, targeted metabolomic methods has allowed for the analysis of biological samples using analytical techniques such as LC-MS/MS. This paper presents a large-scale metabolomics method for analysis of biological samples, with a focus on quantification of metabolites found in blood plasma. The method comprises a 10-min chromatographic separation using HILIC and RP stationary phases combined with positive and negative electrospray ionization in order to maximize metabolome coverage. Complete analysis of a single sample can be achieved in as little as 40 min using the two columns and dual modes of ionization. With 540 metabolites and the inclusion of over 200 analytical standards, this method is comprehensive and quantitatively robust when compared to current targeted metabolomics methods. This study uses a large-scale evaluation of metabolite recovery from plasma that enables absolute quantification of metabolites by correcting for analyte loss throughout processes such as extraction, handling, or storage. In addition, the method was applied to plasma collected from adjuvant breast cancer patients to confirm the suitability of the method to clinical samples.

Delayed gut microbiota maturation in the first year of life is a hallmark of pediatric allergic disease

Allergic diseases affect millions of people worldwide. An increase in their prevalence has been associated with alterations in the gut microbiome, i.e., the microorganisms and their genes within the gastrointestinal tract. Maturation of the infant immune system and gut microbiota occur in parallel; thus, the conformation of the microbiome may determine if tolerant immune programming arises within the infant. Here we show, using deeply phenotyped participants in the CHILD birth cohort (n = 1115), that there are early-life influences and microbiome features which are uniformly associated with four distinct allergic diagnoses at 5 years: atopic dermatitis (AD, n = 367), asthma (As, n = 165), food allergy (FA, n = 136), and allergic rhinitis (AR, n = 187). In a subset with shotgun metagenomic and metabolomic profiling (n = 589), we discover that impaired 1-year microbiota maturation may be universal to pediatric allergies (AD p = 0.000014; As p = 0.0073; FA p = 0.00083; and AR p = 0.0021). Extending this, we find a core set of functional and metabolic imbalances characterized by compromised mucous integrity, elevated oxidative activity, decreased secondary fermentation, and elevated trace amines, to be a significant mediator between microbiota maturation at age 1 year and allergic diagnoses at age 5 years (βindirect = -2.28; p = 0.0020). Microbiota maturation thus provides a focal point to identify deviations from normative development to predict and prevent allergic disease.

Identification of Salivary Metabolic Signatures Associated with Primary Sjögren's Disease

Sjögren's disease (SjD) is the second most prevalent autoimmune disorder that involves chronic inflammation of exocrine glands. Correct diagnosis of primary SjD (pSjD) can span over many years since disease symptoms manifest only in advanced stages of salivary and lachrymal glandular destruction, and consensus diagnostic methods have critical sensitivity and selectivity limitations. Using nuclear magnetic resonance (NMR) spectroscopy, we determined the composition of metabolites in unstimulated saliva samples from 30 pSjD subjects and 30 participants who do not have Sjögren's disease (non-Sjögren's control group, NS-C). Thirty-four metabolites were quantified in each sample, and analysis was conducted on both non-normalized (concentration) and normalized metabolomics data from all study participants (ages 23-78) and on an age-restricted subset of the data (ages 30-70) while applying false discovery rate correction in determining data significance. The normalized data of saliva samples from all study participants, and of the age-restricted subset, indicated significant increases in the levels of glucose, glycerol, taurine, and lactate, as well as significant decreases in the levels of 5-aminopentanoate, acetate, butyrate and propionate, in subjects with pSjD compared to subjects in the NS-C group. Additionally, a significant increase in choline was found only in the age-restricted subset, and a significant decrease in fucose was found only in the whole study population in normalized data of saliva samples from the pSjD group compared to the NS-C group. Metabolite concentration data of saliva samples from all study participants, but not from the age-restricted subset, indicated significant increases in the levels of glucose, glycerol, taurine, and lactate in subjects with pSjD compared to controls. The study showed that NMR metabolomics can be implemented in defining salivary metabolic signatures that are associated with disease status, and can contribute to differential analysis between subjects with pSjD and those who are not affected with this disease, in the clinic.

Gut microbiome modified by bariatric surgery improves insulin sensitivity and correlates with increased brown fat activity and energy expenditure

Alterations in the microbiome correlate with improved metabolism in patients following bariatric surgery. While fecal microbiota transplantation (FMT) from obese patients into germ-free (GF) mice has suggested a significant role of the gut microbiome in metabolic improvements following bariatric surgery, causality remains to be confirmed. Here, we perform paired FMT from the same obese patients (BMI > 40; four patients), pre- and 1 or 6 months post-Roux-en-Y gastric bypass (RYGB) surgery, into Western diet-fed GF mice. Mice colonized by FMT from patients' post-surgery stool exhibit significant changes in microbiota composition and metabolomic profiles and, most importantly, improved insulin sensitivity compared with pre-RYGB FMT mice. Mechanistically, mice harboring the post-RYGB microbiome show increased brown fat mass and activity and exhibit increased energy expenditure. Moreover, improvements in immune homeostasis within the white adipose tissue are also observed. Altogether, these findings point to a direct role for the gut microbiome in mediating improved metabolic health post-RYGB surgery.

Identification of Metabolite Interference Is Necessary for Accurate LC-MS Targeted Metabolomics Analysis

Targeted metabolomics has been broadly used for metabolite measurement due to its good quantitative linearity and simple metabolite annotation workflow. However, metabolite interference, the phenomenon where one metabolite generates a peak in another metabolite's MRM setting (Q1/Q3) with a close retention time (RT), may lead to inaccurate metabolite annotation and quantification. Besides isomeric metabolites having the same precursor and product ions that may interfere with each other, we found other metabolite interferences as the result of inadequate mass resolution of triple-quadruple mass spectrometry and in-source fragmentation of metabolite ions. Characterizing the targeted metabolomics data using 334 metabolite standards revealed that about 75% of the metabolites generated measurable signals in at least one other metabolite's MRM setting. Different chromatography techniques can resolve 65-85% of these interfering signals among standards. Metabolite interference analysis combined with the manual inspection of cell lysate and serum data suggested that about 10% out of ∼180 annotated metabolites were mis-annotated or mis-quantified. These results highlight that a thorough investigation of metabolite interference is necessary for accurate metabolite measurement in targeted metabolomics.

The resting serum metabolome in response to short-term sprint interval training

PURPOSE

To investigate the response of a targeted fraction of (168 metabolites) of the resting serum metabolome to 9 sessions of sprint interval training (SIT).

METHODS

Thirty-four recreationally active males provided resting blood samples before (baseline) and 48-72 h after (post) a short-term (9 sessions) cycle ergometer-based SIT intervention. A targeted analysis of 168 metabolites was performed on serum using liquid chromatography mass spectrometry (LC-MS). 160 distinct metabolites were identified and combined with 4 calculated metabolite sums and 3 calculated metabolite ratios creating a panel of 167 individual factors. Data were analysed using principal component analysis and univariate testing of all factors classified into 5 metabolite subgroups.

RESULTS

SIT improved anaerobic capacity measured by average power output during a Wingate test (p < 0.01; mean difference = 38 W, 95% confidence interval [26, 51]) and aerobic capacity measured by average power output in a 20 min cycling test (p < 0.01; 17 W [12, 23]). Limited separation was discernible in the targeted serum metabolome between baseline and post-intervention when projected on the first and second principal component(s). However, univariate testing identified 11 fatty acids that had lower concentrations (false discovery rate < 0.05) in post-intervention samples.

CONCLUSIONS

These findings demonstrate that this short-term SIT intervention had limited effect on the serum metabolome at rest, but a subfraction of fatty acids are potentially sensitive to short-term exercise training.

The Effect of Night Shifts on 24-h Rhythms in the Urinary Metabolome of Police Officers on a Rotating Work Schedule

Shift workers face an increased risk of metabolic health problems, but the direct metabolic response to working nights is not fully understood. The aim of this study was to investigate the effect of night shifts on the 24-h urinary metabolome of shift workers. Eleven police officers working rotating shifts completed two 24-h laboratory visits that took place before and after they worked 7 consecutive nights. Sleep and meals were scheduled on a day schedule in the first visit and then on a night schedule (i.e., sleep and meals shifted by approximately 12 h) in the second visit. Targeted metabolomic analysis was performed on urine samples collected throughout these laboratory visits. Differential rhythmicity analysis was used to compare 24-h rhythms in urinary metabolites in both conditions. Our results show that on the day schedule, 24-h rhythms are present in the urinary levels of the majority of metabolites, but that this is significantly reduced on the night schedule, partly due to loss of organic acid rhythmicity. Furthermore, misalignment of 24-h metabolite rhythms with the shifted behavioral cycles in the night schedule was observed in more than half of the metabolites that were rhythmic in both conditions (all acylcarnitines). These results show that working nights alters the daily rhythms of the urinary metabolome in rotating shift workers, with the most notable impact observed for acylcarnitines and organic acids, 2 metabolite classes involved in mitochondrial function. Further research is warranted to study how these changes relate to the increased metabolic risks associated with shift work.

Identification of urinary biomarkers of colorectal cancer: Towards the development of a colorectal screening test in limited resource settings

BACKGROUND

African colorectal cancer (CRC) rates are rising rapidly. A low-cost CRC screening approach is needed to identify CRC from non-CRC patients who should be sent for colonoscopy (a scarcity in Africa).

OBJECTIVE

To identify urinary metabolite biomarkers that, combined with easy-to-measure clinical variables, would identify patients that should be further screened for CRC by colonoscopy. Ideal metabolites would be water-soluble and easily translated into a sensitive, low-cost point-of-care (POC) test.

METHODS

Liquid-chromatography mass spectrometry (LC-MS/MS) was used to quantify 142 metabolites in spot urine samples from 514 Nigerian CRC patients and healthy controls. Metabolite concentration data and clinical characteristics were used to determine optimal sets of biomarkers for identifying CRC from non-CRC subjects.

RESULTS

Our statistical analysis identified N1, N12-diacetylspermine, hippurate, p-hydroxyhippurate, and glutamate as the best metabolites to discriminate CRC patients via POC screening. Logistic regression modeling using these metabolites plus clinical data achieved an area under the receiver-operator characteristic (AUCs) curves of 89.2% for the discovery set, and 89.7% for a separate validation set.

CONCLUSIONS

Effective urinary biomarkers for CRC screening do exist. These results could be transferred into a simple, POC urinary test for screening CRC patients in Africa.

Inulin and Chinese Gallotannin Affect Meat Quality and Lipid Metabolism on Hu Sheep

The aim of this study was to investigate the impacts of inulin and Chinese gallotannin on the meat fatty acids and urinary metabolites in sheep. Twenty-four healthy (25.80 ± 3.85 kg) weaned Hu lambs of approximately 4.5 months old were equally divided into four groups: control group (basal diet), treatment group I (basal diet + 0.1% inulin), treatment group II (basal diet + 0.1% inulin + 2% Chinese gallotannin), and treatment group III (basal diet + 0.1% inulin + 2% Chinese gallotannin + 4% PEG). The contents of myristic acid (C14:0) and palmitic acid (C16:0) were found to be lower in treatment group II than in the control group (p < 0.05). Moreover, the palmitoleic acid (C16:1) content in treatment group II was notably higher than that in the control group (p < 0.05), while the elaidic acid (C18:1n9t) content in treatment group II was higher than that in other groups (p < 0.05). Besides, the linoleic acid (C18:2n6c) content was higher in the treatment II and control groups than in the treatment I and III groups. Furthermore, compared with the control group, both 4-pyridoxic acid and creatinine in treatment groups I and II were upregulated (p < 0.05), while other metabolites, such as nicotinuric acid, l-threonine, palmitic acid, and oleic acid, were drastically downregulated (p < 0.05). These differential metabolites were found to be mainly involved in nicotinate and nicotinamide metabolism (ko00760), vitamin B6 metabolism (ko00750), and the fatty acid biosynthesis pathway (ko00061). It is concluded that the combination of inulin and Chinese gallotannin in the diet could improve the energy and lipid metabolism of sheep, which may improve both mutton quality and production performance.

A Scoping Review: Urinary Markers of Metabolic Maturation in Preterm Infants and Future Interventions to Improve Growth

Background: Growth failure in infants born preterm is a significant issue, increasing the risk of poorer neurodevelopmental outcomes and metabolic syndrome later in life. During the first 1000 days of life biological systems mature rapidly involving developmental programming, cellular senescence, and metabolic maturation, regulating normal growth and development. However, little is known about metabolic maturation in infants born preterm and the relationship with growth. Objective: To examine the available evidence on urinary markers of metabolic maturation and their relationship with growth in infants born preterm. Eligibility criteria: Studies including in this scoping review using qualitative or quantitative methods to describe urinary markers of metabolic maturation and the relationship with growth in infants born preterm. Results: After a screening process 15 titles were included in this review, from 1998–2021 drawing from China (n = 1), Italy (n = 3), Germany (n = 3), Greece (n = 1), Japan (n = 2), Norway (n = 1), Portugal (n = 1), Spain (n = 2) and USA (n = 1). The included studies examined urinary metabolites in 1131 infants. A content analysis identified 4 overarching themes relating to; (i) metabolic maturation relative to gestational age, (ii) metabolic signature and changes in urinary metabolites over time, (iii) nutrition and (iv) growth. Conclusion: The results of this scoping review suggest there are considerable gaps in our knowledge relating to factors associated with metabolic instability, what constitutes normal maturation of preterm infants, and how the development of reference phenome age z scores for metabolites of interest could improve nutritional and growth outcomes.

LC-MS metabolomics of urine reveals distinct profiles for non-muscle-invasive and muscle-invasive bladder cancer

PURPOSE

Bladder cancer (BC) is among the most frequent malignancies worldwide. Novel non-invasive markers are needed to diagnose and stage BC with more accuracy than invasive procedures like cystoscopy. To date, no study has identified urine metabolites characteristic of all BC stages. To discover novel urine metabolomic profiles to diagnose and stage non-muscle-invasive (NMIBC) and muscle-invasive (MIBC) patients using mass spectrometry-based metabolomics.

METHODS

We prospectively recruited 198 BC patients and 98 age- and sex-matched healthy volunteers without evidence of renal or bladder condition confirmed by ultrasound, from whom we collected a first morning urine sample (before surgery in patients). In a discovery stage, an untargeted metabolomic analysis was conducted in urine samples of a selection of 64 BC patients (19 TaG1, 11 TaG3, 20 T1G3, 12 T2G3, 1 T2G2, 1 T3G3) and 20 controls to identify dysregulated metabolites. Next, after exhaustive multivariate analysis, confirmed dysregulated metabolites were validated in an independent cohort of 134 BC patients (19 TaG1, 62 TaG2, 9 TaG3, 15 T1G2, 16 T1G3, 4 T2G2, 9 T2G3) and 78 controls.

RESULTS

We validated p-cresol glucuronide as potential diagnostic biomarker for BC patients compared to controls (AUC = 0.79). For NMIBC, p-cresol glucuronide was valuable as staging biomarker (AUC = 0.803). And among NMIBCs, p-coumaric acid may be a potential specific staging biomarker for the TaG1 NMIBC; however, future validation experiments should be conducted once the precise version of the standard is commercially available. Remarkably, for MIBC we validated spermine as potential specific staging biomarker (AUC = 0.882).

CONCLUSION

Ours is the first metabolomics study conducted in urine of a thoroughly characterized cohort comprising all stages of NMIBC, MIBC and healthy controls in which we identified non-invasive diagnostic and staging biomarkers. These may improve BC management, thus reducing the use of current harmful diagnostic techniques.

Metabolomic Analysis of Key Regulatory Metabolites in the Urine of Flavivirus-Infected Mice

Objective

Dengue virus (DENV), Japanese encephalitis virus (JEV), and Zika virus (ZIKV) are several important flaviviruses, and infections caused by these flaviviruses remain worldwide health problems. Different flaviviruses exhibit different biological characteristics and pathogenicity. Metabolomics is an emerging research perspective to uncover and observe the pathogenesis of certain infections.

Methods

To improve the understanding of the specific metabolic changes that occur during infection with different flaviviruses, considering the principle of noninvasive sampling, this article describes our comprehensive analysis of metabolites in urine samples from the three kinds of flavivirus-infected mice using a liquid chromatography tandem mass spectrometry method to better understand their infection mechanisms.

Results

The urine of DENV-, JEV-, and ZIKV-infected mice had 68, 64, and 47 different differential metabolites, respectively, compared with the urine of control mice. Among the metabolic pathways designed by these metabolites, ABC transporters, arginine and proline metabolism, and regulation of lipolysis play an important role. Furthermore, we predicted and fitted potential relationships between metabolites and pathways.

Conclusions

These virus-specific altered metabolites may be associated with their unique biological properties and pathogenicity. The metabolomic analysis of urine is very important for the analysis of flavivirus infection.

A rapid LC-MS/MS assay for detection and monitoring of underivatized branched-chain amino acids in maple syrup urine disease

•

Branched chain amino acid (BCAA) testing is crucial in the diagnosis and monitoring of maple syrup urine disease (MSUD).

•

Mixed mode chromatography can be applied to separate BCAAs without requiring sample derivatization.

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A rapid, clinically validated LC-MS/MS-based assay for analysis of underivatized BCAA in human plasma was developed.

•

The assay involves minimal sample preparation without derivatization, rapid chromatographic separation, and requires only 20 µL of sample.

Introduction

Quantitation of the isomeric branched-chain amino acids (BCAA; valine, alloisoleucine, isoleucine, leucine) is a challenging task that typically requires derivatization steps or long runtimes if a traditional chromatographic method involving a ninhydrin ion pairing reagent is used.

Objectives

To develop and perform clinical validation of a rapid, LC-MS/MS-based targeted metabolomics assay for detection and monitoring of underivatized BCAA in human plasma.

Methods:

Various columns and modes of chromatography were tested. The final optimized method utilized mixed mode chromatography with an Intrada column under isocratic condition. Sample preparation utilized the 96-well format. Briefly, extraction solvent containing the internal standard is added to 20 uL of sample, followed by shaking and positive pressure filtering, and the resulting extracted sample is analyzed. The assay was validated based on accepted quality standards (e.g., CLIA and CLSI) for clinical assays.

Results

The method is linear over a wide range of concentrations, 2.0–1500 µM, with LOD of 0.60 µM and LOQ of 2.0 µM. The precision of the assay was 4–10% across analytes. The method was also validated against reference laboratories via blinded split-sample analysis and demonstrated good agreement with accuracy: 89–95% relative to the external group mean.

Conclusion

We have developed a method that is accurate, rapid, and reliable for routine clinical testing of patient sample BCAA, which is used in the diagnosis and management of maple syrup urine disease (MSUD). The assay also has desirable characteristics, such as short run time, small sample volume requirement, simple sample preparation without the need for derivatization, and high throughput.

Combined Plasma and Urinary Metabolomics Uncover Metabolic Perturbations Associated with Severe Respiratory Syncytial Viral Infection and Future Development of Asthma in Infant Patients

A large percentage of infants develop viral bronchiolitis needing medical intervention and often develop further airway disease such as asthma. To characterize metabolic perturbations in acute respiratory syncytial viral (RSV) bronchiolitis, we compared metabolomic profiles of moderate and severe RSV patients versus sedation controls. RSV patients were classified as moderate or severe based on the need for invasive mechanical ventilation. Whole blood and urine samples were collected at two time points (baseline and 72 h). Plasma and urinary metabolites were extracted in cold methanol and analyzed by liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS), and data from the two biofluids were combined for multivariate data analysis. Metabolite profiles were clustered according to severity, characterized by unique metabolic changes in both plasma and urine. Plasma metabolites that correlated with severity included intermediates in the sialic acid biosynthesis, while urinary metabolites included citrate as well as multiple nucleotides. Furthermore, metabolomic profiles were predictive of future development of asthma, with urinary metabolites exhibiting higher predictive power than plasma. These metabolites may offer unique insights into the pathology of RSV bronchiolitis and may be useful in identifying patients at risk for developing asthma.

Yolk-shell Ni/NiO anchored on N-doped graphene synthesized as dual-ion MALDI matrix for detecting and imaging bioactive small molecules

Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is an attractive tool to analyze the bioactive small molecules but remains a great challenge owing to the serious background interference from conventional matrix with m/z < 1000. Herein, we reported a dual-ion MALDI matrix of yolk-shell Ni/NiO nanoparticles anchored on nitrogen-doped graphene (Ni/NiO/N-Gr) to enhance MALDI performance. The Ni/NiO/N-Gr was synthesized via the pyrolysis and controllable oxidation strategy based on the nanoscale regulation of Kirkendall effect. The novel matrix showed the superior behavior for the analysis of various small molecular metabolites (amino acids, saccharides, spermidine, creatinine, hippuric acid, dopamine, and ascorbic acid) with high sensitivity, excellent salt tolerance, and favorable reproducibility in dual-ion modes compared to the traditional α-cyano-4-hydroxycinnamic acid (CHCA) and control substances (Ni/N-Gr and NiO/N-Gr). Meanwhile, we have realized accurate quantitation of blood glucose in mice with a linearity concentration range of 0.2-7.5 mM and qualitative detection of various endogenous small molecular metabolites in mice serum and urine samples. Especially, the Ni/NiO/N-Gr assisted LDI MS imaging (MSI) has exhibited the excellent spatial distribution of lipids in hippocampus region of mice brain. These results may provide an approach to explore the MALDI MS and MSI applications in clinical diagnosis.

Kynurenine and Hemoglobin as Sex-Specific Variables in COVID-19 Patients: A Machine Learning and Genetic Algorithms Approach

Differences in clinical manifestations, immune response, metabolic alterations, and outcomes (including disease severity and mortality) between men and women with COVID-19 have been reported since the pandemic outbreak, making it necessary to implement sex-specific biomarkers for disease diagnosis and treatment. This study aimed to identify sex-associated differences in COVID-19 patients by means of a genetic algorithm (GALGO) and machine learning, employing support vector machine (SVM) and logistic regression (LR) for the data analysis. Both algorithms identified kynurenine and hemoglobin as the most important variables to distinguish between men and women with COVID-19. LR and SVM identified C10:1, cough, and lysoPC a 14:0 to discriminate between men with COVID-19 from men without, with LR being the best model. In the case of women with COVID-19 vs. women without, SVM had a higher performance, and both models identified a higher number of variables, including 10:2, lysoPC a C26:0, lysoPC a C28:0, alpha-ketoglutaric acid, lactic acid, cough, fever, anosmia, and dysgeusia. Our results demonstrate that differences in sexes have implications in the diagnosis and outcome of the disease. Further, genetic and machine learning algorithms are useful tools to predict sex-associated differences in COVID-19.

Analysis of urinary organic acids by gas chromatography tandem mass spectrometry method for metabolic profiling applications

A sensitive, accurate and precise method was developed for the quantification of a large number of organic acids in human urine by GC-MS/MS. The analytes were selected based on their role as key metabolic intermediates; intermediates of Krebs cycle, fatty acid oxidation, glycolysis, down-stream metabolites of neurotransmitter synthesis and degradation, metabolites indicative of nutritional deficiencies, byproducts of microbial activity in the gastrointestinal tract (GI) etc. The most efficient sample preparation protocol was selected based on tests for extraction with different solvents such as MTBE and ethyl acetate under acidic conditions, whereas finally a more general protocol was applied with methanol. Regarding derivatization, methoxyamine with MSTFA, 1% TMCS was applied. The method was extensively validated, including stability study, ensuring accurate determination of the studied organic acids in human urine. Proof of its utility was exhibited in a set of samples from human volunteers. The method can find wide applicability in the context of metabolomics for clinical or nutritional studies.

Urinary Metabolomic Profile of Neonates Born to Women with Gestational Diabetes Mellitus

Gestational diabetes mellitus (GDM) is one of the most frequent pregnancy complications with potential adverse outcomes for mothers and newborns. Its effects on the newborn appear during the neonatal period or early childhood. Therefore, an early diagnosis is crucial to prevent the development of chronic diseases later in adult life. In this study, the urinary metabolome of babies born to GDM mothers was characterized. In total, 144 neonatal and maternal (second and third trimesters of pregnancy) urinary samples were analyzed using targeted metabolomics, combining liquid chromatographic mass spectrometry (LC-MS/MS) and flow injection analysis mass spectrometry (FIA-MS/MS) techniques. We provide here the neonatal urinary concentration values of 101 metabolites for 26 newborns born to GDM mothers and 22 newborns born to healthy mothers. The univariate analysis of these metabolites revealed statistical differences in 11 metabolites. Multivariate analyses revealed a differential metabolic profile in newborns of GDM mothers characterized by dysregulation of acylcarnitines, amino acids, and polyamine metabolism. Levels of hexadecenoylcarnitine (C16:1) and spermine were also higher in newborns of GDM mothers. The maternal urinary metabolome revealed significant differences in butyric, isobutyric, and uric acid in the second and third trimesters of pregnancy. These metabolic alterations point to the impact of GDM in the neonatal period.

Immunometabolic signatures predict risk of progression to sepsis in COVID-19

Viral sepsis has been proposed as an accurate term to describe all multisystemic dysregulations and clinical findings in severe and critically ill COVID-19 patients. The adoption of this term may help the implementation of more accurate strategies of early diagnosis, prognosis, and in-hospital treatment. We accurately quantified 110 metabolites using targeted metabolomics, and 13 cytokines/chemokines in plasma samples of 121 COVID-19 patients with different levels of severity, and 37 non-COVID-19 individuals. Analyses revealed an integrated host-dependent dysregulation of inflammatory cytokines, neutrophil activation chemokines, glycolysis, mitochondrial metabolism, amino acid metabolism, polyamine synthesis, and lipid metabolism typical of sepsis processes distinctive of a mild disease. Dysregulated metabolites and cytokines/chemokines showed differential correlation patterns in mild and critically ill patients, indicating a crosstalk between metabolism and hyperinflammation. Using multivariate analysis, powerful models for diagnosis and prognosis of COVID-19 induced sepsis were generated, as well as for mortality prediction among septic patients. A metabolite panel made of kynurenine/tryptophan ratio, IL-6, LysoPC a C18:2, and phenylalanine discriminated non-COVID-19 from sepsis patients with an area under the curve (AUC (95%CI)) of 0.991 (0.986-0.995), with sensitivity of 0.978 (0.963-0.992) and specificity of 0.920 (0.890-0.949). The panel that included C10:2, IL-6, NLR, and C5 discriminated mild patients from sepsis patients with an AUC (95%CI) of 0.965 (0.952-0.977), with sensitivity of 0.993(0.984-1.000) and specificity of 0.851 (0.815-0.887). The panel with citric acid, LysoPC a C28:1, neutrophil-lymphocyte ratio (NLR) and kynurenine/tryptophan ratio discriminated severe patients from sepsis patients with an AUC (95%CI) of 0.829 (0.800-0.858), with sensitivity of 0.738 (0.695-0.781) and specificity of 0.781 (0.735-0.827). Septic patients who survived were different from those that did not survive with a model consisting of hippuric acid, along with the presence of Type II diabetes, with an AUC (95%CI) of 0.831 (0.788-0.874), with sensitivity of 0.765 (0.697-0.832) and specificity of 0.817 (0.770-0.865).

A Workflow to Perform Targeted Metabolomics at the Untargeted Scale on a Triple Quadrupole Mass Spectrometer

The thousands of features commonly observed when performing untargeted metabolomics with quadrupole time-of-flight (QTOF) and Orbitrap mass spectrometers often correspond to only a few hundred unique metabolites of biological origin, which is in the range of what can be assayed in a single targeted metabolomics experiment by using a triple quadrupole (QqQ) mass spectrometer. A major benefit of performing targeted metabolomics with QqQ mass spectrometry is the affordability of the instruments relative to high-resolution QTOF and Orbitrap platforms. Optimizing targeted methods to profile hundreds of metabolites on a QqQ mass spectrometer, however, has historically been limited by the availability of authentic standards, particularly for "unknowns" that have yet to be structurally identified. Here, we report a strategy to develop multiple reaction monitoring (MRM) methods for QqQ instruments on the basis of high-resolution spectra, thereby enabling us to use data from untargeted metabolomics to design targeted experiments without the need for authentic standards. We demonstrate that using high-resolution fragmentation data alone to design MRM methods results in the same quantitative performance as when methods are optimized by measuring authentic standards on QqQ instruments, as is conventionally done. The approach was validated by showing that Orbitrap ID-X data can be used to establish MRM methods on a Thermo TSQ Altis and two Agilent QqQs for hundreds of metabolites, including unknowns, without a dependence on standards. Finally, we highlight an application where metabolite profiling was performed on an ID-X and a QqQ by using the strategy introduced here, with both data sets yielding the same result. The described approach therefore allows us to use QqQ instruments, which are often associated with targeted metabolomics, to profile knowns and unknowns at a comprehensive scale that is typical of untargeted metabolomics.

Metabolomics meets lipidomics: Assessing the small molecule component of metabolism

Metabolomics, including lipidomics, is emerging as a quantitative biology approach for the assessment of energy flow through metabolism and information flow through metabolic signaling; thus, providing novel insights into metabolism and its regulation, in health, healthy ageing and disease. In this forward-looking review we provide an overview on the origins of metabolomics, on its role in this postgenomic era of biochemistry and its application to investigate metabolite role and (bio)activity, from model systems to human population studies. We present the challenges inherent to this analytical science, and approaches and modes of analysis that are used to resolve, characterize and measure the infinite chemical diversity contained in the metabolome (including lipidome) of complex biological matrices. In the current outbreak of metabolic diseases such as cardiometabolic disorders, cancer and neurodegenerative diseases, metabolomics appears to be ideally situated for the investigation of disease pathophysiology from a metabolite perspective.

Targeted metabolomics identifies high performing diagnostic and prognostic biomarkers for COVID-19

Research exploring the development and outcome of COVID-19 infections has led to the need to find better diagnostic and prognostic biomarkers. This cross-sectional study used targeted metabolomics to identify potential COVID-19 biomarkers that predicted the course of the illness by assessing 110 endogenous plasma metabolites from individuals admitted to a local hospital for diagnosis/treatment. Patients were classified into four groups (≈ 40 each) according to standard polymerase chain reaction (PCR) COVID-19 testing and disease course: PCR-/controls (i.e., non-COVID controls), PCR+/not-hospitalized, PCR+/hospitalized, and PCR+/intubated. Blood samples were collected within 2 days of admission/PCR testing. Metabolite concentration data, demographic data and clinical data were used to propose biomarkers and develop optimal regression models for the diagnosis and prognosis of COVID-19. The area under the receiver operating characteristic curve (AUC; 95% CI) was used to assess each models' predictive value. A panel that included the kynurenine: tryptophan ratio, lysoPC a C26:0, and pyruvic acid discriminated non-COVID controls from PCR+/not-hospitalized (AUC = 0.947; 95% CI 0.931-0.962). A second panel consisting of C10:2, butyric acid, and pyruvic acid distinguished PCR+/not-hospitalized from PCR+/hospitalized and PCR+/intubated (AUC = 0.975; 95% CI 0.968-0.983). Only lysoPC a C28:0 differentiated PCR+/hospitalized from PCR+/intubated patients (AUC = 0.770; 95% CI 0.736-0.803). If additional studies with targeted metabolomics confirm the diagnostic value of these plasma biomarkers, such panels could eventually be of clinical use in medical practice.

Use of nuclear magnetic resonance spectroscopy in diagnosis of inborn errors of metabolism

Nuclear Magnetic Resonance (NMR) spectroscopy has been applied in many fields of science and is increasingly being considered as a tool in the clinical setting. This review examines its application for diagnosis of inborn errors of metabolism (IEMs). IEMs, whether involving deficiency in the synthesis and degradation of metabolites, or in lipoprotein metabolism, affect nearly 3% of the global population. NMR is a preferred method for comprehensive evaluation of complex biofluids such as blood or urine, as it can provide a relatively unbiased overview of all compounds that are present and does not destroy or otherwise chemically alter the sample. While current newborn screening programs take advantage of other more sensitive methods, such as mass spectrometry, NMR has advantages especially for urine analysis with respect to ease of sample preparation and the reproducibility of results. NMR spectroscopy is particularly compatible with analysis of lipoproteins because it provides information about their size and density, not easily attained by other methods, that can help the clinician to better manage patients with dyslipidemia. We believe that NMR holds great potential for expanding clinical diagnosis in the future, in the field of IEMs and beyond.

A Comprehensive Targeted Metabolomics Assay for Crop Plant Sample Analysis

Metabolomics plays an important role in various fields from health to agriculture. However, the comprehensive quantitative metabolomic analysis of plants and plant metabolites has not been widely performed. Liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS)-based plant metabolomics offers the sensitivity and breadth of coverage for both phenotyping and disease diagnosis of plants. Here, we report a high-coverage and quantitative MS-based assay for plant metabolite analysis. The assay detects and quantifies 206 primary and secondary plant metabolites, including many key plant hormones. In total, it measures 28 amino acids and derivatives, 27 organic acids, 20 biogenic amines and derivatives, 40 acylcarnitines, 90 phospholipids and C-6 sugars. All the analysis methods in this assay are based on LC-MS/MS techniques using both positive and negative-mode multiple reaction monitoring (MRM). The recovery rates of spiked plant samples at three different concentration levels (low, medium and high) ranged from 80% to 120%, with satisfactory precision values of less than 20%. This targeted plant metabolomic assay has been successfully applied to the analysis of large numbers of pine and spruce needle samples, canola root samples, as well as cannabis samples. Moreover, the assay was specifically developed in a 96-well plate format, which enables automated, high-throughput sample analysis. This assay has already been used to analyze over 1500 crop plant samples in less than two months.

High-Precision Automated Workflow for Urinary Untargeted Metabolomic Epidemiology

Urine is a noninvasive biofluid that is rich in polar metabolites and well suited for metabolomic epidemiology. However, because of individual variability in health and hydration status, the physiological concentration of urine can differ >15-fold, which can pose major challenges in untargeted liquid chromatography-mass spectrometry (LC-MS) metabolomics. Although numerous urine normalization methods have been implemented (e.g., creatinine, specific gravity-SG), most are manual and, therefore, not practical for population-based studies. To address this issue, we developed a method to measure SG in 96-well-plates using a refractive index detector (RID), which exhibited accuracy within 85-115% and <3.4% precision. Bland-Altman statistics showed a mean deviation of -0.0001 SG units (limits of agreement: -0.0014 to 0.0011) relative to a hand-held refractometer. Using this RID-based SG normalization, we developed an automated LC-MS workflow for untargeted urinary metabolomics in a 96-well-plate format. The workflow uses positive and negative ionization HILIC chromatography and acquires mass spectra in data-independent acquisition (DIA) mode at three collision energies. Five technical internal standards (tISs) were used to monitor data quality in each method, all of which demonstrated raw coefficients of variation (CVs) < 10% in the quality controls (QCs) and < 20% in the samples for a small cohort (n = 87 urine samples, n = 22 QCs). Application in a large cohort (n = 842 urine samples, n = 248 QCs) demonstrated CVQC < 5% and CVsamples < 16% for 4/5 tISs after signal drift correction by cubic spline regression. The workflow identified >540 urinary metabolites including endogenous and exogenous compounds. This platform is suitable for performing urinary untargeted metabolomic epidemiology and will be useful for applications in population-based molecular phenotyping.