Amino acids in a targeted versus a non-targeted metabolomics LC-MS/MS assay. Are the results consistent?

A systematic review of metabolomic findings in adult and pediatric renal disease

Chronic kidney disease (CKD) affects over 0.5 billion people worldwide across their lifetimes. Despite a growingly ageing world population, an increase in all-age prevalence of kidney disease persists. Adult-onset forms of kidney disease often result from lifestyle-modifiable metabolic illnesses such as type 2 diabetes. Pediatric and adolescent forms of renal disease are primarily caused by morphological abnormalities of the kidney, as well as immunological, infectious and inherited metabolic disorders. Alterations in energy metabolism are observed in CKD of varying causes, albeit the molecular mechanisms underlying pathology are unclear. A systematic indexing of metabolites identified in plasma and urine of patients with kidney disease alongside disease enrichment analysis uncovered inborn errors of metabolism as a framework that links features of adult and pediatric kidney disease. The relationship of genetics and metabolism in kidney disease could be classified into three distinct landscapes: (i) Normal genotypes that develop renal damage because of lifestyle and / or comorbidities; (ii) Heterozygous genetic variants and polymorphisms that result in unique metabotypes that may predispose to the development of kidney disease via synergistic heterozygosity, and (iii) Homozygous genetic variants that cause renal impairment by perturbing metabolism, as found in children with monogenic inborn errors of metabolism. Interest in the identification of early biomarkers of onset and progression of CKD has grown steadily in the last years, though it has not translated into clinical routine yet. This systematic review indexes findings of differential concentration of metabolites and energy pathway dysregulation in kidney disease and appraises their potential use as biomarkers.

Impact of Ultrasonication on African Oil Bean (Pentaclethra macrophylla Benth) Protein Extraction and Properties

African oil bean (Pentaclethra macrophylla Benth) is an underutilised edible oil seed that could represent a sustainable protein source. In this study, the impact of ultrasonication on the extraction efficiency and properties of protein from African oil bean (AOB) seeds was evaluated. The increase in the duration of extraction favoured the extraction of AOB proteins. This was observed by an increase in extraction yield from 24% to 42% (w/w) when the extraction time was increased from 15 min to 60 min. Desirable properties were observed in extracted AOB proteins; the amino acid profile of protein isolates revealed higher ratios of hydrophobic to hydrophilic amino acids compared to those of the defatted seeds, suggesting alterations in their functional properties. This was also supported by the higher proportion of hydrophobic amino acids and high surface hydrophobicity index value (3813) in AOB protein isolates. The foaming capacity of AOB proteins was above 200%, with an average foaming stability of 92%. The results indicate that AOB protein isolates can be considered promising food ingredients and could help stimulate the growth of the food industry in tropical Sub-Saharan regions where AOB seeds thrive.

Acupuncture at Taichong and Zusanli points exerts hypotensive effect in spontaneously hypertensive rats by metabolomic analysis

BACKGROUND

The development of hypertension affects several target organs, the kidneys being one of them. Acupuncture has been used to treat hypertension for a long time. Several mechanisms of acupuncture on hypotensive effect have been reveled, while the effects of acupuncture on the alterations in renal cortex from a metabolomic perspective are still unclear.

METHODS

Twelve male Wistar rats served as the control group (Wistar Group). Twenty-four male spontaneously hypertensive rats (SHR) were randomly divided into two groups: the model group (SHR Group) and the acupuncture group (AC Group). In the AC Group, milli-needle acupuncture was used to puncture the bilateral Taichong (LR3) and Zusanli (ST36) points. Blood pressure values were measured weekly and the rats were euthanized after three weeks. Renal cortical tissues were collected for non-targeted and targeted metabolomic analyses.

RESULTS

Acupuncture reduced blood pressure values in rats (Compared with the SHR Group, P < 0.001). Thirteen metabolites with significant differences and three metabolic pathways were screened by untargeted metabolomics. The SHR Group was compared with the Wistar Group and AC Group both involving metabolites and pathways related to bile acid metabolism. Furthermore, targeted metabolomics quantification of four bile acids, Cholic acid (CA), Allocholic acid (ACA), Deoxycholic acid (DCA) and Chenodeoxycholic acid (CDCA), revealed that all bile acid concentrations were relatively high in the SHR Group, except for ACA.

CONCLUSION

This study indicate that abnormal bile acid metabolism may be an independent risk factor the development of hypertension. Acupuncture at Taichong and at Zusanli points effectively modulated bile acids metabolism in SHR renal cortex tissues to exert a hypotensive effect, and CA may be able to be a new target for the treatment of hypertension.

A rapid and robust method for amino acid quantification using a simple N-hydroxysuccinimide ester derivatization and liquid chromatography-ion mobility-mass spectrometry

The vast majority of mass spectrometry (MS)-based metabolomics studies employ reversed-phase liquid chromatography (RPLC) to separate analytes prior to MS detection. Highly polar metabolites, such as amino acids (AAs), are poorly retained by RPLC, making quantitation of these key species challenging across the broad concentration ranges typically observed in biological specimens, such as cell extracts. To improve the detection and quantitation of AAs in microglial cell extracts, the implementation of a 4-dimethylaminobenzoylamido acetic acid N-hydroxysuccinimide ester (DBAA-NHS) derivatization agent was explored for its ability to improve both analyte retention and detection limits in RPLC-MS. In addition to the introduction of the DBAA-NHS labeling reagent, a uniformly (U) ¹³C-labeled yeast extract was also introduced during the sample preparation workflow as an internal standard (IS) to eliminate artifacts and to enable targeted quantitation of AAs, as well as untargeted amine submetabolome profiling. To improve method sensitivity and selectivity, multiplexed drift-tube ion mobility (IM) was integrated into the LC-MS workflow, facilitating the separation of isomeric metabolites, and improving the structural identification of unknown metabolites. Implementation of the U-¹³C-labeled yeast extract during the multiplexed LC-IM-MS analysis enabled the quantitation of 19 of the 20 common AAs, supporting a linear dynamic range spanning up to three orders of magnitude in concentration for microglial cell extracts, in addition to reducing the required cell count for reliable quantitation from 10 to 5 million cells per sample.

An Approach to Biomarker Discovery of Cannabis Use Utilizing Proteomic, Metabolomic, and Lipidomic Analyses

Introduction: Relatively little is known about the molecular pathways influenced by cannabis use in humans. We used a multi-omics approach to examine protein, metabolomic, and lipid markers in plasma differentiating between cannabis users and nonusers to understand markers associated with cannabis use. Methods: Eight discordant twin pairs and four concordant twin pairs for cannabis use completed a blood draw, urine and plasma toxicology testing, and provided information about their past 30-day cannabis use and other substance use patterns. The 24 twins were all non-Hispanic whites. Sixty-six percent were female. Median age was 30 years. Fifteen participants reported that they had used cannabis in the last 30 days, including eight participants that used every day or almost every day (29-30 of 30 days). Of these 15 participants, plasma 11-nor-9-carboxy-Δ⁹-tetrahydrocannabinol (THC-COOH) and total tetrahydrocannabinol (THC) concentrations were detectable in 12 participants. Among the eight "heavy users" the amount of total THC (sum of THC and its metabolites) and plasma THC-COOH concentrations varied widely, with ranges of 13.1-1713 ng/mL and 2.7-284 ng/mL, respectively. A validated liquid chromatography-tandem mass spectrometry (LC-MS/MS) assay measured plasma THC-COOH, THC, and other cannabinoids and metabolites. Plasma THC-COOH was used as the primary measure. Expression levels of 1305 proteins were measured using SOMAScan assay, and 34 lipid mediators and 314 metabolites were measured with LC-MS/MS. Analyses examined associations between markers and THC-COOH levels with and without taking genetic relatedness into account. Results: Thirteen proteins, three metabolites, and two lipids were identified as associated with THC-COOH levels. Myc proto-oncogene was identified as associated with THC-COOH levels in both molecular insight and potential marker analyses. Five pathways (interleukin-6 production, T lymphocyte regulation, apoptosis, kinase signaling pathways, and nuclear factor kappa-light-chain-enhancer of activated B cells) were linked with molecules identified in these analyses. Conclusions: THC-COOH levels are associated with immune system-related pathways. This study presents a feasible approach to identify additional molecular markers associated with THC-COOH levels.

Targeted Metabolomics Analysis of Bile Acids in Patients with Idiosyncratic Drug-Induced Liver Injury

Drug-induced liver injury (DILI) is rare but clinically important due to a high rate of mortality. However, specific biomarkers for diagnosing and predicting the severity and prognosis of DILI are lacking. Here, we used targeted metabolomics to identify and quantify specific types of bile acids that can predict the severity of DILI. A total of 161 DILI patients were enrolled in this prospective cohort study, as well as 31 health controls. A targeted metabolomics method was used to identify 24 types of bile acids. DILI patients were divided into mild, moderate, and severe groups according to disease severity. A multivariate analysis was performed to identify characteristic bile acids. Then the patients were divided into severe and non-severe groups, and logistic regression was used to identify bile acids that could predict DILI severity. Among the enrolled DILI patients, 32 were in the mild group, 90 were in the moderate group, and 39 were in the severe group. Orthogonal partial least squares-discriminant analysis (OPLS-DA) modeling clearly discriminated among the different groups. Among the four groups, glycochenodeoxycholate (GCDCA), taurochenodeoxycholate (TCDCA), deoxycholic acid (DCA), Nor Cholic acid (NorCA), glycocholic acid (GCA), and taurocholic acid (TCA) showed significant differences in concentration between at least two groups. NorCA, GCDCA, and TCDCA were all independent risk factors that differentiated severe DILI patients from the other groups. The area under the receiver operating characteristic curve (AUROC) of GCDCA, TCDCA, and NorCA was 0.856, 0.792, and 0.753, respectively. Together, these three bile acids had an AUROC of 0.895 for predicting severe DILI patients. DILI patients with different disease severities have specific bile acid metabolomics. NorCA, GCDTA, and TCDCA were independent risk factors for differentiating severe DILI patients from less-severe patients and have the potential to predict DILI severity.

Serum metabolic profile of postoperative acute kidney injury following infant cardiac surgery with cardiopulmonary bypass

BACKGROUND

We sought to determine differences in the circulating metabolic profile of infants with or without acute kidney injury (AKI) following cardiothoracic surgery with cardiopulmonary bypass (CPB).

METHODS

We performed a secondary analysis of preoperative and 24-h postoperative serum samples from infants ≤ 120 days old undergoing CPB. Metabolic profiling of the serum samples was performed by targeted analysis of 165 serum metabolites via tandem mass spectrometry. We then compared infants who did or did not develop AKI in the first 72 h postoperatively to determine global differences in the preoperative and 24-h metabolic profiles in addition to specific differences in individual metabolites.

RESULTS

A total of 57 infants were included in the study. Six infants (11%) developed KDIGO stage 2/3 AKI and 13 (23%) developed stage 1 AKI. The preoperative metabolic profile did not differentiate between infants with or without AKI. Infants with severe AKI could be moderately distinguished from infants without AKI by their 24-h metabolic profile, while infants with stage 1 AKI segregated into two groups, overlapping with either the no AKI or severe AKI groups. Differences in these 24-h metabolic profiles were driven by 21 metabolites significant at an adjusted false discovery rate of < 0.05. Prominently altered pathways include purine, methionine, and kynurenine/nicotinamide metabolism.

CONCLUSION

Moderate-to-severe AKI after infant cardiac surgery is associated with changes in the serum metabolome, including prominent changes to purine, methionine, and kynurenine/nicotinamide metabolism. A portion of infants with mild AKI demonstrated similar metabolic changes, suggesting a potential role for metabolic analysis in the evaluation of lower stage injury.

Current progress in metabolomics of gestational diabetes mellitus

Gestational diabetes mellitus (GDM) is one of the most common metabolic disorders of pregnancy and can cause short- and long-term adverse effects in both pregnant women and their offspring. However, the etiology and pathogenesis of GDM are still unclear. As a metabolic disease, GDM is well suited to metabolomics study, which can monitor the changes in small molecular metabolites induced by maternal stimuli or perturbations in real time. The application of metabolomics in GDM can be used to discover diagnostic biomarkers, evaluate the prognosis of the disease, guide the application of diet or drugs, evaluate the curative effect, and explore the mechanism. This review provides comprehensive documentation of metabolomics research methods and techniques as well as the current progress in GDM research. We anticipate that the review will contribute to identifying gaps in the current knowledge or metabolomics technology, provide evidence-based information, and inform future research directions in GDM.

Attenuation of doxorubicin-induced oxidative damage in rat brain by regulating amino acid homeostasis with Astragali Radix

The nervous system disorders caused by doxorubicin (DOX) are among the severe adverse effects that dramatically reduce the quality of life of cancer survivors. Astragali Radix (AR), a popular herbal drug and dietary supplement, is believed to help treat brain diseases by reducing oxidative stress and maintaining metabolic homeostasis. Here we show the protective effects of AR against DOX-induced oxidative damage in rat brain via regulating amino acid homeostasis. By constructing a clinically relevant low-dose DOX-induced toxicity rat model, we first performed an untargeted metabolomics analysis to discover specific metabolic features in the brain after DOX treatment and AR co-treatment. It was found that the amino acid (AA) metabolism pathways altered most significantly. To accurately characterize the brain AA profile, we established a sensitive, fast, and reproducible hydrophilic interaction chromatography-tandem mass spectrometry method for the simultaneous quantification of 22 AAs. The targeted analysis further confirmed the changes of AAs between different groups of rat brain. Specifically, the levels of six AAs, including glutamate, glycine, serine, alanine, citrulline, and ornithine, correlated (Pearson |r| >  0.47, p < 0.05) with the brain oxidative damage that was caused by DOX and rescued by AR. These findings present that AAs are among the regulatory targets of DOX-induced brain toxicity, and AR is a promising therapeutic agent for it.

Alterations in Metabolites Associated with Hypoxemia in Neonates and Infants with Congenital Heart Disease

Objectives:

(1) To measure the global shift in the metabolome in hypoxemic versus non-hypoxemic infants with congenital heart disease; (2) To identify metabolites and metabolic pathways that are altered in hypoxemia.

Study Design:

Analysis of serum samples obtained prior to cardiopulmonary bypass from 82 infants ≤120 days old with congenital heart disease requiring surgery at Children’s Hospital Colorado. Infants were divided into groups based on pre-operative oxygen saturations: non-hypoxemic (>92%), mild hypoxemia (85–92%), and severe hypoxemia (<85%). Tandem mass spectrometry was used to analyze 165 targeted metabolites. Partial least squares discriminant analysis and t-tests were used to determine differences among metabolic profiles and individual metabolites respectively.

Results:

The broad metabolic fingerprint of neonates or older infants did not vary by degree of hypoxemia. There were 12 individual metabolites that differed between hypoxemic and non-hypoxemic neonates, including lower methylmalonic acid (p = 2.44 × 10⁻⁴), glutamate (p = 0.001), and hypoxanthine (p = 0.003), and higher thymine (p = 8.67 × 10⁻⁴) and myo-inositol (p = 0.014) seen in hypoxemic neonates. Individual metabolites did not vary significantly between older infants with or without hypoxemia.

Conclusions:

We did not find evidence supporting global metabolic changes associated with cyanotic congenital heart disease in neonates or older infants. However, specific metabolites did discriminate between hypoxemic and non-hypoxemic neonates. These include methylmalonic acid, as well as several metabolites known to change in hypoxia-reoxygenation states (hypoxanthine) and chronic hypoxemic states (glutamate, thymine, myo-inositol) and may represent specific metabolic changes triggered by hypoxemia among neonates with cyanotic congenital heart disease.

Analytical methods for amino acid determination in organisms

Amino acids are important metabolites for tissue metabolism, growth, maintenance, and repair, which are basic life necessities. Therefore, summarizing analytical methods for amino acid determination in organisms is important. In the past decades, analytical methods for amino acids have developed rapidly but have not been fully explored. Thus, this article provides reference to analytical methods for amino acids in organisms for food and human research. Present amino acid analysis methods include thin-layer chromatography, high-performance liquid chromatography, liquid chromatography-mass spectrometer, gas chromatography-mass spectrometry, capillary electrophoresis, nuclear magnetic resonance, and amino acid analyzer analysis.

Simultaneous Determination of 34 Amino Acids in Tumor Tissues from Colorectal Cancer Patients Based on the Targeted UHPLC-MS/MS Method

A targeted ultrahigh-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) method was established and validated for the simultaneous determination of 34 amino acids in tissue samples from colorectal cancer (CRC) patients. The chromatographic separation was achieved on an Agilent ZORBAX SB-C₁₈ column (3.0 × 150 mm, 5 μm) with a binary gradient elution system (A, 0.02% heptafluorobutyric acid and 0.2% formic acid in water, v/v; B, methanol). The run time was 10 min. The multiple reaction monitoring mode was chosen with an electrospray ionization source operating in the positive ionization mode for data acquisition. The linear correlation coefficients were >0.99 for all the analytes in their corresponding calibration ranges. The sample was pretreated based on tissue homogenate and protein precipitation with a 100 mg aliquot sample. The average recovery and matrix effect for 34 amino acids and 3 internal standards were 39.00%∼146.95% and 49.45%∼173.63%, respectively. The intra- and interday accuracy for all the analytes ranged from -13.52% to 14.21% (RSD ≤8.57%) and from -14.52% to 12.59% (RSD ≤10.31%), respectively. Deviations of stability under different conditions were within ±15% for all the analytes. This method was applied to simultaneous quantification of 34 amino acids in tissue samples from 94 CRC patients.

Metabolomics Research Conducted by Nurse Scientists: A Systematic Scoping Review

Metabolomics, one of the newest omics, allows for investigation of holistic responses of living systems to myriad biological, behavioral, and environmental factors. Researcher use metabolomics to examine the underlying mechanisms of clinically observed phenotypes. However, these methods are complex, potentially impeding their uptake by scientists. In this scoping review, we summarize literature illustrating nurse scientists' use of metabolomics. Using electronic search methods, we identified metabolomics investigations conducted by nurse scientists and published in English-language journals between 1990 and November 2019. Of the studies included in the review (N = 30), 9 (30%) listed first and/or senior authors that were nurses. Studies were conducted predominantly in the United States and focused on a wide array of clinical conditions across the life span. The upward trend we note in the use of these methods by nurse scientists over the past 2 decades mirrors a similar trend across scientists of all backgrounds. A broad range of study designs were represented in the literature we reviewed, with the majority involving untargeted metabolomics (n = 16, 53.3%) used to generate hypotheses (n = 13, 76.7%) of potential metabolites and/or metabolic pathways as mechanisms of clinical conditions. Metabolomics methods match well with the unique perspective of nurse researchers, who seek to integrate the experiences of individuals to develop a scientific basis for clinical practice that emphasizes personalized approaches. Although small in number, metabolomics investigations by nurse scientists can serve as the foundation for robust programs of research to answer essential questions for nursing.

Metabolomics Signatures in Type 2 Diabetes: A Systematic Review and Integrative Analysis

OBJECTIVE

Metabolic signatures have emerged as valuable signaling molecules in the biochemical process of type 2 diabetes (T2D). To summarize and identify metabolic biomarkers in T2D, we performed a systematic review and meta-analysis of the associations between metabolites and T2D using high-throughput metabolomics techniques.

METHODS

We searched relevant studies from MEDLINE (PubMed), Embase, Web of Science, and Cochrane Library as well as Chinese databases (Wanfang, Vip, and CNKI) inception through 31 December 2018. Meta-analysis was conducted using STATA 14.0 under random effect. Besides, bioinformatic analysis was performed to explore molecule mechanism by MetaboAnalyst and R 3.5.2.

RESULTS

Finally, 46 articles were included in this review on metabolites involved amino acids, acylcarnitines, lipids, carbohydrates, organic acids, and others. Results of meta-analysis in prospective studies indicated that isoleucine, leucine, valine, tyrosine, phenylalanine, glutamate, alanine, valerylcarnitine (C5), palmitoylcarnitine (C16), palmitic acid, and linoleic acid were associated with higher T2D risk. Conversely, serine, glutamine, and lysophosphatidylcholine C18:2 decreased risk of T2D. Arginine and glycine increased risk of T2D in the Western countries subgroup, and betaine was negatively correlated with T2D in nested case-control subgroup. In addition, slight improvements in T2D prediction beyond traditional risk factors were observed when adding these metabolites in predictive analysis. Pathway analysis identified 17 metabolic pathways may alter in the process of T2D and metabolite-related genes were also enriched in functions and pathways associated with T2D.

CONCLUSIONS

Several metabolites and metabolic pathways associated with T2D have been identified, which provide valuable biomarkers and novel targets for prevention and drug therapy.

Biological System Responses of Dairy Cows to Aflatoxin B1 Exposure Revealed with Metabolomic Changes in Multiple Biofluids

Research on mycotoxins now requires a systematic study of post-exposure organisms. In this study, the effects of aflatoxin B1 (AFB1) on biofluids biomarkers were examined with metabolomics and biochemical tests. The results showed that milk concentration of aflatoxin M1 changed with the addition or removal of AFB1. AFB1 significantly affected serum concentrations of superoxide dismutase (SOD) and malon dialdehyde (MDA), SOD/MDA, and the total antioxidant capacity. Significant differences of volatile fatty acids and NH₃-N were detected in the rumen fluid. Eighteen rumen fluid metabolites, 11 plasma metabolites, and 9 milk metabolites were significantly affected by the AFB1. These metabolites are mainly involved in the pathway of amino acids metabolism. Our results suggest that not only is the study of macro-indicators (milk composition and production) important, but that more attention should be paid to micro-indicators (biomarkers) when assessing the risks posed by mycotoxins to dairy cows.

The metabolome as a biomarker of mortality risk in the common marmoset

Recently, the common marmoset has been proposed as a non-human primate model of aging. Their short lifespan coupled with pathologies that are similar to humans make them an ideal model to understand the genetic, metabolic, and environmental factors that influence aging and longevity. However, many of the underlying physiological changes that occur with age in the marmoset are unknown. Here, we attempt to determine if individual metabolites are predictive of future death and to recapitulate past metabolomic results after a change in environment (move across the country) was imposed on a colony of marmosets. We first determined that low levels of tryptophan metabolism metabolites were associated with risk of death in a 2-year follow-up in the animals, suggesting these metabolites may be used as future biomarkers of mortality. We also discovered that betaine metabolism and methionine metabolism are associated with aging regardless of environment for the animals, or of metabolomic assay technique. These two metabolic pathways are therefore of particular interest to examine as future targets for health and lifespan extending interventions. Many of the pathways associated with age in our first study of marmoset metabolomics were not found to have significant age effects in our second study, suggesting more work is needed to understand the reproducibility of large scale metabolomic studies in mammalian models. Overall, we were able to show that while several metabolomics markers show promise in understanding health and lifespan relationships with aging, it is possible that choice of technique for assay and reproducibility in these types of studies are still issues that need to be examined further.

Metabolomic Fingerprinting of Infants Undergoing Cardiopulmonary Bypass: Changes in Metabolic Pathways and Association With Mortality and Cardiac Intensive Care Unit Length of Stay

Background Mortality for infants undergoing complex cardiac surgery is >10% with a 30% to 40% risk of complications. Early identification and treatment of high-risk infants remains challenging. Metabolites are small molecules that determine the minute-to-minute cellular phenotype, making them ideal biomarkers for postsurgical monitoring and potential targets for intervention. Methods and Results We measured 165 serum metabolites by tandem mass spectroscopy in infants ≤120 days old undergoing cardiopulmonary bypass. Samples were collected prebypass, during rewarming, and 24 hours after surgery. Partial least squares-discriminant analysis, pathway analysis, and receiver operator characteristic curve analysis were used to evaluate changes in the metabolome, assess altered metabolic pathways, and discriminate between survivors/nonsurvivors as well as upper/lower 50% intensive care unit length of stay. Eighty-two infants had preoperative samples for analysis; 57 also had rewarming and 24-hour samples. Preoperation, the metabolic fingerprint of neonates differed from older infants ( R2=0.89, Q2=0.77; P<0.001). Cardiopulmonary bypass resulted in progressive, age-independent metabolic disturbance ( R2=0.92, Q2=0.83; P<0.001). Multiple pathways demonstrated changes, with arginine/proline ( P=1.2×10-35), glutathione ( P=3.3×10-39), and alanine/aspartate/glutamate ( P=1.4×10-26) metabolism most affected. Six subjects died. Nonsurvivors demonstrated altered aspartate ( P=0.007) and nicotinate/nicotinamide metabolism ( P=0.005). The combination of 24-hour aspartate and methylnicotinamide identified nonsurvivors versus survivors (area under the curve, 0.86; P<0.01), as well as upper/lower 50% intensive care unit length of stay (area under the curve, 0.89; P<0.01). Conclusions The preoperative metabolic fingerprint of neonates differed from older infants. Large metabolic shifts occurred after cardiopulmonary bypass, independent of age. Nonsurvivors and subjects requiring longer intensive care unit length of stay showed distinct changes in metabolism. Specific metabolites, including aspartate and methylnicotinamide, may differentiate sicker patients from those experiencing a more benign course.

Diagnostic value of plasma tryptophan and symmetric dimethylarginine levels for acute kidney injury among tacrolimus-treated kidney transplant patients by targeted metabolomics analysis

Few literatures have evaluated the exact role of metabolomics in the identification process of potential biomarkers for acute kidney injury among the patients receiving renal transplantation. On top of this, the success of metabolomics in biomarker translation seems to lie in the robust quantitative method. As such, a single-center retrospective observational study was conducted enrolling 42 patients underwent renal transplantation with/without acute kidney injury, as well as 24 healthy volunteers, in Shanghai Changzheng Hospital. Plasma amino acid metabolic patterns for the participants were investigated by targeted UHPLC-MS/MS metabolic profiling. The most significant changes of the explored metabolites were related to the disturbance of tryptophan metabolism and arginine metabolism. Abnormal circulating tryptophan and symmetric dimethylarginine were identified to be potential biomarkers of acute kidney injury, combination of which showed a higher area under receiver-operator curve value (AUC = 0.901), improved sensitivity (0.889) and specificity (0.831) compared with creatinine only. Overall, these results revealed that targeted metabolomics analysis would be a potent and promising strategy for identification and pre-validation of biomarkers of acute kidney injury in renal transplantation patients.

From Discovery to Translation: Characterization of C-Mannosyltryptophan and Pseudouridine as Markers of Kidney Function

Using a non-targeted metabolomics platform, we recently identified C-mannosyltryptophan and pseudouridine as non-traditional kidney function markers. The aims of this study were to obtain absolute concentrations of both metabolites in blood and urine from individuals with and without CKD to provide reference ranges and to assess their fractional excretions (FE), and to assess the agreement with their non-targeted counterparts. In individuals without/with CKD, mean plasma and urine concentrations for C-mannosyltryptophan were 0.26/0.72 µmol/L and 3.39/4.30 µmol/mmol creatinine, respectively. The respective concentrations for pseudouridine were 2.89/5.67 µmol/L and 39.7/33.9 µmol/mmol creatinine. Median (25^th, 75^th percentiles) FEs were 70.8% (65.6%, 77.8%) for C-mannosyltryptophan and 76.0% (68.6%, 82.4%) for pseudouridine, indicating partial net reabsorption. Association analyses validated reported associations between single metabolites and eGFR. Targeted measurements of both metabolites agreed well with the non-targeted measurements, especially in urine. Agreement for composite nephrological measures FE and urinary metabolite-to-creatinine ratio was lower, but could be improved by replacing non-targeted creatinine measurements with a standard clinical creatinine test. In summary, targeted quantification and additional characterization in relevant populations are necessary steps in the translation of non-traditional biomarkers in nephrology from non-targeted discovery to clinical application.

Comprehensive Metabolomic Profiling and Incident Cardiovascular Disease: A Systematic Review

BACKGROUND

Metabolomics is a promising tool of cardiovascular biomarker discovery. We systematically reviewed the literature on comprehensive metabolomic profiling in association with incident cardiovascular disease (CVD).

METHODS AND RESULTS

We searched MEDLINE and EMBASE from inception to January 2016. Studies were eligible if they pertained to adult humans; followed an agnostic and/or comprehensive approach; used serum or plasma (not urine or other biospecimens); conducted metabolite profiling at baseline in the context of examining prospective disease; and included myocardial infarction, stroke, and/or CVD death in the CVD outcome definition. We identified 12 original articles (9 cohort and 3 nested case-control studies); participant numbers ranged from 67 to 7256. Mass spectrometry was the predominant analytical method. The number and chemical diversity of metabolites were very heterogeneous, ranging from 31 to >10 000 features. Four studies used untargeted profiling. Different types of metabolites were associated with CVD risk: acylcarnitines, dicarboxylacylcarnitines, and several amino acids and lipid classes. Only tiny improvements in CVD prediction beyond traditional risk factors were observed using these metabolites (C index improvement ranged from 0.006 to 0.05).

CONCLUSIONS

There are a limited number of longitudinal studies assessing associations between comprehensive metabolomic profiles and CVD risk. Quantitatively synthesizing the literature is challenging because of the widely varying analytical tools and the diversity of methodological and statistical approaches. Although some results are promising, more research is needed, notably standardization of metabolomic techniques and statistical approaches. Replication and combinations of novel and holistic methodological approaches would move the field toward the realization of its promise.

High-Speed Quantitative UPLC-MS Analysis of Multiple Amines in Human Plasma and Serum via Precolumn Derivatization with 6-Aminoquinolyl-N-hydroxysuccinimidyl Carbamate: Application to Acetaminophen-Induced Liver Failure

A targeted reversed-phase gradient UPLC-MS/MS assay has been developed for the quantification /monitoring of 66 amino acids and amino-containing compounds in human plasma and serum using precolumn derivatization with 6-aminoquinolyl-N-hydroxysuccinimidyl carbamate (AccQTag Ultra). Derivatization of the target amines required minimal sample preparation and resulted in analytes with excellent chromatographic and mass spectrometric detection properties. The resulting method, which requires only 10 μL of sample, provides the reproducible and robust separation of 66 analytes in 7.5 min, including baseline resolution of isomers such as leucine and isoleucine. The assay has been validated for the quantification of 33 amino compounds (predominantly amino acids) over a concentration range from 2 to 20 and 800 μM. Intra- and interday accuracy of between 0.05 and 15.6 and 0.78-13.7% and precision between 0.91 and 16.9% and 2.12-15.9% were obtained. A further 33 biogenic amines can be monitored in samples for relative changes in concentration rather than quantification. Application of the assay to samples derived from healthy controls and patients suffering from acetaminophen (APAP, paracetamol)-induced acute liver failure (ALF) showed significant differences in the amounts of aromatic and branched chain amino acids between the groups as well as a number of other analytes, including the novel observation of increased concentrations of sarcosine in ALF patients. The properties of the developed assay, including short analysis time, make it suitable for high-throughput targeted UPLC-ESI-MS/MS metabonomic analysis in clinical and epidemiological environments.

Technical Challenges in Mass Spectrometry-Based Metabolomics

Metabolomics is a strategy for analysis, and quantification of the complete collection of metabolites present in biological samples. Metabolomics is an emerging area of scientific research because there are many application areas including clinical, agricultural, and medical researches for the biomarker discovery and the metabolic system analysis by employing widely targeted analysis of a few hundred preselected metabolites from 10-100 biological samples. Further improvement in technologies of mass spectrometry in terms of experimental design for larger scale analysis, computational methods for tandem mass spectrometry-based elucidation of metabolites, and specific instrumentation for advanced bioanalysis will enable more comprehensive metabolome analysis for exploring the hidden secrets of metabolism.