Validation of a dual LC-HRMS platform for clinical metabolic diagnosis in serum, bridging quantitative analysis and untargeted metabolomics

Determination of alkaloid-inspired molecule vindeburnol in rabbit plasma by UPLC-HRMS and its application to pharmacokinetic studies and preliminary metabolite identification

The eburnamine-vincamine alkaloids exhibit a range of pharmacological activities. There is a limited understanding of the pharmacokinetics and pharmacodynamics of vindeburnol, a synthetic derivative of this chemical class of alkaloids. A fast and reliable UPLC-HRMS method was developed and validated to quantify vindeburnol in Soviet Chinchilla rabbit plasma from pharmacokinetics studies. An ultra-performance liquid chromatography system equipped with a Waters Acquity UPLC HSS T3 column was used for chromatographic separation by gradient elution with 0.1% (v/v) formic acid in water and acetonitrile. An Impact II QqTOF high-resolution mass spectrometer equipped with an Apollo II electrospray ionization source was used for analysis in positive mode; the ions [M+H]+m/z 269.1648 ± 0.003 and m/z 351.2067 ± 0.003 were monitored for vindeburnol and internal standard (vinpocetine), respectively. Preliminary metabolite profiling was also performed, and the pharmacokinetics of the identified metabolites were evaluated. The mean retention times for vindeburnol and vinpocetine were 2.0 and 3.5 min. The UPLC-HRMS method was validated with accuracy and precision within the 15% acceptance limit (8.2% and 11.0%, respectively). The mean extraction recovery value of vindeburnol from rabbit plasma was 77%. Pharmacokinetic evaluation of vindeburnol revealed that the compound is distributed rapidly with a short elimination half-life. Vindeburnol undergoes extensive first-pass metabolism and is metabolized into hydroxyvindeburnol and vindeburnol glucuronide.

Detecting lysosomal storage disorders by glycomic profiling using liquid chromatography mass spectrometry

BACKGROUND

Urine and plasma biomarker testing for lysosomal storage disorders by liquid chromatography mass spectrometry (LC-MS) currently requires multiple analytical methods to detect the abnormal accumulation of oligosaccharides, mucopolysaccharides, and glycolipids. To improve clinical testing efficiency, we developed a single LC-MS method to simultaneously identify disorders of oligosaccharide, mucopolysaccharide, and glycolipid metabolism with minimal sample preparation.

METHODS

We created a single chromatographic method for separating free glycans and glycolipids in their native form, using an amide column and high pH conditions. We used this glycomic profiling method both in untargeted analyses of patient and control urines using LC ion-mobility high-resolution MS (biomarker discovery), and targeted analyses of urine, serum, and dried blood spot samples by LC-MS/MS (clinical validation).

RESULTS

Untargeted glycomic profiling revealed twenty biomarkers that could identify and subtype mucopolysaccharidoses. We incorporated these with known oligosaccharide and glycolipid biomarkers into a rapid test that identifies at least 27 lysosomal storage disorders, including oligosaccharidoses, mucopolysaccharidoses, sphingolipidoses, glycogen storage disorders, and congenital disorders of glycosylation and de-glycosylation. In a validation set containing 115 urine samples from patients with lysosomal storage disorders, all were unambiguously distinguished from normal controls, with correct disease subtyping for 88% (101/115) of cases. Glucosylsphingosine was reliably elevated in dried blood spots from Gaucher disease patients with baseline resolution from galactosylsphingosine.

CONCLUSION

Glycomic profiling by liquid chromatography mass spectrometry identifies a range of lysosomal storage disorders. This test can be used in clinical evaluations to rapidly focus a diagnosis, as well as to clarify or support additional gene sequencing and enzyme studies.

Challenges in LC-MS-based metabolomics for Alzheimer's disease early detection: targeted approaches versus untargeted approaches

BACKGROUND

Alzheimer's disease (AD) is one of the most common causes of dementia in old people. Neuronal deficits such as loss of memory, language and problem-solving are severely compromised in affected patients. The molecular features of AD are Aβ deposits in plaques or in oligomeric structures and neurofibrillary tau tangles in brain. However, the challenge is that Aβ is only one piece of the puzzle, and recent findings continue to support the hypothesis that their presence is not sufficient to predict decline along the AD outcome. In this regard, metabolomic-based techniques are acquiring a growing interest for either the early diagnosis of diseases or the therapy monitoring. Mass spectrometry is one the most common analytical platforms used for detection, quantification, and characterization of metabolic biomarkers. In the past years, both targeted and untargeted strategies have been applied to identify possible interesting compounds.

AIM OF REVIEW

The overall goal of this review is to guide the reader through the most recent studies in which LC-MS-based metabolomics has been proposed as a powerful tool for the identification of new diagnostic biomarkers in AD. To this aim, herein studies spanning the period 2009-2020 have been reported. Advantages and disadvantages of targeted vs untargeted metabolomic approaches have been outlined and critically discussed.

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.

A merged method for targeted analysis of amino acids and derivatives using parallel reaction monitoring combined with untargeted profiling by HILIC-Q-Orbitrap HRMS

With continuously increased scan rate and sensitivity, high resolution mass spectrometry (HRMS) allows for both reliable targeted analysis (e.g., parallel reaction monitoring, PRM) and a global overview for discovery-based untargeted profiling (e.g., data dependent acquisition, DDA) to be performed. Based on previous study on PRM for large scale targeted metabolomics quantification, we developed an innovative method merged targeted and untargeted approaches in a single run. In our workflow, the scheduled PRM for targeted analysis of amino acids and derivatives combined with the full scan was acquired in every sample injection by hydrophilic interaction liquid chromatography tandem quadrupole-Orbitrap high resolution mass spectrometry (HILIC-Q-Orbitrap HRMS). The identification of metabolic features from full scan was further performed with DDA methodology on grouped quality control (QC) samples and matched with available database. Specifically, 20 amino acids and 40 derivatives were selected for targeted analysis with optimal chromatographic separation and PRM parameters. All isomers within the selected metabolites were totally separated in the robust HILIC condition. 36 of selected metabolites were well-detected and showed a good linearity and reproducibility in NIST SRM 1950 plasma. Moreover, the absolute quantification performance of targeted PRM method was systematically validated using 10 amino acids with the corresponding stable isotope-labeled internal standards (SIL-IS). Finally, the newly developed method was successfully applied to analysis of the plasma samples from patients of pancreatic benign tumor and pancreatic cancer. The significant reduction of circulating amino acids in patients with pancreatic malignancy was confirmed by targeted PRM method and other amino acids modifications as well as polar metabolites were identified with untargeted profiling. Therefore, we have established a workflow that combines specifically and reliably targeted PRM method as well as broad-coverage untargeted profiling, which provides an innovative strategy for basic and clinical metabolomics study.

Tumor Metabolism and Neurocognition in CNS Lymphoma

BACKGROUND

The mechanistic basis for neurocognitive deficits in CNS lymphoma and other brain tumors is incompletely understood. We tested the hypothesis that tumor metabolism impairs neurotransmitter pathways and neurocognitive function.

METHODS

We performed serial cerebrospinal fluid (CSF) metabolomic analyses using liquid chromatography-electrospray tandem mass spectrometry to evaluate changes in the tumor microenvironment in 14 patients with recurrent CNS lymphoma, focusing on 18 metabolites involved in neurotransmission and bioenergetics. These were paired with serial mini-mental state examinations (MMSE) and MRI studies for tumor volumetric analyses. Patients were analyzed in the setting of the phase I trial of lenalidomide/rituximab. Associations were assessed by Pearson and Spearman correlation coefficient. Generalized estimating equation (gee) models were also established, adjusting for within-subject repeated measures.

RESULTS

Of 18 metabolites, elevated CSF lactate correlated most strongly with lower MMSE score (p<8E-8, rho=-0.67). High lactate was associated with lower GABA, higher glutamate/GABA ratio and dopamine. Conversely, high succinate correlated with higher MMSE score. Serial analysis demonstrated a reproducible, time-dependent, reciprocal correlation between changes in lactate and GABA concentrations. While high lactate and low GABA correlated with tumor contrast enhancing volume, they correlated more significantly with lower MMSE scores than tumor volumes.

CONCLUSIONS

We provide evidence that lactate production and Warburg metabolism may impact neurotransmitter dysregulation and neurocognition in CNS lymphomas. We identify novel metabolomic biomarkers that may be applied in future studies of neurocognition in CNS lymphomas. Elucidation of mechanistic interactions between lymphoma metabolism, neurotransmitter imbalance and neurocognition may promote interventions that preserve cognitive function.

Untargeted metabolomics changes on Gammarus pulex induced by propranolol, triclosan, and nimesulide pharmaceutical drugs

The presence of pharmaceuticals and personal care products (PPCPs) in natural water resources due to incomplete removal in Wastewater Treatment Plants (WWTPs) is a serious environmental concern at present. In this work, the effects of three pharmaceuticals (propranolol, triclosan, and nimesulide) on Gammarus pulex metabolic profiles at different doses and times of exposure have been investigated by liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS). The complex data sets generated in the different exposure experiments were analyzed with the ROIMCR procedure, based on the selection of the MS regions of interest (ROI) data and on their analysis by the Multivariate Curve-Resolution Alternating Least Squares (MCR-ALS) chemometrics method. This approach, allowed the resolution and identification of the metabolites present in the analyzed samples, as well as the estimation of their concentration changes due to the exposure experiments. ANOVA Simultaneous Component Analysis (ASCA) and Partial Least Squares Discriminant Analysis (PLS-DA) were then conducted to assess the changes in the concentration of the metabolites for the three pharmaceuticals at the different conditions of exposure. The three tested pharmaceuticals changed the concentrations of metabolites, which were related to different KEGG functional classes. These changes summarize the biochemical response of Gammarus pulex to the exposure by the three investigated pharmaceuticals. Possible pathway alterations related to protein synthesis and oxidative stress were observed in the concentration of identified metabolites.

Cardiac tissue citric acid cycle intermediates in exercised very long-chain acyl-CoA dehydrogenase-deficient mice fed triheptanoin or medium-chain triglyceride

Anaplerotic odd-chain fatty acid supplementation has been suggested as an approach to replenish citric acid cycle intermediate (CACi) pools and facilitate adenosine triphosphate (ATP) production in subjects with long-chain fatty acid oxidation disorders, but the evidence that cellular CACi depletion exists and that repletion occurs following anaplerotic substrate supplementation is limited. We exercised very long-chain acyl-CoA dehydrogenase-deficient (VLCAD-/-) and wild-type (WT) mice to exhaustion and collected cardiac tissue for measurement of CACi by targeted metabolomics. In a second experimental group, VLCAD-/- and WT mice that had been fed chow prepared with either medium-chain triglyceride (MCT) oil or triheptanoin for 4 weeks were exercised for 60 minutes. VLCAD-/- mice exhibited lower succinate in cardiac muscle at exhaustion than WT mice suggesting lower CACi in VLCAD-/- with prolonged exercise. In mice fed either MCT or triheptanoin, succinate and malate were greater in VLCAD-/- mice fed triheptanoin compared to VLCAD-/- animals fed MCT but lower than WT mice fed triheptanoin. Long-chain odd acylcarnitines such as C19 were elevated in VLCAD-/- and WT mice fed triheptanoin suggesting some elongation of the heptanoate, but it is unknown what proportion of heptanoate was oxidized vs elongated. Prolonged exercise was associated with decreased cardiac muscle succinate in VLCAD-/- mice in comparison to WT mice. VLCAD-/- fed triheptanoin had increased succinate compared to VLCAD-/- mice fed MCT but lower than WT mice fed triheptanoin. Cardiac CACi were higher following dietary ingestion of an anaplerotic substrate, triheptanoin, in comparison to MCT.

Identification of pathognomonic purine synthesis biomarkers by metabolomic profiling of adolescents with obesity and type 2 diabetes

The incidence of type 2 diabetes is increasing more rapidly in adolescents than in any other age group. We identified and compared metabolite signatures in obese children with type 2 diabetes (T2D), obese children without diabetes (OB), and healthy, age- and gender-matched normal weight controls (NW) by measuring 273 analytes in fasting plasma and 24-hour urine samples from 90 subjects by targeted LC-MS/MS. Diabetic subjects were within 2 years of diagnosis in an attempt to capture early-stage disease prior to declining renal function. We found 22 urine metabolites that were uniquely associated with T2D when compared to OB and NW groups. The metabolites most significantly elevated in T2D youth included members of the betaine pathway, nucleic acid metabolism, and branched-chain amino acids (BCAAs) and their catabolites. Notably, the metabolite pattern in OB and T2D groups differed between urine and plasma, suggesting that urinary BCAAs and their intermediates behaved as a more specific biomarker for T2D, while plasma BCAAs associated with the obese, insulin resistant state independent of diabetes status. Correlative analysis of metabolites in the T2D signature indicated that betaine metabolites, BCAAs, and aromatic amino acids were associated with hyperglycemia, but BCAA acylglycine derivatives and nucleic acid metabolites were linked to insulin resistance. Of major interest, we found that urine levels of succinylaminoimidazole carboxamide riboside (SAICA-riboside) were increased in diabetic youth, identifying urine SAICA-riboside as a potential biomarker for T2D.

Staring into the void: demystifying microbial metabolomics

Metabolites give us a window into the chemistry of microbes and are split into two subclasses: primary and secondary. Primary metabolites are required for life whereas secondary metabolites have historically been classified as those appearing after exponential growth and are not necessarily needed for survival. Many microbial species are estimated to produce hundreds of metabolites and can be affected by differing nutrients. Using various analytical techniques, metabolites can be directly detected in order to elucidate their biological significance. Currently, a single experiment can produce anywhere from megabytes to terabytes of data. This big data has motivated scientists to develop informatics tools to help target specific metabolites or sets of metabolites. Broadly, it is imperative to identify clear biological questions before embarking on a study of metabolites (metabolomics). For instance, studying the effect of a transposon insertion on phenazine biosynthesis in Pseudomonas is a very different from asking what molecules are present in a specific banana-derived strain of Pseudomonas. This review is meant to serve as a primer for a 'choose your own adventure' approach for microbiologists with limited mass spectrometry expertise, with a strong focus on liquid chromatography mass spectrometry based workflows developed or optimized within the past five years.

A High-Resolution Mass Spectrometry-Based Quantitative Metabolomic Workflow Highlights Defects in 5-Fluorouracil Metabolism in Cancer Cells with Acquired Chemoresistance

Currently, 5-fluorouracil (5-FU)-based combination chemotherapy is the mainstay in the treatment of metastatic colorectal cancer (CRC), which benefits approximately 50% of the patients. However, these tumors inevitably acquire chemoresistance resulting in treatment failure. The molecular mechanisms driving acquired chemotherapeutic drug resistance in CRC is fundamental for the development of novel strategies for circumventing resistance. However, the specific phenomenon that drives the cancer cells to acquire resistance is poorly understood. Understanding the molecular mechanisms that regulate chemoresistance will uncover new avenues for the treatment of CRC. Among the various mechanisms of acquired chemoresistance, defects in the drug metabolism pathways could play a major role. In the case of 5-FU, it gets converted into various active metabolites, which, directly or indirectly, interferes with the replication and transcription of dividing cells causing DNA and RNA damage. In this project, we developed a high-resolution mass spectrometry-based method to effectively extract and quantify levels of the 5-FU metabolites in cell lysates and media of parental and 5-FU resistant LIM1215 CRC cells. The analysis highlighted that the levels of 5-FU metabolites are significantly reduced in 5-FU resistant cells. Specifically, the level of the nucleotide fluorodeoxyuridine monophosphate (FdUMP) is reduced with treatment of 5-FU clarifying the compromised 5-FU metabolism in resistant cells. Corroborating the metabolomic analysis, treatment of the resistant cells with FdUMP, an active metabolite of 5-FU, resulted in effective killing of the resistant cells. Overall, in this study, an effective protocol was developed for comparative quantitation of polar metabolites and nucleotide analogues from the adherent cells efficiently. Furthermore, the utility of FdUMP as an alternative for CRC therapy is highlighted.

Metabolic profiling by reversed-phase/ion-exchange mass spectrometry

Metabolic profiling is commonly achieved by mass spectrometry (MS) following reversed-phase (RP) and hydrophilic interaction chromatography (HILIC) either performed independently, leading to overlapping datasets, or in a coupled configuration, requiring multiple liquid chromatography (LC) systems. To overcome these limitations, we developed a single, 20-minute chromatographic method using an in-line RP-ion-exchange (IEX) column arrangement and a single LC system. This configuration separates clinically significant polar and non-polar compounds without derivatization or ion-pairing reagents, allowing ionization in both polarities. An in-house library was created with 397 authentic standards, including acylcarnitines, amino acids, bile acids, nucleosides, organic acids, steroid hormones, and vitamins. Analysis of pooled plasma and urine samples revealed 5445 and 4111 ion features, leading to 88 and 82 confirmed metabolite identifications, respectively. Metabolites were detected at clinically relevant concentrations with good precision, and good chromatographic separation was demonstrated for clinically significant isomers including methylmalonic acid and succinic acid, as well as alloisoleucine and isoleucine/leucine. Evaluation of the samples by unsupervised principal component analysis showed excellent analytical quality.

Development and validation of a rapid, specific and sensitive LC-MS/MS bioanalytical method for eicosanoid quantification - assessment of arachidonic acid metabolic pathway activity in hypertensive rats

Lipid mediators such as eicosanoids maintain various physiological processes, and their alterations are involved in the development of numerous cardiovascular diseases. Therefore, the reliable assessment of their profile could be helpful in diagnosis as well as in eicosanoid biomarker-based treatment. Hence, the presented study aimed to develop and validate a new rapid, specific and sensitive LC-MS/MS method for quantification of arachidonic acid-derived eicosanoids in plasma, including lipid mediators generated via COX-, LOX- and CYP450-dependent pathways. The developed method features high sensitivity because the lower limit of quantification ranged from 0.05 to 0.50 ng mL^-1 as well as the accuracy and precision estimated within 88.88-111.25% and 1.03-11.82%, respectively. An application of a simple and fast liquid-liquid extraction procedure for sample cleaning resulted in a highly satisfactory recovery of the analytes (>88.30%). Additionally, the method was validated using artificial plasma, an approach that enabled the elimination of the matrix effect caused by an endogenous concentration of studied lipid mediators. Importantly, the presented LC-MS/MS method allowed for simultaneous quantitative and qualitative [quan/qual] analysis of the selected eicosanoids, leading to an additional improvement of the method specificity. Moreover, the validated method was successfully applied for eicosanoid profiling in rat, mouse and human plasma samples, clearly demonstrating the heterogeneity of the profile of studied lipid mediators in those species.

Analysis of Tryptophan Metabolites in Serum Using Wide-Isolation Strategies for UHPLC-HRMS/MS

Current targeted metabolomic workflows are limited by design and thus sacrifice crucial information from a profiling standpoint that could lead to a more fundamental understanding of the metabolic processes of interest. One drawback to performing targeted analysis on ion trapping instruments is the potential for increased variability in analysis when analytes and standards are isolated and trapped individually for fragmentation. In addition, this sequential isolation process increases the duty cycle of the mass spectrometer and reduces the number of points collected across a chromatographic peak. To address this, the use of a wide-isolation window (12 Da) to encompass the target analyte and the isotope standard within a single fragmentation window ensures that fragmentation is consistent when quantitation relies on the ratio of the target to the internal standard. Additionally, the preservation of a faster scan rate ensures that optimal representation of chromatographic peaks is preserved for the purposes of both quantitative and qualitative analyses that require peak integration for statistical analysis. The use of this flexible method is promising in the investigation of pathways that require multiple targets and are highly integrated within the system. Here, we demonstrate the application of this method in a fast ultra-high performance liquid chromatography (UHPLC) analysis to integrate wide-isolation quantitative strategies for high-resolution mass spectrometry (HRMS) combined with profiling qualitative metabolomics for the analysis of tryptophan degradation metabolites in mouse serum. Analysis of tryptophan-deficient states as compared to control in both germ-free or E. coli gut microbiota states was used to quantitate pathway-specific metabolites as well as obtain full profiling information. The quantitative and qualitative results revealed the preservation of the primary pathways of degradation in the kynurenine pathway to potentially produce primary products such as nicotinamide during stress-induced dietary states.

Complex Oscillatory Waves Emerging from Cortical Organoids Model Early Human Brain Network Development

Structural and transcriptional changes during early brain maturation follow fixed developmental programs defined by genetics. However, whether this is true for functional network activity remains unknown, primarily due to experimental inaccessibility of the initial stages of the living human brain. Here, we developed human cortical organoids that dynamically change cellular populations during maturation and exhibited consistent increases in electrical activity over the span of several months. The spontaneous network formation displayed periodic and regular oscillatory events that were dependent on glutamatergic and GABAergic signaling. The oscillatory activity transitioned to more spatiotemporally irregular patterns, and synchronous network events resembled features similar to those observed in preterm human electroencephalography. These results show that the development of structured network activity in a human neocortex model may follow stable genetic programming. Our approach provides opportunities for investigating and manipulating the role of network activity in the developing human cortex.

An endogenous peptide marker differentiates SOD1 stability and facilitates pharmacodynamic monitoring in SOD1 amyotrophic lateral sclerosis

The discovery of novel biomarkers has emerged as a critical need for therapeutic development in amyotrophic lateral sclerosis (ALS). For some subsets of ALS, such as the genetic superoxide dismutase 1 (SOD1) form, exciting new treatment strategies, such as antisense oligonucleotide-mediated (ASO-mediated) SOD1 silencing, are being tested in clinical trials, so the identification of pharmacodynamic biomarkers for therapeutic monitoring is essential. We identify increased levels of a 7-amino acid endogenous peptide of SOD1 in cerebrospinal fluid (CSF) of human SOD1 mutation carriers but not in other neurological cases or nondiseased controls. Levels of peptide elevation vary based on the specific SOD1 mutation (ranging from 1.1-fold greater than control in D90A to nearly 30-fold greater in V148G) and correlate with previously published measurements of SOD1 stability. Using a mass spectrometry-based method (liquid chromatography-mass spectrometry), we quantified peptides in both extracellular samples (CSF) and intracellular samples (spinal cord from rat) to demonstrate that the peptide distinguishes mutation-specific differences in intracellular SOD1 degradation. Furthermore, 80% and 63% reductions of the peptide were measured in SOD1G93A and SOD1H46R rat CSF samples, respectively, following treatment with ASO, with an improved correlation to mRNA levels in spinal cords compared with the ELISA measuring intact SOD1 protein. These data demonstrate the potential of this peptide as a pharmacodynamic biomarker.

High-throughput quantification of carboxymethyl lysine in serum and plasma using high-resolution accurate mass Orbitrap mass spectrometry

BACKGROUND

Carboxymethyl lysine is an advanced glycation end product of interest as a potential biomarker of cardiovascular and other diseases. Available methods involve ELISA, with potential interference, or isotope dilution mass spectrometry (IDMS), with low-throughput sample preparation.

METHODS

A high-throughput sample preparation method based on 96-well plates was developed. Protein-bound carboxymethyl lysine and lysine were quantified by IDMS using reversed phase chromatography coupled to a high-resolution accurate mass Orbitrap Exactive mass spectrometer. The carboxymethyl lysine concentration (normalized to lysine concentration) was measured in 1714 plasma samples from the British Regional Heart Study (BRHS).

RESULTS

For carboxymethyl lysine, the lower limit of quantification (LLOQ) was estimated at 0.16 μM and the assay was linear between 0.25 and 10 μM. For lysine, the LLOQ was estimated at 3.79 mM, and the assay was linear between 2.5 and 100 mM. The intra-assay coefficient of variation was 17.2% for carboxymethyl lysine, 9.3% for lysine and 10.5% for normalized carboxymethyl lysine. The inter-assay coefficient of variation was 18.1% for carboxymethyl lysine, 14.8 for lysine and 16.2% for normalized carboxymethyl lysine. The median and inter-quartile range of all study samples in each batch were monitored. A mean carboxymethyl lysine concentration of 2.7 μM (IQR 2.0-3.2 μM, range 0.2-17.4 μM) and a mean normalized carboxymethyl lysine concentration of 69 μM/M lysine (IQR 54-76 μM/M, range 19-453 μM/M) were measured in the BRHS.

CONCLUSION

This high-throughput sample preparation method makes it possible to analyse large cohorts required to determine the potential of carboxymethyl lysine as a biomarker.

Recent Advances and Challenges in Steroid Metabolomics for Biomarker Discovery

BACKGROUND

Steroid hormones belong to a group of low-molecular weight compounds which are responsible for maintenance of various body functions, thus, their accurate assessment is crucial for evaluation of biosynthetic defects. The development of reliable methods allowing disease diagnosis is essential to improve early detection of various disorders connected with altered steroidogenesis. Currently, the field of metabolomics offers several improvements in terms of sensitivity and specificity of the diagnostic methods when opposed to classical diagnostic approaches. The combination of hyphenated techniques and pattern recognition methods allows to carry out a comprehensive assessment of the slightest alterations in steroid metabolic pathways and can be applied as a tool for biomarker discovery.

METHODS

We have performed an extensive literature search applying various bibliographic databases for peer-reviewed articles concentrating on the applications of hyphenated techniques and pattern recognition methods incorporated into the steroid metabolomic approach for biomarker discovery.

RESULTS

The review discusses strengths, challenges and recent developments in steroidbased metabolomics. We present methods of sample collection and preparation, methods of separation and detection of steroid hormones in biological material, data analysis, and interpretation as well as examples of applications of steroid metabolomics for biomarker discovery (cancer, mental and central nervous system disorders, endocrine diseases, monitoring of drug therapy and doping control).

CONCLUSION

Information presented in this review will be valuable to anyone interested in the application of metabolomics for biomarker discovery with a special emphasis on disorders of steroid hormone synthesis and metabolism.

Phase 1 investigation of lenalidomide/rituximab plus outcomes of lenalidomide maintenance in relapsed CNS lymphoma

There is an unmet need for effective biological therapies for relapsed central nervous system (CNS) lymphoma. Lenalidomide is active in activated B-cell type diffuse large B-cell lymphoma and rituximab is effective in CNS lymphoma. These observations are the basis for this first trial of an immunomodulatory drug as monotherapy in CNS lymphoma, and, in patients with inadequate responses to lenalidomide, with rituximab. In an independent cohort, we evaluated lenalidomide maintenance after salvage with high-dose methotrexate or focal irradiation in relapsed primary CNS lymphoma (PCNSL). We determined safety, efficacy, and cerebrospinal fluid (CSF) penetration of lenalidomide at 10-, 15-, and 20-mg dose levels in 14 patients with refractory CD20⁺ CNS lymphoma. Nine subjects with relapsed, refractory CNS lymphoma achieved better than partial response with lenalidomide monotherapy, 6 maintained response ≥9 months, and 4 maintained response ≥18 months. Median progression-free survival for lenalidomide/rituximab was 6 months. In the independent cohort, response duration with lenalidomide maintenance after complete responses 2 through 5 were significantly longer than response durations after standard therapy. The CSF/plasma partition coefficient of lenalidomide was ≥20% at 15- and 20-mg dose levels. Change in CSF interleukin-10 at 1 month correlated with clinical response and response duration to lenalidomide. Metabolomic profiling of CSF identified novel biomarkers, including lactate, and implicated indoleamine-2,3 dioxygenase activity with CNS lymphoma progression on lenalidomide. We conclude that lenalidomide penetrates ventricular CSF and is active as monotherapy in relapsed CNS lymphomas. We provide evidence that maintenance lenalidomide potentiates response duration after salvage in relapsed PCNSL and delays whole brain radiotherapy (WBRT). This trial was registered at www.clinicaltrials.gov as #NCT01542918.

Exploiting High-Resolution Mass Spectrometry for Targeted Metabolite Quantification and 13C-Labeling Metabolism Analysis

Quantification of targeted metabolites, especially trace metabolites and structural isomers, in complex biological materials is an ongoing challenge for metabolomics. In this chapter, we summarize high-resolution mass spectrometry-based approaches mainly used for targeted metabolite and metabolomics analysis, and then introduce an MS1/MS2-combined PRM workflow for quantification of central carbon metabolism intermediates, amino acids, and shikimate pathway-related metabolites. Major steps in the workflow, including cell culture, metabolite extraction, LC-MS analysis and data processing, are described. Furthermore, we adapt this new approach to a dynamic ¹³C-labeling experiment and demonstrate its unique advantage in capturing and correcting isotopomer labeling curves to facilitate nonstationary ¹³C-labeling metabolism analysis.

Use of Liquid Chromatography-Mass Spectrometry-Based Metabolomics to Identify Biomarkers of Tuberculosis

Liquid chromatography-mass spectrometry (LC-MS) based metabolomics has proven to be a powerful analytical tool for biomarker screening. Here we describe two workflows which employ untargeted metabolomics to study serum biomarkers in tuberculosis patients. Expression profiles for samples of hydrophilic metabolites and hydrophobic metabolites (lipids) may be obtained by this method.

Analytical considerations for (un)-targeted metabolomic studies with special focus on forensic applications

Over the past few years, the interest in metabolomics has increased in various fields including forensic toxicology. Forensic analysis typically requires a high degree of accuracy, which is often a problem in metabolomics applications. We aimed for a systematic evaluation of different analytical considerations of a metabolomics workflow allowing a targeted approach within an untargeted setup. Samples with 69 metabolites from different chemical classes were qualitatively and quantitatively analyzed on a high resolution quadrupole time of flight mass spectrometer coupled to liquid chromatography (UHPLC-QTOF). Three issues were addressed: (a) Two different approaches on "blind matrix" a simulated body fluid (SBF) and plasma-filtrate, were tested for calibration samples; (b) comparison of two different HPLC columns, reverse-phase (RP) and hydrophilic interaction chromatography (HILIC); and (c) comparison of three different acquisition modes (TOF-MS, information dependent data acquisition (IDA), and sequential window acquisition of all theoretical fragment-ion spectra (SWATH). Samples were measured repeatedly for method comparison based on sensitivity, accuracy, precision, and detection robustness. The blind matrices showed similar accuracy for most analytes, while SBF provided an easier preparation with satisfying results. To cover a wide part of the human metabolome, a combination of RP and HILIC showed the best results. The different scan modes performed equally regarding metabolite quantification while TOF-MS was more sensitive but lacked MS/MS spectra generation. IDA and SWATH files were aligned to various databases where IDA showed good MS/MS spectra matches. SWATH seemed to be beneficial in detection rate but was incompatible with many important software tools in metabolomics.

[Development of LC-MS and LC-MS/MS Methods for Free Asparagine in Grains]

New analytical methods for the determination of free asparagine （Asn）, which is a precursor of acrylamide, in grains were developed using LC-MS and LC-MS/MS. Asn was extracted from a sample with 5％ （w/v） aqueous trichloroacetic acid solution, appropriately diluted with 0.1％ （v/v） formic acid solution, and then analyzed by LC-MS or LC-MS/MS. HPLC separation was performed by isocratic elution on a Penta Fluoro Phenyl （PFP） column using 0.1％ （v/v） formic acid and acetonitrile mixture as the mobile phase. The calibration curve was linear in the range of 0.005-0.1 μg/mL. The mean recoveries from potato starch, non-glutinous rice flour and whole wheat flour ranged from 95.4 to 100.9％, repeatability （RSD） ranged from 0.9 to 6.0％, and within-laboratory reproducibility （RSDwr） ranged from 2.8 to 7.1％. Limits of quantitation （LOQs） were 7 mg/kg for potato starch, and 5 mg/kg for non-glutinous rice flour. In addition, an inter-laboratory study was performed in 10 laboratories using 5 kinds of grains （non-glutinous brown rice flour, corn flour, strong flour, whole wheat flour, and whole rye flour）, which naturally contained free asparagine. The HORRAT_R values ranged from 0.4 to 1.0. These results are within the range of the procedural manual of the Codex Alimentarius Commission, confirming the effectiveness of the developed procedures.

Strategies for large-scale targeted metabolomics quantification by liquid chromatography-mass spectrometry

Advances in liquid chromatography-mass spectrometry (LC-MS) instruments and analytical strategies have brought about great progress in targeted metabolomics analysis. This methodology is now capable of performing precise targeted measurement of dozens or hundreds of metabolites in complex biological samples. Classic targeted quantification assay using the multiple reaction monitoring (MRM) mode has been the foundation of high-quality metabolite quantitation. However, utilization of this strategy in biological studies has been limited by its relatively low metabolite coverage and throughput capacity. A number of methods for large-scale targeted metabolomics assay which have been developed overcome these limitations. These strategies have enabled extended metabolite coverage which is defined as targeting of large numbers of metabolites, while maintaining reliable quantification performance. These recently developed techniques thus bridge the gap between traditional targeted metabolite quantification and untargeted metabolomics profiling, and have proven to be powerful tools for metabolomics study. Although the LC-MRM-MS strategy has been used widely in large-scale metabolomics quantification analysis due to its fast scan speed and ideal analytic stability, there are still drawbacks which are due to the low resolution of the triple quadrupole instruments used for MRM assays. New approaches have been developed to expand the options for large-scale targeted metabolomics study, using high-resolution instruments such as parallel reaction monitoring (PRM). MRM and PRM-based techniques are now attractive strategies for quantitative metabolomics analysis and high-throughput biomarker discovery. Here we provide an overview of the major developments in LC-MS-based strategies for large-scale targeted metabolomics quantification in biological samples. The advantages of LC-MRM/PRM-MS based analytical strategies which may be used in multiplexed and high throughput quantitation for a wide range of metabolites are highlighted. In particular, PRM and MRM strategies are compared, and we summarize the work flow commonly used for large-scale targeted metabolomics analysis including sample preparation, LC separation and data analysis, as well as recent applications in biological studies.

Metabolomics: a challenge for detecting and monitoring inborn errors of metabolism

Timely newborn screening and genetic profiling are crucial in early recognition and treatment of inborn errors of metabolism (IEMs). A proposed nosology of IEMs has inserted 1,015 well-characterized IEMs causing alterations in specific metabolic pathways. With the increasing expansion of metabolomics in clinical biochemistry and laboratory medicine communities, several research groups have focused their interest on the analysis of metabolites and their interconnections in IEMs. Metabolomics has the potential to extend metabolic information, thus allowing to achieve an accurate diagnosis for the individual patient and to discover novel IEMs. Structural and functional information on 247 metabolites associated with 147 IEMs and 202 metabolic pathways involved in various IEMs have been reported in the human metabolome data base (HMDB). For each metabolic gene, a new computational approach can be developed for predicting a set of metabolites, whose concentration is predicted to change after gene knockout in urine, blood and other biological fluids. Both targeted and untargeted mass spectrometry (MS)-based metabolomic approaches have been used to expand the range of disease-associate metabolites. The quantitative targeted approach, in conjunction with chemometrics, can be considered a basic tool for validating known diagnostic biomarkers in various metabolic disorders. The untargeted approach broadens the identification of new biomarkers in known IEMs and allows pathways analysis. Urine is an ideal biological fluid for metabolomics in neonatology; however, the lack of standardization of preanalytical phase may generate potential interferences in metabolomic studies. The integration of genomic and metabolomic data represents the current challenge for improving diagnosis and prognostication of IEMs. The goals consist in identifying both metabolically active loci and genes relevant to a disease phenotype, which means deriving disease-specific biological insights.

Improving metabolome coverage and data quality: advancing metabolomics and lipidomics for biomarker discovery

This Feature Article highlights some of the key challenges within the field of metabolomics and examines what role separation and analytical sciences can play to improve the use of metabolomics in biomarker discovery and personalized medicine. Recent progress in four key areas is highlighted: (i) improving metabolite coverage, (ii) developing accurate methods for unstable metabolites including in vivo global metabolomics methods, (iii) advancing inter-laboratory studies and reference materials and (iv) improving data quality, standardization and quality control of metabolomics studies.

LC-HRMS Metabolomics for Untargeted Diagnostic Screening in Clinical Laboratories: A Feasibility Study

Today’s high-resolution mass spectrometers (HRMS) allow bioanalysts to perform untargeted/global determinations that can reveal unexpected compounds or concentrations in a patient’s sample. This could be performed for preliminary diagnosis attempts when usual diagnostic processes and targeted determinations fail. We have evaluated an untargeted diagnostic screening (UDS) procedure. UDS is a metabolome analysis that compares one sample (e.g., a patient) with control samples (a healthy population). Using liquid chromatography (LC)-HRMS full-scan analysis of human serum extracts and unsupervised data treatment, we have compared individual samples that were spiked with one xenobiotic or a higher level of one endogenous compound with control samples. After the use of different filters that drastically reduced the number of metabolites detected, the spiked compound was eventually revealed in each test sample and ranked. The proposed UDS procedure appears feasible and reliable to reveal unexpected xenobiotics (toxicology) or higher concentrations of endogenous metabolites. HRMS-based untargeted approaches could be useful as preliminary diagnostic screening when canonical processes do not reveal disease etiology nor establish a clear diagnosis and could reduce misdiagnosis. On the other hand, the risk of overdiagnosis of this approach should be reduced with mandatory biomedical interpretation of the patient’s UDS results and with confirmatory targeted and quantitative determinations.

Promises and pitfalls of untargeted metabolomics

Metabolomics is one of the newer omics fields, and has enabled researchers to complement genomic and protein level analysis of disease with both semi-quantitative and quantitative metabolite levels, which are the chemical mediators that constitute a given phenotype. Over more than a decade, methodologies have advanced for both targeted (quantification of specific analytes) as well as untargeted metabolomics (biomarker discovery and global metabolite profiling). Untargeted metabolomics is especially useful when there is no a priori metabolic hypothesis. Liquid chromatography coupled to mass spectrometry (LC-MS) has been the preferred choice for untargeted metabolomics, given the versatility in metabolite coverage and sensitivity of these instruments. Resolving and profiling many hundreds to thousands of metabolites with varying chemical properties in a biological sample presents unique challenges, or pitfalls. In this review, we address the various obstacles and corrective measures available in four major aspects associated with an untargeted metabolomics experiment: (1) experimental design, (2) pre-analytical (sample collection and preparation), (3) analytical (chromatography and detection), and (4) post-analytical (data processing).

Transplantation of wild-type mouse hematopoietic stem and progenitor cells ameliorates deficits in a mouse model of Friedreich's ataxia

Friedreich's ataxia (FRDA) is an incurable autosomal recessive neurodegenerative disease caused by reduced expression of the mitochondrial protein frataxin due to an intronic GAA-repeat expansion in the FXN gene. We report the therapeutic efficacy of transplanting wild-type mouse hematopoietic stem and progenitor cells (HSPCs) into the YG8R mouse model of FRDA. In the HSPC-transplanted YG8R mice, development of muscle weakness and locomotor deficits was abrogated as was degeneration of large sensory neurons in the dorsal root ganglia (DRGs) and mitochondrial capacity was improved in brain, skeletal muscle, and heart. Transplanted HSPCs engrafted and then differentiated into microglia in the brain and spinal cord and into macrophages in the DRGs, heart, and muscle of YG8R FRDA mice. We observed the transfer of wild-type frataxin and Cox8 mitochondrial proteins from HSPC-derived microglia/macrophages to FRDA mouse neurons and muscle myocytes in vivo. Our results show the HSPC-mediated phenotypic rescue of FRDA in YG8R mice and suggest that this approach should be investigated further as a strategy for treating FRDA.

Quality assurance procedures for mass spectrometry untargeted metabolomics. a review

Untargeted metabolomics, as a global approach, has already proven its great potential and capabilities for the investigation of health and disease, as well as the wide applicability for other research areas. Although great progress has been made on the feasibility of metabolomics experiments, there are still some challenges that should be faced and that includes all sources of fluctuations and bias affecting every step involved in multiplatform untargeted metabolomics studies. The identification and reduction of the main sources of unwanted variation regarding the pre-analytical, analytical and post-analytical phase of metabolomics experiments is essential to ensure high data quality. Nowadays, there is still a lack of information regarding harmonized guidelines for quality assurance as those available for targeted analysis. In this review, sources of variations to be considered and minimized along with methodologies and strategies for monitoring and improvement the quality of the results are discussed. The given information is based on evidences from different groups among our own experiences and recommendations for each stage of the metabolomics workflow. The comprehensive overview with tools presented here might serve other researchers interested in monitoring, controlling and improving the reliability of their findings by implementation of good experimental quality practices in the untargeted metabolomics study.

Proposed Confidence Scale and ID Score in the Identification of Known-Unknown Compounds Using High Resolution MS Data

High-resolution (HR) MS instruments recording HR-full scan allow analysts to go further beyond pre-acquisition choices. Untargeted acquisition can reveal unexpected compounds or concentrations and can be performed for preliminary diagnosis attempt. Then, revealed compounds will have to be identified for interpretations. Whereas the need of reference standards is mandatory to confirm identification, the diverse information collected from HRMS allows identifying unknown compounds with relatively high degree of confidence without reference standards injected in the same analytical sequence. However, there is a necessity to evaluate the degree of confidence in putative identifications, possibly before further targeted analyses. This is why a confidence scale and a score in the identification of (non-peptidic) known-unknown, defined as compounds with entries in database, is proposed for (LC-) HRMS data. The scale is based on two representative documents edited by the European Commission (2007/657/EC) and the Metabolomics Standard Initiative (MSI), in an attempt to build a bridge between the communities of metabolomics and screening labs. With this confidence scale, an identification (ID) score is determined as [a number, a letter, and a number] (e.g., 2D3), from the following three criteria: I, a General Identification Category (1, confirmed, 2, putatively identified, 3, annotated compounds/classes, and 4, unknown); II, a Chromatography Class based on the relative retention time (from the narrowest tolerance, A, to no chromatographic references, D); and III, an Identification Point Level (1, very high, 2, high, and 3, normal level) based on the number of identification points collected. Three putative identification examples of known-unknown will be presented. Graphical Abstract ᅟ.

Untargeted LC-HRMS-Based Metabolomics for Searching New Biomarkers of Pancreatic Ductal Adenocarcinoma: A Pilot Study

Pancreatic ductal adenocarcinoma is one of the most lethal tumors since it is usually detected at an advanced stage in which surgery and/or current chemotherapy have limited efficacy. The lack of sensitive and specific markers for diagnosis leads to a dismal prognosis. The purpose of this study is to identify metabolites in serum of pancreatic ductal adenocarcinoma patients that could be used as diagnostic biomarkers of this pathology. We used liquid chromatography-high-resolution mass spectrometry for a nontargeted metabolomics approach with serum samples from 28 individuals, including 16 patients with pancreatic ductal adenocarcinoma and 12 healthy controls. Multivariate statistical analysis, which included principal component analysis and partial least squares, revealed clear separation between the patient and control groups analyzed by liquid chromatography-high-resolution mass spectrometry using a nontargeted metabolomics approach. The metabolic analysis showed significantly lower levels of phospholipids in the serum from patients with pancreatic ductal adenocarcinoma compared with serum from controls. Our results suggest that the liquid chromatography-high-resolution mass spectrometry-based metabolomics approach provides a potent and promising tool for the diagnosis of pancreatic ductal adenocarcinoma patients using the specific metabolites identified as novel biomarkers that could be used for an earlier detection and treatment of these patients.

Clinical Metabolomics: The New Metabolic Window for Inborn Errors of Metabolism Investigations in the Post-Genomic Era

Inborn errors of metabolism (IEM) represent a group of about 500 rare genetic diseases with an overall estimated incidence of 1/2500. The diversity of metabolic pathways involved explains the difficulties in establishing their diagnosis. However, early diagnosis is usually mandatory for successful treatment. Given the considerable clinical overlap between some inborn errors, biochemical and molecular tests are crucial in making a diagnosis. Conventional biological diagnosis procedures are based on a time-consuming series of sequential and segmented biochemical tests. The rise of “omic” technologies offers holistic views of the basic molecules that build a biological system at different levels. Metabolomics is the most recent “omic” technology based on biochemical characterization of metabolites and their changes related to genetic and environmental factors. This review addresses the principles underlying metabolomics technologies that allow them to comprehensively assess an individual biochemical profile and their reported applications for IEM investigations in the precision medicine era.

Applications of Fourier Transform Ion Cyclotron Resonance (FT-ICR) and Orbitrap Based High Resolution Mass Spectrometry in Metabolomics and Lipidomics

Metabolomics, along with other "omics" approaches, is rapidly becoming one of the major approaches aimed at understanding the organization and dynamics of metabolic networks. Mass spectrometry is often a technique of choice for metabolomics studies due to its high sensitivity, reproducibility and wide dynamic range. High resolution mass spectrometry (HRMS) is a widely practiced technique in analytical and bioanalytical sciences. It offers exceptionally high resolution and the highest degree of structural confirmation. Many metabolomics studies have been conducted using HRMS over the past decade. In this review, we will explore the latest developments in Fourier transform mass spectrometry (FTMS) and Orbitrap based metabolomics technology, its advantages and drawbacks for using in metabolomics and lipidomics studies, and development of novel approaches for processing HRMS data.

Methods used to increase the comprehensive coverage of urinary and plasma metabolomes by MS

Metabolomics, focusing on comprehensive analysis of all the metabolites in a biological system, provides a direct signature of biochemical activity. Using emerging technologies in MS, it is possible to simultaneously and rapidly analyze thousands of metabolites. However, due to the chemical and physical diversity of metabolites, it is difficult to acquire a comprehensive and reliable profiling of the whole metabolome. Here, we summarize the state of the art in metabolomics research, focusing on efforts to provide a more comprehensive metabolome coverage via improvements in two fundamental processes: sample preparation and MS analysis. Additionally, the reliable analysis is also highlighted via the combinations of multiple methods (e.g., targeted and untargeted approaches), and analytical quality control and calibration methods.

A method for comparative metabolomics in urine using high resolution mass spectrometry

Developing a workflow for metabolite profiling from biological fluids using mass spectrometry is imperative to extract accurate information. In this study, urine samples from smokers (n=10) and nonsmokers (n=10) were analyzed using an ultrahigh performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS) system. For the analysis, two different chromatographic methods [Reversed phase chromatography (RPC) and Hydrophilic interaction liquid chromatography (HILIC)], in two ionization modes (positive and negative) were used. Spiked reserpine (positive ion mode) or taurocholate (negative ion mode) were used for data extraction and normalization. Quality controls (QCs), prepared by pooling urine samples from both smokers and non-smokers (each n=10), were used to assess the reproducibility of the method. The final data output from SIEVE 2.2 after applying a cut-off for QC coefficient of variation (CV) <20% and p-value <0.05 showed 165, 83, 177 and 100 unique components in RP positive/negative, HILIC positive/negative modes, respectively. Statistical analysis showed clustering of the two groups and the QCs, while the variable importance in projection (VIP) scores for the top fifteen metabolites in each of the four modes indicated the metabolites most responsible for the differences. Application of the developed workflow for comparative metabolomic analysis of urine in different diseased models will be of great use in the field of clinical metabolomics.

p300 is not required for metabolic adaptation to endurance exercise training

The acetyltransferase, E1a-binding protein (p300), is proposed to regulate various aspects of skeletal muscle development, metabolism, and mitochondrial function,viaits interaction with numerous transcriptional regulators and other proteins. Remarkably, however, the contribution of p300 to skeletal muscle function and metabolism,in vivo, is poorly understood. To address this, we used Cre-LoxP methodology to generate mice with skeletal muscle-specific knockout of E1a-binding protein (mKO). mKO mice were indistinguishable from their wild-type/floxed littermates, with no differences in lean mass, skeletal muscle structure, fiber type, respirometry flux, or metabolites of fatty acid and amino acid metabolism.Ex vivomuscle function in extensor digitorum longus and soleus muscles, including peak stress and time to fatigue, as well asin vivorunning capacity were also comparable. Moreover, expected adaptations to a 20 d voluntary wheel running regime were not compromised in mKO mice. Taken together, these findings demonstrate that p300 is not required for the normal development or functioning of adult skeletal muscle, nor is it required for endurance exercise-mediated mitochondrial adaptations.-LaBarge, S. A., Migdal, C. W., Buckner, E. H., Okuno, H., Gertsman, I., Stocks, B., Barshop, B. A., Nalbandian, S. R., Philp, A., McCurdy, C. E., Schenk, S. p300 is not required for metabolic adaptation to endurance exercise training.

Perturbations of tyrosine metabolism promote the indolepyruvate pathway via tryptophan in host and microbiome

The drug nitisinone (NTBC) is used to treat tyrosinemia type I, and more recently has been also used for the treatment of another disorder of tyrosine metabolism, alkaptonuria. While studying the dose effects of NTBC treatment on alkaptonuria, untargeted metabolomics revealed perturbations in a completely separate pathway, that of tryptophan metabolism. Significant elevations in several indolic compounds associated with the indolepyruvate pathway of tryptophan metabolism were present in NTBC-treated patient sera and correlated with elevations of an intermediate of tyrosine metabolism. Indolic compounds of this pathway have long been associated with commensal bacterial and plant metabolism. These exogenous sources of indoles have been more recently implicated in affecting mammalian cell function and disease. We studied the correlation of these indolic compounds in other disorders of tyrosine metabolism including tyrosinemia types I and II as well as transient tyrosinemia, and demonstrated that 4-hydroxyphenylpyruvate (4-HPP) was directly responsible for the promotion of this pathway. We then investigated the regulation of the indolepyruvate pathway and the role of 4-HPP further in both mammalian cells and intestinal microbial cultures. We demonstrated that several of the indolic products, including indolepyruvate and indolelactate, were in fact generated by human cell metabolism, while the downstream indole metabolite, indolecarboxaldehyde, was produced exclusively by microbial cultures of human gut flora. This study describes a symbiotic perturbation in host and microbiome tryptophan metabolism in response to elevations related to defects of tyrosine metabolism and concomitant drug treatment.

Metabolic Effects of Increasing Doses of Nitisinone in the Treatment of Alkaptonuria

Alkaptonuria is an autosomal recessive disease involving a deficiency of the enzyme homogentisate dioxygenase, which is involved in the tyrosine degradation pathway. The enzymatic deficiency results in high concentrations of homogentisic acid (HGA), which results in orthopedic and cardiac complications, among other symptoms. Nitisinone (NTBC) has been shown to effectively treat alkaptonuria by blocking the conversion of 4-hydroxyphenylpyruvate to HGA, but there have been concerns that using doses higher than about 2 mg/day could cause excessively high levels of tyrosine, resulting in crystal deposition and corneal pathology. We have enrolled seven patients in a study to determine whether higher doses of NTBC were effective at further reducing HGA levels while maintaining tyrosine at acceptable levels. Patients were given varying doses of NTBC (ranging from 2 to 8 mg/day) over the course of between 0.5 and 3.5 years. Urine HGA, plasma tyrosine levels, and plasma NTBC were then measured longitudinally at various doses. We found that tyrosine concentrations plateaued and did not reach significantly higher levels as NTBC doses were increased above 2 mg/day, while a significant drop in HGA continued from 2 to 4 mg/day, with no significant changes at higher doses. We also demonstrated using untargeted metabolomics that elevations in tyrosine from treatment resulted in proportional elevations in alternative tyrosine metabolic products, that of N-acetyltyrosine and γ-glutamyltyrosine.