Detection ofClostridioides difficileinfection by assessment of exhaled breath volatile organic compounds.

Clostridioides difficileinfection (CDI) is the leading cause of hospital-acquired infective diarrhea. Current methods for diagnosing CDI have limitations; enzyme immunoassays for toxin have low sensitivity andClostridioides difficilepolymerase chain reaction cannot differentiate infection from colonization. An ideal diagnostic test that incorporates microbial factors, host factors, and host-microbe interaction might characterize true infection. Assessing volatile organic compounds (VOCs) in exhaled breath may be a useful test for identifying CDI. To identify a wide selection of VOCs in exhaled breath, we used thermal desorption-gas chromatography-mass spectrometry to study breath samples from 17 patients with CDI. Age- and sex-matched patients with diarrhea and negativeC.difficiletesting (no CDI) were used as controls. Of the 65 VOCs tested, 9 were used to build a quadratic discriminant model that showed a final cross-validated accuracy of 74%, a sensitivity of 71%, a specificity of 76%, and a receiver operating characteristic area under the curve of 0.72. If these findings are proven by larger studies, breath VOC analysis may be a helpful adjunctive diagnostic test for CDI.

Breathing new life into clinical testing and diagnostics: perspectives on volatile biomarkers from breath.

Human breath offers several benefits for diagnostic applications, including simple, noninvasive collection. Breath is a rich source of clinically-relevant biological information; this includes a volatile fraction, where greater than 1,000 volatile organic compounds (VOCs) have been described so far, and breath aerosols that carry nucleic acids, proteins, signaling molecules, and pathogens. Many of these factors, especially VOCs, are delivered to the lung by the systemic circulation, and diffusion of candidate biomarkers from blood into breath allows systematic profiling of organismal health. Biomarkers on breath offer the capability to advance early detection and precision medicine in areas of global clinical need. Breath tests are noninvasive and can be performed at home or in a primary care setting, which makes them well-suited for the kind of public screening program that could dramatically improve the early detection of conditions such as lung cancer. Since measurements of VOCs on breath largely report on metabolic changes, this too aids in the early detection of a broader range of illnesses and can be used to detect metabolic shifts that could be targeted through precision medicine. Furthermore, the ability to perform frequent sampling has envisioned applications in monitoring treatment responses. Breath has been investigated in respiratory, liver, gut, and neurological diseases and in contexts as diverse as infectious diseases and cancer. Preclinical research studies using breath have been ongoing for some time, yet only a few breath-based diagnostics tests are currently available and in widespread clinical use. Most recently, tests assessing the gut microbiome using hydrogen and methane on breath, in addition to tests using urea to detect Helicobacter pylori infections have been released, yet there are many more applications of breath tests still to be realized. Here, we discuss the strengths of breath as a clinical sampling matrix and the technical challenges to be addressed in developing it for clinical use. Historically, a lack of standardized methodologies has delayed the discovery and validation of biomarker candidates, resulting in a proliferation of early-stage pilot studies. We will explore how advancements in breath collection and analysis are in the process of driving renewed progress in the field, particularly in the context of gastrointestinal and chronic liver disease. Finally, we will provide a forward-looking outlook for developing the next generation of clinically relevant breath tests and how they may emerge into clinical practice.

Long-Chain Acylcholines Link Butyrylcholinesterase to Regulation of Non-neuronal Cholinergic Signaling.

Acylcholines are comprised of an acyl chain esterified to a choline moiety; acetylcholine is the best-characterized member of this class, functioning as a neurotransmitter in the central and peripheral nervous systems as well as an inhibitor of cytokine production by macrophages and other innate immune cells. Acylcholines are metabolized by a class of cholinesterases, including acetylcholinesterase (a specific regulator of acetylcholine levels) and butyrylcholinesterase (BChE, an enigmatic enzyme whose function has not been resolved by genetic knockout models). BChE provides reserve capacity to hydrolyze acetylcholine, but its importance is arguable given acetylcholinesterase is the most catalytically efficient enzyme characterized to date. While known to be substrates of BChE in vitro, endogenous production of long-chain acylcholines is a recent discovery enabled by untargeted metabolomics. Compared to acetylcholine, long-chain acylcholines show greater stability in circulation with homeostatic levels-dictated by synthesis and clearance-suggested to impact cholinergic receptor sensitivity of acetylcholine with varying levels of antagonism. Acylcholines then provide a link between BChE and non-neuronal acetylcholine signaling, filling a gap in understanding around how imbalances between acylcholines and BChE could modulate inflammatory disease, such as the "cytokine storm" identified in severe COVID-19. Areas for further research, development, and clinical testing are outlined.

Comparison of Untargeted Metabolomic Profiling vs Traditional Metabolic Screening to Identify Inborn Errors of Metabolism.

Importance: Recent advances in newborn screening (NBS) have improved the diagnosis of inborn errors of metabolism (IEMs); however, many potentially treatable IEMs are not included on NBS panels, nor are they covered in standard, first-line biochemical testing. Objective: To examine the utility of untargeted metabolomics as a primary screening tool for IEMs by comparing the diagnostic rate of clinical metabolomics with the recommended traditional metabolic screening approach. Design, Setting, and Participants: This cross-sectional study compares data from 4464 clinical samples received from 1483 unrelated families referred for trio testing of plasma amino acids, plasma acylcarnitine profiling, and urine organic acids (June 2014 to October 2018) and 2000 consecutive plasma samples from 1807 unrelated families (July 2014 to February 2019) received for clinical metabolomic screening at a College of American Pathologists and Clinical Laboratory Improvement Amendments-certified biochemical genetics laboratory. Data analysis was performed from September 2019 to August 2020. Exposures: Metabolic and molecular tests performed at a genetic testing reference laboratory in the US and available clinical information for each patient were assessed to determine diagnostic rate. Main Outcomes and Measures: The diagnostic rate of traditional metabolic screening compared with clinical metabolomic profiling was assessed in the context of expanded NBS. Results: Of 1483 cases screened by the traditional approach, 912 patients (61.5%) were male and 1465 (98.8%) were pediatric (mean [SD] age, 4.1 [6.0] years; range, 0-65 years). A total of 19 families were identified with IEMs, resulting in a 1.3% diagnostic rate. A total of 14 IEMs were detected, including 3 conditions not included in the Recommended Uniform Screening Panel for NBS. Of the 1807 unrelated families undergoing plasma metabolomic profiling, 1059 patients (58.6%) were male, and 1665 (92.1%) were pediatric (mean [SD] age, 8.1 [10.4] years; range, 0-80 years). Screening identified 128 unique cases with IEMs, giving an overall diagnostic rate of 7.1%. In total, 70 different metabolic conditions were identified, including 49 conditions not presently included on the Recommended Uniform Screening Panel for NBS. Conclusions and Relevance: These findings suggest that untargeted metabolomics provided a 6-fold higher diagnostic yield compared with the conventional screening approach and identified a broader spectrum of IEMs. Notably, with the expansion of NBS programs, traditional metabolic testing approaches identify few disorders beyond those covered on the NBS. These data support the capability of clinical untargeted metabolomics in screening for IEMs and suggest that broader screening approaches should be considered in the initial evaluation for metabolic disorders.

Effects of Weight Loss and Moderate-Protein, High-Fiber Diet Consumption on the Fasted Serum Metabolome of Cats.

Feline obesity elicits a plethora of metabolic responses leading to comorbidities, with potential reversal during weight loss. The specific metabolic alterations and biomarkers of organ dysfunction are not entirely understood. Untargeted, high-throughput metabolomic technologies may allow the identification of biological components that change with weight status in cats, increasing our understanding of feline metabolism. The objective of this study was to utilize untargeted metabolomic techniques to identify biomarkers and gain mechanistic insight into the serum metabolite changes associated with reduced food intake and weight loss in overweight cats. During a four-wk baseline period, cats were fed to maintain body weight. For 18 wk following baseline, cats were fed to lose weight at a rate of ~1.5% body weight/wk. Blood serum metabolites were measured at wk 0, 1, 2, 4, 8, 12, and 16. A total of 535 named metabolites were identified, with up to 269 of them being altered (p- and q-values < 0.05) at any time point. A principal component analysis showed a continual shift in metabolite profile as weight loss progressed, with early changes being distinct from those over the long term. The majority of lipid metabolites decreased with weight loss; however, ketone bodies and small lipid particles increased with weight loss. The majority of carbohydrate metabolites decreased with weight loss. Protein metabolites had a variable result, with some increasing, but others decreasing with weight loss. Metabolic mediators of inflammation, oxidative stress, xenobiotics, and insulin resistance decreased with weight loss. In conclusion, global metabolomics identified biomarkers of reduced food intake and weight loss in cats, including decreased markers of inflammation and/or altered macronutrient metabolism.

The SARS-CoV-2 viral load in COVID-19 patients is lower on face mask filters than on nasopharyngeal swabs.

Face masks and personal respirators are used to curb the transmission of SARS-CoV-2 in respiratory droplets; filters embedded in some personal protective equipment could be used as a non-invasive sample source for applications, including at-home testing, but information is needed about whether filters are suited to capture viral particles for SARS-CoV-2 detection. In this study, we generated inactivated virus-laden aerosols of 0.3-2 microns in diameter (0.9 µm mean diameter by mass) and dispersed the aerosolized viral particles onto electrostatic face mask filters. The limit of detection for inactivated coronaviruses SARS-CoV-2 and HCoV-NL63 extracted from filters was between 10 to 100 copies/filter for both viruses. Testing for SARS-CoV-2, using face mask filters and nasopharyngeal swabs collected from hospitalized COVID-19-patients, showed that filter samples offered reduced sensitivity (8.5% compared to nasopharyngeal swabs). The low concordance of SARS-CoV-2 detection between filters and nasopharyngeal swabs indicated that number of viral particles collected on the face mask filter was below the limit of detection for all patients but those with the highest viral loads. This indicated face masks are unsuitable to replace diagnostic nasopharyngeal swabs in COVID-19 diagnosis. The ability to detect nucleic acids on face mask filters may, however, find other uses worth future investigation.

Beta-aminoisobutyric acid is released by contracting human skeletal muscle and lowers insulin release from INS-1 832/3 cells by mediating mitochondrial energy metabolism.

AIMS/HYPOTHESIS: This study aimed to examine if beta-aminoisobutyric acid (BAIBA) is (i) secreted by skeletal muscle in humans during exercise, (ii) associated with insulin secretory function in vivo, and (iii) directly linked with acute glucose-mediated insulin release by pancreatic beta cells in vitro. METHODS: Following 2-weeks of single-leg immobilization, plasma BAIBA concentrations were measured in the brachial artery and the femoral veins of each leg in healthy male subjects, at rest and during two-legged dynamic knee-extensor exercise. During a 2-h hyperglycamic clamp, insulin secretory function and levels of plasma BAIBA were assessed in non-diabetic individuals, non-diabetic individuals following 24-h hyperglycemia and patients with type 2 diabetes. Direct effects of BAIBA on acute glucose-mediated insulin release were probed in INS-1832/3 cells under normal and 'diabetes-like' conditions. Finally, the effect of BAIBA on mitochondrial function was assessed in INS-1832/3 cells using extracellular flux analysis. RESULTS: (i) BAIBA is released from skeletal muscle at rest and during exercise under healthy conditions but is suppressed during exercise following leg immobilization, (ii) plasma BAIBA concentrations inversely associate with insulin secretory function in humans, (iii) BAIBA lowers mitochondrial energy metabolism in INS-1 832/3 cells in parallel with decreased insulin secretionConclusion/interpretation: BAIBA is a myokine released by skeletal muscle during exercise and indepedantly alters the triggering pathway of insulin secretion in cultured INS-1832/3 cells.

Untargeted metabolomics as an unbiased approach to the diagnosis of inborn errors of metabolism of the non-oxidative branch of the pentose phosphate pathway.

Inborn errors of metabolism (IEM) involving the non-oxidative pentose phosphate pathway (PPP) include the two relatively rare conditions, transketolase deficiency and transaldolase deficiency, both of which can be difficult to diagnosis given their non-specific clinical presentations. Current biochemical testing approaches require an index of suspicion to consider targeted urine polyol testing. To determine whether a broad-spectrum biochemical test could accurately identify a specific metabolic pattern defining IEMs of the non-oxidative PPP, we employed the use of clinical metabolomic profiling as an unbiased novel approach to diagnosis. Subjects with molecularly confirmed IEMs of the PPP were included in this study. Targeted quantitative analysis of polyols in urine and plasma samples was accomplished with chromatography and mass spectrometry. Semi-quantitative unbiased metabolomic analysis of urine and plasma samples was achieved by assessing small molecules via liquid chromatography and high-resolution mass spectrometry. Results from untargeted and targeted analyses were then compared and analyzed for diagnostic acuity. Two siblings with transketolase (TKT) deficiency and three unrelated individuals with transaldolase (TALDO) deficiency were identified for inclusion in the study. For both IEMs, targeted polyol testing and untargeted metabolomic testing on urine and/or plasma samples identified typical perturbations of the respective disorder. Additionally, untargeted metabolomic testing revealed elevations in other PPP metabolites not typically measured with targeted polyol testing, including ribonate, ribose, and erythronate for TKT deficiency and ribonate, erythronate, and sedoheptulose 7-phosphate in TALDO deficiency. Non-PPP alternations were also noted involving tryptophan, purine, and pyrimidine metabolism for both TKT and TALDO deficient patients. Targeted polyol testing and untargeted metabolomic testing methods were both able to identify specific biochemical patterns indicative of TKT and TALDO deficiency in both plasma and urine samples. In addition, untargeted metabolomics was able to identify novel biomarkers, thereby expanding the current knowledge of both conditions and providing further insight into potential underlying pathophysiological mechanisms. Furthermore, untargeted metabolomic testing offers the advantage of having a single effective biochemical screening test for identification of rare IEMs, like TKT and TALDO deficiencies, that may otherwise go undiagnosed due to their generally non-specific clinical presentations.

Precision of a Clinical Metabolomics Profiling Platform for Use in the Identification of Inborn Errors of Metabolism.

BACKGROUND: The application of whole-exome sequencing for the diagnosis of genetic disease has paved the way for systems-based approaches in the clinical laboratory. Here, we describe a clinical metabolomics method for the screening of metabolic diseases through the analysis of a multi-pronged mass spectrometry platform. By simultaneously measuring hundreds of metabolites in a single sample, clinical metabolomics offers a comprehensive approach to identify metabolic perturbations across multiple biochemical pathways. METHODS: We conducted a single- and multi-day precision study on hundreds of metabolites in human plasma on 4, multi-arm, high-throughput metabolomics platforms. RESULTS: The average laboratory coefficient of variation (CV) on the 4 platforms was between 9.3 and 11.5% (median, 6.5-8.4%), average inter-assay CV on the 4 platforms ranged from 9.9 to 12.6% (median, 7.0-8.3%) and average intra-assay CV on the 4 platforms ranged from 5.7 to 6.9% (median, 3.5-4.4%). In relation to patient sample testing, the precision of multiple biomarkers associated with IEM disorders showed CVs that ranged from 0.2 to 11.0% across 4 analytical batches. CONCLUSIONS: This evaluation describes single and multi-day precision across 4 identical metabolomics platforms, comprised each of 4 independent method arms, and reproducibility of the method for the measurement of key IEM metabolites in patient samples across multiple analytical batches, providing evidence that the method is robust and reproducible for the screening of patients with inborn errors of metabolism.

Clinical, biochemical, mitochondrial, and metabolomic aspects of methylmalonate semialdehyde dehydrogenase deficiency: Report of a fifth case.

Methylmalonate semialdehyde dehydrogenase deficiency (MMSDD; MIM 614105) is a rare autosomal recessive defect of valine and pyrimidine catabolism. Four prior MMSDD cases are published. We present a fifth case, along with functional and metabolomic analysis. The patient, born to non-consanguineous parents of East African origin, was admitted at two weeks of age for failure to thrive. She was nondysmorphic, had a normal brain MRI, and showed mild hypotonia. Gastroesophageal reflux occurred with feeding. Urine organic acid assessment identified excess 3-hydroxyisobutyrate and 3-hydroxypropionate, while urine amino acid analysis identified elevated concentrations of ß-aminoisobutyrate and ß-alanine. Plasma amino acids showed an elevated concentration of ß-aminoisobutyrate with undetectable ß-alanine. ALDH6A1 gene sequencing identified a homozygous variant of uncertain significance, c.1261C > T (p.Pro421Ser). Management with valine restriction led to reduced concentration of abnormal analytes in blood and urine, improved growth, and reduced gastroesophageal reflux. Western blotting of patient fibroblast extracts demonstrated a large reduction of methylmalonate semialdehyde dehydrogenase (MMSD) protein. Patient cells displayed compromised mitochondrial function with increased superoxide production, reduced oxygen consumption, and reduced ATP production. Metabolomic profiles from patient fibroblasts demonstrated over-representation of fatty acids and fatty acylcarnitines, presumably due to methylmalonate semialdehyde shunting to ß-alanine and subsequently to malonyl-CoA with ensuing increase of fatty acid synthesis. Previously reported cases of MMSDD have shown variable clinical presentation. Our case continues the trend as clinical phenotypes diverge from prior cases. Recognition of mitochondrial dysfunction and novel metabolites in this patient provide the opportunity to assess future patients for secondary changes that may influence clinical outcome.

Metabolomic analysis of Shiga toxin 2a-induced injury in conditionally immortalized glomerular endothelial cells.

INTRODUCTION: Shiga toxin 2a (Stx2a) induces hemolytic uremic syndrome (STEC HUS) by targeting glomerular endothelial cells (GEC). OBJECTIVES: We investigated in a metabolomic analysis the response of a conditionally immortalized, stable glomerular endothelial cell line (ciGEnC) to Stx2a stimulation as a cell culture model for STEC HUS. METHODS: CiGEnC were treated with tumor necrosis factor-(TNF)α, Stx2a or sequentially with TNFα and Stx2a. We performed a metabolomic high-throughput screening by lipid- or gas chromatography and subsequent mass spectrometry. Metabolite fold changes in stimulated ciGEnC compared to untreated cells were calculated. RESULTS: 320 metabolites were identified and investigated. In response to TNFα + Stx2a, there was a predominant increase in intracellular free fatty acids and amino acids. Furthermore, lipid- and protein derived pro-inflammatory mediators, oxidative stress and an augmented intracellular energy turnover were increased in ciGEnC. Levels of most biochemicals related to carbohydrate metabolism remained unchanged. CONCLUSION: Stimulation of ciGEnC with TNFα + Stx2a is associated with profound metabolic changes indicative of increased inflammation, oxidative stress and energy turnover.

2-Pyrrolidinone and Succinimide as Clinical Screening Biomarkers for GABA-Transaminase Deficiency: Anti-seizure Medications Impact Accurate Diagnosis.

Broad-scale untargeted biochemical phenotyping is a technology that supplements widely accepted assays, such as organic acid, amino acid, and acylcarnitine analyses typically utilized for the diagnosis of inborn errors of metabolism. In this study, we investigate the analyte changes associated with 4-aminobutyrate aminotransferase (ABAT, GABA transaminase) deficiency and treatments that affect GABA metabolism. GABA-transaminase deficiency is a rare neurodevelopmental and neurometabolic disorder caused by mutations in ABAT and resulting in accumulation of GABA in the cerebrospinal fluid (CSF). For that reason, measurement of GABA in CSF is currently the primary approach to diagnosis. GABA-transaminase deficiency results in severe developmental delay with intellectual disability, seizures, and movement disorder, and is often associated with death in childhood. Using an untargeted metabolomics platform, we analyzed EDTA plasma, urine, and CSF specimens from four individuals with GABA-transaminase deficiency to identify biomarkers by comparing the biochemical profile of individual patient samples to a pediatric-centric population cohort. Metabolomic analyses of over 1,000 clinical plasma samples revealed a rich source of biochemical information. Three out of four patients showed significantly elevated levels of the molecule 2-pyrrolidinone (Z-score ≥2) in plasma, and whole exome sequencing revealed variants of uncertain significance in ABAT. Additionally, these same patients also had elevated levels of succinimide in plasma, urine, and CSF and/or homocarnosine in urine and CSF. In the analysis of clinical EDTA plasma samples, the levels of succinimide and 2-pyrrolidinone showed a high level of correlation (R = 0.73), indicating impairment in GABA metabolism and further supporting the association with GABA-transaminase deficiency and the pathogenicity of the ABAT variants. Further analysis of metabolomic data across our patient population revealed the association of elevated levels of 2-pyrrolidinone with administration of vigabatrin, a commonly used anti-seizure medication and a known inhibitor of GABA-transaminase. These data indicate that anti-seizure medications may alter the biochemical and metabolomic data, potentially impacting the interpretation and diagnosis for the patient. Further, these data demonstrate the power of combining broad scale genotyping and phenotyping technologies to diagnose inherited neurometabolic disorders and support the use of metabolic phenotyping of plasma to screen for GABA-transaminase deficiency.

Untargeted metabolomics identifies unique though benign biochemical changes in patients with pathogenic variants in UROC1.

Urocanic aciduria is caused by a deficiency in the enzyme urocanase (E.C. 4.2.1.49) encoded by the gene UROC1. In the past, deficiency of urocanase has been associated with intellectual disability in a few case studies with some suggestion that the enzyme deficiency was the causative etiology. Here, we describe two phenotypically normal siblings with compound heterozygous pathogenic variants in UROC1 and characteristic biochemical evidence of urocanase deficiency collected utilizing untargeted metabolomic analysis. These findings suggest that urocanic aciduria may represent an otherwise benign biochemical phenotype and that those individuals with concurrent developmental delay should continue to be evaluated for other underlying causes for their symptoms.

Corrigendum: 2-Pyrrolidinone and Succinimide as Clinical Screening Biomarkers for GABA-Transaminase Deficiency: Anti-seizure Medications Impact Accurate Diagnosis.

[This corrects the article DOI: 10.3389/fnins.2019.00394.].

Metabolomics in the clinic: A review of the shared and unique features of untargeted metabolomics for clinical research and clinical testing.

Metabolomics is the untargeted measurement of the metabolome, which is composed of the complement of small molecules detected in a biological sample. As such, metabolomic analysis produces a global biochemical phenotype. It is a technology that has been utilized in the research setting for over a decade. The metabolome is directly linked to and is influenced by genetics, epigenetics, environmental factors, and the microbiome-all of which affect health. Metabolomics can be applied to human clinical diagnostics and to other fields such as veterinary medicine, nutrition, exercise, physiology, agriculture/plant biochemistry, and toxicology. Applications of metabolomics in clinical testing are emerging, but several aspects of its use as a clinical test differ from applications focused on research or biomarker discovery and need to be considered for metabolomics clinical test data to have optimum impact, be meaningful, and be used responsibly. In this review, we deconstruct aspects and challenges of metabolomics for clinical testing by illustrating the significance of test design, accurate and precise data acquisition, quality control, data processing, n-of-1 comparison to a reference population, and biochemical pathway analysis. We describe how metabolomics technology is integral to defining individual biochemical phenotypes, elaborates on human health and disease, and fits within the precision medicine landscape. Finally, we conclude by outlining some future steps needed to bring metabolomics into the clinical space and to be recognized by the broader medical and regulatory fields.

Increases in bioactive lipids accompany early metabolic changes associated with ß-cell expansion in response to short-term high-fat diet.

Pancreatic ß-cell expansion is a highly regulated metabolic adaptation to increased somatic demands, including obesity and pregnancy; adult ß cells otherwise rarely proliferate. We previously showed that high-fat diet (HFD) feeding induces mouse ß-cell proliferation in less than 1 wk in the absence of insulin resistance. Here we metabolically profiled tissues from a short-term HFD ß-cell expansion mouse model to identify pathways and metabolite changes associated with ß-cell proliferation. Mice fed HFD vs. chow diet (CD) showed a 14.3% increase in body weight after 7 days; ß-cell proliferation increased 1.75-fold without insulin resistance. Plasma from 1-wk HFD-fed mice induced ß-cell proliferation ex vivo. The plasma, as well as liver, skeletal muscle, and bone, were assessed by LC and GC mass-spectrometry for global metabolite changes. Of the 1,283 metabolites detected, 159 showed significant changes [false discovery rate (FDR) < 0.1]. The majority of changes were in liver and muscle. Pathway enrichment analysis revealed key metabolic changes in steroid synthesis and lipid metabolism, including free fatty acids and other bioactive lipids. Other important enrichments included changes in the citric acid cycle and 1-carbon metabolism pathways implicated in DNA methylation. Although the minority of changes were observed in bone and plasma (<20), increased p-cresol sulfate was increased >4 fold in plasma (the largest increase in all tissues), and pantothenate (vitamin B5) decreased >2-fold. The results suggest that HFD-mediated ß-cell expansion is associated with complex, global metabolite changes. The finding could be a significant insight into Type 2 diabetes pathogenesis and potential novel drug targets.

Clinical Metabolomics to Segregate Aromatic Amino Acid Decarboxylase Deficiency From Drug-Induced Metabolite Elevations.

BACKGROUND: Phenotyping technologies featured in the diagnosis of inborn errors of metabolism, such as organic acid, amino acid, and acylcarnitine analyses, recently have been supplemented by broad-scale untargeted metabolomic phenotyping. We investigated the analyte changes associated with aromatic amino acid decarboxylase (AADC) deficiency and dopamine medication treatment. METHODS: Using an untargeted metabolomics platform, we analyzed ethylenediaminetetraacetic acid plasma specimens, and biomarkers were identified by comparing the biochemical profile of individual patient samples to a pediatric-centric population cohort. RESULTS: Elevated 3-methoxytyrosine (average z score 5.88) accompanied by significant decreases of dopamine 3-O-sulfate (-2.77), vanillylmandelate (-2.87), and 3-methoxytyramine sulfate (-1.44) were associated with AADC deficiency in three samples from two patients. In five non-AADC patients treated with carbidopa-levodopa, levels of 3-methoxytyrosine were elevated (7.65); however, the samples from non-AADC patients treated with DOPA-elevating drugs had normal or elevated levels of metabolites downstream of aromatic l-amino acid decarboxylase, including dopamine 3-O-sulfate (2.92), vanillylmandelate (0.33), and 3-methoxytyramine sulfate (5.07). In one example, a plasma metabolomic phenotype pointed to a probable AADC deficiency and prompted the evaluation of whole exome sequencing data, identifying homozygosity for a known pathogenic variant, whereas whole exome analysis in a second patient revealed compound heterozygosity for two variants of unknown significance. CONCLUSIONS: These data demonstrate the power of combining broad-scale genotyping and phenotyping technologies to diagnose inherited neurometabolic disorders and suggest that metabolic phenotyping of plasma can be used to identify AADC deficiency and to distinguish it from non-AADC patients with elevated 3-methoxytyrosine caused by DOPA-raising medications.

Metabolomics Study of the Effects of Inflammation, Hypoxia, and High Glucose on Isolated Human Pancreatic Islets.

The transplantation of human pancreatic islets is a therapeutic possibility for a subset of type 1 diabetic patients who experience severe hypoglycemia. Pre- and post-transplantation loss in islet viability and function, however, is a major efficacy-limiting impediment. To investigate the effects of inflammation and hypoxia, the main obstacles hampering the survival and function of isolated, cultured, and transplanted islets, we conducted a comprehensive metabolomics evaluation of human islets in parallel with dynamic glucose-stimulated insulin release (GSIR) perifusion studies for functional evaluation. Metabolomics profiling of media and cell samples identified a total of 241 and 361 biochemicals, respectively. Metabolites that were altered in highly significant manner in both included, for example, kynurenine, kynurenate, citrulline, and mannitol/sorbitol under inflammation (all elevated) plus lactate (elevated) and N-formylmethionine (depressed) for hypoxia. Dynamic GSIR experiments, which capture both first- and second-phase insulin release, found severely depressed insulin-secretion under hypoxia, whereas elevated baseline and stimulated insulin-secretion was measured for islet exposed to the inflammatory cytokine cocktail (IL-1ß, IFN-γ, and TNF-α). Because of the uniquely large changes observed in kynurenine and kynurenate, they might serve as potential biomarkers of islet inflammation, and indoleamine-2,3-dioxygenase on the corresponding pathway could be a worthwhile therapeutic target to dampen inflammatory effects.

Elucidation of the complex metabolic profile of cerebrospinal fluid using an untargeted biochemical profiling assay.

We sought to determine the molecular composition of human cerebrospinal fluid (CSF) and identify the biochemical pathways represented in CSF to understand the potential for untargeted screening of inborn errors of metabolism (IEMs). Biochemical profiles for each sample were obtained using an integrated metabolomics workflow comprised of four chromatographic techniques followed by mass spectrometry. Secondarily, we wanted to compare the biochemical profile of CSF with those of plasma and urine within the integrated mass spectrometric-based metabolomic workflow. Three sample types, CSF (N=30), urine (N=40) and EDTA plasma (N=31), were analyzed from retrospectively collected pediatric cohorts of equivalent age and gender characteristics. We identified 435 biochemicals in CSF representing numerous biological and chemical/structural families. Sixty-three percent (273 of 435) of the biochemicals detected in CSF also were detected in urine and plasma, another 32% (140 of 435) were detected in either plasma or urine, and 5% (22 of 435) were detected only in CSF. Analyses of several metabolites showed agreement between clinically useful assays and the metabolomics approach. An additional set of CSF and plasma samples collected from the same patient revealed correlation between several biochemicals detected in paired samples. Finally, analysis of CSF from a pediatric case with dihydropteridine reductase (DHPR) deficiency demonstrated the utility of untargeted global metabolic phenotyping as a broad assessment to screen samples from patients with undifferentiated phenotypes. The results indicate a single CSF sample processed with an integrated metabolomics workflow can be used to identify a large breadth of biochemicals that could be useful for identifying disrupted metabolic patterns associated with IEMs.

Lipid profile of human synovial fluid following intra-articular ankle fracture.

This study characterizes the metabolic profile of synovial fluid after intra-articular ankle fracture with an emphasis on changes in the lipid profile. Bilateral ankle synovial fluid from 19 patients with unilateral intra-articular ankle fracture was submitted for metabolic profiling. Contralateral ankle synovial fluid from each patient served as a matched control. Seven patients participated in a second bilateral synovial fluid collection after 6 months. Random forest classification, matched pairs t-tests (α < 0.01), repeated measures ANOVA with post-test contrasts (α < 0.01), correlation to cytokines and matrix metalloproteinases, and fracture and injury classification analyses yielded key lipid biomarkers in synovial fluid following intra-articular fracture. Free fatty acids, sphingomyelins, and lysolipids demonstrated significant elevation in fractured ankles at baseline. Fatty acids and sphingomyelins showed a significant decrease 6 months post-surgery. Random forest analysis showed predominantly fatty acids differentiating between groups. Significant correlations included fatty acids, sphingomyelins, and lysolipids with inflammatory cytokines and matrix metalloproteinases. Fracture classification showed increased fatty acids, lysolipids, and inositol metabolites as fracture severity increased. Fatty acid and sn-1 lysolipid elevation could be detrimental to the joint, as these strongly correlated with matrix metalloproteinases and TNF-α. This elevation also suggests involvement of phospholipase A2 , a potential target for therapeutic intervention. Together with elevated 2-hydroxyl fatty acids, these findings suggest elevated sn-1 lysolipids, sphingomyelins, and subsequent lipid metabolites in synovial fluid as biomarkers of ankle injury. Reversal of this signature after 6 months suggests temporary involvement of these metabolites in disease progression, although they may activate signaling pathways which drive progression to osteoarthritis. © 2016 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 35:657-666, 2017.