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Circulating metabolite levels partly reflect the state of human health and diseases, and can be impacted by genetic determinants. Hundreds of loci associated with circulating metabolites have been identified; however, most findings focus on predominantly European ancestry or single study analyses. Leveraging the rich metabolomics resources generated by the NHLBI Trans-Omics for Precision Medicine (TOPMed) Program, we harmonized and accessibly cataloged 1,729 circulating metabolites among 25,058 ancestrally-diverse samples. We provided recommendations for outlier and imputation handling to process metabolite data, as well as a general analytical framework. We further performed a pooled analysis following our practical recommendations and discovered 1,778 independent loci associated with 667 metabolites. Among 108 novel locus - metabolite pairs, we detected not only novel loci within previously implicated metabolite associated genes, but also novel genes (such as GAB3 and VSIG4 located in the X chromosome) that have putative roles in metabolic regulation. In the sex-stratified analysis, we revealed 85 independent locus-metabolite pairs with evidence of sexual dimorphism, including well-known metabolic genes such as FADS2 , D2HGDH , SUGP1 , UTG2B17 , strongly supporting the importance of exploring sex difference in the human metabolome. Taken together, our study depicted the genetic contribution to circulating metabolite levels, providing additional insight into the understanding of human health.

Plasma metabolomics holds potential for precision medicine, but limited information is available to compare the performance of such methods across multiple cohorts. We compared plasma metabolite profiles after an overnight fast in 11,309 participants of five population-based Swedish cohorts (50-80 years, 52% women). Metabolite profiles were uniformly generated at a core laboratory (Metabolon Inc.) with untargeted liquid chromatography mass spectrometry and a comprehensive reference library. Analysis of a second sample obtained one year later was conducted in a subset. Of 1629 detected metabolites, 1074 (66%) were detected in all cohorts while only 10% were unique to one cohort, most of which were xenobiotics or uncharacterized. The major classes were lipids (28%), xenobiotics (22%), amino acids (14%), and uncharacterized (19%). The most abundant plasma metabolome components were the major dietary fatty acids and amino acids, glucose, lactate and creatinine. Most metabolites displayed a log-normal distribution. Temporal variability was generally similar to clinical chemistry analytes but more pronounced for xenobiotics. Extensive metabolite-metabolite correlations were observed but mainly restricted to within each class. Metabolites were broadly associated with clinical factors, particularly body mass index, sex and renal function. Collectively, our findings inform the conduct and interpretation of metabolite association and precision medicine studies., (© 2024. The Author(s).)

We present a case study of a 20-year-old male with an unknown neurodegenerative disease who was referred to the Undiagnosed Diseases Network Vanderbilt Medical Center site. A previous metabolic panel showed that the patient had a critical deficiency in nicotinamide intermediates that are generated during the biosynthesis of NAD(H). We followed up on these findings by evaluating the patient's ability to metabolize nicotinamide. We performed a global metabolic profiling analysis of plasma samples that were collected: (1) under normal fed conditions (baseline), (2) after the patient had fasted, and (3) after he was challenged with a 500 mg nasogastric tube bolus of nicotinamide following the fast. Our findings showed that the patient's nicotinamide N-methyltransferase (NNMT), a key enzyme in NAD(H) biosynthesis and methionine metabolism, was not functional under normal fed or fasting conditions but was restored in response to the nicotinamide challenge. Altered levels of metabolites situated downstream of NNMT and in neighboring biochemical pathways provided further evidence of a baseline defect in NNMT activity. To date, this is the only report of a critical defect in NNMT activity manifesting in adulthood and leading to neurodegenerative disease. Altogether, this study serves as an important reference in the rare disease literature and also demonstrates the utility of metabolomics as a diagnostic tool for uncharacterized metabolic diseases.

Non-O blood groups are associated with decreased insulin sensitivity and risk of type 2 diabetes. A recent study pinpointed the associations between ABO blood groups and gut microbiome, which may serve as potential mediators for the observed increased disease risks. We aimed to characterize associations between ABO haplotypes and insulin-related traits as well as potential mediating pathways. We assessed insulin homeostasis in African Americans (AAs; n = 109) and non-Hispanic whites ( n = 210) from the Microbiome and Insulin Longitudinal Evaluation Study. The ABO haplotype was determined by six SNPs located in the ABO gene. Based on prior knowledge, we included 21 gut bacteria and 13 plasma metabolites for mediation analysis. In the white study cohort (60 ± 9 years, 42% male), compared to the O1 haplotype, A1 was associated with a higher Matsuda insulin sensitivity index, while a lower relative abundance of Bacteroides massiliensis and lactate levels. Lactate was a likely mediator of this association but not Bacteroides massiliensis . In the AAs group (57 ± 8 years, 33% male), we found no association between any haplotype and insulin-related traits. In conclusion, the A1 haplotype may promote healthy insulin sensitivity in non-Hispanic whites and lactate likely play a role in this process but not selected gut bacteria.

Genome-wide association studies (GWAS) with non-targeted metabolomics have identified many genetic loci of biomedical interest. However, metabolites with a high degree of missingness, such as drug metabolites and xenobiotics, are often excluded from such studies due to a lack of statistical power and higher uncertainty in their quantification. Here we propose ratios between related drug metabolites as GWAS phenotypes that can drastically increase power to detect genetic associations between pairs of biochemically related molecules. As a proof-of-concept we conducted a GWAS with 520 individuals from the Qatar Biobank for who at least five of the nine available acetaminophen metabolites have been detected. We identified compelling evidence for genetic variance in acetaminophen glucuronidation and methylation by UGT2A15 and COMT, respectively. Based on the metabolite ratio association profiles of these two loci we hypothesized the chemical structure of one of their products or substrates as being 3-methoxyacetaminophen, which we then confirmed experimentally. Taken together, our study suggests a novel approach to analyze metabolites with a high degree of missingness in a GWAS setting with ratios, and it also demonstrates how pharmacological pathways can be mapped out using non-targeted metabolomics measurements in large population-based studies.

Modern metabolomics platforms are able to identify many drug-related metabolites in blood samples. Applied to population-based biobank studies, the detection of drug metabolites can then be used as a proxy for medication use or serve as a validation tool for questionnaire-based health assessments. However, it is not clear how well detection of drug metabolites in blood samples matches information on self-reported medication provided by study participants. Here, we curate free-text responses to a drug-usage questionnaire from 6000 participants of the Qatar Biobank (QBB) using standardized WHO Anatomical Therapeutic Chemical (ATC) Classification System codes and compare the occurrence of these ATC terms to the detection of drug-related metabolites in matching blood plasma samples from 2807 QBB participants for which we collected non-targeted metabolomics data. We found that the detection of 22 drug-related metabolites significantly associated with the self-reported use of the corresponding medication. Good agreement of self-reported medication with non-targeted metabolomics was observed, with self-reported drugs and their metabolites being detected in a same blood sample in 79.4% of the cases. On the other hand, only 29.5% of detected drug metabolites matched to self-reported medication. Possible explanations for differences include under-reporting of over-the-counter medications from the study participants, such as paracetamol, misannotation of low abundance metabolites, such as metformin, and inability of the current methods to detect them. Taken together, our study provides a broad real-world view of what to expect from large non-targeted metabolomics measurements in population-based biobank studies and indicates areas where further improvements can be made.

Rice bran contains essential nutrients, antioxidants, and bioactives with anti-inflammatory and diarrheal protective properties important for infants. This 6-month randomized controlled trial investigated the effects of heat-stabilized rice bran supplementation during Malian infant weaning. Fifty healthy 6-month-old infants were randomized to a rice bran intervention (N = 25) or non-intervention control group (N = 25). Intervention infants received dose-escalating rice bran supplementation for 6 months (1-5 g/day). Monthly infant dried blood spot and anthropometric measurements were collected. Dried blood spot metabolite abundances were compared monthly according to diet for six months. Supplementation resulted in favorable weight-for-age and weight-for-length z-score changes. Non-targeted dried blood spot-based metabolomics identified 796 metabolites, of which 33% had significant fold differences between groups (7-12 months). Lipids and amino acids represented 70.6% of the metabolites identified. Rice bran supplementation during infant weaning significantly modulated the metabolites involved in antioxidant defenses and with neuroactive properties including reduced glutathione, glycine, glutamate, cysteinylglycine, tryptophan betaine, and choline. These findings support rice bran as a weaning ingredient to meet infant nutritional requirements and with the potential to reduce oxidative stress and improve cognitive outcomes. This study provides evidence for dried blood spots as a cost-effective tool to detect infant biomarkers of nutritional and metabolic status.

Introduction: Analysis of blood for the evaluation of clinically relevant biomarkers requires precise collection and sample handling by phlebotomists and laboratory staff. An important consideration for the clinical application of metabolomics are the different anticoagulants utilized for sample collection. Most studies that have characterized differences in metabolite levels in various blood collection tubes have focused on single analytes. We define analyte levels on a global metabolomics platform following blood sampling using five different, but commonly used, clinical laboratory blood collection tubes (i.e., plasma anticoagulated with either EDTA, lithium heparin or sodium citrate, along with no additive (serum), and EDTA anticoagulated whole blood)., Methods: Using an untargeted metabolomics platform we analyzed five sample types after all had been collected and stored at -80°C. The biochemical composition was determined and differences between the samples established using matched-pair t-tests., Results: We identified 1,117 biochemicals across all samples and detected a mean of 1,036 in the sample groups. Compared to the levels of metabolites in EDTA plasma, the number of biochemicals present at statistically significant different levels (p<0.05) ranged from 452 (serum) to 917 (whole blood). Several metabolites linked to screening assays for rare diseases including acylcarnitines, bilirubin and heme metabolites, nucleosides, and redox balance metabolites varied significantly across the sample collection types., Conclusions: Our study highlights the widespread effects and importance of using consistent additives for assessing small molecule levels in clinical metabolomics. The biochemistry that occurs during the blood collection process creates a reproducible signal that can identify specimens collected with different anticoagulants in metabolomic studies., Impact Statement: In this manuscript, normal/healthy donors had peripheral blood collected using multiple anticoagulants as well as serum during a fasted blood draw. Global metabolomics is a new technology being utilized to draw clinical conclusions and we interrogated the effects of different anticoagulants on the levels of biochemicals from each of the donors. Characterizing the effects of the anticoagulants on biochemical levels will help researchers leverage the information using global metabolomics in order to make conclusions regarding important disease biomarkers., Competing Interests: AK, LF, BW, JC, JW, MM, AE, DT are employees of Metabolon, Inc. and, as such, have affiliations with or financial involvement with Metabolon, Inc. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Introduction: Clinical metabolomics has utility as a screen for inborn errors of metabolism (IEM) and variant classification in patients with rare disease. It is important to understand and characterize preanalytical factors that influence assay performance during patient sample testing., Objectives: To evaluate the impact of extended thawing of human EDTA plasma samples on ice prior to extraction as well as repeated freeze-thaw cycling of samples to identify compounds that are unstable prior to metabolomic analysis., Methods: Twenty-four (24) donor EDTA plasma samples were collected and immediately frozen at - 80 °C. Twelve samples were thawed on ice and extracted for analysis at time 0, 2, 4, and 6 h. Twelve other donor samples were repeatedly thawed and frozen up to four times and analyzed at each cycle. Compound levels at each time point/freeze-thaw cycle were compared to the control samples using matched-paired t tests to identify analytes affected by each condition., Results: We identified 1026 biochemicals across all samples. Incubation of thawed EDTA plasma samples on ice for up to 6 h resulted in < 1% of biochemicals changing significantly. Freeze-thaw cycles affected a greater percentage of the metabolome; ~ 2% of biochemicals changed after 3 freeze-thaw cycles., Conclusions: Our study highlights that the number and magnitude of these changes are not as widespread as other aspects of improper sample handling. In total, < 3% of the metabolome detected on our clinical metabolomics platform should be disqualified when multiple freeze-thaw cycles or extended thawing at 4 °C are performed on a given sample.

Background: Stool metabolites provide essential insights into the function of the gut microbiome. The current gold standard for storage of stool samples for metabolomics is flash-freezing at - 80 °C which can be inconvenient and expensive. Ambient temperature storage of stool is more practical, however no available methodologies adequately preserve the metabolomic profile of stool. A novel sampling kit (OMNImet.GUT; DNA Genotek, Inc.) was introduced for ambient temperature storage and stabilization of feces for metabolomics; we aimed to test the performance of this kit vs. flash-freezing. To do this stool was collected from an infant's diaper was divided into two aliquots: 1) flash-frozen and 2) stored in an OMNImet.GUT tube at ambient temperature for 3-4 days. Samples from the same infant were collected at 2 different time points to assess metabolite changes over time. Subsequently, all samples underwent metabolomic analysis by liquid chromatography - tandem mass spectrometry (LC-MS/MS)., Results: Paired fecal samples (flash-frozen and ambient temperature) from 16 infants were collected at 2 time points (32 individual samples, 64 aliquots). Similar numbers of metabolites were detected in both the frozen and ambient temperature samples (1126 in frozen, 1107 in ambient temperature, 1064 shared between sample types). Metabolite abundances were strongly correlated between storage methods (median Spearman correlation Rs = 0.785 across metabolites). Hierarchical clustering analysis and principal component analysis showed that samples from the same individuals at a given time point clustered closely, regardless of the storage method. Repeat samples from the same individual were compared by paired t-test, separately for the frozen and OMNImet.GUT. The number of metabolites in each biochemical class that significantly changed (p < 0.05) at timepoint 2 relative to timepoint 1 was similar in flash-frozen versus ambient temperature storage. Changes in microbiota modified metabolites over time were also consistent across both methodologies., Conclusion: Ambient temperature storage and stabilization of stool in the OMNImet.GUT device yielded comparable metabolomic results to flash freezing in terms of 1) the identity and abundance of detected biochemicals 2) the distinct metabolomic profiles of subjects and 3) changes in metabolites over time that are plausibly microbiota-induced. This method potentially provides a more convenient, less expensive home collection and storage option for stool metabolomic analysis.

Introduction: The metabolomics quality assurance and quality control consortium (mQACC) evolved from the recognized need for a community-wide consensus on improving and systematizing quality assurance (QA) and quality control (QC) practices for untargeted metabolomics., Objectives: In this work, we sought to identify and share the common and divergent QA and QC practices amongst mQACC members and collaborators who use liquid chromatography-mass spectrometry (LC-MS) in untargeted metabolomics., Methods: All authors voluntarily participated in this collaborative research project by providing the details of and insights into the QA and QC practices used in their laboratories. This sharing was enabled via a six-page questionnaire composed of over 120 questions and comment fields which was developed as part of this work and has proved the basis for ongoing mQACC outreach., Results: For QA, many laboratories reported documenting maintenance, calibration and tuning (82%); having established data storage and archival processes (71%); depositing data in public repositories (55%); having standard operating procedures (SOPs) in place for all laboratory processes (68%) and training staff on laboratory processes (55%). For QC, universal practices included using system suitability procedures (100%) and using a robust system of identification (Metabolomics Standards Initiative level 1 identification standards) for at least some of the detected compounds. Most laboratories used QC samples (>86%); used internal standards (91%); used a designated analytical acquisition template with randomized experimental samples (91%); and manually reviewed peak integration following data acquisition (86%). A minority of laboratories included technical replicates of experimental samples in their workflows (36%)., Conclusions: Although the 23 contributors were researchers with diverse and international backgrounds from academia, industry and government, they are not necessarily representative of the worldwide pool of practitioners due to the recruitment method for participants and its voluntary nature. However, both questionnaire and the findings presented here have already informed and led other data gathering efforts by mQACC at conferences and other outreach activities and will continue to evolve in order to guide discussions for recommendations of best practices within the community and to establish internationally agreed upon reporting standards. We very much welcome further feedback from readers of this article.

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., (© American Association for Clinical Chemistry 2020. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

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.

Background and Objectives: Data are scarce on blood metabolite associations with proteinuria, a strong risk factor for adverse kidney outcomes. We sought to investigate associations of proteinuria with serum metabolites identified using untargeted profiling in populations with CKD., Design, Setting, Participants, & Measurements: Using stored serum samples from the African American Study of Kidney Disease and Hypertension (AASK; n =962) and the Modification of Diet in Renal Disease (MDRD) study ( n =620), two rigorously conducted clinical trials with per-protocol measures of 24-hour proteinuria and GFR, we evaluated cross-sectional associations between urine protein-to-creatinine ratio and 637 known, nondrug metabolites, adjusting for key clinical covariables. Metabolites significantly associated with proteinuria were tested for associations with CKD progression., Results: In the AASK and the MDRD study, respectively, the median urine protein-to-creatinine ratio was 80 (interquartile range [IQR], 28-359) and 188 (IQR, 54-894) mg/g, mean age was 56 and 52 years, 39% and 38% were women, 100% and 7% were black, and median measured GFR was 48 (IQR, 35-57) and 28 (IQR, 18-39) ml/min per 1.73 m 2 . Linear regression identified 66 serum metabolites associated with proteinuria in one or both studies after Bonferroni correction ( P <7.8×10 -5 ), 58 of which were statistically significant in a meta-analysis ( P <7.8×10 -4 ). The metabolites with the lowest P values ( P <10 -27 ) were 4-hydroxychlorthalonil and 1,5-anhydroglucitol; all six quantified metabolites in the phosphatidylethanolamine pathway were also significant. Of the 58 metabolites associated with proteinuria, four were associated with ESKD in both the AASK and the MDRD study., Conclusions: We identified 58 serum metabolites with cross-sectional associations with proteinuria, some of which were also associated with CKD progression., Podcast: This article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2019&#95;02&#95;07&#95;CJASNPodcast&#95;19&#95;03&#95;.mp3., (Copyright © 2019 by the American Society of Nephrology.)

Background and Objectives: Nontargeted metabolomics can measure thousands of low-molecular-weight biochemicals, but important gaps limit its utility for biomarker discovery in CKD. These include the need to characterize technical and intraperson analyte variation, to pool data across platforms, and to outline analyte relationships with eGFR., Design, Setting, Participants, & Measurements: Plasma samples from 49 individuals with CKD (eGFR<60 ml/min per 1.73 m 2 and/or ≥1 g proteinuria) were examined from two study visits; 20 samples were repeated as blind replicates. To enable comparison across two nontargeted platforms, samples were profiled at Metabolon and the Broad Institute., Results: The Metabolon platform reported 837 known metabolites and 483 unnamed compounds (selected from 44,953 unknown ion features). The Broad Institute platform reported 594 known metabolites and 26,106 unknown ion features. Median coefficients of variation (CVs) across blind replicates were 14.6% (Metabolon) and 6.3% (Broad Institute) for known metabolites, and 18.9% for (Metabolon) unnamed compounds and 24.5% for (Broad Institute) unknown ion features. Median CVs for day-to-day variability were 29.0% (Metabolon) and 24.9% (Broad Institute) for known metabolites, and 41.8% for (Metabolon) unnamed compounds and 40.9% for (Broad Institute) unknown ion features. A total of 381 known metabolites were shared across platforms (median correlation 0.89). Many metabolites were negatively correlated with eGFR at P <0.05, including 35.7% (Metabolon) and 18.9% (Broad Institute) of known metabolites., Conclusions: Nontargeted metabolomics quantifies >1000 analytes with low technical CVs, and agreement for overlapping metabolites across two leading platforms is excellent. Many metabolites demonstrate substantial intraperson variation and correlation with eGFR., (Copyright © 2019 by the American Society of Nephrology.)

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., (© 2018 John Wiley & Sons, Ltd.)

Background Metabolite levels reflect physiologic homeostasis and may serve as biomarkers of disease progression. Identifying metabolites associated with APOL1 risk alleles-genetic variants associated with CKD risk commonly present in persons of African descent-may reveal novel markers of CKD progression relevant to other populations. Methods We evaluated associations between the number of APOL1 risk alleles and 760 serum metabolites identified via untargeted profiling in participants of the African American Study of Kidney Disease and Hypertension (AASK) ( n =588; Bonferroni significance threshold P <6.5×10 -5 ) and replicated findings in 678 black participants with CKD in Bio Me , an electronic medical record-linked biobank. We tested the metabolite association with CKD progression in AASK, Bio Me , and the Modification of Diet in Renal Disease (MDRD) Study. Results One metabolite, 6-bromotryptophan, was significant in AASK ( P =4.7×10 -5 ) and replicated in Bio Me ( P =5.7×10 -3 ) participants, with lower levels associated with more APOL1 risk alleles. Lower levels of 6-bromotryptophan were associated with CKD progression in AASK and Bio Me participants and in white participants in the MDRD Study, independent of demographics and clinical characteristics, including baseline GFR (adjusted hazard ratio per two-fold higher 6-bromotryptophan level, AASK, 0.76; 95% confidence interval [95% CI], 0.64 to 0.91; Bio Me , 0.61; 95% CI, 0.43 to 0.85; MDRD, 0.52; 95% CI, 0.34 to 0.79). The interaction between the APOL1 risk alleles and 6-bromotryptophan was not significant. The identity of 6-bromotryptophan was confirmed in experiments comparing its molecular signature with that of authentic standards of other bromotryptophan isomers. Conclusions Serum 6-bromotryptophan is a consistent and novel risk factor for CKD progression., (Copyright © 2018 by the American Society of Nephrology.)

The chemical structure of x11564, a new endogenous organosulfur metabolite, was elucidated by de novo interpretation of mass spectrometric data. The structure was confirmed by comparison to a synthetic standard. Metabolite x11564 is structurally related to intermediates in the methionine salvage pathway.

Identification of metabolites in non-targeted metabolomics continues to be a bottleneck in metabolomics studies in large human cohorts. Unidentified metabolites frequently emerge in the results of association studies linking metabolite levels to, for example, clinical phenotypes. For further analyses these unknown metabolites must be identified. Current approaches utilize chemical information, such as spectral details and fragmentation characteristics to determine components of unknown metabolites. Here, we propose a systems biology model exploiting the internal correlation structure of metabolite levels in combination with existing biochemical and genetic information to characterize properties of unknown molecules. Levels of 758 metabolites (439 known, 319 unknown) in human blood samples of 2279 subjects were measured using a non-targeted metabolomics platform (LC-MS and GC-MS). We reconstructed the structure of biochemical pathways that are imprinted in these metabolomics data by building an empirical network model based on 1040 significant partial correlations between metabolites. We further added associations of these metabolites to 134 genes from genome-wide association studies as well as reactions and functional relations to genes from the public database Recon 2 to the network model. From the local neighborhood in the network, we were able to predict the pathway annotation of 180 unknown metabolites. Furthermore, we classified 100 pairs of known and unknown and 45 pairs of unknown metabolites to 21 types of reactions based on their mass differences. As a proof of concept, we then looked further into the special case of predicted dehydrogenation reactions leading us to the selection of 39 candidate molecules for 5 unknown metabolites. Finally, we could verify 2 of those candidates by applying LC-MS analyses of commercially available candidate substances. The formerly unknown metabolites X-13891 and X-13069 were shown to be 2-dodecendioic acid and 9-tetradecenoic acid, respectively. Our data-driven approach based on measured metabolite levels and genetic associations as well as information from public resources can be used alone or together with methods utilizing spectral patterns as a complementary, automated and powerful method to characterize unknown metabolites., (Copyright © 2017. Published by Elsevier B.V.)

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., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Genetic factors modifying the blood metabolome have been investigated through genome-wide association studies (GWAS) of common genetic variants and through exome sequencing. We conducted a whole-genome sequencing study of common, low-frequency and rare variants to associate genetic variations with blood metabolite levels using comprehensive metabolite profiling in 1,960 adults. We focused the analysis on 644 metabolites with consistent levels across three longitudinal data collections. Genetic sequence variations at 101 loci were associated with the levels of 246 (38%) metabolites (P ≤ 1.9 × 10 -11 ). We identified 113 (10.7%) among 1,054 unrelated individuals in the cohort who carried heterozygous rare variants likely influencing the function of 17 genes. Thirteen of the 17 genes are associated with inborn errors of metabolism or other pediatric genetic conditions. This study extends the map of loci influencing the metabolome and highlights the importance of heterozygous rare variants in determining abnormal blood metabolic phenotypes in adults.

Introduction: A major bottleneck in metabolomic studies is metabolite identification from accurate mass spectrometric data. Metabolite x17299 was identified in plasma as an unknown in a metabolomic study using a compound-centric approach where the associated ion features of the compound were used to determine the true molecular mass., Objectives: The aim of this work is to elucidate the chemical structure of x17299, a new compound by de novo interpretation of mass spectrometric data., Methods: An Orbitrap Elite mass spectrometer was used for acquisition of mass spectra up to MS 4 at high resolution. Synthetic standards of N,N,N -trimethyl-l-alanyl-l-proline betaine (l,l-TMAP), a diastereomer, and an enantiomer were chemically prepared., Results: The planar structure of x17299 was successfully proposed by de novo mechanistic interpretation of mass spectrometric data without any laborious purification and nuclear magnetic resonance spectroscopic analysis. The proposed structure was verified by deuterium exchanged mass spectrometric analysis and confirmed by comparison to a synthetic standard. Relative configuration of x17299 was determined by direct chromatographic comparison to a pair of synthetic diastereomers. Absolute configuration was assigned after derivatization of x17299 with a chiral auxiliary group followed by its chromatographic comparison to a pair of synthetic standards., Conclusion: The chemical structure of metabolite x17299 was determined to be l,l-TMAP.

Aims: We wished to determine the efficacy of using urine as an analyte to screen for a broad range of metabolic products associated with multiple different types of inborn errors of metabolism (IEMs), using an automated mass spectrometry-based assay. Urine was compared with plasma samples from a similar cohort analyzed using the same assay. Specimens were analyzed using two different commonly utilized urine normalization methods based on creatinine and osmolality, respectively., Methods: Biochemical profiles for each sample (from both affected and unaffected subjects) were obtained using a mass spectrometry-based platform and population-based statistical analyses., Results: We identified over 1200 biochemicals from among 100 clinical urine samples and identified clear biochemical signatures for 16 of 18 IEM diseases tested. The two diseases that did not result in clear signatures, X-linked creatine transporter deficiency and ornithine transcarbamylase deficiency, were from individuals under treatment, which masked biomarker signatures. Overall the process variability and coefficient of variation for isolating and identifying biochemicals by running technical replicates of each urine sample was 10%., Conclusions: A single urine sample analyzed with our integrated metabolomic platform can identify signatures of IEMs that are traditionally identified using many different assays and multiple sample types. Creatinine and osmolality-normalized data were robust to the detection of the disorders and samples tested here.

Background . The Alpha-Tocopherol, Beta-Carotene Cancer Prevention (ATBC) Study, a randomized controlled cancer prevention trial, showed a 32% reduction in prostate cancer incidence in response to vitamin E supplementation. Two other trials were not confirmatory, however. Objective . We compared the change in serum metabolome of the ATBC Study participants randomized to receive vitamin E to those who were not by randomly selecting 50 men from each of the intervention groups (50 mg/day all- rac - α -tocopheryl acetate (ATA), 20 mg/day β -carotene, both, placebo). Methods . Metabolomic profiling was conducted on baseline and follow-up fasting serum (Metabolon, Inc.). Results . After correction for multiple comparisons, five metabolites were statistically significantly altered ( β is the change in metabolite level expressed as number of standard deviations on the log scale): α -CEHC sulfate ( β = 1.51, p = 1.45 × 10 -38 ), α -CEHC glucuronide (β = 1.41, p = 1.02 × 10 -31 ), α -tocopherol ( β = 0.97, p = 2.22 × 10 -13 ), γ -tocopherol ( β = -0.90, p = 1.76 × 10 -11 ), and β -tocopherol ( β = -0.73, p = 9.40 × 10 -8 ). Glutarylcarnitine, beta-alanine, ornithine, and N6-acetyllysine were also decreased by ATA supplementation ( β range 0.40 to -0.36), but not statistically significantly. Conclusions . Comparison of the observed metabolite alterations resulting from ATA supplementation to those in other vitamin E trials of different populations, dosages, or formulations may shed light on the apparently discordant vitamin E-prostate cancer risk findings.

Precision medicine, taking account of human individuality in genes, environment, and lifestyle for early disease diagnosis and individualized therapy, has shown great promise to transform medical care. Nontargeted metabolomics, with the ability to detect broad classes of biochemicals, can provide a comprehensive functional phenotype integrating clinical phenotypes with genetic and nongenetic factors. To test the application of metabolomics in individual diagnosis, we conducted a metabolomics analysis on plasma samples collected from 80 volunteers of normal health with complete medical records and three-generation pedigrees. Using a broad-spectrum metabolomics platform consisting of liquid chromatography and GC coupled with MS, we profiled nearly 600 metabolites covering 72 biochemical pathways in all major branches of biosynthesis, catabolism, gut microbiome activities, and xenobiotics. Statistical analysis revealed a considerable range of variation and potential metabolic abnormalities across the individuals in this cohort. Examination of the convergence of metabolomics profiles with whole-exon sequences (WESs) provided an effective approach to assess and interpret clinical significance of genetic mutations, as shown in a number of cases, including fructose intolerance, xanthinuria, and carnitine deficiency. Metabolic abnormalities consistent with early indications of diabetes, liver dysfunction, and disruption of gut microbiome homeostasis were identified in several volunteers. Additionally, diverse metabolic responses to medications among the volunteers may assist to identify therapeutic effects and sensitivity to toxicity. The results of this study demonstrate that metabolomics could be an effective approach to complement next generation sequencing (NGS) for disease risk analysis, disease monitoring, and drug management in our goal toward precision care.

Serum urate, the final breakdown product of purine metabolism, is causally involved in the pathogenesis of gout, and implicated in cardiovascular disease and type 2 diabetes. Serum urate levels highly differ between men and women; however the underlying biological processes in its regulation are still not completely understood and are assumed to result from a complex interplay between genetic, environmental and lifestyle factors. In order to describe the metabolic vicinity of serum urate, we analyzed 355 metabolites in 1,764 individuals of the population-based KORA F4 study and constructed a metabolite network around serum urate using Gaussian Graphical Modeling in a hypothesis-free approach. We subsequently investigated the effect of sex and urate lowering medication on all 38 metabolites assigned to the network. Within the resulting network three main clusters could be detected around urate, including the well-known pathway of purine metabolism, as well as several dipeptides, a group of essential amino acids, and a group of steroids. Of the 38 assigned metabolites, 25 showed strong differences between sexes. Association with uricostatic medication intake was not only confined to purine metabolism but seen for seven metabolites within the network. Our findings highlight pathways that are important in the regulation of serum urate and suggest that dipeptides, amino acids, and steroid hormones are playing a role in its regulation. The findings might have an impact on the development of specific targets in the treatment and prevention of hyperuricemia.

Background: Two chemoprevention trials found that supplementation with β-carotene increased the risk of lung cancer and overall mortality. The biologic basis of these findings remains poorly understood., Objective: The objective was to compare the on-study change in metabolomic profiles of men randomly assigned to receive or not receive β-carotene supplements in the Alpha-Tocopherol, Beta-Carotene Cancer Prevention (ATBC) Study., Design: The ATBC Study was a randomized, double-blind, placebo-controlled, primary cancer prevention trial; participants were Finnish male smokers assigned to 1 of 4 intervention groups: 1) α-tocopherol, 2) β-carotene, 3) both, or 4) placebo. Fifty participants with both baseline and follow-up fasting serum samples were randomly selected from each of these groups. Metabolomic profiling was conducted by mass spectrometry. The association between change in each metabolite over time and trial assignment (β-carotene or no β-carotene) was estimated by linear regression., Results: We measured 489 metabolites, and 17 changed significantly (P < 0.05) in response to β-carotene supplementation. More of these 17 metabolites were of xenobiotic origin than would be expected by chance (9 of 60, or 15%; P = 0.00004). We also found a suggestive association with 1,5-anhydroglucitol-a marker of glycemic control (β = -0.379, P = 0.0071)., Conclusions: Male smokers supplemented with β-carotene developed metabolomic profiles consistent with the induction of cytochrome P450 enzymes, the primary metabolizers of xenobiotics in humans. These findings may shed light on the increased mortality associated with β-carotene supplementation in the ATBC Study and suggest the need to explore potential interactions between medication use and dietary supplements, particularly among smokers. This trial was registered at clinicaltrials.gov as NCT00342992.

Background: Individuals with negative affectivity who are inhibited in social situations are characterized as distressed, or Type D, and have an increased risk of cardiovascular disease (CVD). The underlying biomechanisms that link this psychological affect to a pathological state are not well understood. This study applied a metabolomic approach to explore biochemical pathways that may contribute to the Type D personality., Methods: Type D personality was determined by the Type D Scale-14. Small molecule biochemicals were measured using two complementary mass-spectrometry based metabolomics platforms. Metabolic profiles of Type D and non-Type D participants within a population-based study in Southern Germany were compared in cross-sectional regression analyses. The PHQ-9 and GAD-7 instruments were also used to assess symptoms of depression and anxiety, respectively, within this metabolomic study., Results: 668 metabolites were identified in the serum of 1502 participants (age 32-77); 386 of these individuals were classified as Type D. While demographic and biomedical characteristics were equally distributed between the groups, a higher level of depression and anxiety was observed in Type D individuals. Significantly lower levels of the tryptophan metabolite kynurenine were associated with Type D (p-value corrected for multiple testing=0.042), while no significant associations could be found for depression and anxiety. A Gaussian graphical model analysis enabled the identification of four potentially interesting metabolite networks that are enriched in metabolites (androsterone sulfate, tyrosine, indoxyl sulfate or caffeine) that associate nominally with Type D personality., Conclusions: This study identified novel biochemical pathways associated with Type D personality and demonstrates that the application of metabolomic approaches in population studies can reveal mechanisms that may contribute to psychological health and disease., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

Recent genome-wide association studies (GWAS) with metabolomics data linked genetic variation in the human genome to differences in individual metabolite levels. A strong relevance of this metabolic individuality for biomedical and pharmaceutical research has been reported. However, a considerable amount of the molecules currently quantified by modern metabolomics techniques are chemically unidentified. The identification of these "unknown metabolites" is still a demanding and intricate task, limiting their usability as functional markers of metabolic processes. As a consequence, previous GWAS largely ignored unknown metabolites as metabolic traits for the analysis. Here we present a systems-level approach that combines genome-wide association analysis and Gaussian graphical modeling with metabolomics to predict the identity of the unknown metabolites. We apply our method to original data of 517 metabolic traits, of which 225 are unknowns, and genotyping information on 655,658 genetic variants, measured in 1,768 human blood samples. We report previously undescribed genotype-metabotype associations for six distinct gene loci (SLC22A2, COMT, CYP3A5, CYP2C18, GBA3, UGT3A1) and one locus not related to any known gene (rs12413935). Overlaying the inferred genetic associations, metabolic networks, and knowledge-based pathway information, we derive testable hypotheses on the biochemical identities of 106 unknown metabolites. As a proof of principle, we experimentally confirm nine concrete predictions. We demonstrate the benefit of our method for the functional interpretation of previous metabolomics biomarker studies on liver detoxification, hypertension, and insulin resistance. Our approach is generic in nature and can be directly transferred to metabolomics data from different experimental platforms., Competing Interests: AME, MWM, RPM, and MVM are employees of Metabolon. A patent application for the unknown identification method has been filed: “Identity Elucidation of Unknown Metabolites,” U.S. Patent Application No. 61503673, unpublished – filing date July 1, 2011 (MVM, applicant).

Background: Metabolomics experiments involve generating and comparing small molecule (metabolite) profiles from complex mixture samples to identify those metabolites that are modulated in altered states (e.g., disease, drug treatment, toxin exposure). One non-targeted metabolomics approach attempts to identify and interrogate all small molecules in a sample using GC or LC separation followed by MS or MSn detection. Analysis of the resulting large, multifaceted data sets to rapidly and accurately identify the metabolites is a challenging task that relies on the availability of chemical libraries of metabolite spectral signatures. A method for analyzing spectrometry data to identify and Quantify Individual Components in a Sample, (QUICS), enables generation of chemical library entries from known standards and, importantly, from unknown metabolites present in experimental samples but without a corresponding library entry. This method accounts for all ions in a sample spectrum, performs library matches, and allows review of the data to quality check library entries. The QUICS method identifies ions related to any given metabolite by correlating ion data across the complete set of experimental samples, thus revealing subtle spectral trends that may not be evident when viewing individual samples and are likely to be indicative of the presence of one or more otherwise obscured metabolites., Results: LC-MS/MS or GC-MS data from 33 liver samples were analyzed simultaneously which exploited the inherent biological diversity of the samples and the largely non-covariant chemical nature of the metabolites when viewed over multiple samples. Ions were partitioned by both retention time (RT) and covariance which grouped ions from a single common underlying metabolite. This approach benefitted from using mass, time and intensity data in aggregate over the entire sample set to reject outliers and noise thereby producing higher quality chemical identities. The aggregated data was matched to reference chemical libraries to aid in identifying the ion set as a known metabolite or as a new unknown biochemical to be added to the library., Conclusion: The QUICS methodology enabled rapid, in-depth evaluation of all possible metabolites (known and unknown) within a set of samples to identify the metabolites and, for those that did not have an entry in the reference library, to create a library entry to identify that metabolite in future studies.

To address the challenges associated with metabolomics analyses, such as identification of chemical structures and elimination of experimental artifacts, we developed a platform that integrated the chemical analysis, including identification and relative quantification, data reduction, and quality assurance components of the process. The analytical platform incorporated two separate ultrahigh performance liquid chromatography/tandem mass spectrometry (UHPLC/MS/MS(2)) injections; one injection was optimized for basic species, and the other was optimized for acidic species. This approach permitted the detection of 339 small molecules, a total instrument analysis time of 24 min (two injections at 12 min each), while maintaining a median process variability of 9%. The resulting MS/MS(2) data were searched against an in-house generated authentic standard library that included retention time, molecular weight (m/z), preferred adducts, and in-source fragments as well as their associated MS/MS spectra for all molecules in the library. The library allowed the rapid and high-confidence identification of the experimentally detected molecules based on a multiparameter match without need for additional analyses. This integrated platform enabled the high-throughput collection and relative quantitative analysis of analytical data and identified a large number and broad spectrum of molecules with a high degree of confidence.

Objective: It is well established that disease states are associated with biochemical changes (e.g., diabetes/glucose, cardiovascular disease/cholesterol), as are responses to chemical agents (e.g., medications, toxins, xenobiotics). Recently, nontargeted methods have been used to identify the small molecules (metabolites) in a biological sample to uncover many of the biochemical changes associated with a disease state or chemical response. Given that these experimental results may be influenced by the composition of the cohort, in the present study we assessed the effects of age, sex and race on the relative concentrations of small molecules (metabolites) in the blood of healthy adults., Methods: Using gas- and liquid-chromatography in combination with mass spectrometry, a nontargeted metabolomic analysis was performed on plasma collected from an age- and sex-balanced cohort of 269 individuals., Results: Of the more than 300 unique compounds that were detected, significant changes in the relative concentration of more than 100 metabolites were associated with age. Many fewer differences were associated with sex and fewer still with race. Changes in protein, energy and lipid metabolism, as well as oxidative stress, were observed with increasing age. Tricarboxylic acid intermediates, creatine, essential and nonessential amino acids, urea, ornithine, polyamines and oxidative stress markers (e.g., oxoproline, hippurate) increased with age. Compounds related to lipid metabolism, including fatty acids, carnitine, beta-hydroxybutyrate and cholesterol, were lower in the blood of younger individuals. By contrast, relative concentrations of dehydroepiandrosterone-sulfate (a proposed antiaging androgen) were lowest in the oldest age group. Certain xenobiotics (e.g., caffeine) were higher in older subjects, possibly reflecting decreases in hepatic cytochrome P450 activity., Conclusions: Our nontargeted analytical approach detected a large number of metabolites, including those that were found to be statistically altered with age, sex or race. Age-associated changes were more pronounced than those related to differences in sex or race in the population group we studied. Age, sex and race can be confounding factors when comparing different groups in clinical studies. Future studies to determine the influence of diet, lifestyle and medication are also warranted.

Alterations in phosphorylation of cellular proteins are a hallmark of malignant transformation. Degradation of these phosphoproteins could generate cancer-specific class I MHC-associated phosphopeptides recognizable by CD8+ T lymphocytes. In a comparative analysis of phosphopeptides presented on the surface of melanoma, ovarian carcinoma, and B lymphoblastoid cells, we find 5 of 36 that are restricted to the solid tumors and common to both cancers. Differential presentation of these peptides can result from differential phosphorylation of the source proteins. Recognition of the peptides on cancer cells by phosphopeptide-specific CD8+ T lymphocytes validates the potential of these phosphopeptides as immunotherapeutic targets.

Minor histocompatibility antigens (mHAg's) are peptides encoded by polymorphic genes that are presented by major histocompatibility complex (MHC) molecules and recognized by T cells in recipients of allogeneic hematopoietic cell transplants. Here we report that an alternative transcript of the proliferation-associated nuclear element 1 (PANE1) gene encodes a novel human leukocyte antigen (HLA)-A(*)0301-restricted mHAg that is selectively expressed in B-lymphoid cells. The antigenic peptide is entirely encoded within a unique exon not present in other PANE1 transcripts. Sequencing of PANE1 alleles in mHAg-positive and mHAg-negative cells demonstrates that differential T-cell recognition is due to a single nucleotide polymorphism within the variant exon that replaces an arginine codon with a translation termination codon. The PANE1 transcript that encodes the mHAg is expressed at high levels in resting CD19(+) B cells and B-lineage chronic lymphocytic leukemia (B-CLL) cells, and at significantly lower levels in activated B cells. Activation of B-CLL cells through CD40 ligand (CD40L) stimulation decreases expression of the mHAg-encoding PANE1 transcript and reciprocally increases expression of PANE1 transcripts lacking the mHAg-encoding exon. These studies suggest distinct roles for different PANE1 isoforms in resting compared with activated CD19(+) cells, and identify PANE1 as a potential therapeutic target in B-CLL.

Receptor-triggered control of local postsynaptic protein synthesis plays a crucial role for enabling long lasting changes in synaptic functions, but signaling pathways that link receptor stimulation with translational control remain poorly known. Among the putative regulatory factors are mRNA-binding proteins (messenger ribonucleoprotein, mRNP), which control the fate of cytosolic localized mRNAs. Based on the assumption that a subset of mRNA is maintained in an inactive state, mRNP-mRNA complexes were separated into polysome-bound (translated) and polysome-free (nontranslated) fractions by sucrose density centrifugation. Poly(A) mRNA-mRNP complexes were purified from a postmitochondrial extract of rat cerebral cortex by oligo(dT)-cellulose affinity chromatography. The mRNA processing proteins were characterized, from solution, by a nanoflow reverse phase-high pressure liquid chromatography-mu-electrospray ionization mass spectrometry. The majority of detected mRNA-binding proteins was found in both fractions. However, a small number of proteins appeared to be fraction-specific. This subset of proteins is by far the most interesting because the proteins are potentially involved in controlling an activity-dependent onset of translation. They include transducer proteins, kinases, and anchor proteins. This study of the mRNP proteome is the first step in allowing future experimentation to characterize individual proteins responsible for mRNA processing and translation in dendrites.

Tapasin has been proposed to function as a peptide editor to displace lower affinity peptides and/or to favor the binding of high affinity peptides. Consistent with this, cell surface HLA-B8 molecules in tapasin-deficient cells were less stable and the peptide repertoire was substantially altered. However, the binding affinities of peptides expressed in the absence of tapasin were unexpectedly higher, not lower. The peptide repertoire from cells expressing soluble tapasin was similar in both appearance and affinity to that presented in the presence of full-length tapasin, but the HLA-B8 molecules showed altered cell surface stability characteristics. Similarly, the binding affinities of HLA-A*0201-associated peptides from tapasin(+) and tapasin(-) cells were equivalent, although steady state HLA-A*0201 cell surface expression was decreased and the molecules demonstrated reduced cell surface stability on tapasin(-) cells. These data are inconsistent with a role for tapasin as a peptide editor. Instead, we propose that tapasin acts as a peptide facilitator. In this role, it stabilizes the peptide-free conformation of class I MHC molecules in the endoplasmic reticulum and thus increases the number and variety of peptides bound to class I MHC. Full-length tapasin then confers additional stability on class I MHC molecules that are already associated with peptides.

Type 1 diabetes is an autoimmune disease in which autoreactive T cells attack and destroy the insulin-producing pancreatic beta cells. CD8+ T cells are essential for this beta cell destruction, yet their specific antigenic targets are largely unknown. Here, we reveal that the autoantigen targeted by a prevalent population of pathogenic CD8+ T cells in nonobese diabetic mice is islet-specific glucose-6-phosphatase catalytic subunit-related protein (IGRP). Through tetramer technology, IGRP-reactive T cells are readily detected in islets and peripheral blood directly ex vivo. The human IGRP gene maps to a diabetes susceptibility locus, suggesting that IGRP also may be an antigen for pathogenic T cells in human type 1 diabetes and, thus, a new, potential target for diagnostic and therapeutic approaches.

Long-lasting changes in synaptic functions after an appropriate stimulus require altered protein expression at the synapse. To restrict changes in protein composition to activated synapses, proteins may be synthesized locally as a result of transmitter receptor-triggered signaling pathways. Second messenger-controlled mechanisms that affect mRNA translation are essentially unknown. Here we report that a receptor for activated C kinase, RACK1, is a component of messenger ribonucleoprotein (mRNP) complexes. RACK1 is predominantly associated with polysome-bound, polyA-mRNAs that are being actively translated. We find it to be present in a complex with beta-tubulin and at least two mRNA-binding proteins, polyA-binding protein 1 and a 130 kDa polyA-mRNA binding protein (KIAA0217). Activation of PKCbeta2 in vitro by phosphatidylserine/diacylglycerol or in hippocampal slices by metabotropic glutamate receptor stimulation increased the amount of RACK1/PKCbeta2 associated with polysome-bound polyA-mRNAs. In vitro, PKCbeta2 can phosphorylate a subset of polyA-mRNA-associated proteins that are also phosphorylated under in vivo conditions. On the basis of these findings plus the somatodendritic localization of RACK1, we hypothesize that metabotropic glutamate receptor-triggered binding of activated PKCbeta2 to mRNP complexes bound to polyA-mRNAs is involved in activity-triggered control of protein synthesis.