Loss of succinyl-CoA synthetase in mouse forebrain results in hypersuccinylation with perturbed neuronal transcription and metabolism.

Lysine succinylation is a subtype of protein acylation associated with metabolic regulation of succinyl-CoA in the tricarboxylic acid cycle. Deficiency of succinyl-CoA synthetase (SCS), the tricarboxylic acid cycle enzyme catalyzing the interconversion of succinyl-CoA to succinate, results in mitochondrial encephalomyopathy in humans. This report presents a conditional forebrain-specific knockout (KO) mouse model of Sucla2, the gene encoding the ATP-specific beta isoform of SCS, resulting in postnatal deficiency of the entire SCS complex. Results demonstrate that accumulation of succinyl-CoA in the absence of SCS leads to hypersuccinylation within the murine cerebral cortex. Specifically, increased succinylation is associated with functionally significant reduced activity of respiratory chain complex I and widescale alterations in chromatin landscape and gene expression. Integrative analysis of the transcriptomic data also reveals perturbations in regulatory networks of neuronal transcription in the KO forebrain. Together, these findings provide evidence that protein succinylation plays a significant role in the pathogenesis of SCS deficiency.

Specifications of the ACMG/AMP guidelines for ACADVL variant interpretation.

Very long-chain acyl-CoA dehydrogenase (VLCAD) deficiency (VLCADD) is a relatively common inborn error of metabolism, but due to difficulty in accurately predicting affected status through newborn screening, molecular confirmation of the causative variants by sequencing of the ACADVL gene is necessary. Although the ACMG/AMP guidelines have helped standardize variant classification, ACADVL variant classification remains disparate due to a phenotype that can be nonspecific, the possibility of variants that produce late-onset disease, and relatively high carrier frequency, amongst other challenges. Therefore, an ACADVL-specific variant curation expert panel (VCEP) was created to facilitate the specification of the ACMG/AMP guidelines for VLCADD. We expect these guidelines to help streamline, increase concordance, and expedite the classification of ACADVL variants.

Dicarboxylic acylcarnitine biomarkers in peroxisome biogenesis disorders.

The peroxisome is an essential eukaryotic organelle with diverse metabolic functions. Inherited peroxisomal disorders are associated with a wide spectrum of clinical outcomes and are broadly divided into two classes, those impacting peroxisome biogenesis (PBD) and those impacting specific peroxisomal factors. Prior studies have indicated a role for acylcarnitine testing in the diagnosis of some peroxisomal diseases through the detection of long chain dicarboxylic acylcarnitine abnormalities (C16-DC and C18-DC). However, there remains limited independent corroboration of these initial findings and acylcarnitine testing for peroxisomal diseases has not been widely adopted in clinical laboratories. To explore the utility of acylcarnitine testing in the diagnosis of peroxisomal disorders we applied a LC-MS/MS acylcarnitine method to study a heterogenous clinical sample set (n = 598) that included residual plasma specimens from nineteen patients with PBD caused by PEX1 or PEX6 deficiency, ranging in severity from lethal neonatal onset to mild late onset forms. Multiple dicarboxylic acylcarnitines were significantly elevated in PBD patients including medium to long chain (C8-DC to C18-DC) species as well as previously undescribed elevations of malonylcarnitine (C3-DC) and very long chain dicarboxylic acylcarnitines (C20-DC and C22-DC). The best performing plasma acylcarnitine biomarkers, C20-DC and C22-DC, were detected at elevated levels in 100% and 68% of PBD patients but were rarely elevated in patients that did not have a PBD. We extended our analysis to residual newborn screening blood spot cards and were able to detect dicarboxylic acylcarnitine abnormalities in a newborn with a PBD caused by PEX6 deficiency. Similar to prior studies, we failed to detect substantial dicarboxylic acylcarnitine abnormalities in blood spot cards from patients with x-linked adrenoleukodystrophy (x-ald) indicating that these biomarkers may have utility in quickly narrowing the differential diagnosis in patients with a positive newborn screen for x-ald. Overall, our study identifies widespread dicarboxylic acylcarnitine abnormalities in patients with PBD and highlights key acylcarnitine biomarkers for the detection of this class of inherited metabolic disease.

Biochemical and molecular confirmation of alkaptonuria in a Sumatran orangutan (Pongo abelii).

A 6-yr-old female orangutan presented with a history of dark urine that turned brown upon standing since birth. Repeated routine urinalysis and urine culture were unremarkable. Urine organic acid analysis showed elevation in homogentisic acid consistent with alkaptonuria. Sequence analysis identified a homozygous missense variant, c.1081G>A (p.Gly361Arg), of the homogentisate 1,2-dioxygenase (HGD) gene. Familial studies, molecular modeling, and comparison to human variant databases support this variant as the underlying cause of alkaptonuria in this orangutan. This is the first report of molecular confirmation of alkaptonuria in a nonhuman primate.

Expansion of the clinical and molecular spectrum of WWOX-related epileptic encephalopathy.

WWOX biallelic loss-of-function pathogenic single nucleotide variants (SNVs) and copy number variants (CNVs) including exonic deletions and duplications cause WWOX-related epileptic encephalopathy (WOREE) syndrome. This disorder is characterized by refractory epilepsy, axial hypotonia, peripheral hypertonia, progressive microcephaly, and premature death. Here we report five patients with WWOX biallelic predicted null variants identified by exome sequencing (ES), genome sequencing (GS), and/or chromosomal microarray analysis (CMA). SNVs and intragenic deletions of one or more exons were commonly reported in WOREE syndrome patients which made the genetic diagnosis challenging and required a combination of different diagnostic technologies. These patients presented with severe, developmental and epileptic encephalopathy (DEE), and other cardinal features consistent with WOREE syndrome. This report expands the clinical phenotype associated with this condition, including failure to thrive in most patients and epilepsy that responded to a ketogenic diet in three patients. Dysmorphic features and abnormal prenatal findings were not commonly observed. Additionally, recurrent pancreatitis and sensorineural hearing loss each were observed in single patients. In summary, these phenotypic features broaden the clinical spectrum of WOREE syndrome.

GCN2 eIF2 kinase promotes prostate cancer by maintaining amino acid homeostasis.

A stress adaptation pathway termed the integrated stress response has been suggested to be active in many cancers including prostate cancer (PCa). Here, we demonstrate that the eIF2 kinase GCN2 is required for sustained growth in androgen-sensitive and castration-resistant models of PCa both in vitro and in vivo, and is active in PCa patient samples. Using RNA-seq transcriptome analysis and a CRISPR-based phenotypic screen, GCN2 was shown to regulate expression of over 60 solute-carrier (SLC) genes, including those involved in amino acid transport and loss of GCN2 function reduces amino acid import and levels. Addition of essential amino acids or expression of 4F2 (SLC3A2) partially restored growth following loss of GCN2, suggesting that GCN2 targeting of SLC transporters is required for amino acid homeostasis needed to sustain tumor growth. A small molecule inhibitor of GCN2 showed robust in vivo efficacy in androgen-sensitive and castration-resistant mouse models of PCa, supporting its therapeutic potential for the treatment of PCa.

Prostate cancer is the fourth most common cancer worldwide, affecting over a million people each year. Existing drug treatments work by blocking the effects or reducing the levels of the hormone testosterone. However, these drug regimens are not always effective, so finding alternative treatments is an important area of research. One option is to target the 'integrated stress response', a pathway that acts as a genetic switch, turning on a group of genes that counteract cellular stress and are essential for the survival of cancer cells. The reason cancer cells are under stress is because they are hungry. They need to make a lot of proteins and other metabolic intermediates to grow and divide, which means they need plenty of amino acids, the building blocks that make up proteins and fuel metabolism. Amino acids enter cells through molecular gates called amino acid transporters, and scientists think the integrated stress response might play a role in this process. One of the integrated stress response components is a protein called General Control Nonderepressible 2, or GCN2 for short. In healthy cells, this protein helps to boost amino acid levels when supplies start to run low. Cordova et al. examined human prostate cancer cells to find out what role GCN2 plays in this cancer. In both lab-grown cells and tissue from patients, GCN2 was active and played a critical role in prostate tumor growth by turning on the genes for amino acid transporters to increase the levels of amino acids entering the cancer cells. Deleting the gene for GCN2, or blocking its effects with an experimental drug, slowed the growth of cultured prostate cancer cells and reduced tumor growth in mice. In these early experiments, Cordova et al. did not notice any toxic side effects to healthy tissues. If GCN2 works in the same way in humans as it does in mice, blocking it might help to control prostate cancer growth. The integrated stress response is also active in other cancer types, so the same logic might apply to different tumors. However, before GCN2 blockers can become treatments, researchers need a more complete understanding of their molecular effects.

Clinical diagnosis of metabolic disorders using untargeted metabolomic profiling and disease-specific networks learned from profiling data.

Untargeted metabolomics is a global molecular profiling technology that can be used to screen for inborn errors of metabolism (IEMs). Metabolite perturbations are evaluated based on current knowledge of specific metabolic pathway deficiencies, a manual diagnostic process that is qualitative, has limited scalability, and is not equipped to learn from accumulating clinical data. Our purpose was to improve upon manual diagnosis of IEMs in the clinic by developing novel computational methods for analyzing untargeted metabolomics data. We employed CTD, an automated computational diagnostic method that "connects the dots" between metabolite perturbations observed in individual metabolomics profiling data and modules identified in disease-specific metabolite co-perturbation networks learned from prior profiling data. We also extended CTD to calculate distances between any two individuals (CTDncd) and between an individual and a disease state (CTDdm), to provide additional network-quantified predictors for use in diagnosis. We show that across 539 plasma samples, CTD-based network-quantified measures can reproduce accurate diagnosis of 16 different IEMs, including adenylosuccinase deficiency, argininemia, argininosuccinic aciduria, aromatic L-amino acid decarboxylase deficiency, cerebral creatine deficiency syndrome type 2, citrullinemia, cobalamin biosynthesis defect, GABA-transaminase deficiency, glutaric acidemia type 1, maple syrup urine disease, methylmalonic aciduria, ornithine transcarbamylase deficiency, phenylketonuria, propionic acidemia, rhizomelic chondrodysplasia punctata, and the Zellweger spectrum disorders. Our approach can be used to supplement information from biochemical pathways and has the potential to significantly enhance the interpretation of variants of uncertain significance uncovered by exome sequencing. CTD, CTDdm, and CTDncd can serve as an essential toolset for biological interpretation of untargeted metabolomics data that overcomes limitations associated with manual diagnosis to assist diagnosticians in clinical decision-making. By automating and quantifying the interpretation of perturbation patterns, CTD can improve the speed and confidence by which clinical laboratory directors make diagnostic and treatment decisions, while automatically improving performance with new case data.

A clinically validated method to separate and quantify underivatized acylcarnitines and carnitine metabolic intermediates using mixed-mode chromatography with tandem mass spectrometry.

Acylcarnitines are intermediate metabolites of the mitochondria that serve as biomarkers for inherited disorders of fatty acid oxidation and amino acid metabolism. The prevailing clinical method used to quantify acylcarnitines involves flow-injection tandem mass spectrometry, an approach with a number of limitations; foremost the inability to separate and therefore distinguish key isobaric acylcarnitine species. To address these issues, we report a clinically validated liquid chromatography tandem mass spectrometry method to quantify acylcarnitines, free carnitine, and carnitine metabolic intermediates in human plasma. Importantly, this method resolves clinically relevant isobaric and isomeric acylcarnitine species in a single 22 min analysis without the use of ion pairing or derivatization reagents. This unique combination of features is not achievable by existing acylcarnitine methods and is made possible by the use of a novel mixed-mode chromatographic separation. Further clinical validation studies demonstrate excellent limits of quantification, linearity, accuracy, and inter-assay precision for analyses of 38 different calibrated analytes. An additional 28 analytes are semi-quantitatively analyzed using surrogate calibrators. The study of residual patient specimens confirms the clinical utility of this method and suggests expanded applicability to the diagnosis of peroxisomal disorders. In summary, we report a clinically validated acylcarnitine method that utilizes a novel mixed-mode chromatographic separation to provide a number of advantages in terms of specificity, accuracy, sample preparation time, and clinical utility.

Correction: CTD: An information-theoretic algorithm to interpret sets of metabolomic and transcriptomic perturbations in the context of graphical models.

[This corrects the article DOI: 10.1371/journal.pcbi.1008550.].

CTD: An information-theoretic algorithm to interpret sets of metabolomic and transcriptomic perturbations in the context of graphical models.

We consider the following general family of algorithmic problems that arises in transcriptomics, metabolomics and other fields: given a weighted graph G and a subset of its nodes S, find subsets of S that show significant connectedness within G. A specific solution to this problem may be defined by devising a scoring function, the Maximum Clique problem being a classic example, where S includes all nodes in G and where the score is defined by the size of the largest subset of S fully connected within G. Major practical obstacles for the plethora of algorithms addressing this type of problem include computational efficiency and, particularly for more complex scores which take edge weights into account, the computational cost of permutation testing, a statistical procedure required to obtain a bound on the p-value for a connectedness score. To address these problems, we developed CTD, "Connect the Dots", a fast algorithm based on data compression that detects highly connected subsets within S. CTD provides information-theoretic upper bounds on p-values when S contains a small fraction of nodes in G without requiring computationally costly permutation testing. We apply the CTD algorithm to interpret multi-metabolite perturbations due to inborn errors of metabolism and multi-transcript perturbations associated with breast cancer in the context of disease-specific Gaussian Markov Random Field networks learned directly from respective molecular profiling data.

Laboratory analysis of acylcarnitines, 2020 update: a technical standard of the American College of Medical Genetics and Genomics (ACMG).

Acylcarnitine analysis is a useful test for identifying patients with inborn errors of mitochondrial fatty acid ß-oxidation and certain organic acidemias. Plasma is routinely used in the diagnostic workup of symptomatic patients. Urine analysis of targeted acylcarnitine species may be helpful in the diagnosis of glutaric acidemia type I and other disorders in which polar acylcarnitine species accumulate. For newborn screening applications, dried blood spot acylcarnitine analysis can be performed as a multiplex assay with other analytes, including amino acids, succinylacetone, guanidinoacetate, creatine, and lysophosphatidylcholines. Tandem mass spectrometric methodology, established more than 30 years ago, remains a valid approach for acylcarnitine analysis. The method involves flow-injection analysis of esterified or underivatized acylcarnitines species and detection using a precursor-ion scan. Alternative methods utilize liquid chromatographic separation of isomeric and isobaric species and/or detection by selected reaction monitoring. These technical standards were developed as a resource for diagnostic laboratory practices in acylcarnitine analysis, interpretation, and reporting.

Rapid quantification of underivatized alloisoleucine and argininosuccinate using mixed-mode chromatography with tandem mass spectrometry.

Plasma elevations of the amino acids alloisoleucine and argininosuccinic acid (ASA) are pathognomonic for maple syrup urine disease and argininosuccinate lyase deficiency, respectively. Reliable detection of these biomarkers is typically achieved using methods with tedious sample preparations or long chromatographic separations, and many published amino acid assays report poor specificity and/or sensitivity for one or both of these compounds. This report describes a novel liquid chromatography tandem mass spectrometry (LC-MS/MS) method that provides rapid quantification of alloisoleucine and ASA in human plasma. The basis of this method is a mixed-mode solid phase separation that achieves baseline resolution of alloisoleucine from isobaric interferents without the use of derivatization or ion pairing agents. The inject-to-inject time is 6 min including elution, column washing and re-equilibration. Validation studies demonstrate excellent limits of quantification (1 µmol/L), linearity (r = 0.999 from 1 to 250 µmol/L), accuracy (bias = -3.8% and -10.1%), and inter-assay imprecision (CV < 8.06%) for plasma analyses. Data from long-term clinical application confirms chromatographic consistency equivalent to more traditional reversed-phase or HILIC based columns. Additional matrix studies indicate low suppression (<10%) for a wide range of amino acids and compatibility with other matrixes such as blood spot analyses. Finally, analysis of our first 257 clinical specimens demonstrates high analytic specificity and sensitivity, allowing the detection of subtle but clinically relevant elevations of alloisoleucine and ASA that may be missed by other less sensitive methods. In conclusion, the novel LC-MS/MS method reported here overcomes a number of the challenges associated with alloisoleucine and ASA quantification. Combining this approach with published incomplete amino acid quantification methods allows, for the first time, a rapid and comprehensive LC-MS/MS analysis of underivatized amino acids without the use of ion pairing agents.

Untargeted metabolomic profiling reveals multiple pathway perturbations and new clinical biomarkers in urea cycle disorders.

PURPOSE: Untargeted metabolomic analysis is increasingly being used in the screening and management of individuals with inborn errors of metabolism (IEM). We aimed to test whether untargeted metabolomic analysis in plasma might be useful for monitoring the disease course and management of urea cycle disorders (UCDs). METHODS: Untargeted mass spectrometry-based metabolomic analysis was used to generate z-scores for more than 900 metabolites in plasma from 48 individuals with various UCDs. Pathway analysis was used to identify common pathways that were perturbed in each UCD. RESULTS: Our metabolomic analysis in plasma identified multiple potentially neurotoxic metabolites of arginine in arginase deficiency and, thus, may have utility in monitoring the efficacy of treatment in arginase deficiency. In addition, we were also able to detect multiple biochemical perturbations in all UCDs that likely reflect clinical management, including metabolite alterations secondary to dietary and medication management. CONCLUSION: In addition to utility in screening for IEM, our results suggest that untargeted metabolomic analysis in plasma may be beneficial for monitoring efficacy of clinical management and off-target effects of medications in UCDs and potentially other IEM.

SUMOylome Profiling Reveals a Diverse Array of Nuclear Targets Modified by the SUMO Ligase SIZ1 during Heat Stress.

The posttranslational addition of small ubiquitin-like modifier (SUMO) is an essential protein modification in plants that provides protection against numerous environmental challenges. Ligation is accomplished by a small set of SUMO ligases, with the SAP-MIZ domain-containing SIZ1 and METHYL METHANESULFONATE-SENSITIVE21 (MMS21) ligases having critical roles in stress protection and DNA endoreduplication/repair, respectively. To help identify their corresponding targets in Arabidopsis thaliana, we used siz1 and mms21 mutants for proteomic analyses of SUMOylated proteins enriched via an engineered SUMO1 isoform suitable for mass spectrometric studies. Through multiple data sets from seedlings grown at normal temperatures or exposed to heat stress, we identified over 1000 SUMO targets, most of which are nuclear localized. Whereas no targets could be assigned to MMS21, suggesting that it modifies only a few low abundance proteins, numerous targets could be assigned to SIZ1, including major transcription factors, coactivators/repressors, and chromatin modifiers connected to abiotic and biotic stress defense, some of which associate into multisubunit regulatory complexes. SIZ1 itself is also a target, but studies with mutants protected from SUMOylation failed to uncover a regulatory role. The catalog of SIZ1 substrates indicates that SUMOylation by this ligase provides stress protection by modifying a large array of key nuclear regulators.

A metabolomic map of Zellweger spectrum disorders reveals novel disease biomarkers.

PURPOSE: Peroxisome biogenesis disorders-Zellweger spectrum disorders (PBD-ZSD) are metabolic diseases with multisystem manifestations. Individuals with PBD-ZSD exhibit impaired peroxisomal biochemical functions and have abnormal levels of peroxisomal metabolites, but the broader metabolic impact of peroxisomal dysfunction and the utility of metabolomic methods is unknown. METHODS: We studied 19 individuals with clinically and molecularly characterized PBD-ZSD. We performed both quantitative peroxisomal biochemical diagnostic studies in parallel with untargeted small molecule metabolomic profiling in plasma samples with detection of >650 named compounds. RESULTS: The cohort represented intermediate to mild PBD-ZSD subjects with peroxisomal biochemical alterations on targeted analysis. Untargeted metabolomic profiling of these samples revealed elevations in pipecolic acid and long-chain lysophosphatidylcholines, as well as an unanticipated reduction in multiple sphingomyelin species. These sphingomyelin reductions observed were consistent across the PBD-ZSD samples and were rare in a population of >1,000 clinical samples. Interestingly, the pattern or "PBD-ZSD metabolome" was more pronounced in younger subjects suggesting studies earlier in life reveal larger biochemical changes. CONCLUSION: Untargeted metabolomics is effective in detecting mild to intermediate cases of PBD-ZSD. Surprisingly, dramatic reductions in plasma sphingomyelin are a consistent feature of the PBD-ZSD metabolome. The use of metabolomics in PBD-ZSD can provide insight into novel biomarkers of disease.

Use of Exome Sequencing for Infants in Intensive Care Units: Ascertainment of Severe Single-Gene Disorders and Effect on Medical Management.

Importance: While congenital malformations and genetic diseases are a leading cause of early infant death, to our knowledge, the contribution of single-gene disorders in this group is undetermined. Objective: To determine the diagnostic yield and use of clinical exome sequencing in critically ill infants. Design, Setting, and Participants: Clinical exome sequencing was performed for 278 unrelated infants within the first 100 days of life who were admitted to Texas Children's Hospital in Houston, Texas, during a 5-year period between December 2011 and January 2017. Exome sequencing types included proband exome, trio exome, and critical trio exome, a rapid genomic assay for seriously ill infants. Main Outcomes and Measures: Indications for testing, diagnostic yield of clinical exome sequencing, turnaround time, molecular findings, patient age at diagnosis, and effect on medical management among a group of critically ill infants who were suspected to have genetic disorders. Results: The mean (SEM) age for infants participating in the study was 28.5 (1.7) days; of these, the mean (SEM) age was 29.0 (2.2) days for infants undergoing proband exome sequencing, 31.5 (3.9) days for trio exome, and 22.7 (3.9) days for critical trio exome. Clinical indications for exome sequencing included a range of medical concerns. Overall, a molecular diagnosis was achieved in 102 infants (36.7%) by clinical exome sequencing, with relatively low yield for cardiovascular abnormalities. The diagnosis affected medical management for 53 infants (52.0%) and had a substantial effect on informed redirection of care, initiation of new subspecialist care, medication/dietary modifications, and furthering life-saving procedures in select patients. Critical trio exome sequencing revealed a molecular diagnosis in 32 of 63 infants (50.8%) at a mean (SEM) of 33.1 (5.6) days of life with a mean (SEM) turnaround time of 13.0 (0.4) days. Clinical care was altered by the diagnosis in 23 of 32 patients (71.9%). The diagnostic yield, patient age at diagnosis, and medical effect in the group that underwent critical trio exome sequencing were significantly different compared with the group who underwent regular exome testing. For deceased infants (n = 81), genetic disorders were molecularly diagnosed in 39 (48.1%) by exome sequencing, with implications for recurrence risk counseling. Conclusions and Relevance: Exome sequencing is a powerful tool for the diagnostic evaluation of critically ill infants with suspected monogenic disorders in the neonatal and pediatric intensive care units and its use has a notable effect on clinical decision making.

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.

Analytes related to erythrocyte metabolism are reliable biomarkers for preanalytical error due to delayed plasma processing in metabolomics studies.

BACKGROUND: Delaying plasma separation after phlebotomy (processing delay) can cause perturbations of numerous small molecule analytes. This poses a major challenge to the clinical application of metabolomics analyses. In this study, we further define the analyte changes that occur during processing delays and generate a model for the post hoc detection of this preanalytical error. METHODS: Using an untargeted metabolomics platform we analyzed EDTA-preserved plasma specimens harvested after processing delays lasting from minutes to days. Identified biomarkers were tested on (i) a test-set of samples exposed to either minimal (n=28) or long delays (n=40) and (ii) samples collected in a clinical setting for metabolomics analysis (n=141). RESULTS: A total of 149 of 803 plasma analytes changed significantly during processing delays lasting 0-20h. Biomarkers related to erythrocyte metabolism, e.g., 5-oxoproline, lactate, and an ornithine/arginine ratio, were the strongest predictors of plasma separation delays, providing 100% diagnostic accuracy in the test set. Together these biomarkers could accurately predict processing delays >2h in a pilot study and we found evidence of sample mishandling in 4 of 141 clinically derived specimens. CONCLUSIONS: Our study highlights the widespread effects of processing delays and proposes that erythrocyte metabolism creates a reproducible signal that can identify mishandled specimens in metabolomics studies.

Purification of SUMO Conjugates from Arabidopsis for Mass Spectrometry Analysis.

The posttranslational modification of proteins with small ubiquitin-related modifier (SUMO) is a rapid, robust, and reversible mechanism that impacts a host of eukaryotic processes important to both normal cellular functions and survival during various abiotic and biotic challenges. Essential to defining the breadth of events impacted by SUMOylation is the development of full catalogues of protein targets. Here, we describe a stringent affinity method to purify native SUMO conjugates from the model plant Arabidopsis thaliana based on the expression of modified SUMOs bearing epitope tags. When combined with standard and quantitative mass spectrometric methods, deep datasets of SUMOylated proteins can be acquired. Functional analysis of these lists links SUMO to numerous regulatory events, with an emphasis on those associated with transcription, DNA replication and repair, and chromatin assembly/accessibility.