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Acylcarnitine analysis is a useful test for identifying patients with inborn errors of mitochondrial fatty acid β-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.

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.

The covalent attachment of SUMO (small ubiquitin-like modifier) to other intracellular proteins affects a broad range of nuclear processes in yeast and animals, including chromatin maintenance, transcription, and transport across the nuclear envelope, as well as protects proteins from ubiquitin addition. Substantial increases in SUMOylated proteins upon various stresses have also implicated this modification in the general stress response. To help understand the role(s) of SUMOylation in plants, we developed a stringent method to isolate SUMO-protein conjugates from Arabidopsis thaliana that exploits a tagged SUMO1 variant that faithfully replaces the wild-type protein. Following purification under denaturing conditions, SUMOylated proteins were identified by tandem mass spectrometry from both nonstressed plants and those exposed to heat and oxidative stress. The list of targets is enriched for factors that direct SUMOylation and for nuclear proteins involved in chromatin remodeling/repair, transcription, RNA metabolism, and protein trafficking. Targets of particular interest include histone H2B, components in the LEUNIG/TOPLESS corepressor complexes, and proteins that control histone acetylation and DNA methylation, which affect genome-wide transcription. SUMO attachment site(s) were identified in a subset of targets, including SUMO1 itself to confirm the assembly of poly-SUMO chains. SUMO1 also becomes conjugated with ubiquitin during heat stress, thus connecting these two posttranslational modifications in plants. Taken together, we propose that SUMOylation represents a rapid and global mechanism for reversibly manipulating plant chromosomal functions, especially during environmental stress.

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.

Global metabolic profiling currently achievable by untargeted mass spectrometry-based metabolomic platforms has great potential to advance our understanding of human disease states, including potential utility in the detection of novel and known inborn errors of metabolism (IEMs). There are few studies of the technical reproducibility, data analysis methods, and overall diagnostic capabilities when this technology is applied to clinical specimens for the diagnosis of IEMs. We explored the clinical utility of a metabolomic workflow capable of routinely generating semi-quantitative z-score values for ~900 unique compounds, including ~500 named human analytes, in a single analysis of human plasma. We tested the technical reproducibility of this platform and applied it to the retrospective diagnosis of 190 individual plasma samples, 120 of which were collected from patients with a confirmed IEM. Our results demonstrate high intra-assay precision and linear detection for the majority compounds tested. Individual metabolomic profiles provided excellent sensitivity and specificity for the detection of a wide range of metabolic disorders and identified novel biomarkers for some diseases. With this platform, it is possible to use one test to screen for dozens of IEMs that might otherwise require ordering multiple unique biochemical tests. However, this test may yield false negative results for certain disorders that would be detected by a more well-established quantitative test and in its current state should be considered a supplementary test. Our findings describe a novel approach to metabolomic analysis of clinical specimens and demonstrate the clinical utility of this technology for prospective screening of IEMs.

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 “ c onnects t he d ots” between metabolite perturbations observed in individual metabolomics profiling data and modules identified in disease­specific 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 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.

The posttranslational addition of small ubiquitin-like modifiers (SUMOs) to other intracellular proteins has been implicated in a variety of eukaryotic functions, including modifying cytoplasmic signal transduction, nuclear import and subnuclear compartmentalization, DNA repair, and transcription regulation. For plants, in particular, both genetic analyses and the rapid accumulation of SUMO conjugates in response to various adverse environmental conditions suggest that SUMOylation plays a key role in the stress response. Through genetic analyses of various SUMO conjugation mutants, we show here that the SUMO1 and SUMO2 isoforms, in particular, and SUMOylation, in general, are essential for viability in Arabidopsis (Arabidopsis thaliana). Null T-DNA insertion mutants affecting the single genes encoding the SUMO-activating enzyme subunit SAE2 and the SUMO-conjugating enzyme SCE1 are embryonic lethal, with arrest occurring early in embryo development. Whereas the single genes encoding the SUMO1 and SUMO2 isoforms are not essential by themselves, double mutants missing both are also embryonic lethal. Viability can be restored by reintroduction of SUMO1 expression in the homozygous sum1-1 sum2-1 background. Various stresses, like heat shock, dramatically increase the pool of SUMO conjugates in planta. This increase involves SUMO1 and SUMO2 and is mainly driven by the SUMO protein ligase SIZ1, with most of the conjugates accumulating in the nucleus. Taken together, it appears that SIZ1-mediated conjugation of SUMO1 and SUMO2 to other intracellular proteins is essential in Arabidopsis, possibly through stress-induced modification of a potentially diverse pool of nuclear proteins.