Bar coding MS2 spectra for metabolite identification

[Image: see text] Metabolite identifications are most frequently achieved in untargeted metabolomics by matching precursor mass and full, high-resolution MS(2) spectra to metabolite databases and standards. Here we considered an alternative approach for establishing metabolite identifications that does not rely on full, high-resolution MS(2) spectra. First, we select mass-to-charge regions containing the most informative metabolite fragments and designate them as bins. We then translate each metabolite fragmentation pattern into a binary code by assigning 1’s to bins containing fragments and 0’s to bins without fragments. With 20 bins, this binary-code system is capable of distinguishing 96% of the compounds in the METLIN MS(2) library. A major advantage of the approach is that it extends untargeted metabolomics to low-resolution triple quadrupole (QqQ) instruments, which are typically less expensive and more robust than other types of mass spectrometers. We demonstrate a method of acquiring MS(2) data in which the third quadrupole of a QqQ instrument cycles over 20 wide isolation windows (coinciding with the location and width of our bins) for each precursor mass selected by the first quadrupole. Operating the QqQ instrument in this mode yields diagnostic bar codes for each precursor mass that can be matched to the bar codes of metabolite standards. Furthermore, our data suggest that using low-resolution bar codes enables QqQ instruments to make MS(2)-based identifications in untargeted metabolomics with a specificity and sensitivity that is competitive to high-resolution time-of-flight technologies

Evidence that 2-hydroxyglutarate is not readily metabolized in colorectal carcinoma cells

BACKGROUND: Two-hydroxyglutarate (2HG) is present at low concentrations in healthy mammalian cells as both an L and D enantiomer. Both the L and D enantiomers have been implicated in regulating cellular physiology by mechanisms that are only partially characterized. In multiple human cancers, the D enantiomer accumulates due to gain-of-function mutations in the enzyme isocitrate dehydrogenase (IDH) and has been hypothesized to drive malignancy through mechanisms that remain incompletely understood. While much attention has been dedicated to identifying the route of 2HG synthesis, the metabolic fate of 2HG has not been studied in detail. Yet the metabolism of 2HG may have important mechanistic consequences influencing cell function and cancer pathogenesis, such as modulating redox potential or producing unknown products with unique modes of action. RESULTS: By applying our isotope-based metabolomic platform, we unbiasedly and comprehensively screened for products of L- and D-2HG in HCT116 colorectal carcinoma cells harboring a mutation in IDH1. After incubating HCT116 cells in uniformly (13)C-labeled 2HG for 24 h, we used liquid chromatography/mass spectrometry to track the labeled carbons in small molecules. Strikingly, we did not identify any products of 2HG metabolism from the thousands of metabolomic features that we screened. Consistent with these results, we did not detect any significant changes in the labeling patterns of tricarboxylic acid cycle metabolites from wild type or IDH1 mutant cells cultured in (13)C-labeled glucose upon the addition of L, D, or racemic mixtures of 2HG. A more sensitive, targeted analysis revealed trace levels of isotopic enrichment (<1 %) in some central carbon metabolites from (13)C-labeled 2HG. However, we found that cells do not deplete 2HG from the media at levels above our detection limit over a 48 h time period. CONCLUSIONS: Taken together, we conclude that 2HG carbon is not readily transformed in the HCT116 cell line. These data indicate that the phenotypic alterations induced by 2HG are not a result of its metabolic products

The disruption of Celf6, a gene identified by translational profiling of serotonergic neurons, results in autism-related behaviors

The immense molecular diversity of neurons challenges our ability to understand the genetic and cellular etiology of neuropsychiatric disorders. Leveraging knowledge from neurobiology may help parse the genetic complexity: identifying genes important for a circuit that mediates a particular symptom of a disease may help identify polymorphisms that contribute to risk for the disease as a whole. The serotonergic system has long been suspected in disorders that have symptoms of repetitive behaviors and resistance to change, including autism. We generated a bacTRAP mouse line to permit translational profiling of serotonergic neurons. From this, we identified several thousand serotonergic-cell expressed transcripts, of which 174 were highly enriched, including all known markers of these cells. Analysis of common variants near the corresponding genes in the AGRE collection implicated the RNA binding protein CELF6 in autism risk. Screening for rare variants in CELF6 identified an inherited premature stop codon in one of the probands. Subsequent disruption of Celf6 in mice resulted in animals exhibiting resistance to change and decreased ultrasonic vocalization as well as abnormal levels of serotonin in the brain. This work provides a reproducible and accurate method to profile serotonergic neurons under a variety of conditions and suggests a novel paradigm for gaining information on the etiology of psychiatric disorders

Systems-Level Annotation of a Metabolomics Data Set Reduces 25 000 Features to Fewer than 1000 Unique Metabolites

When using liquid chromatography/mass spectrometry (LC/MS) to perform untargeted metabolomics, it is now routine to detect tens of thousands of features from biological samples. Poor understanding of the data, however, has complicated interpretation and masked the number of unique metabolites actually being measured in an experiment. Here we place an upper bound on the number of unique metabolites detected in Escherichia coli samples analyzed with one untargeted metabolomics method. We first group multiple features arising from the same analyte, which we call “degenerate features”, using a context-driven annotation approach. Surprisingly, this analysis revealed thousands of previously unreported degeneracies that reduced the number of unique analytes to ∼2961. We then applied an orthogonal approach to remove nonbiological features from the data using the ¹³C-based credentialing technology. This further reduced the number of unique analytes to less than 1000. Our 90% reduction in data is 5-fold greater than previously published studies. On the basis of the results, we propose an alternative approach to untargeted metabolomics that relies on thoroughly annotated reference data sets. To this end, we introduce the creDBle database (http://creDBle.wustl.edu), which contains accurate mass, retention time, and MS/MS fragmentation data as well as annotations of all credentialed features

Consumption of NADPH for 2-HG Synthesis Increases Pentose Phosphate Pathway Flux and Sensitizes Cells to Oxidative Stress

Summary: Gain-of-function mutations in isocitrate dehydrogenase 1 (IDH1) occur in multiple types of human cancer. Here, we show that these mutations significantly disrupt NADPH homeostasis by consuming NADPH for 2-hydroxyglutarate (2-HG) synthesis. Cells respond to 2-HG synthesis, but not exogenous administration of 2-HG, by increasing pentose phosphate pathway (PPP) flux. We show that 2-HG production competes with reductive biosynthesis and the buffering of oxidative stress, processes that also require NADPH. IDH1 mutants have a decreased capacity to synthesize palmitate and an increased sensitivity to oxidative stress. Our results demonstrate that, even when NADPH is limiting, IDH1 mutants continue to synthesize 2-HG at the expense of other NADPH-requiring pathways that are essential for cell viability. Thus, rather than attempting to decrease 2-HG synthesis in the clinic, the consumption of NADPH by mutant IDH1 may be exploited as a metabolic weakness that sensitizes tumor cells to ionizing radiation, a commonly used anti-cancer therapy. : Using liquid chromatography/mass spectrometry (LC/MS) and stable isotope tracing, Gelman et al. find that 2-HG production in cells with IDH1 mutations leads to increased pentose phosphate pathway activity to generate NADPH. Production of 2-HG competes with other NADPH-dependent pathways and sensitizes cells to redox stress. Keywords: 2-hydroxyglutarate, cancer metabolism, LC/MS, metabolomcis, pentose phosphate pathway, redox regulatio

Defining and Detecting Complex Peak Relationships in Mass Spectral Data: The Mz.unity Algorithm

Analysis of a single analyte by mass spectrometry can result in the detection of more than 100 degenerate peaks. These degenerate peaks complicate spectral interpretation and are challenging to annotate. In mass spectrometry-based metabolomics, this degeneracy leads to inflated false discovery rates, data sets containing an order of magnitude more features than analytes, and an inefficient use of resources during data analysis. Although software has been introduced to annotate spectral degeneracy, current approaches are unable to represent several important classes of peak relationships. These include heterodimers and higher complex adducts, distal fragments, relationships between peaks in different polarities, and complex adducts between features and background peaks. Here we outline sources of peak degeneracy in mass spectra that are not annotated by current approaches and introduce a software package called mz.unity to detect these relationships in accurate mass data. Using mz.unity, we find that data sets contain many more complex relationships than we anticipated. Examples include the adduct of glutamate and nicotinamide adenine dinucleotide (NAD), fragments of NAD detected in the same or opposite polarities, and the adduct of glutamate and a background peak. Further, the complex relationships we identify show that several assumptions commonly made when interpreting mass spectral degeneracy do not hold in general. These contributions provide new tools and insight to aid in the annotation of complex spectral relationships and provide a foundation for improved data set identification. Mz.unity is an R package and is freely available at https://github.com/nathaniel-mahieu/mz.unity as well as our laboratory Web site http://pattilab.wustl.edu/software/

An Untargeted Metabolomic Workflow to Improve Structural Characterization of Metabolites

Mass spectrometry-based metabolomics relies on MS² data for structural characterization of metabolites. To obtain the high-quality MS² data necessary to support metabolite identifications, ions of interest must be purely isolated for fragmentation. Here, we show that metabolomic MS² data are frequently characterized by contaminating ions that prevent structural identification. Although using narrow-isolation windows can minimize contaminating MS² fragments, even narrow windows are not always selective enough, and they can complicate data analysis by removing isotopic patterns from MS² spectra. Moreover, narrow windows can significantly reduce sensitivity. In this work, we introduce a novel, two-part approach for performing metabolomic identifications that addresses these issues. First, we collect MS² scans with less stringent isolation settings to obtain improved sensitivity at the expense of specificity. Then, by evaluating MS² fragment intensities as a function of retention time and precursor mass targeted for MS² analysis, we obtain deconvolved MS² spectra that are consistent with pure standards and can therefore be used for metabolite identification. The value of our approach is highlighted with metabolic extracts from brain, liver, astrocytes, as well as nerve tissue, and performance is evaluated by using pure metabolite standards in combination with simulations based on raw MS² data from the METLIN metabolite database. A R package implementing the algorithms used in our workflow is available on our laboratory website (http://pattilab.wustl.edu/decoms2.php)

Differential Incorporation of Glucose into Biomass during Warburg Metabolism

It is well established that most cancer cells take up an increased amount of glucose relative to that taken up by normal differentiated cells. The majority of this glucose carbon is secreted from the cell as lactate. The fate of the remaining glucose carbon, however, has not been well-characterized. Here we apply a novel combination of metabolomic technologies to track uniformly labeled glucose in HeLa cancer cells. We provide a list of specific intracellular metabolites that become enriched after being labeled for 48 h and quantitate the fraction of consumed glucose that ends up in proteins, peptides, sugars/glycerol, and lipids