Kevin Cho

An accelerated workflow for untargeted metabolomics using the METLIN database.

Metabolites, typically recognized as small molecules that are involved in cellular reactions, provide a functional signature of phenotype that is complimentary to the upstream biochemical information obtained from genes, transcripts, and proteins. The high-level of correlation between metabolites and phenotype has created a surge of interest in the field that is reflected in the number of metabolomic publications growing from just a few articles in 1999 to over five thousand in 2011. Although relatively new compared to its genomic and proteomic predecessors, already metabolomics has led to the discovery of biomarkers for disease, fundamental insights into cellular biochemistry, and clues related to disease pathogenesis.1,2

Metabolism Links Bacterial Biofilms and Colon Carcinogenesis

Bacterial biofilms in the colon alter the host tissue microenvironment. A role for biofilms in colon cancer metabolism has been suggested but to date has not been evaluated. Using metabolomics, we investigated the metabolic influence that microbial biofilms have on colon tissues and the related occurrence of cancer. Patient-matched colon cancers and histologically normal tissues, with or without biofilms, were examined. We show the upregulation of polyamine metabolites in tissues from cancer hosts with significant enhancement of N(1), N(12)-diacetylspermine in both biofilm-positive cancer and normal tissues. Antibiotic treatment, which cleared biofilms, decreased N(1), N(12)-diacetylspermine levels to those seen in biofilm-negative tissues, indicating that host cancer and bacterial biofilm structures contribute to the polyamine metabolite pool. These results show that colonic mucosal biofilms alter the cancer metabolome to produce a regulator of cellular proliferation and colon cancer growth potentially affecting cancer development and progression.

X13CMS: Global Tracking of Isotopic Labels in Untargeted Metabolomics

Studies of isotopically labeled compounds have been fundamental to understanding metabolic pathways and fluxes. They have traditionally, however, been used in conjunction with targeted analyses that identify and quantify a limited number of labeled downstream metabolites. Here we describe an alternative workflow that leverages recent advances in untargeted metabolomic technologies to track the fates of isotopically labeled metabolites in a global, unbiased manner. This untargeted approach can be applied to discover novel biochemical pathways and characterize changes in the fates of labeled metabolites as a function of altered biological conditions such as disease. To facilitate the data analysis, we introduce X13CMS, an extension of the widely used mass spectrometry-based metabolomic software package XCMS. X13CMS uses the XCMS platform to detect metabolite peaks and perform retention-time alignment in liquid chromatography/mass spectrometry (LC/MS) data. With the use of the XCMS output, the program then identifies isotopologue groups that correspond to isotopically labeled compounds. The retrieval of these groups is done without any a priori knowledge besides the following input parameters: (i) the mass difference between the unlabeled and labeled isotopes, (ii) the mass accuracy of the instrument used in the analysis, and (iii) the estimated retention-time reproducibility of the chromatographic method. Despite its name, X13CMS can be used to track any isotopic label. Additionally, it detects differential labeling patterns in biological samples collected from parallel control and experimental conditions. We validated the ability of X13CMS to accurately retrieve labeled metabolites from complex biological matrices both with targeted LC/MS/MS analysis of a subset of the hits identified by the program and with labeled standards spiked into cell extracts. We demonstrate the full functionality of X13CMS with an analysis of cultured rat astrocytes treated with uniformly labeled (U-)13C-glucose during lipopolysaccharide (LPS) challenge. Our results show that out of 223 isotopologue groups enriched from U-13C-glucose, 95 have statistically significant differential labeling patterns in astrocytes challenged with LPS compared to unchallenged control cells. Only two of these groups overlap with the 32 differentially regulated peaks identified by XCMS, indicating that X13CMS uncovers different and complementary information from untargeted metabolomic studies. Like XCMS, X13CMS is implemented in R. It is available from our laboratory website at http://pattilab.wustl.edu/x13cms.php.

isoMETLIN: a database for isotope-based metabolomics.

The METLIN metabolite database has become one of the most widely used resources in metabolomics for making metabolite identifications. However, METLIN is not designed to identify metabolites that have been isotopically labeled. As a result, unbiasedly tracking the transformation of labeled metabolites with isotope-based metabolomics is a challenge. Here, we introduce a new database, called isoMETLIN (http://isometlin.scripps.edu/), that has been developed specifically to identify metabolites incorporating isotopic labels. isoMETLIN enables users to search all computed isotopologues derived from METLIN on the basis of mass-to-charge values and specified isotopes of interest, such as (13)C or (15)N. Additionally, isoMETLIN contains experimental MS/MS data on hundreds of isotopomers. These data assist in localizing the position of isotopic labels within a metabolite. From these experimental MS/MS isotopomer spectra, precursor atoms can be mapped to fragments. The MS/MS spectra of additional isotopomers can then be computationally generated and included within isoMETLIN. Given that isobaric isotopomers cannot be separated chromatographically or by mass but are likely to occur simultaneously in a biological system, we have also implemented a spectral-mixing function in isoMETLIN. This functionality allows users to combine MS/MS spectra from various isotopomers in different ratios to obtain a theoretical MS/MS spectrum that matches the MS/MS spectrum from a biological sample. Thus, by searching MS and MS/MS experimental data, isoMETLIN facilitates the identification of isotopologues as well as isotopomers from biological samples and provides a platform to drive the next generation of isotope-based metabolomic studies.

After the feature presentation: technologies bridging untargeted metabolomics and biology.

Liquid chromatography/mass spectrometry-based untargeted metabolomics is now an established experimental approach that is being broadly applied by many laboratories worldwide. Interpreting untargeted metabolomic data, however, remains a challenge and limits the translation of results into biologically relevant conclusions. Here we review emerging technologies that can be applied after untargeted profiling to extend biological interpretation of metabolomic data. These technologies include advances in bioinformatic software that enable identification of isotopes and adducts, comprehensive pathway mapping, deconvolution of MS(2) data, and tracking of isotopically labeled compounds. There are also opportunities to gain additional biological insight by complementing the metabolomic analysis of homogenized samples with recently developed technologies for metabolite imaging of intact tissues. To maximize the value of these emerging technologies, a unified workflow is discussed that builds on the traditional untargeted metabolomic pipeline. Particularly when integrated together, the combination of the advances highlighted in this review helps transform lists of masses and fold changes characteristic of untargeted profiling results into structures, absolute concentrations, pathway fluxes, and localization patterns that are typically needed to understand biology.

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.

Inflammation triggers production of dimethylsphingosine from oligodendrocytes

Neuropathic pain is a chronic, refractory condition that arises after damage to the nervous system. We previously showed that an increased level of the endogenous metabolite N,N-dimethylsphingosine (DMS) in the central nervous system (CNS) is sufficient to induce neuropathic pain-like behavior in rats. However, several important questions remain. First, it has not yet been demonstrated that DMS is produced in humans and its value as a therapeutic target is therefore unknown. Second, the cell types within the CNS that produce DMS are currently unidentified. Here we provide evidence that DMS is present in human CNS tissue. We show that DMS levels increase in demyelinating lesions isolated from patients with multiple sclerosis, an autoimmune disease in which the majority of patients experience chronic pain. On the basis of these results, we hypothesized that oligodendrocytes may be a cellular source of DMS. We show that human oligodendrocytes produce DMS in culture and that the levels of DMS increase when oligodendrocytes are challenged with agents that damage white matter. These results suggest that damage to oligodendrocytes leads to increased DMS production which in turn drives inflammatory astrocyte responses involved in sensory neuron sensitization. Interruption of this pathway in patients may provide analgesia without the debilitating side effects that are commonly observed with other chronic pain therapies.

Bar Coding MS2 Spectra for Metabolite Identification

Metabolite identifications are most frequently achieved in untargeted metabolomics by matching precursor mass and full, high-resolution MS2 spectra to metabolite databases and standards. Here we considered an alternative approach for establishing metabolite identifications that does not rely on full, high-resolution MS2 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 MS2 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 MS2 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 MS2-based identifications in untargeted metabolomics with a specificity and sensitivity that is competitive to high-resolution time-of-flight technologies.

Mechanism of High-Level Daptomycin Resistance in Corynebacterium striatum

Antimicrobial resistance threatens the efficacy of antimicrobial treatment options, including last-line-of-defense drugs. Understanding how this resistance develops can help direct antimicrobial stewardship efforts and is critical to designing the next generation of antimicrobial therapies. Here we determine how Corynebacterium striatum, a skin commensal and opportunistic pathogen, evolved high-level resistance to a drug of last resort, daptomycin. Through a single mutation, this pathogen was able to remove the daptomycin’s target, phosphatidylglycerol (PG), from the membrane and evade daptomycin’s bactericidal activity. We found that additional compensatory changes were not necessary to support the removal of PG and replacement with phosphatidylinositol (PI). The ease with which C. striatum evolved high-level resistance is cause for alarm and highlights the importance of screening new antimicrobials against a wide range of clinical pathogens which may harbor unique capacities for resistance evolution. ABSTRACT Daptomycin, a last-line-of-defense antibiotic for treating Gram-positive infections, is experiencing clinical failure against important infectious agents, including Corynebacterium striatum. The recent transition of daptomycin to generic status is projected to dramatically increase availability, use, and clinical failure. Here we confirm the genetic mechanism of high-level daptomycin resistance (HLDR; MIC = >256 µg/ml) in C. striatum, which evolved within a patient during daptomycin therapy, a phenotype recapitulated in vitro. In all 8 independent cases tested, loss-of-function mutations in phosphatidylglycerol synthase (pgsA2) were necessary and sufficient for high-level daptomycin resistance. Through lipidomic and biochemical analysis, we demonstrate that daptomycin’s activity is dependent on the membrane phosphatidylglycerol (PG) concentration. Until now, the verification of PG as the in vivo target of daptomycin has proven difficult since tested cell model systems were not viable without membrane PG. C. striatum becomes daptomycin resistant at a high level by removing PG from the membrane and changing the membrane composition to maintain viability. This work demonstrates that loss-of-function mutation in pgsA2 and the loss of membrane PG are necessary and sufficient to produce high-level resistance to daptomycin in C. striatum. IMPORTANCE Antimicrobial resistance threatens the efficacy of antimicrobial treatment options, including last-line-of-defense drugs. Understanding how this resistance develops can help direct antimicrobial stewardship efforts and is critical to designing the next generation of antimicrobial therapies. Here we determine how Corynebacterium striatum, a skin commensal and opportunistic pathogen, evolved high-level resistance to a drug of last resort, daptomycin. Through a single mutation, this pathogen was able to remove the daptomycin’s target, phosphatidylglycerol (PG), from the membrane and evade daptomycin’s bactericidal activity. We found that additional compensatory changes were not necessary to support the removal of PG and replacement with phosphatidylinositol (PI). The ease with which C. striatum evolved high-level resistance is cause for alarm and highlights the importance of screening new antimicrobials against a wide range of clinical pathogens which may harbor unique capacities for resistance evolution.