PGRMC2 is an intracellular haem chaperone critical for adipocyte function.

Haem is an essential prosthetic group of numerous proteins and a central signalling molecule in many physiologic processes1,2. The chemical reactivity of haem means that a network of intracellular chaperone proteins is required to avert the cytotoxic effects of free haem, but the constituents of such trafficking pathways are unknown3,4. Haem synthesis is completed in mitochondria, with ferrochelatase adding iron to protoporphyrin IX. How this vital but highly reactive metabolite is delivered from mitochondria to haemoproteins throughout the cell remains poorly defined3,4. Here we show that progesterone receptor membrane component 2 (PGRMC2) is required for delivery of labile, or signalling haem, to the nucleus. Deletion of PGMRC2 in brown fat, which has a high demand for haem, reduced labile haem in the nucleus and increased stability of the haem-responsive transcriptional repressors Rev-Erbα and BACH1. Ensuing alterations in gene expression caused severe mitochondrial defects that rendered adipose-specific PGRMC2-null mice unable to activate adaptive thermogenesis and prone to greater metabolic deterioration when fed a high-fat diet. By contrast, obese-diabetic mice treated with a small-molecule PGRMC2 activator showed substantial improvement of diabetic features. These studies uncover a role for PGRMC2 in intracellular haem transport, reveal the influence of adipose tissue haem dynamics on physiology and suggest that modulation of PGRMC2 may revert obesity-linked defects in adipocytes.

Chemical genomics with pyrvinium identifies C1orf115 as a regulator of drug efflux.

Pyrvinium is a quinoline-derived cyanine dye and an approved anti-helminthic drug reported to inhibit WNT signaling and have anti-proliferative effects in various cancer cell lines. To further understand the mechanism by which pyrvinium is cytotoxic, we conducted a pooled genome-wide CRISPR loss-of-function screen in the human HAP1 cell model. The top drug-gene sensitizer interactions implicated the malate-aspartate and glycerol-3-phosphate shuttles as mediators of cytotoxicity to mitochondrial complex I inhibition including pyrvinium. By contrast, perturbation of the poorly characterized gene C1orf115/RDD1 resulted in strong resistance to the cytotoxic effects of pyrvinium through dysregulation of the major drug efflux pump ABCB1/MDR1. Interestingly, C1orf115/RDD1 was found to physically associate with ABCB1/MDR1 through proximity-labeling experiments and perturbation of C1orf115 led to mis-localization of ABCB1/MDR1. Our results are consistent with a model whereby C1orf115 modulates drug efflux through regulation of the major drug exporter ABCB1/MDR1.

Computational Expansion of High-Resolution-MSn Spectral Libraries.

Commonly, in MS-based untargeted metabolomics, some metabolites cannot be confidently identified due to ambiguities in resolving isobars and structurally similar species. To address this, analytical techniques beyond traditional MS2 analysis, such as MSn fragmentation, can be applied to probe metabolites for additional structural information. In MSn fragmentation, recursive cycles of activation are applied to fragment ions originating from the same precursor ion detected on an MS1 spectrum. This resonant-type collision-activated dissociation (CAD) can yield information that cannot be ascertained from MS2 spectra alone, which helps improve the performance of metabolite identification workflows. However, most approaches for metabolite identification require mass-to-charge (m/z) values measured with high resolution, as this enables the determination of accurate mass values. Unfortunately, high-resolution-MSn spectra are relatively rare in spectral libraries. Here, we describe a computational approach to generate a database of high-resolution-MSn spectra by converting existing low-resolution-MSn spectra using complementary high-resolution-MS2 spectra generated by beam-type CAD. Using this method, we have generated a database, derived from the NIST20 MS/MS database, of MSn spectral trees representing 9637 compounds and 19386 precursor ions where at least 90% of signal intensity was converted from low-to-high resolution.

XCMS-MRM and METLIN-MRM: a cloud library and public resource for targeted analysis of small molecules.

We report XCMS-MRM and METLIN-MRM ( http://xcmsonline-mrm.scripps.edu/ and http://metlin.scripps.edu/ ), a cloud-based data-analysis platform and a public multiple-reaction monitoring (MRM) transition repository for small-molecule quantitative tandem mass spectrometry. This platform provides MRM transitions for more than 15,500 molecules and facilitates data sharing across different instruments and laboratories.

Metabolomics-based discovery of a metabolite that enhances oligodendrocyte maturation.

Endogenous metabolites play essential roles in the regulation of cellular identity and activity. Here we have investigated the process of oligodendrocyte precursor cell (OPC) differentiation, a process that becomes limiting during progressive stages of demyelinating diseases, including multiple sclerosis, using mass-spectrometry-based metabolomics. Levels of taurine, an aminosulfonic acid possessing pleotropic biological activities and broad tissue distribution properties, were found to be significantly elevated (∼20-fold) during the course of oligodendrocyte differentiation and maturation. When added exogenously at physiologically relevant concentrations, taurine was found to dramatically enhance the processes of drug-induced in vitro OPC differentiation and maturation. Mechanism of action studies suggest that the oligodendrocyte-differentiation-enhancing activities of taurine are driven primarily by its ability to directly increase available serine pools, which serve as the initial building block required for the synthesis of the glycosphingolipid components of myelin that define the functional oligodendrocyte cell state.

Autonomous METLIN-Guided In-source Fragment Annotation for Untargeted Metabolomics.

Computational metabolite annotation in untargeted profiling aims at uncovering neutral molecular masses of underlying metabolites and assign those with putative identities. Existing annotation strategies rely on the observation and annotation of adducts to determine metabolite neutral masses. However, a significant fraction of features usually detected in untargeted experiments remains unannotated, which limits our ability to determine neutral molecular masses. Despite the availability of tools to annotate, relatively few of them benefit from the inherent presence of in-source fragments in liquid chromatography-electrospray ionization-mass spectrometry. In this study, we introduce a strategy to annotate in-source fragments in untargeted data using low-energy tandem mass spectrometry (MS) spectra from the METLIN library. Our algorithm, MISA (METLIN-guided in-source annotation), compares detected features against low-energy fragments from MS/MS spectra, enabling robust annotation and putative identification of metabolic features based on low-energy spectral matching. The algorithm was evaluated through an annotation analysis of a total of 140 metabolites across three different sets of biological samples analyzed with liquid chromatography-mass spectrometry. Results showed that, in cases where adducts were not formed or detected, MISA was able to uncover neutral molecular masses by in-source fragment matching. MISA was also able to provide putative metabolite identities via two annotation scores. These scores take into account the number of in-source fragments matched and the relative intensity similarity between the experimental data and the reference low-energy MS/MS spectra. Overall, results showed that in-source fragmentation is a highly frequent phenomena that should be considered for comprehensive feature annotation. Thus, combined with adduct annotation, this strategy adds a complementary annotation layer, enabling in-source fragments to be annotated and increasing putative identification confidence. The algorithm is integrated into the XCMS Online platform and is freely available at http://xcmsonline.scripps.edu .

METLIN: A Technology Platform for Identifying Knowns and Unknowns.

METLIN originated as a database to characterize known metabolites and has since expanded into a technology platform for the identification of known and unknown metabolites and other chemical entities. Through this effort it has become a comprehensive resource containing over 1 million molecules including lipids, amino acids, carbohydrates, toxins, small peptides, and natural products, among other classes. METLIN's high-resolution tandem mass spectrometry (MS/MS) database, which plays a key role in the identification process, has data generated from both reference standards and their labeled stable isotope analogues, facilitated by METLIN-guided analysis of isotope-labeled microorganisms. The MS/MS data, coupled with the fragment similarity search function, expand the tool's capabilities into the identification of unknowns. Fragment similarity search is performed independent of the precursor mass, relying solely on the fragment ions to identify similar structures within the database. Stable isotope data also facilitate characterization by coupling the similarity search output with the isotopic m/ z shifts. Examples of both are demonstrated here with the characterization of four previously unknown metabolites. METLIN also now features in silico MS/MS data, which has been made possible through the creation of algorithms trained on METLIN's MS/MS data from both standards and their isotope analogues. With these informatic and experimental data features, METLIN is being designed to address the characterization of known and unknown molecules.

Data Streaming for Metabolomics: Accelerating Data Processing and Analysis from Days to Minutes.

The speed and throughput of analytical platforms has been a driving force in recent years in the "omics" technologies and while great strides have been accomplished in both chromatography and mass spectrometry, data analysis times have not benefited at the same pace. Even though personal computers have become more powerful, data transfer times still represent a bottleneck in data processing because of the increasingly complex data files and studies with a greater number of samples. To meet the demand of analyzing hundreds to thousands of samples within a given experiment, we have developed a data streaming platform, XCMS Stream, which capitalizes on the acquisition time to compress and stream recently acquired data files to data processing servers, mimicking just-in-time production strategies from the manufacturing industry. The utility of this XCMS Online-based technology is demonstrated here in the analysis of T cell metabolism and other large-scale metabolomic studies. A large scale example on a 1000 sample data set demonstrated a 10 000-fold time savings, reducing data analysis time from days to minutes. Further, XCMS Stream has the capability to increase the efficiency of downstream biochemical dependent data acquisition (BDDA) analysis by initiating data conversion and data processing on subsets of data acquired, expanding its application beyond data transfer to smart preliminary data decision-making prior to full acquisition.

Exposome-Scale Investigations Guided by Global Metabolomics, Pathway Analysis, and Cognitive Computing.

Concurrent exposure to a wide variety of xenobiotics and their combined toxic effects can play a pivotal role in health and disease, yet are largely unexplored. Investigating the totality of these exposures, i.e., the "exposome", and their specific biological effects constitutes a new paradigm for environmental health but still lacks high-throughput, user-friendly technology. We demonstrate the utility of mass spectrometry-based global exposure metabolomics combined with tailored database queries and cognitive computing for comprehensive exposure assessment and the straightforward elucidation of biological effects. The METLIN Exposome database has been redesigned to help identify environmental toxicants, food contaminants and supplements, drugs, and antibiotics as well as their biotransformation products, through its expansion with over 700 000 chemical structures to now include more than 950 000 unique small molecules. More importantly, we demonstrate how the XCMS/METLIN platform now allows for the readout of the biological effect of a toxicant through metabolomic-derived pathway analysis, and further, artificial intelligence provides a means of assessing the role of a potential toxicant. The presented workflow addresses many of the methodological challenges current exposomics research is facing and will serve to gain a deeper understanding of the impact of environmental exposures and combinatory toxic effects on human health.

Smartphone Analytics: Mobilizing the Lab into the Cloud for Omic-Scale Analyses.

Active data screening is an integral part of many scientific activities, and mobile technologies have greatly facilitated this process by minimizing the reliance on large hardware instrumentation. In order to meet with the increasingly growing field of metabolomics and heavy workload of data processing, we designed the first remote metabolomic data screening platform for mobile devices. Two mobile applications (apps), XCMS Mobile and METLIN Mobile, facilitate access to XCMS and METLIN, which are the most important components in the computer-based XCMS Online platforms. These mobile apps allow for the visualization and analysis of metabolic data throughout the entire analytical process. Specifically, XCMS Mobile and METLIN Mobile provide the capabilities for remote monitoring of data processing, real time notifications for the data processing, visualization and interactive analysis of processed data (e.g., cloud plots, principle component analysis, box-plots, extracted ion chromatograms, and hierarchical cluster analysis), and database searching for metabolite identification. These apps, available on Apple iOS and Google Android operating systems, allow for the migration of metabolomic research onto mobile devices for better accessibility beyond direct instrument operation. The utility of XCMS Mobile and METLIN Mobile functionalities was developed and is demonstrated here through the metabolomic LC-MS analyses of stem cells, colon cancer, aging, and bacterial metabolism.

Lipid profiling of polarized human monocyte-derived macrophages.

The highly orchestrated transcriptional and metabolic reprogramming during activation drastically transforms the main functions and physiology of human macrophages across the polarization spectrum. Lipids, for example, can modify protein function by acting remotely as signaling molecules but also locally by altering the physical properties of cellular membranes. These changes play key roles in the functions of highly plastic immune cells due to their involvement in inflammation, immune responses, phagocytosis and wound healing processes. We report an analysis of major membrane lipids of distinct phenotypes of resting (M0), classically activated (M1), alternatively activated (M2a) and deactivated (M2c) human monocyte derived macrophages from different donors. Samples were subjected to supercritical fluid chromatography-ion mobility-mass spectrometry analysis, which allowed separations based on lipid class, facilitating the profiling of their fatty acid composition. Different levels of arachidonic acid mobilization as well as other fatty acid changes were observed for different lipid classes in the distinct polarization phenotypes, suggesting the activation of highly orchestrated and specific enzymatic processes in the biosynthesis of lipid signaling molecules and cell membrane remodeling. Thromboxane A2 production appeared to be a specific marker of M1 polarization. These alterations to the global composition of lipid bi-layer membranes in the cell provide a potential methodology for the definition and determination of cellular and tissue activation states.

Novel behavior of the chromatographic separation of linear and cyclic polymers.

In various polymerization processes, the formation of a wide variety of chains, not only in length but also in chemical composition, broadly complicates comprehensive polymer characterization. In this communication, we compare different stationary and mobile phases for the analysis of complex polymer mixtures via size-exclusion chromatography-mass spectrometry (SEC-MS). To the best of our knowledge, we report novel chromatographic effects for the separation of linear and cyclic oligomers for polyesters (PE) and polyurethanes (PUR). A complete separation for the different structures was achieved for both polymer types with a single-solvent system (acetonitrile, ACN) and without extensive optimization. Additionally, cyclic species were found to show an inverse elution profile compared to their linear counterparts, suggesting distinct physical properties between species.

Unusual kinetic isotope effects of deuterium reinforced polyunsaturated fatty acids in tocopherol-mediated free radical chain oxidations.

Substitution of -CD2- at the reactive centers of linoleic and linolenic acids reduces the rate of abstraction of D by a tocopheryl radical by as much as 36-fold, compared to the abstraction of H from a corresponding -CH2- center. This H atom transfer reaction is the rate-determining step in the tocopherol-mediated peroxidation of lipids in human low-density lipoproteins, a process that has been linked to coronary artery disease. The unanticipated large kinetic isotope effects reported here for the tocopherol-mediated oxidation of linoleic and linolenic acids and esters suggests that tunneling makes this process favorable.

Protocol for mapping the metabolome and lipidome of medulloblastoma cells using liquid chromatography-mass spectrometry.

Liquid chromatography-mass spectrometry (LC-MS)-based metabolomics and lipidomics have recently been used to show that MYC-amplified group 3 medulloblastoma tumors are driven by metabolic reprogramming. Here, we present a protocol to extract metabolites and lipids from human medulloblastoma brain tumor-initiating cells and normal neural stem cells. We describe untargeted LC-MS methods that can be used to achieve extensive coverage of the polar metabolome and lipidome. Finally, we detail strategies for metabolite identification and data analysis. For complete details on the use and execution of this protocol, please refer to Gwynne et al.1.

Drug-Initiated Activity Metabolomics Identifies Myristoylglycine as a Potent Endogenous Metabolite for Human Brown Fat Differentiation.

Worldwide, obesity rates have doubled since the 1980s and in the USA alone, almost 40% of adults are obese, which is closely associated with a myriad of metabolic diseases such as type 2 diabetes and arteriosclerosis. Obesity is derived from an imbalance between energy intake and consumption, therefore balancing energy homeostasis is an attractive target for metabolic diseases. One therapeutic approach consists of increasing the number of brown-like adipocytes in the white adipose tissue (WAT). Whereas WAT stores excess energy, brown adipose tissue (BAT) can dissipate this energy overload in the form of heat, increasing energy expenditure and thus inhibiting metabolic diseases. To facilitate BAT production a high-throughput screening approach was developed on previously known drugs using human Simpson-Golabi-Behmel Syndrome (SGBS) preadipocytes. The screening allowed us to discover that zafirlukast, an FDA-approved small molecule drug commonly used to treat asthma, was able to differentiate adipocyte precursors and white-biased adipocytes into functional brown adipocytes. However, zafirlukast is toxic to human cells at higher dosages. Drug-Initiated Activity Metabolomics (DIAM) was used to investigate zafirlukast as a BAT inducer, and the endogenous metabolite myristoylglycine was then discovered to mimic the browning properties of zafirlukast without impacting cell viability. Myristoylglycine was found to be bio-synthesized upon zafirlukast treatment and was unique in inducing brown adipocyte differentiation, raising the possibility of using endogenous metabolites and bypassing the exogenous drugs to potentially alleviate disease, in this case, obesity and other related metabolic diseases.

The Trisubstituted Isoxazole MMV688766 Exerts Broad-Spectrum Activity against Drug-Resistant Fungal Pathogens through Inhibition of Lipid Homeostasis.

Candida species are among the most prevalent causes of systemic fungal infection, posing a growing threat to public health. While Candida albicans is the most common etiological agent of systemic candidiasis, the frequency of infections caused by non-albicans Candida species is rising. Among these is Candida auris, which has emerged as a particular concern. Since its initial discovery in 2009, it has been identified worldwide and exhibits resistance to all three principal antifungal classes. Here, we endeavored to identify compounds with novel bioactivity against C. auris from the Medicines for Malaria Venture's Pathogen Box library. Of the five hits identified, the trisubstituted isoxazole MMV688766 emerged as the only compound displaying potent fungicidal activity against C. auris, as well as other evolutionarily divergent fungal pathogens. Chemogenomic profiling, as well as subsequent metabolomic and phenotypic analyses, revealed that MMV688766 disrupts cellular lipid homeostasis, driving a decrease in levels of early sphingolipid intermediates and fatty acids and a concomitant increase in lysophospholipids. Experimental evolution to further probe MMV688766's mode of action in the model fungus Saccharomyces cerevisiae revealed that loss of function of the transcriptional regulator HAL9 confers resistance to MMV688766, in part through the upregulation of the lipid-binding chaperone HSP12, a response that appears to assist in tolerating MMV688766-induced stress. The novel mode of action we have uncovered for MMV688766 against drug-resistant fungal pathogens highlights the broad utility of targeting lipid homeostasis to disrupt fungal growth and how screening structurally-diverse chemical libraries can provide new insights into resistance-conferring stress responses of fungi. IMPORTANCE As widespread antimicrobial resistance threatens to propel the world into a postantibiotic era, there is a pressing need to identify mechanistically distinct antimicrobial agents. This is of particular concern when considering the limited arsenal of drugs available to treat fungal infections, coupled with the emergence of highly drug-resistant fungal pathogens, including Candida auris. In this work, we demonstrate that existing libraries of drug-like chemical matter can be rich resources for antifungal molecular scaffolds. We discovered that the small molecule MMV688766, from the Pathogen Box library, displays previously undescribed broad-spectrum fungicidal activity through perturbation of lipid homeostasis. Characterization of the mode of action of MMV688766 provided new insight into the protective mechanisms fungi use to cope with the disruption of lipid homeostasis. Our findings highlight that elucidating the genetic circuitry required to survive in the presence of cellular stress offers powerful insights into the biological pathways that govern this important phenotype.

Cancer-selective metabolic vulnerabilities in MYC-amplified medulloblastoma.

MYC-driven medulloblastoma (MB) is an aggressive pediatric brain tumor characterized by therapy resistance and disease recurrence. Here, we integrated data from unbiased genetic screening and metabolomic profiling to identify multiple cancer-selective metabolic vulnerabilities in MYC-driven MB tumor cells, which are amenable to therapeutic targeting. Among these targets, dihydroorotate dehydrogenase (DHODH), an enzyme that catalyzes de novo pyrimidine biosynthesis, emerged as a favorable candidate for therapeutic targeting. Mechanistically, DHODH inhibition acts on target, leading to uridine metabolite scarcity and hyperlipidemia, accompanied by reduced protein O-GlcNAcylation and c-Myc degradation. Pyrimidine starvation evokes a metabolic stress response that leads to cell-cycle arrest and apoptosis. We further show that an orally available small-molecule DHODH inhibitor demonstrates potent mono-therapeutic efficacy against patient-derived MB xenografts in vivo. The reprogramming of pyrimidine metabolism in MYC-driven medulloblastoma represents an unappreciated therapeutic strategy and a potential new class of treatments with stronger cancer selectivity and fewer neurotoxic sequelae.

Metabolomics activity screening of T cell-induced colitis reveals anti-inflammatory metabolites.

Untargeted metabolomics of disease-associated intestinal microbiota can detect quantitative changes in metabolite profiles and complement other methodologies to reveal the full effect of intestinal dysbiosis. Here, we used the T cell transfer mouse model of colitis to identify small-molecule metabolites with altered abundance due to intestinal inflammation. We applied untargeted metabolomics to detect metabolite signatures in cecal, colonic, and fecal samples from healthy and colitic mice and to uncover differences that would aid in the identification of colitis-associated metabolic processes. We provided an unbiased spatial survey of the GI tract for small molecules, and we identified the likely source of metabolites and biotransformations. Several prioritized metabolites that we detected as being altered in colitis were evaluated for their ability to induce inflammatory signaling in cultured macrophages, such as NF-κB signaling and the expression of cytokines and chemokines upon LPS stimulation. Multiple previously uncharacterized anti-inflammatory and inflammation-augmenting metabolites were thus identified, with phytosphingosine showing the most effective anti-inflammatory activity in vitro. We further demonstrated that oral administration of phytosphingosine decreased inflammation in a mouse model of colitis induced by the compound TNBS. The collection of distinct metabolites we identified and characterized, many of which have not been previously associated with colitis, may offer new biological insight into IBD-associated inflammation and disease pathogenesis.