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.

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.

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.

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.

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.

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.

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.

Cognitive analysis of metabolomics data for systems biology.

Cognitive computing is revolutionizing the way big data are processed and integrated, with artificial intelligence (AI) natural language processing (NLP) platforms helping researchers to efficiently search and digest the vast scientific literature. Most available platforms have been developed for biomedical researchers, but new NLP tools are emerging for biologists in other fields and an important example is metabolomics. NLP provides literature-based contextualization of metabolic features that decreases the time and expert-level subject knowledge required during the prioritization, identification and interpretation steps in the metabolomics data analysis pipeline. Here, we describe and demonstrate four workflows that combine metabolomics data with NLP-based literature searches of scientific databases to aid in the analysis of metabolomics data and their biological interpretation. The four procedures can be used in isolation or consecutively, depending on the research questions. The first, used for initial metabolite annotation and prioritization, creates a list of metabolites that would be interesting for follow-up. The second workflow finds literature evidence of the activity of metabolites and metabolic pathways in governing the biological condition on a systems biology level. The third is used to identify candidate biomarkers, and the fourth looks for metabolic conditions or drug-repurposing targets that the two diseases have in common. The protocol can take 1-4 h or more to complete, depending on the processing time of the various software used.

Metabolic adaptation to calorie restriction.

Calorie restriction (CR) enhances health span (the length of time that an organism remains healthy) and increases longevity across species. In mice, these beneficial effects are partly mediated by the lowering of core body temperature that occurs during CR. Conversely, the favorable effects of CR on health span are mitigated by elevating ambient temperature to thermoneutrality (30°C), a condition in which hypothermia is blunted. In this study, we compared the global metabolic response to CR of mice housed at 22°C (the standard housing temperature) or at 30°C and found that thermoneutrality reverted 39 and 78% of total systemic or hypothalamic metabolic variations caused by CR, respectively. Systemic changes included pathways that control fuel use and energy expenditure during CR. Cognitive computing-assisted analysis of these metabolomics results helped to prioritize potential active metabolites that modulated the hypothermic response to CR. Last, we demonstrated with pharmacological approaches that nitric oxide (NO) produced through the citrulline-NO pathway promotes CR-triggered hypothermia and that leucine enkephalin directly controls core body temperature when exogenously injected into the hypothalamus. Because thermoneutrality counteracts CR-enhanced health span, the multiple metabolites and pathways altered by thermoneutrality may represent targets for mimicking CR-associated effects.

METLIN: A Tandem Mass Spectral Library of Standards.

Untargeted mass spectrometry metabolomics studies rely on accurate databases for the identification of metabolic features. Leveraging unique fragmentation patterns as well as characteristic dissociation routes allows for structural information to be gained for specific metabolites and molecular classes, respectively. Here we describe the evolution of METLIN as a resource for small molecule analysis as well as the tools (e.g., Fragment Similarity Search and Neutral Loss Search) used to query the database and their workflows for the identification of molecular entities. Additionally, we will discuss the functionalities of isoMETLIN, a database of isotopic metabolites, and the latest addition to the METLIN family, METLIN-MRM, which facilitates the analysis of quantitative mass spectrometry data generated with triple quadrupole instrumentation.

Activation of Kappa Opioid Receptor Regulates the Hypothermic Response to Calorie Restriction and Limits Body Weight Loss.

Mammals maintain a nearly constant core body temperature (Tb) by balancing heat production and heat dissipation. This comes at a high metabolic cost that is sustainable if adequate calorie intake is maintained. When nutrients are scarce or experimentally reduced such as during calorie restriction (CR), endotherms can reduce energy expenditure by lowering Tb [1-6]. This adaptive response conserves energy, limiting the loss of body weight due to low calorie intake [7-10]. Here we show that this response is regulated by the kappa opioid receptor (KOR). CR is associated with increased hypothalamic levels of the endogenous opioid Leu-enkephalin, which is derived from the KOR agonist precursor dynorphin [11]. Pharmacological inhibition of KOR, but not of the delta or the mu opioid receptor subtypes, fully blocked CR-induced hypothermia and increased weight loss during CR independent of calorie intake. Similar results were seen with DIO mice subjected to CR. In contrast, inhibiting KOR did not change Tb in animals fed ad libitum (AL). Chemogenetic inhibition of KOR neurons in the hypothalamic preoptic area reduced the CR-induced hypothermia, whereas chemogenetic activation of prodynorphin-expressing neurons in the arcuate or the parabrachial nucleus lowered Tb. These data indicate that KOR signaling is a pivotal regulator of energy homeostasis and can affect body weight during dieting by modulating Tb and energy expenditure.

The METLIN small molecule dataset for machine learning-based retention time prediction.

Machine learning has been extensively applied in small molecule analysis to predict a wide range of molecular properties and processes including mass spectrometry fragmentation or chromatographic retention time. However, current approaches for retention time prediction lack sufficient accuracy due to limited available experimental data. Here we introduce the METLIN small molecule retention time (SMRT) dataset, an experimentally acquired reverse-phase chromatography retention time dataset covering up to 80,038 small molecules. To demonstrate the utility of this dataset, we deployed a deep learning model for retention time prediction applied to small molecule annotation. Results showed that in 70[Formula: see text] of the cases, the correct molecular identity was ranked among the top 3 candidates based on their predicted retention time. We anticipate that this dataset will enable the community to apply machine learning or first principles strategies to generate better models for retention time prediction.

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.

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 .

Nanoformulated Antiretroviral Therapy Attenuates Brain Metabolic Oxidative Stress.

Antiretroviral therapy (ART) restricts human immunodeficiency virus type one (HIV-1) replication and by so doing, improves the quality and longevity of life for infected people. Nonetheless, treatment can also lead to adverse clinical outcomes such as drug resistance and systemic adverse events. Both could be affected by long-acting slow effective release ART. Indeed, maintenance of sustained plasma drug levels, for weeks or months, after a single high-level dosing, could improve regimen adherence but, at the same time, affect systemic toxicities. Of these, the most troubling are those that affect the central nervous system (CNS). To address this, dolutegravir (Tivicay, DTG), a potent and durable HIV integrase inhibitor used effectively in combination ART was tested. Rodents were administered parenteral 45-mg/kg doses. DTG-associated changes in CNS homeostasis were assessed by measuring brain metabolic activities. After antiretroviral treatment, brain subregions were dissected and screened by mass spectrometry-based metabolomics. Metabolic drug-related dysregulation of energy and oxidative stress were readily observed within the cerebellum and frontal cortex following native drug administrations. Each was associated with alterations in neural homeostasis and depleted canonical oxidation protection pools that included glutathione and ascorbic acid. Surprisingly, the oxidative stress-related metabolites were completely attenuated when DTG was administered as nanoformulations. These data demonstrate the importance of formulation design in control of DTG or perhaps other antiretroviral drug-associated CNS events.

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 activity screening for identifying metabolites that modulate phenotype.

Metabolomics, in which small-molecule metabolites (the metabolome) are identified and quantified, is broadly acknowledged to be the omics discipline that is closest to the phenotype. Although appreciated for its role in biomarker discovery programs, metabolomics can also be used to identify metabolites that could alter a cell's or an organism's phenotype. Metabolomics activity screening (MAS) as described here integrates metabolomics data with metabolic pathways and systems biology information, including proteomics and transcriptomics data, to produce a set of endogenous metabolites that can be tested for functionality in altering phenotypes. A growing literature reports the use of metabolites to modulate diverse processes, such as stem cell differentiation, oligodendrocyte maturation, insulin signaling, T-cell survival and macrophage immune responses. This opens up the possibility of identifying and applying metabolites to affect phenotypes. Unlike genes or proteins, metabolites are often readily available, which means that MAS is broadly amenable to high-throughput screening of virtually any biological system.

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.

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.