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.
Metabolite identification is still considered an imposing bottleneck in liquid chromatography mass spectrometry (LC/MS) untargeted metabolomics. The identification workflow usually begins with ...detecting relevant LC/MS peaks via peak-picking algorithms and retrieving putative identities based on accurate mass searching. However, accurate mass search alone provides poor evidence for metabolite identification. For this reason, computational annotation is used to reveal the underlying metabolites monoisotopic masses, improving putative identification in addition to confirmation with tandem mass spectrometry. This review examines LC/MS data from a computational and analytical perspective, focusing on the occurrence of neutral losses and in-source fragments, to understand the challenges in computational annotation methodologies. Herein, we examine the state-of-the-art strategies for computational annotation including: (i) peak grouping or full scan (MS
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) pseudo-spectra extraction, i.e., clustering all mass spectral signals stemming from each metabolite; (ii) annotation using ion adduction and mass distance among ion peaks; (iii) incorporation of biological knowledge such as biotransformations or pathways; (iv) tandem MS data; and (v) metabolite retention time calibration, usually achieved by prediction from molecular descriptors. Advantages and pitfalls of each of these strategies are discussed, as well as expected future trends in computational annotation.
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 70Formula: 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.
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.
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.
Post-stroke patients usually exhibit reduced peak muscular torque (PT) and/or force steadiness during submaximal exercise. Brain stimulation techniques have been proposed to improve neural plasticity ...and help to restore motor performance in post-stroke patients. The present study compared the effects of bihemispheric motor cortex transcranial direct current stimulation (tDCS) on PT and force steadiness during maximal and submaximal resistance exercise performed by post-stroke patients vs. healthy controls. A double-blind randomized crossover controlled trial (identification number: TCTR20151112001; URL: http://www.clinicaltrials.in.th/) was conducted involving nine healthy and 10 post-stroke hemiparetic individuals who received either tDCS (2 mA) or sham stimulus upon the motor cortex for 20 min. PT and force steadiness (reflected by the coefficient of variation (CV) of muscular torque) were assessed during unilateral knee extension and flexion at maximal and submaximal workloads (1 set of 3 repetitions at 100% PT and 2 sets of 10 repetitions at 50% PT, respectively). No significant change in PT was observed in post-stroke and healthy subjects. Force steadiness during knee extension (~25-35%, P < 0.001) and flexion (~22-33%, P < 0.001) improved after tDCS compared to the sham condition in post-stroke patients, but improved only during knee extension (~13-27%, P < 0.001) in healthy controls. These results suggest that tDCS may improve force steadiness, but not PT in post-stroke hemiparetic patients, which might be relevant in the context of motor rehabilitation programs.
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
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
confers resistance to MMV688766, in part through the upregulation of the lipid-binding chaperone
, 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.
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.
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.
The result of the multidisciplinary collaboration of researchers from different areas of knowledge to validate a solar radiation model is presented. The MAPsol is a 3D local-scale adaptive solar ...radiation model that allows us to estimate direct, diffuse, and reflected irradiance for clear sky conditions. The model includes the adaptation of the mesh to complex orography and albedo, and considers the shadows cast by the terrain and buildings. The surface mesh generation is based on surface refinement, smoothing and parameterization techniques and allows the generation of high-quality adapted meshes with a reasonable number of elements. Another key aspect of the paper is the generation of a high-resolution digital elevation model (DEM). This high-resolution DEM is constructed from LiDAR data, and its resolution is two times more accurate than the publicly available DEMs. The validation process uses direct and global solar irradiance data obtained from pyranometers at the University of Salamanca located in an urban area affected by systematic shading from nearby buildings. This work provides an efficient protocol for studying solar resources, with particular emphasis on areas of complex orography and dense buildings where shadows can potentially make solar energy production facilities less efficient.
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.
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•Calorie restriction (CR) causes hypothermia and elevates Leu-enkephalin•Activation of prodynorphin neurons lowers temperature•Inhibition of kappa opioid receptor (KOR) neurons reduces CR-induced hypothermia•Blocking KOR halts CR-induced hypothermia, promoting weight loss during dieting
During calorie restriction, endotherms lower core body temperature to limit energy expenditure and weight loss. Cintron-Colon et al. show in mice that this response is regulated centrally by prodynorphin and by kappa opioid receptor (KOR) neurons and that pharmacological inhibition of KOR during dieting prevents hypothermia, increasing weight loss.
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