FAMIN Is a Multifunctional Purine Enzyme Enabling the Purine Nucleotide Cycle.

Mutations in FAMIN cause arthritis and inflammatory bowel disease in early childhood, and a common genetic variant increases the risk for Crohn's disease and leprosy. We developed an unbiased liquid chromatography-mass spectrometry screen for enzymatic activity of this orphan protein. We report that FAMIN phosphorolytically cleaves adenosine into adenine and ribose-1-phosphate. Such activity was considered absent from eukaryotic metabolism. FAMIN and its prokaryotic orthologs additionally have adenosine deaminase, purine nucleoside phosphorylase, and S-methyl-5'-thioadenosine phosphorylase activity, hence, combine activities of the namesake enzymes of central purine metabolism. FAMIN enables in macrophages a purine nucleotide cycle (PNC) between adenosine and inosine monophosphate and adenylosuccinate, which consumes aspartate and releases fumarate in a manner involving fatty acid oxidation and ATP-citrate lyase activity. This macrophage PNC synchronizes mitochondrial activity with glycolysis by balancing electron transfer to mitochondria, thereby supporting glycolytic activity and promoting oxidative phosphorylation and mitochondrial H+ and phosphate recycling.

A region-resolved proteomic map of the human brain enabled by high-throughput proteomics.

Substantial efforts are underway to deepen our understanding of human brain morphology, structure, and function using high-resolution imaging as well as high-content molecular profiling technologies. The current work adds to these approaches by providing a comprehensive and quantitative protein expression map of 13 anatomically distinct brain regions covering more than 11,000 proteins. This was enabled by the optimization, characterization, and implementation of a high-sensitivity and high-throughput microflow liquid chromatography timsTOF tandem mass spectrometry system (LC-MS/MS) capable of analyzing more than 2,000 consecutive samples prepared from formalin-fixed paraffin embedded (FFPE) material. Analysis of this proteomic resource highlighted brain region-enriched protein expression patterns and functional protein classes, protein localization differences between brain regions and individual markers for specific areas. To facilitate access to and ease further mining of the data by the scientific community, all data can be explored online in a purpose-built R Shiny app (https://brain-region-atlas.proteomics.ls.tum.de).

Predicting glycan structure from tandem mass spectrometry via deep learning.

Glycans constitute the most complicated post-translational modification, modulating protein activity in health and disease. However, structural annotation from tandem mass spectrometry (MS/MS) data is a bottleneck in glycomics, preventing high-throughput endeavors and relegating glycomics to a few experts. Trained on a newly curated set of 500,000 annotated MS/MS spectra, here we present CandyCrunch, a dilated residual neural network predicting glycan structure from raw liquid chromatography-MS/MS data in seconds (top-1 accuracy: 90.3%). We developed an open-access Python-based workflow of raw data conversion and prediction, followed by automated curation and fragment annotation, with predictions recapitulating and extending expert annotation. We demonstrate that this can be used for de novo annotation, diagnostic fragment identification and high-throughput glycomics. For maximum impact, this entire pipeline is tightly interlaced with our glycowork platform and can be easily tested at https://colab.research.google.com/github/BojarLab/CandyCrunch/blob/main/CandyCrunch.ipynb . We envision CandyCrunch to democratize structural glycomics and the elucidation of biological roles of glycans.

Quantitative, high-resolution proteomics for data-driven systems biology.

Systems biology requires comprehensive data at all molecular levels. Mass spectrometry (MS)-based proteomics has emerged as a powerful and universal method for the global measurement of proteins. In the most widespread format, it uses liquid chromatography (LC) coupled to high-resolution tandem mass spectrometry (MS/MS) to identify and quantify peptides at a large scale. This peptide intensity information is the basic quantitative proteomic data type. It is used to quantify proteins between different proteome states, including the temporal variation of the proteome, to determine the complete primary structure of proteins including posttranslational modifications, to localize proteins to organelles, and to determine protein interactions. Here, we describe the principles of analysis and the areas of biology where proteomics can make unique contributions. The large-scale nature of proteomics data and its high accuracy pose special opportunities as well as challenges in systems biology that have been largely untapped so far.

Integrative open workflow for confident annotation and molecular networking of metabolomics MSE/DIA data.

Liquid chromatography coupled with high-resolution mass spectrometry data-independent acquisition (LC-HRMS/DIA), including MSE, enable comprehensive metabolomics analyses though they pose challenges for data processing with automatic annotation and molecular networking (MN) implementation. This motivated the present proposal, in which we introduce DIA-IntOpenStream, a new integrated workflow combining open-source software to streamline MSE data handling. It provides 'in-house' custom database construction, allows the conversion of raw MSE data to a universal format (.mzML) and leverages open software (MZmine 3 and MS-DIAL) all advantages for confident annotation and effective MN data interpretation. This pipeline significantly enhances the accessibility, reliability and reproducibility of complex MSE/DIA studies, overcoming previous limitations of proprietary software and non-universal MS data formats that restricted integrative analysis. We demonstrate the utility of DIA-IntOpenStream with two independent datasets: dataset 1 consists of new data from 60 plant extracts from the Ocotea genus; dataset 2 is a publicly available actinobacterial extract spiked with authentic standard for detailed comparative analysis with existing methods. This user-friendly pipeline enables broader adoption of cutting-edge MS tools and provides value to the scientific community. Overall, it holds promise for speeding up metabolite discoveries toward a more collaborative and open environment for research.

Mass Spectrometry Profiling of HLA-Associated Peptidomes in Mono-allelic Cells Enables More Accurate Epitope Prediction.

Identification of human leukocyte antigen (HLA)-bound peptides by liquid chromatography-tandem mass spectrometry (LC-MS/MS) is poised to provide a deep understanding of rules underlying antigen presentation. However, a key obstacle is the ambiguity that arises from the co-expression of multiple HLA alleles. Here, we have implemented a scalable mono-allelic strategy for profiling the HLA peptidome. By using cell lines expressing a single HLA allele, optimizing immunopurifications, and developing an application-specific spectral search algorithm, we identified thousands of peptides bound to 16 different HLA class I alleles. These data enabled the discovery of subdominant binding motifs and an integrative analysis quantifying the contribution of factors critical to epitope presentation, such as protein cleavage and gene expression. We trained neural-network prediction algorithms with our large dataset (>24,000 peptides) and outperformed algorithms trained on datasets of peptides with measured affinities. We thus demonstrate a strategy for systematically learning the rules of endogenous antigen presentation.

Exploration of Nitrotyrosine-Containing Proteins and Peptides by Antibody-Based Enrichment Strategies.

Nitrotyrosine, or 3-nitrotyrosine, is an oxidative post-translational modification induced by reactive nitrogen species. Although nitrotyrosine is considered a marker of oxidative stress and has been associated with inflammation, neurodegeneration, cardiovascular disease, and cancer, identification of nitrotyrosine-modified proteins remains challenging owing to its low stoichiometric levels in biological samples. To facilitate a comprehensive analysis of proteins and peptides containing nitrotyrosine, we optimized an immunoprecipitation-based enrichment workflow using a cell line model. The identification of proteins and peptides containing nitrotyrosine residues was carried out after peroxynitrite treatment of cell lysates, which generated modified nitrotyrosine residues on susceptible sites on proteins. We evaluated the efficacy of enriching nitrotyrosine-modified proteins and peptides by employing four different commercially available monoclonal antibodies directed against nitrotyrosine. LC-MS/MS analysis resulted in the identification of 1377 and 1624 nitrotyrosine-containing peptides from protein- and peptide-based enrichment experiments, respectively. Although the yield of nitrotyrosine-containing peptides was higher in experiments where peptides rather than proteins were enriched, we found a substantial proportion (37-65%) of identified nitrotyrosine-containing peptides contained nitrotyrosine at the N-terminus. However, in protein-based immunoprecipitation <9% of nitrotyrosine-containing peptides had nitrotyrosine modification at the N-terminus of the peptide. Overall, our study resulted in the identification of 2603 nitrotyrosine-containing peptides of which >2000 have not previously been reported. We synthesized 101 novel nitrotyrosine-containing peptides identified in our analysis and analyzed them by LC-MS/MS to validate our findings. We have confirmed the validity of 70% of these peptides, as they demonstrated a similarity score exceeding 0.7 when compared to peptides identified through experimental methods. Finally, we also validated the presence of nitrotyrosine modification on PKM and EF2 proteins in peroxynitrite-treated samples by immunoblot analysis. The large catalog presented in this study along with the workflow should facilitate the investigation of nitrotyrosine as an oxidative modification in a variety of settings in greater detail.

Systematic Optimization of Automated Phosphopeptide Enrichment for High-Sensitivity Phosphoproteomics.

Improving coverage, robustness, and sensitivity is crucial for routine phosphoproteomics analysis by single-shot liquid chromatography-tandem mass spectrometry (LC-MS/MS) from minimal peptide inputs. Here, we systematically optimized key experimental parameters for automated on-bead phosphoproteomics sample preparation with a focus on low-input samples. Assessing the number of identified phosphopeptides, enrichment efficiency, site localization scores, and relative enrichment of multiply-phosphorylated peptides pinpointed critical variables influencing the resulting phosphoproteome. Optimizing glycolic acid concentration in the loading buffer, percentage of ammonium hydroxide in the elution buffer, peptide-to-beads ratio, binding time, sample, and loading buffer volumes allowed us to confidently identify >16,000 phosphopeptides in half-an-hour LC-MS/MS on an Orbitrap Exploris 480 using 30 µg of peptides as starting material. Furthermore, we evaluated how sequential enrichment can boost phosphoproteome coverage and showed that pooling fractions into a single LC-MS/MS analysis increased the depth. We also present an alternative phosphopeptide enrichment strategy based on stepwise addition of beads thereby boosting phosphoproteome coverage by 20%. Finally, we applied our optimized strategy to evaluate phosphoproteome depth with the Orbitrap Astral MS using a cell dilution series and were able to identify >32,000 phosphopeptides from 0.5 million HeLa cells in half-an-hour LC-MS/MS using narrow-window data-independent acquisition (nDIA).

A Targeted LC-MRM3 Proteomic Approach for the Diagnosis of SARS-CoV-2 Infection in Nasopharyngeal Swabs.

Since its first appearance, severe acute respiratory syndrome coronavirus 2 quickly spread around the world and the lack of adequate PCR testing capacities, especially during the early pandemic, led the scientific community to explore new approaches such as mass spectrometry (MS). We developed a proteomics workflow to target several tryptic peptides of the nucleocapsid protein. A highly selective multiple reaction monitoring-cubed (MRM3) strategy provided a sensitivity increase in comparison to conventional MRM acquisition. Our MRM3 approach was first tested on an Amsterdam public health cohort (alpha-variant, 760 participants) detecting viral nucleocapsid protein peptides from nasopharyngeal swabs samples presenting a cycle threshold value down to 35 with sensitivity and specificity of 94.2% and 100.0%, without immunopurification. A second iteration of the MS-diagnostic test, able to analyze more than 400 samples per day, was clinically validated on a Leiden-Rijswijk public health cohort (delta-variant, 2536 participants) achieving 99.9% specificity and 93.1% sensitivity for patients with cycle threshold values up to 35. In this manuscript, we also developed and brought the first proof of the concept of viral variant monitoring in a complex matrix using targeted MS.

Quantitative proteomics reveals CLR interactome in primary human cells.

The G protein-coupled receptor (GPCR) calcitonin receptor-like receptor (CLR) mediates essential functions in several cell types and is implicated in cardiovascular pathologies, skin diseases, migraine, and cancer. To date, the network of proteins interacting with CLR ("CLR interactome") in primary cells, where this GPCR is expressed at endogenous (physiologically relevant) levels, remains unknown. To address this knowledge gap, we established a novel integrative methodological workflow/approach for conducting a comprehensive/proteome-wide analysis of Homo sapiens CLR interactome. We used primary human dermal lymphatic endothelial cells and combined immunoprecipitation utilizing anti-human CLR antibody with label-free quantitative nano LC-MS/MS and quantitative in situ proximity ligation assay. By using this workflow, we identified 37 proteins interacting with endogenously expressed CLR amongst 4902 detected members of the cellular proteome (by quantitative nano LC-MS/MS) and revealed direct interactions of two kinases and two transporters with this GPCR (by in situ proximity ligation assay). All identified interactors have not been previously reported as members of CLR interactome. Our approach and findings uncover the hitherto unrecognized compositional complexity of the interactome of endogenously expressed CLR and contribute to fundamental understanding of the biology of this GPCR. Collectively, our study provides a first-of-its-kind integrative methodological approach and datasets as valuable resources and robust platform/springboard for advancing the discovery and comprehensive characterization of physiologically relevant CLR interactome at a proteome-wide level in a range of cell types and diseases in future studies.

The Michael addition of thiols to 13-oxo-octadecadienoate (13-oxo-ODE) with implications for LC-MS analysis of glutathione conjugation.

Unsaturated fatty acid ketones with αß,γδ conjugation are susceptible to Michael addition of thiols, with unresolved issues on the site of adduction and precise structures of the conjugates. Herein we reacted 13-keto-octadecadienoic acid (13-oxo-ODE or 13-KODE) with glutathione (GSH), N-acetyl-cysteine, and ß-mercaptoethanol and identified the adducts. HPLC-UV analyses indicated none of the products exhibit a conjugated enone UV chromophore, a result that conflicts with the literature and is relevant to the mass spectral interpretation of 1,4 versus 1,6 thiol adduction. Aided by the development of an HPLC solvent system that separates the GSH diastereomers and thus avoids overlap of signals in proton NMR experiments, we established the two major conjugates are formed by 1,6 addition of GSH at the 9-carbon of 13-oxo-ODE with the remaining double bond α to the thiol in the 10,11 position. N-acetyl cysteine reacts similarly, while ß-mercaptoethanol gives equal amounts of 1,4 and 1,6 addition products. Equine glutathione transferase catalyzed 1,6 addition of GSH to the two major diastereomers in 44:56 proportions. LC-MS in positive ion mode gives a product ion interpreted before as evidence of 1,4-thiol adduction, whereas here we find this ion using the authentic 1,6 adduct. LC-MS with negative ion APCI gave a fragment selective for 1,4 adduction. These results clarify the structures of thiol conjugates of a prototypical unsaturated keto-fatty acid and have relevance to the application of LC-MS for the structural analysis of keto-fatty acid glutathione conjugation.

A global LC-MS2 -based methodology to identify and quantify anionic phospholipids in plant samples.

Anionic phospholipids (PS, PA, PI, PIPs) are low-abundant phospholipids with impactful functions in cell signaling, membrane trafficking and cell differentiation processes. They can be quickly metabolized and can transiently accumulate at defined spots within the cell or an organ to respond to physiological or environmental stimuli. As even a small change in their composition profile will produce a significant effect on biological processes, it is crucial to develop a sensitive and optimized analytical method to accurately detect and quantify them. While thin-layer chromatography (TLC) separation coupled with gas chromatography (GC) detection methods already exist, they do not allow for precise, sensitive, and accurate quantification of all anionic phospholipid species. Here we developed a method based on high-performance liquid chromatography (HPLC) combined with two-dimensional mass spectrometry (MS2 ) by MRM mode to detect and quantify all molecular species and classes of anionic phospholipids in one shot. This method is based on a derivatization step by methylation that greatly enhances the ionization, the separation of each peak, the peak resolution as well as the limit of detection and quantification for each individual molecular species, and more particularly for PA and PS. Our method universally works in various plant samples. Remarkably, we identified that PS is enriched with very long chain fatty acids in the roots but not in aerial organs of Arabidopsis thaliana. Our work thus paves the way for new studies on how the composition of anionic lipids is finely tuned during plant development and environmental responses.

Comprehensive investigation of pathway enrichment methods for functional interpretation of LC-MS global metabolomics data.

BACKGROUND: Global or untargeted metabolomics is widely used to comprehensively investigate metabolic profiles under various pathophysiological conditions such as inflammations, infections, responses to exposures or interactions with microbial communities. However, biological interpretation of global metabolomics data remains a daunting task. Recent years have seen growing applications of pathway enrichment analysis based on putative annotations of liquid chromatography coupled with mass spectrometry (LC-MS) peaks for functional interpretation of LC-MS-based global metabolomics data. However, due to intricate peak-metabolite and metabolite-pathway relationships, considerable variations are observed among results obtained using different approaches. There is an urgent need to benchmark these approaches to inform the best practices. RESULTS: We have conducted a benchmark study of common peak annotation approaches and pathway enrichment methods in current metabolomics studies. Representative approaches, including three peak annotation methods and four enrichment methods, were selected and benchmarked under different scenarios. Based on the results, we have provided a set of recommendations regarding peak annotation, ranking metrics and feature selection. The overall better performance was obtained for the mummichog approach. We have observed that a ~30% annotation rate is sufficient to achieve high recall (~90% based on mummichog), and using semi-annotated data improves functional interpretation. Based on the current platforms and enrichment methods, we further propose an identifiability index to indicate the possibility of a pathway being reliably identified. Finally, we evaluated all methods using 11 COVID-19 and 8 inflammatory bowel diseases (IBD) global metabolomics datasets.

Advances in proteomics methods for the analysis of exhaled breath condensate.

The analysis of exhaled breath condensate (EBC) demonstrates a promising avenue of minimally invasive biopsies for diagnostics. EBC is obtained by cooling exhaled air and collecting the condensation to be utilized for downstream analysis using various analytical methods. The aqueous phase of breath contains a large variety of miscible small compounds including polar electrolytes, amino acids, cytokines, chemokines, peptides, small proteins, metabolites, nucleic acids, and lipids/eicosanoids-however, these analytes are typically present at minuscule levels in EBC, posing a considerable technical challenge. Along with recent improvements in devices for breath collection, the sensitivity and resolution of liquid chromatography coupled to online mass spectrometry-based proteomics has attained subfemtomole sensitivity, vastly enhancing the quality of EBC sample analysis. As a result, proteomics analysis of EBC has been expanding the field of breath biomarker research. We present an au courant overview of the achievements in proteomics of EBC, the advancement of EBC collection devices, and the current and future applications for EBC biomarker analysis.

Identification of a novel cationic glycolipid in Streptococcus agalactiae that contributes to brain entry and meningitis.

Bacterial membrane lipids are critical for membrane bilayer formation, cell division, protein localization, stress responses, and pathogenesis. Despite their critical roles, membrane lipids have not been fully elucidated for many pathogens. Here, we report the discovery of a novel cationic glycolipid, lysyl-glucosyl-diacylglycerol (Lys-Glc-DAG), which is synthesized in high abundance by the bacterium Streptococcus agalactiae (Group B Streptococcus, GBS). To our knowledge, Lys-Glc-DAG is more positively charged than any other known lipids. Lys-Glc-DAG carries 2 positive net charges per molecule, distinct from the widely described lysylated phospholipid lysyl-phosphatidylglycerol (Lys-PG) that carries one positive net charge due to the presence of a negatively charged phosphate moiety. We use normal phase liquid chromatography (NPLC) coupled with electrospray ionization (ESI) high-resolution tandem mass spectrometry (HRMS/MS) and genetic approaches to determine that Lys-Glc-DAG is synthesized by the enzyme MprF in GBS, which covalently modifies the neutral glycolipid Glc-DAG with the cationic amino acid lysine. GBS is a leading cause of neonatal meningitis, which requires traversal of the endothelial blood-brain barrier (BBB). We demonstrate that GBS strains lacking mprF exhibit a significant decrease in the ability to invade BBB endothelial cells. Further, mice challenged with a GBSΔmprF mutant developed bacteremia comparably to wild-type (WT) infected mice yet had less recovered bacteria from brain tissue and a lower incidence of meningitis. Thus, our data suggest that Lys-Glc-DAG may contribute to bacterial uptake into host cells and disease progression. Importantly, our discovery provides a platform for further study of cationic lipids at the host-pathogen interface.

Common data models to streamline metabolomics processing and annotation, and implementation in a Python pipeline.

To standardize metabolomics data analysis and facilitate future computational developments, it is essential to have a set of well-defined templates for common data structures. Here we describe a collection of data structures involved in metabolomics data processing and illustrate how they are utilized in a full-featured Python-centric pipeline. We demonstrate the performance of the pipeline, and the details in annotation and quality control using large-scale LC-MS metabolomics and lipidomics data and LC-MS/MS data. Multiple previously published datasets are also reanalyzed to showcase its utility in biological data analysis. This pipeline allows users to streamline data processing, quality control, annotation, and standardization in an efficient and transparent manner. This work fills a major gap in the Python ecosystem for computational metabolomics.

LC-MS/MS platform-based serum untargeted screening reveals the diagnostic biomarker panel and molecular mechanism of breast cancer.

BACKGROUND: Breast cancer (BC), the most common form of malignant cancer affecting women worldwide, was characterized by heterogeneous metabolic disorder and lack of effective biomarkers for diagnosis. The purpose of this study is to search for reliable metabolite biomarkers of BC as well as triple-negative breast cancer (TNBC) using serum metabolomics approach. METHODS: In this study, an untargeted metabolomics technique based on ultra-high performance liquid chromatography combined with mass spectrometry (UHPLC-MS) was utilized to investigate the differences in serum metabolic profile between the BC group (n = 53) and non-BC group (n = 57), as well as between TNBC patients (n = 23) and non-TNBC subjects (n = 30). The multivariate data analysis, determination of the fold change and the Mann-Whitney U test were used to screen out the differential metabolites. Additionally, machine learning methods including receiver operating curve analysis and logistic regression analysis were conducted to establish diagnostic biomarker panels. RESULTS: There were 36 metabolites found to be significantly different between BC and non-BC groups, and 12 metabolites discovered to be significantly different between TNBC and non-TNBC patients. Results also showed that four metabolites, including N-acetyl-D-tryptophan, 2-arachidonoylglycerol, pipecolic acid and oxoglutaric acid, were considered as vital biomarkers for the diagnosis of BC and non-BC with an area under the curve (AUC) of 0.995. Another two-metabolite panel of N-acetyl-D-tryptophan and 2-arachidonoylglycerol was discovered to discriminate TNBC from non-TNBC and produced an AUC of 0.965. CONCLUSION: This study demonstrated that serum metabolomics can be used to identify BC specifically and identified promising serum metabolic markers for TNBC diagnosis.

Bioinertization of NanoLC/MS/MS Systems by Depleting Metal Ions From the Mobile Phases for Phosphoproteomics.

We have successfully developed a bioinertized nanoflow LC/MS/MS (nanoLC/MS/MS) system for the highly sensitive analysis of phosphopeptides by depleting metal ions from the mobile phase. We found that not only direct contact of phosphopeptides with metal components, but also indirect contact with nanoLC pumps through the mobile phase causes significant losses during the recovery of phosphopeptides. Moreover, electrospray ionization was adversely affected by the mobile phase containing multiple metal ions as well as by the sample solvents contaminated with metal ions used in immobilized metal ion affinity chromatography for phosphopeptide enrichment. To solve these problems, metal ions were depleted by inserting an online metal ion removal device containing metal-chelating membranes between the gradient mixer and the autosampler. As a result, the peak areas of the identified phosphopeptides increased an average of 9.9-fold overall and 77-fold for multiply phosphorylated peptides with the insertion of the online metal ion removal system. This strategy would be applicable to the highly sensitive analysis of other phosphorylated biomolecules by microscale-LC/MS/MS.

Robust and High-Throughput Analytical Flow Proteomics Analysis of Cynomolgus Monkey and Human Matrices With Zeno SWATH Data-Independent Acquisition.

Modern mass spectrometers routinely allow deep proteome coverage in a single experiment. These methods are typically operated at nanoflow and microflow regimes, but they often lack throughput and chromatographic robustness, which is critical for large-scale studies. In this context, we have developed, optimized, and benchmarked LC-MS methods combining the robustness and throughput of analytical flow chromatography with the added sensitivity provided by the Zeno trap across a wide range of cynomolgus monkey and human matrices of interest for toxicological studies and clinical biomarker discovery. Sequential Window Acquisition of All Theoretical Fragment Ion Mass Spectra (SWATH) data-independent acquisition (DIA) experiments with Zeno trap activated (Zeno SWATH DIA) provided a clear advantage over conventional SWATH DIA in all sample types tested with improved sensitivity, quantitative robustness, and signal linearity as well as increased protein coverage by up to 9-fold. Using a 10-min gradient chromatography, up to 3300 proteins were identified in tissues at 2 µg peptide load. Importantly, the performance gains with Zeno SWATH translated into better biological pathway representation and improved the ability to identify dysregulated proteins and pathways associated with two metabolic diseases in human plasma. Finally, we demonstrate that this method is highly stable over time with the acquisition of reliable data over the injection of 1000+ samples (14.2 days of uninterrupted acquisition) without the need for human intervention or normalization. Altogether, Zeno SWATH DIA methodology allows fast, sensitive, and robust proteomic workflows using analytical flow and is amenable to large-scale studies.

The Thing Metabolome Repository family (XMRs): comparable untargeted metabolome databases for analyzing sample-specific unknown metabolites.

The identification of unknown chemicals has emerged as a significant issue in untargeted metabolome analysis owing to the limited availability of purified standards for identification; this is a major bottleneck for the accumulation of reusable metabolome data in systems biology. Public resources for discovering and prioritizing the unknowns that should be subject to practical identification, as well as further detailed study of spending costs and the risks of misprediction, are lacking. As such a resource, we released databases, Food-, Plant- and Thing-Metabolome Repository (http://metabolites.in/foods, http://metabolites.in/plants, and http://metabolites.in/things, referred to as XMRs) in which the sample-specific localization of unknowns detected by liquid chromatography-mass spectrometry in a wide variety of samples can be examined, helping to discover and prioritize the unknowns. A set of application programming interfaces for the XMRs facilitates the use of metabolome data for large-scale analysis and data mining. Several applications of XMRs, including integrated metabolome and genome analyses, are presented. Expanding the concept of XMRs will accelerate the identification of unknowns and increase the discovery of new knowledge.