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Supplementary Fig. 1. Levels of folate enzymes in E-Wn, M-Wnt or metM-Wntliver cells measured by a dot blot assay. Supplementary Fig. 2. Folate restriction inhibits cellular migration.

Objective: There is known heterogeneity in the relationship between diet quality and cardiovascular disease by self-reported race and sex, which may underlie inequities in cardiovascular health. Methods: We used data from 2,832 participants in the Coronary Artery Risk Development in Young Adults (CARDIA) study (2005-06, aged 37-55, 57% women, 45% Black). Using fasted blood samples, we obtained ultra-high-performance liquid chromatography high resolution mass spectrometry untargeted metabolomics and derived an a priori diet quality score using a validated diet history questionnaire, based on classification of 46 food groups with potential beneficial (n=20), adverse (n=13), or neutral (n=13) implications for cardiovascular health. We tested effect modification of associations between metabolites and diet quality by race and sex, separately, using multivariable-adjusted linear regression for 7,522 metabolite peaks, accounting for multiple comparisons, adjusted for batch, field center, demographics, lifestyle behaviors, total energy intake, hypertension and diabetes status, medication use, and BMI. We also used race- and sex-stratified multivariate Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) analyses to examine how metabolites distinguish by diet quality 1 st and 4 th quartiles. We identified differing metabolite pathways by race and sex through biochemical pathway analysis (Mummichog version 1.0.10) from regression and OPLS-DA models. Results: In linear regression, race significantly modified metabolite-diet quality associations for 231 metabolite peaks; sex modified associations for 1 peak (FDR Conclusions: Metabolite pathways were statistically different by self-reported race, and to a lesser extent, by sex. Differences in metabolite-diet quality associations may reflect differences in contextual or social variables, and potentially, heterogeneity in metabolism.

Background: While obesity is associated with hypertension, there is heterogeneity in risk by weight status and we know little about whether and which metabolic pathways play a role in exacerbating differential risk across weight status categories. Methods: Fasting blood was collected at the Yr. 20 follow-up exam in the Coronary Artery Risk Development in Young Adults (CARDIA) Study. We conducted Ultra-High-Performance Liquid Chromatography high resolution mass spectrometry untargeted metabolomics in 1,769 individuals (ages 37-54, 58% female, 66% Black) classified as normotensive (no hypertension diagnosis or medication and SBP/DBP ≤ 130/80). We identified 7,522 metabolomic peaks and 8 principal components, which we included in a multivariable-adjusted proportional hazards model with an interaction term to assess whether the association between these metabolite measures and incident hypertension (HTN) varied by BMI. We then employed multivariate analysis using Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) models using 70% of the sample within each obesity category to predict incident HTN at ten years in normal weight, overweight and obese groups based on the high dimensional metabolomics data. The remaining 30% of individuals in each obesity category were then used to evaluate the predictive value of the OPLS-DA models. Finally, we used mummichog software and Fisher’s exact tests, to conduct pathway enrichment analysis using data from both the individual peak and the OPLS-DA models. Results: We identified 723 metabolites where the association with long term risk of HTN differed by BMI, although none were statistically significant after adjustment for multiple comparisons. However, we found strong evidence of effect modification for metabolites via principal components and OPLS-DA. For the 3 rd principal component, which explained 5% of the variation across all peaks, we found a significant interaction with BMI (p-value = 0.0043). In external test data sets, OPLS-DA models predicted incident HTN relatively well in normal weight individuals (AUC of 77%) but not well in overweight and obese individuals (AUC of 49% and 56% respectively). We identified several pathways with a differential HTN risk by BMI shown in the OPLS-DA and proportional hazards models. In the phenylamine pathway there was substantial differentiation between those with and without incident HTN risk for normal weight only and across the lysine degradation pathway only for individuals who were obese. Conclusions: Our findings identify variation in hypertension risk by BMI, with suggestive evidence for a role of metabolomic pathways and combinations of metabolites. Overall, metabolites predicted 10-year incident hypertension very well in individuals of normal weight (but not obese status), indicating heterogeneity in hypertension risk by weight status.

Untargeted lipidomics allows analysis of a broader range of lipids than targeted methods and permits discovery of unknown compounds. Previous ring trials have evaluated the reproducibility of targeted lipidomics methods, but inter-laboratory comparison of compound identification and unknown feature detection in untargeted lipidomics has not been attempted. To address this gap, five laboratories analyzed a set of mammalian tissue and biofluid reference samples using both their own untargeted lipidomics procedures and a common chromatographic and data analysis method. While both methods yielded informative data, the common method improved chromatographic reproducibility and resulted in detection of more shared features between labs. Spectral search against the LipidBlast in silico library enabled identification of over 2,000 unique lipids. Further examination of LC-MS/MS and ion mobility data, aided by hybrid search and spectral networking analysis, revealed spectral and chromatographic patterns useful for classification of unknown features, a subset of which were highly reproducible between labs. Overall, our method offers enhanced compound identification performance compared to targeted lipidomics, demonstrates the potential of harmonized methods to improve inter-site reproducibility for quantitation and feature alignment, and can serve as a reference to aid future annotation of untargeted lipidomics data.

A growing number of software tools have been developed for metabolomics data processing and analysis. Many new tools are contributed by metabolomics practitioners who have limited prior experience with software development, and the tools are subsequently implemented by users with expertise that ranges from basic point-and-click data analysis to advanced coding. This Perspective is intended to introduce metabolomics software users and developers to important considerations that determine the overall impact of a publicly available tool within the scientific community. The recommendations reflect the collective experience of an NIH-sponsored Metabolomics Consortium working group that was formed with the goal of researching guidelines and best practices for metabolomics tool development. The recommendations are aimed at metabolomics researchers with little formal background in programming and are organized into three stages: (i) preparation, (ii) tool development, and (iii) distribution and maintenance.

We report the development of a spectral knowledgebase named ADAP-KDB for tracking and prioritizing unknown gas chromatography-mass spectrometry (GC-MS) spectra in the NIH's Metabolomics Data Repository-a national and international repository for metabolomics data. ADAP-KDB consists of two parts. One part is a computational workflow that preprocesses raw mass spectrometry data and derives consensus mass spectra. The other part is a web portal for users to browse the consensus spectra and match query spectra against them. For each consensus spectrum, the Gini-Simpson diversity index and the p-value from χ2 goodness-of-fit test are calculated to measure its statistical significance, which enables prioritization of unknown mass spectra for subsequent costly compound identification.

ALDH1L1 (10-formyltetrahydrofolate dehydrogenase), an enzyme of folate metabolism highly expressed in liver, metabolizes 10-formyltetrahydrofolate to produce tetrahydrofolate (THF). This reaction might have a regulatory function towards reduced folate pools, de novo purine biosynthesis, and the flux of folate-bound methyl groups. To understand the role of the enzyme in cellular metabolism, Aldh1l1−/− mice were generated using an ES cell clone (C57BL/6N background) from KOMP repository. Though Aldh1l1−/− mice were viable and did not have an apparent phenotype, metabolomic analysis indicated that they had metabolic signs of folate deficiency. Specifically, the intermediate of the histidine degradation pathway and a marker of folate deficiency, formiminoglutamate, was increased more than 15-fold in livers of Aldh1l1−/− mice. At the same time, blood folate levels were not changed and the total folate pool in the liver was decreased by only 20%. A two-fold decrease in glycine and a strong drop in glycine conjugates, a likely result of glycine shortage, were also observed in Aldh1l1−/− mice. Our study indicates that in the absence of ALDH1L1 enzyme, 10-formyl-THF cannot be efficiently metabolized in the liver. This leads to the decrease in THF causing reduced generation of glycine from serine and impaired histidine degradation, two pathways strictly dependent on THF.

Summary Diagnosis of neurodegenerative diseases hinges on “seed” proteins detected in disease-specific aggregates. These inclusions contain diverse constituents, adhering through aberrant interactions that our prior data indicate are nonrandom. To define preferential protein-protein contacts mediating aggregate coalescence, we created click-chemistry reagents that cross-link neighboring proteins within human, APPSw-driven, neuroblastoma-cell aggregates. These reagents incorporate a biotinyl group to efficiently recover linked tryptic-peptide pairs. Mass-spectroscopy outputs were screened for all possible peptide pairs in the aggregate proteome. These empirical linkages, ranked by abundance, implicate a protein-adherence network termed the “aggregate contactome.” Critical hubs and hub-hub interactions were assessed by RNAi-mediated rescue of chemotaxis in aging nematodes, and aggregation-driving properties were inferred by multivariate regression and neural-network approaches. Aspirin, while disrupting aggregation, greatly simplified the aggregate contactome. This approach, and the dynamic model of aggregate accrual it implies, reveals the architecture of insoluble-aggregate networks and may reveal targets susceptible to interventions to ameliorate protein-aggregation diseases., Graphical Abstract, Highlights • Cross-link data support a preferred hierarchy of protein accrual into aggregates • Contact networks can predict proteins that contribute functionally to aggregation • RNAi knockdowns of key hubs and hub connectors imply functional roles in accrual • Aspirin opposes protein aggregation by reducing contactome interactions >5-fold, Neuroscience; Molecular Neuroscience; Neural Networks; Proteomics

We report a multivariate curve resolution (MCR)-based spectral deconvolution workflow for untargeted gas chromatography–mass spectrometry metabolomics. As an essential step in preprocessing such data, spectral deconvolution computationally separates ions that are in the same mass spectrum but belong to coeluting compounds that are not resolved completely by chromatography. As a result of this computational separation, spectral deconvolution produces pure fragmentation mass spectra. Traditionally, spectral deconvolution has been achieved by using a model peak approach. We describe the fundamental differences between the model peak-based and the MCR-based spectral deconvolution and report ADAP-GC 4.0 that employs the latter approach while overcoming the associated computational complexity. ADAP-GC 4.0 has been evaluated using GC–TOF data sets from a 27-standards mixture at different dilutions and urine with the mixture spiked in, and GC Orbitrap data sets from mixtures of different standards. It produced the average matching scores 960, 959, and 926 respectively. Moreover, its performance has been compared against MS-DIAL, eRah, and ADAP-GC 3.2, and ADAP-GC 4.0 demonstrated a higher number of matched compounds and up to 6% increase of the average matching score.

Background Mitochondrial folate enzyme ALDH1L2 (aldehyde dehydrogenase 1 family member L2) converts 10-formyltetrahydrofolate to tetrahydrofolate and CO2 simultaneously producing NADPH. We have recently reported that the lack of the enzyme due to compound heterozygous mutations was associated with neuro-ichthyotic syndrome in a male patient. Here, we address the role of ALDH1L2 in cellular metabolism and highlight the mechanism by which the enzyme regulates lipid oxidation. Methods We generated Aldh1l2 knockout (KO) mouse model, characterized its phenotype, tissue histology, and levels of reduced folate pools and applied untargeted metabolomics to determine metabolic changes in the liver, pancreas, and plasma caused by the enzyme loss. We have also used NanoString Mouse Inflammation V2 Code Set to analyze inflammatory gene expression and evaluate the role of ALDH1L2 in the regulation of inflammatory pathways. Results Both male and female Aldh1l2 KO mice were viable and did not show an apparent phenotype. However, H&E and Oil Red O staining revealed the accumulation of lipid vesicles localized between the central veins and portal triads in the liver of Aldh1l2-/- male mice indicating abnormal lipid metabolism. The metabolomic analysis showed vastly changed metabotypes in the liver and plasma in these mice suggesting channeling of fatty acids away from β-oxidation. Specifically, drastically increased plasma acylcarnitine and acylglycine conjugates were indicative of impaired β-oxidation in the liver. Our metabolomics data further showed that mechanistically, the regulation of lipid metabolism by ALDH1L2 is linked to coenzyme A biosynthesis through the following steps. ALDH1L2 enables sufficient NADPH production in mitochondria to maintain high levels of glutathione, which in turn is required to support high levels of cysteine, the coenzyme A precursor. As the final outcome, the deregulation of lipid metabolism due to ALDH1L2 loss led to decreased ATP levels in mitochondria. Conclusions The ALDH1L2 function is important for CoA-dependent pathways including β-oxidation, TCA cycle, and bile acid biosynthesis. The role of ALDH1L2 in the lipid metabolism explains why the loss of this enzyme is associated with neuro-cutaneous diseases. On a broader scale, our study links folate metabolism to the regulation of lipid homeostasis and the energy balance in the cell.

We engineered a machine learning approach, MSHub, to enable auto-deconvolution of gas chromatography–mass spectrometry (GC–MS) data. We then designed workflows to enable the community to store, process, share, annotate, compare and perform molecular networking of GC–MS data within the Global Natural Product Social (GNPS) Molecular Networking analysis platform. MSHub/GNPS performs auto-deconvolution of compound fragmentation patterns via unsupervised non-negative matrix factorization and quantifies the reproducibility of fragmentation patterns across samples., Fil: Aksenov, Alexander A. University of California. Skaggs School of Pharmacy and Pharmaceutical Sciences; Estados Unidos, Fil: Aksenov, Alexander A. University of California. Skaggs School of Pharmacy and Pharmaceutical Sciences. Collaborative Mass Spectrometry Innovation Center; Estados Unidos, Fil: Laponogov, Ivan. South Kensington Campus. Imperial College London. Department of Surgery and Cancer; Reino Unido, Fil: Zhang, Zheng. University of California. Skaggs School of Pharmacy and Pharmaceutical Sciences; Estados Unidos, Fil: Doran, Sophie L. F. South Kensington Campus. Imperial College London. Department of Surgery and Cancer; Reino Unido, Fil: Belluomo, Ilaria. South Kensington Campus. Imperial College London. Department of Surgery and Cancer; Reino Unido, Fil: Veselkov, Dennis. Intelligify Limited. Reino Unido. South Kensington Campus. Imperial College. Department of Computing; Reino Unido, Fil: Bittremieux, Wout. University of California. Skaggs School of Pharmacy and Pharmaceutical Sciences; Estados Unidos, Fil: Bittremieux, Wout. University of California. Skaggs School of Pharmacy and Pharmaceutical Sciences. Collaborative Mass Spectrometry Innovation Center; Estados Unidos, Fil: Bittremieux, Wout. University of Antwerp. Department of Computer Science; Belgica, Fil: Nothias, Louis Felix. University of California. Skaggs School of Pharmacy and Pharmaceutical Sciences; Estados Unidos, Fil: Nothias, Louis Felix. University of California. Skaggs School of Pharmacy and Pharmaceutical Sciences. Collaborative Mass Spectrometry Innovation Center; Estados Unidos, Fil: Nothias-Esposito, Mélissa. University of California. Skaggs School of Pharmacy and Pharmaceutical Sciences; Estados Unidos, Fil: Nothias-Esposito, Mélissa. University of California. Skaggs School of Pharmacy and Pharmaceutical Sciences. Collaborative Mass Spectrometry Innovation Center; Estados Unidos, Fil: Maloney, Katherine N. University of California. Skaggs School of Pharmacy and Pharmaceutical Sciences; Estados Unidos, Fil: Maloney, Katherine N. Point Loma Nazarene University. Department of Chemistry; Estados Unidos, Fil: Misra, Biswapriya B. Wake Forest School of Medicine. Section of Molecular Medicine. Department of Internal Medicine. Center for Precision Medicine; Estados Unidos, Fil: Melnik, Alexey V. University of California. Skaggs School of Pharmacy and Pharmaceutical Sciences; Estados Unidos, Fil: Smirnov, Aleksandr. University of North Carolina at Charlotte. Department of Bioinformatics and Genomics; Estados Unidos, Fil: Du, Xiuxia. University of North Carolina at Charlotte. Department of Bioinformatics and Genomics; Estados Unidos, Fil: Jones, Kenneth L. University of North Carolina at Charlotte. Department of Bioinformatics and Genomics; Estados Unidos, Fil: Dorrestein, Kathleen. University of California. Skaggs School of Pharmacy and Pharmaceutical Sciences; Estados Unidos, Fil: Dorrestein, Kathleen. University of California. Skaggs School of Pharmacy and Pharmaceutical Sciences. Collaborative Mass Spectrometry Innovation Center; Estados Unidos, Fil: Panitchpakdi, Morgan. University of California. Skaggs School of Pharmacy and Pharmaceutical Sciences; Estados Unidos, Fil: Ernst, Madeleine. University of California. Skaggs School of Pharmacy and Pharmaceutical Sciences; Estados Unidos, Fil: Ernst, Madeleine. Statens Serum Institut. Danish Center for Neonatal Screening. Department of Congenital Disorders. Section for Clinical Mass Spectrometry; Dinamarca, Fil: van der Hooft, Justin J. J. University of California. Skaggs School of Pharmacy and Pharmaceutical Sciences; Estados Unidos, Fil: van der Hooft, Justin J. J. Wageningen University. Bioinformatics Group; Países Bajos, Fil: Gonzalez, Mabel. Universidad de los Andes. Department of Chemistry; Colombia, Fil: Carazzone, Chiara. Universidad de los Andes. Department of Chemistry; Colombia, Fil: Amézquita, Adolfo. Universidad de los Andes. Department of Biological Sciences; Colombia, Fil: Callewaert, Chris. Center for Microbial Ecology and Technology; Belgica, Fil: Callewaert, Chris. University of California. Department of Pediatrics; Estados Unidos, Fil: Morton, James T. University of California. Department of Pediatrics; Estados Unidos, Fil: Morton, James T. Simons Foundation. Flatiron Institute. Center for Computational Biology; Estados Unidos, Fil: Quinn, Robert A. Michigan State University. Department of Biochemistry and Molecular Biology; Estados Unidos, Fil: Bouslimani, Amina. University of California. Skaggs School of Pharmacy and Pharmaceutical Sciences; Estados Unidos, Fil: Bouslimani, Amina. University of California. Skaggs School of Pharmacy and Pharmaceutical Sciences. Collaborative Mass Spectrometry Innovation Center; Estados Unidos, Fil: Orio, Andrea Albarracín. Universidad Católica de Córdoba. IRNASUS. CONICET. Facultad de Ciencias Agropecuarias; Argentina, Fil: Petras, Daniel. University of California. Skaggs School of Pharmacy and Pharmaceutical Sciences; Estados Unidos, Fil: Petras, Daniel. University of California. Skaggs School of Pharmacy and Pharmaceutical Sciences. Collaborative Mass Spectrometry Innovation Center; Estados Unidos, Fil: Smania, Andrea M. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Departamento de Química Biológica Ranwel Caputto; Argentina, Fil: Smania, Andrea M. Centro de Investigaciones en Química Biológica de Córdoba. Universidad Nacional de Córdoba. CONICET; Argentina, Fil: Couvillion, Sneha P. Pacific Northwest National Laboratory. Biological Sciences Division; Estados Unidos, Fil: Burnet, Meagan C. Pacific Northwest National Laboratory. Biological Sciences Division; Estados Unidos, Fil: Nicora, Carrie D. Pacific Northwest National Laboratory. Biological Sciences Division; Estados Unidos, Fil: Zink, Erika. Pacific Northwest National Laboratory. Biological Sciences Division; Estados Unidos, Fil: Metz, Thomas O. Pacific Northwest National Laboratory. Biological Sciences Division; Estados Unidos, Fil: Artaev, Viatcheslav. LECO Corporation; Estados Unidos, Fil: Humston-Fulmer, Elizabeth. LECO Corporation; Estados Unidos, Fil: Gregor, Rachel. Ben-Gurion University of the Negev. Department of Chemistry and the National Institute for Biotechnology in the Negev; Israel, Fil: Meijler, Michael M. Ben-Gurion University of the Negev. Department of Chemistry and the National Institute for Biotechnology in the Negev; Israel, Fil: Mizrahi, Itzhak. Ben-Gurion University of the Negev. Department of Life Sciences and the National Institute for Biotechnology in the Negev; Israel, Fil: Eyal, Stav. Ben-Gurion University of the Negev. Department of Life Sciences and the National Institute for Biotechnology in the Negev; Israel, Fil: Anderson, Brooke. University of California. Division of Biological Sciences; Estados Unidos, Fil: Dutton, Rachel. University of California. Division of Biological Sciences; Estados Unidos, Fil: Lugan, Raphaël. Université d’Avignon et des Pays du Vaucluse, Agrosciences. UMR Qualisud; Francia, Fil: Boulch, Pauline Le. Université d’Avignon et des Pays du Vaucluse, Agrosciences. UMR Qualisud; Francia, Fil: Guitton, Yann. INRAe. Oniris. Laboratoire d’Etude des Résidus et Contaminants dans les Aliments (LABERCA); Francia, Fil: Prevost, Stephanie. INRAe. Oniris. Laboratoire d’Etude des Résidus et Contaminants dans les Aliments (LABERCA); Francia, Fil: Poirier, Audrey. INRAe. Oniris. Laboratoire d’Etude des Résidus et Contaminants dans les Aliments (LABERCA); Francia, Fil: Dervilly, Gaud. INRAe. Oniris. Laboratoire d’Etude des Résidus et Contaminants dans les Aliments (LABERCA); Francia, Fil: Le Bizec, Bruno. Oniris. Laboratoire d’Etude des Résidus et Contaminants dans les Aliments (LABERCA); Francia, Fil: Fait, Aaron. Ben Gurion University of the Negev. Sede Boqer Campus. The Jacob Blaustein Institutes for Desert Research. The French Associates Institute for Agriculture and Biotechnology of Dryland; Israel, Fil: Persi, Noga Sikron. Ben Gurion University of the Negev. Sede Boqer Campus. The Jacob Blaustein Institutes for Desert Research. The French Associates Institute for Agriculture and Biotechnology of Dryland; Israel, Fil: Song, Chao. Ben Gurion University of the Negev. Sede Boqer Campus. The Jacob Blaustein Institutes for Desert Research. The French Associates Institute for Agriculture and Biotechnology of Dryland; Israel, Fil: Gashu, Kelem. Ben Gurion University of the Negev. Sede Boqer Campus. The Jacob Blaustein Institutes for Desert Research. The French Associates Institute for Agriculture and Biotechnology of Dryland; Israel, Fil: Coras, Roxana. University of California. Department of Medicine. Division of Rheumatology; Estados Unidos, Fil: Guma, Monica. University of California. Department of Medicine. Division of Rheumatology; Estados Unidos, Fil: Manasson, Julia. New York University School of Medicine. Department of Medicine. Division of Rheumatology; Estados Unidos, Fil: Scher, Jose U. New York University School of Medicine. Department of Medicine. Division of Rheumatology; Estados Unidos, Fil: Barupal, Dinesh Kumar. Icahn School of Medicine at Mount Sinai. Department of Environmental Medicine and Public Health; Estados Unidos, Fil: Alseekh, Saleh. Max Planck Institute for Molecular Plant Physiology; Alemania, Fil: Alseekh, Saleh. Center of Plant Systems Biology and Biotechnology (CPSBB); Bulgaria, Fil: Fernie, Alisdair R. Max Planck Institute for Molecular Plant Physiology; Alemania, Fil: Fernie, Alisdair R. Center of Plant Systems Biology and Biotechnology (CPSBB); Bulgaria, Fil: Mirnezami, Reza. Royal Free Hospital NHS Foundation Trust. Department of Colorectal Surgery; Reino Unido, Fil: Vasiliou, Vasilis. Yale University. Yale School of Public Health. Department of Environmental Health Sciences; Estados Unidos, Fil: Schmid, Robin. University of Münster. Institute of Inorganic and Analytical Chemistry; Alemania, Fil: Borisov, Roman S. A.V. Topchiev Institute of Petrochemical Synthesis RAS; Rusia, Fil: Kulikova, Larisa N. Рeoples’ Friendship University of Russia (RUDN University); Rusia, Fil: Knight, Rob. University of California. Department of Pediatrics; Estados Unidos, Fil: Knight, Rob. University of California. UCSD Center for Microbiome Innovation; Estados Unidos, Fil: Knight, Rob. University of California. Department of Bioengineering; Estados Unidos, Fil: Wang, Mingxun. University of California. Skaggs School of Pharmacy and Pharmaceutical Sciences; Estados Unidos, Fil: Wang, Mingxun. University of California. Skaggs School of Pharmacy and Pharmaceutical Sciences. Collaborative Mass Spectrometry Innovation Center; Estados Unidos, Fil: Hanna, George B. South Kensington Campus. Imperial College London. Department of Surgery and Cancer; Reino Unido, Fil: Dorrestein, Pieter C. University of California. Skaggs School of Pharmacy and Pharmaceutical Sciences; Estados Unidos, Fil: Dorrestein, Pieter C. University of California. Skaggs School of Pharmacy and Pharmaceutical Sciences. Collaborative Mass Spectrometry Innovation Center; Estados Unidos, Fil: Dorrestein, Pieter C. University of California. Department of Pediatrics; Estados Unidos, Fil: Veselkov, Kirill. South Kensington Campus. Imperial College London. Department of Surgery and Cancer; Reino Unido

Additional file 1.

The informatics pipeline for making sense of untargeted LC-MS or GC-MS data starts with preprocessing the raw data. Results from data preprocessing undergo statistical analysis and subsequently mapped to metabolic pathways for placing untargeted metabolomics data in the biological context. ADAP is a suite of computational algorithms that has been developed specifically for preprocessing LC-MS and GC-MS data. It consists of two separate computational workflows that extract compound-relevant information from raw LC-MS and GC-MS data, respectively. Computational steps include construction of extracted ion chromatograms, detection of chromatographic peaks, spectral deconvolution, and alignment. The two workflows have been incorporated into the cross-platform and graphical MZmine 2 framework and ADAP-specific graphical user interfaces have been developed for using ADAP with ease. This chapter summarizes the algorithmic principles underlying key steps in the two workflows and illustrates how to apply ADAP to preprocess LC-MS and GC-MS data.

The Mass Spectrometry Development Kit (MSDK) is a Java library of algorithms for processing mass spectrometry data in metabolomics. It provides a flexible data model with Java interfaces for mass-spectrometry-related objects, and offers support for reading and writing many commonly used file formats. MSDK currently integrates a variety of algorithms for raw mass spectra filtering, feature detection, compound identification, or database search, into a single, easy-to-deploy package. The source codes of MSDK are available on GitHub under a dual GNU Lesser General Public License/Eclipse Public License, and binary artifacts of MSDK modules are deployed into the Maven Central repository.

Additional file 8.

MZmine is an open-source, multi-platform, user-friendly, graphical software for mass spectrometry data processing, with the main focus on small molecule mass spectrometry applications such as metabolomics and lipidomics. It provides a set of modules that cover the entire data-processing workflow for liquid chromatography–mass spectrometry. Moreover, the recent addition of the automated data analysis pipeline (ADAP) algorithms for feature detection and spectral deconvolution has further expanded the scope of MZmine to gas chromatography–mass spectrometry as well. The metabolite identification capability of MZmine has been greatly improved by integration of the SIRIUS/CSI:FingerID structure-prediction algorithm, as well as new modules for interactive lipid identification and for direct submission of feature-based molecular networking jobs to the Global Natural Products Social Molecular Networking (GNPS) web server. MZmine also supports the creation and querying of local mass spectral databases and direct deposition of spectra to the GNPS database. In addition to the interactive user interface, batch processing is available for large-scale, automated data analyses. Over its 15-year history, MZmine has matured into a worldwide collaborative project, with many academic labs having contributed new data processing and visualization modules.

Simple Summary Cancers often loose the enzyme of folate metabolism ALDH1L1. We proposed that such loss is advantageous for the malignant tumor growth and tested this hypothesis in mice proficient or deficient (gene knockout) in ALDH1L1 expression. Liver cancer in both groups was induced by injection of chemical carcinogen diethylnitrosamine. While the number of tumors observed in ALDH1L1 proficient and deficient mice was similar, tumors grew faster and to a larger size in the knockout mice. We conclude that the ALDH1L1 loss promotes liver tumor growth without affecting tumor initiation or multiplicity. Accelerated growth of tumors lacking the enzyme was linked to several metabolic pathways, which are beneficial for rapid proliferation. Abstract Cytosolic 10-formyltetrahydrofolate dehydrogenase (ALDH1L1) is commonly downregulated in human cancers through promoter methylation. We proposed that ALDH1L1 loss promotes malignant tumor growth. Here, we investigated the effect of the Aldh1l1 mouse knockout (Aldh1l1−/−) on hepatocellular carcinoma using a chemical carcinogenesis model. Fifteen-day-old male Aldh1l1 knockout mice and their wild-type littermate controls (Aldh1l1+/+) were injected intraperitoneally with 20 μg/g body weight of DEN (diethylnitrosamine). Mice were sacrificed 10, 20, 28, and 36 weeks post-DEN injection, and livers were examined for tumor multiplicity and size. We observed that while tumor multiplicity did not differ between Aldh1l1−/− and Aldh1l1+/+ animals, larger tumors grew in Aldh1l1−/− compared to Aldh1l1+/+ mice at 28 and 36 weeks. Profound differences between Aldh1l1−/− and Aldh1l1+/+ mice in the expression of inflammation-related genes were seen at 10 and 20 weeks. Of note, large tumors from wild-type mice showed a strong decrease of ALDH1L1 protein at 36 weeks. Metabolomic analysis of liver tissues at 20 weeks showed stronger differences in Aldh1l1+/+ versus Aldh1l1−/− metabotypes than at 10 weeks, which underscores metabolic pathways that respond to DEN in an ALDH1L1-dependent manner. Our study indicates that Aldh1l1 knockout promoted liver tumor growth without affecting tumor initiation or multiplicity.

ADAP-GC is an automated computational workflow for extracting metabolite information from raw, untargeted gas chromatography-mass spectrometry metabolomics data. Deconvolution of coeluting analytes is a critical step in the workflow, and the underlying algorithm is able to extract fragmentation mass spectra of coeluting analytes with high accuracy. However, its latest version ADAP-GC 3.0 was not user-friendly. To make ADAP-GC easier to use, we have developed ADAP-GC 3.2 and describe here the improvements on three aspects. First, all of the algorithms in ADAP-GC 3.0 written in R have been replaced by their analogues in Java and incorporated into MZmine 2 to make the workflow user-friendly. Second, the clustering algorithm DBSCAN has replaced the original hierarchical clustering to allow faster spectral deconvolution. Finally, algorithms originally developed for constructing extracted ion chromatograms (EICs) and detecting EIC peaks from LC-MS data are incorporated into the ADAP-GC workflow, allowing the latter to process high mass resolution data. Performance of ADAP-GC 3.2 has been evaluated using unit mass resolution data from standard-mixture and urine samples. The identification and quantitation results were compared with those produced by ADAP-GC 3.0, AMDIS, AnalyzerPro, and ChromaTOF. Identification results for high mass resolution data derived from standard-mixture samples are presented as well.

XCMS and MZmine 2 are two widely used software packages for preprocessing untargeted LC/MS metabolomics data. Both construct extracted ion chromatograms (EICs) and detect peaks from the EICs, the first two steps in the data preprocessing workflow. While both packages have performed admirably in peak picking, they also detect a problematic number of false positive EIC peaks and can also fail to detect real EIC peaks. The former and latter translate downstream into spurious and missing compounds and present significant limitations with most existing software packages that preprocess untargeted mass spectrometry metabolomics data. We seek to understand the specific reasons why XCMS and MZmine 2 find the false positive EIC peaks that they do and in what ways they fail to detect real compounds. We investigate differences of EIC construction methods in XCMS and MZmine 2 and find several problems in the XCMS centWave peak detection algorithm which we show are partly responsible for the false positive and false negative compound identifications. In addition, we find a problem with MZmine 2's use of centWave. We hope that a detailed understanding of the XCMS and MZmine 2 algorithms will allow users to work with them more effectively and will also help with future algorithmic development.

Folate coenzymes are involved in biochemical reactions of one-carbon transfer, and deficiency of this vitamin impairs cellular proliferation, migration, and survival in many cell types. Here, the effect of folate restriction on mammary cancer was evaluated using three distinct breast cancer subtypes differing in their aggressiveness and metastatic potential: noninvasive basal-like (E-Wnt), invasive but minimally metastatic claudin-low (M-Wnt), and highly metastatic claudin-low (metM-Wntliver) cell lines, each derived from the same pool of MMTV-Wnt-1 transgenic mouse mammary tumors. NMR-based metabolomics was used to quantitate 41 major metabolites in cells grown in folate-free medium versus standard medium. Each cell line demonstrated metabolic reprogramming when grown in folate-free medium. In E-Wnt, M-Wnt, and metM-Wntliver cells, 12, 29, and 25 metabolites, respectively, were significantly different (P < 0.05 and at least 1.5-fold change). The levels of eight metabolites (aspartate, ATP, creatine, creatine phosphate, formate, serine, taurine and β-alanine) were changed in each folate-restricted cell line. Increased glucose, decreased lactate, and inhibition of glycolysis, cellular proliferation, migration, and invasion occurred in M-Wnt and metM-Wntliver cells (but not E-Wnt cells) grown in folate-free versus standard medium. These effects were accompanied by altered levels of several folate-metabolizing enzymes, indicating that the observed metabolic reprogramming may result from both decreased folate availability and altered folate metabolism. These findings reveal that folate restriction results in metabolic and bioenergetic changes and a less aggressive cancer cell phenotype. Implications: Metabolic reprogramming driven by folate restriction represents a therapeutic target for reducing the burden of breast cancer. Mol Cancer Res; 15(2); 189–200. ©2016 AACR.

Diagnosis of neurodegenerative diseases hinges on detection of “seed” proteins in disease-specific aggregates. These inclusions contain diverse constituents, adhering through aberrant interactions that our prior data indicate are nonrandom. To define preferential protein-protein contacts mediating aggregate coalescence, we created click-chemistry reagents that crosslink neighboring proteins within human, APPSw-driven, neuroblastoma-cell aggregates. These reagents incorporate a biotinyl group to efficiently recover linked tryptic-peptide pairs. Mass-spectroscopy outputs were screened for all possible peptide pairs in the aggregate proteome. These empirical linkages, ranked by abundance, implicate a protein-adherence network termed the “aggregate-contactome.” Critical hubs and hub-hub interactions were assessed by RNAi-mediated rescue of chemotaxis in aging nematodes, and aggregation-driving properties were inferred by multivariate-regression and neural-network approaches. Aspirin, while disrupting aggregation, greatly simplified the aggregate contactome. This approach, and the dynamic model of aggregate accrual it implies, reveal the architecture of insoluble-aggregate networks and highlight influential proteins asnovel targets to ameliorate protein-aggregation diseases.

ADAP-GC is an automated computational pipeline for untargeted, GC/MS-based metabolomics studies. It takes raw mass spectrometry data as input and carries out a sequence of data processing steps including construction of extracted ion chromatograms, detection of chromatographic peak features, deconvolution of coeluting compounds, and alignment of compounds across samples. Despite the increased accuracy from the original version to version 2.0 in terms of extracting metabolite information for identification and quantitation, ADAP-GC 2.0 requires appropriate specification of a number of parameters and has difficulty in extracting information on compounds that are in low concentration. To overcome these two limitations, ADAP-GC 3.0 was developed to improve both the robustness and sensitivity of compound detection. In this paper, we report how these goals were achieved and compare ADAP-GC 3.0 against three other software tools including ChromaTOF, AnalyzerPro, and AMDIS that are widely used in the metabolomics community.

Metabolomics, a systems biology discipline representing analysis of known and unknown pathways of metabolism, has grown tremendously over the past 20 years. Because of its comprehensive nature, metabolomics requires careful consideration of the question(s) being asked, the scale needed to answer the question(s), collection and storage of the sample specimens, methods for extraction of the metabolites from biological matrices, the analytical method(s) to be employed and the quality control of the analyses, how collected data are correlated, the statistical methods to determine metabolites undergoing significant change, putative identification of metabolites and the use of stable isotopes to aid in verifying metabolite identity and establishing pathway connections and fluxes. This second part of a comprehensive description of the methods of metabolomics focuses on data analysis, emerging methods in metabolomics and the future of this discipline. Copyright © 2016 John Wiley & Sons, Ltd.

False positive and false negative peaks detected from extracted ion chromatograms (EIC) are an urgent problem with existing software packages that preprocess untargeted liquid or gas chromatography-mass spectrometry metabolomics data because they can translate downstream into spurious or missing compound identifications. We have developed new algorithms that carry out the sequential construction of EICs and detection of EIC peaks. We compare the new algorithms to two popular software packages XCMS and MZmine 2 and present evidence that these new algorithms detect significantly fewer false positives. Regarding the detection of compounds known to be present in the data, the new algorithms perform at least as well as XCMS and MZmine 2. Furthermore, we present evidence that mass tolerance in m/z should be favored rather than mass tolerance in ppm in the process of constructing EICs. The mass tolerance parameter plays a critical role in the EIC construction process and can have immense impact on the detection of EIC peaks.

Metabolomics is perhaps the most challenging of the -omics fields, given the complexity of an organism's metabolome and the rapid rate at which it changes. When one sets out to study metabolism there are numerous dynamic variables that can influence metabolism that must be considered. Recognizing the experimental challenges confronting researchers who undertake metabolism studies, workshops like the one at University of Alabama at Birmingham have been established to offer instructional guidance. A summary of the UAB course training materials is being published as a two-part Special Feature Tutorial. In this month's Part I the authors discuss details of good experimental design and sample collection and handling. In an upcoming Part II, the authors discuss in detail the various aspects of data analysis.

Metabolomics, a systems biology discipline representing analysis of known and unknown pathways of metabolism, has grown tremendously over the past 20 years. Because of its comprehensive nature, metabolomics requires careful consideration of the question(s) being asked, the scale needed to answer the question(s), collection and storage of the sample specimens, methods for extraction of the metabolites from biological matrices, the analytical method(s) to be employed and the quality control of the analyses, how collected data are correlated, the statistical methods to determine metabolites undergoing significant change, putative identification of metabolites and the use of stable isotopes to aid in verifying metabolite identity and establishing pathway connections and fluxes. This second part of a comprehensive description of the methods of metabolomics focuses on data analysis, emerging methods in metabolomics and the future of this discipline. Copyright © 2016 John WileySons, Ltd.

Modern mass spectrometry (MS) technologies have provided a versatile platform that can be combined with a large number of techniques to analyze protein structure and dynamics. These techniques include the three detailed in this chapter: (1) hydrogen/deuterium exchange (HDX), (2) limited proteolysis, and (3) chemical crosslinking (CX). HDX relies on the change in mass of a protein upon its dilution into deuterated buffer, which results in varied deuterium content within its backbone amides. Structural information on surface exposed, flexible or disordered linker regions of proteins can be achieved through limited proteolysis, using a variety of proteases and only small extents of digestion. CX refers to the covalent coupling of distinct chemical species and has been used to analyze the structure, function and interactions of proteins by identifying crosslinking sites that are formed by small multi-functional reagents, termed crosslinkers. Each of these MS applications is capable of revealing structural information for proteins when used either with or without other typical high resolution techniques, including NMR and X-ray crystallography.

The NuRD (nucleosome remodeling and deacetylase) complex serves as a crucial epigenetic regulator of cell differentiation, proliferation, and hematopoietic development by coupling the deacetylation and demethylation of histones, nucleosome mobilization, and the recruitment of transcription factors. The core nucleosome remodeling function of the mammalian NuRD complex is executed by the helicase-domain-containing ATPase CHD4 (Mi-2β) subunit, which also contains N-terminal plant homeodomain (PHD) and chromo domains. The mode of regulation of chromatin remodeling by CHD4 is not well understood, nor is the role of its PHD and chromo domains. Here, we use small-angle X-ray scattering, nucleosome binding ATPase and remodeling assays, limited proteolysis, cross-linking, and tandem mass spectrometry to propose a three-dimensional structural model describing the overall shape and domain interactions of CHD4 and discuss the relevance of these for regulating the remodeling of chromatin by the NuRD complex., Graphical Abstract Highlights ► The ATPase CHD4 mediates nucleosome remodeling by the NuRD complex. ► We present a three-dimensional small-angle X-ray scattering model of CHD4 and define its interdomain interactions. ► Cross-linking and limited proteolysis studies validate our model. ► Functional and binding assays suggest a regulatory role for the PHD and chromo domains.

ADAP-GC 2.0 has been developed to deconvolute coeluting metabolites that frequently exist in real biological samples of metabolomics studies. Deconvolution is based on a chromatographic model peak approach that combines five metrics of peak qualities for constructing/selecting model peak features. Prior to deconvolution, ADAP-GC 2.0 takes raw mass spectral data as input, extracts ion chromatograms for all the observed masses, and detects chromatographic peak features. After deconvolution, it aligns components across samples and exports the qualitative and quantitative information of all of the observed components. Centered on the deconvolution, the entire data analysis workflow is fully automated. ADAP-GC 2.0 has been tested using three different types of samples. The testing results demonstrate significant improvements of ADAP-GC 2.0, compared to the previous ADAP 1.0, to identify and quantify metabolites from gas chromatography/time-of-flight mass spectrometry (GC/TOF-MS) data in untargeted metabolomics studies.

Recent technological advances have made it possible to carry out high-throughput metabonomics studies using gas chromatography coupled with time-of-flight mass spectrometry. Large volumes of data are produced from these studies and there is a pressing need for algorithms that can efficiently process and analyze data in a high-throughput fashion as well. We present an Automated Data Analysis Pipeline (ADAP) that has been developed for this purpose. ADAP consists of peak detection, deconvolution, peak alignment, and library search. It allows data to flow seamlessly through the analysis steps without any human intervention and features two novel algorithms in the analysis. Specifically, clustering is successfully applied in deconvolution to resolve coeluting compounds that are very common in complex samples and a two-phase alignment process has been implemented to enhance alignment accuracy. ADAP is written in standard C++ and R and uses parallel computing via Message Passing Interface for fast peak detection and deconvolution. ADAP has been applied to analyze both mixed standards samples and serum samples and identified and quantified metabolites successfully. ADAP is available at http://www.du-lab.org .

ADAP-GC is an automated computational pipeline for untargeted, GC-MS-based metabolomics studies. It takes raw mass spectrometry data as input and carries out a sequence of data processing steps including construction of extracted ion chromatograms, detection of chromatographic peak features, deconvolution of co-eluting compounds, and alignment of compounds across samples. Despite the increased accuracy from the original version to version 2.0 in terms of extracting metabolite information for identification and quantitation, ADAP-GC 2.0 requires appropriate specification of a number of parameters and has difficulty in extracting information of compounds that are in low concentration. To overcome these two limitations, ADAP-GC 3.0 was developed to improve both the robustness and sensitivity of compound detection. In this paper, we report how these goals were achieved and compare ADAP-GC 3.0 against three other software tools including ChromaTOF, AnalyzerPro, and AMDIS that are widely used in the metabolomics community.

This paper introduces a simple recursive algorithm for nonlinear dynamic system identification using linear-in-the-parameters models for NARX or RBF network where both the structure and parameters can be obtained simultaneously and recursively. The main objective is to improve the numerical stability when the model terms are highly correlated. This is based on the “innovation” idea and net contribution criteria. Using the recursive formulae for the computation of the Moore-Penrose inverse of matrices and the net contribution of model terms, it is possible to combine the structure term determination and parameters estimation within one framework by adding and deleting an item in the selected candidate model. The formulae for enhancing and reducing a matrix are given. Simulation results show the proposed method is numerically more stable than existing approaches.

Reversible protein phosphorylation is a central cellular regulatory mechanism in modulating protein activity and propagating signals within cellular pathways and networks. Development of more effective methods for the simultaneous identification of phosphorylation sites and quantification of temporal changes in protein phosphorylation could provide important insights into molecular signaling mechanisms in various cellular processes. Here we present an integrated quantitative phosphoproteomics approach and its application for comparative analysis of Cos-7 cells in response to lysophosphatidic acid (LPA) gradient stimulation. The approach combines trypsin-catalyzed (16)O/ (18)O labeling plus (16)O/ (18)O-methanol esterification for quantitation, a macro-immobilized metal-ion affinity chromatography trap for phosphopeptide enrichment, and LC-MS/MS analysis. LC separation and MS/MS are followed by neutral loss-dependent MS/MS/MS for phosphopeptide identification using a linear ion trap (LTQ)-FT mass spectrometer. A variety of phosphorylated proteins were identified and quantified including receptors, kinases, proteins associated with small GTPases, and cytoskeleton proteins. A number of hypothetical proteins were also identified as differentially expressed followed by LPA stimulation, and we have shown evidence of pseudopodia subcellular localization of one of these candidate proteins. These results demonstrate the efficiency of this quantitative phosphoproteomics approach and its application for rapid discovery of phosphorylation events associated with LPA gradient sensing and cell chemotaxis.