45 results on '"H. Paul Benton"'
Search Results
2. The METLIN small molecule dataset for machine learning-based retention time prediction
- Author
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Xavier Domingo-Almenara, Carlos Guijas, Elizabeth Billings, J. Rafael Montenegro-Burke, Winnie Uritboonthai, Aries E. Aisporna, Emily Chen, H. Paul Benton, and Gary Siuzdak
- Subjects
Science - Abstract
The use of machine learning for identifying small molecules through their retention time’s predictions has been challenging so far. Here the authors combine a large database of liquid chromatography retention time with a deep learning approach to enable accurate metabolites’s identification.
- Published
- 2019
- Full Text
- View/download PDF
3. LipidFinder 2.0: advanced informatics pipeline for lipidomics discovery applications.
- Author
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Jorge álvarez-Jarreta, Patricia Rodrigues, Eoin Fahy, Anne O'connor, Anna Price, Caroline Gaud, Simon Andrews, H. Paul Benton, Gary Siuzdak, Jade I Hawksworth, Maria Valdivia-Garcia, Stuart M. Allen, and Valerie B. O'Donnell
- Published
- 2021
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- View/download PDF
4. Determining conserved metabolic biomarkers from a million database queries.
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Michael E. Kurczy, Julijana Ivanisevic, Caroline H. Johnson, Winnie Uritboonthai, Linh Hoang, Mingliang Fang, Matthew Hicks, Anthony Aldebot, Duane Rinehart, Lisa J. Mellander, Ralf Tautenhahn, Gary J. Patti, Mary E. Spilker, H. Paul Benton, and Gary Siuzdak
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- 2015
- Full Text
- View/download PDF
5. Cognitive analysis of metabolomics data for systems biology
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Markus M. Rinschen, Elizabeth Billings, Carlos Guijas, Erica L.-W. Majumder, J. Rafael Montenegro-Burke, Bradley A. Tagtow, Gary Siuzdak, H. Paul Benton, Richard L. Martin, Robert S. Plumb, Amelia Palermo, and Xavier Domingo-Almenara
- Subjects
0303 health sciences ,Computer science ,business.industry ,Systems biology ,Cognitive computing ,Big data ,Scientific literature ,Data science ,General Biochemistry, Genetics and Molecular Biology ,03 medical and health sciences ,ComputingMethodologies_PATTERNRECOGNITION ,0302 clinical medicine ,Metabolomics ,Workflow ,Identification (biology) ,business ,Protocol (object-oriented programming) ,030217 neurology & neurosurgery ,030304 developmental biology - Abstract
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. This protocol describes how to use natural language processing software to analyze metabolomics data to prioritize metabolites for further study, identify candidates for unique disease biomarkers and elucidate their function on a pathway level.
- Published
- 2021
6. Neutral Loss Mass Spectral Data Enhances Molecular Similarity Analysis in METLIN
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Aries Aisporna, H. Paul Benton, Andy Chen, Rico J. E. Derks, Jean Marie Galano, Martin Giera, and Gary Siuzdak
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Structural Biology ,Spectroscopy ,Article - Abstract
Neutral loss (NL) spectral data presents a mirror of MS(2) data, and is a valuable yet largely untapped resource for molecular discovery and similarity analysis. Tandem mass spectrometry (MS(2)) data is effective for the identification of known molecules and the putative identification of novel, previously uncharacterized molecules (unknowns). Yet, MS(2) data alone is limited in characterizing structurally related molecules. To facilitate unknown identification and complement the METLIN-MS(2) fragment ion database for characterizing structurally related molecules, we have created a MS(2) to NL converter as a part of the METLIN platform. The converter has been used to transform METLIN’s MS(2) data into a neutral loss database (METLIN-NL) on over 860,000 individual molecular standards. The platform includes both the MS(2) to NL converter and a graphical user interface enabling comparative analyses between MS(2) and NL data. Examples of NL spectral data are shown with oxylipin analogues and two structurally related statin molecules to demonstrate NL spectra and their ability to help characterize structural similarity. Mirroring MS(2) data to generate NL spectral data offers a unique dimension for chemical and metabolite structure characterization.
- Published
- 2022
7. Enhanced in-Source Fragmentation Annotation Enables Novel Data Independent Acquisition and Autonomous METLIN Molecular Identification
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John Isbell, Gary Siuzdak, Jingchuan Xue, Carlos Guijas, Xavier Domingo-Almenara, Markus M. Rinschen, Amelia Palermo, and H. Paul Benton
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Spectrometry, Mass, Electrospray Ionization ,Chemistry ,Electrospray ionization ,010401 analytical chemistry ,Polyatomic ion ,Analytical chemistry ,010402 general chemistry ,Mass spectrometry ,Tandem mass spectrometry ,01 natural sciences ,Article ,0104 chemical sciences ,Analytical Chemistry ,Fragmentation (mass spectrometry) ,Tandem Mass Spectrometry ,Ionization ,Data-independent acquisition ,Organic Chemicals ,METLIN - Abstract
Electrospray ionization (ESI) in-source fragmentation (ISF) has traditionally been minimized to promote precursor molecular ion formation, and therefore its value in molecular identification is underappreciated. In-source annotation algorithms have been shown to increase confidence in putative identifications by using ubiquitous in-source fragments. However, these in-source annotation algorithms are limited by ESI sources that are generally designed to minimize ISF. In this study, enhanced in-source fragmentation annotation (eISA) was created by tuning the ISF conditions to generate in-source fragmentation patterns comparable with higher energy fragments generated at higher collision energies as deposited in the METLIN MS/MS library, without compromising the intensity of precursor ions (median loss ≤10% in both positive and negative ionization modes). The analysis of 50 molecules was used to validate the approach in comparison to MS/MS spectra produced via data dependent acquisition (DDA) and data independent acquisition (DIA) mode with quadrupole time-of-flight mass spectrometry (QTOF-MS). Enhanced ISF as compared to QTOF DDA enabled higher peak intensities for the precursor ions (median: 18 times in negative mode and 210 times in positive mode), with the eISA fragmentation patterns consistent with METLIN for over 90% of the molecules with respect to fragment relative intensity and m/z. eISA also provides higher peak intensity as opposed to QTOF DIA for over 60% of the precursor ions in negative mode (median increase: 20%) and for 88% of the precursor ions in positive mode (median increase: 80%). Molecular identification with eISA was also successfully validated from the analysis of a metabolic extract from macrophages. An interesting side benefit of enhanced ISF is that it significantly improved molecular identification confidence with low resolution single quadrupole mass-spectrometry-based untargeted LC/MS experiments. Overall, enhanced ISF allowed for eISA to be used as a more sensitive alternative to other QTOF DIA and DDA approaches, and further, it enabled the acquisition of ESI TOF and ESI single quadrupole mass spectrometry instrumentation spectra with improved molecular identification confidence.
- Published
- 2020
8. Accelerated lysine metabolism conveys kidney protection in salt-sensitive hypertension
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Markus M. Rinschen, Oleg Palygin, Ashraf El-Meanawy, Xavier Domingo-Almenara, Amelia Palermo, Lashodya V. Dissanayake, Daria Golosova, Michael A. Schafroth, Carlos Guijas, Fatih Demir, Johannes Jaegers, Megan L. Gliozzi, Jingchuan Xue, Martin Hoehne, Thomas Benzing, Bernard P. Kok, Enrique Saez, Markus Bleich, Nina Himmerkus, Ora A. Weisz, Benjamin F. Cravatt, Marcus Krüger, H. Paul Benton, Gary Siuzdak, and Alexander Staruschenko
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Malonyl Coenzyme A ,Disease Models, Animal ,Multidisciplinary ,Albumins ,Lysine ,Hypertension ,General Physics and Astronomy ,Animals ,General Chemistry ,Kidney ,General Biochemistry, Genetics and Molecular Biology ,Carbon ,Rats - Abstract
Hypertension and kidney disease have been repeatedly associated with genomic variants and alterations of lysine metabolism. Here, we combined stable isotope labeling with untargeted metabolomics to investigate lysine’s metabolic fate in vivo. Dietary 13C6 labeled lysine was tracked to lysine metabolites across various organs. Globally, lysine reacts rapidly with molecules of the central carbon metabolism, but incorporates slowly into proteins and acylcarnitines. Lysine metabolism is accelerated in a rat model of hypertension and kidney damage, chiefly through N-alpha-mediated degradation. Lysine administration diminished development of hypertension and kidney injury. Protective mechanisms include diuresis, further acceleration of lysine conjugate formation, and inhibition of tubular albumin uptake. Lysine also conjugates with malonyl-CoA to form a novel metabolite Nε-malonyl-lysine to deplete malonyl-CoA from fatty acid synthesis. Through conjugate formation and excretion as fructoselysine, saccharopine, and Nε-acetyllysine, lysine lead to depletion of central carbon metabolites from the organism and kidney. Consistently, lysine administration to patients at risk for hypertension and kidney disease inhibited tubular albumin uptake, increased lysine conjugate formation, and reduced tricarboxylic acid (TCA) cycle metabolites, compared to kidney-healthy volunteers. In conclusion, lysine isotope tracing mapped an accelerated metabolism in hypertension, and lysine administration could protect kidneys in hypertensive kidney disease.
- Published
- 2020
9. Cloud‐based archived metabolomics data: A resource for in‐source fragmentation/annotation, meta‐analysis and systems biology
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Valerie B. O'Donnell, Markus M. Rinschen, Jingchuan Xue, Tao Huan, Shuzhao Li, Shankar Subramaniam, Duane Rinehart, Eoin Fahy, Gary Siuzdak, Amelia Palermo, and H. Paul Benton
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Annotation ,Computer science ,business.industry ,Systems biology ,Fragmentation (computing) ,General Earth and Planetary Sciences ,Cloud computing ,business ,Data science ,Article ,General Environmental Science ,Metabolomics data - Abstract
Archived metabolomics data represent a broad resource for the scientific community. However, the absence of tools for the meta-analysis of heterogeneous data types makes it challenging to perform direct comparisons in a single and cohesive workflow. Here we present a framework for the meta-analysis of metabolic pathways and interpretation with proteomic and transcriptomic data. This framework facilitates the comparison of heterogeneous types of metabolomics data from online repositories (e.g., XCMS Online, Metabolomics Workbench, GNPS, and MetaboLights) representing tens of thousands of studies, as well as locally acquired data. As a proof of concept, we apply the workflow for the meta-analysis of i) independent colon cancer studies, further interpreted with proteomics and transcriptomics data, ii) multimodal data from Alzheimer’s disease and mild cognitive impairment studies, demonstrating its high-throughput capability for the systems level interpretation of metabolic pathways. Moreover, the platform has been modified for improved knowledge dissemination through a collaboration with Metabolomics Workbench and LIPID MAPS. We envision that this meta-analysis tool will help overcome the primary bottleneck in analyzing diverse datasets and facilitate the full exploitation of archival metabolomics data for addressing a broad array of questions in metabolism research and systems biology.
- Published
- 2020
10. Cognitive analysis of metabolomics data for systems biology
- Author
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Erica L-W, Majumder, Elizabeth M, Billings, H Paul, Benton, Richard L, Martin, Amelia, Palermo, Carlos, Guijas, Markus M, Rinschen, Xavier, Domingo-Almenara, J Rafael, Montenegro-Burke, Bradley A, Tagtow, Robert S, Plumb, and Gary, Siuzdak
- Subjects
Big Data ,Data Analysis ,Databases, Factual ,Artificial Intelligence ,Systems Biology ,Animals ,Humans ,Metabolomics ,Mass Spectrometry ,Metabolic Networks and Pathways ,Software ,Natural Language Processing ,Workflow - Abstract
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.
- Published
- 2020
11. Enhanced Electrospray In-source Fragmentation for Higher Sensitivity Data Independent Acquisition and Autonomous METLIN Molecular Identification
- Author
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Gary Siuzdak, H. Paul Benton, Markus Rinschen, Amelia Palermo, Carlos Guijas, Xavier Domingo-Almenara, and jingchuan xue
- Abstract
Electrospray ionization (ESI) in-source fragmentation (ISF) has traditionally been minimized to promote precursor molecular ion formation, and therefore its value in molecular identification underappreciated. Recently a METLIN-guided in-source annotation (MISA) algorithm was introduced to increase confidence in putative identifications by using ubiquitous in-source fragments. However, MISA is limited by ESI sources that are generally designed to minimize ISF. In this study, enhanced ISF with MISA (eMISA) was created by tuning the ISF conditions to generate in-source fragmentation patterns comparable with higher energy fragments generated at higher collision energies as deposited in the METLIN MS/MS library, without compromising the intensity of precursor ions (median loss ≤ 10% in both positive and negative ionization modes). The analysis of 50 molecules was used to validate the approach in comparison to MS/MS spectra produced via data dependent acquisition (DDA) and data independent acquisition mode (DIA) with quadrupole time-of-flight mass spectrometry (QTOF-MS). Enhanced ISF as compared to QTOF DDA, enables for higher peak intensities for the precursor ions (median: 18 times at negative mode and 210 times at positive mode), with the eMISA fragmentation patterns consistent with METLIN for over 90% of the molecules with respect to fragment relative intensity and m/z. eMISA also provides higher peak intensity as opposed to QTOF DIA with a median increase of 20% at negative mode and 80% at positive mode for all precursor ions. Metabolite identification with eMISA was also successfully validated from the analysis of a metabolic extract from macrophages. An interesting side benefit of enhanced ISF is that it significantly improved the compound identification confidence with low resolution single quadrupole mass spectrometry-based untargeted LC/MS experiments. Overall, enhanced ISF allowed for eMISA to be used as a more sensitive alternative to other QTOF DIA and DDA approaches, and further, it enables the acquisition of ESI TOF and ESI single quadrupole mass spectrometry instrumentation spectra with higher sensitivity and improved molecular identification confidence.
- Published
- 2020
12. The METLIN small molecule dataset for machine learning-based retention time prediction
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Emily I. Chen, J. Rafael Montenegro-Burke, Aries E. Aisporna, Elizabeth Billings, H. Paul Benton, Winnie Uritboonthai, Gary Siuzdak, Xavier Domingo-Almenara, and Carlos Guijas
- Subjects
0301 basic medicine ,Time Factors ,Computer science ,Science ,education ,Datasets as Topic ,General Physics and Astronomy ,Machine learning ,computer.software_genre ,01 natural sciences ,Article ,General Biochemistry, Genetics and Molecular Biology ,Small Molecule Libraries ,03 medical and health sciences ,Deep Learning ,METLIN ,Chromatography, Reverse-Phase ,Multidisciplinary ,Mass spectrometry ,business.industry ,Extramural ,Cheminformatics ,Deep learning ,010401 analytical chemistry ,Experimental data ,Scientific data ,General Chemistry ,Small molecule ,0104 chemical sciences ,030104 developmental biology ,Models, Chemical ,Artificial intelligence ,business ,Retention time ,computer - Abstract
Machine learning has been extensively applied in small molecule analysis to predict a wide range of molecular properties and processes including mass spectrometry fragmentation or chromatographic retention time. However, current approaches for retention time prediction lack sufficient accuracy due to limited available experimental data. Here we introduce the METLIN small molecule retention time (SMRT) dataset, an experimentally acquired reverse-phase chromatography retention time dataset covering up to 80,038 small molecules. To demonstrate the utility of this dataset, we deployed a deep learning model for retention time prediction applied to small molecule annotation. Results showed that in 70\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document}% 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., The use of machine learning for identifying small molecules through their retention time’s predictions has been challenging so far. Here the authors combine a large database of liquid chromatography retention time with a deep learning approach to enable accurate metabolites’s identification.
- Published
- 2019
13. Fluorinated Gold Nanoparticles for Nanostructure Imaging Mass Spectrometry
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Ellen Kuang, Amelia Palermo, Gary Siuzdak, Benedikt Warth, H. Paul Benton, Aries E. Aisporna, Elizabeth Billings, David Berry, and Erica M. Forsberg
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0301 basic medicine ,Nanostructure ,Halogenation ,Colon ,Carbohydrates ,General Physics and Astronomy ,Photochemistry ,01 natural sciences ,Mass Spectrometry ,Article ,Mass spectrometry imaging ,Bacteroides fragilis ,Bile Acids and Salts ,Mice ,03 medical and health sciences ,chemistry.chemical_compound ,Metabolomics ,Desorption ,Ionization ,Animals ,General Materials Science ,Amino Acids ,Perfluorohexane ,Nucleotides ,Optical Imaging ,010401 analytical chemistry ,General Engineering ,Lipids ,Nanostructures ,0104 chemical sciences ,Mice, Inbred C57BL ,Matrix-assisted laser desorption/ionization ,030104 developmental biology ,chemistry ,Colloidal gold ,Gold ,Sulfur - Abstract
Nanostructure imaging mass spectrometry (NIMS) with fluorinated gold nanoparticles (f-AuNPs) is a nanoparticle assisted laser desorption/ionization approach that requires low laser energy and has demonstrated high sensitivity. Here we describe NIMS with f-AuNPs for the comprehensive analysis of metabolites in biological tissues. F-AuNPs assist in desorption/ionization by laser-induced release of the fluorocarbon chains with minimal background noise. Since the energy barrier required to release the fluorocarbons from the AuNPs is minimal, the energy of the laser is maintained in the low μJ/pulse range, thus limiting metabolite in-source fragmentation. Electron microscopy analysis of tissue samples after f-AuNP NIMS shows a distinct "raising" of the surface as compared to matrix assisted laser desorption ionization ablation, indicative of a gentle desorption mechanism aiding in the generation of intact molecular ions. Moreover, the use of perfluorohexane to distribute the f-AuNPs on the tissue creates a hydrophobic environment minimizing metabolite solubilization and spatial dislocation. The transfer of the energy from the incident laser to the analytes through the release of the fluorocarbon chains similarly enhances the desorption/ionization of metabolites of different chemical nature, resulting in heterogeneous metabolome coverage. We performed the approach in a comparative study of the colon of mice exposed to three different diets. F-AuNP NIMS allows the direct detection of carbohydrates, lipids, bile acids, sulfur metabolites, amino acids, nucleotide precursors as well as other small molecules of varied biological origins. Ultimately, the diversified molecular coverage obtained provides a broad picture of a tissue's metabolic organization.
- Published
- 2018
14. Autonomous Multimodal Metabolomics Data Integration for Comprehensive Pathway Analysis and Systems Biology
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Gary Siuzdak, Duane Rinehart, H. Paul Benton, Xavier Domingo-Almenara, Amelia Palermo, David Edler, Tao Huan, Carlos Guijas, Julijana Ivanisevic, Thiery Phommavongsay, and Benedikt Warth
- Subjects
0301 basic medicine ,Data processing ,Chemistry ,Systems biology ,010401 analytical chemistry ,Pathway analysis ,Proteomics ,computer.software_genre ,01 natural sciences ,0104 chemical sciences ,Analytical Chemistry ,Metabolomics data ,03 medical and health sciences ,030104 developmental biology ,Metabolomics ,Proof of concept ,Multimodal analysis ,Data mining ,computer - Abstract
Comprehensive metabolomic data can be achieved using multiple orthogonal separation and mass spectrometry (MS) analytical techniques. However, drawing biologically relevant conclusions from this data and combining it with additional layers of information collected by other omic technologies present a significant bioinformatic challenge. To address this, a data processing approach was designed to automate the comprehensive prediction of dysregulated metabolic pathways/networks from multiple data sources. The platform autonomously integrates multiple MS-based metabolomics data types without constraints due to different sample preparation/extraction, chromatographic separation, or MS detection method. This multimodal analysis streamlines the extraction of biological information from the metabolomics data as well as the contextualization within proteomics and transcriptomics data sets. As a proof of concept, this multimodal analysis approach was applied to a colorectal cancer (CRC) study, in which complementa...
- Published
- 2018
15. Annotation: A Computational Solution for Streamlining Metabolomics Analysis
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J. Rafael Montenegro-Burke, H. Paul Benton, Gary Siuzdak, and Xavier Domingo-Almenara
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0301 basic medicine ,Spectrometry, Mass, Electrospray Ionization ,Chromatography ,Chemistry ,Metabolite ,Computational Biology ,Computational biology ,Tandem mass spectrometry ,Mass spectrometry ,Article ,Analytical Chemistry ,03 medical and health sciences ,chemistry.chemical_compound ,Annotation ,030104 developmental biology ,Metabolomics ,Liquid chromatography–mass spectrometry ,Animals ,Humans ,Monoisotopic mass ,Cluster analysis ,Algorithms ,Chromatography, Liquid - Abstract
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 (MS1) 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.
- Published
- 2017
16. Correction of mass calibration gaps in liquid chromatography-mass spectrometry metabolomics data.
- Author
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H. Paul Benton, Elizabeth J. Want, and Timothy M. D. Ebbels
- Published
- 2010
- Full Text
- View/download PDF
17. METLIN MS2 molecular standards database: a broad chemical and biological resource
- Author
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Carlos Guijas, H. Paul Benton, Gary Siuzdak, Benedikt Warth, and Jingchuan Xue
- Subjects
Resource (biology) ,Extramural ,Computer science ,MEDLINE ,Cell Biology ,Molecular Biology ,Biochemistry ,Data science ,METLIN ,Biotechnology - Published
- 2020
18. Data Streaming for Metabolomics: Accelerating Data Processing and Analysis from Days to Minutes
- Author
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H. Paul Benton, Brian T. Abe, J. Rafael Montenegro-Burke, Luc Teyton, Mingliang Fang, Aries E. Aisporna, Julijana Ivanisevic, Duane Rinehart, Tao Huan, Luke L. Lairson, Erica M. Forsberg, Dennis W. Wolan, Benedikt Warth, and Gary Siuzdak
- Subjects
0301 basic medicine ,Complex data type ,Data processing ,Time Factors ,Chemistry ,T-Lymphocytes ,Real-time computing ,Data Compression ,Bottleneck ,Article ,Analytical Chemistry ,Workflow ,03 medical and health sciences ,Data Compression/economics ,Data Compression/methods ,Data Mining/economics ,Data Mining/methods ,Humans ,Metabolomics/economics ,Metabolomics/methods ,Software ,T-Lymphocytes/metabolism ,030104 developmental biology ,Metabolomics ,Server ,Data file ,Data Mining ,Throughput (business) ,Data transmission - Abstract
The speed and throughput of analytical platforms has been a driving force in recent years in the "omics" technologies and while great strides have been accomplished in both chromatography and mass spectrometry, data analysis times have not benefited at the same pace. Even though personal computers have become more powerful, data transfer times still represent a bottleneck in data processing because of the increasingly complex data files and studies with a greater number of samples. To meet the demand of analyzing hundreds to thousands of samples within a given experiment, we have developed a data streaming platform, XCMS Stream, which capitalizes on the acquisition time to compress and stream recently acquired data files to data processing servers, mimicking just-in-time production strategies from the manufacturing industry. The utility of this XCMS Online-based technology is demonstrated here in the analysis of T cell metabolism and other large-scale metabolomic studies. A large scale example on a 1000 sample data set demonstrated a 10 000-fold time savings, reducing data analysis time from days to minutes. Further, XCMS Stream has the capability to increase the efficiency of downstream biochemical dependent data acquisition (BDDA) analysis by initiating data conversion and data processing on subsets of data acquired, expanding its application beyond data transfer to smart preliminary data decision-making prior to full acquisition.
- Published
- 2016
19. Autonomous METLIN-Guided In-source Fragment Annotation for Untargeted Metabolomics
- Author
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Carlos Guijas, Erica L.-W. Majumder, J. Rafael Montenegro-Burke, H. Paul Benton, Xavier Domingo-Almenara, and Gary Siuzdak
- Subjects
Databases, Factual ,Chemistry ,010401 analytical chemistry ,Brain ,Computational biology ,010402 general chemistry ,Tandem mass spectrometry ,Mass spectrometry ,Creatine ,01 natural sciences ,Article ,0104 chemical sciences ,Analytical Chemistry ,Annotation ,Mice ,Untargeted metabolomics ,Tandem Mass Spectrometry ,Metabolome ,Animals ,Metabolomics ,Amino Acids ,METLIN ,Algorithms ,Chromatography, High Pressure Liquid - Abstract
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 .
- Published
- 2019
20. Peer review of 'PhenoMeNal: Processing and analysis of Metabolomics data in the Cloud '
- Author
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H Paul Benton
- Abstract
This is the open peer reviewers comments and recommendations regarding the submitted GigaScience article and/or dataset.
- Published
- 2019
- Full Text
- View/download PDF
21. Smartphone Analytics: Mobilizing the Lab into the Cloud for Omic-Scale Analyses
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Gary Siuzdak, Duane Rinehart, Paul D. Robbins, J. Rafael Montenegro-Burke, Farris L. Poole, Erica M. Forsberg, Luke L. Lairson, Tao Huan, Michael P. Thorgersen, Gregory P. Krantz, Thiery Phommavongsay, Matthew W. Fields, H. Paul Benton, Aries E. Aisporna, Michael W. W. Adams, Laura J. Niedernhofer, and Trent R. Northen
- Subjects
0301 basic medicine ,Data Interpretation ,Cloud computing ,01 natural sciences ,Article ,Mass Spectrometry ,Analytical Chemistry ,03 medical and health sciences ,Humans ,Metabolomics ,Mobile technology ,Android (operating system) ,METLIN ,Chromatography ,Liquid ,Principal Component Analysis ,Internet ,Chemistry ,business.industry ,010401 analytical chemistry ,Statistical ,Chemical Engineering ,Mobile Applications ,Data science ,0104 chemical sciences ,Visualization ,Networking and Information Technology R&D ,030104 developmental biology ,Networking and Information Technology R&D (NITRD) ,Analytics ,Data Interpretation, Statistical ,The Internet ,Smartphone ,Other Chemical Sciences ,business ,Mobile device ,Chromatography, Liquid - Abstract
© 2016 American Chemical Society. Active data screening is an integral part of many scientific activities, and mobile technologies have greatly facilitated this process by minimizing the reliance on large hardware instrumentation. In order to meet with the increasingly growing field of metabolomics and heavy workload of data processing, we designed the first remote metabolomic data screening platform for mobile devices. Two mobile applications (apps), XCMS Mobile and METLIN Mobile, facilitate access to XCMS and METLIN, which are the most important components in the computer-based XCMS Online platforms. These mobile apps allow for the visualization and analysis of metabolic data throughout the entire analytical process. Specifically, XCMS Mobile and METLIN Mobile provide the capabilities for remote monitoring of data processing, real time notifications for the data processing, visualization and interactive analysis of processed data (e.g., cloud plots, principle component analysis, box-plots, extracted ion chromatograms, and hierarchical cluster analysis), and database searching for metabolite identification. These apps, available on Apple iOS and Google Android operating systems, allow for the migration of metabolomic research onto mobile devices for better accessibility beyond direct instrument operation. The utility of XCMS Mobile and METLIN Mobile functionalities was developed and is demonstrated here through the metabolomic LC-MS analyses of stem cells, colon cancer, aging, and bacterial metabolism.
- Published
- 2016
22. Training in metabolomics research. II. Processing and statistical analysis of metabolomics data, metabolite identification, pathway analysis, applications of metabolomics and its future
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Jeevan K. Prasain, Xiangqin Cui, Shuzhao Li, H. Paul Benton, Stephen Barnes, Sara J. Cooper, Hemant K. Tiwari, Matthew B. Renfrow, Xiuxia Du, Wimal Pathmasiri, Jeffrey A. Engler, Janusz H. Kabarowski, and Krista Casazza
- Subjects
0301 basic medicine ,Systems biology ,Metabolite ,010401 analytical chemistry ,Computational biology ,Pathway analysis ,01 natural sciences ,0104 chemical sciences ,Metabolomics data ,03 medical and health sciences ,chemistry.chemical_compound ,030104 developmental biology ,Metabolomics ,chemistry ,Identification (biology) ,Statistical analysis ,Spectroscopy - Abstract
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.
- Published
- 2016
23. Training in metabolomics research. I. Designing the experiment, collecting and extracting samples and generating metabolomics data
- Author
-
Matthew B. Renfrow, Xiuxia Du, Jeffrey A. Engler, Xiangqin Cui, Wimal Pathmasiri, H. Paul Benton, Janusz H. Kabarowski, Krista Casazza, Jeevan K. Prasain, Stephen Barnes, Sara J. Cooper, Hemant K. Tiwari, and Shuzhao Li
- Subjects
0301 basic medicine ,Chemistry ,business.industry ,010401 analytical chemistry ,Machine learning ,computer.software_genre ,01 natural sciences ,0104 chemical sciences ,Metabolomics data ,03 medical and health sciences ,030104 developmental biology ,Metabolomics ,Artificial intelligence ,business ,computer ,Spectroscopy - Published
- 2016
24. Metabolizing Data in the Cloud
- Author
-
H. Paul Benton, John R. Teijaro, Gary Siuzdak, Benedikt Warth, Duane Rinehart, and Nadine Levin
- Subjects
0301 basic medicine ,Data processing ,Government ,business.industry ,Computer science ,010401 analytical chemistry ,Multitude ,Information Storage and Retrieval ,Bioengineering ,Cloud computing ,Cloud Computing ,01 natural sciences ,Data science ,Article ,0104 chemical sciences ,Biotechnology ,03 medical and health sciences ,030104 developmental biology ,Metabolomics ,business ,Omics technologies - Abstract
Cloud-based bioinformatic platforms address the fundamental demands of creating a flexible scientific environment, facilitating data processing and general accessibility independent of a countries’ affluence. These platforms have a multitude of advantages as demonstrated by omics technologies, helping to support both government and scientific mandates of a more open environment.
- Published
- 2017
25. Data processing, multi-omic pathway mapping, and metabolite activity analysis using XCMS Online
- Author
-
Tao Huan, Brian Hilmers, Benedikt Warth, H. Paul Benton, Erica M. Forsberg, Gary Siuzdak, and Duane Rinehart
- Subjects
0301 basic medicine ,Computer science ,Systems biology ,Computational biology ,01 natural sciences ,General Biochemistry, Genetics and Molecular Biology ,Article ,03 medical and health sciences ,Metabolomics ,Software ,Data processing ,Electronic Data Processing ,Internet ,business.industry ,Electronic data processing ,Systems Biology ,010401 analytical chemistry ,Data interpretation ,Computational Biology ,0104 chemical sciences ,Visualization ,030104 developmental biology ,Workflow ,Metabolism ,business - Abstract
Systems biology is the study of complex living organisms, and as such, analysis on a systems-wide scale involves the collection of information-dense data sets that are representative of an entire phenotype. To uncover dynamic biological mechanisms, bioinformatics tools have become essential to facilitating data interpretation in large-scale analyses. Global metabolomics is one such method for performing systems biology, as metabolites represent the downstream functional products of ongoing biological processes. We have developed XCMS Online, a platform that enables online metabolomics data processing and interpretation. A systems biology workflow recently implemented within XCMS Online enables rapid metabolic pathway mapping using raw metabolomics data for investigating dysregulated metabolic processes. In addition, this platform supports integration of multi-omic (such as genomic and proteomic) data to garner further systems-wide mechanistic insight. Here, we provide an in-depth procedure showing how to effectively navigate and use the systems biology workflow within XCMS Online without a priori knowledge of the platform, including uploading liquid chromatography (LC)-mass spectrometry (MS) data from metabolite-extracted biological samples, defining the job parameters to identify features, correcting for retention time deviations, conducting statistical analysis of features between sample classes and performing predictive metabolic pathway analysis. Additional multi-omics data can be uploaded and overlaid with previously identified pathways to enhance systems-wide analysis of the observed dysregulations. We also describe unique visualization tools to assist in elucidation of statistically significant dysregulated metabolic pathways. Parameter input takes 5-10 min, depending on user experience; data processing typically takes 1-3 h, and data analysis takes ∼30 min.
- Published
- 2018
26. METLIN: A Technology Platform for Identifying Knowns and Unknowns
- Author
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J. Rafael Montenegro-Burke, Amelia Palermo, Gerrit Hermann, Gunda Koellensperger, H. Paul Benton, Gary Siuzdak, Tao Huan, Carlos Guijas, Benedikt Warth, Winnie Uritboonthai, Mary E. Spilker, Aries E. Aisporna, Xavier Domingo-Almenara, and Dennis W. Wolan
- Subjects
Cell Extracts ,0301 basic medicine ,Chemistry ,Extramural ,010401 analytical chemistry ,Datasets as Topic ,Computational biology ,Tandem mass spectrometry ,01 natural sciences ,Pichia ,Article ,0104 chemical sciences ,Analytical Chemistry ,03 medical and health sciences ,Identification (information) ,030104 developmental biology ,Tandem Mass Spectrometry ,Small peptide ,Metabolomics ,METLIN ,Reference standards ,Databases, Chemical ,Function (biology) - Abstract
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.
- Published
- 2018
27. Thermal Degradation of Small Molecules: A Global Metabolomic Investigation
- Author
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Mingliang Fang, Gary J. Patti, Julijana Ivanisevic, Linh Hoang, Michael E. Kurczy, Caroline H. Johnson, H. Paul Benton, Gary Siuzdak, and Winnie Uritboonthai
- Subjects
Inosine monophosphate ,Male ,Spectrometry, Mass, Electrospray Ionization ,Chromatography ,Metabolite ,Electrospray ionization ,Temperature ,Mass spectrometry ,Small molecule ,Article ,Gas Chromatography-Mass Spectrometry ,Analytical Chemistry ,chemistry.chemical_compound ,Blood ,chemistry ,medicine ,Humans ,Metabolomics ,Gas chromatography–mass spectrometry ,Inosine ,Derivatization ,medicine.drug ,Chromatography, Liquid - Abstract
Thermal processes are widely used in small molecule chemical analysis and metabolomics for derivatization, vaporization, chromatography, and ionization, especially in gas chromatography mass spectrometry (GC/MS). In this study the effect of heating was examined on a set of 64 small molecule standards and, separately, on human plasma metabolite extracts. The samples, either derivatized or underivatized, were heated at three different temperatures (60, 100, and 250 °C) at different exposure times (30 s, 60 s, and 300 s). All the samples were analyzed by liquid chromatography coupled to electrospray ionization mass spectrometry (LC/MS) and the data processed by XCMS Online ( xcmsonline.scripps.edu ). The results showed that heating at an elevated temperature of 100 °C had an appreciable effect on both the underivatized and derivatized molecules, and heating at 250 °C created substantial changes in the profile. For example, over 40% of the molecular peaks were altered in the plasma metabolite analysis after heating (250 °C, 300s) with a significant formation of degradation and transformation products. The analysis of 64 small molecule standards validated the temperature-induced changes observed on the plasma metabolites, where most of the small molecules degraded at elevated temperatures even after minimal exposure times (30 s). For example, tri- and diorganophosphates (e.g., adenosine triphosphate and adenosine diphosphate) were readily degraded into a mono-organophosphate (e.g., adenosine monophosphate) during heating. Nucleosides and nucleotides (e.g., inosine and inosine monophosphate) were also found to be transformed into purine derivatives (e.g., hypoxanthine). A newly formed transformation product, oleoyl ethyl amide, was identified in both the underivatized and derivatized forms of the plasma extracts and small molecule standard mixture, and was likely generated from oleic acid. Overall these analyses show that small molecules and metabolites undergo significant time-sensitive alterations when exposed to elevated temperatures, especially those conditions that mimic sample preparation and analysis in GC/MS experiments.
- Published
- 2015
28. Determining conserved metabolic biomarkers from a million database queries
- Author
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Gary J. Patti, Duane Rinehart, Julijana Ivanisevic, Michael E. Kurczy, Matthew Hicks, Caroline H. Johnson, H. Paul Benton, Mary E. Spilker, Mingliang Fang, Linh Hoang, Ralf Tautenhahn, Winnie Uritboonthai, Gary Siuzdak, Lisa Mellander, and Anthony Aldebot
- Subjects
Statistics and Probability ,Databases, Factual ,Metabolite ,Biology ,computer.software_genre ,Biochemistry ,Mass Spectrometry ,chemistry.chemical_compound ,Metabolomics ,Metabolome ,Humans ,Biomarker discovery ,Molecular Biology ,METLIN ,Metabolic biomarkers ,Database ,Search analytics ,Data science ,Computer Science Applications ,Discovery Note ,Computational Mathematics ,Computational Theory and Mathematics ,chemistry ,Potential biomarkers ,computer ,Biomarkers - Abstract
Motivation: Metabolite databases provide a unique window into metabolome research allowing the most commonly searched biomarkers to be catalogued. Omic scale metabolite profiling, or metabolomics, is finding increased utility in biomarker discovery largely driven by improvements in analytical technologies and the concurrent developments in bioinformatics. However, the successful translation of biomarkers into clinical or biologically relevant indicators is limited. Results: With the aim of improving the discovery of translatable metabolite biomarkers, we present search analytics for over one million METLIN metabolite database queries. The most common metabolites found in METLIN were cross-correlated against XCMS Online, the widely used cloud-based data processing and pathway analysis platform. Analysis of the METLIN and XCMS common metabolite data has two primary implications: these metabolites, might indicate a conserved metabolic response to stressors and, this data may be used to gauge the relative uniqueness of potential biomarkers. Availability and implementation. METLIN can be accessed by logging on to: https://metlin.scripps.edu Contact: siuzdak@scripps.edu Supplementary information: Supplementary data are available at Bioinformatics online.
- Published
- 2015
29. Autonomous Metabolomics for Rapid Metabolite Identification in Global Profiling
- Author
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Duane Rinehart, Elizabeth R Valentine, Baljit K. Ubhi, Lauren Franco, Gary J. Patti, Matthew W. Fields, Andrew Gieschen, Nathaniel G. Mahieu, Julijana Ivanisevic, Michael E. Kurczy, Caroline H. Johnson, H. Paul Benton, Harsha Gowda, Ralf Tautenhahn, and Gary Siuzdak
- Subjects
Electronic Data Processing ,Databases, Factual ,Metabolite ,Analytical chemistry ,Computational Biology ,Computational biology ,Tandem mass spectrometry ,Mass spectrometry ,Article ,Analytical Chemistry ,chemistry.chemical_compound ,Workflow ,Metabolomics ,Data acquisition ,chemistry ,Tandem Mass Spectrometry ,Profiling (information science) ,Desulfovibrio vulgaris ,METLIN ,Software ,Chromatography, Liquid - Abstract
An autonomous metabolomic workflow combining mass spectrometry analysis with tandem mass spectrometry data acquisition was designed to allow for simultaneous data processing and metabolite characterization. Although previously tandem mass spectrometry data have been generated on the fly, the experiments described herein combine this technology with the bioinformatic resources of XCMS and METLIN. As a result of this unique integration, we can analyze large profiling datasets and simultaneously obtain structural identifications. Validation of the workflow on bacterial samples allowed the profiling on the order of a thousand metabolite features with simultaneous tandem mass spectra data acquisition. The tandem mass spectrometry data acquisition enabled automatic search and matching against the METLIN tandem mass spectrometry database, shortening the current workflow from days to hours. Overall, the autonomous approach to untargeted metabolomics provides an efficient means of metabolomic profiling, and will ultimately allow the more rapid integration of comparative analyses, metabolite identification, and data analysis at a systems biology level.
- Published
- 2014
30. An interactive cluster heat map to visualize and explore multidimensional metabolomic data
- Author
-
Adrian A. Epstein, Julijana Ivanisevic, Michael D. Boska, H. Paul Benton, Howard E. Gendelman, Gary Siuzdak, Duane Rinehart, and Michael E. Kurczy
- Subjects
Computer science ,business.industry ,Endocrinology, Diabetes and Metabolism ,Clinical Biochemistry ,computer.software_genre ,Biochemistry ,Article ,Visualization ,Data set ,Metadata ,Metabolomics ,Software ,Clickable ,Data mining ,Zoom ,business ,METLIN ,computer - Abstract
Heat maps are a commonly used visualization tool for metabolomic data where the relative abundance of ions detected in each sample is represented with color intensity. A limitation of applying heat maps to global metabolomic data, however, is the large number of ions that have to be displayed and the lack of information provided about important metabolomic parameters such as m/z and retention time. Here we address these challenges by introducing the interactive cluster heat map in the data-processing software XCMS Online. XCMS Online (xcmsonline.scripps.edu) is a cloud-based informatic platform designed to process, statistically evaluate, and visualize mass-spectrometry based metabolomic data. An interactive heat map is provided for all data processed by XCMS Online. The heat map is clickable, allowing users to zoom and explore specific metabolite metadata (EICs, Box-and-whisker plots, mass spectra) that are linked to the METLIN metabolite database. The utility of the XCMS interactive heat map is demonstrated on metabolomic data set generated from different anatomical regions of the mouse brain.
- Published
- 2014
31. Exposome-Scale Investigations Guided by Global Metabolomics, Pathway Analysis, and Cognitive Computing
- Author
-
Brian Hilmers, J. Rafael Montenegro-Burke, Duane Rinehart, Mingliang Fang, Scott Spangler, Antony J. Williams, Gary Siuzdak, Susan D. Richardson, Winnie Uritboonthai, Erica M. Forsberg, Aries E. Aisporna, Caroline H. Johnson, H. Paul Benton, Ana Granados, Linh Hoang, Tao Huan, Benedikt Warth, Richard L. Martin, and Xavier Domingo-Almenara
- Subjects
0301 basic medicine ,Male ,Exposome ,Chemistry ,Scale (chemistry) ,010401 analytical chemistry ,Cognitive computing ,Computational biology ,Genomics ,Pathway analysis ,01 natural sciences ,0104 chemical sciences ,Analytical Chemistry ,03 medical and health sciences ,030104 developmental biology ,Metabolomics ,Artificial Intelligence ,Environmental chemistry ,Databases, Genetic ,Humans ,METLIN ,Exposure assessment - Abstract
Concurrent exposure to a wide variety of xenobiotics and their combined toxic effects can play a pivotal role in health and disease, yet are largely unexplored. Investigating the totality of these exposures, i.e., the "exposome", and their specific biological effects constitutes a new paradigm for environmental health but still lacks high-throughput, user-friendly technology. We demonstrate the utility of mass spectrometry-based global exposure metabolomics combined with tailored database queries and cognitive computing for comprehensive exposure assessment and the straightforward elucidation of biological effects. The METLIN Exposome database has been redesigned to help identify environmental toxicants, food contaminants and supplements, drugs, and antibiotics as well as their biotransformation products, through its expansion with over 700 000 chemical structures to now include more than 950 000 unique small molecules. More importantly, we demonstrate how the XCMS/METLIN platform now allows for the readout of the biological effect of a toxicant through metabolomic-derived pathway analysis, and further, artificial intelligence provides a means of assessing the role of a potential toxicant. The presented workflow addresses many of the methodological challenges current exposomics research is facing and will serve to gain a deeper understanding of the impact of environmental exposures and combinatory toxic effects on human health.
- Published
- 2017
32. XCMS-MRM and METLIN-MRM: a cloud library and public resource for targeted analysis of small molecules
- Author
-
Jonathan Sidibé, H. Paul Benton, Julijana Ivanisevic, Aurélien Thomas, Jeremy K. Nicholson, Aries E. Aisporna, Matthew R. Lewis, Carlos Guijas, Duane Rinehart, J. Rafael Montenegro-Burke, Tony Teav, Linh Hoang, María Gómez-Romero, Luke Whiley, Anders Nordström, Xavier Domingo-Almenara, and Gary Siuzdak
- Subjects
0301 basic medicine ,Computer science ,business.industry ,Extramural ,Computational Biology ,Cloud computing ,Cell Biology ,Cloud Computing ,Biochemistry ,Data science ,Article ,Small Molecule Libraries ,03 medical and health sciences ,030104 developmental biology ,Tandem Mass Spectrometry ,Metabolomics ,business ,Molecular Biology ,METLIN ,Biotechnology ,Public resource ,Chromatography, Liquid - Abstract
Small molecule quantitative tandem mass spectrometry analysis(1) is now widely used in life sciences and medicine. Quantification is usually accomplished by prior fragmentation of standard materials and the use of commercial software to quantitate the resulting peaks(2,3). Despite its widespread application, there is a paucity of public libraries to expedite assay development. Here, we introduce a new library, METLIN-MRM, comprised of more than 15,500 optimized transitions for multiple reaction monitoring of a wide variety of low molecular weight compounds. METLIN-MRM includes (i) transitions optimized following the established protocol with standard materials and (ii) transitions computationally optimized for selectivity. This computational optimization was achieved by the analysis of a large collection of tandem mass spectrometry spectra, where an algorithm selected the most unique transitions for a given compound in comparison with other compounds with a mass within the error of the mass spectrometer. METLIN-MRM streamlines quantitative analyses with minimal resources and development time and also serves as a public repository, allowing the community to upload, share and cite experimental transitions through accession numbers. Additionally, this library has been integrated with XCMS-MRM, a cloud-based data analysis platform that allows for data analysis and sharing across different platforms and laboratories. This platform is publicly accessible at http://metlin.scripps.edu/ and http://xcmsonline-mrm.scripps.edu
- Published
- 2017
33. Exposing the Exposome with Global Metabolomics and Cognitive Computing
- Author
-
Brian Hilmers, Winnie Uritboonthai, Xavier Domingo-Almenara, Richard L. Martin, Linh Hoang, Tao Huan, Gary Siuzdak, J. Rafael Montenegro-Burke, Caroline H. Johnson, Aries E. Aisporna, Mingliang Fang, Benedikt Warth, Antony J. Williams, H. Paul Benton, Susan D. Richardson, Erica M. Forsberg, Ana Granados, Scott Spangler, and Duane Rinehart
- Subjects
0303 health sciences ,Exposome ,Cognitive computing ,010501 environmental sciences ,Biology ,Biological effect ,Bioinformatics ,01 natural sciences ,Data science ,03 medical and health sciences ,chemistry.chemical_compound ,Metabolomics ,Workflow ,chemistry ,METLIN ,030304 developmental biology ,0105 earth and related environmental sciences ,Toxicant ,Exposure assessment - Abstract
Concurrent exposure to a wide variety of xenobiotics and their combined toxic effects can play a pivotal role in health and disease, yet are largely unexplored. Investigating the totality of these exposures, i.e. theexposome, and their specific biological effects constitutes a new paradigm for environmental health but still lacks high-throughput, user-friendly technology. We demonstrate the utility of mass spectrometry-based global exposure metabolomics combined with tailored database queries and cognitive computing for comprehensive exposure assessment and the straightforward elucidation of biological effects. The METLIN Exposome database has been redesigned to help identify environmental toxicants, food contaminants and supplements, drugs, and antibiotics as well as their biotransformation products, through its expansion with over 700,000 chemical structures to now include more than 950,000 unique small molecules. More importantly, we demonstrate how the XCMS/METLIN platform now allows for the readout of the biological effect of a toxicant through metabolomic-derived pathway analysis and further, cognitive computing provides a means of assessing the role of a potential toxicant. The presented workflow addresses many of the outstanding methodological challenges current exposome research is facing and will serve to gain a deeper understanding of the impact of environmental exposures and combinatory toxic effects on human health.
- Published
- 2017
34. Systems Biology Guided by XCMS Online Metabolomics
- Author
-
Trey Ideker, Duane Rinehart, Paul D. Robbins, Aries E. Aisporna, Farris L. Poole, Royston Goodacre, Michael W. W. Adams, Mingliang Fang, Nicholas J. W. Rattray, Gary Siuzdak, Julijana Ivanisevic, Tao Huan, Matthew W. Fields, Brian Hilmers, Caroline H. Johnson, H. Paul Benton, Luke L. Lairson, Judy D. Wall, Erica M. Forsberg, Michael P. Thorgersen, Gregory P. Krantz, Laura J. Niedernhofer, and Erica L.-W. Majumder
- Subjects
0301 basic medicine ,Extramural ,Systems biology ,Systems Biology ,Cell Biology ,Computational biology ,Biology ,ResearchInstitutes_Networks_Beacons/manchester_institute_of_biotechnology ,Biochemistry ,Mass Spectrometry ,Article ,RS ,03 medical and health sciences ,Multiple data ,030104 developmental biology ,0302 clinical medicine ,Metabolomics ,Manchester Institute of Biotechnology ,Molecular Biology ,030217 neurology & neurosurgery ,Software ,Biotechnology - Abstract
Systems biology is the study of complex living organisms, and as such, analysis on a systems-wide scale involves the collection of information-dense data sets that are representative of an entire phenotype. To uncover dynamic biological mechanisms, bioinformatics tools have become essential to facilitating data interpretation in large-scale analyses. Global metabolomics is one such method for performing systems biology, as metabolites represent the downstream functional products of ongoing biological processes. We have developed XCMS Online, a platform that enables online metabolomics data processing and interpretation. A systems biology workflow recently implemented within XCMS Online enables rapid metabolic pathway mapping using raw metabolomics data for investigating dysregulated metabolic processes. In addition, this platform supports integration of multi-omic (such as genomic and proteomic) data to garner further systems-wide mechanistic insight. Here, we provide an in-depth procedure showing how to effectively navigate and use the systems biology workflow within XCMS Online without a priori knowledge of the platform, including uploading liquid chromatography (LCLC)–mass spectrometry (MS) data from metabolite-extracted biological samples, defining the job parameters to identify features, correcting for retention time deviations, conducting statistical analysis of features between sample classes and performing predictive metabolic pathway analysis. Additional multi-omics data can be uploaded and overlaid with previously identified pathways to enhance systems-wide analysis of the observed dysregulations. We also describe unique visualization tools to assist in elucidation of statistically significant dysregulated metabolic pathways. Parameter input takes 5–10 min, depending on user experience; data processing typically takes 1–3 h, and data analysis takes ~30 min.
- Published
- 2017
35. Metabolomic data streaming for biology-dependent data acquisition
- Author
-
Duane Rinehart, Gary Siuzdak, Gary J. Patti, Caroline H. Johnson, Jessica Lloyd, H. Paul Benton, Julijana Ivanisevic, Thomas Nguyen, Adam M. Deutschbauer, and Adam P. Arkin
- Subjects
Biomedical Engineering ,MEDLINE ,Information Storage and Retrieval ,Bioengineering ,Biology ,Applied Microbiology and Biotechnology ,Article ,Data acquisition ,Metabolomics ,Text mining ,Metabolome ,Data Mining ,Databases, Protein ,Internet ,Information retrieval ,Extramural ,business.industry ,Data Compression ,Database Management Systems ,Molecular Medicine ,The Internet ,business ,Algorithms ,Biotechnology ,Data compression - Published
- 2014
36. Training in metabolomics research. II. Processing and statistical analysis of metabolomics data, metabolite identification, pathway analysis, applications of metabolomics and its future
- Author
-
Stephen, Barnes, H Paul, Benton, Krista, Casazza, Sara J, Cooper, Xiangqin, Cui, Xiuxia, Du, Jeffrey, Engler, Janusz H, Kabarowski, Shuzhao, Li, Wimal, Pathmasiri, Jeevan K, Prasain, Matthew B, Renfrow, and Hemant K, Tiwari
- Subjects
Isotopes ,Animals ,Humans ,Metabolomics ,Gas Chromatography-Mass Spectrometry ,Mass Spectrometry ,Article ,Chromatography, Liquid - Abstract
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.
- Published
- 2016
37. Global Isotope Metabolomics Reveals Adaptive Strategies for Nitrogen Assimilation
- Author
-
Dwayne A. Elias, Erica M. Forsberg, H. Paul Benton, Minerva Tran, Michael W. W. Adams, Michael E. Kurczy, Judy D. Wall, Michael P. Thorgersen, Gary Siuzdak, Julijana Ivanisevic, and Farris L. Poole
- Subjects
0301 basic medicine ,Denitrification ,Nitrogen assimilation ,Biology ,Biochemistry ,Article ,03 medical and health sciences ,Denitrifying bacteria ,chemistry.chemical_compound ,Nitrate ,Ammonia ,Nitrogen Fixation ,Pseudomonas ,Botany ,Metabolomics ,Amino Acids ,Nitrogen cycle ,Nitrates ,Nitrogen Radioisotopes ,Nucleotides ,Computational Biology ,Assimilation (biology) ,General Medicine ,biology.organism_classification ,Pseudomonas stutzeri ,030104 developmental biology ,Pyrimidines ,chemistry ,Purines ,Nitrogen fixation ,Molecular Medicine - Abstract
Nitrogen cycling is a microbial metabolic process essential for global ecological/agricultural balance. To investigate the link between the well-established ammonium and the alternative nitrate assimilation metabolic pathways, global isotope metabolomics was employed to examine three nitrate reducing bacteria using (15)NO3 as a nitrogen source. In contrast to a control (Pseudomonas stutzeri RCH2), the results show that two of the isolates from Oak Ridge, Tennessee (Pseudomonas N2A2 and N2E2) utilize nitrate and ammonia for assimilation concurrently with differential labeling observed across multiple classes of metabolites including amino acids and nucleotides. The data reveal that the N2A2 and N2E2 strains conserve nitrogen-containing metabolites, indicating that the nitrate assimilation pathway is a conservation mechanism for the assimilation of nitrogen. Co-utilization of nitrate and ammonia is likely an adaption to manage higher levels of nitrite since the denitrification pathways utilized by the N2A2 and N2E2 strains from the Oak Ridge site are predisposed to the accumulation of the toxic nitrite. The use of global isotope metabolomics allowed for this adaptive strategy to be investigated, which would otherwise not have been possible to decipher.
- Published
- 2016
38. Training in metabolomics research. I. Designing the experiment, collecting and extracting samples and generating metabolomics data
- Author
-
Stephen, Barnes, H Paul, Benton, Krista, Casazza, Sara J, Cooper, Xiangqin, Cui, Xiuxia, Du, Jeffrey, Engler, Janusz H, Kabarowski, Shuzhao, Li, Wimal, Pathmasiri, Jeevan K, Prasain, Matthew B, Renfrow, and Hemant K, Tiwari
- Subjects
Magnetic Resonance Spectroscopy ,Research Design ,Metabolome ,Animals ,Electrophoresis, Capillary ,Humans ,Metabolomics ,Gas Chromatography-Mass Spectrometry ,Mass Spectrometry ,Article ,Chromatography, Liquid - Abstract
The study of metabolism has had a long history. 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. The National Institutes of Health Common Fund Metabolomics Program was established in 2012 to stimulate interest in the approaches and technologies of metabolomics. To deliver one of the program's goals, the University of Alabama at Birmingham has hosted an annual 4-day short course in metabolomics for faculty, postdoctoral fellows and graduate students from national and international institutions. This paper is the first part of a summary of the training materials presented in the course to be used as a resource for all those embarking on metabolomics research. The complete set of training materials including slide sets and videos can be viewed at http://www.uab.edu/proteomics/metabolomics/workshop/workshop_june_2015.php. Copyright © 2016 John WileySons, Ltd.
- Published
- 2015
39. Metabolic oxidation regulates embryonic stem cell differentiation
- Author
-
Gary J. Patti, Caroline Desponts, Sunia A. Trauger, Gary Siuzdak, Sheng Ding, Antonio Sánchez-Ruiz, Julie Clark, Diana M. Wong, Oscar Yanes, and H. Paul Benton
- Subjects
Proteome ,Cellular differentiation ,Carboxylic Acids ,Oxidative phosphorylation ,Biology ,Article ,Metabolomics ,Carnitine ,Metabolome ,Humans ,Amino Acids ,Molecular Biology ,Embryonic Stem Cells ,Stem Cells ,Cell Differentiation ,Cell Biology ,Ascorbic acid ,Embryonic stem cell ,Glutathione ,Cell biology ,Phenotype ,Biochemistry ,Gene Expression Regulation ,Eicosanoids ,Stem cell ,Signal transduction ,Oxidation-Reduction ,Software - Abstract
Metabolites offer an important unexplored complement to understanding the pluripotency of stem cells. Using mass spectrometry-based metabolomics, we show that embryonic stem cells are characterized by abundant metabolites with highly unsaturated structures whose levels decrease upon differentiation. By monitoring the reduced and oxidized glutathione ratio as well as ascorbic acid levels, we demonstrate that the stem cell redox status is regulated during differentiation. Based on the oxidative biochemistry of the unsaturated metabolites, we experimentally manipulated specific pathways in embryonic stem cells while monitoring the effects on differentiation. Inhibition of the eicosanoid signaling pathway promoted pluripotency and maintained levels of unsaturated fatty acids. In contrast, downstream oxidized metabolites (e.g., neuroprotectin D1) and substrates of pro-oxidative reactions (e.g., acyl-carnitines), promoted neuronal and cardiac differentiation. We postulate that the highly unsaturated metabolome sustained by stem cells makes them particularly attuned to differentiate in response to in vivo oxidative processes such as inflammation.
- Published
- 2010
40. Metabolomics Reveals that Dietary Xenoestrogens Alter Cellular Metabolism Induced by Palbociclib/Letrozole Combination Cancer Therapy
- Author
-
Tao Huan, Laura H. Goetz, Caroline H. Johnson, Mingliang Fang, H. Paul Benton, Erica M. Forsberg, Gary Siuzdak, Benedikt Warth, Philipp Raffeiner, Ana Granados, and School of Civil and Environmental Engineering
- Subjects
0301 basic medicine ,Pyridines ,Metabolite ,Clinical Biochemistry ,Estrogen receptor ,Genistein ,Breast Neoplasms ,Phytoestrogens ,Biology ,Pharmacology ,Palbociclib ,Biochemistry ,Piperazines ,Article ,03 medical and health sciences ,chemistry.chemical_compound ,Combinatory Chemotherapy ,0302 clinical medicine ,Metabolomics ,Combination cancer therapy ,Drug Discovery ,Ibrance ,medicine ,Metabolome ,Humans ,Molecular Biology ,030304 developmental biology ,0303 health sciences ,Principal Component Analysis ,Letrozole ,Biological sciences [Science] ,Carbon ,Diet ,3. Good health ,030104 developmental biology ,Receptors, Estrogen ,chemistry ,030220 oncology & carcinogenesis ,MCF-7 Cells ,Zearalenone ,Molecular Medicine ,Female ,medicine.drug - Abstract
HighlightsSynergism of combined palbociclib/letrozole chemotherapy was examined by global metabolomicsCombination therapy led to more pronounced effects on the MCF-7 metabolome than single agentsDietary phyto- and mycoestrogens significantly affected the metabolic and anti-oncogenic response of the drugsImplications of these bio-active chemicals on therapeutic success in breast cancer patients appear plausibleIn BriefWarth et al. used innovative global metabolomics and pathway prediction technology to describe the metabolic effects of the combined palbociclib/letrozole breast cancer therapy. Moreover, the role of dietary xenoestrogens on this treatment was examined by metabolite data, proliferation experiments, and functional assays.SummaryRecently, the palbociclib/letrozole combination therapy was granted accelerated FDA approval for the treatment of estrogen receptor (ER) positive breast cancer. Since the underlying metabolic effects of these drugs are yet unknown, we investigated their synergism at the metabolome level in MCF-7 cells. As xenoestrogens interact with the ER, we additionally aimed at deciphering the impact of the phytoestrogen genistein, and the estrogenic mycotoxin zearalenone on this treatment. A global metabolomics approach was applied to unravel metabolite and pathway modifications. The results clearly showed that the combined effects of palbociclib and letrozole on cellular metabolism were far more pronounced than that of each agent alone and potently influenced by xenoestrogens. This behavior was confirmed in proliferation experiments and functional assays. Specifically, amino acids and central carbon metabolites were attenuated while higher abundances were observed for fatty acids and most nucleic acid related metabolites. Interestingly, exposure to model xenoestrogens appeared to partially counteract these effects.
- Published
- 2018
41. Bioinformatics: the next frontier of metabolomics
- Author
-
Caroline H. Johnson, H. Paul Benton, Julijana Ivanisevic, and Gary Siuzdak
- Subjects
medicine.drug_class ,Chemistry ,Metabolite ,Computational Biology ,Disease ,Review ,Bioinformatics ,Antimetabolite ,Fold change ,Analytical Chemistry ,Biological pathway ,chemistry.chemical_compound ,Metabolomics ,medicine ,Biomarker (medicine) ,Animals ,Humans ,Biomarker discovery - Abstract
Bioinformatic tools are required to carry out essential functions such as statistical analyses and database functionalities. Now, they are also needed for one of the most difficult tasks, helping researchers decide which metabolites are the most biologically meaningful. This can be achieved through aiding the identification process, reducing feature redundancy, putting forward better candidates for tandem mass spectrometry (MS/MS), speeding up or automating the workflow, deconvolving the feature list through meta-analysis or multigroup analysis, or using stable isotopes and pathway mapping. This review thus focuses on the most recent and innovative bioinformatic advancements for identifying metabolites. A primary objective of metabolomics beyond biomarker discovery is to identify the most meaningful metabolites that correlate with disease pathogenesis or other perturbations of metabolism. Metabolites play important roles in biological pathways; their flux or differential regulation (dysregulation) can reveal novel insights into disease and environmental influences. Therefore, one of the most important goals of metabolomic analysis has been to assign metabolite identity so they can be used for further statistical and informed pathway analysis.1,2 Over the past few years, technologies for analyzing metabolites by untargeted or targeted metabolomics have undergone extensive improvements. Strides to establish the most efficient protocols for experimental design, sample extraction techniques, and data acquisition have paid off providing robust complex data sets.3−9 As more is being required of these data sets such as assigning identity and biological meaning to the features, bioinformatics is the area of metabolomics which is currently undergoing the most needed growth. It is often the case that metabolomic analysis results in a list of metabolites with low specificity for the disease or stimulus being studied (Figure (Figure1).1). Some of these metabolites seem to be dysregulated in a variety of diseases such as acylcarnitines10−13 and fatty acids.14−17 They may be more indicative of a perturbed systemic cause (appetite, physical activity, diurnal rhythm changes, etc..), sample contamination, or instrumental/bioinformatic noise, rather than a specific biomarker of disease. An example of this can be seen in the analysis of urinary biomarkers of ionizing radiation, where dicarboxylic acids were downregulated in the rat after radiation exposure. It was proven that this observation was actually caused by a decreased appetite after radiation exposure perturbing the β-oxidation pathway and not from radiation-induced cellular changes.18,19 Furthermore, dicarboxylic acids can leach out from plastics during the extraction process, further adding to the ambiguity of their role in ionizing radiation.20 Figure 1 Biomarkers that have high vs low disease specificity. As well as identifying the correct source of the biomarkers, it is also important to identify their physiological role and how to utilize them as therapeutic targets. This first has to start with the identification of the metabolite and is determined by filtering thresholds set by the user which is intrinsically biased. These thresholds include those for fold change and p-value, which are highly dependent on the experiment; in vitro experiments would exhibit lower variation between biological replicates than in vivo. The ease of identifying the metabolite is also determined by its concentration in the sample and previous annotation in metabolite databases. Filtering thresholds for metabolite intensity that are set too high may omit important biologically meaningful metabolites rather than noise. Furthermore, a metabolite that is novel or not curated in a database may not be taken into consideration based on the chemical knowledge of the researcher and what they deem as meaningful. In order to transform the complex list of identified metabolites into markers of disease, or assign what role they play, bioinformatic tools can aid in identifying the potential pathways that the metabolite may belong to. It is then that the researcher can use this knowledge surrounding the biology of the metabolite to probe the mechanism of the disease. Untargeted metabolomics has already been used in such a manner to find the source of neuropathic pain.21N,N-Dimethylsphingosine was dysregulated in a rat model of neuropathic pain, furthermore when dosed to control rats it induced mechanical hypersensitivity. This metabolite implicated the sphingomyelin-ceramide pathway as a potential therapeutic target. Antimetabolite inhibitors of enzymes in this pathway were tested and were able to ameliorate neuropathic pain (unpublished data). This study holds promise for other metabolomic studies to maximize the potential information contained within the data for finding therapeutics of disease rather than only providing lists of dysregulated metabolites.
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- 2014
42. Interactive XCMS Online: simplifying advanced metabolomic data processing and subsequent statistical analyses
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Michael E. Kurczy, Jayashree Ray, Adam P. Arkin, Junhua Wang, Bernardo Arevalo, Peter D. Westenskow, Duane Rinehart, Gary J. Patti, Adam M. Deutschbauer, Julijana Ivanisevic, Caroline H. Johnson, Thomas Nguyen, H. Paul Benton, Jennifer V. Kuehl, Gary Siuzdak, and Harsha Gowda
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Male ,Multivariate statistics ,Data Interpretation ,Databases, Factual ,Lymphoma ,computer.software_genre ,01 natural sciences ,Plot (graphics) ,Article ,Mass Spectrometry ,Analytical Chemistry ,03 medical and health sciences ,User-Computer Interface ,Databases ,Meta-Analysis as Topic ,Feature (machine learning) ,Humans ,Metabolomics ,Interactive visualization ,Factual ,030304 developmental biology ,Statistical hypothesis testing ,0303 health sciences ,Box plot ,Principal Component Analysis ,Internet ,Chemistry ,010401 analytical chemistry ,Univariate ,Statistical ,0104 chemical sciences ,Blood ,Data Interpretation, Statistical ,Principal component analysis ,Multivariate Analysis ,Desulfovibrio ,Female ,Data mining ,Other Chemical Sciences ,computer ,Software - Abstract
XCMS Online (xcmsonline.scripps.edu) is a cloud-based informatic platform designed to process and visualize mass-spectrometry-based, untargeted metabolomic data. Initially, the platform was developed for two-group comparisons to match the independent, "control" versus "disease" experimental design. Here, we introduce an enhanced XCMS Online interface that enables users to perform dependent (paired) two-group comparisons, meta-analysis, and multigroup comparisons, with comprehensive statistical output and interactive visualization tools. Newly incorporated statistical tests cover a wide array of univariate analyses. Multigroup comparison allows for the identification of differentially expressed metabolite features across multiple classes of data while higher order meta-analysis facilitates the identification of shared metabolic patterns across multiple two-group comparisons. Given the complexity of these data sets, we have developed an interactive platform where users can monitor the statistical output of univariate (cloud plots) and multivariate (PCA plots) data analysis in real time by adjusting the threshold and range of various parameters. On the interactive cloud plot, metabolite features can be filtered out by their significance level (p-value), fold change, mass-to-charge ratio, retention time, and intensity. The variation pattern of each feature can be visualized on both extracted-ion chromatograms and box plots. The interactive principal component analysis includes scores, loadings, and scree plots that can be adjusted depending on scaling criteria. The utility of XCMS functionalities is demonstrated through the metabolomic analysis of bacterial stress response and the comparison of lymphoblastic leukemia cell lines.
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- 2014
43. Intra- and interlaboratory reproducibility of ultra performance liquid chromatography-time-of-flight mass spectrometry for urinary metabolic profiling
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H. Paul Benton, Bernhard Walther, Francoise Goldfain-Blanc, Jeremy K. Nicholson, Michael D. Reily, Hector C. Keun, Alexander Amberg, Timothy M. D. Ebbels, Elaine Holmes, John C. Lindon, Robert S. Plumb, and Elizabeth J. Want
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Reproducibility ,Spectrometry, Mass, Electrospray Ionization ,Chromatography ,Chemistry ,Analytical chemistry ,Reproducibility of Results ,Urine ,Repeatability ,Urinalysis ,Mass spectrometry ,High-performance liquid chromatography ,Analytical Chemistry ,Dilution ,Ionization ,Isotope Labeling ,Metabolome ,Humans ,Time-of-flight mass spectrometry ,Dimerization ,Chromatography, High Pressure Liquid - Abstract
Liquid chromatography coupled to mass spectrometry (LC-MS) is a major platform in metabolic profiling but has not yet been comprehensively assessed as to its repeatability and reproducibility across multiple spectrometers and laboratories. Here we report results of a large interlaboratory reproducibility study of ultra performance (UP) LC-MS of human urine. A total of 14 stable isotope labeled standard compounds were spiked into a pooled human urine sample, which was subject to a 2- to 16-fold dilution series and run by UPLC coupled to time-of-flight MS at three different laboratories all using the same platform. In each lab, identical samples were run in two phases, separated by at least 1 week, to assess between-day reproducibility. Overall, platform reproducibility was good with median mass accuracies below 12 ppm, median retention time drifts of less than 0.73 s and coefficients of variation of intensity of less than 18% across laboratories and ionization modes. We found that the intensity response was highly linear within each run, with a median R(2) of 0.95 and 0.93 in positive and negative ionization modes. Between-day reproducibility was also high with a mean R(2) of 0.93 for a linear relationship between the intensities of ions recorded in the two phases across the laboratories and modes. Most importantly, between-lab reproducibility was excellent with median R(2) values of 0.96 and 0.98 for positive and negative ionization modes, respectively, across all pairs of laboratories. Interestingly, the three laboratories observed different amounts of adduct formation, but this did not appear to be related to reproducibility observed in each laboratory. These studies show that UPLC-MS is fit for the purpose of targeted urinary metabolite analysis but that care must be taken to optimize laboratory systems for quantitative detection due to variable adduct formation over many compound classes.
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- 2012
44. Metabolic drift in the aging brain
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Howard S. Fox, Gary Siuzdak, Howard E. Gendelman, Mingliang Fang, H. Paul Benton, Adrian A. Epstein, Michael D. Boska, Santhi Gorantla, Michael Petrascheck, Minerva Tran, Linh Hoang, Kelly L. Stauch, Julijana Ivanisevic, Michael E. Kurczy, and School of Civil and Environmental Engineering
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0301 basic medicine ,Purine ,Proteomics ,medicine.medical_specialty ,Aging ,Metabolite ,Healthy brain aging ,Oxidative phosphorylation ,Biology ,Aging/metabolism ,Animals ,Brain/metabolism ,Energy Metabolism/physiology ,Metabolomics ,Mice ,Oxidative Phosphorylation ,energy metabolism ,healthy brain aging ,metabolic drift ,metabolomics ,proteomics ,03 medical and health sciences ,chemistry.chemical_compound ,0302 clinical medicine ,Internal medicine ,medicine ,Aging brain ,Metabolic drift ,Brain ,Cell Biology ,030104 developmental biology ,Endocrinology ,chemistry ,NAD+ kinase ,030217 neurology & neurosurgery ,Homeostasis ,Research Paper - Abstract
Brain function is highly dependent upon controlled energy metabolism whose loss heralds cognitive impairments. This is particularly notable in the aged individuals and in age-related neurodegenerative diseases. However, how metabolic homeostasis is disrupted in the aging brain is still poorly understood. Here we performed global, metabolomic and proteomic analyses across different anatomical regions of mouse brain at different stages of its adult lifespan. Interestingly, while severe proteomic imbalance was absent, global-untargeted metabolomics revealed an energymetabolic drift or significant imbalance in core metabolite levels in aged mouse brains. Metabolic imbalance was characterized by compromised cellular energy status (NAD decline, increased AMP/ATP, purine/pyrimidine accumulation) and significantly altered oxidative phosphorylation and nucleotide biosynthesis and degradation. The central energy metabolic drift suggests a failure of the cellular machinery to restore metabostasis (metabolite homeostasis) in the aged brain and therefore an inability to respond properly to external stimuli, likely driving the alterations in signaling activity and thus in neuronal function and communication. Published version
45. Global metabolomics reveals metabolic dysregulation in ischemic retinopathy
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Liliana P Paris, Toshihide Kurihara, Edith Aguilar, Yoshihiko Usui, Gary J. Patti, Gary Siuzdak, Kevin Cho, Shunichiro Ueda, Jennifer K Trombley, Peter D. Westenskow, Julijana Ivanisevic, Daniel Feitelberg, Caroline H. Johnson, Lihn T. Hoang, Kinya Tsubota, H. Paul Benton, Yoshihiro Wakabayashi, and Martin Friedlander
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0301 basic medicine ,Arginine ,endocrine system diseases ,Endocrinology, Diabetes and Metabolism ,Clinical Biochemistry ,Arginine metabolism ,Pharmacology ,Bioinformatics ,Biochemistry ,03 medical and health sciences ,Metabolomics ,Downregulation and upregulation ,medicine ,Proliferative diabetic retinopathy ,Pathological ,Untargeted metabolomics ,business.industry ,Pathway enrichment analysis ,Diabetic retinopathy ,medicine.disease ,Molecular medicine ,eye diseases ,3. Good health ,030104 developmental biology ,Targeted mass spectrometry ,Urea cycle ,Original Article ,sense organs ,business - Abstract
Proliferative diabetic retinopathy (PDR) is the most severe form of diabetic retinopathy and, along with diabetic macular edema, is responsible for the majority of blindness in adults below the age of 65. Therapeutic strategies for PDR are ineffective at curtailing disease progression in all cases; however a deeper understanding of the ocular metabolic landscape in PDR through metabolomic analysis may offer new therapeutic targets. Here, global and targeted mass spectrometry-based metabolomics were used to investigate metabolism. Initial analyses on vitreous humor from patients with PDR (n = 9) and non-diabetic controls (n = 11) revealed an increase of arginine and acylcarnitine metabolism in PDR. The oxygen-induced-retinopathy (OIR) mouse model, which exhibits comparable pathological manifestations to human PDR, revealed similar increases of arginine and other metabolites in the urea cycle, as well as downregulation of purine metabolism. We validated our findings by targeted multiple reaction monitoring and through the analysis of a second set of patient samples [PDR (n = 11) and non-diabetic controls (n = 20)]. These results confirmed a predominant and consistent increase in proline in both the OIR mouse model and vitreous samples from patients with PDR, suggesting that over activity in the arginine-to-proline pathway could be used as a therapeutic target in diabetic retinopathy. Electronic supplementary material The online version of this article (doi:10.1007/s11306-015-0877-5) contains supplementary material, which is available to authorized users.
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