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Dodia H, Sunder AV, Borkar Y, Wangikar PP. Precision fermentation with mass spectrometry-based spent media analysis. Biotechnol Bioeng 2023; 120:2809-2826. [PMID: 37272489 DOI: 10.1002/bit.28450] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 05/13/2023] [Accepted: 05/15/2023] [Indexed: 06/06/2023]
Abstract
Optimization and monitoring of bioprocesses requires the measurement of several process parameters and quality attributes. Mass spectrometry (MS)-based techniques such as those coupled to gas chromatography (GCMS) and liquid Chromatography (LCMS) enable the simultaneous measurement of hundreds of metabolites with high sensitivity. When applied to spent media, such metabolome analysis can help determine the sequence of substrate uptake and metabolite secretion, consequently facilitating better design of initial media and feeding strategy. Furthermore, the analysis of metabolite diversity and abundance from spent media will aid the determination of metabolic phases of the culture and the identification of metabolites as surrogate markers for product titer and quality. This review covers the recent advances in metabolomics analysis applied to the development and monitoring of bioprocesses. In this regard, we recommend a stepwise workflow and guidelines that a bioprocesses engineer can adopt to develop and optimize a fermentation process using spent media analysis. Finally, we show examples of how the use of MS can revolutionize the design and monitoring of bioprocesses.
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Affiliation(s)
- Hardik Dodia
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Powai, Mumbai, India
| | | | - Yogen Borkar
- Clarity Bio Systems India Pvt. Ltd., Pune, India
| | - Pramod P Wangikar
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Powai, Mumbai, India
- Clarity Bio Systems India Pvt. Ltd., Pune, India
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Mapar M, Rydzak T, Groves RA, Lewis IA. Biomarker enrichment medium: A defined medium for metabolomic analysis of microbial pathogens. Front Microbiol 2022; 13:957158. [PMID: 35935218 PMCID: PMC9354526 DOI: 10.3389/fmicb.2022.957158] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 06/27/2022] [Indexed: 11/13/2022] Open
Abstract
Microbes have diverse metabolic capabilities and differences in these phenotypes are critical for differentiating strains, species, and broader taxa of microorganisms. Recent advances in liquid chromatography-mass spectrometry (LC-MS) allow researchers to track the complex combinations of molecules that are taken up by each cell type and to quantify the rates that individual metabolites enter or exit the cells. This metabolomics-based approach allows complex metabolic phenotypes to be captured in a single assay, enables computational models of microbial metabolism to be constructed, and can serve as a diagnostic approach for clinical microbiology. Unfortunately, metabolic phenotypes are directly affected by the molecular composition of the culture medium and many traditional media are subject to molecular-level heterogeneity. Herein, we show that commercially sourced Mueller Hinton (MH) medium, a Clinical and Laboratory Standards Institute (CLSI) approved medium for clinical microbiology, has significant lot-to-lot and supplier-to-supplier variability in the concentrations of individual nutrients. We show that this variability does not affect microbial growth rates but does affect the metabolic phenotypes observed in vitro—including metabolic phenotypes that distinguish six common pathogens. To address this, we used a combination of isotope-labeling, substrate exclusion, and nutritional supplementation experiments using Roswell Park Memorial Institute (RPMI) medium to identify the specific nutrients used by the microbes to produce diagnostic biomarkers, and to formulate a Biomarker Enrichment Medium (BEM) as an alternative to complex undefined media for metabolomics research, clinical diagnostics, antibiotic susceptibility testing, and other applications where the analysis of stable microbial metabolic phenotypes is important.
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Jeffryes JG, Lerma-Ortiz C, Liu F, Golubev A, Niehaus TD, Elbadawi-Sidhu M, Fiehn O, Hanson AD, Tyo KE, Henry CS. Chemical-damage MINE: A database of curated and predicted spontaneous metabolic reactions. Metab Eng 2021; 69:302-312. [PMID: 34958914 DOI: 10.1016/j.ymben.2021.11.009] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Revised: 10/05/2021] [Accepted: 11/23/2021] [Indexed: 01/27/2023]
Abstract
Spontaneous reactions between metabolites are often neglected in favor of emphasizing enzyme-catalyzed chemistry because spontaneous reaction rates are assumed to be insignificant under physiological conditions. However, synthetic biology and engineering efforts can raise natural metabolites' levels or introduce unnatural ones, so that previously innocuous or nonexistent spontaneous reactions become an issue. Problems arise when spontaneous reaction rates exceed the capacity of a platform organism to dispose of toxic or chemically active reaction products. While various reliable sources list competing or toxic enzymatic pathways' side-reactions, no corresponding compilation of spontaneous side-reactions exists, nor is it possible to predict their occurrence. We addressed this deficiency by creating the Chemical Damage (CD)-MINE resource. First, we used literature data to construct a comprehensive database of metabolite reactions that occur spontaneously in physiological conditions. We then leveraged this data to construct 148 reaction rules describing the known spontaneous chemistry in a substrate-generic way. We applied these rules to all compounds in the ModelSEED database, predicting 180,891 spontaneous reactions. The resulting (CD)-MINE is available at https://minedatabase.mcs.anl.gov/cdmine/#/home and through developer tools. We also demonstrate how damage-prone intermediates and end products are widely distributed among metabolic pathways, and how predicting spontaneous chemical damage helps rationalize toxicity and carbon loss using examples from published pathways to commercial products. We explain how analyzing damage-prone areas in metabolism helps design effective engineering strategies. Finally, we use the CD-MINE toolset to predict the formation of the novel damage product N-carbamoyl proline, and present mass spectrometric evidence for its presence in Escherichia coli.
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Affiliation(s)
- James G Jeffryes
- Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL, 60439, USA
| | - Claudia Lerma-Ortiz
- Consortium for Advanced Science and Engineering, University of Chicago, Chicago, IL, 60637, USA; Department of Data Science and Learning, Argonne National Laboratory, Argonne, IL, 60439, USA
| | - Filipe Liu
- Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL, 60439, USA; Consortium for Advanced Science and Engineering, University of Chicago, Chicago, IL, 60637, USA
| | - Alexey Golubev
- Department of Carcinogenesis and Oncogerontology, N.N. Petrov National Medical Research Center of Oncology, Saint Petersburg, 197758, Russia
| | - Thomas D Niehaus
- Horticultural Sciences Department, University of Florida, Gainesville, FL, 32611, USA; Plant and Microbial Biology Department, University of Minnesota, Saint Paul, MN, 55108, USA
| | | | - Oliver Fiehn
- West Coast Metabolomics Center, University of California, Davis, CA, USA
| | - Andrew D Hanson
- Horticultural Sciences Department, University of Florida, Gainesville, FL, 32611, USA
| | - Keith Ej Tyo
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, 60208, USA
| | - Christopher S Henry
- Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL, 60439, USA; Consortium for Advanced Science and Engineering, University of Chicago, Chicago, IL, 60637, USA.
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Synergism between SLC6A14 blockade and gemcitabine in pancreactic cancer: a 1H-NMR-based metabolomic study in pancreatic cancer cells. Biochem J 2020; 477:1923-1937. [PMID: 32379301 DOI: 10.1042/bcj20200275] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 05/04/2020] [Accepted: 05/07/2020] [Indexed: 12/14/2022]
Abstract
Gemcitabine is the first-line chemotherapy for pancreatic cancer. To overcome the often-acquired gemcitabine resistance, other drugs are used in combination with gemcitabine. It is well-known that cancer cells reprogram cellular metabolism, coupled with the up-regulation of selective nutrient transporters to feed into the altered metabolic pathways. Our previous studies have demonstrated that the amino acid transporter SLC6A14 is markedly up-regulated in pancreatic cancer and that it is a viable therapeutic target. α-Methyltryptophan (α-MT) is a blocker of SLC6A14 and is effective against pancreatic cancer in vitro and in vivo. In the present study, we tested the hypothesis that α-MT could synergize with gemcitabine in the treatment of pancreatic cancer. We investigated the effects of combination of α-MT and gemcitabine on proliferation, migration, and apoptosis in a human pancreatic cancer cell line, and examined the underlying mechanisms using 1H-NMR-based metabolomic analysis. These studies examined the intracellular metabolite profile and the extracellular metabolite profile separately. Combination of α-MT with gemcitabine elicited marked changes in a wide variety of metabolic pathways, particularly amino acid metabolism with notable alterations in pathways involving tryptophan, branched-chain amino acids, ketone bodies, and membrane phospholipids. The metabolomic profiles of untreated control cells and cells treated with gemcitabine or α-MT were distinctly separable, and the combination regimen showed a certain extent of overlap with the individual α-MT and gemcitabine groups. This represents the first study detailing the metabolomic basis of the anticancer efficacy of gemcitabine, α-MT and their combination.
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Gopalakrishnan S, Dash S, Maranas C. K-FIT: An accelerated kinetic parameterization algorithm using steady-state fluxomic data. Metab Eng 2020; 61:197-205. [DOI: 10.1016/j.ymben.2020.03.001] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Revised: 01/31/2020] [Accepted: 03/02/2020] [Indexed: 12/12/2022]
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Lee JY, Styczynski MP. NS-kNN: a modified k-nearest neighbors approach for imputing metabolomics data. Metabolomics 2018; 14:153. [PMID: 30830437 PMCID: PMC6532628 DOI: 10.1007/s11306-018-1451-8] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/06/2018] [Accepted: 11/15/2018] [Indexed: 01/28/2023]
Abstract
INTRODUCTION A common problem in metabolomics data analysis is the existence of a substantial number of missing values, which can complicate, bias, or even prevent certain downstream analyses. One of the most widely-used solutions to this problem is imputation of missing values using a k-nearest neighbors (kNN) algorithm to estimate missing metabolite abundances. kNN implicitly assumes that missing values are uniformly distributed at random in the dataset, but this is typically not true in metabolomics, where many values are missing because they are below the limit of detection of the analytical instrumentation. OBJECTIVES Here, we explore the impact of nonuniformly distributed missing values (missing not at random, or MNAR) on imputation performance. We present a new model for generating synthetic missing data and a new algorithm, No-Skip kNN (NS-kNN), that accounts for MNAR values to provide more accurate imputations. METHODS We compare the imputation errors of the original kNN algorithm using two distance metrics, NS-kNN, and a recently developed algorithm KNN-TN, when applied to multiple experimental datasets with different types and levels of missing data. RESULTS Our results show that NS-kNN typically outperforms kNN when at least 20-30% of missing values in a dataset are MNAR. NS-kNN also has lower imputation errors than KNN-TN on realistic datasets when at least 50% of missing values are MNAR. CONCLUSION Accounting for the nonuniform distribution of missing values in metabolomics data can significantly improve the results of imputation algorithms. The NS-kNN method imputes missing metabolomics data more accurately than existing kNN-based approaches when used on realistic datasets.
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Affiliation(s)
- Justin Y Lee
- School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, 311 Ferst Drive, Atlanta, GA, 30332-0100, USA
| | - Mark P Styczynski
- School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, 311 Ferst Drive, Atlanta, GA, 30332-0100, USA.
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Miguez AM, McNerney MP, Styczynski MP. Metabolomics Analysis of the Toxic Effects of the Production of Lycopene and Its Precursors. Front Microbiol 2018; 9:760. [PMID: 29774011 PMCID: PMC5944366 DOI: 10.3389/fmicb.2018.00760] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2017] [Accepted: 04/04/2018] [Indexed: 01/01/2023] Open
Abstract
Using cells as microbial factories enables highly specific production of chemicals with many advantages over chemical syntheses. A number of exciting new applications of this approach are in the area of precision metabolic engineering, which focuses on improving the specificity of target production. In recent work, we have used precision metabolic engineering to design lycopene-producing Escherichia coli for use as a low-cost diagnostic biosensor. To increase precursor availability and thus the rate of lycopene production, we heterologously expressed the mevalonate pathway. We found that simultaneous induction of these pathways increases lycopene production, but induction of the mevalonate pathway before induction of the lycopene pathway decreases both lycopene production and growth rate. Here, we aim to characterize the metabolic changes the cells may be undergoing during expression of either or both of these heterologous pathways. After establishing an improved method for quenching E. coli for metabolomics analysis, we used two-dimensional gas chromatography coupled to mass spectrometry (GCxGC-MS) to characterize the metabolomic profile of our lycopene-producing strains in growth conditions characteristic of our biosensor application. We found that the metabolic impacts of producing low, non-toxic levels of lycopene are of much smaller magnitude than the typical metabolic changes inherent to batch growth. We then used metabolomics to study differences in metabolism caused by the time of mevalonate pathway induction and the presence of the lycopene biosynthesis genes. We found that overnight induction of the mevalonate pathway was toxic to cells, but that the cells could recover if the lycopene pathway was not also heterologously expressed. The two pathways appeared to have an antagonistic metabolic effect that was clearly reflected in the cells’ metabolic profiles. The metabolites homocysteine and homoserine exhibited particularly interesting behaviors and may be linked to the growth inhibition seen when the mevalonate pathway is induced overnight, suggesting potential future work that may be useful in engineering increased lycopene biosynthesis.
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Affiliation(s)
- April M Miguez
- School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA, United States
| | - Monica P McNerney
- School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA, United States
| | - Mark P Styczynski
- School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA, United States
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Time-Dependent Lactate Production and Amino Acid Utilization in Cultured Astrocytes Under High Glucose Exposure. Mol Neurobiol 2017; 55:1112-1122. [DOI: 10.1007/s12035-016-0360-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2016] [Accepted: 12/28/2016] [Indexed: 02/07/2023]
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Luo X, Zhao S, Huan T, Sun D, Friis RMN, Schultz MC, Li L. High-Performance Chemical Isotope Labeling Liquid Chromatography-Mass Spectrometry for Profiling the Metabolomic Reprogramming Elicited by Ammonium Limitation in Yeast. J Proteome Res 2016; 15:1602-12. [PMID: 26947805 DOI: 10.1021/acs.jproteome.6b00070] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Information about how yeast metabolism is rewired in response to internal and external cues can inform the development of metabolic engineering strategies for food, fuel, and chemical production in this organism. We report a new metabolomics workflow for the characterization of such metabolic rewiring. The workflow combines efficient cell lysis without using chemicals that may interfere with downstream sample analysis and differential chemical isotope labeling liquid chromatography mass spectrometry (CIL LC-MS) for in-depth yeast metabolome profiling. Using (12)C- and (13)C-dansylation (Dns) labeling to analyze the amine/phenol submetabolome, we detected and quantified a total of 5719 peak pairs or metabolites. Among them, 120 metabolites were positively identified using a library of 275 Dns-metabolite standards, and 2980 metabolites were putatively identified based on accurate mass matches to metabolome databases. We also applied (12)C- and (13)C-dimethylaminophenacyl (DmPA) labeling to profile the carboxylic acid submetabolome and detected over 2286 peak pairs, from which 33 metabolites were positively identified using a library of 188 DmPA-metabolite standards, and 1595 metabolites were putatively identified. Using this workflow for metabolomic profiling of cells challenged by ammonium limitation revealed unexpected links between ammonium assimilation and pantothenate accumulation that might be amenable to engineering for better acetyl-CoA production in yeast. We anticipate that efforts to improve other schemes of metabolic engineering will benefit from application of this workflow to multiple cell types.
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Affiliation(s)
- Xian Luo
- Department of Chemistry and ‡Department of Biochemistry, University of Alberta , Edmonton, Alberta, T6G 2R3 Canada
| | - Shuang Zhao
- Department of Chemistry and ‡Department of Biochemistry, University of Alberta , Edmonton, Alberta, T6G 2R3 Canada
| | - Tao Huan
- Department of Chemistry and ‡Department of Biochemistry, University of Alberta , Edmonton, Alberta, T6G 2R3 Canada
| | - Difei Sun
- Department of Chemistry and ‡Department of Biochemistry, University of Alberta , Edmonton, Alberta, T6G 2R3 Canada
| | - R Magnus N Friis
- Department of Chemistry and ‡Department of Biochemistry, University of Alberta , Edmonton, Alberta, T6G 2R3 Canada
| | - Michael C Schultz
- Department of Chemistry and ‡Department of Biochemistry, University of Alberta , Edmonton, Alberta, T6G 2R3 Canada
| | - Liang Li
- Department of Chemistry and ‡Department of Biochemistry, University of Alberta , Edmonton, Alberta, T6G 2R3 Canada
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Xu H, Kim S, Sorek H, Lee Y, Jeong D, Kim J, Oh EJ, Yun EJ, Wemmer DE, Kim KH, Kim SR, Jin YS. PHO13 deletion-induced transcriptional activation prevents sedoheptulose accumulation during xylose metabolism in engineered Saccharomyces cerevisiae. Metab Eng 2016; 34:88-96. [DOI: 10.1016/j.ymben.2015.12.007] [Citation(s) in RCA: 70] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2015] [Revised: 11/30/2015] [Accepted: 12/17/2015] [Indexed: 11/28/2022]
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Hill CB, Czauderna T, Klapperstück M, Roessner U, Schreiber F. Metabolomics, Standards, and Metabolic Modeling for Synthetic Biology in Plants. Front Bioeng Biotechnol 2015; 3:167. [PMID: 26557642 PMCID: PMC4617106 DOI: 10.3389/fbioe.2015.00167] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2015] [Accepted: 10/05/2015] [Indexed: 12/23/2022] Open
Abstract
Life on earth depends on dynamic chemical transformations that enable cellular functions, including electron transfer reactions, as well as synthesis and degradation of biomolecules. Biochemical reactions are coordinated in metabolic pathways that interact in a complex way to allow adequate regulation. Biotechnology, food, biofuel, agricultural, and pharmaceutical industries are highly interested in metabolic engineering as an enabling technology of synthetic biology to exploit cells for the controlled production of metabolites of interest. These approaches have only recently been extended to plants due to their greater metabolic complexity (such as primary and secondary metabolism) and highly compartmentalized cellular structures and functions (including plant-specific organelles) compared with bacteria and other microorganisms. Technological advances in analytical instrumentation in combination with advances in data analysis and modeling have opened up new approaches to engineer plant metabolic pathways and allow the impact of modifications to be predicted more accurately. In this article, we review challenges in the integration and analysis of large-scale metabolic data, present an overview of current bioinformatics methods for the modeling and visualization of metabolic networks, and discuss approaches for interfacing bioinformatics approaches with metabolic models of cellular processes and flux distributions in order to predict phenotypes derived from specific genetic modifications or subjected to different environmental conditions.
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Affiliation(s)
- Camilla Beate Hill
- School of BioSciences, University of Melbourne, Parkville, VIC, Australia
| | - Tobias Czauderna
- Faculty of Information Technology, Monash University, Clayton, VIC, Australia
| | | | - Ute Roessner
- School of BioSciences, University of Melbourne, Parkville, VIC, Australia
| | - Falk Schreiber
- Faculty of Information Technology, Monash University, Clayton, VIC, Australia
- Institute of Computer Science, Martin Luther University Halle-Wittenberg, Halle, Germany
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Jeffryes JG, Colastani RL, Elbadawi-Sidhu M, Kind T, Niehaus TD, Broadbelt LJ, Hanson AD, Fiehn O, Tyo KEJ, Henry CS. MINEs: open access databases of computationally predicted enzyme promiscuity products for untargeted metabolomics. J Cheminform 2015; 7:44. [PMID: 26322134 PMCID: PMC4550642 DOI: 10.1186/s13321-015-0087-1] [Citation(s) in RCA: 135] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2015] [Accepted: 07/06/2015] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND In spite of its great promise, metabolomics has proven difficult to execute in an untargeted and generalizable manner. Liquid chromatography-mass spectrometry (LC-MS) has made it possible to gather data on thousands of cellular metabolites. However, matching metabolites to their spectral features continues to be a bottleneck, meaning that much of the collected information remains uninterpreted and that new metabolites are seldom discovered in untargeted studies. These challenges require new approaches that consider compounds beyond those available in curated biochemistry databases. DESCRIPTION Here we present Metabolic In silico Network Expansions (MINEs), an extension of known metabolite databases to include molecules that have not been observed, but are likely to occur based on known metabolites and common biochemical reactions. We utilize an algorithm called the Biochemical Network Integrated Computational Explorer (BNICE) and expert-curated reaction rules based on the Enzyme Commission classification system to propose the novel chemical structures and reactions that comprise MINE databases. Starting from the Kyoto Encyclopedia of Genes and Genomes (KEGG) COMPOUND database, the MINE contains over 571,000 compounds, of which 93% are not present in the PubChem database. However, these MINE compounds have on average higher structural similarity to natural products than compounds from KEGG or PubChem. MINE databases were able to propose annotations for 98.6% of a set of 667 MassBank spectra, 14% more than KEGG alone and equivalent to PubChem while returning far fewer candidates per spectra than PubChem (46 vs. 1715 median candidates). Application of MINEs to LC-MS accurate mass data enabled the identity of an unknown peak to be confidently predicted. CONCLUSIONS MINE databases are freely accessible for non-commercial use via user-friendly web-tools at http://minedatabase.mcs.anl.gov and developer-friendly APIs. MINEs improve metabolomics peak identification as compared to general chemical databases whose results include irrelevant synthetic compounds. Furthermore, MINEs complement and expand on previous in silico generated compound databases that focus on human metabolism. We are actively developing the database; future versions of this resource will incorporate transformation rules for spontaneous chemical reactions and more advanced filtering and prioritization of candidate structures. Graphical abstractMINE database construction and access methods. The process of constructing a MINE database from the curated source databases is depicted on the left. The methods for accessing the database are shown on the right.
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Affiliation(s)
- James G Jeffryes
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL USA ; Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL USA
| | - Ricardo L Colastani
- Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL USA
| | | | - Tobias Kind
- West Coast Metabolomics Center, University of California, Davis, CA USA
| | - Thomas D Niehaus
- Horticultural Sciences Department, University of Florida, Gainesville, FL USA
| | - Linda J Broadbelt
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL USA
| | - Andrew D Hanson
- Horticultural Sciences Department, University of Florida, Gainesville, FL USA
| | - Oliver Fiehn
- West Coast Metabolomics Center, University of California, Davis, CA USA ; Biochemistry Department, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Keith E J Tyo
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL USA
| | - Christopher S Henry
- Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL USA
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