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Li J, Liu D, Cui M, Wei Z. Screening by Q Exactive liquid chromatography/tandem mass spectrometry identified Choline, 25-hydroxyvitamin D2, and SM(d18:0/16:1(9Z) (OH)) as biomarkers for high-grade serous ovarian cancer. J Proteomics 2024; 299:105154. [PMID: 38471622 DOI: 10.1016/j.jprot.2024.105154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Revised: 12/15/2023] [Accepted: 03/05/2024] [Indexed: 03/14/2024]
Abstract
High-grade serous ovarian cancer (HGSOC) has a high death rate and poor prognosis. The main causes of poor prognosis are asymptomatic early disease, no effective screening method at present, and advanced disease. Changes in cellular metabolism are characteristic of cancer, and plasma metabolome analysis can be used to identify biomarkers. In this study, we used Q Exactive liquid chromatography tandem mass spectrometry (LC-MS/MS, QE) to compare the differentiation between plasma samples (22 HGSOC samples and 22 normal samples). In total, we detected 124 metabolites, and an orthogonal partial least-squares-discriminant analysis (OPLS-DA) model was useful to distinguish HGSOC patients from healthy controls. Choline, 25-hydroxyvitamin D2, and sphingomyelin (d18:0/16:1(9Z) (OH))/SM(d18:0/16:1(9Z) (OH)) showed significantly differential plasma levels in HGSOC patients under the conditions of variable importance in projection (VIP) > 1, p < 0.05 using Student's t-test, and fold change (FC) ≥ 1.5 or ≤ 0.667. Metabolic pathway analysis can provide valuable information to enhance the understanding of the underlying pathophysiology of HGSOC. In conclusion, the Q Exactive LC/MS/MS method validation-based plasma metabolomics approach may have potential as a convenient screening method for HGSOC and may be a method to monitor tumor recurrence in patients with HGSOC after surgery SIGNIFICANCE: High-grade serous ovarian cancer (HGSOC) has a high death rate and poor prognosis. The main causes of poor prognosis are asymptomatic early disease, no effective screening method at present, and advanced disease. Changes in cellular metabolism are characteristic of cancer, and plasma metabolome analysis can be used to identify biomarkers. In this study, we used Q Exactive liquid chromatography tandem mass spectrometry (LC-MS/MS, QE) to compare the differentiation between plasma samples (20 HGSOC samples and 20 normal samples). In total, we detected 124 metabolites, and an orthogonal partial least-squares-discriminant analysis (OPLS-DA) model was useful to distinguish HGSOC patients from healthy controls. Choline, 25-hydroxyvitamin D2, and sphingomyelin (d18:0/16:1(9Z) (OH))/SM(d18:0/16:1(9Z) (OH)) showed significantly differential plasma levels in HGSOC patients under the conditions of variable importance in projection (VIP) > 1, p < 0.05 using Student's t-test, and fold change (FC) ≥ 1.5 or ≤ 0.667. Metabolic pathway analysis can provide valuable information to enhance the understanding of the underlying pathophysiology of HGSOC. In conclusion, the Q Exactive LC/MS/MS method validation-based plasma metabolomics approach may have potential as a convenient screening method for HGSOC and may be a method to monitor tumor recurrence in patients with HGSOC after surgery.
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Affiliation(s)
- Jiajia Li
- Department of Gynecologic Oncology, Gynecologic and Obstetrics Centre, the First Hospital of Jilin University, Changchun, Jilin 130012, China
| | - Dongzhen Liu
- Department of Gynecologic Oncology, Gynecologic and Obstetrics Centre, the First Hospital of Jilin University, Changchun, Jilin 130012, China
| | - Man Cui
- First Department of General Gynecology, Gynecologic and Obstetrics Centre, The First Hospital of Jilin University, Changchun 130021, Jilin, China
| | - Zhentong Wei
- Department of Gynecologic Oncology, Gynecologic and Obstetrics Centre, the First Hospital of Jilin University, Changchun, Jilin 130012, China.
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Perez de Souza L, Fernie AR. Computational methods for processing and interpreting mass spectrometry-based metabolomics. Essays Biochem 2024; 68:5-13. [PMID: 37999335 PMCID: PMC11065554 DOI: 10.1042/ebc20230019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 11/10/2023] [Accepted: 11/15/2023] [Indexed: 11/25/2023]
Abstract
Metabolomics has emerged as an indispensable tool for exploring complex biological questions, providing the ability to investigate a substantial portion of the metabolome. However, the vast complexity and structural diversity intrinsic to metabolites imposes a great challenge for data analysis and interpretation. Liquid chromatography mass spectrometry (LC-MS) stands out as a versatile technique offering extensive metabolite coverage. In this mini-review, we address some of the hurdles posed by the complex nature of LC-MS data, providing a brief overview of computational tools designed to help tackling these challenges. Our focus centers on two major steps that are essential to most metabolomics investigations: the translation of raw data into quantifiable features, and the extraction of structural insights from mass spectra to facilitate metabolite identification. By exploring current computational solutions, we aim at providing a critical overview of the capabilities and constraints of mass spectrometry-based metabolomics, while introduce some of the most recent trends in data processing and analysis within the field.
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Affiliation(s)
- Leonardo Perez de Souza
- Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476 Potsdam-Golm, Germany
| | - Alisdair R Fernie
- Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476 Potsdam-Golm, Germany
- Center for Plant Systems Biology and Biotechnology, 4000 Plovdiv, Bulgaria
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3
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Mahajan P, Fiehn O, Barupal D. IDSL.GOA: gene ontology analysis for interpreting metabolomic datasets. Sci Rep 2024; 14:1299. [PMID: 38221536 PMCID: PMC10788336 DOI: 10.1038/s41598-024-51992-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Accepted: 01/11/2024] [Indexed: 01/16/2024] Open
Abstract
Biological interpretation of metabolomic datasets often ends at a pathway analysis step to find the over-represented metabolic pathways in the list of statistically significant metabolites. However, definitions of biochemical pathways and metabolite coverage vary among different curated databases, leading to missed interpretations. For the lists of genes, transcripts and proteins, Gene Ontology (GO) terms over-presentation analysis has become a standardized approach for biological interpretation. But, GO analysis has not been achieved for metabolomic datasets. We present a new knowledgebase (KB) and the online tool, Gene Ontology Analysis by the Integrated Data Science Laboratory for Metabolomics and Exposomics (IDSL.GOA) to conduct GO over-representation analysis for a metabolite list. The IDSL.GOA KB covers 2393 metabolic GO terms and associated 3144 genes, 1,492 EC annotations, and 2621 metabolites. IDSL.GOA analysis of a case study of older versus young female brain cortex metabolome highlighted 82 GO terms being significantly overrepresented (FDR < 0.05). We showed how IDSL.GOA identified key and relevant GO metabolic processes that were not yet covered in other pathway databases. Overall, we suggest that interpretation of metabolite lists should not be limited to only pathway maps and can also leverage GO terms as well. IDSL.GOA provides a useful tool for this purpose, allowing for a more comprehensive and accurate analysis of metabolite pathway data. IDSL.GOA tool can be accessed at https://goa.idsl.me/ .
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Affiliation(s)
- Priyanka Mahajan
- Integrated Data Science Laboratory for Metabolomics and Exposomics, Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, 10954, USA
| | - Oliver Fiehn
- NIH-West Coast Metabolomics Center, University of California, Davis, CA, 95616, USA
| | - Dinesh Barupal
- Integrated Data Science Laboratory for Metabolomics and Exposomics, Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, 10954, USA.
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4
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Mahajan P, Fiehn O, Barupal D. IDSL.GOA: Gene Ontology Analysis for Interpreting Metabolomic datasets. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.03.25.534225. [PMID: 37034715 PMCID: PMC10081191 DOI: 10.1101/2023.03.25.534225] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/17/2023]
Abstract
Biological interpretation of metabolomic datasets often ends at a pathway analysis step to find the over-represented metabolic pathways in the list of statistically significant metabolites. However, definitions of biochemical pathways and metabolite coverage vary among different curated databases, leading to missed interpretations. For the lists of genes, transcripts and proteins, Gene Ontology (GO) terms over-presentation analysis has become a standardized approach for biological interpretation. But, GO analysis has not been achieved for metabolomic datasets. We present a new knowledgebase (KB) and the online tool, Gene Ontology Analysis by the Integrated Data Science Laboratory for Metabolomics and Exposomics (IDSL.GOA) to conduct GO over-representation analysis for a metabolite list. The IDSL.GOA KB covers 2,393 metabolic GO terms and associated 3,144 genes, 1,492 EC annotations, and 2,621 metabolites. IDSL.GOA analysis of a case study of older vs young female brain cortex metabolome highlighted 82 GO terms being significantly overrepresented (FDR <0.05). We showed how IDSL.GOA identified key and relevant GO metabolic processes that were not yet covered in other pathway databases. Overall, we suggest that interpretation of metabolite lists should not be limited to only pathway maps and can also leverage GO terms as well. IDSL.GOA provides a useful tool for this purpose, allowing for a more comprehensive and accurate analysis of metabolite pathway data. IDSL.GOA tool can be accessed at https://goa.idsl.me/.
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Affiliation(s)
- Priyanka Mahajan
- Integrated Data Science Laboratory for Metabolomics and Exposomics, Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, USA 10954
| | - Oliver Fiehn
- NIH-West Coast Metabolomics Center, University of California, Davis, California, 95616, USA
| | - Dinesh Barupal
- Integrated Data Science Laboratory for Metabolomics and Exposomics, Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, USA 10954
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5
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Arıkan M, Muth T. Integrated multi-omics analyses of microbial communities: a review of the current state and future directions. Mol Omics 2023; 19:607-623. [PMID: 37417894 DOI: 10.1039/d3mo00089c] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/08/2023]
Abstract
Integrated multi-omics analyses of microbiomes have become increasingly common in recent years as the emerging omics technologies provide an unprecedented opportunity to better understand the structural and functional properties of microbial communities. Consequently, there is a growing need for and interest in the concepts, approaches, considerations, and available tools for investigating diverse environmental and host-associated microbial communities in an integrative manner. In this review, we first provide a general overview of each omics analysis type, including a brief history, typical workflow, primary applications, strengths, and limitations. Then, we inform on both experimental design and bioinformatics analysis considerations in integrated multi-omics analyses, elaborate on the current approaches and commonly used tools, and highlight the current challenges. Finally, we discuss the expected key advances, emerging trends, potential implications on various fields from human health to biotechnology, and future directions.
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Affiliation(s)
- Muzaffer Arıkan
- Regenerative and Restorative Medicine Research Center (REMER), Research Institute for Health Sciences and Technologies (SABITA), Istanbul Medipol University, Istanbul, Turkey.
- Department of Medical Biology, Faculty of Medicine, Istanbul Medipol University, Istanbul, Turkey
| | - Thilo Muth
- Section eScience (S.3), Federal Institute for Materials Research and Testing (BAM), Berlin, Germany.
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6
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Moorthy A, Kearsley A, Mallard W, Wallace W, Stein S. Inferring the Nominal Molecular Mass of an Analyte from Its Electron Ionization Mass Spectrum. Anal Chem 2023; 95:13132-13139. [PMID: 37610141 PMCID: PMC10560098 DOI: 10.1021/acs.analchem.3c01815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
The performance of three algorithms for predicting nominal molecular mass from an analyte's electron ionization mass spectrum is presented. The Peak Interpretation Method (PIM) attempts to quantify the likelihood that a molecular ion peak is contained in the mass spectrum, whereas the Simple Search Hitlist Method (SS-HM) and iterative Hybrid Search Hitlist Method (iHS-HM) leverage results from mass spectral library searching. These predictions can be employed in combination (recommended) or independently. The methods were tested on two sets of query mass spectra searched against libraries that did not contain the reference mass spectra of the same compounds: 19,074 spectra of various organic molecules searched against the NIST17 mass spectral library and 162 spectra of small molecule drugs searched against SWGDRUG version 3.3. Individually, each molecular mass prediction method had computed precisions (the fraction of positive predictions that were correct) of 91, 89, and 74%, respectively. The methods become more valuable when predictions are taken together. When all three predictions were identical, which occurred in 33% of the test cases, the predicted molecular mass was almost always correct (>99%).
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Affiliation(s)
- A.S. Moorthy
- Mass Spectrometry Data Center, Biomolecular Measurement Division, National Institute of Standards and Technology, Gaithersburg, MD, USA
| | - A.J. Kearsley
- Mathematical Analysis and Modeling Group, Applied and Computational Mathematics Division, National Institute of Standards and Technology, Gaithersburg, MD, USA
| | - W.G. Mallard
- Mass Spectrometry Data Center, Biomolecular Measurement Division, National Institute of Standards and Technology, Gaithersburg, MD, USA
| | - W.E. Wallace
- Mass Spectrometry Data Center, Biomolecular Measurement Division, National Institute of Standards and Technology, Gaithersburg, MD, USA
| | - S.E. Stein
- Mass Spectrometry Data Center, Biomolecular Measurement Division, National Institute of Standards and Technology, Gaithersburg, MD, USA
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7
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Miller WM, Ziegler KM, Yilmaz A, Saiyed N, Ustun I, Akyol S, Idler J, Sims MD, Maddens ME, Graham SF. Association of Metabolomic Biomarkers with Sleeve Gastrectomy Weight Loss Outcomes. Metabolites 2023; 13:metabo13040506. [PMID: 37110164 PMCID: PMC10145663 DOI: 10.3390/metabo13040506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Revised: 03/29/2023] [Accepted: 03/29/2023] [Indexed: 04/03/2023] Open
Abstract
This prospective observational study aimed to evaluate the association of metabolomic alterations with weight loss outcomes following sleeve gastrectomy (SG). We evaluated the metabolomic profile of serum and feces prior to SG and three months post-SG, along with weight loss outcomes in 45 adults with obesity. The percent total weight loss for the highest versus the lowest weight loss tertiles (T3 vs. T1) was 17.0 ± 1.3% and 11.1 ± 0.8%, p < 0.001. Serum metabolite alterations specific to T3 at three months included a decrease in methionine sulfoxide concentration as well as alterations to tryptophan and methionine metabolism (p < 0.03). Fecal metabolite changes specific to T3 included a decrease in taurine concentration and perturbations to arachidonic acid metabolism, and taurine and hypotaurine metabolism (p < 0.002). Preoperative metabolites were found to be highly predictive of weight loss outcomes in machine learning algorithms, with an average area under the curve of 94.6% for serum and 93.4% for feces. This comprehensive metabolomics analysis of weight loss outcome differences post-SG highlights specific metabolic alterations as well as machine learning algorithms predictive of weight loss. These findings could contribute to the development of novel therapeutic targets to enhance weight loss outcomes after SG.
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Affiliation(s)
- Wendy M. Miller
- Department of Nutrition and Preventive Medicine, Corewell Health William Beaumont University Hospital, 3601 W. 13 Mile Road, Royal Oak, MI 48073, USA
- Oakland University William Beaumont School of Medicine, 586 Pioneer Dr, Rochester, MI 48309, USA
| | - Kathryn M. Ziegler
- Department of Nutrition and Preventive Medicine, Corewell Health William Beaumont University Hospital, 3601 W. 13 Mile Road, Royal Oak, MI 48073, USA
- Oakland University William Beaumont School of Medicine, 586 Pioneer Dr, Rochester, MI 48309, USA
| | - Ali Yilmaz
- Beaumont Research Institute, 3811 W 13 Mile Rd, Royal Oak, MI 48073, USA
| | - Nazia Saiyed
- Beaumont Research Institute, 3811 W 13 Mile Rd, Royal Oak, MI 48073, USA
| | - Ilyas Ustun
- DePaul University Jarvis College of Computing and Digital Media, 243 S Wabash Ave, Chicago, IL 60604, USA
| | - Sumeyya Akyol
- NX Prenatal Inc. Laboratory, 4800 Fournace Place, Suite BW28, Bellaire, TX 77401, USA
| | - Jay Idler
- Allegheny Health Network, West Penn Hospital, 4815 Liberty Ave, Suite GR50, Pittsburgh, PA 15224, USA
- Drexel University College of Medicine, 2900 W Queen Ln, Philadelphia, PA 19129, USA
| | - Matthew D. Sims
- Department of Nutrition and Preventive Medicine, Corewell Health William Beaumont University Hospital, 3601 W. 13 Mile Road, Royal Oak, MI 48073, USA
- Beaumont Research Institute, 3811 W 13 Mile Rd, Royal Oak, MI 48073, USA
| | - Michael E. Maddens
- Department of Nutrition and Preventive Medicine, Corewell Health William Beaumont University Hospital, 3601 W. 13 Mile Road, Royal Oak, MI 48073, USA
- Oakland University William Beaumont School of Medicine, 586 Pioneer Dr, Rochester, MI 48309, USA
| | - Stewart F. Graham
- Department of Nutrition and Preventive Medicine, Corewell Health William Beaumont University Hospital, 3601 W. 13 Mile Road, Royal Oak, MI 48073, USA
- Beaumont Research Institute, 3811 W 13 Mile Rd, Royal Oak, MI 48073, USA
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8
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Fabrile MP, Ghidini S, Conter M, Varrà MO, Ianieri A, Zanardi E. Filling gaps in animal welfare assessment through metabolomics. Front Vet Sci 2023; 10:1129741. [PMID: 36925610 PMCID: PMC10011658 DOI: 10.3389/fvets.2023.1129741] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 02/09/2023] [Indexed: 03/08/2023] Open
Abstract
Sustainability has become a central issue in Italian livestock systems driving food business operators to adopt high standards of production concerning animal husbandry conditions. Meat sector is largely involved in this ecological transition with the introduction of new label claims concerning the defense of animal welfare (AW). These new guarantees referred to AW provision require new tools for the purpose of authenticity and traceability to assure meat supply chain integrity. Over the years, European Union (EU) Regulations, national, and international initiatives proposed provisions and guidelines for assuring AW introducing requirements to be complied with and providing tools based on scoring systems for a proper animal status assessment. However, the comprehensive and objective assessment of the AW status remains challenging. In this regard, phenotypic insights at molecular level may be investigated by metabolomics, one of the most recent high-throughput omics techniques. Recent advances in analytical and bioinformatic technologies have led to the identification of relevant biomarkers involved in complex clinical phenotypes of diverse biological systems suggesting that metabolomics is a key tool for biomarker discovery. In the present review, the Five Domains model has been employed as a vademecum describing AW. Starting from the individual Domains-nutrition (I), environment (II), health (III), behavior (IV), and mental state (V)-applications and advances of metabolomics related to AW setting aimed at investigating phenotypic outcomes on molecular scale and elucidating the biological routes most perturbed from external solicitations, are reviewed. Strengths and weaknesses of the current state-of-art are highlighted, and new frontiers to be explored for AW assessment throughout the metabolomics approach are argued. Moreover, a detailed description of metabolomics workflow is provided to understand dos and don'ts at experimental level to pursue effective results. Combining the demand for new assessment tools and meat market trends, a new cross-strategy is proposed as the promising combo for the future of AW assessment.
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Affiliation(s)
| | - Sergio Ghidini
- Department of Food and Drug, University of Parma, Parma, Italy
| | - Mauro Conter
- Department of Veterinary Science, University of Parma, Parma, Italy
| | | | - Adriana Ianieri
- Department of Food and Drug, University of Parma, Parma, Italy
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Glass KA, Germain A, Huang YV, Hanson MR. Urine Metabolomics Exposes Anomalous Recovery after Maximal Exertion in Female ME/CFS Patients. Int J Mol Sci 2023; 24:3685. [PMID: 36835097 PMCID: PMC9958671 DOI: 10.3390/ijms24043685] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 02/06/2023] [Accepted: 02/09/2023] [Indexed: 02/16/2023] Open
Abstract
Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a debilitating disease with unknown etiology or effective treatments. Post-exertional malaise (PEM) is a key symptom that distinguishes ME/CFS patients. Investigating changes in the urine metabolome between ME/CFS patients and healthy subjects following exertion may help us understand PEM. The aim of this pilot study was to comprehensively characterize the urine metabolomes of eight female healthy sedentary control subjects and ten female ME/CFS patients in response to a maximal cardiopulmonary exercise test (CPET). Each subject provided urine samples at baseline and 24 h post-exercise. A total of 1403 metabolites were detected via LC-MS/MS by Metabolon® including amino acids, carbohydrates, lipids, nucleotides, cofactors and vitamins, xenobiotics, and unknown compounds. Using a linear mixed effects model, pathway enrichment analysis, topology analysis, and correlations between urine and plasma metabolite levels, significant differences were discovered between controls and ME/CFS patients in many lipid (steroids, acyl carnitines and acyl glycines) and amino acid subpathways (cysteine, methionine, SAM, and taurine; leucine, isoleucine, and valine; polyamine; tryptophan; and urea cycle, arginine and proline). Our most unanticipated discovery is the lack of changes in the urine metabolome of ME/CFS patients during recovery while significant changes are induced in controls after CPET, potentially demonstrating the lack of adaptation to a severe stress in ME/CFS patients.
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Affiliation(s)
| | | | | | - Maureen R. Hanson
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY 14853, USA
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Scott-Fordsmand JJ, Amorim MJB. Using Machine Learning to make nanomaterials sustainable. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 859:160303. [PMID: 36410486 DOI: 10.1016/j.scitotenv.2022.160303] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 11/06/2022] [Accepted: 11/15/2022] [Indexed: 06/16/2023]
Abstract
Sustainable development is a key challenge for contemporary human societies; failure to achieve sustainability could threaten human survival. In this review article, we illustrate how Machine Learning (ML) could support more sustainable development, covering the basics of data gathering through each step of the Environmental Risk Assessment (ERA). The literature provides several examples showing how ML can be employed in most steps of a typical ERA.A key observation is that there are currently no clear guidance for using such autonomous technologies in ERAs or which standards/checks are required. Steering thus seems to be the most important task for supporting the use of ML in the ERA of nano- and smart-materials. Resources should be devoted to developing a strategy for implementing ML in ERA with a strong emphasis on data foundations, methodologies, and the related sensitivities/uncertainties. We should recognise historical errors and biases (e.g., in data) to avoid embedding them during ML programming.
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Affiliation(s)
| | - Mónica J B Amorim
- Department of Biology & CESAM, University of Aveiro, 3810-193 Aveiro, Portugal.
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11
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Integrated Metabolomics and Morpho-Biochemical Analyses Reveal a Better Performance of Azospirillum brasilense over Plant-Derived Biostimulants in Counteracting Salt Stress in Tomato. Int J Mol Sci 2022; 23:ijms232214216. [PMID: 36430691 PMCID: PMC9698407 DOI: 10.3390/ijms232214216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Revised: 11/11/2022] [Accepted: 11/14/2022] [Indexed: 11/18/2022] Open
Abstract
Increased soil salinity is one of the main concerns in agriculture and food production, and it negatively affects plant growth and crop productivity. In order to mitigate the adverse effects of salinity stress, plant biostimulants (PBs) have been indicated as a promising approach. Indeed, these products have a beneficial effect on plants by acting on primary and secondary metabolism and by inducing the accumulation of protective molecules against oxidative stress. In this context, the present work is aimed at comparatively investigating the effects of microbial (i.e., Azospirillum brasilense) and plant-derived biostimulants in alleviating salt stress in tomato plants by adopting a multidisciplinary approach. To do so, the morphological and biochemical effects were assessed by analyzing the biomass accumulation and root characteristics, the activity of antioxidant enzymes and osmotic stress protection. Furthermore, modifications in the metabolomic profiles of both leaves and root exudates were also investigated by ultra-high performance liquid chromatography/quadrupole time-of-flight mass spectrometry (UHPLC/QTOF-MS). According to the results, biomass accumulation decreased under high salinity. However, the treatment with A. brasilense considerably improved root architecture and increased root biomass by 156% and 118% in non-saline and saline conditions, respectively. The antioxidant enzymes and proline production were enhanced in salinity stress at different levels according to the biostimulant applied. Moreover, the metabolomic analyses pointed out a wide set of processes being affected by salinity and biostimulant interactions. Crucial compounds belonging to secondary metabolism (phenylpropanoids, alkaloids and other N-containing metabolites, and membrane lipids) and phytohormones (brassinosteroids, cytokinins and methylsalicylate) showed the most pronounced modulation. Overall, our results suggest a better performance of A. brasilense in alleviating high salinity than the vegetal-derived protein hydrolysates herein evaluated.
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Shansky Y, Bespyatykh J. Bile Acids: Physiological Activity and Perspectives of Using in Clinical and Laboratory Diagnostics. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27227830. [PMID: 36431930 PMCID: PMC9692537 DOI: 10.3390/molecules27227830] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 11/09/2022] [Accepted: 11/10/2022] [Indexed: 11/16/2022]
Abstract
Bile acids play a significant role in the digestion of nutrients. In addition, bile acids perform a signaling function through their blood-circulating fraction. They regulate the activity of nuclear and membrane receptors, located in many tissues. The gut microbiota is an important factor influencing the effects of bile acids via enzymatic modification. Depending on the rate of healthy and pathogenic microbiota, a number of bile acids may support lipid and glucose homeostasis as well as shift to more toxic compounds participating in many pathological conditions. Thus, bile acids can be possible biomarkers of human pathology. However, the chemical structure of bile acids is similar and their analysis requires sensitive and specific methods of analysis. In this review, we provide information on the chemical structure and the biosynthesis of bile acids, their regulation, and their physiological role. In addition, the review describes the involvement of bile acids in various diseases of the digestive system, the approaches and challenges in the analysis of bile acids, and the prospects of their use in omics technologies.
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Affiliation(s)
- Yaroslav Shansky
- Department of Molecular Medicine, Center of Molecular Medicine and Diagnostics, Federal Research and Clinical Center of Physical-Chemical Medicine of Federal Medical Biological Agency, Malaya Pirogovskaya Str., 1a, 119435 Moscow, Russia
- Correspondence:
| | - Julia Bespyatykh
- Department of Molecular Medicine, Center of Molecular Medicine and Diagnostics, Federal Research and Clinical Center of Physical-Chemical Medicine of Federal Medical Biological Agency, Malaya Pirogovskaya Str., 1a, 119435 Moscow, Russia
- Department of Expertise in Doping and Drug Control, Mendeleev University of Chemical Technology of Russia, Miusskaya Square, 9, 125047 Moscow, Russia
- Department of Public Health and Health Care, Federal Scientific State Budgetary Institution «N.A. Semashko National Research Institute of Public Health», Vorontsovo Pole Str., 12-1, 105064 Moscow, Russia
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13
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Banimfreg BH, Shamayleh A, Alshraideh H. Survey for Computer-Aided Tools and Databases in Metabolomics. Metabolites 2022; 12:metabo12101002. [PMID: 36295904 PMCID: PMC9610953 DOI: 10.3390/metabo12101002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Revised: 10/08/2022] [Accepted: 10/12/2022] [Indexed: 11/14/2022] Open
Abstract
Metabolomics has advanced from innovation and functional genomics tools and is currently a basis in the big data-led precision medicine era. Metabolomics is promising in the pharmaceutical field and clinical research. However, due to the complexity and high throughput data generated from such experiments, data mining and analysis are significant challenges for researchers in the field. Therefore, several efforts were made to develop a complete workflow that helps researchers analyze data. This paper introduces a review of the state-of-the-art computer-aided tools and databases in metabolomics established in recent years. The paper provides computational tools and resources based on functionality and accessibility and provides hyperlinks to web pages to download or use. This review aims to present the latest computer-aided tools, databases, and resources to the metabolomics community in one place.
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Lao D, Liu R, Liang J. Study on plasma metabolomics for HIV/AIDS patients treated by HAART based on LC/MS-MS. Front Pharmacol 2022; 13:885386. [PMID: 36105186 PMCID: PMC9465010 DOI: 10.3389/fphar.2022.885386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 07/21/2022] [Indexed: 11/13/2022] Open
Abstract
Background: Metabolomics can be applied to the clinical diagnosis and treatment evaluation of acquired immune deficiency syndrome (AIDS). AIDS biomarkers have become a new direction of AIDS research providing clinical guidance for diagnosis. Objective: We sought to apply both untargeted and targeted metabolomic profiling to identify potential biomarkers for AIDS patients. Methods: A liquid chromatography-tandem mass spectrometry (LC-MS/MS) based untargeted metabolomic profiling was performed on plasma samples of patients before and after highly active antiretroviral therapy (HAART) treatment as well as healthy volunteers to identify potential AIDS biomarkers. Targeted quantitative analysis was performed on the potential biomarkers screened from untargeted metabolic profiling for verification. Results: Using the Mass Profiler Professional and the MassHunter, several potential biomarkers have been found by LC-MS/MS in the untargeted metabolomic study. High-resolution MS and MS/MS were used to analyze fragmentation rules of the metabolites, with comparisons of related standards. Several potential biomarkers have been identified, including PS(O-18:0/0:0), sphingosine, PE (21:0/0:0), and 1-Linoleoyl Glycerol. Targeted quantitative analysis showed that sphingosine and 1-Linoleoyl Glycerol might be closely related to HIV/AIDS, which may be the potential biomarkers to the diagnosis. Conclusion: We conducted untargeted metabolomic profiling, which indicates that several metabolites should be considered potential biomarkers for AIDS patients. Further targeted metabolomic research verified that d-Sphingosine and 1-Linoleoyl glycerol as the diagnostic biomarker of AIDS.
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Affiliation(s)
- Donghui Lao
- Department of Pharmacy, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Rong Liu
- Department of Pharmacy, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
- *Correspondence: Rong Liu, ; Jianying Liang,
| | - Jianying Liang
- School of Pharmacy, Fudan University, Shanghai, China
- *Correspondence: Rong Liu, ; Jianying Liang,
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Barupal DK, Mahajan P, Fakouri-Baygi S, Wright RO, Arora M, Teitelbaum SL. CCDB: A database for exploring inter-chemical correlations in metabolomics and exposomics datasets. ENVIRONMENT INTERNATIONAL 2022; 164:107240. [PMID: 35461097 PMCID: PMC9195052 DOI: 10.1016/j.envint.2022.107240] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 04/01/2022] [Accepted: 04/08/2022] [Indexed: 05/18/2023]
Abstract
Inter-chemical correlations in metabolomics and exposomics datasets provide valuable information for studying relationships among chemicals reported for human specimens. With an increase in the number of compounds for these datasets, a network graph analysis and visualization of the correlation structure is difficult to interpret. We have developed the Chemical Correlation Database (CCDB), as a systematic catalogue of inter-chemical correlation in publicly available metabolomics and exposomics studies. The database has been provided via an online interface to create single compound-centric views. We have demonstrated various applications of the database to explore: 1) the chemicals from a chemical class such as Per- and Polyfluoroalkyl Substances (PFAS), polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), phthalates and tobacco smoke related metabolites; 2) xenobiotic metabolites such as caffeine and acetaminophen; 3) endogenous metabolites (acyl-carnitines); and 4) unannotated peaks for PFAS. The database has a rich collection of 35 human studies, including the National Health and Nutrition Examination Survey (NHANES) and high-quality untargeted metabolomics datasets. CCDB is supported by a simple, interactive and user-friendly web-interface to retrieve and visualize the inter-chemical correlation data. The CCDB has the potential to be a key computational resource in metabolomics and exposomics facilitating the expansion of our understanding about biological and chemical relationships among metabolites and chemical exposures in the human body. The database is available at www.ccdb.idsl.me site.
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Affiliation(s)
- Dinesh Kumar Barupal
- Department of Environmental Medicine and Public Health, Institute for Exposomic Research, Icahn School of Medicine at Mount Sinai, 17 E 102nd St, CAM Building, New York 10029, USA.
| | - Priyanka Mahajan
- Department of Environmental Medicine and Public Health, Institute for Exposomic Research, Icahn School of Medicine at Mount Sinai, 17 E 102nd St, CAM Building, New York 10029, USA
| | - Sadjad Fakouri-Baygi
- Department of Environmental Medicine and Public Health, Institute for Exposomic Research, Icahn School of Medicine at Mount Sinai, 17 E 102nd St, CAM Building, New York 10029, USA
| | - Robert O Wright
- Department of Environmental Medicine and Public Health, Institute for Exposomic Research, Icahn School of Medicine at Mount Sinai, 17 E 102nd St, CAM Building, New York 10029, USA
| | - Manish Arora
- Department of Environmental Medicine and Public Health, Institute for Exposomic Research, Icahn School of Medicine at Mount Sinai, 17 E 102nd St, CAM Building, New York 10029, USA
| | - Susan L Teitelbaum
- Department of Environmental Medicine and Public Health, Institute for Exposomic Research, Icahn School of Medicine at Mount Sinai, 17 E 102nd St, CAM Building, New York 10029, USA
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Hall RD, D'Auria JC, Silva Ferreira AC, Gibon Y, Kruszka D, Mishra P, van de Zedde R. High-throughput plant phenotyping: a role for metabolomics? TRENDS IN PLANT SCIENCE 2022; 27:549-563. [PMID: 35248492 DOI: 10.1016/j.tplants.2022.02.001] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Revised: 01/18/2022] [Accepted: 02/02/2022] [Indexed: 05/17/2023]
Abstract
High-throughput (HTP) plant phenotyping approaches are developing rapidly and are already helping to bridge the genotype-phenotype gap. However, technologies should be developed beyond current physico-spectral evaluations to extend our analytical capacities to the subcellular level. Metabolites define and determine many key physiological and agronomic features in plants and an ability to integrate a metabolomics approach within current HTP phenotyping platforms has huge potential for added value. While key challenges remain on several fronts, novel technological innovations are upcoming yet under-exploited in a phenotyping context. In this review, we present an overview of the state of the art and how current limitations might be overcome to enable full integration of metabolomics approaches into a generic phenotyping pipeline in the near future.
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Affiliation(s)
- Robert D Hall
- BU Bioscience, Wageningen University & Research, 6700 AA, Wageningen, The Netherlands; Laboratory of Plant Physiology, Wageningen University, 6700 AA, Wageningen, The Netherlands; Netherlands Metabolomics Centre, Einsteinweg 55, Leiden, The Netherlands.
| | - John C D'Auria
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK Gatersleben), Gatersleben, Corrensstraße 3, 06466 Seeland, Germany
| | - Antonio C Silva Ferreira
- Universidade Católica Portuguesa, CBQF-Centro de Biotecnologia e Química Fina-Laboratório Associado, Escola Superior de Biotecnologia, Rua Arquiteto Lobão Vital, Apartado 2511, 4202-401 Porto, Portugal; Faculty of AgriSciences, University of Stellenbosch, Matieland 7602, South Africa; Cork Supply Portugal, S.A., Rua Nova do Fial, 4535, Portugal
| | - Yves Gibon
- UMR 1332 Biologie du Fruit et Pathologie, INRAE, Univ. Bordeaux, INRAE Nouvelle Aquitaine - Bordeaux, Avenue Edouard Bourlaux, Villenave d'Ornon, France; Bordeaux Metabolome, MetaboHUB, INRAE, Univ. Bordeaux, Avenue Edouard Bourlaux, Villenave d'Ornon, France PMB-Metabolome, INRAE, Centre INRAE de Nouvelle, Aquitaine-Bordeaux, Villenave d'Ornon, France
| | - Dariusz Kruszka
- Institute of Plant Genetics, Polish Academy of Sciences, 60-479 Poznan, Poland
| | - Puneet Mishra
- Food and Biobased Research, Wageningen University & Research, 6708 WE, Wageningen, The Netherlands
| | - Rick van de Zedde
- Plant Sciences Group, Wageningen University & Research, 6700 AA, Wageningen, The Netherlands
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17
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Shu N, Chen X, Sun X, Cao X, Liu Y, Xu YJ. Metabolomics identify landscape of food sensory properties. Crit Rev Food Sci Nutr 2022; 63:8478-8488. [PMID: 35435783 DOI: 10.1080/10408398.2022.2062698] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
Sensory evaluation is a key component of food production strategy. The classical food sensory evaluation method is time-consuming, laborious, costly, and highly subjective. Since flavor (taste and smell), texture, and mouthfeel are all related to the chemical properties of food, there has been a growing interest in how they affect the senses of food. In the past decades, emerging metabolomics has received much attention in the field of sensory evaluation, because it not only offers a broad picture of chemical composition for sensory properties but also revealed their changes and functions in food proceeding. This article reviewed food chemicals regarding the flavor, smell, and texture of foods, and discussed the advantages and limitations of applying metabolomics approaches to sensory evaluation, including GC-MS, LC-MS, and NMR. Taken together, this review gives a comprehensive, critical overview of the current state, future challenges, and trends in metabolomics on food sensory properties.
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Affiliation(s)
- Nanxi Shu
- State Key Laboratory of Food Science and Technology, School of Food Science and Technology, National Engineering Research Center for Function Food, National Engineering Laboratory for Cereal Fermentation Technology, Collaborative Innovation Center of Food Safety and Quality Control, Jiangnan University, Wuxi, China
| | - Xiaoying Chen
- State Key Laboratory of Food Science and Technology, School of Food Science and Technology, National Engineering Research Center for Function Food, National Engineering Laboratory for Cereal Fermentation Technology, Collaborative Innovation Center of Food Safety and Quality Control, Jiangnan University, Wuxi, China
| | - Xian Sun
- State Key Laboratory of Food Science and Technology, School of Food Science and Technology, National Engineering Research Center for Function Food, National Engineering Laboratory for Cereal Fermentation Technology, Collaborative Innovation Center of Food Safety and Quality Control, Jiangnan University, Wuxi, China
| | - Xinyu Cao
- State Key Laboratory of Food Science and Technology, School of Food Science and Technology, National Engineering Research Center for Function Food, National Engineering Laboratory for Cereal Fermentation Technology, Collaborative Innovation Center of Food Safety and Quality Control, Jiangnan University, Wuxi, China
| | - Yuanfa Liu
- State Key Laboratory of Food Science and Technology, School of Food Science and Technology, National Engineering Research Center for Function Food, National Engineering Laboratory for Cereal Fermentation Technology, Collaborative Innovation Center of Food Safety and Quality Control, Jiangnan University, Wuxi, China
| | - Yong-Jiang Xu
- State Key Laboratory of Food Science and Technology, School of Food Science and Technology, National Engineering Research Center for Function Food, National Engineering Laboratory for Cereal Fermentation Technology, Collaborative Innovation Center of Food Safety and Quality Control, Jiangnan University, Wuxi, China
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Ford L, Mitchell M, Wulff J, Evans A, Kennedy A, Elsea S, Wittmann B, Toal D. Clinical metabolomics for inborn errors of metabolism. Adv Clin Chem 2022; 107:79-138. [PMID: 35337606 DOI: 10.1016/bs.acc.2021.09.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Metabolism is a highly regulated process that provides nutrients to cells and essential building blocks for the synthesis of protein, DNA and other macromolecules. In healthy biological systems, metabolism maintains a steady state in which the concentrations of metabolites are relatively constant yet are subject to metabolic demands and environmental stimuli. Rare genetic disorders, such as inborn errors of metabolism (IEM), cause defects in regulatory enzymes or proteins leading to metabolic pathway disruption and metabolite accumulation or deficiency. Traditionally, the laboratory diagnosis of IEMs has been limited to analytical methods that target specific metabolites such as amino acids and acyl carnitines. This approach is effective as a screening method for the most common IEM disorders but lacks the comprehensive coverage of metabolites that is necessary to identify rare disorders that present with nonspecific clinical symptoms. Fortunately, advancements in technology and data analytics has introduced a new field of study called metabolomics which has allowed scientists to perform comprehensive metabolite profiling of biological systems to provide insight into mechanism of action and gene function. Since metabolomics seeks to measure all small molecule metabolites in a biological specimen, it provides an innovative approach to evaluating disease in patients with rare genetic disorders. In this review we provide insight into the appropriate application of metabolomics in clinical settings. We discuss the advantages and limitations of the method and provide details related to the technology, data analytics and statistical modeling required for metabolomic profiling of patients with IEMs.
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Affiliation(s)
- Lisa Ford
- Metabolon, Inc., Morrisville, NC, United States
| | | | - Jacob Wulff
- Metabolon, Inc., Morrisville, NC, United States
| | - Annie Evans
- Metabolon, Inc., Morrisville, NC, United States
| | | | - Sarah Elsea
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, United States
| | | | - Douglas Toal
- Metabolon, Inc., Morrisville, NC, United States.
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Qi Y, Zhang T, Wu Y, Yao Z, Qiu X, Pu P, Tang X, Fu J, Yang W. A Multilevel Assessment of Plasticity in Response to High-Altitude Environment for Agama Lizards. Front Ecol Evol 2022. [DOI: 10.3389/fevo.2022.845072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Upslope range shifting has been documented in diverse species in response to global warming. Plasticity, which refers to the ability of organisms to alter their phenotypes in changing environments, is crucial for the survival of those that newly migrated to a high-altitude environment. The scope and mechanisms of plasticity across biological levels, however, have rarely been examined. We used two agama lizards (genus Phrynocephalus) as model systems and a transplant experiment to comprehensively assess their plasticity on multiple organization levels. Two low-altitude (934 m) agama species, Phrynocephalus axillaris (oviparous) and P. forsythii (viviparous), were transplanted to a high-altitude site (3,400 m). After acclimation for 6 weeks in seminatural enclosures, plasticity was measured from bite force, tail display behavior, gene expression, and metabolome. Both lizards were capable of acclimating to the high-altitude environment without sacrificing their performance in bite force, but they also showed high plasticity in tail display behavior by either decreasing the intensity of a specific display component (P. forsythii) or by the trade-off between display components (P. axillaris). Genes and metabolites associated with lipids, especially fatty acid metabolism, exhibited significant differentiation in expression, compared to individuals from their native habitats. Improved fatty acid storage and metabolism appeared to be a common response among animals at high altitudes. Despite distinct reproductive modes that may differ in response to physiological pressure, the two lizards demonstrated high concordance in plasticity when they faced a novel environment at high altitudes. Taken together, lizards likely acclimate to high-altitude environments by reducing behavioral activity and increasing energy efficiency after range shifting. Our results provide new insights into our understanding of phenotypic plasticity and its importance in today’s changing climate.
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Kanazawa S, Shimizu S, Kajihara S, Mukai N, Iida J, Matsuda F. Automated Recommendation of Research Keywords from PubMed That Suggest the Molecular Mechanism Associated with Biomarker Metabolites. Metabolites 2022; 12:metabo12020133. [PMID: 35208208 PMCID: PMC8875447 DOI: 10.3390/metabo12020133] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 01/29/2022] [Accepted: 01/29/2022] [Indexed: 12/05/2022] Open
Abstract
Metabolomics can help identify candidate biomarker metabolites whose levels are altered in response to disease development or drug administration. However, assessment of the underlying molecular mechanism is challenging considering it depends on the researcher’s knowledge. This study reports a novel method for the automated recommendation of keywords known in the literature that may be overlooked by researchers. The proposed method aided in the identification of Medical Subject Headings (MeSH) terms in PubMed using MeSH co-occurrence data. The intended users are biocurators who have identified specific biomarker metabolites from a metabolomics study and would like to identify literature-reported molecular mechanisms that are associated with both the metabolite and their research area of interest. The proposed method finds MeSH terms that co-occur with a MeSH term of the candidate biomarker metabolite as well as a MeSH term of a researcher’s known keyword, such as the name of a disease. The connectivity score S was determined using association analysis. Pilot analyses demonstrated that, while the biological significance of the obtained MeSH terms could not be guaranteed, the developed method can be useful for finding keywords to further investigate molecular mechanisms in association with candidate biomarker molecules.
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Affiliation(s)
- Shinji Kanazawa
- Shimadzu Corporation, Kyoto 604-8511, Japan; (S.K.); (S.S.); (S.K.); (N.M.); (J.I.)
- Osaka University Shimadzu Omics Innovation Research Laboratories, Osaka University, Osaka 565-0871, Japan
- Graduate School of Information Science and Technology, Osaka University, Osaka 565-0871, Japan
| | - Satoshi Shimizu
- Shimadzu Corporation, Kyoto 604-8511, Japan; (S.K.); (S.S.); (S.K.); (N.M.); (J.I.)
| | - Shigeki Kajihara
- Shimadzu Corporation, Kyoto 604-8511, Japan; (S.K.); (S.S.); (S.K.); (N.M.); (J.I.)
| | - Norio Mukai
- Shimadzu Corporation, Kyoto 604-8511, Japan; (S.K.); (S.S.); (S.K.); (N.M.); (J.I.)
| | - Junko Iida
- Shimadzu Corporation, Kyoto 604-8511, Japan; (S.K.); (S.S.); (S.K.); (N.M.); (J.I.)
- Osaka University Shimadzu Omics Innovation Research Laboratories, Osaka University, Osaka 565-0871, Japan
| | - Fumio Matsuda
- Graduate School of Information Science and Technology, Osaka University, Osaka 565-0871, Japan
- Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Osaka 565-0871, Japan
- Correspondence: ; Tel.: +81-6-6879-7433
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21
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Zenda T, Liu S, Dong A, Li J, Wang Y, Liu X, Wang N, Duan H. Omics-Facilitated Crop Improvement for Climate Resilience and Superior Nutritive Value. FRONTIERS IN PLANT SCIENCE 2021; 12:774994. [PMID: 34925418 PMCID: PMC8672198 DOI: 10.3389/fpls.2021.774994] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 11/08/2021] [Indexed: 05/17/2023]
Abstract
Novel crop improvement approaches, including those that facilitate for the exploitation of crop wild relatives and underutilized species harboring the much-needed natural allelic variation are indispensable if we are to develop climate-smart crops with enhanced abiotic and biotic stress tolerance, higher nutritive value, and superior traits of agronomic importance. Top among these approaches are the "omics" technologies, including genomics, transcriptomics, proteomics, metabolomics, phenomics, and their integration, whose deployment has been vital in revealing several key genes, proteins and metabolic pathways underlying numerous traits of agronomic importance, and aiding marker-assisted breeding in major crop species. Here, citing several relevant examples, we appraise our understanding on the recent developments in omics technologies and how they are driving our quest to breed climate resilient crops. Large-scale genome resequencing, pan-genomes and genome-wide association studies are aiding the identification and analysis of species-level genome variations, whilst RNA-sequencing driven transcriptomics has provided unprecedented opportunities for conducting crop abiotic and biotic stress response studies. Meanwhile, single cell transcriptomics is slowly becoming an indispensable tool for decoding cell-specific stress responses, although several technical and experimental design challenges still need to be resolved. Additionally, the refinement of the conventional techniques and advent of modern, high-resolution proteomics technologies necessitated a gradual shift from the general descriptive studies of plant protein abundances to large scale analysis of protein-metabolite interactions. Especially, metabolomics is currently receiving special attention, owing to the role metabolites play as metabolic intermediates and close links to the phenotypic expression. Further, high throughput phenomics applications are driving the targeting of new research domains such as root system architecture analysis, and exploration of plant root-associated microbes for improved crop health and climate resilience. Overall, coupling these multi-omics technologies to modern plant breeding and genetic engineering methods ensures an all-encompassing approach to developing nutritionally-rich and climate-smart crops whose productivity can sustainably and sufficiently meet the current and future food, nutrition and energy demands.
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Affiliation(s)
- Tinashe Zenda
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, Baoding, China
- Department of Crop Genetics and Breeding, College of Agronomy, Hebei Agricultural University, Baoding, China
- Department of Crop Science, Faculty of Agriculture and Environmental Science, Bindura University of Science Education, Bindura, Zimbabwe
| | - Songtao Liu
- Academy of Agriculture and Forestry Sciences, Hebei North University, Zhangjiakou, China
| | - Anyi Dong
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, Baoding, China
- Department of Crop Genetics and Breeding, College of Agronomy, Hebei Agricultural University, Baoding, China
| | - Jiao Li
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, Baoding, China
- Department of Crop Genetics and Breeding, College of Agronomy, Hebei Agricultural University, Baoding, China
| | - Yafei Wang
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, Baoding, China
- Department of Crop Genetics and Breeding, College of Agronomy, Hebei Agricultural University, Baoding, China
| | - Xinyue Liu
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, Baoding, China
- Department of Crop Genetics and Breeding, College of Agronomy, Hebei Agricultural University, Baoding, China
| | - Nan Wang
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, Baoding, China
- Department of Crop Genetics and Breeding, College of Agronomy, Hebei Agricultural University, Baoding, China
| | - Huijun Duan
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, Baoding, China
- Department of Crop Genetics and Breeding, College of Agronomy, Hebei Agricultural University, Baoding, China
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Heiling S, Knutti N, Scherr F, Geiger J, Weikert J, Rose M, Jahns R, Ceglarek U, Scherag A, Kiehntopf M. Metabolite Ratios as Quality Indicators for Pre-Analytical Variation in Serum and EDTA Plasma. Metabolites 2021; 11:638. [PMID: 34564454 PMCID: PMC8465943 DOI: 10.3390/metabo11090638] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 09/13/2021] [Accepted: 09/14/2021] [Indexed: 12/18/2022] Open
Abstract
In clinical diagnostics and research, blood samples are one of the most frequently used materials. Nevertheless, exploring the chemical composition of human plasma and serum is challenging due to the highly dynamic influence of pre-analytical variation. A prominent example is the variability in pre-centrifugation delay (time-to-centrifugation; TTC). Quality indicators (QI) reflecting sample TTC are of utmost importance in assessing sample history and resulting sample quality, which is essential for accurate diagnostics and conclusive, reproducible research. In the present study, we subjected human blood to varying TTCs at room temperature prior to processing for plasma or serum preparation. Potential sample QIs were identified by Ultra high pressure liquid chromatography tandem mass spectrometry (UHPLC-MS/MS) based metabolite profiling in samples from healthy volunteers (n = 10). Selected QIs were validated by a targeted MS/MS approach in two independent sets of samples from patients (n = 40 and n = 70). In serum, the hypoxanthine/guanosine (HG) and hypoxanthine/inosine (HI) ratios demonstrated high diagnostic performance (Sensitivity/Specificity > 80%) for the discrimination of samples with a TTC > 1 h. We identified several eicosanoids, such as 12-HETE, 15-(S)-HETE, 8-(S)-HETE, 12-oxo-HETE, (±)13-HODE and 12-(S)-HEPE as QIs for a pre-centrifugation delay > 2 h. 12-HETE, 12-oxo-HETE, 8-(S)-HETE, and 12-(S)-HEPE, and the HI- and HG-ratios could be validated in patient samples.
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Affiliation(s)
- Sven Heiling
- Institute of Clinical Chemistry and Laboratory Diagnostics and Integrated Biobank Jena (IBBJ), University Hospital Jena, Am Klinikum 1, 07747 Jena, Germany; (N.K.); (F.S.); (M.R.)
| | - Nadine Knutti
- Institute of Clinical Chemistry and Laboratory Diagnostics and Integrated Biobank Jena (IBBJ), University Hospital Jena, Am Klinikum 1, 07747 Jena, Germany; (N.K.); (F.S.); (M.R.)
| | - Franziska Scherr
- Institute of Clinical Chemistry and Laboratory Diagnostics and Integrated Biobank Jena (IBBJ), University Hospital Jena, Am Klinikum 1, 07747 Jena, Germany; (N.K.); (F.S.); (M.R.)
| | - Jörg Geiger
- Interdisciplinary Bank of Biological Material and Data Würzburg (IBDW), Straubmühlweg 2a, Haus A9, 97078 Würzburg, Germany; (J.G.); (R.J.)
| | - Juliane Weikert
- Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University Hospital Leipzig, 04103 Leipzig, Germany; (J.W.); (U.C.)
- LIFE Leipzig Research Center for Civilization Diseases, University of Leipzig, 04103 Leipzig, Germany
| | - Michael Rose
- Institute of Clinical Chemistry and Laboratory Diagnostics and Integrated Biobank Jena (IBBJ), University Hospital Jena, Am Klinikum 1, 07747 Jena, Germany; (N.K.); (F.S.); (M.R.)
| | - Roland Jahns
- Interdisciplinary Bank of Biological Material and Data Würzburg (IBDW), Straubmühlweg 2a, Haus A9, 97078 Würzburg, Germany; (J.G.); (R.J.)
| | - Uta Ceglarek
- Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University Hospital Leipzig, 04103 Leipzig, Germany; (J.W.); (U.C.)
- LIFE Leipzig Research Center for Civilization Diseases, University of Leipzig, 04103 Leipzig, Germany
| | - André Scherag
- Institute of Medical Statistics, Computer and Data Sciences, Jena University Hospital, Bachstrasse 18, 07743 Jena, Germany;
| | - Michael Kiehntopf
- Institute of Clinical Chemistry and Laboratory Diagnostics and Integrated Biobank Jena (IBBJ), University Hospital Jena, Am Klinikum 1, 07747 Jena, Germany; (N.K.); (F.S.); (M.R.)
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23
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Chemometric applications in metabolomic studies using chromatography-mass spectrometry. Trends Analyt Chem 2021. [DOI: 10.1016/j.trac.2020.116165] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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Du Y, Wei J, Zhang Z, Yang X, Wang M, Wang Y, Qi X, Zhao L, Tian Y, Guo W, Wang Q, Deng W, Li M, Lin D, Li T, Ma X. Plasma Metabolomics Profiling of Metabolic Pathways Affected by Major Depressive Disorder. Front Psychiatry 2021; 12:644555. [PMID: 34646171 PMCID: PMC8502978 DOI: 10.3389/fpsyt.2021.644555] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Accepted: 08/17/2021] [Indexed: 02/05/2023] Open
Abstract
Background: Major depressive disorder (MDD) is a common disease which is complicated by metabolic disorder. Although MDD has been studied relatively intensively, its metabolism is yet to be elucidated. Methods: To profile the global pathophysiological processes of MDD patients, we used metabolomics to identify differential metabolites and applied a new database Metabolite set enrichment analysis (MSEA) to discover dysfunctions of metabolic pathways of this disease. Hydrophilic metabolomics were applied to identify metabolites by profiling the plasma from 55 MDD patients and 100 sex-, gender-, BMI-matched healthy controls. The metabolites were then analyzed in MSEA in an attempt to discover different metabolic pathways. To investigate dysregulated pathways, we further divided MDD patients into two cohorts: (1) MDD patients with anxiety symptoms and (2) MDD patients without anxiety symptoms. Results: Metabolites which were hit in those pathways correlated with depressive and anxiety symptoms. Altogether, 17 metabolic pathways were enriched in MDD patients, and 23 metabolites were hit in those pathways. Three metabolic pathways were enriched in MDD patients without anxiety, including glycine and serine metabolism, arginine and proline metabolism, and phenylalanine and tyrosine metabolism. In addition, L-glutamic acid was positively correlated with the severity of depression and retardation if hit in MDD patients without anxiety symptoms. Conclusions: Different kinds of metabolic pathophysiological processes were found in MDD patients. Disorder of glycine and serine metabolism was observed in both MDD patients with anxiety and those without.
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Affiliation(s)
- Yue Du
- Psychiatric Laboratory and Mental Health Center, West China Hospital of Sichuan University, Chengdu, China
| | - Jinxue Wei
- Psychiatric Laboratory and Mental Health Center, West China Hospital of Sichuan University, Chengdu, China.,West China Brain Research Center, West China Hospital of Sichuan University, Chengdu, China
| | - Zijian Zhang
- Psychiatric Laboratory and Mental Health Center, West China Hospital of Sichuan University, Chengdu, China
| | - Xiao Yang
- Psychiatric Laboratory and Mental Health Center, West China Hospital of Sichuan University, Chengdu, China
| | - Min Wang
- Psychiatric Laboratory and Mental Health Center, West China Hospital of Sichuan University, Chengdu, China
| | - Yu Wang
- Psychiatric Laboratory and Mental Health Center, West China Hospital of Sichuan University, Chengdu, China
| | - Xiongwei Qi
- Psychiatric Laboratory and Mental Health Center, West China Hospital of Sichuan University, Chengdu, China
| | - Liansheng Zhao
- Psychiatric Laboratory and Mental Health Center, West China Hospital of Sichuan University, Chengdu, China.,West China Brain Research Center, West China Hospital of Sichuan University, Chengdu, China
| | - Yang Tian
- Psychiatric Laboratory and Mental Health Center, West China Hospital of Sichuan University, Chengdu, China
| | - Wanjun Guo
- Psychiatric Laboratory and Mental Health Center, West China Hospital of Sichuan University, Chengdu, China.,West China Brain Research Center, West China Hospital of Sichuan University, Chengdu, China
| | - Qiang Wang
- Psychiatric Laboratory and Mental Health Center, West China Hospital of Sichuan University, Chengdu, China.,West China Brain Research Center, West China Hospital of Sichuan University, Chengdu, China
| | - Wei Deng
- Psychiatric Laboratory and Mental Health Center, West China Hospital of Sichuan University, Chengdu, China.,West China Brain Research Center, West China Hospital of Sichuan University, Chengdu, China
| | - Minli Li
- Psychiatric Laboratory and Mental Health Center, West China Hospital of Sichuan University, Chengdu, China.,West China Brain Research Center, West China Hospital of Sichuan University, Chengdu, China
| | - Dongtao Lin
- College of Foreign Languages and Cultures, Sichuan University, Chengdu, China
| | - Tao Li
- Psychiatric Laboratory and Mental Health Center, West China Hospital of Sichuan University, Chengdu, China.,West China Brain Research Center, West China Hospital of Sichuan University, Chengdu, China
| | - Xiaohong Ma
- Psychiatric Laboratory and Mental Health Center, West China Hospital of Sichuan University, Chengdu, China.,West China Brain Research Center, West China Hospital of Sichuan University, Chengdu, China
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Hoang Anh N, Min JE, Kim SJ, Phuoc Long N. Biotherapeutic Products, Cellular Factories, and Multiomics Integration in Metabolic Engineering. ACTA ACUST UNITED AC 2020; 24:621-633. [DOI: 10.1089/omi.2020.0112] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Nguyen Hoang Anh
- College of Pharmacy, Seoul National University, Seoul, South Korea
| | - Jung Eun Min
- College of Pharmacy, Seoul National University, Seoul, South Korea
| | - Sun Jo Kim
- College of Pharmacy, Seoul National University, Seoul, South Korea
| | - Nguyen Phuoc Long
- Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul, South Korea
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Bedair M, Glenn KC. Evaluation of the use of untargeted metabolomics in the safety assessment of genetically modified crops. Metabolomics 2020; 16:111. [PMID: 33037482 PMCID: PMC7547035 DOI: 10.1007/s11306-020-01733-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Accepted: 09/29/2020] [Indexed: 01/22/2023]
Abstract
BACKGROUND The safety assessment of foods and feeds from genetically modified (GM) crops includes the comparison of key characteristics, such as crop composition, agronomic phenotype and observations from animal feeding studies compared to conventional counterpart varieties that have a history of safe consumption, often including a near isogenic variety. The comparative compositional analysis of GM crops has been based on targeted, validated, quantitative analytical methods for the key food and feed nutrients and antinutrients for each crop, as identified by Organization of Economic Co-operation and Development (OCED). As technologies for untargeted metabolomic methods have evolved, proposals have emerged for their use to complement or replace targeted compositional analytical methods in regulatory risk assessments of GM crops to increase the number of analyzed metabolites. AIM OF REVIEW The technical opportunities, challenges and strategies of including untargeted metabolomics analysis in the comparative safety assessment of GM crops are reviewed. The results from metabolomics studies of GM and conventional crops published over the last eight years provide context to enable the discussion of whether metabolomics can materially improve the risk assessment of food and feed from GM crops beyond that possible by the Codex-defined practices used worldwide for more than 25 years. KEY SCIENTIFIC CONCEPTS OF REVIEW Published studies to date show that environmental and genetic factors affect plant metabolomics profiles. In contrast, the plant biotechnology process used to make GM crops has little, if any consequence, unless the inserted GM trait is intended to alter food or feed composition. The nutritional value and safety of food and feed from GM crops is well informed by the quantitative, validated compositional methods for list of key analytes defined by crop-specific OECD consensus documents. Untargeted metabolic profiling has yet to provide data that better informs the safety assessment of GM crops than the already rigorous Codex-defined quantitative comparative assessment. Furthermore, technical challenges limit the implementation of untargeted metabolomics for regulatory purposes: no single extraction method or analytical technique captures the complete plant metabolome; a large percentage of metabolites features are unknown, requiring additional research to understand if differences for such unknowns affect food/feed safety; and standardized methods are needed to provide reproducible data over time and laboratories.
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Apolipoprotein A1 and Ceruloplasmin, the key crosstalk players between the liver and adipose tissue during early postpartum of buffaloes: An in-Silico transcriptome based network analysis. Comput Biol Med 2020; 126:104024. [PMID: 33059235 DOI: 10.1016/j.compbiomed.2020.104024] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Revised: 09/04/2020] [Accepted: 09/26/2020] [Indexed: 11/20/2022]
Abstract
Physiological transition from pregnancy to lactation during early postpartum creates a high-energy demand in females towards milk production for the survival of new offspring. Ruminant milk contains high amount of lactose, a disaccharide of the glucose and galactose. The milk yield of the animals is also dependent on the lactose amount. In ruminants, glucose is majorly supplied by the liver, which obtains energy from the adipose tissue during energy demands. Therefore, there should be an intricate crosstalk between these two tissues for efficient maintenance of energy for normal physiological needs and milk production. In the present study, we analyzed the transcriptome data previously obtained from the buffalo liver and adipose tissue on the 15th day and 30th day of lactation by using several bioinformatics tools such as PANTHER, Secretome-P, STRING and CPDB. Our analysis identified a total of 24 signaling molecules as interactive players between the liver and adipose tissue during early postpartum of buffaloes. Particularly, the liver appears to interact with the adipose tissue and itself majorly through an endocrine/autocrine molecule, APOA1. Similarly, the adipose tissue appears to interact with the liver and itself majorly through an endocrine/autocrine molecule, CP (ceruloplasmin). The APOA1 and CP may counteract with each other on lipolysis in buffalo adipose tissue because of their common signaling molecules being shared. In addition, the interaction between the adipose derived ceruloplasmin and the liver derived lactoferrin seems to be important during early postpartum of buffaloes. The importance of this interaction needs to be studied in further studies.
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Abstract
This manuscript outlines a straight-forward procedure for generating a map of similarity between spectra of a set. When applied to a reference set of spectra for Type I fentanyl analogs (molecules differing from fentanyl by a single modification), the map illuminates clustering that is applicable to automated structure assignment of unidentified molecules. An open-source software implementation that generates mass spectral similarity mappings of unknowns against a library of Type I fentanyl analog spectra is available at http://github.com/asm3-nist/FentanylClassifier.
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30
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Li S, Tian Y, Jiang P, Lin Y, Liu X, Yang H. Recent advances in the application of metabolomics for food safety control and food quality analyses. Crit Rev Food Sci Nutr 2020; 61:1448-1469. [PMID: 32441547 DOI: 10.1080/10408398.2020.1761287] [Citation(s) in RCA: 151] [Impact Index Per Article: 37.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
As one of the omics fields, metabolomics has unique advantages in facilitating the understanding of physiological and pathological activities in biology, physiology, pathology, and food science. In this review, based on developments in analytical chemistry tools, cheminformatics, and bioinformatics methods, we highlight the current applications of metabolomics in food safety, food authenticity and quality, and food traceability. Additionally, the combined use of metabolomics with other omics techniques for "foodomics" is comprehensively described. Finally, the latest developments and advances, practical challenges and limitations, and requirements related to the application of metabolomics are critically discussed, providing new insight into the application of metabolomics in food analysis.
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Affiliation(s)
- Shubo Li
- College of Light Industry and Food Engineering, Guangxi University, Nanning, China
| | - Yufeng Tian
- College of Light Industry and Food Engineering, Guangxi University, Nanning, China
| | - Pingyingzi Jiang
- College of Light Industry and Food Engineering, Guangxi University, Nanning, China
| | - Ying Lin
- College of Light Industry and Food Engineering, Guangxi University, Nanning, China
| | - Xiaoling Liu
- College of Light Industry and Food Engineering, Guangxi University, Nanning, China
| | - Hongshun Yang
- Department of Food Science & Technology, National University of Singapore, Singapore, Singapore
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Waller TC, Berg JA, Lex A, Chapman BE, Rutter J. Compartment and hub definitions tune metabolic networks for metabolomic interpretations. Gigascience 2020; 9:giz137. [PMID: 31972021 PMCID: PMC6977586 DOI: 10.1093/gigascience/giz137] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2018] [Revised: 08/31/2019] [Accepted: 10/27/2019] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Metabolic networks represent all chemical reactions that occur between molecular metabolites in an organism's cells. They offer biological context in which to integrate, analyze, and interpret omic measurements, but their large scale and extensive connectivity present unique challenges. While it is practical to simplify these networks by placing constraints on compartments and hubs, it is unclear how these simplifications alter the structure of metabolic networks and the interpretation of metabolomic experiments. RESULTS We curated and adapted the latest systemic model of human metabolism and developed customizable tools to define metabolic networks with and without compartmentalization in subcellular organelles and with or without inclusion of prolific metabolite hubs. Compartmentalization made networks larger, less dense, and more modular, whereas hubs made networks larger, more dense, and less modular. When present, these hubs also dominated shortest paths in the network, yet their exclusion exposed the subtler prominence of other metabolites that are typically more relevant to metabolomic experiments. We applied the non-compartmental network without metabolite hubs in a retrospective, exploratory analysis of metabolomic measurements from 5 studies on human tissues. Network clusters identified individual reactions that might experience differential regulation between experimental conditions, several of which were not apparent in the original publications. CONCLUSIONS Exclusion of specific metabolite hubs exposes modularity in both compartmental and non-compartmental metabolic networks, improving detection of relevant clusters in omic measurements. Better computational detection of metabolic network clusters in large data sets has potential to identify differential regulation of individual genes, transcripts, and proteins.
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Affiliation(s)
- T Cameron Waller
- Division of Medical Genetics, Department of Medicine, School of Medicine, University of California San Diego, Room 1318A, 9500 Gilman Drive #0606, La Jolla, California 92093-0606, United States of America
- Department of Biochemistry, School of Medicine, University of Utah, Room 4100, 15 North Medical Drive East, Salt Lake City, Utah 84112, USA
| | - Jordan A Berg
- Department of Biochemistry, School of Medicine, University of Utah, Room 4100, 15 North Medical Drive East, Salt Lake City, Utah 84112, USA
| | - Alexander Lex
- School of Computing, University of Utah, Room 3190, 50 South Central Campus Drive, Salt Lake City, Utah 84112, USA
- Scientific Computing and Imaging Institute, University of Utah, Room 3750, 72 South Central Campus Drive, Salt Lake City, Utah 84112, USA
| | - Brian E Chapman
- Department of Radiology and Imaging Sciences, School of Medicine, University of Utah, Room 1A071, 30 North 1900 East, Salt Lake City, Utah 84132, USA
- Department of Biomedical Informatics, School of Medicine, University of Utah, Suite 140, 421 Wakara Way, Salt Lake City, Utah 84108, USA
| | - Jared Rutter
- Department of Biochemistry, School of Medicine, University of Utah, Room 4100, 15 North Medical Drive East, Salt Lake City, Utah 84112, USA
- Howard Hughes Medical Institute, School of Medicine, University of Utah, Room AC101, 30 North 1900 East, Salt Lake City, Utah 84132, USA
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Reisdorph NA, Walmsley S, Reisdorph R. A Perspective and Framework for Developing Sample Type Specific Databases for LC/MS-Based Clinical Metabolomics. Metabolites 2019; 10:metabo10010008. [PMID: 31877765 PMCID: PMC7023092 DOI: 10.3390/metabo10010008] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2019] [Revised: 12/10/2019] [Accepted: 12/18/2019] [Indexed: 02/06/2023] Open
Abstract
Metabolomics has the potential to greatly impact biomedical research in areas such as biomarker discovery and understanding molecular mechanisms of disease. However, compound identification (ID) remains a major challenge in liquid chromatography mass spectrometry-based metabolomics. This is partly due to a lack of specificity in metabolomics databases. Though impressive in depth and breadth, the sheer magnitude of currently available databases is in part what makes them ineffective for many metabolomics studies. While still in pilot phases, our experience suggests that custom-built databases, developed using empirical data from specific sample types, can significantly improve confidence in IDs. While the concept of sample type specific databases (STSDBs) and spectral libraries is not entirely new, inclusion of unique descriptors such as detection frequency and quality scores, can be used to increase confidence in results. These features can be used alone to judge the quality of a database entry, or together to provide filtering capabilities. STSDBs rely on and build upon several available tools for compound ID and are therefore compatible with current compound ID strategies. Overall, STSDBs can potentially result in a new paradigm for translational metabolomics, whereby investigators confidently know the identity of compounds following a simple, single STSDB search.
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Affiliation(s)
- Nichole A. Reisdorph
- Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, 12850 East Montview Boulevard, Aurora, CO 80045, USA;
- Correspondence: ; Tel.: +1-303-724-9234
| | - Scott Walmsley
- Masonic Cancer Center, University of Minnesota, 516 Delaware St. SE, Minneapolis, MN 55455, USA;
- Institute for Health Informatics, University of Minnesota, 516 Delaware St. SE, Minneapolis, MN 55455, USA
| | - Rick Reisdorph
- Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, 12850 East Montview Boulevard, Aurora, CO 80045, USA;
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Lucaciu R, Pelikan C, Gerner SM, Zioutis C, Köstlbacher S, Marx H, Herbold CW, Schmidt H, Rattei T. A Bioinformatics Guide to Plant Microbiome Analysis. FRONTIERS IN PLANT SCIENCE 2019; 10:1313. [PMID: 31708944 PMCID: PMC6819368 DOI: 10.3389/fpls.2019.01313] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/24/2019] [Accepted: 09/20/2019] [Indexed: 05/18/2023]
Abstract
Recent evidence for intimate relationship of plants with their microbiota shows that plants host individual and diverse microbial communities that are essential for their survival. Understanding their relatedness using genome-based and high-throughput techniques remains a hot topic in microbiome research. Molecular analysis of the plant holobiont necessitates the application of specific sampling and preparatory steps that also consider sources of unwanted information, such as soil, co-amplified plant organelles, human DNA, and other contaminations. Here, we review state-of-the-art and present practical guidelines regarding experimental and computational aspects to be considered in molecular plant-microbiome studies. We discuss sequencing and "omics" techniques with a focus on the requirements needed to adapt these methods to individual research approaches. The choice of primers and sequence databases is of utmost importance for amplicon sequencing, while the assembly and binning of shotgun metagenomic sequences is crucial to obtain quality data. We discuss specific bioinformatic workflows to overcome the limitation of genome database resources and for covering large eukaryotic genomes such as fungi. In transcriptomics, it is necessary to account for the separation of host mRNA or dual-RNAseq data. Metaproteomics approaches provide a snapshot of the protein abundances within a plant tissue which requires the knowledge of complete and well-annotated plant genomes, as well as microbial genomes. Metabolomics offers a powerful tool to detect and quantify small molecules and molecular changes at the plant-bacteria interface if the necessary requirements with regard to (secondary) metabolite databases are considered. We highlight data integration and complementarity which should help to widen our understanding of the interactions among individual players of the plant holobiont in the future.
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Affiliation(s)
| | | | | | | | | | | | | | - Hannes Schmidt
- Department of Microbiology and Ecosystem Science, University of Vienna, Vienna, Austria
| | - Thomas Rattei
- Department of Microbiology and Ecosystem Science, University of Vienna, Vienna, Austria
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Bioinformatics for Marine Products: An Overview of Resources, Bottlenecks, and Perspectives. Mar Drugs 2019; 17:md17100576. [PMID: 31614509 PMCID: PMC6835618 DOI: 10.3390/md17100576] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2019] [Revised: 10/01/2019] [Accepted: 10/02/2019] [Indexed: 12/13/2022] Open
Abstract
The sea represents a major source of biodiversity. It exhibits many different ecosystems in a huge variety of environmental conditions where marine organisms have evolved with extensive diversification of structures and functions, making the marine environment a treasure trove of molecules with potential for biotechnological applications and innovation in many different areas. Rapid progress of the omics sciences has revealed novel opportunities to advance the knowledge of biological systems, paving the way for an unprecedented revolution in the field and expanding marine research from model organisms to an increasing number of marine species. Multi-level approaches based on molecular investigations at genomic, metagenomic, transcriptomic, metatranscriptomic, proteomic, and metabolomic levels are essential to discover marine resources and further explore key molecular processes involved in their production and action. As a consequence, omics approaches, accompanied by the associated bioinformatic resources and computational tools for molecular analyses and modeling, are boosting the rapid advancement of biotechnologies. In this review, we provide an overview of the most relevant bioinformatic resources and major approaches, highlighting perspectives and bottlenecks for an appropriate exploitation of these opportunities for biotechnology applications from marine resources.
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López-Ibáñez J, Martín LT, Chagoyen M, Pazos F. Bacterial Feature Finder (BaFF)-a system for extracting features overrepresented in sets of prokaryotic organisms. Bioinformatics 2019; 35:3482-3483. [PMID: 30844057 DOI: 10.1093/bioinformatics/btz099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2018] [Revised: 12/21/2018] [Accepted: 02/12/2019] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION The results of some experimental and computational techniques are given in terms of large sets of organisms, especially prokaryotic. While their distinctive features can provide useful data regarding specific phenomenon, there are no automated tools for extracting them. RESULTS We present here the Bacterial Feature Finder web server, a tool to automatically interrogate sets of prokaryotic organisms provided by the user to evaluate their specific biological features. At the core of the system is a searchable database of qualitative and quantitative features compiled for more than 23 000 prokaryotic organisms. Both the input set of organisms and the background set used to calculate the enriched features can be directly provided by the user, or they can be obtained by searching the database. The results are presented via an interactive graphical interface, with links to external resources. AVAILABILITY AND IMPLEMENTATION The web server is freely available at http://csbg.cnb.csic.es/BaFF. It has been tested in the main web browsers and does not require any especial plug-ins or additional software. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Javier López-Ibáñez
- Computational Systems Biology Group, Systems Biology Program, Spanish National Centre for Biotechnology (CNB-CSIC), Madrid, Spain
| | - Laura T Martín
- Computational Systems Biology Group, Systems Biology Program, Spanish National Centre for Biotechnology (CNB-CSIC), Madrid, Spain
| | - Mónica Chagoyen
- Computational Systems Biology Group, Systems Biology Program, Spanish National Centre for Biotechnology (CNB-CSIC), Madrid, Spain
| | - Florencio Pazos
- Computational Systems Biology Group, Systems Biology Program, Spanish National Centre for Biotechnology (CNB-CSIC), Madrid, Spain
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Nuñez JR, Colby SM, Thomas DG, Tfaily MM, Tolic N, Ulrich EM, Sobus JR, Metz TO, Teeguarden JG, Renslow RS. Evaluation of In Silico Multifeature Libraries for Providing Evidence for the Presence of Small Molecules in Synthetic Blinded Samples. J Chem Inf Model 2019; 59:4052-4060. [PMID: 31430141 DOI: 10.1021/acs.jcim.9b00444] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The current gold standard for unambiguous molecular identification in metabolomics analysis is comparing two or more orthogonal properties from the analysis of authentic reference materials (standards) to experimental data acquired in the same laboratory with the same analytical methods. This represents a significant limitation for comprehensive chemical identification of small molecules in complex samples. The process is time consuming and costly, and the majority of molecules are not yet represented by standards. Thus, there is a need to assemble evidence for the presence of small molecules in complex samples through the use of libraries containing calculated chemical properties. To address this need, we developed a Multi-Attribute Matching Engine (MAME) and a library derived in part from our in silico chemical library engine (ISiCLE). Here, we describe an initial evaluation of these methods in a blinded analysis of synthetic chemical mixtures as part of the U.S. Environmental Protection Agency's (EPA) Non-Targeted Analysis Collaborative Trial (ENTACT, Phase 1). For molecules in all mixtures, the initial blinded false negative rate (FNR), false discovery rate (FDR), and accuracy were 57%, 77%, and 91%, respectively. For high evidence scores, the FDR was 35%. After unblinding of the sample compositions, we optimized the scoring parameters to better exploit the available evidence and increased the accuracy for molecules suspected as present. The final FNR, FDR, and accuracy were 67%, 53%, and 96%, respectively. For high evidence scores, the FDR was 10%. This study demonstrates that multiattribute matching methods in conjunction with in silico libraries may one day enable reduced reliance on experimentally derived libraries for building evidence for the presence of molecules in complex samples.
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Affiliation(s)
- Jamie R Nuñez
- Earth and Biological Sciences Directorate , Pacific Northwest National Laboratory , Richland , Washington 99354 , United States
| | - Sean M Colby
- Earth and Biological Sciences Directorate , Pacific Northwest National Laboratory , Richland , Washington 99354 , United States
| | - Dennis G Thomas
- Earth and Biological Sciences Directorate , Pacific Northwest National Laboratory , Richland , Washington 99354 , United States
| | - Malak M Tfaily
- Earth and Biological Sciences Directorate , Pacific Northwest National Laboratory , Richland , Washington 99354 , United States.,Department of Environmental Science , University of Arizona , Tucson 85712 , United States
| | - Nikola Tolic
- Earth and Biological Sciences Directorate , Pacific Northwest National Laboratory , Richland , Washington 99354 , United States
| | - Elin M Ulrich
- U.S. Environmental Protection Agency, Office of Research and Development , National Exposure Research Laboratory , Research Triangle Park , North Carolina 27711 , United States
| | - Jon R Sobus
- U.S. Environmental Protection Agency, Office of Research and Development , National Exposure Research Laboratory , Research Triangle Park , North Carolina 27711 , United States
| | - Thomas O Metz
- Earth and Biological Sciences Directorate , Pacific Northwest National Laboratory , Richland , Washington 99354 , United States
| | - Justin G Teeguarden
- Earth and Biological Sciences Directorate , Pacific Northwest National Laboratory , Richland , Washington 99354 , United States.,Department of Environmental and Molecular Toxicology , Oregon State University , Corvallis , Oregon 97331 , United States
| | - Ryan S Renslow
- Earth and Biological Sciences Directorate , Pacific Northwest National Laboratory , Richland , Washington 99354 , United States
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37
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Playdon MC, Joshi AD, Tabung FK, Cheng S, Henglin M, Kim A, Lin T, van Roekel EH, Huang J, Krumsiek J, Wang Y, Mathé E, Temprosa M, Moore S, Chawes B, Eliassen AH, Gsur A, Gunter MJ, Harada S, Langenberg C, Oresic M, Perng W, Seow WJ, Zeleznik OA. Metabolomics Analytics Workflow for Epidemiological Research: Perspectives from the Consortium of Metabolomics Studies (COMETS). Metabolites 2019; 9:E145. [PMID: 31319517 PMCID: PMC6681081 DOI: 10.3390/metabo9070145] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2019] [Revised: 06/28/2019] [Accepted: 07/04/2019] [Indexed: 12/13/2022] Open
Abstract
The application of metabolomics technology to epidemiological studies is emerging as a new approach to elucidate disease etiology and for biomarker discovery. However, analysis of metabolomics data is complex and there is an urgent need for the standardization of analysis workflow and reporting of study findings. To inform the development of such guidelines, we conducted a survey of 47 cohort representatives from the Consortium of Metabolomics Studies (COMETS) to gain insights into the current strategies and procedures used for analyzing metabolomics data in epidemiological studies worldwide. The results indicated a variety of applied analytical strategies, from biospecimen and data pre-processing and quality control to statistical analysis and reporting of study findings. These strategies included methods commonly used within the metabolomics community and applied in epidemiological research, as well as novel approaches to pre-processing pipelines and data analysis. To help with these discrepancies, we propose use of open-source initiatives such as the online web-based tool COMETS Analytics, which includes helpful tools to guide analytical workflow and the standardized reporting of findings from metabolomics analyses within epidemiological studies. Ultimately, this will improve the quality of statistical analyses, research findings, and study reproducibility.
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Affiliation(s)
- Mary C Playdon
- Department of Nutrition and Integrative Physiology, College of Health, University of Utah, Salt Lake City, UT 84112, USA.
- Division of Cancer Population Sciences, Huntsman Cancer Institute, Salt Lake City, UT 84112, USA.
| | - Amit D Joshi
- Clinical and Translational Epidemiology Unit, Mongan Institute, Massachusetts General Hospital, Boston, MA 02114, USA
- Division of Gastroenterology, Department of Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
- Program in Genetic Epidemiology and Statistical Genetics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA
| | - Fred K Tabung
- Division of Medical Oncology, Department of Internal Medicine, The Ohio State University College of Medicine, Columbus, OH 43210, USA
- The Ohio State University Comprehensive Cancer Center, Arthur G. James Cancer Hospital and Richard J. Solove Research Institute, Columbus, OH 43210, USA
- Division of Epidemiology, The Ohio State University College of Public Health, Columbus, OH 43210, USA
| | - Susan Cheng
- Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Mir Henglin
- Cardiovascular Division, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Andy Kim
- Cardiovascular Division, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Tengda Lin
- Division of Cancer Population Sciences, Huntsman Cancer Institute, Salt Lake City, UT 84112, USA
- Department of Population Health Sciences, School of Medicine, University of Utah, Salt Lake City, UT 84112, USA
| | - Eline H van Roekel
- Department of Epidemiology, GROW School for Oncology and Developmental Biology, Maastricht University, 6200 MD Maastricht, The Netherlands
| | - Jiaqi Huang
- Division of Cancer Epidemiology and Genetics, Metabolic Epidemiology Branch, National Cancer Institute, Rockville, MD 20850, USA
| | - Jan Krumsiek
- Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10021, USA
| | - Ying Wang
- Behavioral and Epidemiology Research Group, American Cancer Society, Atlanta, GA 30303, USA
| | - Ewy Mathé
- College of Medicine, Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA
| | - Marinella Temprosa
- Department of Epidemiology and Biostatistics, Milken Institute School of Public Health, George Washington University, Washington, DC 20052, USA
| | - Steven Moore
- Division of Cancer Epidemiology and Genetics, Metabolic Epidemiology Branch, National Cancer Institute, Rockville, MD 20850, USA
| | - Bo Chawes
- COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, 1165 Copenhagen, Denmark
| | - A Heather Eliassen
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Andrea Gsur
- Institute of Cancer Research, Department of Medicine, Medical University of Vienna, 1090 Vienna, Austria
| | - Marc J Gunter
- Section of Nutrition and Metabolism, International Agency for Research on Cancer, World Health Organization, 69008 Lyon, France
| | - Sei Harada
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo 160-8582, Japan
| | - Claudia Langenberg
- MRC Epidemiology Unit, Public Health, University of Cambridge, Cambridge CB2 1 TN, UK
- The Francis Crick Institute, London NW1 1ST, UK
| | - Matej Oresic
- Turku Centre for Biotechnology, University of Turku, 20500 Turku, Finland
- School of Medical Sciences, Örebro University, 702 81 Örebro, Sweden
| | - Wei Perng
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO 80045, USA
- Life course epidemiology of adiposity and diabetes (LEAD) Center, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Wei Jie Seow
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore 117549, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore 119228, Singapore
| | - Oana A Zeleznik
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
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Vodopivec M, Lah L, Narat M, Curk T. Metabolomic profiling of CHO fed-batch growth phases at 10, 100, and 1,000 L. Biotechnol Bioeng 2019; 116:2720-2729. [PMID: 31184374 DOI: 10.1002/bit.27087] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2019] [Revised: 05/17/2019] [Accepted: 06/03/2019] [Indexed: 01/08/2023]
Abstract
Established bioprocess monitoring is based on quick and reliable methods, including cell count and viability measurement, extracellular metabolite measurement, and the measurement of physicochemical qualities of the cultivation medium. These methods are sufficient for monitoring of process performance, but rarely give insight into the actual physiological states of the cell culture. However, understanding of the latter is essential for optimization of bioprocess development. Our study used LC-MS metabolomics as a tool for additional resolution of bioprocess monitoring and was designed at three bioreactors scales (10 L, 100 L, and 1,000 L) to gain insight into the basal metabolic states of the Chinese hamster ovary (CHO) cell culture during fed-batch. Metabolites characteristics of the four growth stages (early and late exponential phase, stationary phase, and the phase of decline) were identified by multivariate analysis. Enriched metabolic pathways were then established for each growth phase using the CHO metabolic network model. Biomass generation and nucleotide synthesis were enriched in early exponential phase, followed by increased protein production and imbalanced glutathione metabolism in late exponential phase. Glycolysis became downregulated in stationary phase and amino-acid metabolism increased. Phase of culture decline resulted in rise of oxidized glutathione and fatty acid concentrations. Intracellular metabolic profiles of the CHO fed-batch culture were also shown to be consistent with scale and thus demonstrate metabolomic profiling as an informative method to gain physiological insight into the cell culture states during bioprocess regardless of scale.
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Affiliation(s)
- Maja Vodopivec
- Bioprocess Development, Technical Development Biologics Mengeš, Novartis Technical Research & Development, Lek Pharmaceuticals d.d, Slovenia
| | - Ljerka Lah
- Bioprocess Development, Technical Development Biologics Mengeš, Novartis Technical Research & Development, Lek Pharmaceuticals d.d, Slovenia
| | - Mojca Narat
- Department of Animal Science, Biotechnical Faculty, University of Ljubljana, Ljubljana, Slovenia
| | - Tomaž Curk
- Bioinformatics Laboratory, Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenija
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González-Peña D, Brennan L. Recent Advances in the Application of Metabolomics for Nutrition and Health. Annu Rev Food Sci Technol 2019; 10:479-519. [DOI: 10.1146/annurev-food-032818-121715] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Metabolomics is the study of small molecules called metabolites in biological samples. Application of metabolomics to nutrition research has expanded in recent years, with emerging literature supporting multiple applications. Key examples include applications of metabolomics in the identification and development of objective biomarkers of dietary intake, in developing personalized nutrition strategies, and in large-scale epidemiology studies to understand the link between diet and health. In this review, we provide an overview of the current applications and identify key challenges that need to be addressed for the further development of the field. Successful development of metabolomics for nutrition research has the potential to improve dietary assessment, help deliver personalized nutrition, and enhance our understanding of the link between diet and health.
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Affiliation(s)
- Diana González-Peña
- School of Agriculture and Food Science, Institute of Food and Health, University College Dublin, Dublin 4, Ireland;,
| | - Lorraine Brennan
- School of Agriculture and Food Science, Institute of Food and Health, University College Dublin, Dublin 4, Ireland;,
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40
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Kennedy AD, Wittmann BM, Evans AM, Miller LAD, Toal DR, Lonergan S, Elsea SH, Pappan KL. Metabolomics in the clinic: A review of the shared and unique features of untargeted metabolomics for clinical research and clinical testing. JOURNAL OF MASS SPECTROMETRY : JMS 2018; 53:1143-1154. [PMID: 30242936 DOI: 10.1002/jms.4292] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/29/2018] [Revised: 09/10/2018] [Accepted: 09/17/2018] [Indexed: 06/08/2023]
Abstract
Metabolomics is the untargeted measurement of the metabolome, which is composed of the complement of small molecules detected in a biological sample. As such, metabolomic analysis produces a global biochemical phenotype. It is a technology that has been utilized in the research setting for over a decade. The metabolome is directly linked to and is influenced by genetics, epigenetics, environmental factors, and the microbiome-all of which affect health. Metabolomics can be applied to human clinical diagnostics and to other fields such as veterinary medicine, nutrition, exercise, physiology, agriculture/plant biochemistry, and toxicology. Applications of metabolomics in clinical testing are emerging, but several aspects of its use as a clinical test differ from applications focused on research or biomarker discovery and need to be considered for metabolomics clinical test data to have optimum impact, be meaningful, and be used responsibly. In this review, we deconstruct aspects and challenges of metabolomics for clinical testing by illustrating the significance of test design, accurate and precise data acquisition, quality control, data processing, n-of-1 comparison to a reference population, and biochemical pathway analysis. We describe how metabolomics technology is integral to defining individual biochemical phenotypes, elaborates on human health and disease, and fits within the precision medicine landscape. Finally, we conclude by outlining some future steps needed to bring metabolomics into the clinical space and to be recognized by the broader medical and regulatory fields.
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Affiliation(s)
| | | | | | | | | | | | - Sarah H Elsea
- Department of Molecular and Human Genetics and Baylor Genetics, Baylor College of Medicine, Houston, TX, USA
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Kelly RS, McGeachie MJ, Lee-Sarwar KA, Kachroo P, Chu SH, Virkud YV, Huang M, Litonjua AA, Weiss ST, Lasky-Su J. Partial Least Squares Discriminant Analysis and Bayesian Networks for Metabolomic Prediction of Childhood Asthma. Metabolites 2018; 8:metabo8040068. [PMID: 30360514 PMCID: PMC6316795 DOI: 10.3390/metabo8040068] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2018] [Revised: 10/18/2018] [Accepted: 10/18/2018] [Indexed: 01/07/2023] Open
Abstract
To explore novel methods for the analysis of metabolomics data, we compared the ability of Partial Least Squares Discriminant Analysis (PLS-DA) and Bayesian networks (BN) to build predictive plasma metabolite models of age three asthma status in 411 three year olds (n = 59 cases and 352 controls) from the Vitamin D Antenatal Asthma Reduction Trial (VDAART) study. The standard PLS-DA approach had impressive accuracy for the prediction of age three asthma with an Area Under the Curve Convex Hull (AUCCH) of 81%. However, a permutation test indicated the possibility of overfitting. In contrast, a predictive Bayesian network including 42 metabolites had a significantly higher AUCCH of 92.1% (p for difference < 0.001), with no evidence that this accuracy was due to overfitting. Both models provided biologically informative insights into asthma; in particular, a role for dysregulated arginine metabolism and several exogenous metabolites that deserve further investigation as potential causative agents. As the BN model outperformed the PLS-DA model in both accuracy and decreased risk of overfitting, it may therefore represent a viable alternative to typical analytical approaches for the investigation of metabolomics data.
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Affiliation(s)
- Rachel S Kelly
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA.
- Harvard Medical School, Boston, MA 02115, USA.
| | - Michael J McGeachie
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA.
- Harvard Medical School, Boston, MA 02115, USA.
| | - Kathleen A Lee-Sarwar
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA.
- Harvard Medical School, Boston, MA 02115, USA.
- Division of Rheumatology, Immunology and Allergy, Brigham and Women's Hospital, Boston, MA 02115, USA.
| | - Priyadarshini Kachroo
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA.
- Harvard Medical School, Boston, MA 02115, USA.
| | - Su H Chu
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA.
- Harvard Medical School, Boston, MA 02115, USA.
| | - Yamini V Virkud
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA.
- Department of Pediatrics, Massachusetts General Hospital for Children, Boston, MA 02114, USA.
| | - Mengna Huang
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA.
- Harvard Medical School, Boston, MA 02115, USA.
| | - Augusto A Litonjua
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA.
- Harvard Medical School, Boston, MA 02115, USA.
- Division of Pediatric Pulmonary Medicine, Department of Pediatrics, University of Rochester Medical Center, Rochester, NY 14642, USA.
| | - Scott T Weiss
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA.
- Harvard Medical School, Boston, MA 02115, USA.
| | - Jessica Lasky-Su
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA.
- Harvard Medical School, Boston, MA 02115, USA.
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Vuckovic D. Improving metabolome coverage and data quality: advancing metabolomics and lipidomics for biomarker discovery. Chem Commun (Camb) 2018; 54:6728-6749. [PMID: 29888773 DOI: 10.1039/c8cc02592d] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
This Feature Article highlights some of the key challenges within the field of metabolomics and examines what role separation and analytical sciences can play to improve the use of metabolomics in biomarker discovery and personalized medicine. Recent progress in four key areas is highlighted: (i) improving metabolite coverage, (ii) developing accurate methods for unstable metabolites including in vivo global metabolomics methods, (iii) advancing inter-laboratory studies and reference materials and (iv) improving data quality, standardization and quality control of metabolomics studies.
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Affiliation(s)
- Dajana Vuckovic
- Department of Chemistry and Biochemistry, Concordia University, 7141 Sherbrooke Street West, Montréal, Québec H4B 1R6, Canada.
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Tugizimana F, Djami-Tchatchou AT, Steenkamp PA, Piater LA, Dubery IA. Metabolomic Analysis of Defense-Related Reprogramming in Sorghum bicolor in Response to Colletotrichum sublineolum Infection Reveals a Functional Metabolic Web of Phenylpropanoid and Flavonoid Pathways. FRONTIERS IN PLANT SCIENCE 2018; 9:1840. [PMID: 30662445 PMCID: PMC6328496 DOI: 10.3389/fpls.2018.01840] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2018] [Accepted: 11/27/2018] [Indexed: 05/02/2023]
Abstract
The metabolome of a biological system provides a functional readout of the cellular state, thus serving as direct signatures of biochemical events that define the dynamic equilibrium of metabolism and the correlated phenotype. Hence, to elucidate biochemical processes involved in sorghum responses to fungal infection, a liquid chromatography-mass spectrometry-based untargeted metabolomic study was designed. Metabolic alterations of three sorghum cultivars responding to Colletotrichum sublineolum, were investigated. At the 4-leaf growth stage, the plants were inoculated with fungal spore suspensions and the infection monitored over time: 0, 3, 5, 7, and 9 days post inoculation. Non-infected plants were used as negative controls. The metabolite composition of aqueous-methanol extracts were analyzed on an ultra-high performance liquid chromatography system coupled to high-definition mass spectrometry. The acquired multidimensional data were processed to create data matrices for multivariate statistical analysis and chemometric modeling. The computed chemometric models indicated time- and cultivar-related metabolic changes that reflect sorghum responses to the fungal infection. Metabolic pathway and correlation-based network analyses revealed that this multi-component defense response is characterized by a functional metabolic web, containing defense-related molecular cues to counterattack the pathogen invasion. Components of this network are metabolites from a range of interconnected metabolic pathways with the phenylpropanoid and flavonoid pathways being the central hub of the web. One of the key features of this altered metabolism was the accumulation of an array of phenolic compounds, particularly de novo biosynthesis of the antifungal 3-deoxyanthocynidin phytoalexins, apigeninidin, luteolinidin, and related conjugates. The metabolic results were complemented by qRT-PCR gene expression analyses that showed upregulation of defense-related marker genes. Unraveling key characteristics of the biochemical mechanism underlying sorghum-C. sublineolum interactions, provided valuable insights with potential applications in breeding crop plants with enhanced disease resistance. Furthermore, the study contributes to ongoing efforts toward a comprehensive understanding of the regulation and reprogramming of plant metabolism under biotic stress.
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An Overview of Metabolomics Data Analysis: Current Tools and Future Perspectives. COMPREHENSIVE ANALYTICAL CHEMISTRY 2018. [DOI: 10.1016/bs.coac.2018.07.001] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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