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Iman MN, Haslam DE, Liang L, Guo K, Joshipura K, Pérez CM, Clish C, Tucker KL, Manson JE, Bhupathiraju SN, Fukusaki E, Lasky-Su J, Putri SP. Multidisciplinary approach combining food metabolomics and epidemiology identifies meglutol as an important bioactive metabolite in tempe, an Indonesian fermented food. Food Chem 2024; 446:138744. [PMID: 38432131 PMCID: PMC11247955 DOI: 10.1016/j.foodchem.2024.138744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 02/09/2024] [Accepted: 02/10/2024] [Indexed: 03/05/2024]
Abstract
This study introduces a multidisciplinary approach to investigate bioactive food metabolites often overlooked due to their low concentrations. We integrated an in-house food metabolite library (n = 494), a human metabolite library (n = 891) from epidemiological studies, and metabolite pharmacological databases to screen for food metabolites with potential bioactivity. We identified six potential metabolites, including meglutol (3-hydroxy-3-methylglutarate), an understudied low-density lipoprotein (LDL)-lowering compound. We further focused on meglutol as a case study to showcase the range of characterizations achievable with this approach. Green pea tempe was identified to contain the highest meglutol concentration (21.8 ± 4.6 mg/100 g). Furthermore, we identified a significant cross-sectional association between plasma meglutol (per 1-standard deviation) and lower LDL cholesterol in two Hispanic adult cohorts (n = 1,628) (β [standard error]: -5.5 (1.6) mg/dl, P = 0.0005). These findings highlight how multidisciplinary metabolomics can serve as a systematic tool for discovering and enhancing bioactive metabolites in food, such as meglutol, with potential applications in personalized dietary approaches for disease prevention.
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Affiliation(s)
- Marvin N Iman
- Department of Biotechnology, Graduate School of Engineering, Osaka University, Japan
| | - Danielle E Haslam
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Liming Liang
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Kai Guo
- Center for Clinical Research and Health Promotion, Graduate School of Public Health, University of Puerto Rico Medical Sciences Campus, Puerto Rico, USA
| | - Kaumudi Joshipura
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Center for Clinical Research and Health Promotion, Graduate School of Public Health, University of Puerto Rico Medical Sciences Campus, Puerto Rico, USA
| | - Cynthia M Pérez
- Department of Biostatistics and Epidemiology, Graduate School of Public Health, University of Puerto Rico Medical Sciences Campus, Puerto Rico, USA
| | - Clary Clish
- Broad Institute of Massachusetts Institute of Technology and Harvard, USA
| | - Katherine L Tucker
- Department of Biomedical and Nutritional Sciences, University of Massachusetts Lowell, USA
| | - JoAnn E Manson
- Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Shilpa N Bhupathiraju
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Eiichiro Fukusaki
- Department of Biotechnology, Graduate School of Engineering, Osaka University, Japan; Industrial Biotechnology Initiative Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Japan; Osaka University-Shimadzu Omics Innovation Research Laboratories, Osaka University, Japan
| | - Jessica Lasky-Su
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Sastia P Putri
- Department of Biotechnology, Graduate School of Engineering, Osaka University, Japan; Osaka University-Shimadzu Omics Innovation Research Laboratories, Osaka University, Japan.
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Inoue N, Shibata T, Tanaka Y, Taguchi H, Sawada R, Goto K, Momokita S, Aoyagi M, Hirao T, Yamanishi Y. Revealing Comprehensive Food Functionalities and Mechanisms of Action through Machine Learning. J Chem Inf Model 2024. [PMID: 38950938 DOI: 10.1021/acs.jcim.4c00061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/03/2024]
Abstract
Foods possess a range of unexplored functionalities; however, fully identifying these functions through empirical means presents significant challenges. In this study, we have proposed an in silico approach to comprehensively predict the functionalities of foods, encompassing even processed foods. This prediction is accomplished through the utilization of machine learning on biomedical big data. Our focus revolves around disease-related protein pathways, wherein we statistically evaluate how the constituent compounds collaboratively regulate these pathways. The proposed method has been employed across 876 foods and 83 diseases, leading to an extensive revelation of both food functionalities and their underlying operational mechanisms. Additionally, this approach identifies food combinations that potentially affect molecular pathways based on interrelationships between food functions within disease-related pathways. Our proposed method holds potential for advancing preventive healthcare.
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Affiliation(s)
- Nanako Inoue
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
| | - Tomokazu Shibata
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
| | - Yusuke Tanaka
- Research & Development Headquarters, House Foods Group Inc., 1-4 Takanodai, Yotsukaido, Chiba 284-0033, Japan
| | - Hiromu Taguchi
- Research & Development Headquarters, House Foods Group Inc., 1-4 Takanodai, Yotsukaido, Chiba 284-0033, Japan
| | - Ryusuke Sawada
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
- Graduate School of Medicine, Dentistry and Pharmaceutical Science, Okayama University, Shikata-cho, Kita-ku, Okayama 700-8558, Japan
| | - Kenshin Goto
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
| | - Shogo Momokita
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
| | - Morihiro Aoyagi
- Research & Development Headquarters, House Foods Group Inc., 1-4 Takanodai, Yotsukaido, Chiba 284-0033, Japan
| | - Takashi Hirao
- Research & Development Headquarters, House Foods Group Inc., 1-4 Takanodai, Yotsukaido, Chiba 284-0033, Japan
| | - Yoshihiro Yamanishi
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
- Graduate School of Informatics, Nagoya University, Chikusa, Nagoya, Aichi 464-8601, Japan
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Avellaneda-Tamayo JF, Chávez-Hernández AL, Prado-Romero DL, Medina-Franco JL. Chemical Multiverse and Diversity of Food Chemicals. J Chem Inf Model 2024; 64:1229-1244. [PMID: 38356237 PMCID: PMC10900296 DOI: 10.1021/acs.jcim.3c01617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/16/2024]
Abstract
Food chemicals have a fundamental role in our lives, with an extended impact on nutrition, disease prevention, and marked economic implications in the food industry. The number of food chemical compounds in public databases has substantially increased in the past few years, which can be characterized using chemoinformatics approaches. We and other groups explored public food chemical libraries containing up to 26,500 compounds. This study aimed to analyze the chemical contents, diversity, and coverage in the chemical space of food chemicals and additives and, from here on, food components. The approach to food components addressed in this study is a public database with more than 70,000 compounds, including those predicted via omics techniques. It was concluded that food components have distinctive physicochemical properties and constitutional descriptors despite sharing many chemical structures with natural products. Food components, on average, have large molecular weights and several apolar structures with saturated hydrocarbons. Compared to reference databases, food component structures have low scaffold and fingerprint-based diversity and high structural complexity, as measured by the fraction of sp3 carbons. These structural features are associated with a large fraction of macronutrients as lipids. Lipids in food components were decompiled by an analysis of the maximum common substructures. The chemical multiverse representation of food chemicals showed a larger coverage of chemical space than natural products and FDA-approved drugs by using different sets of representations.
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Affiliation(s)
- Juan F Avellaneda-Tamayo
- DIFACQUIM Research Group, Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, Avenida Universidad 3000, Mexico City 04510, Mexico
| | - Ana L Chávez-Hernández
- DIFACQUIM Research Group, Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, Avenida Universidad 3000, Mexico City 04510, Mexico
| | - Diana L Prado-Romero
- DIFACQUIM Research Group, Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, Avenida Universidad 3000, Mexico City 04510, Mexico
| | - José L Medina-Franco
- DIFACQUIM Research Group, Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, Avenida Universidad 3000, Mexico City 04510, Mexico
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Gil-Pichardo A, Sánchez-Ruiz A, Colmenarejo G. Analysis of metabolites in human gut: illuminating the design of gut-targeted drugs. J Cheminform 2023; 15:96. [PMID: 37833792 PMCID: PMC10571276 DOI: 10.1186/s13321-023-00768-y] [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] [Received: 08/17/2023] [Accepted: 10/06/2023] [Indexed: 10/15/2023] Open
Abstract
Gut-targeted drugs provide a new drug modality besides that of oral, systemic molecules, that could tap into the growing knowledge of gut metabolites of bacterial or host origin and their involvement in biological processes and health through their interaction with gut targets (bacterial or host, too). Understanding the properties of gut metabolites can provide guidance for the design of gut-targeted drugs. In the present work we analyze a large set of gut metabolites, both shared with serum or present only in gut, and compare them with oral systemic drugs. We find patterns specific for these two subsets of metabolites that could be used to design drugs targeting the gut. In addition, we develop and openly share a Super Learner model to predict gut permanence, in order to aid in the design of molecules with appropriate profiles to remain in the gut, resulting in molecules with putatively reduced secondary effects and better pharmacokinetics.
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Affiliation(s)
- Alberto Gil-Pichardo
- Biostatistics and Bioinformatics Unit, IMDEA Food, CEI UAM+CSIC, 28049, Madrid, Spain
| | - Andrés Sánchez-Ruiz
- Biostatistics and Bioinformatics Unit, IMDEA Food, CEI UAM+CSIC, 28049, Madrid, Spain
| | - Gonzalo Colmenarejo
- Biostatistics and Bioinformatics Unit, IMDEA Food, CEI UAM+CSIC, 28049, Madrid, Spain.
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Wang D, Li X, Miao Y, Zhang Q. Profiling Chemobiological Connection between Natural Product and Target Space Based on Systematic Analysis. Int J Mol Sci 2023; 24:11265. [PMID: 37511025 PMCID: PMC10378764 DOI: 10.3390/ijms241411265] [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] [Received: 06/15/2023] [Revised: 07/06/2023] [Accepted: 07/07/2023] [Indexed: 07/30/2023] Open
Abstract
Natural products provide valuable starting points for new drugs with unique chemical structures. Here, we retrieve and join the LOTUS natural product database and ChEMBL interaction database to explore the relations and rhythm between chemical features of natural products and biotarget spaces. Our analysis revealed relations between the biogenic pathways of natural products and species taxonomy. Nitrogen-containing natural products were more likely to achieve high activity and have a higher potential to become candidate compounds. An apparent trend existed in the target space of natural products originating from different biological sources. Highly active alkaloids were more related to targets of neurodegenerative or neural diseases. Oligopeptides and polyketides were mainly associated with protein phosphorylation and HDAC receptors. Fatty acids readily intervened in various physiological processes involving prostanoids and leukotrienes. We also used FusionDTA, a deep learning model, to predict the affinity between all LOTUS natural products and 622 therapeutic drug targets, exploring the potential target space for natural products. Our data exploration provided a global perspective on the gaps in the chemobiological space of natural compounds through systematic analysis and prediction of their target space, which can be used for new drug design or natural drug repurposing.
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Affiliation(s)
- Disheng Wang
- Shaanxi Key Laboratory of Natural Products & Chemical Biology, College of Chemistry & Pharmacy, Northwest A&F University, Yangling 712100, China
| | - Xue Li
- Shaanxi Key Laboratory of Natural Products & Chemical Biology, College of Chemistry & Pharmacy, Northwest A&F University, Yangling 712100, China
| | - Yicheng Miao
- Shaanxi Key Laboratory of Natural Products & Chemical Biology, College of Chemistry & Pharmacy, Northwest A&F University, Yangling 712100, China
| | - Qiang Zhang
- Shaanxi Key Laboratory of Natural Products & Chemical Biology, College of Chemistry & Pharmacy, Northwest A&F University, Yangling 712100, China
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Prediction, Discovery, and Characterization of Plant- and Food-Derived Health-Beneficial Bioactive Peptides. Nutrients 2022; 14:nu14224810. [PMID: 36432497 PMCID: PMC9697201 DOI: 10.3390/nu14224810] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 10/31/2022] [Accepted: 11/11/2022] [Indexed: 11/16/2022] Open
Abstract
Nature may have the answer to many of our questions about human, animal, and environmental health. Natural bioactives, especially when harvested from sustainable plant and food sources, provide a plethora of molecular solutions to nutritionally actionable, chronic conditions. The spectrum of these conditions, such as metabolic, immune, and gastrointestinal disorders, has changed with prolonged human life span, which should be matched with an appropriately extended health span, which would in turn favour more sustainable health care: "adding years to life and adding life to years". To date, bioactive peptides have been undervalued and underexploited as food ingredients and drugs. The future of translational science on bioactive peptides-and natural bioactives in general-is being built on (a) systems-level rather than reductionist strategies for understanding their interdependent, and at times synergistic, functions; and (b) the leverage of artificial intelligence for prediction and discovery, thereby significantly reducing the time from idea and concept to finished solutions for consumers and patients. This new strategy follows the path from benefit definition via design to prediction and, eventually, validation and production.
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