1
|
Meng W, Pan H, Sha Y, Zhai X, Xing A, Lingampelly SS, Sripathi SR, Wang Y, Li K. Metabolic Connectome and Its Role in the Prediction, Diagnosis, and Treatment of Complex Diseases. Metabolites 2024; 14:93. [PMID: 38392985 PMCID: PMC10890086 DOI: 10.3390/metabo14020093] [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: 12/15/2023] [Revised: 01/17/2024] [Accepted: 01/25/2024] [Indexed: 02/25/2024] Open
Abstract
The interconnectivity of advanced biological systems is essential for their proper functioning. In modern connectomics, biological entities such as proteins, genes, RNA, DNA, and metabolites are often represented as nodes, while the physical, biochemical, or functional interactions between them are represented as edges. Among these entities, metabolites are particularly significant as they exhibit a closer relationship to an organism's phenotype compared to genes or proteins. Moreover, the metabolome has the ability to amplify small proteomic and transcriptomic changes, even those from minor genomic changes. Metabolic networks, which consist of complex systems comprising hundreds of metabolites and their interactions, play a critical role in biological research by mediating energy conversion and chemical reactions within cells. This review provides an introduction to common metabolic network models and their construction methods. It also explores the diverse applications of metabolic networks in elucidating disease mechanisms, predicting and diagnosing diseases, and facilitating drug development. Additionally, it discusses potential future directions for research in metabolic networks. Ultimately, this review serves as a valuable reference for researchers interested in metabolic network modeling, analysis, and their applications.
Collapse
Affiliation(s)
- Weiyu Meng
- Center for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macau SAR 999078, China
| | - Hongxin Pan
- Center for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macau SAR 999078, China
| | - Yuyang Sha
- Center for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macau SAR 999078, China
| | - Xiaobing Zhai
- Center for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macau SAR 999078, China
| | - Abao Xing
- Center for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macau SAR 999078, China
| | | | - Srinivasa R Sripathi
- Henderson Ocular Stem Cell Laboratory, Retina Foundation of the Southwest, Dallas, TX 75231, USA
| | - Yuefei Wang
- National Key Laboratory of Chinese Medicine Modernization, State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin 301617, China
| | - Kefeng Li
- Center for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macau SAR 999078, China
| |
Collapse
|
2
|
Maciejewska-Skrendo A, Massidda M, Tocco F, Leźnicka K. The Influence of the Differentiation of Genes Encoding Peroxisome Proliferator-Activated Receptors and Their Coactivators on Nutrient and Energy Metabolism. Nutrients 2022; 14:nu14245378. [PMID: 36558537 PMCID: PMC9782515 DOI: 10.3390/nu14245378] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 11/27/2022] [Accepted: 12/15/2022] [Indexed: 12/23/2022] Open
Abstract
Genetic components may play an important role in the regulation of nutrient and energy metabolism. In the presence of specific genetic variants, metabolic dysregulation may occur, especially in relation to the processes of digestion, assimilation, and the physiological utilization of nutrients supplied to the body, as well as the regulation of various metabolic pathways and the balance of metabolic changes, which may consequently affect the effectiveness of applied reduction diets and weight loss after training. There are many well-documented studies showing that the presence of certain polymorphic variants in some genes can be associated with specific changes in nutrient and energy metabolism, and consequently, with more or less desirable effects of applied caloric reduction and/or exercise intervention. This systematic review focused on the role of genes encoding peroxisome proliferator-activated receptors (PPARs) and their coactivators in nutrient and energy metabolism. The literature review prepared showed that there is a link between the presence of specific alleles described at different polymorphic points in PPAR genes and various human body characteristics that are crucial for the efficacy of nutritional and/or exercise interventions. Genetic analysis can be a valuable element that complements the work of a dietitian or trainer, allowing for the planning of a personalized diet or training that makes the best use of the innate metabolic characteristics of the person who is the subject of their interventions.
Collapse
Affiliation(s)
- Agnieszka Maciejewska-Skrendo
- Faculty of Physical Culture, Gdansk University of Physical Education and Sport, 80-336 Gdansk, Poland
- Institute of Physical Culture Sciences, University of Szczecin, 71-065 Szczecin, Poland
- Correspondence:
| | - Myosotis Massidda
- Department of Medical Sciences and Public Health, Faculty of Medicine and Surgery, Sport and Exercise Sciences Degree Courses, University of Cagliari, 72-09124 Cagliari, Italy
| | - Filippo Tocco
- Department of Medical Sciences and Public Health, Faculty of Medicine and Surgery, Sport and Exercise Sciences Degree Courses, University of Cagliari, 72-09124 Cagliari, Italy
| | - Katarzyna Leźnicka
- Faculty of Physical Culture, Gdansk University of Physical Education and Sport, 80-336 Gdansk, Poland
| |
Collapse
|
3
|
Redman AD, Bietz J, Davis JW, Lyon D, Maloney E, Ott A, Otte JC, Palais F, Parsons JR, Wang N. Moving persistence assessments into the 21st century: A role for weight-of-evidence and overall persistence. INTEGRATED ENVIRONMENTAL ASSESSMENT AND MANAGEMENT 2022; 18:868-887. [PMID: 34730270 PMCID: PMC9299815 DOI: 10.1002/ieam.4548] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 10/06/2021] [Accepted: 10/21/2021] [Indexed: 05/29/2023]
Abstract
Assessing the persistence of chemicals in the environment is a key element in existing regulatory frameworks to protect human health and ecosystems. Persistence in the environment depends on many fate processes, including abiotic and biotic transformations and physical partitioning, which depend on substances' physicochemical properties and environmental conditions. A main challenge in persistence assessment is that existing frameworks rely on simplistic and reductionist evaluation schemes that may lead substances to be falsely assessed as persistent or the other way around-to be falsely assessed as nonpersistent. Those evaluation schemes typically assess persistence against degradation half-lives determined in single-compartment simulation tests or against degradation levels measured in stringent screening tests. Most of the available test methods, however, do not apply to all types of substances, especially substances that are poorly soluble, complex in composition, highly sorptive, or volatile. In addition, the currently applied half-life criteria are derived mainly from a few legacy persistent organic pollutants, which do not represent the large diversity of substances entering the environment. Persistence assessment would undoubtedly benefit from the development of more flexible and holistic evaluation schemes including new concepts and methods. A weight-of-evidence (WoE) approach incorporating multiple influencing factors is needed to account for chemical fate and transformation in the whole environment so as to assess overall persistence. The present paper's aim is to begin to develop an integrated assessment framework that combines multimedia approaches to organize and interpret data using a clear WoE approach to allow for a more consistent, transparent, and thorough assessment of persistence. Integr Environ Assess Manag 2022;18:868-887. © 2021 ExxonMobil Biomedical Sciences, Inc. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).
Collapse
Affiliation(s)
| | - Jens Bietz
- Clariant Produkte (Deutschland) GmbHSulzbachGermany
| | - John W. Davis
- Dow, Inc.MidlandMichiganUSA
- John Davis Consulting, LLCMidlandMichiganUSA
| | | | | | - Amelie Ott
- Newcastle University, School of EngineeringNewcastle upon TyneUK
- European Centre for Ecotoxicology and Toxicology of Chemicals (ECETOC)BrusselsBelgium
| | | | - Frédéric Palais
- SOLVAY, HSE PRA‐PS, RICL—Antenne de GenasSaint‐FonsCedexFrance
| | - John R. Parsons
- Institute for Biodiversity and Ecosystem DynamicsUniversity of AmsterdamAmsterdamThe Netherlands
| | - Neil Wang
- TotalEnergies Marketing & ServicesParis la DéfenseFrance
| |
Collapse
|
4
|
Wang P, Schumacher AM, Shiu SH. Computational prediction of plant metabolic pathways. CURRENT OPINION IN PLANT BIOLOGY 2022; 66:102171. [PMID: 35078130 DOI: 10.1016/j.pbi.2021.102171] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 12/07/2021] [Accepted: 12/18/2021] [Indexed: 06/14/2023]
Abstract
Uncovering genes encoding enzymes responsible for the biosynthesis of diverse plant metabolites is essential for metabolic engineering and production of plant metabolite-derived medicine. With the availability of multi-omics data for an ever-increasing number of plant species and the development of computational approaches, the metabolic pathways of many important plant compounds can be predicted, complementing a more traditional genetic and/or biochemical approach. Here, we summarize recent progress in predicting plant metabolic pathways using genome, transcriptome, proteome, interactome, and/or metabolome data, and the utility of integrating these data with machine learning to further improve metabolic pathway predictions.
Collapse
Affiliation(s)
- Peipei Wang
- Department of Plant Biology, Michigan State University, East Lansing, MI, 48824, USA.
| | - Ally M Schumacher
- Department of Plant Biology, Michigan State University, East Lansing, MI, 48824, USA
| | - Shin-Han Shiu
- Department of Plant Biology, Michigan State University, East Lansing, MI, 48824, USA; Department of Computational Mathematics, Science, and Engineering, Michigan State University, East Lansing, MI, 48824, USA.
| |
Collapse
|
5
|
Shah HA, Liu J, Yang Z, Feng J. Review of Machine Learning Methods for the Prediction and Reconstruction of Metabolic Pathways. Front Mol Biosci 2021; 8:634141. [PMID: 34222327 PMCID: PMC8247443 DOI: 10.3389/fmolb.2021.634141] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Accepted: 06/01/2021] [Indexed: 11/13/2022] Open
Abstract
Prediction and reconstruction of metabolic pathways play significant roles in many fields such as genetic engineering, metabolic engineering, drug discovery, and are becoming the most active research topics in synthetic biology. With the increase of related data and with the development of machine learning techniques, there have many machine leaning based methods been proposed for prediction or reconstruction of metabolic pathways. Machine learning techniques are showing state-of-the-art performance to handle the rapidly increasing volume of data in synthetic biology. To support researchers in this field, we briefly review the research progress of metabolic pathway reconstruction and prediction based on machine learning. Some challenging issues in the reconstruction of metabolic pathways are also discussed in this paper.
Collapse
Affiliation(s)
- Hayat Ali Shah
- Institute of Artificial Intelligence, School of Computer Science, Wuhan University, Wuhan, China
| | - Juan Liu
- Institute of Artificial Intelligence, School of Computer Science, Wuhan University, Wuhan, China
| | - Zhihui Yang
- Institute of Artificial Intelligence, School of Computer Science, Wuhan University, Wuhan, China
| | - Jing Feng
- Institute of Artificial Intelligence, School of Computer Science, Wuhan University, Wuhan, China
| |
Collapse
|
6
|
Motwalli O, Uludag M, Mijakovic I, Alazmi M, Bajic VB, Gojobori T, Gao X, Essack M. PATH cre8: A Tool That Facilitates the Searching for Heterologous Biosynthetic Routes. ACS Synth Biol 2020; 9:3217-3227. [PMID: 33198455 DOI: 10.1021/acssynbio.0c00058] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Developing computational tools that can facilitate the rational design of cell factories producing desired products at increased yields is challenging, as the tool needs to take into account that the preferred host organism usually has compounds that are consumed by competing reactions that reduce the yield of the desired product. On the other hand, the preferred host organisms may not have the native metabolic reactions needed to produce the compound of interest; thus, the computational tool needs to identify the metabolic reactions that will most efficiently produce the desired product. In this regard, we developed the generic tool PATHcre8 to facilitate an optimized search for heterologous biosynthetic pathway routes. PATHcre8 finds and ranks biosynthesis routes in a large number of organisms, including Cyanobacteria. The tool ranks the pathways based on feature scores that reflect reaction thermodynamics, the potentially toxic products in the pathway (compound toxicity), intermediate products in the pathway consumed by competing reactions (product consumption), and host-specific information such as enzyme copy number. A comparison with several other similar tools shows that PATHcre8 is more efficient in ranking functional pathways. To illustrate the effectiveness of PATHcre8, we further provide case studies focused on isoprene production and the biodegradation of cocaine. PATHcre8 is free for academic and nonprofit users and can be accessed at https://www.cbrc.kaust.edu.sa/pathcre8/.
Collapse
Affiliation(s)
- Olaa Motwalli
- Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
- Saudi Electronic University (SEU), College of Computing and Informatics, Madinah 41538-53307, Kingdom of Saudi Arabia
| | - Mahmut Uludag
- Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Ivan Mijakovic
- Chalmers University of Technology, Division of Systems & Synthetic Biology, Department of Biology and Biological Engineering, Kemivägen 10, 41296 Gothenburg, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
| | - Meshari Alazmi
- Department of Information and Computer Science, College of Computer Science and Engineering, University of Ha’il, P.O. Box 2440, Ha’il 81411, Kingdom of Saudi Arabia
| | - Vladimir B. Bajic
- Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Takashi Gojobori
- Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
- Biological and Environmental Sciences and Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Xin Gao
- Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Magbubah Essack
- Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
| |
Collapse
|
7
|
Hussan JR, Hunter PJ. Our natural "makeup" reveals more than it hides: Modeling the skin and its microbiome. WIREs Mech Dis 2020; 13:e1497. [PMID: 32539232 DOI: 10.1002/wsbm.1497] [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: 03/26/2020] [Revised: 05/06/2020] [Accepted: 05/07/2020] [Indexed: 01/23/2023]
Abstract
Skin is our primary interface with the environment. A structurally and functionally complex organ that hosts a dynamic ecosystem of microbes, and synthesizes many compounds that affect our well-being and psychosocial interactions. It is a natural platform of signal exchange between internal organs, skin resident microbes, and the environment. These interactions have gained a great deal of attention due to the increased prevalence of atopic diseases, and the co-occurrence of multiple allergic diseases related to allergic sensitization in early life. Despite significant advances in experimentally characterizing the skin, its microbial ecology, and disease phenotypes, high-levels of variability in these characteristics even for the same clinical phenotype are observed. Addressing this variability and resolving the relevant biological processes requires a systems approach. This review presents some of our current understanding of the skin, skin-immune, skin-neuroendocrine, skin-microbiome interactions, and computer-based modeling approaches to simulate this ecosystem in the context of health and disease. The review highlights the need for a systems-based understanding of this sophisticated ecosystem. This article is categorized under: Infectious Diseases > Computational Models.
Collapse
Affiliation(s)
- Jagir R Hussan
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Peter J Hunter
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| |
Collapse
|
8
|
Perez De Souza L, Alseekh S, Brotman Y, Fernie AR. Network-based strategies in metabolomics data analysis and interpretation: from molecular networking to biological interpretation. Expert Rev Proteomics 2020; 17:243-255. [PMID: 32380880 DOI: 10.1080/14789450.2020.1766975] [Citation(s) in RCA: 61] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
INTRODUCTION Metabolomics has become a crucial part of systems biology; however, data analysis is still often undertaken in a reductionist way focusing on changes in individual metabolites. Whilst such approaches indeed provide relevant insights into the metabolic phenotype of an organism, the intricate nature of metabolic relationships may be better explored when considering the whole system. AREAS COVERED This review highlights multiple network strategies that can be applied for metabolomics data analysis from different perspectives including: association networks based on quantitative information, mass spectra similarity networks to assist metabolite annotation and biochemical networks for systematic data interpretation. We also highlight some relevant insights into metabolic organization obtained through the exploration of such approaches. EXPERT OPINION Network based analysis is an established method that allows the identification of non-intuitive metabolic relationships as well as the identification of unknown compounds in mass spectrometry. Additionally, the representation of data from metabolomics within the context of metabolic networks is intuitive and allows for the use of statistical analysis that can better summarize relevant metabolic changes from a systematic perspective.
Collapse
Affiliation(s)
- Leonardo Perez De Souza
- Department of molecular physiology, Max-Planck-Institute of Molecular Plant Physiology , Potsdam-Golm, Germany
| | - Saleh Alseekh
- Department of molecular physiology, Max-Planck-Institute of Molecular Plant Physiology , Potsdam-Golm, Germany.,Department of plant metabolomics, Centre of Plant Systems Biology and Biotechnology , Plovdiv, Bulgaria
| | - Yariv Brotman
- Department of Life Sciences, Ben-Gurion University of the Negev , Beersheba, Israel
| | - Alisdair R Fernie
- Department of molecular physiology, Max-Planck-Institute of Molecular Plant Physiology , Potsdam-Golm, Germany.,Department of plant metabolomics, Centre of Plant Systems Biology and Biotechnology , Plovdiv, Bulgaria
| |
Collapse
|
9
|
Riaz MR, Preston GM, Mithani A. MAPPS: A Web-Based Tool for Metabolic Pathway Prediction and Network Analysis in the Postgenomic Era. ACS Synth Biol 2020; 9:1069-1082. [PMID: 32347714 DOI: 10.1021/acssynbio.9b00397] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Comparative and evolutionary analyses of metabolic networks have a wide range of applications, ranging from research into metabolic evolution through to practical applications in drug development, synthetic biology, and biodegradation. We present MAPPS: Metabolic network Analysis and Pathway Prediction Server (https://mapps.lums.edu.pk), a web-based tool to study functions and evolution of metabolic networks using traditional and 'omics data sets. MAPPS provides diverse functionalities including an interactive interface, graphical visualization of results, pathway prediction and network comparison, identification of potential drug targets, in silico metabolic engineering, host-microbe interactions, and ancestral network building. Importantly, MAPPS also allows users to upload custom data, thus enabling metabolic analyses on draft and custom genomes, and has an 'omics pipeline to filter pathway results, making it relevant in today's postgenomic era.
Collapse
Affiliation(s)
- Muhammad Rizwan Riaz
- Department of Biology, Syed Babar Ali School of Science and Engineering, Lahore University of Management Sciences (LUMS), DHA, Lahore 54792, Pakistan
| | - Gail M. Preston
- Department of Plant Sciences, University of Oxford, South Parks Road, Oxford OX1 3RB, U.K
| | - Aziz Mithani
- Department of Biology, Syed Babar Ali School of Science and Engineering, Lahore University of Management Sciences (LUMS), DHA, Lahore 54792, Pakistan
| |
Collapse
|
10
|
Vazquez-Hernandez C, Loza A, Peguero-Sanchez E, Segovia L, Gutierrez-Rios RM. Identification of reaction organization patterns that naturally cluster enzymatic transformations. BMC SYSTEMS BIOLOGY 2018; 12:63. [PMID: 29848336 PMCID: PMC5977463 DOI: 10.1186/s12918-018-0583-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/13/2017] [Accepted: 05/09/2018] [Indexed: 11/10/2022]
Abstract
BACKGROUND Metabolic reactions are chemical transformations commonly catalyzed by enzymes. In recent years, the explosion of genomic data and individual experimental characterizations have contributed to the construction of databases and methodologies for the analysis of metabolic networks. Some methodologies based on graph theory organize compound networks into metabolic functional categories without preserving biochemical pathways. Other methods based on chemical group exchange and atom flow trace the conversion of substrates into products in detail, which is useful for inferring metabolic pathways. METHODS Here, we present a novel rule-based approach incorporating both methods that decomposes each reaction into architectures of compound pairs and loner compounds that can be organized into tree structures. We compared the tree structure-compound pairs to those reported in the KEGG-RPAIR dataset and obtained a match precision of 81%. The generated tree structures naturally clustered all reactions into general reaction patterns of compounds with similar chemical transformations. The match precision of each cluster was calculated and used to suggest reactant-pairs for which manual curation can be avoided because this is the main goal of the method. We evaluated catalytic processes in the clusters based on Enzyme Commission categories that revealed preferential use of enzyme classes. CONCLUSIONS We demonstrate that the application of simple rules can enable the identification of reaction patterns reflecting metabolic reactions that transform substrates into products and the types of catalysis involved in these transformations. Our rule-based approach can be incorporated as the input in pathfinders or as a tool for the construction of reaction classifiers, indicating its usefulness for predicting enzyme catalysis.
Collapse
Affiliation(s)
- Carlos Vazquez-Hernandez
- Departamento de Microbiología Molecular, Instituto de Biotecnología Universidad Nacional Autónoma de México, Apdo, Postal 510-3, 62250, Cuernavaca, Morelos, Mexico
| | - Antonio Loza
- Departamento de Microbiología Molecular, Instituto de Biotecnología Universidad Nacional Autónoma de México, Apdo, Postal 510-3, 62250, Cuernavaca, Morelos, Mexico
| | - Esteban Peguero-Sanchez
- Departamento de Microbiología Molecular, Instituto de Biotecnología Universidad Nacional Autónoma de México, Apdo, Postal 510-3, 62250, Cuernavaca, Morelos, Mexico
| | - Lorenzo Segovia
- Departamento de Ingeniería Celular y Biocatálisis, Instituto de Biotecnología Universidad Nacional Autónoma de México, Apdo, Postal 510-3, 62250, Cuernavaca, Morelos, Mexico
| | - Rosa-Maria Gutierrez-Rios
- Departamento de Microbiología Molecular, Instituto de Biotecnología Universidad Nacional Autónoma de México, Apdo, Postal 510-3, 62250, Cuernavaca, Morelos, Mexico.
| |
Collapse
|
11
|
Muller EE, Faust K, Widder S, Herold M, Martínez Arbas S, Wilmes P. Using metabolic networks to resolve ecological properties of microbiomes. ACTA ACUST UNITED AC 2018. [DOI: 10.1016/j.coisb.2017.12.004] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
|
12
|
Rai A, Saito K, Yamazaki M. Integrated omics analysis of specialized metabolism in medicinal plants. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2017; 90:764-787. [PMID: 28109168 DOI: 10.1111/tpj.13485] [Citation(s) in RCA: 125] [Impact Index Per Article: 17.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/24/2016] [Revised: 01/10/2017] [Accepted: 01/11/2017] [Indexed: 05/19/2023]
Abstract
Medicinal plants are a rich source of highly diverse specialized metabolites with important pharmacological properties. Until recently, plant biologists were limited in their ability to explore the biosynthetic pathways of these metabolites, mainly due to the scarcity of plant genomics resources. However, recent advances in high-throughput large-scale analytical methods have enabled plant biologists to discover biosynthetic pathways for important plant-based medicinal metabolites. The reduced cost of generating omics datasets and the development of computational tools for their analysis and integration have led to the elucidation of biosynthetic pathways of several bioactive metabolites of plant origin. These discoveries have inspired synthetic biology approaches to develop microbial systems to produce bioactive metabolites originating from plants, an alternative sustainable source of medicinally important chemicals. Since the demand for medicinal compounds are increasing with the world's population, understanding the complete biosynthesis of specialized metabolites becomes important to identify or develop reliable sources in the future. Here, we review the contributions of major omics approaches and their integration to our understanding of the biosynthetic pathways of bioactive metabolites. We briefly discuss different approaches for integrating omics datasets to extract biologically relevant knowledge and the application of omics datasets in the construction and reconstruction of metabolic models.
Collapse
Affiliation(s)
- Amit Rai
- Graduate School of Pharmaceutical Sciences, Chiba University, 1-8-1 Inohana, Chuo-ku, Chiba, 260-8675, Japan
| | - Kazuki Saito
- Graduate School of Pharmaceutical Sciences, Chiba University, 1-8-1 Inohana, Chuo-ku, Chiba, 260-8675, Japan
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, 230-0045, Japan
| | - Mami Yamazaki
- Graduate School of Pharmaceutical Sciences, Chiba University, 1-8-1 Inohana, Chuo-ku, Chiba, 260-8675, Japan
| |
Collapse
|
13
|
Li Y, Italiani P, Casals E, Valkenborg D, Mertens I, Baggerman G, Nelissen I, Puntes VF, Boraschi D. Assessing the Immunosafety of Engineered Nanoparticles with a Novel in Vitro Model Based on Human Primary Monocytes. ACS APPLIED MATERIALS & INTERFACES 2016; 8:28437-28447. [PMID: 27670583 DOI: 10.1021/acsami.6b06278] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
The possibility that nanomaterials could perturb the normal course of an inflammatory response is a key issue when assessing nanoimmunosafety. The alteration of the normal progress of an inflammatory response may have pathological consequences, since inflammation is a major defensive mechanism and its efficiency maintains the body's health. The immunosafety of engineered nanoparticles at nontoxic concentrations was investigated with the use of a human primary monocyte-based in vitro system, which reproduces in a simplified fashion the full course of the physiological inflammatory response, from initiation and development to resolution. The kinetics of expression and production of inflammatory and anti-inflammatory cytokines and the proteomic profiles were used for describing the inflammatory defensive response. We assessed the ability of gold and silver nanoparticles to trigger inflammation and to interfere with the course of an ongoing defensive reaction. While neither nanoparticle type was able to directly activate monocytes, silver nanoparticles could exacerbate the inflammatory response of monocytes but did not interfere with the resolution of the inflammatory reaction. These findings support the use of human primary monocyte-based in vitro assays for realistically investigating the effects of engineered nanoparticles on human innate immune responses, in order to predict the immunological risk of nanomaterials and implement safe nanoparticle-based applications.
Collapse
Affiliation(s)
- Yang Li
- Institute of Protein Biochemistry, National Research Council , 80131 Napoli, Italy
| | - Paola Italiani
- Institute of Protein Biochemistry, National Research Council , 80131 Napoli, Italy
| | - Eudald Casals
- Institut Català de Nanotecnologia , Campus of the UAB, 08193 Bellaterra, Spain
| | - Dirk Valkenborg
- Flemish Institute for Technological Research , 2400 Mol, Belgium
- Center for Proteomics, University of Antwerp , 2020 Antwerp, Belgium
| | - Inge Mertens
- Flemish Institute for Technological Research , 2400 Mol, Belgium
- Center for Proteomics, University of Antwerp , 2020 Antwerp, Belgium
| | - Geert Baggerman
- Flemish Institute for Technological Research , 2400 Mol, Belgium
- Center for Proteomics, University of Antwerp , 2020 Antwerp, Belgium
| | - Inge Nelissen
- Flemish Institute for Technological Research , 2400 Mol, Belgium
| | - Victor F Puntes
- Institut Català de Nanotecnologia , Campus of the UAB, 08193 Bellaterra, Spain
- Vall d'Hebron Institute of Research (VHIR) , 08035 Barcelona, Spain
- Institut Català de Recerca i Estudis Avançats, ICREA , 08010 Barcelona, Spain
| | - Diana Boraschi
- Institute of Protein Biochemistry, National Research Council , 80131 Napoli, Italy
| |
Collapse
|
14
|
Making sense of genomes of parasitic worms: Tackling bioinformatic challenges. Biotechnol Adv 2016; 34:663-686. [DOI: 10.1016/j.biotechadv.2016.03.001] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2015] [Revised: 02/25/2016] [Accepted: 03/01/2016] [Indexed: 01/25/2023]
|
15
|
Gerard MF, Stegmayer G, Milone DH. Evolutionary algorithm for metabolic pathways synthesis. Biosystems 2016; 144:55-67. [PMID: 27080162 DOI: 10.1016/j.biosystems.2016.04.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2015] [Revised: 03/01/2016] [Accepted: 04/01/2016] [Indexed: 11/19/2022]
Abstract
Metabolic pathway building is an active field of research, necessary to understand and manipulate the metabolism of organisms. There are different approaches, mainly based on classical search methods, to find linear sequences of reactions linking two compounds. However, an important limitation of these methods is the exponential increase of search trees when a large number of compounds and reactions is considered. Besides, such models do not take into account all substrates for each reaction during the search, leading to solutions that lack biological feasibility in many cases. This work proposes a new evolutionary algorithm that allows searching not only linear, but also branched metabolic pathways, formed by feasible reactions that relate multiple compounds simultaneously. Tests performed using several sets of reactions show that this algorithm is able to find feasible linear and branched metabolic pathways.
Collapse
Affiliation(s)
- Matias F Gerard
- Research Institute for Signals, Systems and Computational Intelligence (sinc(i)), FICH-UNL/CONICET, Argentina.
| | - Georgina Stegmayer
- Research Institute for Signals, Systems and Computational Intelligence (sinc(i)), FICH-UNL/CONICET, Argentina.
| | - Diego H Milone
- Research Institute for Signals, Systems and Computational Intelligence (sinc(i)), FICH-UNL/CONICET, Argentina.
| |
Collapse
|
16
|
Frainay C, Jourdan F. Computational methods to identify metabolic sub-networks based on metabolomic profiles. Brief Bioinform 2016; 18:43-56. [PMID: 26822099 DOI: 10.1093/bib/bbv115] [Citation(s) in RCA: 42] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2015] [Revised: 12/16/2015] [Indexed: 11/13/2022] Open
Abstract
Untargeted metabolomics makes it possible to identify compounds that undergo significant changes in concentration in different experimental conditions. The resulting metabolomic profile characterizes the perturbation concerned, but does not explain the underlying biochemical mechanisms. Bioinformatics methods make it possible to interpret results in light of the whole metabolism. This knowledge is modelled into a network, which can be mined using algorithms that originate in graph theory. These algorithms can extract sub-networks related to the compounds identified. Several attempts have been made to adapt them to obtain more biologically meaningful results. However, there is still no consensus on this kind of analysis of metabolic networks. This review presents the main graph approaches used to interpret metabolomic data using metabolic networks. Their advantages and drawbacks are discussed, and the impacts of their parameters are emphasized. We also provide some guidelines for relevant sub-network extraction and also suggest a range of applications for most methods.
Collapse
|
17
|
Gerard MF, Stegmayer G, Milone DH. EvoMS: An evolutionary tool to find de novo metabolic pathways. Biosystems 2015; 134:43-7. [PMID: 26092635 DOI: 10.1016/j.biosystems.2015.04.006] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2014] [Revised: 04/17/2015] [Accepted: 04/21/2015] [Indexed: 11/19/2022]
Abstract
The evolutionary metabolic synthesizer (EvoMS) is an evolutionary tool capable of finding novel metabolic pathways linking several compounds through feasible reactions. It allows system biologists to explore different alternatives for relating specific metabolites, offering the possibility of indicating the initial compound or allowing the algorithm to automatically select it. Searching process can be followed graphically through several plots of the evolutionary process. Metabolic pathways found are displayed in a web browser as directed graphs. In all cases, solutions are networks of reactions that produce linear or branched metabolic pathways which are feasible from the specified set of available compounds. Source code of EvoMS is available at http://sourceforge.net/projects/sourcesinc/files/evoms/. Subsets of reactions are provided, as well as four examples for searching metabolic pathways among several compounds. Available as a web service at http://fich.unl.edu.ar/sinc/web-demo/evoms/.
Collapse
Affiliation(s)
- Matias F Gerard
- Research Institute for Signals, Systems and Computational Intelligence (sinc(i)), FICH-UNL/CONICET, Argentina.
| | - Georgina Stegmayer
- Research Institute for Signals, Systems and Computational Intelligence (sinc(i)), FICH-UNL/CONICET, Argentina.
| | - Diego H Milone
- Research Institute for Signals, Systems and Computational Intelligence (sinc(i)), FICH-UNL/CONICET, Argentina.
| |
Collapse
|
18
|
Qi Q, Li J, Cheng J. Reconstruction of metabolic pathways by combining probabilistic graphical model-based and knowledge-based methods. BMC Proc 2014; 8:S5. [PMID: 25374614 PMCID: PMC4202177 DOI: 10.1186/1753-6561-8-s6-s5] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Automatic reconstruction of metabolic pathways for an organism from genomics and transcriptomics data has been a challenging and important problem in bioinformatics. Traditionally, known reference pathways can be mapped into an organism-specific ones based on its genome annotation and protein homology. However, this simple knowledge-based mapping method might produce incomplete pathways and generally cannot predict unknown new relations and reactions. In contrast, ab initio metabolic network construction methods can predict novel reactions and interactions, but its accuracy tends to be low leading to a lot of false positives. Here we combine existing pathway knowledge and a new ab initio Bayesian probabilistic graphical model together in a novel fashion to improve automatic reconstruction of metabolic networks. Specifically, we built a knowledge database containing known, individual gene / protein interactions and metabolic reactions extracted from existing reference pathways. Known reactions and interactions were then used as constraints for Bayesian network learning methods to predict metabolic pathways. Using individual reactions and interactions extracted from different pathways of many organisms to guide pathway construction is new and improves both the coverage and accuracy of metabolic pathway construction. We applied this probabilistic knowledge-based approach to construct the metabolic networks from yeast gene expression data and compared its results with 62 known metabolic networks in the KEGG database. The experiment showed that the method improved the coverage of metabolic network construction over the traditional reference pathway mapping method and was more accurate than pure ab initio methods.
Collapse
Affiliation(s)
- Qi Qi
- Department of Computer Science, University of Missouri, Columbia, MO 65201, USA
| | - Jilong Li
- Department of Computer Science, University of Missouri, Columbia, MO 65201, USA
| | - Jianlin Cheng
- Department of Computer Science, University of Missouri, Columbia, MO 65201, USA ; Informatics Institute, University of Missouri, Columbia, MO 65201, USA
| |
Collapse
|
19
|
Krishnakumar S, Durai DA, Wangikar PP, Viswanathan GA. SHARP: genome-scale identification of gene-protein-reaction associations in cyanobacteria. PHOTOSYNTHESIS RESEARCH 2013; 118:181-190. [PMID: 23975204 DOI: 10.1007/s11120-013-9910-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/25/2013] [Accepted: 08/07/2013] [Indexed: 06/02/2023]
Abstract
Genome scale metabolic model provides an overview of an organism's metabolic capability. These genome-specific metabolic reconstructions are based on identification of gene to protein to reaction (GPR) associations and, in turn, on homology with annotated genes from other organisms. Cyanobacteria are photosynthetic prokaryotes which have diverged appreciably from their nonphotosynthetic counterparts. They also show significant evolutionary divergence from plants, which are well studied for their photosynthetic apparatus. We argue that context-specific sequence and domain similarity can add to the repertoire of the GPR associations and significantly expand our view of the metabolic capability of cyanobacteria. We took an approach that combines the results of context-specific sequence-to-sequence similarity search with those of sequence-to-profile searches. We employ PSI-BLAST for the former, and CDD, Pfam, and COG for the latter. An optimization algorithm was devised to arrive at a weighting scheme to combine the different evidences with KEGG-annotated GPRs as training data. We present the algorithm in the form of software "Systematic, Homology-based Automated Re-annotation for Prokaryotes (SHARP)." We predicted 3,781 new GPR associations for the 10 prokaryotes considered of which eight are cyanobacteria species. These new GPR associations fall in several metabolic pathways and were used to annotate 7,718 gaps in the metabolic network. These new annotations led to discovery of several pathways that may be active and thereby providing new directions for metabolic engineering of these species for production of useful products. Metabolic model developed on such a reconstructed network is likely to give better phenotypic predictions.
Collapse
Affiliation(s)
- S Krishnakumar
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Powai, Mumbai, 400076, India
| | | | | | | |
Collapse
|
20
|
De Filippo C, Ramazzotti M, Fontana P, Cavalieri D. Bioinformatic approaches for functional annotation and pathway inference in metagenomics data. Brief Bioinform 2013; 13:696-710. [PMID: 23175748 PMCID: PMC3505041 DOI: 10.1093/bib/bbs070] [Citation(s) in RCA: 60] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Metagenomic approaches are increasingly recognized as a baseline for understanding the
ecology and evolution of microbial ecosystems. The development of methods for pathway
inference from metagenomics data is of paramount importance to link a phenotype to a
cascade of events stemming from a series of connected sets of genes or proteins.
Biochemical and regulatory pathways have until recently been thought and modelled within
one cell type, one organism, one species. This vision is being dramatically changed by the
advent of whole microbiome sequencing studies, revealing the role of symbiotic microbial
populations in fundamental biochemical functions. The new landscape we face requires a
clear picture of the potentialities of existing tools and development of new tools to
characterize, reconstruct and model biochemical and regulatory pathways as the result of
integration of function in complex symbiotic interactions of ontologically and
evolutionary distinct cell types.
Collapse
|
21
|
Innovation at the intersection of synthetic and systems biology. Curr Opin Biotechnol 2012; 23:712-7. [DOI: 10.1016/j.copbio.2011.12.026] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2011] [Accepted: 12/20/2011] [Indexed: 01/06/2023]
|
22
|
Facchini PJ, Bohlmann J, Covello PS, De Luca V, Mahadevan R, Page JE, Ro DK, Sensen CW, Storms R, Martin VJ. Synthetic biosystems for the production of high-value plant metabolites. Trends Biotechnol 2012; 30:127-31. [DOI: 10.1016/j.tibtech.2011.10.001] [Citation(s) in RCA: 115] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2011] [Revised: 09/29/2011] [Accepted: 10/06/2011] [Indexed: 12/26/2022]
|