1
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Achudhan AB, Kannan P, Gupta A, Saleena LM. A Review of Web-Based Metagenomics Platforms for Analysing Next-Generation Sequence Data. Biochem Genet 2024; 62:621-632. [PMID: 37507643 DOI: 10.1007/s10528-023-10467-w] [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: 05/01/2023] [Accepted: 07/18/2023] [Indexed: 07/30/2023]
Abstract
Metagenomics has now evolved as a promising technology for understanding the microbial population in the environment. By metagenomics, a number of extreme and complex environment has been explored for their microbial population. Using this technology, researchers have brought out novel genes and their potential characteristics, which have robust applications in food, pharmaceutical, scientific research, and other biotechnological fields. A sequencing platform can provide a sequence of microbial populations in any given environment. The sequence needs to be analysed computationally to derive meaningful information. It is presumed that only bioinformaticians with extensive computational skills can process the sequencing data till the downstream end. However, numerous open-source software and online servers are available to analyse the metagenomic data developed for a biologist with less computational skills. This review is focused on bioinformatics tools such as Galaxy, CSI-NGS portal, ANASTASIA and SHAMAN, EBI- metagenomics, IDseq, and MG-RAST for analysing metagenomic data.
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Affiliation(s)
- Arunmozhi Bharathi Achudhan
- Department of Biotechnology, School of Bioengineering, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, India
| | - Priya Kannan
- Department of Biotechnology, School of Bioengineering, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, India
| | - Annapurna Gupta
- Department of Biotechnology, School of Bioengineering, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, India
| | - Lilly M Saleena
- Department of Biotechnology, School of Bioengineering, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, India.
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2
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Ariaeenejad S, Gharechahi J, Foroozandeh Shahraki M, Fallah Atanaki F, Han JL, Ding XZ, Hildebrand F, Bahram M, Kavousi K, Hosseini Salekdeh G. Precision enzyme discovery through targeted mining of metagenomic data. NATURAL PRODUCTS AND BIOPROSPECTING 2024; 14:7. [PMID: 38200389 PMCID: PMC10781932 DOI: 10.1007/s13659-023-00426-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 12/19/2023] [Indexed: 01/12/2024]
Abstract
Metagenomics has opened new avenues for exploring the genetic potential of uncultured microorganisms, which may serve as promising sources of enzymes and natural products for industrial applications. Identifying enzymes with improved catalytic properties from the vast amount of available metagenomic data poses a significant challenge that demands the development of novel computational and functional screening tools. The catalytic properties of all enzymes are primarily dictated by their structures, which are predominantly determined by their amino acid sequences. However, this aspect has not been fully considered in the enzyme bioprospecting processes. With the accumulating number of available enzyme sequences and the increasing demand for discovering novel biocatalysts, structural and functional modeling can be employed to identify potential enzymes with novel catalytic properties. Recent efforts to discover new polysaccharide-degrading enzymes from rumen metagenome data using homology-based searches and machine learning-based models have shown significant promise. Here, we will explore various computational approaches that can be employed to screen and shortlist metagenome-derived enzymes as potential biocatalyst candidates, in conjunction with the wet lab analytical methods traditionally used for enzyme characterization.
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Affiliation(s)
- Shohreh Ariaeenejad
- Department of Systems and Synthetic Biology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREEO), Karaj, Iran
| | - Javad Gharechahi
- Human Genetics Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran
| | - Mehdi Foroozandeh Shahraki
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran
| | - Fereshteh Fallah Atanaki
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran
| | - Jian-Lin Han
- Livestock Genetics Program, International Livestock Research, Institute (ILRI), Nairobi, 00100, Kenya
- CAAS-ILRI Joint Laboratory On Livestock and Forage Genetic Resources, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing, 100193, China
| | - Xue-Zhi Ding
- Key Laboratory of Yak Breeding Engineering, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences (CAAS), Lanzhou, 730050, China
| | - Falk Hildebrand
- Gut Microbes and Health, Quadram Institute Bioscience, Norwich, Norfolk, UK
- Digital Biology, Earlham Institute, Norwich, Norfolk, UK
| | - Mohammad Bahram
- Department of Ecology, Swedish University of Agricultural Sciences, Ulls Väg 16, 756 51, Uppsala, Sweden
- Department of Botany, Institute of Ecology and Earth Sciences, University of Tartu, 40 Lai St, Tartu, Estonia
| | - Kaveh Kavousi
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran.
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3
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Burkhardt C, Baruth L, Neele Meyer-Heydecke, Klippel B, Margaryan A, Paloyan A, Panosyan HH, Antranikian G. Mining thermophiles for biotechnologically relevant enzymes: evaluating the potential of European and Caucasian hot springs. Extremophiles 2023; 28:5. [PMID: 37991546 PMCID: PMC10665251 DOI: 10.1007/s00792-023-01321-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 10/06/2023] [Indexed: 11/23/2023]
Abstract
The development of sustainable and environmentally friendly industrial processes is becoming very crucial and demanding for the rapid implementation of innovative bio-based technologies. Natural extreme environments harbor the potential for discovering and utilizing highly specific and efficient biocatalysts that are adapted to harsh conditions. This review focuses on extremophilic microorganisms and their enzymes (extremozymes) from various hot springs, shallow marine vents, and other geothermal habitats in Europe and the Caucasus region. These hot environments have been partially investigated and analyzed for microbial diversity and enzymology. Hotspots like Iceland, Italy, and the Azores harbor unique microorganisms, including bacteria and archaea. The latest results demonstrate a great potential for the discovery of new microbial species and unique enzymes that can be explored for the development of Circular Bioeconomy.Different screening approaches have been used to discover enzymes that are active at extremes of temperature (up 120 °C), pH (0.1 to 11), high salt concentration (up to 30%) as well as activity in the presence of solvents (up to 99%). The majority of published enzymes were revealed from bacterial or archaeal isolates by traditional activity-based screening techniques. However, the latest developments in molecular biology, bioinformatics, and genomics have revolutionized life science technologies. Post-genomic era has contributed to the discovery of millions of sequences coding for a huge number of biocatalysts. Both strategies, activity- and sequence-based screening approaches, are complementary and contribute to the discovery of unique enzymes that have not been extensively utilized so far.
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Affiliation(s)
- Christin Burkhardt
- Institute of Technical Biocatalysis, Center for Biobased Solutions, Hamburg University of Technology, Am Schwarzenberg-Campus 4, 21073, Hamburg, Germany
| | - Leon Baruth
- Institute of Technical Biocatalysis, Center for Biobased Solutions, Hamburg University of Technology, Am Schwarzenberg-Campus 4, 21073, Hamburg, Germany
| | - Neele Meyer-Heydecke
- Institute of Technical Biocatalysis, Center for Biobased Solutions, Hamburg University of Technology, Am Schwarzenberg-Campus 4, 21073, Hamburg, Germany
| | - Barbara Klippel
- Institute of Technical Biocatalysis, Center for Biobased Solutions, Hamburg University of Technology, Am Schwarzenberg-Campus 4, 21073, Hamburg, Germany
| | - Armine Margaryan
- Department of Biochemistry, Microbiology and Biotechnology, Yerevan State University, Alex Manoogian 1, 0025, Yerevan, Armenia
- Research Institute of Biology, Yerevan State University, Alex Manoogian 1, 0025, Yerevan, Armenia
| | - Ani Paloyan
- Scientific and Production Center, "Armbiotechnology" NAS RA, 14 Gyurjyan Str. 0056, Yerevan, Armenia
| | - Hovik H Panosyan
- Department of Biochemistry, Microbiology and Biotechnology, Yerevan State University, Alex Manoogian 1, 0025, Yerevan, Armenia
- Research Institute of Biology, Yerevan State University, Alex Manoogian 1, 0025, Yerevan, Armenia
| | - Garabed Antranikian
- Institute of Technical Biocatalysis, Center for Biobased Solutions, Hamburg University of Technology, Am Schwarzenberg-Campus 4, 21073, Hamburg, Germany.
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4
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Sartaj K, Patel A, Matsakas L, Prasad R. Unravelling Metagenomics Approach for Microbial Biofuel Production. Genes (Basel) 2022; 13:1942. [PMID: 36360179 PMCID: PMC9689425 DOI: 10.3390/genes13111942] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 10/18/2022] [Accepted: 10/21/2022] [Indexed: 09/29/2023] Open
Abstract
Renewable biofuels, such as biodiesel, bioethanol, and biobutanol, serve as long-term solutions to fossil fuel depletion. A sustainable approach feedstock for their production is plant biomass, which is degraded to sugars with the aid of microbes-derived enzymes, followed by microbial conversion of those sugars to biofuels. Considering their global demand, additional efforts have been made for their large-scale production, which is ultimately leading breakthrough research in biomass energy. Metagenomics is a powerful tool allowing for functional gene analysis and new enzyme discovery. Thus, the present article summarizes the revolutionary advances of metagenomics in the biofuel industry and enlightens the importance of unexplored habitats for novel gene or enzyme mining. Moreover, it also accentuates metagenomics potentials to explore uncultivable microbiomes as well as enzymes associated with them.
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Affiliation(s)
- Km Sartaj
- Department of Biosciences and Bioengineering, Indian Institute of Technology Roorkee, Roorkee 247667, Uttarakhand, India
| | - Alok Patel
- Biochemical Process Engineering, Division of Chemical Engineering, Department of Civil, Environmental, and Natural Resources Engineering, Luleå University of Technology, SE-971 87 Luleå, Sweden
| | - Leonidas Matsakas
- Biochemical Process Engineering, Division of Chemical Engineering, Department of Civil, Environmental, and Natural Resources Engineering, Luleå University of Technology, SE-971 87 Luleå, Sweden
| | - Ramasare Prasad
- Department of Biosciences and Bioengineering, Indian Institute of Technology Roorkee, Roorkee 247667, Uttarakhand, India
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5
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Tatta ER, Imchen M, Moopantakath J, Kumavath R. Bioprospecting of microbial enzymes: current trends in industry and healthcare. Appl Microbiol Biotechnol 2022; 106:1813-1835. [PMID: 35254498 DOI: 10.1007/s00253-022-11859-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 02/15/2022] [Accepted: 02/26/2022] [Indexed: 12/13/2022]
Abstract
Microbial enzymes have an indispensable role in producing foods, pharmaceuticals, and other commercial goods. Many novel enzymes have been reported from all domains of life, such as plants, microbes, and animals. Nonetheless, industrially desirable enzymes of microbial origin are limited. This review article discusses the classifications, applications, sources, and challenges of most demanded industrial enzymes such as pectinases, cellulase, lipase, and protease. In addition, the production of novel enzymes through protein engineering technologies such as directed evolution, rational, and de novo design, for the improvement of existing industrial enzymes is also explored. We have also explored the role of metagenomics, nanotechnology, OMICs, and machine learning approaches in the bioprospecting of novel enzymes. Overall, this review covers the basics of biocatalysts in industrial and healthcare applications and provides an overview of existing microbial enzyme optimization tools. KEY POINTS: • Microbial bioactive molecules are vital for therapeutic and industrial applications. • High-throughput OMIC is the most proficient approach for novel enzyme discovery. • Comprehensive databases and efficient machine learning models are the need of the hour to fast forward de novo enzyme design and discovery.
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Affiliation(s)
- Eswar Rao Tatta
- Department of Genomic Science, School of Biological Sciences, Central University of Kerala, Tejaswini Hills, Periya (PO.), Kasaragod, Kerala, 671320, India
| | - Madangchanok Imchen
- Department of Genomic Science, School of Biological Sciences, Central University of Kerala, Tejaswini Hills, Periya (PO.), Kasaragod, Kerala, 671320, India
| | - Jamseel Moopantakath
- Department of Genomic Science, School of Biological Sciences, Central University of Kerala, Tejaswini Hills, Periya (PO.), Kasaragod, Kerala, 671320, India
| | - Ranjith Kumavath
- Department of Genomic Science, School of Biological Sciences, Central University of Kerala, Tejaswini Hills, Periya (PO.), Kasaragod, Kerala, 671320, India.
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6
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Shahraki MF, Atanaki FF, Ariaeenejad S, Ghaffari MR, Norouzi‐Beirami MH, Maleki M, Salekdeh GH, Kavousi K. A computational learning paradigm to targeted discovery of biocatalysts from metagenomic data: a case study of lipase identification. Biotechnol Bioeng 2022; 119:1115-1128. [DOI: 10.1002/bit.28037] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Revised: 08/18/2021] [Accepted: 12/01/2021] [Indexed: 11/09/2022]
Affiliation(s)
- Mehdi Foroozandeh Shahraki
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Institute of Biochemistry and Biophysics (IBB), University of Tehran Tehran Iran
| | - Fereshteh Fallah Atanaki
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Institute of Biochemistry and Biophysics (IBB), University of Tehran Tehran Iran
| | - Shohreh Ariaeenejad
- Department of Systems and Synthetic Biology Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREEO) Karaj Iran
| | - Mohammad Reza Ghaffari
- Department of Systems and Synthetic Biology Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREEO) Karaj Iran
| | - Mohammad Hossein Norouzi‐Beirami
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Institute of Biochemistry and Biophysics (IBB), University of Tehran Tehran Iran
- Department of Computer Engineering Osku Branch, Islamic Azad University Osku Iran
| | - Morteza Maleki
- Department of Systems and Synthetic Biology Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREEO) Karaj Iran
| | - Ghasem Hosseini Salekdeh
- Department of Systems and Synthetic Biology Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREEO) Karaj Iran
- Department of Molecular Sciences Macquarie University Sydney NSW Australia
| | - Kaveh Kavousi
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Institute of Biochemistry and Biophysics (IBB), University of Tehran Tehran Iran
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7
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Hou Q, Pucci F, Pan F, Xue F, Rooman M, Feng Q. Using metagenomic data to boost protein structure prediction and discovery. Comput Struct Biotechnol J 2022; 20:434-442. [PMID: 35070166 PMCID: PMC8760478 DOI: 10.1016/j.csbj.2021.12.030] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Revised: 12/17/2021] [Accepted: 12/21/2021] [Indexed: 11/19/2022] Open
Abstract
Over the past decade, metagenomic sequencing approaches have been providing an ever-increasing amount of protein sequence data at an astonishing rate. These constitute an invaluable source of information which has been exploited in various research fields such as the study of the role of the gut microbiota in human diseases and aging. However, only a small fraction of all metagenomic sequences collected have been functionally or structurally characterized, leaving much of them completely unexplored. Here, we review how this information has been used in protein structure prediction and protein discovery. We begin by presenting some widely used metagenomic databases and analyze in detail how metagenomic data has contributed to the impressive improvement in the accuracy of structure prediction methods in recent years. We then examine how metagenomic information can be exploited to annotate protein sequences. More specifically, we focus on the role of metagenomes in the discovery of enzymes and new CRISPR-Cas systems, and in the identification of antibiotic resistance genes. With this review, we provide an overview of how metagenomic data is currently revolutionizing our understanding of protein science.
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Affiliation(s)
- Qingzhen Hou
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Shandong 250012, China
- National Institute of Health Data Science of China, Shandong University, Shandong 250002, China
| | - Fabrizio Pucci
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, 1050 Brussels, Belgium
- Interuniversity Institute of Bioinformatics in Brussels, 1050 Brussels, Belgium
| | - Fengming Pan
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Shandong 250012, China
- National Institute of Health Data Science of China, Shandong University, Shandong 250002, China
| | - Fuzhong Xue
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Shandong 250012, China
- National Institute of Health Data Science of China, Shandong University, Shandong 250002, China
| | - Marianne Rooman
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, 1050 Brussels, Belgium
- Interuniversity Institute of Bioinformatics in Brussels, 1050 Brussels, Belgium
| | - Qiang Feng
- Shandong Provincial Key Laboratory of Oral Tissue Regeneration & Shandong Engineering Laboratory for Dental Materials and Oral Tissue Regeneration, Department of Human Microbiome, School of Stomatology, Shandong University, Jinan, Shandong Province 250012, China
- State Key Laboratory of Microbial Technology, Shandong University, Qingdao, Shandong Province 266237, China
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8
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Raj A, Kumar A, Dames JF. Tapping the Role of Microbial Biosurfactants in Pesticide Remediation: An Eco-Friendly Approach for Environmental Sustainability. Front Microbiol 2021; 12:791723. [PMID: 35003022 PMCID: PMC8733403 DOI: 10.3389/fmicb.2021.791723] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 11/22/2021] [Indexed: 11/15/2022] Open
Abstract
Pesticides are used indiscriminately all over the world to protect crops from pests and pathogens. If they are used in excess, they contaminate the soil and water bodies and negatively affect human health and the environment. However, bioremediation is the most viable option to deal with these pollutants, but it has certain limitations. Therefore, harnessing the role of microbial biosurfactants in pesticide remediation is a promising approach. Biosurfactants are the amphiphilic compounds that can help to increase the bioavailability of pesticides, and speeds up the bioremediation process. Biosurfactants lower the surface area and interfacial tension of immiscible fluids and boost the solubility and sorption of hydrophobic pesticide contaminants. They have the property of biodegradability, low toxicity, high selectivity, and broad action spectrum under extreme pH, temperature, and salinity conditions, as well as a low critical micelle concentration (CMC). All these factors can augment the process of pesticide remediation. Application of metagenomic and in-silico tools would help by rapidly characterizing pesticide degrading microorganisms at a taxonomic and functional level. A comprehensive review of the literature shows that the role of biosurfactants in the biological remediation of pesticides has received limited attention. Therefore, this article is intended to provide a detailed overview of the role of various biosurfactants in improving pesticide remediation as well as different methods used for the detection of microbial biosurfactants. Additionally, this article covers the role of advanced metagenomics tools in characterizing the biosurfactant producing pesticide degrading microbes from different environments.
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Affiliation(s)
- Aman Raj
- Metagenomics and Secretomics Research Laboratory, Department of Botany, Dr. Harisingh Gour University (Central University), Sagar, India
| | - Ashwani Kumar
- Metagenomics and Secretomics Research Laboratory, Department of Botany, Dr. Harisingh Gour University (Central University), Sagar, India
- Mycorrhizal Research Laboratory, Department of Biochemistry and Microbiology, Rhodes University, Grahamstown, South Africa
| | - Joanna Felicity Dames
- Mycorrhizal Research Laboratory, Department of Biochemistry and Microbiology, Rhodes University, Grahamstown, South Africa
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9
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Robinson SL, Piel J, Sunagawa S. A roadmap for metagenomic enzyme discovery. Nat Prod Rep 2021; 38:1994-2023. [PMID: 34821235 PMCID: PMC8597712 DOI: 10.1039/d1np00006c] [Citation(s) in RCA: 55] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Indexed: 12/13/2022]
Abstract
Covering: up to 2021Metagenomics has yielded massive amounts of sequencing data offering a glimpse into the biosynthetic potential of the uncultivated microbial majority. While genome-resolved information about microbial communities from nearly every environment on earth is now available, the ability to accurately predict biocatalytic functions directly from sequencing data remains challenging. Compared to primary metabolic pathways, enzymes involved in secondary metabolism often catalyze specialized reactions with diverse substrates, making these pathways rich resources for the discovery of new enzymology. To date, functional insights gained from studies on environmental DNA (eDNA) have largely relied on PCR- or activity-based screening of eDNA fragments cloned in fosmid or cosmid libraries. As an alternative, shotgun metagenomics holds underexplored potential for the discovery of new enzymes directly from eDNA by avoiding common biases introduced through PCR- or activity-guided functional metagenomics workflows. However, inferring new enzyme functions directly from eDNA is similar to searching for a 'needle in a haystack' without direct links between genotype and phenotype. The goal of this review is to provide a roadmap to navigate shotgun metagenomic sequencing data and identify new candidate biosynthetic enzymes. We cover both computational and experimental strategies to mine metagenomes and explore protein sequence space with a spotlight on natural product biosynthesis. Specifically, we compare in silico methods for enzyme discovery including phylogenetics, sequence similarity networks, genomic context, 3D structure-based approaches, and machine learning techniques. We also discuss various experimental strategies to test computational predictions including heterologous expression and screening. Finally, we provide an outlook for future directions in the field with an emphasis on meta-omics, single-cell genomics, cell-free expression systems, and sequence-independent methods.
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Affiliation(s)
| | - Jörn Piel
- Eidgenössische Technische Hochschule (ETH), Zürich, Switzerland.
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10
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Lach J, Jęcz P, Strapagiel D, Matera-Witkiewicz A, Stączek P. The Methods of Digging for "Gold" within the Salt: Characterization of Halophilic Prokaryotes and Identification of Their Valuable Biological Products Using Sequencing and Genome Mining Tools. Genes (Basel) 2021; 12:genes12111756. [PMID: 34828362 PMCID: PMC8619533 DOI: 10.3390/genes12111756] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 10/18/2021] [Accepted: 10/30/2021] [Indexed: 02/06/2023] Open
Abstract
Halophiles, the salt-loving organisms, have been investigated for at least a hundred years. They are found in all three domains of life, namely Archaea, Bacteria, and Eukarya, and occur in saline and hypersaline environments worldwide. They are already a valuable source of various biomolecules for biotechnological, pharmaceutical, cosmetological and industrial applications. In the present era of multidrug-resistant bacteria, cancer expansion, and extreme environmental pollution, the demand for new, effective compounds is higher and more urgent than ever before. Thus, the unique metabolism of halophilic microorganisms, their low nutritional requirements and their ability to adapt to harsh conditions (high salinity, high pressure and UV radiation, low oxygen concentration, hydrophobic conditions, extreme temperatures and pH, toxic compounds and heavy metals) make them promising candidates as a fruitful source of bioactive compounds. The main aim of this review is to highlight the nucleic acid sequencing experimental strategies used in halophile studies in concert with the presentation of recent examples of bioproducts and functions discovered in silico in the halophile's genomes. We point out methodological gaps and solutions based on in silico methods that are helpful in the identification of valuable bioproducts synthesized by halophiles. We also show the potential of an increasing number of publicly available genomic and metagenomic data for halophilic organisms that can be analysed to identify such new bioproducts and their producers.
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Affiliation(s)
- Jakub Lach
- Department of Molecular Microbiology, Faculty of Biology and Environmental Protection, University of Lodz, 93-338 Lodz, Poland; (P.J.); (P.S.)
- Biobank Lab, Department of Molecular Biophysics, Faculty of Environmental Protection, University of Lodz, 93-338 Lodz, Poland;
- Correspondence:
| | - Paulina Jęcz
- Department of Molecular Microbiology, Faculty of Biology and Environmental Protection, University of Lodz, 93-338 Lodz, Poland; (P.J.); (P.S.)
| | - Dominik Strapagiel
- Biobank Lab, Department of Molecular Biophysics, Faculty of Environmental Protection, University of Lodz, 93-338 Lodz, Poland;
| | - Agnieszka Matera-Witkiewicz
- Screening Laboratory of Biological Activity Tests and Collection of Biological Material, Faculty of Pharmacy, Wroclaw Medical University, 50-368 Wroclaw, Poland;
| | - Paweł Stączek
- Department of Molecular Microbiology, Faculty of Biology and Environmental Protection, University of Lodz, 93-338 Lodz, Poland; (P.J.); (P.S.)
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11
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Kant Bhatia S, Vivek N, Kumar V, Chandel N, Thakur M, Kumar D, Yang YH, Pugazendhi A, Kumar G. Molecular biology interventions for activity improvement and production of industrial enzymes. BIORESOURCE TECHNOLOGY 2021; 324:124596. [PMID: 33440311 DOI: 10.1016/j.biortech.2020.124596] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 12/16/2020] [Accepted: 12/18/2020] [Indexed: 06/12/2023]
Abstract
Metagenomics and directed evolution technology have brought a revolution in search of novel enzymes from extreme environment and improvement of existing enzymes and tuning them towards certain desired properties. Using advanced tools of molecular biology i.e. next generation sequencing, site directed mutagenesis, fusion protein, surface display, etc. now researchers can engineer enzymes for improved activity, stability, and substrate specificity to meet the industrial demand. Although many enzymatic processes have been developed up to industrial scale, still there is a need to overcome limitations of maintaining activity during the catalytic process. In this article recent developments in enzymes industrial applications and advancements in metabolic engineering approaches to improve enzymes efficacy and production are reviewed.
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Affiliation(s)
- Shashi Kant Bhatia
- Department of Biological Engineering, College of Engineering, Konkuk University, Seoul 05029, Republic of Korea; Institute for Ubiquitous Information Technology and Application, Konkuk University, Seoul 05029, Republic of Korea
| | - Narisetty Vivek
- Centre for Climate and Environmental Protection, School of Water, Energy and Environment, Cranfield University, Cranfield MK43 0AL, UK
| | - Vinod Kumar
- Centre for Climate and Environmental Protection, School of Water, Energy and Environment, Cranfield University, Cranfield MK43 0AL, UK
| | - Neha Chandel
- School of Medical and Allied Sciences, GD Goenka University, Gurugram 122103, Haryana, India
| | - Meenu Thakur
- Department of Biotechnology, Shoolini Institute of Life Sciences and Business Management, Solan 173212, Himachal Pradesh, India
| | - Dinesh Kumar
- School of Bioengineering & Food Technology, Shoolini University of Biotechnology and Management Sciences, Solan 173229, Himachal Pradesh, India
| | - Yung-Hun Yang
- Department of Biological Engineering, College of Engineering, Konkuk University, Seoul 05029, Republic of Korea; Institute for Ubiquitous Information Technology and Application, Konkuk University, Seoul 05029, Republic of Korea
| | - Arivalagan Pugazendhi
- Innovative Green Product Synthesis and Renewable Environment Development Research Group, Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho ChiMinh City, Viet Nam
| | - Gopalakrishnan Kumar
- Institute of Chemistry, Bioscience and Environmental Engineering, Faculty of Science and Technology, University of Stavanger, Box 8600 Forus, 4036 Stavanger, Norway; School of Civil and Environmental Engineering, Yonsei University, Seoul 03722, Republic of Korea.
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Ghosh A, Firdous S, Saha S. Bioinformatics for Human Microbiome. Adv Bioinformatics 2021. [DOI: 10.1007/978-981-33-6191-1_17] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
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13
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Zarafeta D, Galanopoulou AP, Leni ME, Kaili SI, Chegkazi MS, Chrysina ED, Kolisis FN, Hatzinikolaou DG, Skretas G. XynDZ5: A New Thermostable GH10 Xylanase. Front Microbiol 2020; 11:545. [PMID: 32390953 PMCID: PMC7193231 DOI: 10.3389/fmicb.2020.00545] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2019] [Accepted: 03/12/2020] [Indexed: 12/27/2022] Open
Abstract
Xylanolytic enzymes have a broad range of applications in industrial biotechnology as biocatalytic components of various processes and products, such as food additives, bakery products, coffee extraction, agricultural silage and functional foods. An increasing market demand has driven the growing interest for the discovery of xylanases with specific industrially relevant characteristics, such as stability at elevated temperatures and in the presence of other denaturing factors, which will facilitate their incorporation into industrial processes. In this work, we report the discovery and biochemical characterization of a new thermostable GH10 xylanase, termed XynDZ5, exhibiting only 26% amino acid sequence identity to the closest characterized xylanolytic enzyme. This new enzyme was discovered in an Icelandic hot spring enrichment culture of a Thermoanaerobacterium species using a recently developed bioinformatic analysis platform. XynDZ5 was produced recombinantly in Escherichia coli, purified and characterized biochemically. This analysis revealed that it acts as an endo-1,4-β-xylanase that performs optimally at 65–75°C and pH 7.5. The enzyme is capable of retaining high levels of catalytic efficiency after several hours of incubation at high temperatures, as well as in the presence of significant concentrations of a range of metal ions and denaturing agents. Interestingly, the XynDZ5 biochemical profile was found to be atypical, as it also exhibits significant exo-activity. Computational modeling of its three-dimensional structure predicted a (β/α)8 TIM barrel fold, which is very frequently encountered among family GH10 enzymes. This modeled structure has provided clues about structural features that may explain aspects of its catalytic performance. Our results suggest that XynDZ5 represents a promising new candidate biocatalyst appropriate for several high-temperature biotechnological applications in the pulp, paper, baking, animal-feed and biofuel industries.
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Affiliation(s)
- Dimitra Zarafeta
- Institute of Chemical Biology, National Hellenic Research Foundation, Athens, Greece
| | - Anastasia P Galanopoulou
- Institute of Chemical Biology, National Hellenic Research Foundation, Athens, Greece.,Department of Biology, Enzyme and Microbial Biotechnology Unit, National and Kapodistrian University of Athens, Athens, Greece
| | - Maria Evangelia Leni
- Institute of Chemical Biology, National Hellenic Research Foundation, Athens, Greece
| | - Stavroula I Kaili
- Department of Biology, Enzyme and Microbial Biotechnology Unit, National and Kapodistrian University of Athens, Athens, Greece
| | - Magda S Chegkazi
- Institute of Chemical Biology, National Hellenic Research Foundation, Athens, Greece
| | - Evangelia D Chrysina
- Institute of Chemical Biology, National Hellenic Research Foundation, Athens, Greece
| | - Fragiskos N Kolisis
- Laboratory of Biotechnology, School of Chemical Engineering, National Technical University of Athens, Athens, Greece
| | - Dimitris G Hatzinikolaou
- Department of Biology, Enzyme and Microbial Biotechnology Unit, National and Kapodistrian University of Athens, Athens, Greece
| | - Georgios Skretas
- Institute of Chemical Biology, National Hellenic Research Foundation, Athens, Greece
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14
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Carboxylic Ester Hydrolases in Bacteria: Active Site, Structure, Function and Application. CRYSTALS 2019. [DOI: 10.3390/cryst9110597] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Carboxylic ester hydrolases (CEHs), which catalyze the hydrolysis of carboxylic esters to produce alcohol and acid, are identified in three domains of life. In the Protein Data Bank (PDB), 136 crystal structures of bacterial CEHs (424 PDB codes) from 52 genera and metagenome have been reported. In this review, we categorize these structures based on catalytic machinery, structure and substrate specificity to provide a comprehensive understanding of the bacterial CEHs. CEHs use Ser, Asp or water as a nucleophile to drive diverse catalytic machinery. The α/β/α sandwich architecture is most frequently found in CEHs, but 3-solenoid, β-barrel, up-down bundle, α/β/β/α 4-layer sandwich, 6 or 7 propeller and α/β barrel architectures are also found in these CEHs. Most are substrate-specific to various esters with types of head group and lengths of the acyl chain, but some CEHs exhibit peptidase or lactamase activities. CEHs are widely used in industrial applications, and are the objects of research in structure- or mutation-based protein engineering. Structural studies of CEHs are still necessary for understanding their biological roles, identifying their structure-based functions and structure-based engineering and their potential industrial applications.
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15
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Vekris A, Pilalis E, Chatziioannou A, Petry KG. A Computational Pipeline for the Extraction of Actionable Biological Information From NGS-Phage Display Experiments. Front Physiol 2019; 10:1160. [PMID: 31607941 PMCID: PMC6769401 DOI: 10.3389/fphys.2019.01160] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2018] [Accepted: 08/28/2019] [Indexed: 12/20/2022] Open
Abstract
Phage Display is a powerful method for the identification of peptide binding to targets of variable complexities and tissues, from unique molecules to the internal surfaces of vessels of living organisms. Particularly for in vivo screenings, the resulting repertoires can be very complex and difficult to study with traditional approaches. Next Generation Sequencing (NGS) opened the possibility to acquire high resolution overviews of such repertoires and thus facilitates the identification of binders of interest. Additionally, the ever-increasing amount of available genome/proteome information became satisfactory regarding the identification of putative mimicked proteins, due to the large scale on which partial sequence homology is assessed. However, the subsequent production of massive data stresses the need for high-performance computational approaches in order to perform standardized and insightful molecular network analysis. Systems-level analysis is essential for efficient resolution of the underlying molecular complexity and the extraction of actionable interpretation, in terms of systemic biological processes and pathways that are systematically perturbed. In this work we introduce PepSimili, an integrated workflow tool, which performs mapping of massive peptide repertoires on whole proteomes and delivers a streamlined, systems-level biological interpretation. The tool employs modules for modeling and filtering of background noise due to random mappings and amplifies the biologically meaningful signal through coupling with BioInfoMiner, a systems interpretation tool that employs graph-theoretic methods for prioritization of systemic processes and corresponding driver genes. The current implementation exploits the Galaxy environment and is available online. A case study using public data is presented, with and without a control selection.
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Affiliation(s)
| | - Eleftherios Pilalis
- Metabolic Engineering and Bioinformatics Program, Institute of Chemical Biology, National Hellenic Research Foundation, Athens, Greece.,eNIOS Applications P.C., Athens, Greece
| | - Aristotelis Chatziioannou
- Metabolic Engineering and Bioinformatics Program, Institute of Chemical Biology, National Hellenic Research Foundation, Athens, Greece.,eNIOS Applications P.C., Athens, Greece
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16
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Thang MWC, Chua XY, Price G, Gorse D, Field MA. MetaDEGalaxy: Galaxy workflow for differential abundance analysis of 16s metagenomic data. F1000Res 2019; 8:726. [PMID: 31737256 PMCID: PMC6807170 DOI: 10.12688/f1000research.18866.2] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/16/2019] [Indexed: 12/03/2022] Open
Abstract
Metagenomic sequencing is an increasingly common tool in environmental and biomedical sciences. While software for detailing the composition of microbial communities using 16S rRNA marker genes is relatively mature, increasingly researchers are interested in identifying changes exhibited within microbial communities under differing environmental conditions. In order to gain maximum value from metagenomic sequence data we must improve the existing analysis environment by providing accessible and scalable computational workflows able to generate reproducible results. Here we describe a complete end-to-end open-source metagenomics workflow running within Galaxy for 16S differential abundance analysis. The workflow accepts 454 or Illumina sequence data (either overlapping or non-overlapping paired end reads) and outputs lists of the operational taxonomic unit (OTUs) exhibiting the greatest change under differing conditions. A range of analysis steps and graphing options are available giving users a high-level of control over their data and analyses. Additionally, users are able to input complex sample-specific metadata information which can be incorporated into differential analysis and used for grouping / colouring within graphs. Detailed tutorials containing sample data and existing workflows are available for three different input types: overlapping and non-overlapping read pairs as well as for pre-generated Biological Observation Matrix (BIOM) files. Using the Galaxy platform we developed MetaDEGalaxy, a complete metagenomics differential abundance analysis workflow. MetaDEGalaxy is designed for bench scientists working with 16S data who are interested in comparative metagenomics. MetaDEGalaxy builds on momentum within the wider Galaxy metagenomics community with the hope that more tools will be added as existing methods mature.
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Affiliation(s)
- Mike W C Thang
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, 4000, Australia.,Queensland Facility for Advanced Bioinformatics, University of Queensland, Brisbane, Queensland, 4000, Australia
| | - Xin-Yi Chua
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, 4000, Australia.,Queensland Facility for Advanced Bioinformatics, University of Queensland, Brisbane, Queensland, 4000, Australia
| | - Gareth Price
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, 4000, Australia.,Queensland Facility for Advanced Bioinformatics, University of Queensland, Brisbane, Queensland, 4000, Australia
| | - Dominique Gorse
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, 4000, Australia.,Queensland Facility for Advanced Bioinformatics, University of Queensland, Brisbane, Queensland, 4000, Australia
| | - Matt A Field
- John Curtin School of Medical Research, Australian National University, Canberra, ACT, Australia.,Australian Institute for Tropical Health and Medicine, James Cook University, Smithfield, Queensland, 4878, Australia.,Centre for Tropical Bioinformatics and Molecular Biology, James Cook University, Smithfield, Queensland, 4878, Australia
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