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Medeiros W, Hidalgo K, Leão T, de Carvalho LM, Ziemert N, Oliveira V. Unlocking the biosynthetic potential and taxonomy of the Antarctic microbiome along temporal and spatial gradients. Microbiol Spectr 2024; 12:e0024424. [PMID: 38747631 PMCID: PMC11237469 DOI: 10.1128/spectrum.00244-24] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Accepted: 04/19/2024] [Indexed: 06/06/2024] Open
Abstract
Extreme environments, such as Antarctica, select microbial communities that display a range of evolutionary strategies to survive and thrive under harsh environmental conditions. These include a diversity of specialized metabolites, which have the potential to be a source for new natural product discovery. Efforts using (meta)genome mining approaches to identify and understand biosynthetic gene clusters in Antarctica are still scarce, and the extent of their diversity and distribution patterns in the environment have yet to be discovered. Herein, we investigated the biosynthetic gene diversity of the biofilm microbial community of Whalers Bay, Deception Island, in the Antarctic Peninsula and revealed its distribution patterns along spatial and temporal gradients by applying metagenome mining approaches and multivariable analysis. The results showed that the Whalers Bay microbial community harbors a great diversity of biosynthetic gene clusters distributed into seven classes, with terpene being the most abundant. The phyla Proteobacteria and Bacteroidota were the most abundant in the microbial community and contributed significantly to the biosynthetic gene abundances in Whalers Bay. Furthermore, the results highlighted a significant correlation between the distribution of biosynthetic genes and taxonomic diversity, emphasizing the intricate interplay between microbial taxonomy and their potential for specialized metabolite production.IMPORTANCEThis research on antarctic microbial biosynthetic diversity in Whalers Bay, Deception Island, unveils the hidden potential of extreme environments for natural product discovery. By employing metagenomic techniques, the research highlights the extensive diversity of biosynthetic gene clusters and identifies key microbial phyla, Proteobacteria and Bacteroidota, as significant contributors. The correlation between taxonomic diversity and biosynthetic gene distribution underscores the intricate interplay governing specialized metabolite production. These findings are crucial for understanding microbial adaptation in extreme environments and hold significant implications for bioprospecting initiatives. The study opens avenues for discovering novel bioactive compounds with potential applications in medicine and industry, emphasizing the importance of preserving and exploring these polyextreme ecosystems to advance biotechnological and pharmaceutical research.
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Affiliation(s)
- William Medeiros
- Microbial Resources Division, Research Center for Chemistry, Biology, and Agriculture (CPQBA), Universidade Estadual de Campinas (UNICAMP), Paulínia, São Paulo, Brazil
- Interfaculty Institute of Microbiology, and Infection Medicine Institute for Bioinformatics and Medical Informatics, German Centre for Infection Research (DZIF), Tübingen, Germany
| | - Kelly Hidalgo
- Microbial Resources Division, Research Center for Chemistry, Biology, and Agriculture (CPQBA), Universidade Estadual de Campinas (UNICAMP), Paulínia, São Paulo, Brazil
| | - Tiago Leão
- Chemistry Institute, São Paulo State University (UNESP), Araraquara, São Paulo, Brazil
| | - Lucas Miguel de Carvalho
- Center for Computing in Engineering and Sciences, Universidade Estadual de Campinas (UNICAMP), Campinas, São Paulo, Brazil
| | - Nadine Ziemert
- Interfaculty Institute of Microbiology, and Infection Medicine Institute for Bioinformatics and Medical Informatics, German Centre for Infection Research (DZIF), Tübingen, Germany
| | - Valeria Oliveira
- Microbial Resources Division, Research Center for Chemistry, Biology, and Agriculture (CPQBA), Universidade Estadual de Campinas (UNICAMP), Paulínia, São Paulo, Brazil
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Mugani R, El Khalloufi F, Redouane EM, Haida M, Aba RP, Essadki Y, El Amrani Zerrifi S, Hejjaj A, Ouazzani N, Campos A, Grossart HP, Mandi L, Vasconcelos V, Oudra B. Unlocking the potential of bacterioplankton-mediated microcystin degradation and removal: A bibliometric analysis of sustainable water treatment strategies. WATER RESEARCH 2024; 255:121497. [PMID: 38555787 DOI: 10.1016/j.watres.2024.121497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 01/31/2024] [Accepted: 03/19/2024] [Indexed: 04/02/2024]
Abstract
Microcystins (MCs) constitute a significant threat to human and environmental health, urging the development of effective removal methods for these toxins. In this review, we explore the potential of MC-degrading bacteria as a solution for the removal of MCs from water. The review insights into the mechanisms of action employed by these bacteria, elucidating their ability to degrade and thus remove MCs. After, the review points out the influence of the structural conformation of MCs on their removal, particularly their stability at different water depths within different water bodies. Then, we review the crucial role played by the production of MCs in ensuring the survival and safeguarding of the enzymatic activities of Microcystis cells. This justifies the need for developing effective and sustainable methods for removing MCs from aquatic ecosystems, given their critical ecological function and potential toxicity to humans and animals. Thereafter, challenges and limitations associated with using MC-degrading bacteria in water treatment are discussed, emphasizing the need for further research to optimize the selection of bacterial strains used for MCs biodegradation. The interaction of MCs-degrading bacteria with sediment particles is also crucial for their toxin removal potential and its efficiency. By presenting critical information, this review is a valuable resource for researchers, policymakers, and stakeholders involved in developing sustainable and practical approaches to remove MCs. Our review highlights the potential of various applications of MC-degrading bacteria, including multi-soil-layering (MSL) technologies. It emphasizes the need for ongoing research to optimize the utilization of MC-degrading bacteria in water treatment, ultimately ensuring the safety and quality of water sources. Moreover, this review highlights the value of bibliometric analyses in revealing research gaps and trends, providing detailed insights for further investigations. Specifically, we discuss the importance of employing advanced genomics, especially combining various OMICS approaches to identify and optimize the potential of MCs-degrading bacteria.
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Affiliation(s)
- Richard Mugani
- Water, Biodiversity and Climate Change Laboratory, Faculty of Sciences Semlalia, Cadi Ayyad University, Av. Prince My Abdellah, P.O. Box 2390, Marrakech 40000, Morocco; National Center for Studies and Research on Water and Energy, Cadi Ayyad University, P.O. Box: 511, 40000 Marrakech, Morocco; Department of Plankton and Microbial Ecology, Leibniz-Institute of Freshwater Ecology and Inland Fisheries (IGB), Zur alten Fischerhuette 2, 14775 Stechlin, Germany
| | - Fatima El Khalloufi
- Natural Resources Engineering and Environmental Impacts Team, Multidisciplinary Research and Innovation Laboratory, Polydisciplinary Faculty of Khouribga, Sultan Moulay Slimane University of Beni Mellal, B.P.: 145, 25000, Khouribga, Morocco
| | - El Mahdi Redouane
- Water, Biodiversity and Climate Change Laboratory, Faculty of Sciences Semlalia, Cadi Ayyad University, Av. Prince My Abdellah, P.O. Box 2390, Marrakech 40000, Morocco
| | - Mohammed Haida
- Water, Biodiversity and Climate Change Laboratory, Faculty of Sciences Semlalia, Cadi Ayyad University, Av. Prince My Abdellah, P.O. Box 2390, Marrakech 40000, Morocco
| | - Roseline Prisca Aba
- Water, Biodiversity and Climate Change Laboratory, Faculty of Sciences Semlalia, Cadi Ayyad University, Av. Prince My Abdellah, P.O. Box 2390, Marrakech 40000, Morocco; National Center for Studies and Research on Water and Energy, Cadi Ayyad University, P.O. Box: 511, 40000 Marrakech, Morocco
| | - Yasser Essadki
- Water, Biodiversity and Climate Change Laboratory, Faculty of Sciences Semlalia, Cadi Ayyad University, Av. Prince My Abdellah, P.O. Box 2390, Marrakech 40000, Morocco
| | - Soukaina El Amrani Zerrifi
- Water, Biodiversity and Climate Change Laboratory, Faculty of Sciences Semlalia, Cadi Ayyad University, Av. Prince My Abdellah, P.O. Box 2390, Marrakech 40000, Morocco; Higher Institute of Nurses Professions and Health Techniques of Guelmim, Guelmim, Morocco
| | - Abdessamad Hejjaj
- National Center for Studies and Research on Water and Energy, Cadi Ayyad University, P.O. Box: 511, 40000 Marrakech, Morocco.
| | - Naaila Ouazzani
- Water, Biodiversity and Climate Change Laboratory, Faculty of Sciences Semlalia, Cadi Ayyad University, Av. Prince My Abdellah, P.O. Box 2390, Marrakech 40000, Morocco; National Center for Studies and Research on Water and Energy, Cadi Ayyad University, P.O. Box: 511, 40000 Marrakech, Morocco
| | - Alexandre Campos
- CIIMAR, Interdisciplinary Centre of Marine and Environmental Research, Terminal de Cruzeiros do Porto de Leixões, Av. General Norton de Matos, s/n, 4450-208 Porto, Portugal
| | - Hans-Peter Grossart
- Department of Plankton and Microbial Ecology, Leibniz-Institute of Freshwater Ecology and Inland Fisheries (IGB), Zur alten Fischerhuette 2, 14775 Stechlin, Germany; Institute of Biochemistry and Biology, University of Potsdam, Maulbeeralle 2, 14469 Potsdam, Germany
| | - Laila Mandi
- Water, Biodiversity and Climate Change Laboratory, Faculty of Sciences Semlalia, Cadi Ayyad University, Av. Prince My Abdellah, P.O. Box 2390, Marrakech 40000, Morocco; National Center for Studies and Research on Water and Energy, Cadi Ayyad University, P.O. Box: 511, 40000 Marrakech, Morocco
| | - Vitor Vasconcelos
- CIIMAR, Interdisciplinary Centre of Marine and Environmental Research, Terminal de Cruzeiros do Porto de Leixões, Av. General Norton de Matos, s/n, 4450-208 Porto, Portugal; Department of Biology, Faculty of Sciences, University of Porto, Rua do Campo Alegre, 4169-007 Porto, Portugal.
| | - Brahim Oudra
- Water, Biodiversity and Climate Change Laboratory, Faculty of Sciences Semlalia, Cadi Ayyad University, Av. Prince My Abdellah, P.O. Box 2390, Marrakech 40000, Morocco
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Zdouc MM, van der Hooft JJJ, Medema MH. Metabolome-guided genome mining of RiPP natural products. Trends Pharmacol Sci 2023; 44:532-541. [PMID: 37391295 DOI: 10.1016/j.tips.2023.06.004] [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: 05/24/2023] [Revised: 06/12/2023] [Accepted: 06/12/2023] [Indexed: 07/02/2023]
Abstract
Ribosomally synthesized and post-translationally modified peptides (RiPPs) are a chemically diverse class of metabolites. Many RiPPs show potent biological activities that make them attractive starting points for drug development. A promising approach for the discovery of new classes of RiPPs is genome mining. However, the accuracy of genome mining is hampered by the lack of signature genes shared across different RiPP classes. One way to reduce false-positive predictions is by complementing genomic information with metabolomics data. In recent years, several new approaches addressing such integrative genomics and metabolomics analyses have been developed. In this review, we provide a detailed discussion of RiPP-compatible software tools that integrate paired genomics and metabolomics data. We highlight current challenges in data integration and identify opportunities for further developments targeting new classes of bioactive RiPPs.
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Affiliation(s)
- Mitja M Zdouc
- Bioinformatics Group, Wageningen University & Research, Droevendaalsesteeg 1, 6708 PB, Wageningen, the Netherlands.
| | - Justin J J van der Hooft
- Bioinformatics Group, Wageningen University & Research, Droevendaalsesteeg 1, 6708 PB, Wageningen, the Netherlands; Department of Biochemistry, University of Johannesburg, Auckland Park, Johannesburg 2006, South Africa.
| | - Marnix H Medema
- Bioinformatics Group, Wageningen University & Research, Droevendaalsesteeg 1, 6708 PB, Wageningen, the Netherlands.
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Gaudêncio SP, Bayram E, Lukić Bilela L, Cueto M, Díaz-Marrero AR, Haznedaroglu BZ, Jimenez C, Mandalakis M, Pereira F, Reyes F, Tasdemir D. Advanced Methods for Natural Products Discovery: Bioactivity Screening, Dereplication, Metabolomics Profiling, Genomic Sequencing, Databases and Informatic Tools, and Structure Elucidation. Mar Drugs 2023; 21:md21050308. [PMID: 37233502 DOI: 10.3390/md21050308] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 05/11/2023] [Accepted: 05/12/2023] [Indexed: 05/27/2023] Open
Abstract
Natural Products (NP) are essential for the discovery of novel drugs and products for numerous biotechnological applications. The NP discovery process is expensive and time-consuming, having as major hurdles dereplication (early identification of known compounds) and structure elucidation, particularly the determination of the absolute configuration of metabolites with stereogenic centers. This review comprehensively focuses on recent technological and instrumental advances, highlighting the development of methods that alleviate these obstacles, paving the way for accelerating NP discovery towards biotechnological applications. Herein, we emphasize the most innovative high-throughput tools and methods for advancing bioactivity screening, NP chemical analysis, dereplication, metabolite profiling, metabolomics, genome sequencing and/or genomics approaches, databases, bioinformatics, chemoinformatics, and three-dimensional NP structure elucidation.
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Affiliation(s)
- Susana P Gaudêncio
- Associate Laboratory i4HB-Institute for Health and Bioeconomy, NOVA School of Science and Technology, NOVA University Lisbon, 2819-516 Caparica, Portugal
- UCIBIO-Applied Molecular Biosciences Unit, Chemistry Department, NOVA School of Science and Technology, NOVA University of Lisbon, 2819-516 Caparica, Portugal
| | - Engin Bayram
- Institute of Environmental Sciences, Room HKC-202, Hisar Campus, Bogazici University, Bebek, Istanbul 34342, Turkey
| | - Lada Lukić Bilela
- Department of Biology, Faculty of Science, University of Sarajevo, 71000 Sarajevo, Bosnia and Herzegovina
| | - Mercedes Cueto
- Instituto de Productos Naturales y Agrobiología-CSIC, 38206 La Laguna, Spain
| | - Ana R Díaz-Marrero
- Instituto de Productos Naturales y Agrobiología-CSIC, 38206 La Laguna, Spain
- Instituto Universitario de Bio-Orgánica (IUBO), Universidad de La Laguna, 38206 La Laguna, Spain
| | - Berat Z Haznedaroglu
- Institute of Environmental Sciences, Room HKC-202, Hisar Campus, Bogazici University, Bebek, Istanbul 34342, Turkey
| | - Carlos Jimenez
- CICA- Centro Interdisciplinar de Química e Bioloxía, Departamento de Química, Facultade de Ciencias, Universidade da Coruña, 15071 A Coruña, Spain
| | - Manolis Mandalakis
- Institute of Marine Biology, Biotechnology and Aquaculture, Hellenic Centre for Marine Research, HCMR Thalassocosmos, 71500 Gournes, Crete, Greece
| | - Florbela Pereira
- LAQV, REQUIMTE, Chemistry Department, NOVA School of Science and Technology, NOVA University of Lisbon, 2819-516 Caparica, Portugal
| | - Fernando Reyes
- Fundación MEDINA, Avda. del Conocimiento 34, 18016 Armilla, Spain
| | - Deniz Tasdemir
- GEOMAR Centre for Marine Biotechnology (GEOMAR-Biotech), Research Unit Marine Natural Products Chemistry, GEOMAR Helmholtz Centre for Ocean Research Kiel, Am Kiel-Kanal 44, 24106 Kiel, Germany
- Faculty of Mathematics and Natural Science, Kiel University, Christian-Albrechts-Platz 4, 24118 Kiel, Germany
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Zhao L, Walkowiak S, Fernando WGD. Artificial Intelligence: A Promising Tool in Exploring the Phytomicrobiome in Managing Disease and Promoting Plant Health. PLANTS (BASEL, SWITZERLAND) 2023; 12:plants12091852. [PMID: 37176910 PMCID: PMC10180744 DOI: 10.3390/plants12091852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 04/25/2023] [Accepted: 04/27/2023] [Indexed: 05/15/2023]
Abstract
There is increasing interest in harnessing the microbiome to improve cropping systems. With the availability of high-throughput and low-cost sequencing technologies, gathering microbiome data is becoming more routine. However, the analysis of microbiome data is challenged by the size and complexity of the data, and the incomplete nature of many microbiome databases. Further, to bring microbiome data value, it often needs to be analyzed in conjunction with other complex data that impact on crop health and disease management, such as plant genotype and environmental factors. Artificial intelligence (AI), boosted through deep learning (DL), has achieved significant breakthroughs and is a powerful tool for managing large complex datasets such as the interplay between the microbiome, crop plants, and their environment. In this review, we aim to provide readers with a brief introduction to AI techniques, and we introduce how AI has been applied to areas of microbiome sequencing taxonomy, the functional annotation for microbiome sequences, associating the microbiome community with host traits, designing synthetic communities, genomic selection, field phenotyping, and disease forecasting. At the end of this review, we proposed further efforts that are required to fully exploit the power of AI in studying phytomicrobiomes.
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Affiliation(s)
- Liang Zhao
- Department of Plant Science, University of Manitoba, Winnipeg, MB R3T 2N2, Canada
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6
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Hellinger R, Sigurdsson A, Wu W, Romanova EV, Li L, Sweedler JV, Süssmuth RD, Gruber CW. Peptidomics. NATURE REVIEWS. METHODS PRIMERS 2023; 3:25. [PMID: 37250919 PMCID: PMC7614574 DOI: 10.1038/s43586-023-00205-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 02/09/2023] [Indexed: 05/31/2023]
Abstract
Peptides are biopolymers, typically consisting of 2-50 amino acids. They are biologically produced by the cellular ribosomal machinery or by non-ribosomal enzymes and, sometimes, other dedicated ligases. Peptides are arranged as linear chains or cycles, and include post-translational modifications, unusual amino acids and stabilizing motifs. Their structure and molecular size render them a unique chemical space, between small molecules and larger proteins. Peptides have important physiological functions as intrinsic signalling molecules, such as neuropeptides and peptide hormones, for cellular or interspecies communication, as toxins to catch prey or as defence molecules to fend off enemies and microorganisms. Clinically, they are gaining popularity as biomarkers or innovative therapeutics; to date there are more than 60 peptide drugs approved and more than 150 in clinical development. The emerging field of peptidomics comprises the comprehensive qualitative and quantitative analysis of the suite of peptides in a biological sample (endogenously produced, or exogenously administered as drugs). Peptidomics employs techniques of genomics, modern proteomics, state-of-the-art analytical chemistry and innovative computational biology, with a specialized set of tools. The complex biological matrices and often low abundance of analytes typically examined in peptidomics experiments require optimized sample preparation and isolation, including in silico analysis. This Primer covers the combination of techniques and workflows needed for peptide discovery and characterization and provides an overview of various biological and clinical applications of peptidomics.
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Affiliation(s)
- Roland Hellinger
- Center for Physiology and Pharmacology, Medical University of Vienna, Vienna, Austria
| | - Arnar Sigurdsson
- Institut für Chemie, Technische Universität Berlin, Berlin, Germany
| | - Wenxin Wu
- School of Pharmacy and Department of Chemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Elena V Romanova
- Department of Chemistry, University of Illinois, Urbana, IL, USA
| | - Lingjun Li
- School of Pharmacy and Department of Chemistry, University of Wisconsin-Madison, Madison, WI, USA
| | | | | | - Christian W Gruber
- Center for Physiology and Pharmacology, Medical University of Vienna, Vienna, Austria
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Abstract
A major source of pseudomonad-specialized metabolites is the nonribosomal peptide synthetases (NRPSs) assembling siderophores and lipopeptides. Cyclic lipopeptides (CLPs) of the Mycin and Peptin families are frequently associated with, but not restricted to, phytopathogenic species. We conducted an in silico analysis of the NRPSs encoded by lipopeptide biosynthetic gene clusters in nonpathogenic Pseudomonas genomes, covering 13 chemically diversified families. This global assessment of lipopeptide production capacity revealed it to be confined to the Pseudomonas fluorescens lineage, with most strains synthesizing a single type of CLP. Whereas certain lipopeptide families are specific for a taxonomic subgroup, others are found in distant groups. NRPS activation domain-guided peptide predictions enabled reliable family assignments, including identification of novel members. Focusing on the two most abundant lipopeptide families (Viscosin and Amphisin), a portion of their uncharted diversity was mapped, including characterization of two novel Amphisin family members (nepenthesin and oakridgin). Using NMR fingerprint matching, known Viscosin-family lipopeptides were identified in 15 (type) species spread across different taxonomic groups. A bifurcate genomic organization predominates among Viscosin-family producers and typifies Xantholysin-, Entolysin-, and Poaeamide-family producers but most families feature a single NRPS gene cluster embedded between cognate regulator and transporter genes. The strong correlation observed between NRPS system phylogeny and rpoD-based taxonomic affiliation indicates that much of the structural diversity is linked to speciation, providing few indications of horizontal gene transfer. The grouping of most NRPS systems in four superfamilies based on activation domain homology suggests extensive module dynamics driven by domain deletions, duplications, and exchanges. IMPORTANCE Pseudomonas species are prominent producers of lipopeptides that support proliferation in a multitude of environments and foster varied lifestyles. By genome mining of biosynthetic gene clusters (BGCs) with lipopeptide-specific organization, we mapped the global Pseudomonas lipopeptidome and linked its staggering diversity to taxonomy of the producers, belonging to different groups within the major Pseudomonas fluorescens lineage. Activation domain phylogeny of newly mined lipopeptide synthetases combined with previously characterized enzymes enabled assignment of predicted BGC products to specific lipopeptide families. In addition, novel peptide sequences were detected, showing the value of substrate specificity analysis for prioritization of BGCs for further characterization. NMR fingerprint matching proved an excellent tool to unequivocally identify multiple lipopeptides bioinformatically assigned to the Viscosin family, by far the most abundant one in Pseudomonas and with stereochemistry of all its current members elucidated. In-depth analysis of activation domains provided insight into mechanisms driving lipopeptide structural diversification.
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Talat A, Khan AU. Artificial intelligence as a smart approach to develop antimicrobial drug molecules: A paradigm to combat drug-resistant infections. Drug Discov Today 2023; 28:103491. [PMID: 36646245 DOI: 10.1016/j.drudis.2023.103491] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 01/01/2023] [Accepted: 01/05/2023] [Indexed: 01/15/2023]
Abstract
Antimicrobial resistance (AMR) is a silent pandemic with the third highest global mortality. The antibiotic development pipeline is scarce even though AMR has escalated uncontrollably. Artificial intelligence (AI) is a revolutionary approach, accelerating drug discovery because of its fast pace, cost efficiency, lower labor requirements, and fewer chances of failure. AI has been used to discover several beta-lactamase inhibitors and antibiotic alternatives from antimicrobial peptides (AMPs), nonribosomal peptides, bacteriocins, and marine natural products. The significant recent increase in the use of AI platforms by pharmaceutical companies could result in the discovery of efficient antibiotic alternatives with lower chances of resistance generation.
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Affiliation(s)
- Absar Talat
- Medical Microbiology and Molecular Biology Laboratory, Interdisciplinary Biotechnology Unit, Aligarh Muslim University, Aligarh, India
| | - Asad U Khan
- Medical Microbiology and Molecular Biology Laboratory, Interdisciplinary Biotechnology Unit, Aligarh Muslim University, Aligarh, India.
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Leão TF, Wang M, da Silva R, Gurevich A, Bauermeister A, Gomes PWP, Brejnrod A, Glukhov E, Aron AT, Louwen JJR, Kim HW, Reher R, Fiore MF, van der Hooft JJJ, Gerwick L, Gerwick WH, Bandeira N, Dorrestein PC. NPOmix: A machine learning classifier to connect mass spectrometry fragmentation data to biosynthetic gene clusters. PNAS NEXUS 2022; 1:pgac257. [PMID: 36712343 PMCID: PMC9802219 DOI: 10.1093/pnasnexus/pgac257] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 10/18/2022] [Accepted: 11/25/2022] [Indexed: 06/02/2023]
Abstract
Microbial specialized metabolites are an important source of and inspiration for many pharmaceuticals, biotechnological products and play key roles in ecological processes. Untargeted metabolomics using liquid chromatography coupled with tandem mass spectrometry is an efficient technique to access metabolites from fractions and even environmental crude extracts. Nevertheless, metabolomics is limited in predicting structures or bioactivities for cryptic metabolites. Efficiently linking the biosynthetic potential inferred from (meta)genomics to the specialized metabolome would accelerate drug discovery programs by allowing metabolomics to make use of genetic predictions. Here, we present a k-nearest neighbor classifier to systematically connect mass spectrometry fragmentation spectra to their corresponding biosynthetic gene clusters (independent of their chemical class). Our new pattern-based genome mining pipeline links biosynthetic genes to metabolites that they encode for, as detected via mass spectrometry from bacterial cultures or environmental microbiomes. Using paired datasets that include validated genes-mass spectral links from the Paired Omics Data Platform, we demonstrate this approach by automatically linking 18 previously known mass spectra (17 for which the biosynthesis gene clusters can be found at the MIBiG database plus palmyramide A) to their corresponding previously experimentally validated biosynthetic genes (e.g., via nuclear magnetic resonance or genetic engineering). We illustrated a computational example of how to use our Natural Products Mixed Omics (NPOmix) tool for siderophore mining that can be reproduced by the users. We conclude that NPOmix minimizes the need for culturing (it worked well on microbiomes) and facilitates specialized metabolite prioritization based on integrative omics mining.
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Affiliation(s)
- Tiago F Leão
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA 92093, USA
- Center for Nuclear Energy in Agriculture, University of São Paulo, Piracicaba 13400-970, SP, Brazil
| | - Mingxun Wang
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA 92093, USA
- Center for Computational Mass Spectrometry, University of California San Diego, La Jolla, CA 92093, USA
| | - Ricardo da Silva
- NPPNS, Physic and Chemistry Department, School of Pharmaceutical Sciences of Ribeirão Preto, University of São Paulo, Ribeirão Preto 14040-900, Brazil
| | - Alexey Gurevich
- Center for Algorithmic Biotechnology, St. Petersburg State University, St Petersburg 199004, Russia
| | - Anelize Bauermeister
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA 92093, USA
| | - Paulo Wender P Gomes
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA 92093, USA
| | - Asker Brejnrod
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA 92093, USA
| | - Evgenia Glukhov
- Center for Marine Biotechnology and Biomedicine, Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA 92093, USA
| | - Allegra T Aron
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA 92093, USA
- Department of Chemistry and Biochemistry, University of Denver, Denver, CO 80210, USA
| | - Joris J R Louwen
- Bioinformatics Group, Wageningen University, 6708 PB Wageningen, The Netherlands
| | - Hyun Woo Kim
- College of Pharmacy and Integrated Research Institute for Drug Development, Dongguk University, Gyeonggi-do 10326, Korea
| | - Raphael Reher
- Institute of Pharmaceutical Biology and Biotechnology, University of Marburg, 35043 Marburg, Germany
| | - Marli F Fiore
- Center for Nuclear Energy in Agriculture, University of São Paulo, Piracicaba 13400-970, SP, Brazil
| | - Justin J J van der Hooft
- Bioinformatics Group, Wageningen University, 6708 PB Wageningen, The Netherlands
- Department of Biochemistry, University of Johannesburg, Auckland Park, Johannesburg 2006, South Africa
| | - Lena Gerwick
- Center for Marine Biotechnology and Biomedicine, Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA 92093, USA
| | - William H Gerwick
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA 92093, USA
- Center for Marine Biotechnology and Biomedicine, Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA 92093, USA
| | - Nuno Bandeira
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA 92093, USA
- Center for Computational Mass Spectrometry, University of California San Diego, La Jolla, CA 92093, USA
| | - Pieter C Dorrestein
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA 92093, USA
- Center for Microbiome Innovation, University of California San Diego, La Jolla, CA 92093, USA
- Departments of Pharmacology and Pediatrics, University of California San Diego, La Jolla, CA 92093, USA
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Lin LP, Wu M, Jiang N, Wang W, Tan RX. Carbon-nitrogen bond formation to construct novel polyketide-indole hybrids from the indole-3-carbinol exposed culture of Daldinia eschscholzii. Synth Syst Biotechnol 2022; 7:750-755. [PMID: 35387230 PMCID: PMC8943216 DOI: 10.1016/j.synbio.2022.02.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Revised: 01/29/2022] [Accepted: 02/07/2022] [Indexed: 11/26/2022] Open
Abstract
A plenty of cytochrome P450s have been annotated in the Daldinia eschosholzii genome. Inspired by the fact that some P450s have been reported to catalyze the carbon-nitrogen (C-N) bond formation, we were curious about whether hybrids through C-N bond formation could be generated in the indole-3-carbinol (I3C) exposed culture of D. eschscholzii. As expected, two skeletally undescribed polyketide-indole hybrids, designated as indolpolyketone A and B (1 and 2), were isolated and assigned to be constructed through C-N bond formation. Their structures were elucidated by 1D and 2D NMR spectra. The absolute configurations of 1 and 2 were determined by comparing the recorded and calculated electronic circular dichroism (ECD) spectra. Furthermore, the plausible biosynthetic pathways for 1 and 2 were proposed. Compounds 1 and 2 exhibited significant antiviral activity against H1N1 with IC50 values of 45.2 and 31.4 μM, respectively. In brief, compounds 1 and 2 were reported here for the first time and were the first example of polyketide-indole hybrids pieced together through C-N bond formation in the I3C-exposed culture of D. eschscholzii. Therefore, this study expands the knowledge about the chemical production of D. eschscholzii through precursor-directed biosynthesis (PDB).
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Affiliation(s)
- Li Ping Lin
- State Key Laboratory Cultivation Base for TCM Quality and Efficacy, Nanjing University of Chinese Medicine, Nanjing, 210023, China
| | - Min Wu
- State Key Laboratory Cultivation Base for TCM Quality and Efficacy, Nanjing University of Chinese Medicine, Nanjing, 210023, China
| | - Nan Jiang
- Key Laboratory of Cardiovascular & Cerebrovascular Medicine, School of Pharmacy, Nanjing Medical University, Nanjing, 211166, China
| | - Wei Wang
- Key Laboratory of Marine Drugs, Ministry of Education, Ocean University of China, Qingdao, 266003, China
| | - Ren Xiang Tan
- State Key Laboratory Cultivation Base for TCM Quality and Efficacy, Nanjing University of Chinese Medicine, Nanjing, 210023, China
- State Key Laboratory of Pharmaceutical Biotechnology, Institute of Functional Biomolecules, Nanjing University, Nanjing, 210023, China
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Konanov DN, Krivonos DV, Ilina EN, Babenko VV. BioCAT: search for biosynthetic gene clusters producing nonribosomal peptides with known structure. Comput Struct Biotechnol J 2022; 20:1218-1226. [PMID: 35317229 PMCID: PMC8914306 DOI: 10.1016/j.csbj.2022.02.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 02/14/2022] [Accepted: 02/14/2022] [Indexed: 11/28/2022] Open
Affiliation(s)
- Dmitry N. Konanov
- Federal Research and Clinical Centre of Physical and Chemical Medicine, Federal Medical and Biological Agency of Russia, ul. Malaya Pirogovskaya., 1s3, Moscow 119435, Russian Federation
- Corresponding author.
| | - Danil V. Krivonos
- Federal Research and Clinical Centre of Physical and Chemical Medicine, Federal Medical and Biological Agency of Russia, ul. Malaya Pirogovskaya., 1s3, Moscow 119435, Russian Federation
| | - Elena N. Ilina
- Federal Research and Clinical Centre of Physical and Chemical Medicine, Federal Medical and Biological Agency of Russia, ul. Malaya Pirogovskaya., 1s3, Moscow 119435, Russian Federation
| | - Vladislav V. Babenko
- Federal Research and Clinical Centre of Physical and Chemical Medicine, Federal Medical and Biological Agency of Russia, ul. Malaya Pirogovskaya., 1s3, Moscow 119435, Russian Federation
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