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Cortese N, Procopio A, Merola A, Zaffino P, Cosentino C. Applications of genome-scale metabolic models to the study of human diseases: A systematic review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 256:108397. [PMID: 39232376 DOI: 10.1016/j.cmpb.2024.108397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2024] [Revised: 08/25/2024] [Accepted: 08/25/2024] [Indexed: 09/06/2024]
Abstract
BACKGROUND AND OBJECTIVES Genome-scale metabolic networks (GEMs) represent a valuable modeling and computational tool in the broad field of systems biology. Their ability to integrate constraints and high-throughput biological data enables the study of intricate metabolic aspects and processes of different cell types and conditions. The past decade has witnessed an increasing number and variety of applications of GEMs for the study of human diseases, along with a huge effort aimed at the reconstruction, integration and analysis of a high number of organisms. This paper presents a systematic review of the scientific literature, to pursue several important questions about the application of constraint-based modeling in the investigation of human diseases. Hopefully, this paper will provide a useful reference for researchers interested in the application of modeling and computational tools for the investigation of metabolic-related human diseases. METHODS This systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Elsevier Scopus®, National Library of Medicine PubMed® and Clarivate Web of Science™ databases were enquired, resulting in 566 scientific articles. After applying exclusion and eligibility criteria, a total of 169 papers were selected and individually examined. RESULTS The reviewed papers offer a thorough and up-to-date picture of the latest modeling and computational approaches, based on genome-scale metabolic models, that can be leveraged for the investigation of a large variety of human diseases. The numerous studies have been categorized according to the clinical research area involved in the examined disease. Furthermore, the paper discusses the most typical approaches employed to derive clinically-relevant information using the computational models. CONCLUSIONS The number of scientific papers, utilizing GEM-based approaches for the investigation of human diseases, suggests an increasing interest in these types of approaches; hopefully, the present review will represent a useful reference for scientists interested in applying computational modeling approaches to investigate the aetiopathology of human diseases; we also hope that this work will foster the development of novel applications and methods for the discovery of clinically-relevant insights on metabolic-related diseases.
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Affiliation(s)
- Nicola Cortese
- Department of Experimental and Clinical Medicine, Università degli Studi Magna Græcia, Catanzaro, 88100, Italy
| | - Anna Procopio
- Department of Experimental and Clinical Medicine, Università degli Studi Magna Græcia, Catanzaro, 88100, Italy
| | - Alessio Merola
- Department of Experimental and Clinical Medicine, Università degli Studi Magna Græcia, Catanzaro, 88100, Italy
| | - Paolo Zaffino
- Department of Experimental and Clinical Medicine, Università degli Studi Magna Græcia, Catanzaro, 88100, Italy
| | - Carlo Cosentino
- Department of Experimental and Clinical Medicine, Università degli Studi Magna Græcia, Catanzaro, 88100, Italy.
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2
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Reed EK, Smith KA. Using our understanding of interactions between helminth metabolism and host immunity to target worm survival. Trends Parasitol 2024; 40:549-561. [PMID: 38853079 DOI: 10.1016/j.pt.2024.05.006] [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: 01/29/2024] [Revised: 05/10/2024] [Accepted: 05/12/2024] [Indexed: 06/11/2024]
Abstract
Helminths can adapt to environmental conditions in the host, utilising anaerobic processes like fermentation and malate dismutation to produce energy from carbohydrate. Although targeting carbohydrate metabolism is an established therapeutic strategy to combat helminth infection, questions remain over the metabolic pathways they employ as adults to survive and evade host immunity. Helminths also use amino acid, polyunsaturated fatty acid (PUFA), and cholesterol metabolism, a possible strategy favouring the production of immunomodulatory compounds that may influence survival in the host. Here, we discuss the significance of these differing metabolic pathways and whether targeting of helminth metabolic pathways may allow for the development of novel anthelmintics.
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Affiliation(s)
- Ella K Reed
- Cardiff School of Biosciences, Cardiff University, Cardiff, UK
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3
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Lange E, Kranert L, Krüger J, Benndorf D, Heyer R. Microbiome modeling: a beginner's guide. Front Microbiol 2024; 15:1368377. [PMID: 38962127 PMCID: PMC11220171 DOI: 10.3389/fmicb.2024.1368377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Accepted: 05/27/2024] [Indexed: 07/05/2024] Open
Abstract
Microbiomes, comprised of diverse microbial species and viruses, play pivotal roles in human health, environmental processes, and biotechnological applications and interact with each other, their environment, and hosts via ecological interactions. Our understanding of microbiomes is still limited and hampered by their complexity. A concept improving this understanding is systems biology, which focuses on the holistic description of biological systems utilizing experimental and computational methods. An important set of such experimental methods are metaomics methods which analyze microbiomes and output lists of molecular features. These lists of data are integrated, interpreted, and compiled into computational microbiome models, to predict, optimize, and control microbiome behavior. There exists a gap in understanding between microbiologists and modelers/bioinformaticians, stemming from a lack of interdisciplinary knowledge. This knowledge gap hinders the establishment of computational models in microbiome analysis. This review aims to bridge this gap and is tailored for microbiologists, researchers new to microbiome modeling, and bioinformaticians. To achieve this goal, it provides an interdisciplinary overview of microbiome modeling, starting with fundamental knowledge of microbiomes, metaomics methods, common modeling formalisms, and how models facilitate microbiome control. It concludes with guidelines and repositories for modeling. Each section provides entry-level information, example applications, and important references, serving as a valuable resource for comprehending and navigating the complex landscape of microbiome research and modeling.
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Affiliation(s)
- Emanuel Lange
- Multidimensional Omics Data Analysis, Department for Bioanalytics, Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., Dortmund, Germany
- Graduate School Digital Infrastructure for the Life Sciences, Bielefeld Institute for Bioinformatics Infrastructure (BIBI), Faculty of Technology, Bielefeld University, Bielefeld, Germany
| | - Lena Kranert
- Institute for Automation Engineering, Otto von Guericke University Magdeburg, Magdeburg, Germany
| | - Jacob Krüger
- Engineering of Software-Intensive Systems, Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Dirk Benndorf
- Applied Biosciences and Bioprocess Engineering, Anhalt University of Applied Sciences, Köthen, Germany
| | - Robert Heyer
- Multidimensional Omics Data Analysis, Department for Bioanalytics, Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., Dortmund, Germany
- Graduate School Digital Infrastructure for the Life Sciences, Bielefeld Institute for Bioinformatics Infrastructure (BIBI), Faculty of Technology, Bielefeld University, Bielefeld, Germany
- Multidimensional Omics Data Analysis, Faculty of Technology, Bielefeld University, Bielefeld, Germany
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4
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Valadez-Cano C, Olivares-Hernández R, Espino-Vázquez AN, Partida-Martínez LP. Genome-Scale Model of Rhizopus microsporus: Metabolic integration of a fungal holobiont with its bacterial and viral endosymbionts. Environ Microbiol 2024; 26:e16551. [PMID: 38072824 DOI: 10.1111/1462-2920.16551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 11/24/2023] [Indexed: 01/30/2024]
Abstract
Rhizopus microsporus often lives in association with bacterial and viral symbionts that alter its biology. This fungal model represents an example of the complex interactions established among diverse organisms in functional holobionts. We constructed a Genome-Scale Model (GSM) of the fungal-bacterial-viral holobiont (iHol). We employed a constraint-based method to calculate the metabolic fluxes to decipher the metabolic interactions of the symbionts with their host. Our computational analyses of iHol simulate the holobiont's growth and the production of the toxin rhizoxin. Analyses of the calculated fluxes between R. microsporus in symbiotic (iHol) versus asymbiotic conditions suggest that changes in the lipid and nucleotide metabolism of the host are necessary for the functionality of the holobiont. Glycerol plays a pivotal role in the fungal-bacterial metabolic interaction, as its production does not compromise fungal growth, and Mycetohabitans bacteria can efficiently consume it. Narnavirus RmNV-20S and RmNV-23S affected the nucleotide metabolism without impacting the fungal-bacterial symbiosis. Our analyses highlighted the metabolic stability of Mycetohabitans throughout its co-evolution with the fungal host. We also predicted changes in reactions of the bacterial metabolism required for the active production of rhizoxin. This iHol is the first GSM of a fungal holobiont.
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Affiliation(s)
- Cecilio Valadez-Cano
- Departamento de Ingeniería Genética, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (Cinvestav), Irapuato, Mexico
| | - Roberto Olivares-Hernández
- Departamento de Procesos y Tecnología, Universidad Autónoma Metropolitana, Unidad Cuajimalpa, Ciudad de México, Mexico
| | - Astrid N Espino-Vázquez
- Departamento de Ingeniería Genética, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (Cinvestav), Irapuato, Mexico
| | - Laila P Partida-Martínez
- Departamento de Ingeniería Genética, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (Cinvestav), Irapuato, Mexico
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5
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Bay ÖF, Hayes KS, Schwartz JM, Grencis RK, Roberts IS. A genome-scale metabolic model of parasitic whipworm. Nat Commun 2023; 14:6937. [PMID: 37907472 PMCID: PMC10618284 DOI: 10.1038/s41467-023-42552-4] [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: 03/14/2023] [Accepted: 10/13/2023] [Indexed: 11/02/2023] Open
Abstract
Genome-scale metabolic models are widely used to enhance our understanding of metabolic features of organisms, host-pathogen interactions and to identify therapeutics for diseases. Here we present iTMU798, the genome-scale metabolic model of the mouse whipworm Trichuris muris. The model demonstrates the metabolic features of T. muris and allows the prediction of metabolic steps essential for its survival. Specifically, that Thioredoxin Reductase (TrxR) enzyme is essential, a prediction we validate in vitro with the drug auranofin. Furthermore, our observation that the T. muris genome lacks gsr-1 encoding Glutathione Reductase (GR) but has GR activity that can be inhibited by auranofin indicates a mechanism for the reduction of glutathione by the TrxR enzyme in T. muris. In addition, iTMU798 predicts seven essential amino acids that cannot be synthesised by T. muris, a prediction we validate for the amino acid tryptophan. Overall, iTMU798 is as a powerful tool to study not only the T. muris metabolism but also other Trichuris spp. in understanding host parasite interactions and the rationale design of new intervention strategies.
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Affiliation(s)
- Ömer F Bay
- Division of Infection, Immunity and Respiratory Medicine, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
- Bioinformatics, Abdullah Gül University, Kayseri, Türkiye
- The Lydia Becker Institute of Immunology and Inflammation, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Kelly S Hayes
- Division of Infection, Immunity and Respiratory Medicine, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
- The Lydia Becker Institute of Immunology and Inflammation, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
- The Wellcome Trust Centre for Cell-Matrix Research, University of Manchester, Manchester, UK
| | - Jean-Marc Schwartz
- Division of Evolution, Infection and Genomics, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Richard K Grencis
- Division of Infection, Immunity and Respiratory Medicine, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.
- The Lydia Becker Institute of Immunology and Inflammation, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.
- The Wellcome Trust Centre for Cell-Matrix Research, University of Manchester, Manchester, UK.
| | - Ian S Roberts
- Division of Infection, Immunity and Respiratory Medicine, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.
- The Lydia Becker Institute of Immunology and Inflammation, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.
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6
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Sounart H, Voronin D, Masarapu Y, Chung M, Saarenpää S, Ghedin E, Giacomello S. Miniature spatial transcriptomics for studying parasite-endosymbiont relationships at the micro scale. Nat Commun 2023; 14:6500. [PMID: 37838705 PMCID: PMC10576761 DOI: 10.1038/s41467-023-42237-y] [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: 11/07/2022] [Accepted: 10/03/2023] [Indexed: 10/16/2023] Open
Abstract
Several important human infectious diseases are caused by microscale-sized parasitic nematodes like filarial worms. Filarial worms have their own spatial tissue organization; to uncover this tissue structure, we need methods that can spatially resolve these miniature specimens. Most filarial worms evolved a mutualistic association with endosymbiotic bacteria Wolbachia. However, the mechanisms underlying the dependency of filarial worms on the fitness of these bacteria remain unknown. As Wolbachia is essential for the development, reproduction, and survival of filarial worms, we spatially explored how Wolbachia interacts with the worm's reproductive system by performing a spatial characterization using Spatial Transcriptomics (ST) across a posterior region containing reproductive tissue and developing embryos of adult female Brugia malayi worms. We provide a proof-of-concept for miniature-ST to explore spatial gene expression patterns in small sample types, demonstrating the method's ability to uncover nuanced tissue region expression patterns, observe the spatial localization of key B. malayi - Wolbachia pathway genes, and co-localize the B. malayi spatial transcriptome in Wolbachia tissue regions, also under antibiotic treatment. We envision our approach will open up new avenues for the study of infectious diseases caused by micro-scale parasitic worms.
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Affiliation(s)
- Hailey Sounart
- Department of Gene Technology, KTH Royal Institute of Technology, SciLifeLab, Stockholm, Sweden
| | - Denis Voronin
- Systems Genomics Section, Laboratory of Parasitic Diseases, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Yuvarani Masarapu
- Department of Gene Technology, KTH Royal Institute of Technology, SciLifeLab, Stockholm, Sweden
| | - Matthew Chung
- Systems Genomics Section, Laboratory of Parasitic Diseases, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Sami Saarenpää
- Department of Gene Technology, KTH Royal Institute of Technology, SciLifeLab, Stockholm, Sweden
| | - Elodie Ghedin
- Systems Genomics Section, Laboratory of Parasitic Diseases, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA.
| | - Stefania Giacomello
- Department of Gene Technology, KTH Royal Institute of Technology, SciLifeLab, Stockholm, Sweden.
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7
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Taj B, Adeolu M, Xiong X, Ang J, Nursimulu N, Parkinson J. MetaPro: a scalable and reproducible data processing and analysis pipeline for metatranscriptomic investigation of microbial communities. MICROBIOME 2023; 11:143. [PMID: 37370188 DOI: 10.1186/s40168-023-01562-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Accepted: 04/28/2023] [Indexed: 06/29/2023]
Abstract
BACKGROUND Whole microbiome RNASeq (metatranscriptomics) has emerged as a powerful technology to functionally interrogate microbial communities. A key challenge is how best to process, analyze, and interpret these complex datasets. In a typical application, a single metatranscriptomic dataset may comprise from tens to hundreds of millions of sequence reads. These reads must first be processed and filtered for low quality and potential contaminants, before being annotated with taxonomic and functional labels and subsequently collated to generate global bacterial gene expression profiles. RESULTS Here, we present MetaPro, a flexible, massively scalable metatranscriptomic data analysis pipeline that is cross-platform compatible through its implementation within a Docker framework. MetaPro starts with raw sequence read input (single-end or paired-end reads) and processes them through a tiered series of filtering, assembly, and annotation steps. In addition to yielding a final list of bacterial genes and their relative expression, MetaPro delivers a taxonomic breakdown based on the consensus of complementary prediction algorithms, together with a focused breakdown of enzymes, readily visualized through the Cytoscape network visualization tool. We benchmark the performance of MetaPro against two current state-of-the-art pipelines and demonstrate improved performance and functionality. CONCLUSIONS MetaPro represents an effective integrated solution for the processing and analysis of metatranscriptomic datasets. Its modular architecture allows new algorithms to be deployed as they are developed, ensuring its longevity. To aid user uptake of the pipeline, MetaPro, together with an established tutorial that has been developed for educational purposes, is made freely available at https://github.com/ParkinsonLab/MetaPro . The software is freely available under the GNU general public license v3. Video Abstract.
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Affiliation(s)
- Billy Taj
- Program in Molecular Medicine, The Hospital for Sick Children, Toronto, ON, M5G 0A4, Canada
| | - Mobolaji Adeolu
- Program in Molecular Medicine, The Hospital for Sick Children, Toronto, ON, M5G 0A4, Canada
| | - Xuejian Xiong
- Program in Molecular Medicine, The Hospital for Sick Children, Toronto, ON, M5G 0A4, Canada
| | - Jordan Ang
- Department of Chemical and Physical Sciences, University of Toronto, Mississauga, ON, L5L 1C6, Canada
| | - Nirvana Nursimulu
- Program in Molecular Medicine, The Hospital for Sick Children, Toronto, ON, M5G 0A4, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, M5S 3G4, Canada
| | - John Parkinson
- Program in Molecular Medicine, The Hospital for Sick Children, Toronto, ON, M5G 0A4, Canada.
- Department of Molecular Genetics, University of Toronto, Toronto, ON, M5S 3G4, Canada.
- Department of Biochemistry, University of Toronto, Toronto, ON, M5S 3G4, Canada.
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8
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Wangwiwatsin A, Kulwong S, Phetcharaburanin J, Namwat N, Klanrit P, Loilome W, Maleewong W, Reid AJ. Toward novel treatment against filariasis: Insight into genome-wide co-evolutionary analysis of filarial nematodes and Wolbachia. Front Microbiol 2023; 14:1052352. [PMID: 37032902 PMCID: PMC10073474 DOI: 10.3389/fmicb.2023.1052352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 02/16/2023] [Indexed: 04/11/2023] Open
Abstract
Infectious diseases caused by filarial nematodes are major health problems for humans and animals globally. Current treatment using anti-helminthic drugs requires a long treatment period and is only effective against the microfilarial stage. Most species of filarial nematodes harbor a specific strain of Wolbachia bacteria, which are essential for the survival, development, and reproduction of the nematodes. This parasite-bacteria obligate symbiosis offers a new angle for the cure of filariasis. In this study, we utilized publicly available genome data and putative protein sequences from seven filarial nematode species and their symbiotic Wolbachia to screen for protein-protein interactions that could be a novel target against multiple filarial nematode species. Genome-wide in silico screening was performed to predict molecular interactions based on co-evolutionary signals. We identified over 8,000 pairs of gene families that show evidence of co-evolution based on high correlation score and low false discovery rate (FDR) between gene families and obtained a candidate list that may be keys in filarial nematode-Wolbachia interactions. Functional analysis was conducted on these top-scoring pairs, revealing biological processes related to various signaling processes, adult lifespan, developmental control, lipid and nucleotide metabolism, and RNA modification. Furthermore, network analysis of the top-scoring genes with multiple co-evolving pairs suggests candidate genes in both Wolbachia and the nematode that may play crucial roles at the center of multi-gene networks. A number of the top-scoring genes matched well to known drug targets, suggesting a promising drug-repurposing strategy that could be applicable against multiple filarial nematode species.
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Affiliation(s)
- Arporn Wangwiwatsin
- Department of Biochemistry, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand
- Cholangiocarcinoma Research Institute, Khon Kaen University, Khon Kaen, Thailand
- Khon Kaen University Phenome Centre, Khon Kaen University, Khon Kaen, Thailand
| | - Siriyakorn Kulwong
- Department of Biochemistry, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand
- Cholangiocarcinoma Research Institute, Khon Kaen University, Khon Kaen, Thailand
- Khon Kaen University Phenome Centre, Khon Kaen University, Khon Kaen, Thailand
| | - Jutarop Phetcharaburanin
- Department of Biochemistry, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand
- Cholangiocarcinoma Research Institute, Khon Kaen University, Khon Kaen, Thailand
- Khon Kaen University Phenome Centre, Khon Kaen University, Khon Kaen, Thailand
| | - Nisana Namwat
- Department of Biochemistry, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand
- Cholangiocarcinoma Research Institute, Khon Kaen University, Khon Kaen, Thailand
- Khon Kaen University Phenome Centre, Khon Kaen University, Khon Kaen, Thailand
| | - Poramate Klanrit
- Department of Biochemistry, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand
- Cholangiocarcinoma Research Institute, Khon Kaen University, Khon Kaen, Thailand
- Khon Kaen University Phenome Centre, Khon Kaen University, Khon Kaen, Thailand
| | - Watcharin Loilome
- Department of Biochemistry, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand
- Cholangiocarcinoma Research Institute, Khon Kaen University, Khon Kaen, Thailand
- Khon Kaen University Phenome Centre, Khon Kaen University, Khon Kaen, Thailand
| | - Wanchai Maleewong
- Department of Parasitology, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand
| | - Adam J Reid
- Parasite Genomics Group, Wellcome Sanger Institute, Hinxton, United Kingdom
- The Gurdon Institute, University of Cambridge, Cambridge, United Kingdom
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Hogan AM, Cardona ST. Gradients in gene essentiality reshape antibacterial research. FEMS Microbiol Rev 2022; 46:fuac005. [PMID: 35104846 PMCID: PMC9075587 DOI: 10.1093/femsre/fuac005] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 01/14/2022] [Accepted: 01/24/2022] [Indexed: 02/03/2023] Open
Abstract
Essential genes encode the processes that are necessary for life. Until recently, commonly applied binary classifications left no space between essential and non-essential genes. In this review, we frame bacterial gene essentiality in the context of genetic networks. We explore how the quantitative properties of gene essentiality are influenced by the nature of the encoded process, environmental conditions and genetic background, including a strain's distinct evolutionary history. The covered topics have important consequences for antibacterials, which inhibit essential processes. We argue that the quantitative properties of essentiality can thus be used to prioritize antibacterial cellular targets and desired spectrum of activity in specific infection settings. We summarize our points with a case study on the core essential genome of the cystic fibrosis pathobiome and highlight avenues for targeted antibacterial development.
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Affiliation(s)
- Andrew M Hogan
- Department of Microbiology, University of Manitoba, 45 Chancellor's Circle, Winnipeg, Manitoba R3T 2N2, Canada
| | - Silvia T Cardona
- Department of Microbiology, University of Manitoba, 45 Chancellor's Circle, Winnipeg, Manitoba R3T 2N2, Canada
- Department of Medical Microbiology and Infectious Diseases, Max Rady College of Medicine, University of Manitoba, Room 543 - 745 Bannatyne Avenue, Winnipeg, Manitoba, R3E 0J9, Canada
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10
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High-content approaches to anthelmintic drug screening. Trends Parasitol 2021; 37:780-789. [PMID: 34092518 DOI: 10.1016/j.pt.2021.05.004] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 05/03/2021] [Accepted: 05/11/2021] [Indexed: 11/23/2022]
Abstract
Most anthelmintics were discovered through in vivo screens using animal models of infection. Developing in vitro assays for parasitic worms presents several challenges. The lack of in vitro life cycle culture protocols requires harvesting worms from vertebrate hosts or vectors, limiting assay throughput. Once worms are removed from the host environment, established anthelmintics often show no obvious phenotype - raising concerns about the predictive value of many in vitro assays. However, with recent progress in understanding how anthelmintics subvert host-parasite interactions, and breakthroughs in high-content imaging and machine learning, in vitro assays have the potential to discern subtle cryptic parasite phenotypes. These may prove better endpoints than conventional in vitro viability assays.
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11
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Chung M, Bruno VM, Rasko DA, Cuomo CA, Muñoz JF, Livny J, Shetty AC, Mahurkar A, Dunning Hotopp JC. Best practices on the differential expression analysis of multi-species RNA-seq. Genome Biol 2021; 22:121. [PMID: 33926528 PMCID: PMC8082843 DOI: 10.1186/s13059-021-02337-8] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Accepted: 04/01/2021] [Indexed: 02/07/2023] Open
Abstract
Advances in transcriptome sequencing allow for simultaneous interrogation of differentially expressed genes from multiple species originating from a single RNA sample, termed dual or multi-species transcriptomics. Compared to single-species differential expression analysis, the design of multi-species differential expression experiments must account for the relative abundances of each organism of interest within the sample, often requiring enrichment methods and yielding differences in total read counts across samples. The analysis of multi-species transcriptomics datasets requires modifications to the alignment, quantification, and downstream analysis steps compared to the single-species analysis pipelines. We describe best practices for multi-species transcriptomics and differential gene expression.
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Affiliation(s)
- Matthew Chung
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201 USA
- Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore, MD 21201 USA
| | - Vincent M. Bruno
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201 USA
- Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore, MD 21201 USA
| | - David A. Rasko
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201 USA
- Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore, MD 21201 USA
| | - Christina A. Cuomo
- Infectious Disease and Microbiome Program, Broad Institute, Cambridge, MA 02142 USA
| | - José F. Muñoz
- Infectious Disease and Microbiome Program, Broad Institute, Cambridge, MA 02142 USA
| | - Jonathan Livny
- Infectious Disease and Microbiome Program, Broad Institute, Cambridge, MA 02142 USA
| | - Amol C. Shetty
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201 USA
| | - Anup Mahurkar
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201 USA
| | - Julie C. Dunning Hotopp
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201 USA
- Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore, MD 21201 USA
- Greenebaum Cancer Center, University of Maryland, Baltimore, MD 21201 USA
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12
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Jean-Pierre F, Henson MA, O’Toole GA. Metabolic Modeling to Interrogate Microbial Disease: A Tale for Experimentalists. Front Mol Biosci 2021; 8:634479. [PMID: 33681294 PMCID: PMC7930556 DOI: 10.3389/fmolb.2021.634479] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Accepted: 01/19/2021] [Indexed: 12/14/2022] Open
Abstract
The explosion of microbiome analyses has helped identify individual microorganisms and microbial communities driving human health and disease, but how these communities function is still an open question. For example, the role for the incredibly complex metabolic interactions among microbial species cannot easily be resolved by current experimental approaches such as 16S rRNA gene sequencing, metagenomics and/or metabolomics. Resolving such metabolic interactions is particularly challenging in the context of polymicrobial communities where metabolite exchange has been reported to impact key bacterial traits such as virulence and antibiotic treatment efficacy. As novel approaches are needed to pinpoint microbial determinants responsible for impacting community function in the context of human health and to facilitate the development of novel anti-infective and antimicrobial drugs, here we review, from the viewpoint of experimentalists, the latest advances in metabolic modeling, a computational method capable of predicting metabolic capabilities and interactions from individual microorganisms to complex ecological systems. We use selected examples from the literature to illustrate how metabolic modeling has been utilized, in combination with experiments, to better understand microbial community function. Finally, we propose how such combined, cross-disciplinary efforts can be utilized to drive laboratory work and drug discovery moving forward.
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Affiliation(s)
- Fabrice Jean-Pierre
- Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Hanover, NH, United States
| | - Michael A. Henson
- Department of Chemical Engineering and Institute for Applied Life Sciences, University of Massachusetts, Amherst, MA, United States
| | - George A. O’Toole
- Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Hanover, NH, United States
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