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Oarga A, Bannerman BP, Júlvez J. CONTRABASS: exploiting flux constraints in genome-scale models for the detection of vulnerabilities. Bioinformatics 2023; 39:7000333. [PMID: 36692133 PMCID: PMC9907045 DOI: 10.1093/bioinformatics/btad053] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 11/16/2022] [Accepted: 01/23/2023] [Indexed: 01/25/2023] Open
Abstract
MOTIVATION Despite the fact that antimicrobial resistance is an increasing health concern, the pace of production of new drugs is slow due to the high cost and uncertain success of the process. The development of high-throughput technologies has allowed the integration of biological data into detailed genome-scale models of multiple organisms. Such models can be exploited by means of computational methods to identify system vulnerabilities such as chokepoint reactions and essential reactions. These vulnerabilities are appealing drug targets that can lead to novel drug developments. However, the current approach to compute these vulnerabilities is only based on topological data and ignores the dynamic information of the model. This can lead to misidentified drug targets. RESULTS This work computes flux constraints that are consistent with a certain growth rate of the modelled organism, and integrates the computed flux constraints into the model to improve the detection of vulnerabilities. By exploiting these flux constraints, we are able to obtain a directionality of the reactions of metabolism consistent with a given growth rate of the model, and consequently, a more realistic detection of vulnerabilities can be performed. Several sets of reactions that are system vulnerabilities are defined and the relationships among them are studied. The approach for the detection of these vulnerabilities has been implemented in the Python tool CONTRABASS. Such tool, for which an online web server has also been implemented, computes flux constraints and generates a report with the detected vulnerabilities. AVAILABILITY AND IMPLEMENTATION CONTRABASS is available as an open source Python package at https://github.com/openCONTRABASS/CONTRABASS under GPL-3.0 License. An online web server is available at http://contrabass.unizar.es. SUPPLEMENTARY INFORMATION A glossary of terms are available at Bioinformatics online.
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Affiliation(s)
- Alexandru Oarga
- Department of Computer Science and Systems Engineering, University of Zaragoza, Zaragoza 50018, Spain
| | - Bridget P Bannerman
- Lucy Cavendish College, Biological Sciences, University of Cambridge, Cambridge CB3 0BU, UK.,Science Resources Foundation, Health Unit, London EC1V 2NX, UK
| | - Jorge Júlvez
- Department of Computer Science and Systems Engineering, University of Zaragoza, Zaragoza 50018, Spain
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Bannerman BP, Oarga A, Júlvez J. Mycobacterial metabolic model development for drug target identification. GigaByte 2023; 2023:gigabyte80. [PMID: 37153490 PMCID: PMC10154535 DOI: 10.46471/gigabyte.80] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Accepted: 04/24/2023] [Indexed: 05/09/2023] Open
Abstract
Antibiotic resistance is increasing at an alarming rate, and three related mycobacteria are sources of widespread infections in humans. According to the World Health Organization, Mycobacterium leprae, which causes leprosy, is still endemic in tropical countries; Mycobacterium tuberculosis is the second leading infectious killer worldwide after COVID-19; and Mycobacteroides abscessus, a group of non-tuberculous mycobacteria, causes lung infections and other healthcare-associated infections in humans. Due to the rise in resistance to common antibacterial drugs, it is critical that we develop alternatives to traditional treatment procedures. Furthermore, an understanding of the biochemical mechanisms underlying pathogenic evolution is important for the treatment and management of these diseases. In this study, metabolic models have been developed for two bacterial pathogens, M. leprae and My. abscessus, and a new computational tool has been used to identify potential drug targets, which are referred to as bottleneck reactions. The genes, reactions, and pathways in each of these organisms have been highlighted; the potential drug targets can be further explored as broad-spectrum antibacterials and the unique drug targets for each pathogen are significant for precision medicine initiatives. The models and associated datasets described in this paper are available in GigaDB, Biomodels, and PatMeDB repositories.
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Affiliation(s)
- Bridget P. Bannerman
- Lucy Cavendish College, University of Cambridge, Lady Margaret Rd, Cambridge, CB3 0BU, UK
- Science Resources Foundation, 128 City Road, London, EC1V 2NX, UK
- Corresponding author. E-mail:
| | - Alexandru Oarga
- Department of Computer Science and Systems Engineering, University of Zaragoza, C/María de Luna n 1, 50018, Zaragoza, Spain
| | - Jorge Júlvez
- Department of Computer Science and Systems Engineering, University of Zaragoza, C/María de Luna n 1, 50018, Zaragoza, Spain
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Boeck L, Burbaud S, Skwark M, Pearson WH, Sangen J, Wuest AW, Marshall EKP, Weimann A, Everall I, Bryant JM, Malhotra S, Bannerman BP, Kierdorf K, Blundell TL, Dionne MS, Parkhill J, Andres Floto R. Mycobacterium abscessus pathogenesis identified by phenogenomic analyses. Nat Microbiol 2022; 7:1431-1441. [PMID: 36008617 PMCID: PMC9418003 DOI: 10.1038/s41564-022-01204-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Accepted: 07/19/2022] [Indexed: 12/12/2022]
Abstract
The medical and scientific response to emerging and established pathogens is often severely hampered by ignorance of the genetic determinants of virulence, drug resistance and clinical outcomes that could be used to identify therapeutic drug targets and forecast patient trajectories. Taking the newly emergent multidrug-resistant bacteria Mycobacterium abscessus as an example, we show that combining high-dimensional phenotyping with whole-genome sequencing in a phenogenomic analysis can rapidly reveal actionable systems-level insights into bacterial pathobiology. Through phenotyping of 331 clinical isolates, we discovered three distinct clusters of isolates, each with different virulence traits and associated with a different clinical outcome. We combined genome-wide association studies with proteome-wide computational structural modelling to define likely causal variants, and employed direct coupling analysis to identify co-evolving, and therefore potentially epistatic, gene networks. We then used in vivo CRISPR-based silencing to validate our findings and discover clinically relevant M. abscessus virulence factors including a secretion system, thus illustrating how phenogenomics can reveal critical pathways within emerging pathogenic bacteria.
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Affiliation(s)
- Lucas Boeck
- Molecular Immunity Unit, University of Cambridge Department of Medicine, MRC Laboratory of Molecular Biology, Cambridge, UK
- Cambridge Centre for AI in Medicine, Cambridge, UK
- Wellcome Sanger Institute, Hinxton, UK
- Department of Biomedicine, University of Basel, Basel, Switzerland
| | - Sophie Burbaud
- Molecular Immunity Unit, University of Cambridge Department of Medicine, MRC Laboratory of Molecular Biology, Cambridge, UK
- Cambridge Centre for AI in Medicine, Cambridge, UK
| | - Marcin Skwark
- Department of Biochemistry, University of Cambridge, Cambridge, UK
| | - Will H Pearson
- MRC Centre for Molecular Bacteriology and Infection, Imperial College London, London, UK
- Department of Life Sciences, Imperial College London, London, UK
| | - Jasper Sangen
- Molecular Immunity Unit, University of Cambridge Department of Medicine, MRC Laboratory of Molecular Biology, Cambridge, UK
- Cambridge Centre for AI in Medicine, Cambridge, UK
| | - Andreas W Wuest
- Department of Biomedicine, University of Basel, Basel, Switzerland
| | - Eleanor K P Marshall
- MRC Centre for Molecular Bacteriology and Infection, Imperial College London, London, UK
- Department of Life Sciences, Imperial College London, London, UK
| | - Aaron Weimann
- Molecular Immunity Unit, University of Cambridge Department of Medicine, MRC Laboratory of Molecular Biology, Cambridge, UK
- Cambridge Centre for AI in Medicine, Cambridge, UK
| | | | - Josephine M Bryant
- Molecular Immunity Unit, University of Cambridge Department of Medicine, MRC Laboratory of Molecular Biology, Cambridge, UK
- Cambridge Centre for AI in Medicine, Cambridge, UK
| | - Sony Malhotra
- Department of Biochemistry, University of Cambridge, Cambridge, UK
- Scientific Computing Department, Science and Technology Facilities Council, Harwell, UK
| | - Bridget P Bannerman
- Molecular Immunity Unit, University of Cambridge Department of Medicine, MRC Laboratory of Molecular Biology, Cambridge, UK
- Cambridge Centre for AI in Medicine, Cambridge, UK
- Department of Biochemistry, University of Cambridge, Cambridge, UK
| | - Katrin Kierdorf
- MRC Centre for Molecular Bacteriology and Infection, Imperial College London, London, UK
- Department of Life Sciences, Imperial College London, London, UK
- Institute of Neuropathology, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Tom L Blundell
- Department of Biochemistry, University of Cambridge, Cambridge, UK
| | - Marc S Dionne
- MRC Centre for Molecular Bacteriology and Infection, Imperial College London, London, UK
- Department of Life Sciences, Imperial College London, London, UK
| | - Julian Parkhill
- Department of Veterinary Medicine, University of Cambridge, Cambridge, UK
| | - R Andres Floto
- Molecular Immunity Unit, University of Cambridge Department of Medicine, MRC Laboratory of Molecular Biology, Cambridge, UK.
- Cambridge Centre for AI in Medicine, Cambridge, UK.
- Cambridge Centre for Lung Infection, Royal Papworth Hospital, Cambridge, UK.
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Bannerman BP, Júlvez J, Oarga A, Blundell TL, Moreno P, Floto RA. Integrated human/SARS-CoV-2 metabolic models present novel treatment strategies against COVID-19. Life Sci Alliance 2021; 4:e202000954. [PMID: 34353886 PMCID: PMC8343166 DOI: 10.26508/lsa.202000954] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 07/26/2021] [Accepted: 07/27/2021] [Indexed: 01/20/2023] Open
Abstract
The coronavirus disease 2019 (COVID-19) pandemic caused by the new coronavirus (SARS-CoV-2) is currently responsible for more than 3 million deaths in 219 countries across the world and with more than 140 million cases. The absence of FDA-approved drugs against SARS-CoV-2 has highlighted an urgent need to design new drugs. We developed an integrated model of the human cell and SARS-CoV-2 to provide insight into the virus' pathogenic mechanism and support current therapeutic strategies. We show the biochemical reactions required for the growth and general maintenance of the human cell, first, in its healthy state. We then demonstrate how the entry of SARS-CoV-2 into the human cell causes biochemical and structural changes, leading to a change of cell functions or cell death. A new computational method that predicts 20 unique reactions as drug targets from our models and provides a platform for future studies on viral entry inhibition, immune regulation, and drug optimisation strategies. The model is available in BioModels (https://www.ebi.ac.uk/biomodels/MODEL2007210001) and the software tool, findCPcli, that implements the computational method is available at https://github.com/findCP/findCPcli.
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Affiliation(s)
- Bridget P Bannerman
- Molecular Immunity Unit, Department of Medicine, University of Cambridge, Cambridge, UK
- The Center for Research and Interdisciplinarity, Paris, France
| | - Jorge Júlvez
- Department of Computer Science and Systems Engineering, University of Zaragoza, Zaragoza, Spain
| | - Alexandru Oarga
- Department of Computer Science and Systems Engineering, University of Zaragoza, Zaragoza, Spain
| | - Tom L Blundell
- Department of Biochemistry, University of Cambridge, Cambridge, UK
| | - Pablo Moreno
- EMBL-EBI, European Bioinformatics Institute, Hinxton, UK
| | - R Andres Floto
- Molecular Immunity Unit, Department of Medicine, University of Cambridge, Cambridge, UK
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Beaudoin CA, Jamasb AR, Alsulami AF, Copoiu L, van Tonder AJ, Hala S, Bannerman BP, Thomas SE, Vedithi SC, Torres PH, Blundell TL. Predicted structural mimicry of spike receptor-binding motifs from highly pathogenic human coronaviruses. Comput Struct Biotechnol J 2021; 19:3938-3953. [PMID: 34234921 PMCID: PMC8249111 DOI: 10.1016/j.csbj.2021.06.041] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 06/26/2021] [Accepted: 06/27/2021] [Indexed: 12/19/2022] Open
Abstract
Potential coronavirus spike protein mimicry revealed by structural comparison. Human and non-human protein potential interactions with virus identified. Predicted structural mimicry corroborated by protein–protein docking. Epitope-based alignments may help guide vaccine efforts.
Viruses often encode proteins that mimic host proteins in order to facilitate infection. Little work has been done to understand the potential mimicry of the SARS-CoV-2, SARS-CoV, and MERS-CoV spike proteins, particularly the receptor-binding motifs, which could be important in determining tropism and druggability of the virus. Peptide and epitope motifs have been detected on coronavirus spike proteins using sequence homology approaches; however, comparing the three-dimensional shape of the protein has been shown as more informative in predicting mimicry than sequence-based comparisons. Here, we use structural bioinformatics software to characterize potential mimicry of the three coronavirus spike protein receptor-binding motifs. We utilize sequence-independent alignment tools to compare structurally known protein models with the receptor-binding motifs and verify potential mimicked interactions with protein docking simulations. Both human and non-human proteins were returned for all three receptor-binding motifs. For example, all three were similar to several proteins containing EGF-like domains: some of which are endogenous to humans, such as thrombomodulin, and others exogenous, such as Plasmodium falciparum MSP-1. Similarity to human proteins may reveal which pathways the spike protein is co-opting, while analogous non-human proteins may indicate shared host interaction partners and overlapping antibody cross-reactivity. These findings can help guide experimental efforts to further understand potential interactions between human and coronavirus proteins.
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Affiliation(s)
- Christopher A. Beaudoin
- Department of Biochemistry, Sanger Building, University of Cambridge, Tennis Court Rd, Cambridge CB2 1GA, United Kingdom
- Corresponding authors.
| | - Arian R. Jamasb
- Department of Biochemistry, Sanger Building, University of Cambridge, Tennis Court Rd, Cambridge CB2 1GA, United Kingdom
- Department of Computer Science & Technology, University of Cambridge, JJ Thomson Ave, Cambridge CB3 0FD, United Kingdom
| | - Ali F. Alsulami
- Department of Biochemistry, Sanger Building, University of Cambridge, Tennis Court Rd, Cambridge CB2 1GA, United Kingdom
| | - Liviu Copoiu
- Department of Biochemistry, Sanger Building, University of Cambridge, Tennis Court Rd, Cambridge CB2 1GA, United Kingdom
| | - Andries J. van Tonder
- Department of Veterinary Medicine, University of Cambridge, Madingley Rd, Cambridge CB3 0ES, United Kingdom
| | - Sharif Hala
- King Abdullah International Medical Research Centre – Ministry of National Guard Health Affairs, Jeddah, Saudi Arabia
- King Saud bin Abdulaziz University for Health Sciences, Jeddah, Saudi Arabia
| | - Bridget P. Bannerman
- Department of Biochemistry, Sanger Building, University of Cambridge, Tennis Court Rd, Cambridge CB2 1GA, United Kingdom
| | - Sherine E. Thomas
- Department of Biochemistry, Sanger Building, University of Cambridge, Tennis Court Rd, Cambridge CB2 1GA, United Kingdom
| | - Sundeep Chaitanya Vedithi
- Department of Biochemistry, Sanger Building, University of Cambridge, Tennis Court Rd, Cambridge CB2 1GA, United Kingdom
| | - Pedro H.M. Torres
- Laboratório de Modelagem e Dinâmica Molecular, Instituto de Biofísica Carlos Chagas Filho, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, Brazil
| | - Tom L. Blundell
- Department of Biochemistry, Sanger Building, University of Cambridge, Tennis Court Rd, Cambridge CB2 1GA, United Kingdom
- Corresponding authors.
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Waman VP, Vedithi SC, Thomas SE, Bannerman BP, Munir A, Skwark MJ, Malhotra S, Blundell TL. Mycobacterial genomics and structural bioinformatics: opportunities and challenges in drug discovery. Emerg Microbes Infect 2019; 8:109-118. [PMID: 30866765 PMCID: PMC6334779 DOI: 10.1080/22221751.2018.1561158] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2018] [Revised: 12/03/2018] [Accepted: 12/09/2018] [Indexed: 01/08/2023]
Abstract
Of the more than 190 distinct species of Mycobacterium genus, many are economically and clinically important pathogens of humans or animals. Among those mycobacteria that infect humans, three species namely Mycobacterium tuberculosis (causative agent of tuberculosis), Mycobacterium leprae (causative agent of leprosy) and Mycobacterium abscessus (causative agent of chronic pulmonary infections) pose concern to global public health. Although antibiotics have been successfully developed to combat each of these, the emergence of drug-resistant strains is an increasing challenge for treatment and drug discovery. Here we describe the impact of the rapid expansion of genome sequencing and genome/pathway annotations that have greatly improved the progress of structure-guided drug discovery. We focus on the applications of comparative genomics, metabolomics, evolutionary bioinformatics and structural proteomics to identify potential drug targets. The opportunities and challenges for the design of drugs for M. tuberculosis, M. leprae and M. abscessus to combat resistance are discussed.
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Affiliation(s)
| | | | | | | | - Asma Munir
- Department of Biochemistry, University of Cambridge, Cambridge, UK
| | - Marcin J. Skwark
- Department of Biochemistry, University of Cambridge, Cambridge, UK
| | - Sony Malhotra
- Institute of Structural and Molecular Biology, Department of Biological Sciences, Birkbeck College, University of London, London, UK
| | - Tom L. Blundell
- Department of Biochemistry, University of Cambridge, Cambridge, UK
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Bannerman BP, Kramer S, Dorrell RG, Carrington M. Multispecies reconstructions uncover widespread conservation, and lineage-specific elaborations in eukaryotic mRNA metabolism. PLoS One 2018; 13:e0192633. [PMID: 29561870 PMCID: PMC5862402 DOI: 10.1371/journal.pone.0192633] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2017] [Accepted: 01/26/2018] [Indexed: 12/01/2022] Open
Abstract
The degree of conservation and evolution of cytoplasmic mRNA metabolism pathways across the eukaryotes remains incompletely resolved. In this study, we describe a comprehensive genome and transcriptome-wide analysis of proteins involved in mRNA maturation, translation, and mRNA decay across representative organisms from the six eukaryotic super-groups. We demonstrate that eukaryotes share common pathways for mRNA metabolism that were almost certainly present in the last eukaryotic common ancestor, and show for the first time a correlation between intron density and a selective absence of some Exon Junction Complex (EJC) components in eukaryotes. In addition, we identify pathways that have diversified in individual lineages, with a specific focus on the unique gene gains and losses in members of the Excavata and SAR groups that contribute to their unique gene expression pathways compared to other organisms.
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Affiliation(s)
| | - Susanne Kramer
- Biozentrum, Lehrstuhl für Zell-und Entwicklungsbiologie, Universität Würzburg, Am Hubland, Würzburg, Germany
| | - Richard G. Dorrell
- Institute of Biology, École Normale Supérieure, PSL Research University, Paris, France
| | - Mark Carrington
- Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom
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