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Poddar H, Breitling R, Takano E. Towards engineering and production of artificial spider silk using tools of synthetic biology. ENGINEERING BIOLOGY 2020; 4:1-6. [PMID: 36970229 PMCID: PMC9996717 DOI: 10.1049/enb.2019.0017] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Revised: 01/21/2020] [Accepted: 02/10/2020] [Indexed: 12/18/2022] Open
Abstract
Spider silk is one of the strongest biomaterials available in nature. Its mechanical properties make it a good candidate for applications in various fields ranging from protective armour to bandages for wound dressing to coatings for medical implants. Spider silk is formed by an intricate arrangement of spidroins, which are extremely large proteins containing long stretches of repeating segments rich in alanine and glycine. A large amount of research has been directed towards harnessing the spectacular potential of spider silks and using them for different applications. The interdisciplinary approach of synthetic biology is an ideal tool to study these spider silk proteins and work towards the engineering and production of synthetic spider silk. This review aims to highlight the recent progress that has been made in the study of spider silk proteins using different branches of synthetic biology. Here, the authors discuss the different computational approaches, directed evolution techniques and various expression platforms that have been tested for the successful production of spider silk. Future challenges facing the field and possible solutions offered by synthetic biology are also discussed.
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Affiliation(s)
- Hashwardhan Poddar
- Faculty of Science and Engineering, Manchester Institute of Biotechnology, Manchester Synthetic Biology Research Centre SYNBIOCHEM The University of Manchester Manchester M1 7DN UK
| | - Rainer Breitling
- Faculty of Science and Engineering, Manchester Institute of Biotechnology, Manchester Synthetic Biology Research Centre SYNBIOCHEM The University of Manchester Manchester M1 7DN UK
| | - Eriko Takano
- Faculty of Science and Engineering, Manchester Institute of Biotechnology, Manchester Synthetic Biology Research Centre SYNBIOCHEM The University of Manchester Manchester M1 7DN UK
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Hallinan JS, Wipat A, Kitney R, Woods S, Taylor K, Goñi‐Moreno A. Future‐proofing synthetic biology: educating the next generation. ENGINEERING BIOLOGY 2019. [DOI: 10.1049/enb.2019.0001] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023] Open
Affiliation(s)
| | - Anil Wipat
- School of ComputingNewcastle UniversityNewcastle upon TyneUK
| | - Richard Kitney
- Department of BioengineeringImperial College LondonLondonUK
| | - Simon Woods
- Policy, Ethics and Life Sciences (PEALS) Research CentreNewcastle UniversityNewcastle upon TyneUK
| | - Ken Taylor
- Policy, Ethics and Life Sciences (PEALS) Research CentreNewcastle UniversityNewcastle upon TyneUK
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Acuña-Amador L, Primot A, Cadieu E, Roulet A, Barloy-Hubler F. Genomic repeats, misassembly and reannotation: a case study with long-read resequencing of Porphyromonas gingivalis reference strains. BMC Genomics 2018; 19:54. [PMID: 29338683 PMCID: PMC5771137 DOI: 10.1186/s12864-017-4429-4] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2017] [Accepted: 12/29/2017] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Without knowledge of their genomic sequences, it is impossible to make functional models of the bacteria that make up human and animal microbiota. Unfortunately, the vast majority of publicly available genomes are only working drafts, an incompleteness that causes numerous problems and constitutes a major obstacle to genotypic and phenotypic interpretation. In this work, we began with an example from the class Bacteroidia in the phylum Bacteroidetes, which is preponderant among human orodigestive microbiota. We successfully identify the genetic loci responsible for assembly breaks and misassemblies and demonstrate the importance and usefulness of long-read sequencing and curated reannotation. RESULTS We showed that the fragmentation in Bacteroidia draft genomes assembled from massively parallel sequencing linearly correlates with genomic repeats of the same or greater size than the reads. We also demonstrated that some of these repeats, especially the long ones, correspond to misassembled loci in three reference Porphyromonas gingivalis genomes marked as circularized (thus complete or finished). We prove that even at modest coverage (30X), long-read resequencing together with PCR contiguity verification (rrn operons and an integrative and conjugative element or ICE) can be used to identify and correct the wrongly combined or assembled regions. Finally, although time-consuming and labor-intensive, consistent manual biocuration of three P. gingivalis strains allowed us to compare and correct the existing genomic annotations, resulting in a more accurate interpretation of the genomic differences among these strains. CONCLUSIONS In this study, we demonstrate the usefulness and importance of long-read sequencing in verifying published genomes (even when complete) and generating assemblies for new bacterial strains/species with high genomic plasticity. We also show that when combined with biological validation processes and diligent biocurated annotation, this strategy helps reduce the propagation of errors in shared databases, thus limiting false conclusions based on incomplete or misleading information.
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Affiliation(s)
- Luis Acuña-Amador
- Institut de Génétique et Développement de Rennes, CNRS, UMR6290, Université de Rennes 1, Rennes, France.,Laboratorio de Investigación en Bacteriología Anaerobia, Centro de Investigación en Enfermedades Tropicales, Facultad de Microbiología, Universidad de Costa Rica, San José, Costa Rica
| | - Aline Primot
- Institut de Génétique et Développement de Rennes, CNRS, UMR6290, Université de Rennes 1, Rennes, France
| | - Edouard Cadieu
- Institut de Génétique et Développement de Rennes, CNRS, UMR6290, Université de Rennes 1, Rennes, France
| | - Alain Roulet
- GenoToul Genome & Transcriptome (GeT-PlaGe), INRA, US1426, Castanet-Tolosan, France
| | - Frédérique Barloy-Hubler
- Institut de Génétique et Développement de Rennes, CNRS, UMR6290, Université de Rennes 1, Rennes, France.
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Silva FN, Amancio DR, Bardosova M, Costa LDF, Oliveira ON. Using network science and text analytics to produce surveys in a scientific topic. J Informetr 2016. [DOI: 10.1016/j.joi.2016.03.008] [Citation(s) in RCA: 44] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
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Arend D, Junker A, Scholz U, Schüler D, Wylie J, Lange M. PGP repository: a plant phenomics and genomics data publication infrastructure. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2016; 2016:baw033. [PMID: 27087305 PMCID: PMC4834206 DOI: 10.1093/database/baw033] [Citation(s) in RCA: 64] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/05/2015] [Accepted: 02/26/2016] [Indexed: 11/22/2022]
Abstract
Plant genomics and phenomics represents the most promising tools for accelerating yield gains and overcoming emerging crop productivity bottlenecks. However, accessing this wealth of plant diversity requires the characterization of this material using state-of-the-art genomic, phenomic and molecular technologies and the release of subsequent research data via a long-term stable, open-access portal. Although several international consortia and public resource centres offer services for plant research data management, valuable digital assets remains unpublished and thus inaccessible to the scientific community. Recently, the Leibniz Institute of Plant Genetics and Crop Plant Research and the German Plant Phenotyping Network have jointly initiated the Plant Genomics and Phenomics Research Data Repository (PGP) as infrastructure to comprehensively publish plant research data. This covers in particular cross-domain datasets that are not being published in central repositories because of its volume or unsupported data scope, like image collections from plant phenotyping and microscopy, unfinished genomes, genotyping data, visualizations of morphological plant models, data from mass spectrometry as well as software and documents. The repository is hosted at Leibniz Institute of Plant Genetics and Crop Plant Research using e!DAL as software infrastructure and a Hierarchical Storage Management System as data archival backend. A novel developed data submission tool was made available for the consortium that features a high level of automation to lower the barriers of data publication. After an internal review process, data are published as citable digital object identifiers and a core set of technical metadata is registered at DataCite. The used e!DAL-embedded Web frontend generates for each dataset a landing page and supports an interactive exploration. PGP is registered as research data repository at BioSharing.org, re3data.org and OpenAIRE as valid EU Horizon 2020 open data archive. Above features, the programmatic interface and the support of standard metadata formats, enable PGP to fulfil the FAIR data principles—findable, accessible, interoperable, reusable. Database URL:http://edal.ipk-gatersleben.de/repos/pgp/
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Affiliation(s)
- Daniel Arend
- Leibniz Institute for Plant Genetics and Crop Plant Research (IPK), OT Gatersleben, Corrensstraße 3, Stadt Seeland, 06466, Gatersleben, Germany
| | - Astrid Junker
- Leibniz Institute for Plant Genetics and Crop Plant Research (IPK), OT Gatersleben, Corrensstraße 3, Stadt Seeland, 06466, Gatersleben, Germany
| | - Uwe Scholz
- Leibniz Institute for Plant Genetics and Crop Plant Research (IPK), OT Gatersleben, Corrensstraße 3, Stadt Seeland, 06466, Gatersleben, Germany
| | - Danuta Schüler
- Leibniz Institute for Plant Genetics and Crop Plant Research (IPK), OT Gatersleben, Corrensstraße 3, Stadt Seeland, 06466, Gatersleben, Germany
| | - Juliane Wylie
- Leibniz Institute for Plant Genetics and Crop Plant Research (IPK), OT Gatersleben, Corrensstraße 3, Stadt Seeland, 06466, Gatersleben, Germany
| | - Matthias Lange
- Leibniz Institute for Plant Genetics and Crop Plant Research (IPK), OT Gatersleben, Corrensstraße 3, Stadt Seeland, 06466, Gatersleben, Germany
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Arend D, Lange M, Chen J, Colmsee C, Flemming S, Hecht D, Scholz U. e!DAL--a framework to store, share and publish research data. BMC Bioinformatics 2014; 15:214. [PMID: 24958009 PMCID: PMC4080583 DOI: 10.1186/1471-2105-15-214] [Citation(s) in RCA: 66] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2014] [Accepted: 06/12/2014] [Indexed: 11/10/2022] Open
Abstract
Background The life-science community faces a major challenge in handling “big data”, highlighting the need for high quality infrastructures capable of sharing and publishing research data. Data preservation, analysis, and publication are the three pillars in the “big data life cycle”. The infrastructures currently available for managing and publishing data are often designed to meet domain-specific or project-specific requirements, resulting in the repeated development of proprietary solutions and lower quality data publication and preservation overall. Results e!DAL is a lightweight software framework for publishing and sharing research data. Its main features are version tracking, metadata management, information retrieval, registration of persistent identifiers (DOI), an embedded HTTP(S) server for public data access, access as a network file system, and a scalable storage backend. e!DAL is available as an API for local non-shared storage and as a remote API featuring distributed applications. It can be deployed “out-of-the-box” as an on-site repository. Conclusions e!DAL was developed based on experiences coming from decades of research data management at the Leibniz Institute of Plant Genetics and Crop Plant Research (IPK). Initially developed as a data publication and documentation infrastructure for the IPK’s role as a data center in the DataCite consortium, e!DAL has grown towards being a general data archiving and publication infrastructure. The e!DAL software has been deployed into the Maven Central Repository. Documentation and Software are also available at: http://edal.ipk-gatersleben.de.
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Affiliation(s)
- Daniel Arend
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), OT Gatersleben, Corrensstr, 3, 06466 Stadt Seeland, Germany.
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Camargo-Rodriguez AV, Kim JT. DoGeNetS: using optimisation to discriminate regulatory network topologies based on gene expression data. IET Syst Biol 2012; 6:1-8. [PMID: 22360266 DOI: 10.1049/iet-syb.2011.0004] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Gene regulatory networks (GRNs) determine the dynamics of gene expression. Interest often focuses on the topological structure of a GRN while numerical parameters (e.g. decay rates) are unknown and less important. For larger GRNs, inference of structure from gene expression data is prohibitively difficult. Models are often proposed based on integrative interpretation of multiple sources of information. We have developed DoGeNetS (Discrimination of Gene Network Structures), a method to directly assess candidate models of GRN structure against a target gene expression data set. The transsys language serves to model GRN structures. Numeric parameters are optimised to approximate the target data. Multiple restarts of optimisation yield score sets that provide a basis to statistically discriminate candidate models according to their potential to explain the target data. We demonstrate discrimination power of the DoGeNetS method by relating structural divergence to divergence between gene expression data sets. Known models are used to generate target expression data, and a set of candidate models with a defined structural divergence to the true model is produced. Structural divergence and divergence of expression profiles after optimisation are strongly correlated. We further show that discrimination is possible at noise levels exceeding those typical of contemporary microarray data. DoGeNetS is capable of discriminating the best GRN structure from among a small number of candidates. p values indicate whether differences in divergence of expression are significant. Although this study uses single gene knockouts, the DoGeNetS method can be adapted to simulate a virtually unlimited range of experimental conditions. [Includes supplementary material].
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Abstract
The field of bioinformatics and computational biology has gone through a number of transformations during the past 15 years, establishing itself as a key component of new biology. This spectacular growth has been challenged by a number of disruptive changes in science and technology. Despite the apparent fatigue of the linguistic use of the term itself, bioinformatics has grown perhaps to a point beyond recognition. We explore both historical aspects and future trends and argue that as the field expands, key questions remain unanswered and acquire new meaning while at the same time the range of applications is widening to cover an ever increasing number of biological disciplines. These trends appear to be pointing to a redefinition of certain objectives, milestones, and possibly the field itself.
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Affiliation(s)
- Christos A Ouzounis
- Institute of Agrobiotechnology, Centre for Research & Technology Hellas-CERTH, Thessaloniki, Greece.
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Hallinan J. Data mining for microbiologists. J Microbiol Methods 2012. [DOI: 10.1016/b978-0-08-099387-4.00002-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/12/2023]
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Hallinan JS, James K, Wipat A. Network approaches to the functional analysis of microbial proteins. Adv Microb Physiol 2011; 59:101-33. [PMID: 22114841 DOI: 10.1016/b978-0-12-387661-4.00005-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Large amounts of detailed biological data have been generated over the past few decades. Much of these data is freely available in over 1000 online databases; an enticing, but frustrating resource for microbiologists interested in a systems-level view of the structure and function of microbial cells. The frustration engendered by the need to trawl manually through hundreds of databases in order to accumulate information about a gene, protein, pathway, or organism of interest can be alleviated by the use of computational data integration to generated network views of the system of interest. Biological networks can be constructed from a single type of data, such as protein-protein binding information, or from data generated by multiple experimental approaches. In an integrated network, nodes usually represent genes or gene products, while edges represent some form of interaction between the nodes. Edges between nodes may be weighted to represent the probability that the edge exists in vivo. Networks may also be enriched with ontological annotations, facilitating both visual browsing and computational analysis via web service interfaces. In this review, we describe the construction, analysis of both single-data source and integrated networks, and their application to the inference of protein function in microbes.
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Affiliation(s)
- J S Hallinan
- School of Computing Science, Newcastle University, Newcastle, UK
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Luyf ACM, van Schaik BDC, de Vries M, Baas F, van Kampen AHC, Olabarriaga SD. Initial steps towards a production platform for DNA sequence analysis on the grid. BMC Bioinformatics 2010; 11:598. [PMID: 21156038 PMCID: PMC3018473 DOI: 10.1186/1471-2105-11-598] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2010] [Accepted: 12/14/2010] [Indexed: 11/10/2022] Open
Abstract
Background Bioinformatics is confronted with a new data explosion due to the availability of high throughput DNA sequencers. Data storage and analysis becomes a problem on local servers, and therefore it is needed to switch to other IT infrastructures. Grid and workflow technology can help to handle the data more efficiently, as well as facilitate collaborations. However, interfaces to grids are often unfriendly to novice users. Results In this study we reused a platform that was developed in the VL-e project for the analysis of medical images. Data transfer, workflow execution and job monitoring are operated from one graphical interface. We developed workflows for two sequence alignment tools (BLAST and BLAT) as a proof of concept. The analysis time was significantly reduced. All workflows and executables are available for the members of the Dutch Life Science Grid and the VL-e Medical virtual organizations All components are open source and can be transported to other grid infrastructures. Conclusions The availability of in-house expertise and tools facilitates the usage of grid resources by new users. Our first results indicate that this is a practical, powerful and scalable solution to address the capacity and collaboration issues raised by the deployment of next generation sequencers. We currently adopt this methodology on a daily basis for DNA sequencing and other applications. More information and source code is available via http://www.bioinformaticslaboratory.nl/
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Affiliation(s)
- Angela C M Luyf
- Bioinformatics Laboratory, Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Academic Medical Center, PO Box 22700, 1100 DE Amsterdam, The Netherlands
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Sintchenko V. Informatics for Infectious Disease Research and Control. INFECTIOUS DISEASE INFORMATICS 2010. [PMCID: PMC7120928 DOI: 10.1007/978-1-4419-1327-2_1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The goal of infectious disease informatics is to optimize the clinical and public health management of infectious diseases through improvements in the development and use of antimicrobials, the design of more effective vaccines, the identification of biomarkers for life-threatening infections, a better understanding of host-pathogen interactions, and biosurveillance and clinical decision support. Infectious disease informatics can lead to more targeted and effective approaches for the prevention, diagnosis and treatment of infections through a comprehensive review of the genetic repertoire and metabolic profiles of a pathogen. The developments in informatics have been critical in boosting the translational science and in supporting both reductionist and integrative research paradigms.
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Kouwen TRHM, van Dijl JM. Applications of thiol-disulfide oxidoreductases for optimized in vivo production of functionally active proteins in Bacillus. Appl Microbiol Biotechnol 2009; 85:45-52. [PMID: 19727703 PMCID: PMC2765640 DOI: 10.1007/s00253-009-2212-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2009] [Revised: 08/17/2009] [Accepted: 08/18/2009] [Indexed: 02/01/2023]
Abstract
Bacillus subtilis is a well-established cellular factory for proteins and fine chemicals. In particular, the direct secretion of proteinaceous products into the growth medium greatly facilitates their downstream processing, which is an important advantage of B. subtilis over other biotechnological production hosts, such as Escherichia coli. The application spectrum of B. subtilis is, however, often confined to proteins from Bacillus or closely related species. One of the major reasons for this (current) limitation is the inefficient formation of disulfide bonds, which are found in many, especially eukaryotic, proteins. Future exploitation of B. subtilis to fulfill the ever-growing demand for pharmaceutical and other high-value proteins will therefore depend on overcoming this particular hurdle. Recently, promising advances in this area have been achieved, which focus attention on the need to modulate the cellular levels and activity of thiol-disulfide oxidoreductases (TDORs). These TDORs are enzymes that control the cleavage or formation of disulfide bonds. This review will discuss readily applicable approaches for TDOR modulation and aims to provide leads for further improvement of the Bacillus cell factory for production of disulfide bond-containing proteins.
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Affiliation(s)
- Thijs R H M Kouwen
- Department of Medical Microbiology, University Medical Microbiology, University Medical Center Groningen, Groningen, The Netherlands
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