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      Microbial laboratory evolution in the era of genome-scale science

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Advances in DNA sequencing, high-throughput technologies, and genetic manipulation systems have enabled empirical studies of the molecular and genomic bases of adaptive evolution. This review discusses key insights learned from direct observation of the evolution process.

          Abstract

          Laboratory evolution studies provide fundamental biological insight through direct observation of the evolution process. They not only enable testing of evolutionary theory and principles, but also have applications to metabolic engineering and human health. Genome-scale tools are revolutionizing studies of laboratory evolution by providing complete determination of the genetic basis of adaptation and the changes in the organism's gene expression state. Here, we review studies centered on four central themes of laboratory evolution studies: (1) the genetic basis of adaptation; (2) the importance of mutations to genes that encode regulatory hubs; (3) the view of adaptive evolution as an optimization process; and (4) the dynamics with which laboratory populations evolve.

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          Most cited references88

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          Cluster analysis and display of genome-wide expression patterns.

          P. T. Spellman, P. O. Brown, D Botstein … (1998)
          A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is described that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression. The output is displayed graphically, conveying the clustering and the underlying expression data simultaneously in a form intuitive for biologists. We have found in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function, and we find a similar tendency in human data. Thus patterns seen in genome-wide expression experiments can be interpreted as indications of the status of cellular processes. Also, coexpression of genes of known function with poorly characterized or novel genes may provide a simple means of gaining leads to the functions of many genes for which information is not available currently.
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            Programming cells by multiplex genome engineering and accelerated evolution.

            Harris Wang, Farren J. Isaacs, Peter A Carr … (2009)
            The breadth of genomic diversity found among organisms in nature allows populations to adapt to diverse environments. However, genomic diversity is difficult to generate in the laboratory and new phenotypes do not easily arise on practical timescales. Although in vitro and directed evolution methods have created genetic variants with usefully altered phenotypes, these methods are limited to laborious and serial manipulation of single genes and are not used for parallel and continuous directed evolution of gene networks or genomes. Here, we describe multiplex automated genome engineering (MAGE) for large-scale programming and evolution of cells. MAGE simultaneously targets many locations on the chromosome for modification in a single cell or across a population of cells, thus producing combinatorial genomic diversity. Because the process is cyclical and scalable, we constructed prototype devices that automate the MAGE technology to facilitate rapid and continuous generation of a diverse set of genetic changes (mismatches, insertions, deletions). We applied MAGE to optimize the 1-deoxy-D-xylulose-5-phosphate (DXP) biosynthesis pathway in Escherichia coli to overproduce the industrially important isoprenoid lycopene. Twenty-four genetic components in the DXP pathway were modified simultaneously using a complex pool of synthetic DNA, creating over 4.3 billion combinatorial genomic variants per day. We isolated variants with more than fivefold increase in lycopene production within 3 days, a significant improvement over existing metabolic engineering techniques. Our multiplex approach embraces engineering in the context of evolution by expediting the design and evolution of organisms with new and improved properties.
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              The Gene Ontology Annotation (GOA) Database: sharing knowledge in Uniprot with Gene Ontology.

              Evelyn Camon, Michele Magrane, Daniel Barrell … (2004)
              The Gene Ontology Annotation (GOA) database (http://www.ebi.ac.uk/GOA) aims to provide high-quality electronic and manual annotations to the UniProt Knowledgebase (Swiss-Prot, TrEMBL and PIR-PSD) using the standardized vocabulary of the Gene Ontology (GO). As a supplementary archive of GO annotation, GOA promotes a high level of integration of the knowledge represented in UniProt with other databases. This is achieved by converting UniProt annotation into a recognized computational format. GOA provides annotated entries for nearly 60,000 species (GOA-SPTr) and is the largest and most comprehensive open-source contributor of annotations to the GO Consortium annotation effort. By integrating GO annotations from other model organism groups, GOA consolidates specialized knowledge and expertise to ensure the data remain a key reference for up-to-date biological information. Furthermore, the GOA database fully endorses the Human Proteomics Initiative by prioritizing the annotation of proteins likely to benefit human health and disease. In addition to a non-redundant set of annotations to the human proteome (GOA-Human) and monthly releases of its GO annotation for all species (GOA-SPTr), a series of GO mapping files and specific cross-references in other databases are also regularly distributed. GOA can be queried through a simple user-friendly web interface or downloaded in a parsable format via the EBI and GO FTP websites. The GOA data set can be used to enhance the annotation of particular model organism or gene expression data sets, although increasingly it has been used to evaluate GO predictions generated from text mining or protein interaction experiments. In 2004, the GOA team will build on its success and will continue to supplement the functional annotation of UniProt and work towards enhancing the ability of scientists to access all available biological information. Researchers wishing to query or contribute to the GOA project are encouraged to email: goa@ebi.ac.uk.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Mol Syst Biol
                Title: Molecular Systems Biology
                Publisher: Nature Publishing Group
                ISSN (Electronic): 1744-4292
                Publication date Collection: 2011
                Publication date (Electronic): 05 July 2011
                Publication date PMC-release: 05 July 2011
                Volume: 7
                Page: 509
                Affiliations
                [1 ]simpleDepartment of Bioengineering, University of California San Diego , La Jolla, CA, USA
                Author notes
                [a ]Department of Bioengineering, University of California San Diego, 9500 Gilman Drive, Mail Code 0412, La Jolla, CA 92093-0412, USA. Tel.: +1 858 534 5668; Fax: +1 858 822 3120; palsson@ 123456ucsd.edu
                Article
                Publisher Item ID: msb201142
                DOI: 10.1038/msb.2011.42
                PMC ID: 3159978
                PubMed ID: 21734648
                SO-VID: 091a21d7-787e-4cf1-8051-7dad8ab87ab1
                Copyright © Copyright © 2011, EMBO and Macmillan Publishers Limited
                License:

                This is an open-access article distributed under the terms of the Creative Commons Attribution Noncommercial No Derivative Works 3.0 Unported License, which permits distribution and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation or the creation of derivative works without specific permission.

                History
                Date received : 21 February 2011
                Date accepted : 12 May 2011
                Categories
                Subject: Review Article

                ScienceOpen disciplines: Quantitative & Systems biology
                Keywords: regulatory hub,epistasis,flux-balance analysis,metabolic engineering,mutation
                Data availability:
                ScienceOpen disciplines: Quantitative & Systems biology
                Keywords: regulatory hub, epistasis, flux-balance analysis, metabolic engineering, mutation

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