Insights into metabolic osmoadaptation of the ectoines-producer bacterium  Chromohalobacter salexigens  through a high-quality genome scale metabolic model

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Abstract

Background

The halophilic bacterium Chromohalobacter salexigens is a natural producer of ectoines, compatible solutes with current and potential biotechnological applications. As production of ectoines is an osmoregulated process that draws away TCA intermediates, bacterial metabolism needs to be adapted to cope with salinity changes. To explore and use C. salexigens as cell factory for ectoine(s) production, a comprehensive knowledge at the systems level of its metabolism is essential. For this purpose, the construction of a robust and high-quality genome-based metabolic model of C. salexigens was approached.

Results

We generated and validated a high quality genome-based C. salexigens metabolic model ( iFP764). This comprised an exhaustive reconstruction process based on experimental information, analysis of genome sequence, manual re-annotation of metabolic genes, and in-depth refinement. The model included three compartments (periplasmic, cytoplasmic and external medium), and two salinity-specific biomass compositions, partially based on experimental results from C. salexigens. Using previous metabolic data as constraints, the metabolic model allowed us to simulate and analyse the metabolic osmoadaptation of C. salexigens under conditions for low and high production of ectoines. The iFP764 model was able to reproduce the major metabolic features of C. salexigens. Flux Balance Analysis (FBA) and Monte Carlo Random sampling analysis showed salinity-specific essential metabolic genes and different distribution of fluxes and variation in the patterns of correlation of reaction sets belonging to central C and N metabolism, in response to salinity. Some of them were related to bioenergetics or production of reducing equivalents, and probably related to demand for ectoines. Ectoines metabolic reactions were distributed according to its correlation in four modules. Interestingly, the four modules were independent both at low and high salinity conditions, as they did not correlate to each other, and they were not correlated with other subsystems.

Conclusions

Our validated model is one of the most complete curated networks of halophilic bacteria. It is a powerful tool to simulate and explore C. salexigens metabolism at low and high salinity conditions, driving to low and high production of ectoines. In addition, it can be useful to optimize the metabolism of other halophilic bacteria for metabolite production.

Electronic supplementary material

The online version of this article (10.1186/s12934-017-0852-0) contains supplementary material, which is available to authorized users.

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Most cited references 44

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Network reconstructions are a common denominator in systems biology. Bottom-up metabolic network reconstructions have been developed over the last 10 years. These reconstructions represent structured knowledge bases that abstract pertinent information on the biochemical transformations taking place within specific target organisms. The conversion of a reconstruction into a mathematical format facilitates a myriad of computational biological studies, including evaluation of network content, hypothesis testing and generation, analysis of phenotypic characteristics and metabolic engineering. To date, genome-scale metabolic reconstructions for more than 30 organisms have been published and this number is expected to increase rapidly. However, these reconstructions differ in quality and coverage that may minimize their predictive potential and use as knowledge bases. Here we present a comprehensive protocol describing each step necessary to build a high-quality genome-scale metabolic reconstruction, as well as the common trials and tribulations. Therefore, this protocol provides a helpful manual for all stages of the reconstruction process.

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Accurate multiple alignments of 86 domains that occur in signaling proteins have been constructed and used to provide a Web-based tool (SMART: simple modular architecture research tool) that allows rapid identification and annotation of signaling domain sequences. The majority of signaling proteins are multidomain in character with a considerable variety of domain combinations known. Comparison with established databases showed that 25% of our domain set could not be deduced from SwissProt and 41% could not be annotated by Pfam. SMART is able to determine the modular architectures of single sequences or genomes; application to the entire yeast genome revealed that at least 6.7% of its genes contain one or more signaling domains, approximately 350 greater than previously annotated. The process of constructing SMART predicted (i) novel domain homologues in unexpected locations such as band 4.1-homologous domains in focal adhesion kinases; (ii) previously unknown domain families, including a citron-homology domain; (iii) putative functions of domain families after identification of additional family members, for example, a ubiquitin-binding role for ubiquitin-associated domains (UBA); (iv) cellular roles for proteins, such predicted DEATH domains in netrin receptors further implicating these molecules in axonal guidance; (v) signaling domains in known disease genes such as SPRY domains in both marenostrin/pyrin and Midline 1; (vi) domains in unexpected phylogenetic contexts such as diacylglycerol kinase homologues in yeast and bacteria; and (vii) likely protein misclassifications exemplified by a predicted pleckstrin homology domain in a Candida albicans protein, previously described as an integrin.

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Is Open Access

A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information

Adam M Feist, Christopher S. Henry, Jennifer Reed … (2007)

An updated genome-scale reconstruction of the metabolic network in Escherichia coli K-12 MG1655 is presented. This updated metabolic reconstruction includes: (1) an alignment with the latest genome annotation and the metabolic content of EcoCyc leading to the inclusion of the activities of 1260 ORFs, (2) characterization and quantification of the biomass components and maintenance requirements associated with growth of E. coli and (3) thermodynamic information for the included chemical reactions. The conversion of this metabolic network reconstruction into an in silico model is detailed. A new step in the metabolic reconstruction process, termed thermodynamic consistency analysis, is introduced, in which reactions were checked for consistency with thermodynamic reversibility estimates. Applications demonstrating the capabilities of the genome-scale metabolic model to predict high-throughput experimental growth and gene deletion phenotypic screens are presented. The increased scope and computational capability using this new reconstruction is expected to broaden the spectrum of both basic biology and applied systems biology studies of E. coli metabolism.

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Author and article information

Contributors

Francine Piubeli: piubeli@us.es

Manuel Salvador: msdl@us.es

Montserrat Argandoña: montseab@us.es

Joaquín J. Nieto: jjnieto@us.es

Vicente Bernal: vicente.bernal@gmail.com

Jose M. Pastor: josempastor@um.es

Manuel Cánovas: mcanovas@um.es

Carmen Vargas: +34 95 455 38 06 , cvargas@us.es

Journal

Journal ID (nlm-ta): Microb Cell Fact

Journal ID (iso-abbrev): Microb. Cell Fact

Title: Microbial Cell Factories

Publisher: BioMed Central (London )

ISSN (Electronic): 1475-2859

Publication date (Electronic): 9 January 2018

Publication date PMC-release: 9 January 2018

Publication date Collection: 2018

Volume: 17

Electronic Location Identifier: 2

Affiliations

[1 ]ISNI 0000 0001 2168 1229, GRID grid.9224.d, Department of Microbiology and Parasitology, Faculty of Pharmacy, , University of Sevilla, ; C/Profesor García González 2, 41012 Sevilla, Spain

[2 ]ISNI 0000 0001 2287 8496, GRID grid.10586.3a, Department of Biochemistry and Molecular Biology B and Immunology, Faculty of Chemistry, Campus Regional de Excelencia Internacional “Campus Mare Nostrum”, , University of Murcia, ; 30100 Murcia, Spain

[3 ]Centro de Tecnología de Repsol, REPSOL S.A. Calle Agustín de Betancourt, s/n. 28935, Móstoles, Madrid Spain

Article

Publisher ID: 852

DOI: 10.1186/s12934-017-0852-0

PMC ID: 5759318

PubMed ID: 29316921

SO-VID: 25164810-f62e-4a89-a14d-2e983e57e13e

License:

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

History

Date received : 27 July 2017

Date accepted : 20 December 2017

Funding

Funded by: FundRef http://dx.doi.org/10.13039/501100002878, Consejería de Economía, Innovación, Ciencia y Empleo, Junta de Andalucía;

Award ID: P08-CVI-03724

Award Recipient : Carmen Vargas

Funded by: FundRef http://dx.doi.org/10.13039/501100003329, Ministerio de Economía y Competitividad;

Award ID: BIO2015-63949-R

Award ID: BIO2014-54411-C2-1-R

Award Recipient : Carmen Vargas

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ScienceOpen disciplines: Biotechnology

Keywords: genome–scale metabolic model,flux balance analysis,chromohalobacter salexigens,metabolic osmoadaptation

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ScienceOpen disciplines: Biotechnology

Keywords: genome–scale metabolic model, flux balance analysis, chromohalobacter salexigens, metabolic osmoadaptation

Insights into metabolic osmoadaptation of the ectoines-producer bacterium Chromohalobacter salexigens through a high-quality genome scale metabolic model

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Genome Engineering using CRISPR

Most cited references 44

A protocol for generating a high-quality genome-scale metabolic reconstruction.

SMART, a simple modular architecture research tool: identification of signaling domains.

A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information

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