Evidence map›Paper›PMID 29316921›Full record

ArticleMicrobial cell factories2018

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

Francine Piubeli, Manuel Salvador, Montserrat Argandoña, Joaquín J Nieto, Vicente Bernal, Jose M Pastor, Manuel Cánovas, Carmen Vargas

Open access · goldAbstract read
In one paragraph

Article in Microbial cell factories, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
1.3field-weighted citation impact, top 20% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed, 30 citations in OpenAlex.

  1. Decoding metabolic trade-offs inSynthetic and systems biotechnology · 2025
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  2. Reconstruction and analyses of genome-scaleMetabolic engineering communications · 2024
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  8. Genome analysis of the salt-resistant Paludifilum halophilum DSM 102817World journal of microbiology & biotechnology · 2021
    Article
  9. Article
  10. CEG 2.0: an updated database of clusters of essential genes including eukaryotic organisms.Database : the journal of biological databases and curation · 2020
    Article
  11. Article
  12. Review
  13. Article
  14. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 2 institutions in 1 country.

Francine PiubeliDepartment of Microbiology and Parasitology, Faculty of Pharmacy, University of Sevilla, C/Profesor García González 2, 41012, Sevilla, Spain.
Manuel SalvadorDepartment of Microbiology and Parasitology, Faculty of Pharmacy, University of Sevilla, C/Profesor García González 2, 41012, Sevilla, Spain.
Montserrat ArgandoñaDepartment of Microbiology and Parasitology, Faculty of Pharmacy, University of Sevilla, C/Profesor García González 2, 41012, Sevilla, Spain.
Joaquín J NietoDepartment of Microbiology and Parasitology, Faculty of Pharmacy, University of Sevilla, C/Profesor García González 2, 41012, Sevilla, Spain.
Vicente BernalDepartment 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.
Jose M PastorDepartment 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.
Manuel CánovasDepartment 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.
Carmen VargasDepartment of Microbiology and Parasitology, Faculty of Pharmacy, University of Sevilla, C/Profesor García González 2, 41012, Sevilla, Spain. cvargas@us.es.
Universidad de Sevilla · ESUniversidad de Murcia · ES

Funding

Consejería de Economía, Innovación, Ciencia y Empleo, Junta de Andalucía P08-CVI-03724Ministerio de Economía y Competitividad BIO2014-54411-C2-1-RMinisterio de Economía y Competitividad BIO2015-63949-R
6 · The paper itself

Abstract

backgroundThe 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.

resultsWe 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.

conclusionsOur 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.

Indexed as

Genome, BacterialModels, BiologicalAdaptation, PhysiologicalAmino Acids, DiaminoBiomassChromohalobacterMetabolic Flux AnalysisSalinitySodium ChlorideAmino Acids, DiaminoectoineSodium ChlorideChromohalobacter salexigensFlux balance analysisGenome–scale metabolic modelMetabolic osmoadaptation

Identifiers

PMID29316921
PMCPMC5759318
OpenAlexW2783107179

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.