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Genome-scale metabolic modelling of lifestyle changes in Rhizobium leguminosarum

Abstract:
Biological nitrogen fixation in rhizobium-legume symbioses is of major importance for sustainable agricultural practices. To establish a mutualistic relationship with their plant host, rhizobia transition from free-living bacteria in soil to growth down infection threads inside plant roots and finally differentiate into nitrogen-fixing bacteroids. We reconstructed a genome-scale metabolic model for Rhizobium leguminosarum and integrated the model with transcriptome, proteome, metabolome, and gene essentiality data to investigate nutrient uptake and metabolic fluxes characteristic of these different lifestyles. Synthesis of leucine, polyphosphate, and AICAR is predicted to be important in the rhizosphere, while myo-inositol catabolism is active in undifferentiated nodule bacteria in agreement with experimental evidence. The model indicates that bacteroids utilize xylose and glycolate in addition to dicarboxylates, which could explain previously described gene expression patterns. Histidine is predicted to be actively synthesized in bacteroids, consistent with transcriptome and proteome data for several rhizobial species. These results provide the basis for targeted experimental investigation of metabolic processes specific to the different stages of the rhizobium-legume symbioses.
IMPORTANCE Rhizobia are soil bacteria that induce nodule formation on plant roots and differentiate into nitrogen-fixing bacteroids. A detailed understanding of this complex symbiosis is essential for advancing ongoing efforts to engineer novel symbioses with cereal crops for sustainable agriculture. Here, we reconstruct and validate a genome-scale metabolic model for Rhizobium leguminosarum bv. viciae 3841. By integrating the model with various experimental data sets specific to different stages of symbiosis formation, we elucidate the metabolic characteristics of rhizosphere bacteria, undifferentiated bacteria inside root nodules, and nitrogen-fixing bacteroids. Our model predicts metabolic flux patterns for these three distinct lifestyles, thus providing a framework for the interpretation of genome-scale experimental data sets and identifying targets for future experimental studies.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1128/msystems.00975-21

Authors

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Role:
Author
ORCID:
0000-0002-7574-6259
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Oxford college:
Worcester College
Role:
Author
ORCID:
0000-0002-3565-8967
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Plant Sciences
Role:
Author
ORCID:
0000-0001-5087-6455


Publisher:
American Society for Microbiology
Journal:
mSystems More from this journal
Volume:
7
Issue:
1
Article number:
e00975-21
Publication date:
2022-01-11
Acceptance date:
2021-12-20
DOI:
EISSN:
2379-5077
ISSN:
2379-5077
Pmid:
35014871


Language:
English
Keywords:
Pubs id:
1232466
Local pid:
pubs:1232466
Deposit date:
2022-07-18
ARK identifier:

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