Similar Datasets
Project description:Our study revealed a synergistic effect between biological nitrogen fixation and current generation by G. sulfurreducens, providing a green nitrogen fixation alternative through shifting the nitrogen fixation field from energy consumption to energy production and having implications for N-deficient wastewater treatment.
2021-10-09 | GSE185414 | GEO
Project description:Legumes can utilize atmospheric nitrogen via symbiotic nitrogen fixation, but this process is inhibited by high soil inorganic nitrogen. So far, how high nitrogen inhibits N2 fixation in mature nodules is still poorly understood. Here we construct a co-expression network in soybean nodule and find that a dynamic and reversible transcriptional network underlies the high N inhibition of N2 fixation. Intriguingly, several NAC transcription factors (TFs), designated as Soybean Nitrogen Associated NAPs (SNAPs), are amongst the most connected hub TFs. The nodules of snap1/2/3/4 quadruple mutants show less sensitivity to the high N inhibition of nitrogenase activity and acceleration of senescence. Integrative analysis shows that these SNAP TFs largely influence the high N transcriptional response through direct regulation of a subnetwork of senescence-associated genes and transcriptional regulators. We propose that the SNAP-mediated transcriptional network may trigger nodule senescence in response to high N.
2023-07-05 | GSE236557 | GEO
Project description:A1501 NFI is a genomic island derived from Pseudomonas stutzeri A1501. To study the molecular interactions of the P. stutzeri nif genes with the E. coli genome during nitrogen fixation, the NIF of A1501 was transferred into E. coli and comparative transcriptomics analyses were performed between nitrogen fixation conditions and nitrogen excess conditions.
2015-08-05 | GSE37780 | GEO
Project description:To investigate the effect that biological nitrogen fixation will have on plant responses to nitrogen dose at elevated CO2, alfalfa (Medicago sativa) lines were grown at three nitrogen doses and ambient or elevated CO2. Four lines were used in the study, two lines that can form nodules capable of fixing nitrogen (effective lines) and two lines that can not form nodules capable of nitrogen fixation (ineffective lines). The ineffective lines are the result of a complementary mutation in the same gene.
2024-02-26 | GSE244008 | GEO
Project description:Nitrogen fixation is an important metabolic process carried out by microorganisms, which converts molecular nitrogen into inorganic nitrogenous compounds such as ammonia (NH3). These nitrogenous compounds are crucial for biogeochemical cycles and for the synthesis of essential biomolecules, i.e. nucleic acids, amino acids and proteins. Azotobacter vinelandii is a bacterial non-photosynthetic model organism to study aerobic nitrogen fixation (diazotrophy) and hydrogen production. Moreover, the diazotroph can produce biopolymers like alginate and polyhydroxybutyrate (PHB) that have important industrial applications. However, many metabolic processes such as partitioning of carbon and nitrogen metabolism in A. vinelandii remain unknown to date.
Genome-scale metabolic models (M-models) represent reliable tools to unravel and optimize metabolic functions at genome-scale. M-models are mathematical representations that contain information about genes, reactions, metabolites and their associations. M-models can simulate optimal reaction fluxes under a wide variety of conditions using experimentally determined constraints. Here we report on the development of a M-model of the wild type bacterium A. vinelandii DJ (iDT1278) which consists of 2,003 metabolites, 2,469 reactions, and 1,278 genes. We validated the model using high-throughput phenotypic and physiological data, testing 180 carbon sources and 95 nitrogen sources. iDT1278 was able to achieve an accuracy of 89% and 91% for growth with carbon sources and nitrogen source, respectively. This comprehensive M-model will help to comprehend metabolic processes associated with nitrogen fixation, ammonium assimilation, and production of organic nitrogen in an environmentally important microorganism.
2023-03-10 | MODEL2204190001 | BioModels
Project description:Biological nitrogen fixation, the microbial reduction of atmospheric nitrogen to bioavailable ammonia, represents both a major limitation on biological productivity and a highly desirable engineering target for synthetic biology. However, the engineering of nitrogen fixation requires an integrated understanding of how the gene regulatory dynamics of host diazotrophs respond across sequence-function space of its central catalytic metalloenzyme, nitrogenase. Here, we interrogate this relationship by analyzing the transcriptome of Azotobacter vinelandii engineered with a phylogenetically inferred ancestral nitrogenase protein variant. The engineered strain exhibits reduced cellular nitrogenase activity but recovers wild-type growth rates following an extended lag period. We find that expression of genes within the immediate nitrogen fixation network is resilient to the introduced nitrogenase sequence-level perturbations. Rather the sustained physiological compatibility with the ancestral nitrogenase variant is accompanied by reduced expression of genes that support trace metal and electron resource allocation to nitrogenase. Our results spotlight gene expression changes in cellular processes adjacent to nitrogen fixation as productive engineering considerations to improve compatibility between remodeled nitrogenase proteins and engineered host diazotrophs. IMPORTANCE Azotobacter vinelandii is a key model bacterium for the study of biological nitrogen fixation, an important metabolic process catalyzed by nitrogenase enzymes. Here, we demonstrate that compatibilities between engineered A. vinelandii strains and nitrogenase variants can be modulated at the regulatory level. The engineered strain studied here responds by adjusting the expression of proteins involved in cellular processes adjacent to nitrogen fixation, rather than that of nitrogenase proteins themselves. These insights can inform future strategies to transfer nitrogenase variants to non-native hosts.
2023-06-07 | GSE234075 | GEO
Project description:Resendis-Antonio2007 - Genome-scale metabolic
network of Rhizobium etli (iOR363)
This model is described in the article:
Metabolic reconstruction and
modeling of nitrogen fixation in Rhizobium etli.
Resendis-Antonio O, Reed JL,
Encarnación S, Collado-Vides J, Palsson BØ.
PLoS Comput. Biol. 2007 Oct; 3(10):
1887-1895
Abstract:
Rhizobiaceas are bacteria that fix nitrogen during symbiosis
with plants. This symbiotic relationship is crucial for the
nitrogen cycle, and understanding symbiotic mechanisms is a
scientific challenge with direct applications in agronomy and
plant development. Rhizobium etli is a bacteria which provides
legumes with ammonia (among other chemical compounds), thereby
stimulating plant growth. A genome-scale approach, integrating
the biochemical information available for R. etli, constitutes
an important step toward understanding the symbiotic
relationship and its possible improvement. In this work we
present a genome-scale metabolic reconstruction (iOR363) for R.
etli CFN42, which includes 387 metabolic and transport
reactions across 26 metabolic pathways. This model was used to
analyze the physiological capabilities of R. etli during stages
of nitrogen fixation. To study the physiological capacities in
silico, an objective function was formulated to simulate
symbiotic nitrogen fixation. Flux balance analysis (FBA) was
performed, and the predicted active metabolic pathways agreed
qualitatively with experimental observations. In addition,
predictions for the effects of gene deletions during nitrogen
fixation in Rhizobia in silico also agreed with reported
experimental data. Overall, we present some evidence supporting
that FBA of the reconstructed metabolic network for R. etli
provides results that are in agreement with physiological
observations. Thus, as for other organisms, the reconstructed
genome-scale metabolic network provides an important framework
which allows us to compare model predictions with experimental
measurements and eventually generate hypotheses on ways to
improve nitrogen fixation.
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2015-07-30 | MODEL1507180006 | BioModels