Opening the door to nitrogen fixation in oxygenic phototrophs

Morris, Dianna S; Chowdhury, Niaz Bahar; Kathol, Mark; Saha, Rajib

doi:10.1016/j.tibtech.2023.01.005

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…Using phenotyping, transcriptomic and metabolomic approaches, we have characterized such interaction using two maize lines which benefit from symbiotic association with the fungus in terms of agronomic performance. Genome-scale metabolic modeling (GSM) is a computational tool to model plant metabolism and can highlight potential key metabolic pathways in plant in response to environmental cues (Morris et al ., 2023). Initial efforts on maize metabolic models were limited to its leaf (Simons et al ., 2014) and root (Chowdhury et al ., 2022 b ) GSMs, which showed excellent agreement with existing literature regarding phenotypical predictions.…”

Section: Introductionmentioning

confidence: 99%

“…Genome-scale metabolic modeling (GSM) is a computational tool to model plant metabolism and can highlight potential key metabolic pathways in plant in response to environmental cues (Morris et al, 2023). Initial efforts on maize metabolic models were limited to its leaf (Simons et al, 2014) and root (Chowdhury et al, 2022b) GSMs, which showed excellent agreement with existing literature regarding phenotypical predictions.…”

Section: Introductionmentioning

confidence: 99%

“…It employs techniques such as flux balance analysis (FBA) 28 , flux variability analysis (FVA) 29 , and parsimonious FBA 30 , to predict the flux of reactions accurately. Genome-scale metabolic modelling (GSM) is a computational tool to model plant metabolism and applied to plant system can highlight potential key metabolic pathways in plants in response to environmental cues 31 . Initial efforts on maize metabolic models were limited to its leaf 32 and root 33 GSMs, which showed agreement with existing literature regarding phenotypical predictions.…”

Section: Introduction 908 Wordsmentioning

confidence: 99%

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Unlocking the Mycorrhizal Nitrogen Pathway Puzzle: Metabolic Modelling and multi-omics unveil Pyrimidines’ Role in Maize Nutrition via Arbuscular Mycorrhizal Fungi Amidst Nitrogen Scarcity

Decouard,

Chowdhury,

Saou

et al. 2023

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Maize is currently the most productive cereal crop in the world (www.faostat.org). It can associate with the Arbuscular Mycorrhizal Fungus (AMF)Rhizophagus irregulariswhich can provide additional water and mineral nutrients to the plant in return for C delivered by the host plant. Two maize lines were characterized at the physiological and molecular levels as they displayed contrasting responses to inoculation with the AMF when grown under optimal, medium, or low N fertilization conditions. For both these lines, the presence of the AMF allows development of a beneficial symbiotic association but only under limiting N fertilization conditions and allows to maintain plant biomass production when there is a five fold reduction in N supply. Physio-agronomical phenotyping and transcriptomic and metabolomic characterization of these two lines indicates that, (i) the level of N supply is the major factor affecting all studied traits; (ii) although the two lines display different transcriptomic and metabolomic responses toR. irregularisthe agro-physiological traits are similar; and (iii) inoculation with the AMF relieves N deficiency stress. Analysis of the fungal transcriptome indicated that, like the plant transcriptome, it was also mainly affected by N nutrition level rather than by the maize host genotype. To explore potential metabolic pathways affected by the symbiosis, we integrated the transcriptomic data to model mycorrhized maize metabolism in a multi-organ Genome-scale metabolic model (GSM)iZMA6517, based on a stoichiometric approach. Both maize transcriptomics data integratediZMA6517 model andR. irregularistranscriptome pointed to nucleotide and ureides metabolism as previously unexplored new drivers of the symbiotic N nutrition triggered byR. irregularis that allow maize growth improvement.

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Section: Introductionmentioning

confidence: 99%