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Barbillon, P.

Publications and source records attributed to Barbillon, P..

2 recordsLinked to original sources

Maize (Zea mays L.) interaction with the arbuscular mycorrhizal fungus Rhizophagus irregularis allows mitigation of nitrogen deficiency stress: physiological and molecular characterization

Maize is currently the most productive cereal crop in the world (www.faostat.org). Maize can form a symbiotic relationship with the Arbuscular Mycorrhizal Fungus (AMF), Rhizophagus irregularis. In this relationship, the fungus provides the plant with additional water and mineral nutrients, while the plant supplies carbon compounds to the fungus. Little is known about the N metabolism disruption during symbiosis in both partners. To address this issue, two genetically distant maize lines were studied in terms of physiological and molecular responses to AMF inoculation by dual RNA-seq, metabolomics and phenotyping. Interestingly, the beneficial effects of the AMF were observed mainly under conditions of limited N fertilization. Under such conditions, the AMF helped maintain plant biomass production. The availability of nitrogen was found to be a crucial factor influencing all the traits studied showing that the level of N supply plays a pivotal role in determining how maize plants interact with the AMF. Despite the two maize lines showing different transcriptomic and metabolomic responses to R. irregularis, their agro-physiological traits remained similar. Both the plant and fungal transcriptomes were more significantly influenced by the level of N nutrition rather than the specific maize genotype. This suggests that N availability has a more profound impact on gene expression in both organisms than the genetic makeup of the maize plant. To understand the metabolic implications of this symbiotic relationship, we integrated transcriptomic data into our recently built multi-organ Genome-scale metabolic model (GSM) called iZMA6517. Remarkably, this modelling approach was supported by metabolomics profiling, in particular increased leaf pyrimidine levels in response to AMF inoculation under limiting N supply. Consistently, fungal genes involved in pyrimidine de novo synthesis and salvage were found to be expressed in symbiotic roots. Our work highlights nucleotide and ureides metabolism as previously unrecognized factors contributing to the symbiotic N nutrition facilitated by R. irregularis, thereby enhancing maize growth. This study demonstrates the effectiveness of integrating multi-omics approaches with mathematical modelling to uncover novel metabolic mechanisms associated with AM symbiosis, without a priori.

plant biology↗

Automatic calibration of a functional-structural wheat model using an adaptive design and a metamodelling approach

O_LIBackground and Aims Functional-structural plant models are increasingly being used by plant scientists to address a wide variety of questions. However, the calibration of these complex models is often challenging, mainly because of their high computational cost. In this paper, we applied an automatic method to the calibration of WALTer: a functional-structural wheat model that simulates the plasticity of tillering in response to competition for light. C_LIO_LIMethods We used a Bayesian calibration method to estimate the values of 5 parameters of the WALTer model by fitting the model outputs to tillering dynamics data. The method presented in this paper is based on the Efficient Global Optimisation algorithm. It involves the use of Gaussian process metamodels to generate fast approximations of the model outputs. To account for the uncertainty associated with the metamodels approximations, an adaptive design was used. The efficacy of the method was first assessed using simulated data. The calibration was then applied to experimental data. C_LIO_LIKey Results The method presented here performed well on both simulated and experimental data. In particular, the use of an adaptive design proved to be a very efficient method to improve the quality of the metamodels predictions, especially by reducing the uncertainty in areas of the parameter space that were of interest for the fitting. Moreover, we showed the necessity to have a diversity of field data in order to be able to calibrate the parameters. C_LIO_LIConclusions The method presented in this paper, based on an adaptive design and Gaussian process metamodels, is an efficient approach for the calibration of WALTer and could be of interest for the calibration of other functional-structural plant models. C_LI

bioinformatics↗