Modélisation mathématique du réseau transcriptionnel contrôlé par MYB30 et MYB96, deux facteurs de transcription impliqués dans la réponse de la plante modèle arabidopsis thaliana

Au cours des années, de nombreuses données ont été accumulées concernant le rôle et la régulation des facteurs de transcription MYB30 et MYB96 lors des réponses de défense de la plante Arabidopsis thaliana à l'attaque de bactéries pathogènes. Mon travail de thèse a consisté en la mise en place de méthodes de modélisation mathématique afin d'étudier l'effet de ces facteurs de transcription sur le métabolisme de la plante durant l'infection. Pour cela, j'ai développé des méthodes hybrides capables de combiner l'analyse de réseaux de régulation et du métabolisme. Ces études ont pu mettre en évidence l'importance de MYB96 qui semble réguler de nombreux gènes impliqués dans la biosynthèse d'acides gras à très longue chaîne et de leurs dérivés.Over the years, a lot of data has been accumulated concerning the role and regulation of MYB30 and MYB96 transcription factors during the defence responses of the plant Arabidopsis thaliana in response to pathogenic bacteria. My PhD project consisted in using mathematical modelling methods to study the role of these transcription factors on plant metabolism during infection. I developed hybrid methods capable of combining analyses of regulatory and metabolic networks. These studies showed the importance of MYB96 which seems to positively regulate many genes involved in the biosynthesis of very long chain fatty acids and their derivatives

Mathematical modelling of the transcriptional network controlled by MYB30 and MYB96, two transcription factors involved in the defence response of the model plant Arabidopsis thaliana

Au cours des années, de nombreuses données ont été accumulées concernant le rôle et la régulation des facteurs de transcription MYB30 et MYB96 lors des réponses de défense de la plante Arabidopsis thaliana à l'attaque de bactéries pathogènes. Mon travail de thèse a consisté en la mise en place de méthodes de modélisation mathématique afin d'étudier l'effet de ces facteurs de transcription sur le métabolisme de la plante durant l'infection. Pour cela, j'ai développé des méthodes hybrides capables de combiner l'analyse de réseaux de régulation et du métabolisme. Ces études ont pu mettre en évidence l'importance de MYB96 qui semble réguler de nombreux gènes impliqués dans la biosynthèse d'acides gras à très longue chaîne et de leurs dérivés.Over the years, a lot of data has been accumulated concerning the role and regulation of MYB30 and MYB96 transcription factors during the defence responses of the plant Arabidopsis thaliana in response to pathogenic bacteria. My PhD project consisted in using mathematical modelling methods to study the role of these transcription factors on plant metabolism during infection. I developed hybrid methods capable of combining analyses of regulatory and metabolic networks. These studies showed the importance of MYB96 which seems to positively regulate many genes involved in the biosynthesis of very long chain fatty acids and their derivatives

El defensor : Diario Liberal independiente, protector de los intereses materiales y morales de esta provincia: Año II Epoca 2ª Número 175 - 1904 septiembre 15

Copia digital. Madrid : Ministerio de Cultura. Subdirección General de Coordinación Bibliotecaria, 200

A resource allocation trade-off between virulence and proliferation drives metabolic versatility in the plant pathogen Ralstonia solanacearum

Bacterial pathogenicity relies on a proficient metabolism and there is increasing evidence that metabolic adaptation to exploit host resources is a key property of infectious organisms. In many cases, colonization by the pathogen also implies an intensive multiplication and the necessity to produce a large array of virulence factors, which may represent a significant cost for the pathogen. We describe here the existence of a resource allocation trade-off mechanism in the plant pathogen R. solanacearum. We generated a genome-scale reconstruction of the metabolic network of R. solanacearum, together with a macromolecule network module accounting for the production and secretion of hundreds of virulence determinants. By using a combination of constraint-based modeling and metabolic flux analyses, we quantified the metabolic cost for production of exopolysaccharides, which are critical for disease symptom production, and other virulence factors. We demonstrated that this trade-off between virulence factor production and bacterial proliferation is controlled by the quorum-sensing-dependent regulatory protein PhcA. A phcA mutant is avirulent but has a better growth rate than the wild-type strain. Moreover, a phcA mutant has an expanded metabolic versatility, being able to metabolize 17 substrates more than the wild-type. Model predictions indicate that metabolic pathways are optimally oriented towards proliferation in a phcA mutant and we show that this enhanced metabolic versatility in phcA mutants is to a large extent a consequence of not paying the cost for virulence. This analysis allowed identifying candidate metabolic substrates having a substantial impact on bacterial growth during infection. Interestingly, the substrates supporting well both production of virulence factors and growth are those found in higher amount within the plant host. These findings also provide an explanatory basis to the well-known emergence of avirulent variants in R. solanacearum populations in planta or in stressful environments

Additional file 4 of FlexFlux: combining metabolic flux and regulatory network analyses

Comparison between rFBA and RSA on 125 conditions and 110 gene knock-outs of E. coli regulatory and metabolic networks. (XLSX 43 kb

Virulence factor production restricts the versatility in a <i>phcA</i>-dependent manner.

<p><b>(A)</b> Heatmap of the phenotype microarray data (plate number 1, PM1) of strain GMI1000 (wild-type) and the regulatory mutants GMI1605 (<i>phcA</i>), GMI1755 (<i>hrpG</i>) and GMI1525 (<i>hrpB</i>). The clustering is performed depending on the similarity of substrates usages. <b>(B)</b> Maximum growth rate of the wild-type strain and the mutants in minimal medium supplemented with different carbon sources. Errors bars are 2*σ (95% data dispersion), n = 6, significance level (Student-test): *: p-value <0.05; **: p-value <0.01. <b>(C)</b> Contingency table of the simulated and experimental metabolic capacities of the wild type strain and the <i>phcA</i> mutant determined using phenotype microarray (PM1, 2, 3). The model performance is reported as precision, sensitivity and accuracy for the two data sets.</p