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SeMoVi - 210115 De Vienne Ferro Yvert

This SeMoVi will be held at Émilie du Châtelet Conference Hall, Marie Curie Library, INSA Lyon


14h00 - 15h00

Dominique de Vienne,  INRA et Université Paris-Sud, Gif-sur-Yvette

Genetic consequences of the non-linear genotype-phenotype relationships

15h00 - 15h30 Coffee Break  
15h30 - 16h30

Myriam Ferro, CEA, Grenoble


Gaël Yvert, ENS, Lyon

How proteomics can provide useful information for metabolic engineering purposes


Quantitative genetics of a yeast gene regulatory network

16h30 - 17h00 General Discussion



How proteomics can provide useful information for metabolic engineering purposes


Myriam Ferro


Metabolic engineering aims to design high performance platforms (e.g. E. coli) producing compounds of interest. This requires systems-level understanding; genome-scale models have therefore been developed to predict metabolic fluxes. In that context, multi-omics data including genomics, transcriptomics, fluxomics and proteomics may be required to model the metabolism of potential cell factories. Recent technological advances in quantitative proteomics have made mass spectrometry-based quantitative analyses an interesting way to measure relative or absolute amount of proteins, a quantitative dimension that has to be integrated in metabolism models.

In a first study we developed a quantification workflow aiming to analyze enzymes involved in carbon central metabolism in E.coli. This workflow combined full-length isotopically labelled (15N) standards with Selected Reaction Monitoring (SRM) analysis. Liquid chromatography conditions were specifically optimized for reproducibility and multiplexing capabilities over a single 30-minute LC-MS analysis. This workflow was used to accurately quantify 22 enzymes involved in E. coli central metabolism in a wild-type reference strain and two derived strains, optimized for higher NADPH production. In combination with measurements of metabolic fluxes, proteomics data can be used to assess different levels of regulation, in particular enzyme abundance and catalytic rate. This provides information that can be used to design specific strains used in biotechnology. In addition, accurate measurement of absolute enzyme concentrations is key to the development of predictive kinetic models in the context of metabolic engineering.

In a second study our aim was to model some metabolic pathways occurring in the chloroplast. The chloroplast is a complex and integrated network that produces a high number of metabolites of industrial interest (e.g sugars, vitamins, lipids and pigments). One way to improve our knowledge of such a metabolic factory is to build metabolic pathways with highly curated and integrated knowledge. As current knowledge on chloroplast metabolism is still dispersed we decided to build a chloroplast knowledge base (Chloro-KB) dedicated to chloroplast metabolic pathways.  The aim of Chloro-KB is to build curated metabolic maps, to visualize these maps and to integrate “omics” qualitative and quantitative data required for modeling purposes. In that context we have integrated in Chloro-KB proteomics quantitative data that provide information over the sub-plastidial localization, a critical information to infer functional annotations and to help data curation.



The pdf version of the poster can be found here









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