Mots-Clés

Protéomique des plantes Renouvellement des protéines Biologie des systèmes Bio-informatique Marquage métabolique Modélisation Plant proteomics Protein turnover System biology Bio-informatics

Description

Large scale proteins turnover measurement to investigate the impact of maize genetic variability on proteostasis.

Protein homeostasis (or proteostasis) is crucial to finely tune the abundance of the proteins in a cell by maintaining the equilibrium between protein synthesis and degradation, thus allowing an efficient biological activity [1]. The associated processes to maintain this equilibrium are constantly in action during organism’s development [2] or responses to environmental stress [3]. Proteostasis is a key element among the molecular mechanisms required to maintain cell vital functions through tight regulations from gene transcription to post-translational proteins modifications. It is also responsible for the moderate correlation repeatedly observed between the abundance of proteins and the level of the corresponding transcripts [2]. The protein turnover rate (PTR) is a main parameter of proteostasis and it is usually associated to protein synthesis (Ks) and degradation (Kd) rates. Then, the accurate measurements of these kinetics constants, at the proteome level, are required to better understand the mechanisms of proteostasis regulations and their impact on the development or responses to environmental stresses.

PTR measurement methodologies based on pulse SILAC metabolic labelling are now frequently used for unicellular organisms to small animals model [4] but are not compatible with autotroph species like plants. Hence, this is a great difficulty to shed a new light on the mechanisms regulating plant protein abundance. The Millar group (Uni. West. Australia) [5-7] had some success with a method based on suboptimal 15N metabolic labelling to define the Kd / Ks for a few hundreds of proteins [5]. Unfortunately, this methodology remains difficult to handle due to the complexity of the experimental design and the lack of integrated data processing tools that severely hampered its implementation.

 The main objectives of this project are to develop an alternative metabolic labelling strategy for large scale PTR measurement then, to apply it on maize to evaluate the impact of genetic variability [8] on plant proteostasis. We propose to setup a methodology based on low 15N metabolic labelling in plant combined with standard proteomics approach to determine proteins synthesis and degradation constants at the proteome level. This novel approach takes advantage of recent improvements in sensitivity and accuracy of the mass spectrometers to measure the peptide isotopic distribution [9]. Then, post-acquisition data-processing will take advantage of our locally developed proteomics tools [10, 11] to determine the Ks and Kd for the characterised proteoforms. This development and the proposed application will provide a novel element in multiomics investigations to better the regulation mechanisms of protein abundance and how they affect plant phenotypes.

REFERENCES

1. Orosa B, Ustun S, Calderon Villalobos LIA, Genschik P, Gibbs D, Holdsworth MJ, Isono E, Lois M, Trujillo M, Sadanandom A: Plant proteostasis - shaping the proteome: a research community aiming to understand molecular mechanisms that control protein abundance. New Phytol 2020, 227(4):1028-1033.

2. Belouah I, Nazaret C, Petriacq P, Prigent S, Benard C, Mengin V, Blein-Nicolas M, Denton AK, Balliau T, Auge S et al: Modeling Protein Destiny in Developing Fruit. Plant Physiol 2019, 180(3):1709-1724.

3. Chen Q, Shinozaki D, Luo J, Pottier M, Have M, Marmagne A, Reisdorf-Cren M, Chardon F, Thomine S, Yoshimoto K et al: Autophagy and Nutrients Management in Plants. Cells 2019, 8(11).

4. Beller NC, Hummon AB: Advances in stable isotope labeling: dynamic labeling for spatial and temporal proteomic analysis. Mol Omics 2022, 18(7):579-590.

5. Li L, Nelson CJ, Trosch J, Castleden I, Huang S, Millar AH: Protein Degradation Rate in Arabidopsis thaliana Leaf Growth and Development. Plant Cell 2017, 29(2):207-228.

6. Nelson CJ, Alexova R, Jacoby RP, Millar AH: Proteins with high turnover rate in barley leaves estimated by proteome analysis combined with in planta isotope labeling. Plant Physiol 2014, 166(1):91-108.

7. Li L, Nelson CJ, Solheim C, Whelan J, Millar AH: Determining degradation and synthesis rates of arabidopsis proteins using the kinetics of progressive 15N labeling of two-dimensional gel-separated protein spots. Mol Cell Proteomics 2012, 11(6):M111 010025.

8. Blein-Nicolas M, Negro SS, Balliau T, Welcker C, Cabrera-Bosquet L, Nicolas SD, Charcosset A, Zivy M: A systems genetics approach reveals environment-dependent associations between SNPs, protein coexpression, and drought-related traits in maize. Genome Res 2020, 30(11):1593-1604.

9. Senecaut N, Alves G, Weisser H, Lignieres L, Terrier S, Yang-Crosson L, Poulain P, Lelandais G, Yu YK, Camadro JM: Novel Insights into Quantitative Proteomics from an Innovative Bottom-Up Simple Light Isotope Metabolic (bSLIM) Labeling Data Processing Strategy. J Proteome Res 2021, 20(3):1476-1487.

10. Langella O, Valot B, Balliau T, Blein-Nicolas M, Bonhomme L, Zivy M: X!TandemPipeline: A Tool to Manage Sequence Redundancy for Protein Inference and Phosphosite Identification. J Proteome Res 2017, 16(2):494-503.

11. Valot B, Langella O, Nano E, Zivy M: MassChroQ: a versatile tool for mass spectrometry quantification. Proteomics 2011, 11(17):3572-3577.