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Resolving subcellular plant metabolism.

ABSTRACT: Plant cells are characterized by a high degree of compartmentalization and a diverse proteome and metabolome. Only a very limited number of studies has addressed combined subcellular proteomics and metabolomics which strongly limits biochemical and physiological interpretation of large-scale 'omics data. Our study presents a methodological combination of nonaqueous fractionation, shotgun proteomics, enzyme activities and metabolomics to reveal subcellular diurnal dynamics of plant metabolism. Subcellular marker protein sets were identified and enzymatically validated to resolve metabolism in a four-compartment model comprising chloroplasts, cytosol, vacuole and mitochondria. These marker sets are now available for future studies that aim to monitor subcellular metabolome and proteome dynamics. Comparing subcellular dynamics in wild type plants and HXK1-deficient gin2-1 mutants revealed a strong impact of HXK1 activity on metabolome dynamics in multiple compartments. Glucose accumulation in the cytosol of gin2-1 was accompanied by diminished vacuolar glucose levels. Subcellular dynamics of pyruvate, succinate and fumarate amounts were significantly affected in gin2-1 and coincided with differential mitochondrial proteome dynamics. Lowered mitochondrial glycine and serine amounts in gin2-1 together with reduced abundance of photorespiratory proteins indicated an effect of the gin2-1 mutation on photorespiratory capacity. Our findings highlight the necessity to resolve plant metabolism to a subcellular level to provide a causal relationship between metabolites, proteins and metabolic pathway regulation.

SUBMITTER: Furtauer L

PROVIDER: S-EPMC8653894 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:One of the fundamental goals in proteomics and cell biology is to identify the functions of proteins in various cellular organelles and pathways. Information of subcellular locations of proteins can provide useful insights for revealing their functions and understanding how they interact with each other in cellular network systems. Most of the existing methods in predicting plant protein subcellular localization can only cover three or four location sites, and none of them can be used to deal with multiplex plant proteins that can simultaneously exist at two, or move between, two or more different location sits. Actually, such multiplex proteins might have special biological functions worthy of particular notice. The present study was devoted to improve the existing plant protein subcellular location predictors from the aforementioned two aspects. A new predictor called "Plant-mPLoc" is developed by integrating the gene ontology information, functional domain information, and sequential evolutionary information through three different modes of pseudo amino acid composition. It can be used to identify plant proteins among the following 12 location sites: (1) cell membrane, (2) cell wall, (3) chloroplast, (4) cytoplasm, (5) endoplasmic reticulum, (6) extracellular, (7) Golgi apparatus, (8) mitochondrion, (9) nucleus, (10) peroxisome, (11) plastid, and (12) vacuole. Compared with the existing methods for predicting plant protein subcellular localization, the new predictor is much more powerful and flexible. Particularly, it also has the capacity to deal with multiple-location proteins, which is beyond the reach of any existing predictors specialized for identifying plant protein subcellular localization. As a user-friendly web-server, Plant-mPLoc is freely accessible at http://www.csbio.sjtu.edu.cn/bioinf/plant-multi/. Moreover, for the convenience of the vast majority of experimental scientists, a step-by-step guide is provided on how to use the web-server to get the desired results. It is anticipated that the Plant-mPLoc predictor as presented in this paper will become a very useful tool in plant science as well as all the relevant areas.

Project description:Although compartmentation is a key feature of eukaryotic cells, biological research is frequently limited by methods allowing for the comprehensive subcellular resolution of the metabolome. It has been widely accepted that such a resolution would be necessary in order to approximate cellular biochemistry and metabolic regulation, yet technical challenges still limit both the reproducible subcellular fractionation and the sample throughput being necessary for a statistically robust analysis. Here, we present a method and a detailed protocol which is based on the non-aqueous fractionation technique enabling the assignment of metabolites to their subcellular localization. The presented benchtop method aims at unraveling subcellular metabolome dynamics in a precise and statistically robust manner using a relatively small amount of tissue material. The method is based on the separation of cellular fractions via density gradients consisting of organic, non-aqueous solvents. By determining the relative distribution of compartment-specific marker enzymes together with metabolite profiles over the density gradient it is possible to estimate compartment-specific metabolite concentrations by correlation. To support this correlation analysis, a spreadsheet is provided executing a calculation algorithm to determine the distribution of metabolites over subcellular compartments. The calculation algorithm performs correlation of marker enzyme activity and metabolite abundance accounting for technical errors, reproducibility and the resulting error propagation. The method was developed, tested and validated in three natural accessions of Arabidopsis thaliana showing different ability to acclimate to low temperature. Particularly, amino acids were strongly shuffled between subcellular compartments in a cold-sensitive accession while a cold-tolerant accession was characterized by a stable subcellular metabolic homeostasis. Finally, we conclude that subcellular metabolome analysis is essential to unambiguously unravel regulatory strategies being involved in plant-environment interactions.

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Resolving subcellular plant metabolism.

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