Dataset Information

Screening and Identification of Candidate GUN1-Interacting Proteins in Arabidopsis thaliana.

ABSTRACT: Chloroplasts are semi-autonomous organelles governed by the precise coordination between the genomes of their own and the nucleus for functioning correctly in response to developmental and environmental cues. Under stressed conditions, various plastid-to-nucleus retrograde signals are generated to regulate the expression of a large number of nuclear genes for acclimation. Among these retrograde signaling pathways, the chloroplast protein GENOMES UNCOUPLED 1 (GUN1) is the first component identified. However, in addition to integrating aberrant physiological signals when chloroplasts are challenged by stresses such as photooxidative damage or the inhibition of plastid gene expression, GUN1 was also found to regulate other developmental processes such as flowering. Several partner proteins have been found to interact with GUN1 and facilitate its different regulatory functions. In this study, we report 15 possible interacting proteins identified through yeast two-hybrid (Y2H) screening, among which 11 showed positive interactions by pair-wise Y2H assay. Through the bimolecular fluorescence complementation assay in Arabidopsis protoplasts, two candidate proteins with chloroplast localization, DJC31 and HCF145, were confirmed to interact with GUN1 in planta. Genes for these GUN1-interacting proteins showed different fluctuations in the WT and gun1 mutant under norflurazon and lincomycin treatments. Our results provide novel clues for a better understanding of molecular mechanisms underlying GUN1-mediated regulations.

SUBMITTER: Wang L

PROVIDER: S-EPMC8583188 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Nitric oxide (NO) is an important signaling molecule that regulates many physiological processes in plants. One of the most important regulatory mechanisms of NO is S-nitrosylation-the covalent attachment of NO to cysteine residues. Although the involvement of cysteine S-nitrosylation in the regulation of protein functions is well established, its substrate specificity remains unknown. Identification of candidates for S-nitrosylation and their target cysteine residues is fundamental for studying the molecular mechanisms and regulatory roles of S-nitrosylation in plants. Several experimental methods that are based on the biotin switch have been developed to identify target proteins for S-nitrosylation. However, these methods have their limits. Thus, computational methods are attracting considerable attention for the identification of modification sites in proteins. Using GPS-SNO version 1.0, a recently developed S-nitrosylation site-prediction program, a set of 16,610 candidate proteins for S-nitrosylation containing 31,900 S-nitrosylation sites was isolated from the entire Arabidopsis proteome using the medium threshold. In the compartments "chloroplast," "CUL4-RING ubiquitin ligase complex," and "membrane" more than 70% of the proteins were identified as candidates for S-nitrosylation. The high number of identified candidates in the proteome reflects the importance of redox signaling in these compartments. An analysis of the functional distribution of the predicted candidates showed that proteins involved in signaling processes exhibited the highest prediction rate. In a set of 46 proteins, where 53 putative S-nitrosylation sites were already experimentally determined, the GPS-SNO program predicted 60 S-nitrosylation sites, but only 11 overlap with the results of the experimental approach. In general, a computer-assisted method for the prediction of targets for S-nitrosylation is a very good tool; however, further development, such as including the three dimensional structure of proteins in such analyses, would improve the identification of S-nitrosylation sites.

Project description:Knowledge of protein subcellular localization assists in the elucidation of protein function and understanding of different biological mechanisms that occur at discrete subcellular niches. Organelle-centric proteomics enables localization of thousands of proteins simultaneously. Although such techniques have successfully allowed organelle protein catalogues to be achieved, they rely on the purification or significant enrichment of the organelle of interest, which is not achievable for many organelles. Incomplete separation of organelles leads to false discoveries, with erroneous assignments. Proteomics methods that measure the distribution patterns of specific organelle markers along density gradients are able to assign proteins of unknown localization based on comigration with known organelle markers, without the need for organelle purification. These methods are greatly enhanced when coupled to sophisticated computational tools. Here we apply and compare multiple approaches to establish a high-confidence data set of Arabidopsis root tissue trans-Golgi network (TGN) proteins. The method employed involves immunoisolations of the TGN, coupled to probability-based organelle proteomics techniques. Specifically, the technique known as LOPIT (localization of organelle protein by isotope tagging), couples density centrifugation with quantitative mass-spectometry-based proteomics using isobaric labeling and targeted methods with semisupervised machine learning methods. We demonstrate that while the immunoisolation method gives rise to a significant data set, the approach is unable to distinguish cargo proteins and persistent contaminants from full-time residents of the TGN. The LOPIT approach, however, returns information about many subcellular niches simultaneously and the steady-state location of proteins. Importantly, therefore, it is able to dissect proteins present in more than one organelle and cargo proteins en route to other cellular destinations from proteins whose steady-state location favors the TGN. Using this approach, we present a robust list of Arabidopsis TGN proteins.

Project description:BackgroundMYC2, a basic helix-loop-helix (bHLH) domain-containing transcription factor, participates in the jasmonate (JA) signaling pathway and is involved in the modulation of diverse JA functions. However, a comprehensive list of MYC2-dependent JA-responsive proteins has yet to be defined.ResultsIn this paper, we report the comparative proteomics of wild-type (WT) plants and jin1-9, a MYC2 mutant plant, in response to methyl jasmonate (MeJA) treatment. Proteins from mock/MeJA-treated jin1-9 and WT samples were extracted and separated by two-dimensional gel electrophoresis. Twenty-seven JA-mediated proteins demonstrated differential expression modulated by MYC2. We observed that MYC2 negatively regulates the accumulation of JA-dependent indolic glucosinolate-related proteins and exhibits opposite effects on the biosynthetic enzymes involved aliphatic glucosinolate pathways. In addition, proteins involved in the tricarboxylic acid cycle and a majority of the MeJA-inducible proteins that are involved in multiple protective systems against oxidative stress were reduced in jin1-9/myc2 sample compared to the WT sample. These results support a positive role for MYC2 in regulating JA-mediated carbohydrate metabolism and oxidative stress tolerance.ConclusionsWe have identified MYC2-dependent jasmonate-regulated proteins in Arabidopsis thaliana by performing two-dimensional gel electrophoresis and MALDI-TOF/TOF MS analysis. The observed pattern of protein expression suggests that MYC2 has opposite effects on the biosynthetic enzymes of indolic and aliphatic glucosinolate pathways and positively regulates JA-mediated carbohydrate metabolism and oxidative stress tolerance-related proteins. Furthermore, it is very interesting to note that MYC2 plays opposite roles in the modulation of a subset of JA-regulated photosynthetic proteins during short-term and long-term JA signaling. This study will enhance our understanding of the function of MYC2 in JA signaling in Arabidopsis thaliana.

Dataset Information

Screening and Identification of Candidate GUN1-Interacting Proteins in Arabidopsis thaliana.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets