Dataset Information

Arabidopsis SECstroma - Megadalton complexes in the chloroplast stroma of Arabidopsis thaliana characterized by size exclusion chromatography, mass spectrometry, and hierarchical clustering

ABSTRACT: Native SEC of Arabidopsis chloroplast stroma with BufferA elution, High MWt Fraction1 SDS-PAGE Band1 Tech Rep2, in-gel trypsin digestion LTQ Orbitrap

INSTRUMENT(S): LTQ Orbitrap, instrument model

ORGANISM(S): Arabidopsis Thaliana (mouse-ear Cress)

SUBMITTER: Paul Dominic Olinares

PROVIDER: PRD000197 | Pride | 2010-04-29

REPOSITORIES: Pride

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Dataset's files

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			Action	DRS
	PRIDE_Exp_Complete_Ac_11459.pride.mgf.gz	Mgf
	PRIDE_Exp_Complete_Ac_11459.xml.gz	Xml
	PRIDE_Exp_Complete_Ac_11460.pride.mgf.gz	Mgf
	PRIDE_Exp_Complete_Ac_11460.xml.gz	Xml
	PRIDE_Exp_Complete_Ac_11461.pride.mgf.gz	Mgf

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Megadalton complexes in the chloroplast stroma of Arabidopsis thaliana characterized by size exclusion chromatography, mass spectrometry, and hierarchical clustering.

Olinares Paul Dominic B PD Ponnala Lalit L van Wijk Klaas J KJ

Molecular & cellular proteomics : MCP 20100426 7

To characterize MDa-sized macromolecular chloroplast stroma protein assemblies and to extend coverage of the chloroplast stroma proteome, we fractionated soluble chloroplast stroma in the non-denatured state by size exclusion chromatography with a size separation range up to approximately 5 MDa. To maximize protein complex stability and resolution of megadalton complexes, ionic strength and composition were optimized. Subsequent high accuracy tandem mass spectrometry analysis (LTQ-Orbitrap) iden ...[more]

PMID: 20423899

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Project description:To characterize MDa-sized macromolecular chloroplast stroma protein assemblies and to extend coverage of the chloroplast stroma proteome, we fractionated soluble chloroplast stroma in the non-denatured state by size exclusion chromatography with a size separation range up to approximately 5 MDa. To maximize protein complex stability and resolution of megadalton complexes, ionic strength and composition were optimized. Subsequent high accuracy tandem mass spectrometry analysis (LTQ-Orbitrap) identified 1081 proteins across the complete native mass range. Protein complexes and assembly states above 0.8 MDa were resolved using hierarchical clustering, and protein heat maps were generated from normalized protein spectral counts for each of the size exclusion chromatography fractions; this complemented previous analysis of stromal complexes up to 0.8 MDa (Peltier, J. B., Cai, Y., Sun, Q., Zabrouskov, V., Giacomelli, L., Rudella, A., Ytterberg, A. J., Rutschow, H., and van Wijk, K. J. (2006) The oligomeric stromal proteome of Arabidopsis thaliana chloroplasts. Mol. Cell. Proteomics 5, 114-133). This combined experimental and bioinformatics analyses resolved chloroplast ribosomes in different assembly and functional states (e.g. 30, 50, and 70 S), which enabled the identification of plastid homologues of prokaryotic ribosome assembly factors as well as proteins involved in co-translational modifications, targeting, and folding. The roles of these ribosome-associating proteins will be discussed. Known RNA splice factors (e.g. CAF1/WTF1/RNC1) as well as uncharacterized proteins with RNA-binding domains (pentatricopeptide repeat, RNA recognition motif, and chloroplast ribosome maturation), RNases, and DEAD box helicases were found in various sized complexes. Chloroplast DNA (>3 MDa) was found in association with the complete heteromeric plastid-encoded DNA polymerase complex, and a dozen other DNA-binding proteins, e.g. DNA gyrase, topoisomerase, and various DNA repair enzymes. The heteromeric >or=5-MDa pyruvate dehydrogenase complex and the 0.8-1-MDa acetyl-CoA carboxylase complex associated with uncharacterized biotin carboxyl carrier domain proteins constitute the entry point to fatty acid metabolism in leaves; we suggest that their large size relates to the need for metabolic channeling. Protein annotations and identification data are available through the Plant Proteomics Database, and mass spectrometry data are available through Proteomics Identifications database.

Project description:Extracellular vesicles (EVs) are involved in a wide range of physiological and pathological processes by shuttling material out of and between cells. Tissue EVs may thus lend insights into disease mechanisms and also betray disease when released into easily accessed biological fluids. Since brain-derived EVs (bdEVs) and their cargo may serve as biomarkers of neurodegenerative diseases, we evaluated modifications to a published, rigorous protocol for separation of EVs from brain tissue and studied effects of processing variables on quantitative and qualitative outcomes. To this end, size exclusion chromatography (SEC) and sucrose density gradient ultracentrifugation were compared as final separation steps in protocols involving stepped ultracentrifugation. bdEVs were separated from brain tissues of human, macaque. Effects of post-mortem interval (PMI) before final bdEV separation were probed. MISEV2018-compliant EV characterization was performed, and both small RNA and protein profiling were done. We conclude that the modified, SEC-employing protocol achieves EV separation efficiency roughly similar to a protocol using gradient density ultracentrifugation, while decreasing operator time and, potentially, variability. The protocol appears to yield bdEVs of higher purity for human tissues compared with macaque, suggesting opportunities for optimization. The interval between death/tissue storage/processing and final bdEV separation can also affect bdEV populations and composition and should thus be recorded for rigorous reporting. Different populations of EVs obtained through the modified method reported herein display characteristic RNA and protein content that hint at biomarker potential. To conclude, this study finds that the automatable and increasingly employed technique of SEC can be applied to tissue EV separation, and also reveals more about the importance of species-specific and technical considerations when working with tissue EVs. These results are expected to enhance the use of bdEVs in revealing and understanding brain disease.