Metabolic Labeling of Mammalian Organisms with Stable Isotopes for Quantitative Proteomic Analysis

2004 ◽  
Vol 76 (17) ◽  
pp. 4951-4959 ◽  
Author(s):  
Christine C. Wu ◽  
Michael J. MacCoss ◽  
Kathryn E. Howell ◽  
Dwight E. Matthews ◽  
John R. Yates
Keyword(s):  
Stable Isotopes ◽  
Proteomic Analysis ◽  
Metabolic Labeling ◽  
Quantitative Proteomic Analysis

scholarly journals Quantitative Proteomic Analysis of the Interaction Between the Endophytic Plant-Growth-Promoting Bacterium Gluconacetobacter diazotrophicus and Sugarcane

2011 ◽  
Vol 24 (5) ◽  
pp. 562-576 ◽  
Author(s):  
Letícia M. S. Lery ◽  
Adriana S. Hemerly ◽  
Eduardo M. Nogueira ◽  
Wanda M. A. von Krüger ◽  
Paulo M. Bisch
Keyword(s):  
Plant Growth ◽  
Proteomic Analysis ◽  
Interaction Model ◽  
Metabolic Labeling ◽  
Gluconacetobacter Diazotrophicus ◽  
Quantitative Proteomic Analysis ◽  
Signaling Systems ◽  
Plant Growth Promoting ◽  
Growth Promoting ◽  
Molecular Aspects

Gluconacetobacter diazotrophicus is a plant-growth-promoting bacterium that colonizes sugarcane. In order to investigate molecular aspects of the G. diazotrophicus–sugarcane interaction, we performed a quantitative mass spectrometry-based proteomic analysis by 15N metabolic labeling of bacteria, root samples, and co-cultures. Overall, more than 400 proteins were analyzed and 78 were differentially expressed between the plant–bacterium interaction model and control cultures. A comparative analysis of the G. diazotrophicus in interaction with two distinct genotypes of sugarcane, SP70-1143 and Chunee, revealed proteins with fundamental roles in cellular recognition. G. diazotrophicus presented proteins involved in adaptation to atypical conditions and signaling systems during the interaction with both genotypes. However, SP70-1143 and Chunee, sugarcane genotypes with high and low contribution of biological nitrogen fixation, showed divergent responses in contact with G. diazotrophicus. The SP70-1143 genotype overexpressed proteins from signaling cascades and one from a lipid metabolism pathway, whereas Chunee differentially synthesized proteins involved in chromatin remodeling and protein degradation pathways. In addition, we have identified 30 bacterial proteins in the roots of the plant samples; from those, nine were specifically induced by plant signals. This is the first quantitative proteomic analysis of a bacterium–plant interaction, which generated insights into early signaling of the G. diazotrophicus–sugarcane interaction.

iTRAQ-based quantitative proteomic analysis of silkworm infected with Beauveria bassiana

2021 ◽  
Vol 135 ◽  
pp. 204-216
Author(s):  
Dingding Lü ◽  
Ping Xu ◽  
Chengxiang Hou ◽  
Ruilin Li ◽  
Congwu Hu ◽  
...  
Keyword(s):  
Beauveria Bassiana ◽  
Proteomic Analysis ◽  
Quantitative Proteomic Analysis

scholarly journals Label-free quantitative proteomic analysis of the inhibition effect of Lactobacillus rhamnosus GG on Escherichia coli biofilm formation in co-culture

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Huiyi Song ◽  
Ni Lou ◽  
Jianjun Liu ◽  
Hong Xiang ◽  
Dong Shang
Keyword(s):  
Escherichia Coli ◽  
Proteomic Analysis ◽  
Biofilm Formation ◽  
Lactobacillus Rhamnosus ◽  
Differentially Expressed ◽  
Differentially Expressed Proteins ◽  
Label Free ◽  
Quantitative Proteomic Analysis ◽  
E Coli ◽  
Protein Ubiquitination

Abstract Background Escherichia coli (E. coli) is the principal pathogen that causes biofilm formation. Biofilms are associated with infectious diseases and antibiotic resistance. This study employed proteomic analysis to identify differentially expressed proteins after coculture of E. coli with Lactobacillus rhamnosus GG (LGG) microcapsules. Methods To explore the relevant protein abundance changes after E. coli and LGG coculture, label-free quantitative proteomic analysis and qRT-PCR were applied to E. coli and LGG microcapsule groups before and after coculture, respectively. Results The proteomic analysis characterised a total of 1655 proteins in E. coli K12MG1655 and 1431 proteins in the LGG. After coculture treatment, there were 262 differentially expressed proteins in E. coli and 291 in LGG. Gene ontology analysis showed that the differentially expressed proteins were mainly related to cellular metabolism, the stress response, transcription and the cell membrane. A protein interaction network and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway analysis indicated that the differentiated proteins were mainly involved in the protein ubiquitination pathway and mitochondrial dysfunction. Conclusions These findings indicated that LGG microcapsules may inhibit E. coli biofilm formation by disrupting metabolic processes, particularly in relation to energy metabolism and stimulus responses, both of which are critical for the growth of LGG. Together, these findings increase our understanding of the interactions between bacteria under coculture conditions.

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