Arabidopsis thaliana Early Foliar Proteome Response to Root Exposure to the Rhizobacterium Pseudomonas simiae WCS417.

Pseudomonas simiae WCS417 is a plant growth-promoting rhizobacterium that improves plant health and development. In this study, we investigate the early leaf responses of Arabidopsis thaliana to WCS417 exposure and the possible involvement of formate dehydrogenase (FDH) in such responses. In vitro-grown A. thaliana seedlings expressing an FDH::GUS reporter show a significant increase in FDH promoter activity in their roots and shoots after 7 days of indirect exposure (without contact) to WCS417. After root exposure to WCS417, the leaves of FDH::GUS plants grown in the soil also show an increased FDH promoter activity in hydathodes. To elucidate early foliar responses to WCS417 as well as FDH involvement, the roots of A. thaliana wild-type Col and atfdh1-5 knock-out mutant plants grown in soil were exposed to WCS417, and proteins from rosette leaves were subjected to proteomic analysis. The results reveal that chloroplasts, in particular several components of the photosystems PSI and PSII, as well as members of the glutathione S-transferase family, are among the early targets of the metabolic changes induced by WCS417. Taken together, the alterations in the foliar proteome, as observed in the atfdh1-5 mutant, especially after exposure to WCS417 and involving stress-responsive genes, suggest that FDH is a node in the early events triggered by the interactions between A. thaliana and the rhizobacterium WCS417. [Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Availability of MudPIT data for classification of biological samples.

BACKGROUND: Mass spectrometry is an important analytical tool for clinical proteomics. Primarily employed for biomarker discovery, it is increasingly used for developing methods which may help to provide unambiguous diagnosis of biological samples. In this context, we investigated the classification of phenotypes by applying support vector machine (SVM) on experimental data obtained by MudPIT approach. In particular, we compared the performance capabilities of SVM by using two independent collection of complex samples and different data-types, such as mass spectra (m/z), peptides and proteins. RESULTS: Globally, protein and peptide data allowed a better discriminant informative content than experimental mass spectra (overall accuracy higher than 87% in both collection 1 and 2). These results indicate that sequencing of peptides and proteins reduces the experimental noise affecting the raw mass spectra, and allows the extraction of more informative features available for the effective classification of samples. In addition, proteins and peptides features selected by SVM matched for 80% with the differentially expressed proteins identified by the MAProMa software. CONCLUSIONS: These findings confirm the availability of the most label-free quantitative methods based on processing of spectral count and SEQUEST-based SCORE values. On the other hand, it stresses the usefulness of MudPIT data for a correct grouping of sample phenotypes, by applying both supervised and unsupervised learning algorithms. This capacity permit the evaluation of actual samples and it is a good starting point to translate proteomic methodology to clinical application.