Sira Echevarría-Zomeño

Evaluation of three different protocols of protein extraction for Arabidopsis thaliana leaf proteome analysis by two-dimensional electrophoresis.

This work was performed to compare three precipitation protocols of protein extraction for 2-DE proteomic analysis using Arabidopsis leaf tissue: TCA-acetone, phenol, and TCA-acetone-phenol. There were no statistically significant differences in protein yield between the three methods. Samples were subjected to 2-DE in the 5 to 8 pH and 14-80 kDa ranges. The TCA-acetone-phenol protocol provided the best results in terms of spot focusing, resolved spots, spot intensity, unique spots detected, and reproducibility. In all, 93 qualitative or quantitative statistically significant differential spots were found between the three protocols. The 2-DE map of TCA-acetone-phenol extracts presented more resolved spots above 40 kDa, with no pI-dependent differences observed between the three protocols. 54 spots were selected for trypsin digestion, and the peptides were analyzed by MALDI-TOF-TOF MS. After database search using peptide mass fingerprinting, and MS/MS combined search, 30 proteins were identified, the proteins from chloroplastic photosynthetic and carbohydrate metabolism being those most highly represented. From these data, we were able to conclude that each extraction protocol had its main features. Considering this, the workflow of any standard comparative proteomic experiment should include the optimization and adaptation of the protein extraction protocol to the plant tissue and to the particular objective pursued.

Changes in the protein profile of Quercus ilex leaves in response to drought stress and recovery.

To characterize the molecular response of holm oak to drought stress and its capacity to recover 9-month-old Quercus ilex seedlings were subjected to three treatments for a 14-d period: (i) continuous watering to field capacity (control plants, W), (ii) no irrigation (drought treatment, D), and (iii) no irrigation for 7d followed by a watering period of 7d (recovery treatment, R). In drought plants, leaf water potential decreased from -0.72 (day 0) to -0.99MPa (day 7), and -1.50MPa (day 14). Shoot relative water content decreased from 49.3% (day 0) to 47.7% (day 7) and 40.8% (day 14). Photosystem II quantum yield decreased from 0.80 (day 0) to 0.72 (day 7) and 0.73 (day 14). Plants subjected to water withholding for 7d reached, after a 7-d rewatering period, values similar to those of continuously irrigated control plants. Changes in the leaf protein pattern in response to drought and recovery treatments were analyzed by using a proteomic approach. Twenty-three different spots were observed when comparing the two-dimensional electrophoresis profile of control to both drought and recovered plants. From these, 14 proteins were identified from tryptic peptides tandem mass spectra by using the new Paragon algorithm present in the ProteinPilot software. The proteins identified belong to the photosynthesis, carbohydrate and nitrogen metabolism, and stress-related protein functional categories.

Proteomic analysis of Arabidopsis protein S-nitrosylation in response to inoculation with Pseudomonas syringae

Nitric oxide (NO) is a key signaling molecule in plants, being its biological effects mainly mediated through S-nitrosylation of cysteine thiols. Using the biotin switch method combined with mass spectrometry analysis we have identified 127 targets of S-nitrosylation in Arabidopsis cell suspension cultures and leaves challenged with virulent and avirulent isolates of Pseudomonas syringae pv. tomato. The NO targets are proteins associated with carbon, nitrogen, and sulpfur metabolism, photosynthesis, the cytoskeleton, stress-, pathogen- and redox-related and signaling proteins. Some proteins were previously identified in plants and mammals, while others (63%) represent novel targets of S-nitrosylation. Our data suggest that NO might be orchestrating the whole plant physiology, presumably through covalent modification of proteins.

Guidelines for reporting the use of gel image informatics in proteomics

655 1LGC, Teddington, Middlesex, UK. 2International Graduate School of Arts and Sciences, Yokohama City University, Tsurumi-ku, Yokohama, Kanagawa, Japan. 3Facultad de Farmacia, Universidad San Pablo-CEU, Campus Montepríncipe, Boadilla del Monte, Madrid, Spain. 4Bioproduct Research and Development, Lilly Research Laboratories, Lilly Technology Centre, Indianapolis, Indiana, USA. 5Department of Protein Analytical Chemistry, Genentech Inc., South San Francisco, California, USA. 6Pharmaceutical Sciences Research Division, King’s College London, London, UK. 7School of Biomedical Sciences, University of Ulster, Coleraine, Co. Londonderry, UK. 8Aalen University, Aalen, Germany. 9William Harvey Research Institute, Barts & The London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London, UK. 10Max-Planck-Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany. 11Lilly UK, Speke, Liverpool, UK. 12European Bioinformatics Institute, Hinxton, UK (chris.taylor@ebi.ac.uk).