Proteomic data from leaves of twenty-four sunflower genotypes underwater deficit

This article describes how the proteomic data were produced on sunflower plants subjected to water deficit. Twenty-four sunflower genotypes were selected to represent genetic diversity within cultivated sunflower. They included both inbred lines and their hybrids Water deficit was applied to plants in pots at the vegetative stage using the high-throughput phenotyping platform Heliaphen. Here, we provide proteomic data from sunflower leaves corresponding to the identification of 3062 proteins and the quantification of 1211 of them in these 24 genotypes grown in two watering conditions. These data differentiate both treatment and the different genotypes and constitute a valuable resource to the community to study adaptation of crops to drought and the molecular basis of heterosis.


Value of the Data
-Drought is an important issue for crop yield that will get worse in the context of climate change. Sunflower, the fourth oilseed crop in the world, is particularly impacted by this stress.
-Heterosis is a phenomenon commonly used to improve yield. To analyse its impact on the response to environmental constraints and particularly to drought, twenty-four genotypes of cultivated sunflower comprising four maintainer lines, four restorer lines and their 16 corresponding hybrids were subjected to two treatments (Well-Watered or Water-Deficit).
-Plants were managed on the outdoor Heliaphen high-throughput phenotyping platform.
Leaf samples were collected at the end of the treatments for proteomic analysis.
-This dataset provides identification data for 3062 proteins of sunflower leaves and quantification data for 1211 of them in 24 genotypes grown in two conditions.
-It provides unique access to the genetic variability of the sunflower's molecular response to water deficit. Climate change is a current issue of major concern because of its potential effects on biodiversity and the agricultural sector. Better understanding the adaptation of plants to this recent phenomenon is therefore a major interest for crop science and society. Helianthus annuus L., the domesticated sunflower, is the fourth most important oilseed crop in the world [3] and is promising for agriculture adaptation because it can maintain stable yields across a wide variety of environmental conditions, especially during drought stress [1]. It constitutes an archetypical systems biology model as drought stress response involves many molecular pathways and subsequent physiological processes.

Data
In this data article, we are sharing the proteomic data of 24 genotypes of sunflower grown in two environmental conditions in the outdoor Heliaphen platform. These datasets are part of a larger project that integrates other omics data at different biological levels (like ecophysiological data described in [2]) and could be associated to diverse studies.
The raw data associated with this article (data from the mass spectrometer in mzXML format as well as annotated spectra and quantitative data can be found at the following link https://doi.org/10.15454/TW59-P718 or directly at http://moulon.inra.fr/protic/sunrise. Protein identification data are provided as a supplemental file and can also be found at http://moulon.inra.fr/protic/sunrise . Parameters used for mass spectrometry analyses are shown in Table 1.

Experimental Design, plant material and growth conditions
The experiment was performed from May to July 2013 in the outdoor Heliaphen conditions done in triplicate. Each pot was adequately fertilized and irrigated as in [5] before the beginning of the water deficit application. Pots were saturated with water 35 DAG and after excessive water was drained (~ for two hours), pots were weighed to obtain the full soil water retention mass. Then, irrigation was stopped at 38 DAG (~20-leaf stage corresponding to bud formation phase (stage R1 or R3; [6]) for WD plants (as in work by [4]). All pots were covered with a 3 mm layer of polystyrene sheets at the collar level to limit soil evaporation. Soil evaporation was estimated according to [7]. Both WW and WD plants were weighed three or four times per day by the Heliaphen robot to estimate transpiration [4]. WW plants were re- At harvest, leaves for molecular analysis were cuts without their petiole and immediately frozen in liquid nitrogen from 11 h to 13 h. On sunflower, the mature leaf developmental stage corresponds to a dark green leaf, assumed to be experiencing its highest photosynthetic rate and having recently reached its maximum size [8]. More precisely, we defined the mature leaf as positioned at three-fifths (0.60 ± 0.04 SD) of the plant (leaf 20 ± 2.3 SD) [2]. The selected leaf harvested for the molecular analysis was the n+1 leaves of the mature.

Protein extraction
Leaf proteins were extracted using the TCA-acetone protocol described in [9]. Protein digestion was performed according to the liquid digestion protocol described in [10].

Identification of proteins by LC-MS/MS
Protein identification was performed by searching the Heliagen database using genome HanXRQv1 [1] with the X!Tandem search engine [12]. Data filtering and protein inference were performed by using X!TandemPipeline 3.3.4 [13]. Trypsin digestion was declared with respectively one and five possible miss cleavages in the first and refine pass respectively. Only proteins identified with at least two different peptides in the same sample were considered. [14]

Bioinformatics annotation of proteins and quantification
Quantification was operated using the MassChroQ software [14]. Only proteins quantified with at least 2 specific peptides that were present in at least 90% of the samples