Liver proteome dataset of Sparus aurata exposed to low temperatures

We report the proteomic dataset of livers from Sparus aurata exposed to low temperature during growth. Gilthead sea bream juveniles were reared in Recirculating Aquaculture Systems (RAS) and exposed to a temperature ramp made of two phases of four weeks each: a Cooling phase from 18 °C (t0) to 11 °C (t1) and a Cold Maintenance phase at 11 °C (t1-t2) in a 8 week feeding trial. At the end of the experiment, sea bream livers were collected and analyzed with a shotgun proteomics approach based on filter-aided sample preparation followed by tandem mass spectrometry, peptide identification carried out using Sequest-HT as search engine within the Proteome Discoverer informatic platform, and label-free differential analysis. The mass spectrometry data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD011059 (Vizcaíno et al., 2016; Deutsch et al., 2017; Perez-Riverol et al., 2016). The dataset described here is also related to the research article entitled “Liver proteomics of gilthead sea bream (Sparus aurata) exposed to cold stress” (Ghisaura et al., 2019).


Data
Sea bream livers exposed to low temperatures (Cooling phase and Cold maintenance phase) were characterized with a shotgun proteomic approach. A summary of protein identifications obtained in all samples is provided in Table 1. All protein identifications obtained with the Proteome Discoverer software in gilthead seabream livers exposed to three temperature phases (t0, t1, t2) are listed in Supplementary Table 1.
The differential analysis was carried out with a label-free approach by comparing all different groups according to temperature variations: Cooling phase (t0-t1), Cold Maintenance phase (t1-t2) and Overall changes (t0-t2). The differential proteins passing the significance thresholds (R NSAF > 0.5 or < À0.5; P value < 0.05; FDR < 0.1) are summarized in detail in the related research article [4]. Detailed protein identification and abundance data for the three comparisons (t0 vs t1; t1 vs t2 and t0 vs t2) are provided in Supplementary Table 1. 2. Experimental design, materials, and methods

Sparus aurata liver samples
Gilthead sea bream specimens with an average weight of 82.0 ± 4.5 g were selected for the experimental feeding trial. A total of 60 juveniles were transferred in three 550 L tanks and an Specifications Title: Liver proteomics of gilthead sea bream (Sparus aurata) exposed to cold stress; Journal: Journal of Thermal Biology Value of the data Detailed proteomic dataset of gilthead sea bream livers in fish exposed to low temperature during growth; Differential protein abundances between the two temperature phases: Cooling phase (t0-t1, from 18 C to 11 C) and Cold Maintenance phase (t1-t2, at 11 C) are useful to understand the dynamics and metabolic shifts occurring in sea bream liver with decreasing water temperature; Shotgun proteomics dataset improves previous data on fish hepatic metabolism during cold exposure; The proteomic dataset might be advantageous to other research groups working on the development of feeds designed to compensate the thermal stress encountered by fish in offshore farming conditions. acclimation phase of two weeks from 20 C to 18 C was carried out. Water temperature was gradually lowered as described in Ghisaura et al., 2019 [4]. During the trial, fish were fed with an experimental feed formulation (Aller Aqua, Christiansfeld, Denmark) by hand, once a day. After fish anesthetization with 1,1,1-trichloro-2-methylpropan-2-ol (2% in marine water) and transfer in a mixture of marine water and ice, liver tissues were collected at each time point (t0, t1, t2). The complete procedure of tissue excision and storage is described by Melis et al., 2017 [5].

Protein extraction and digestion
Liver tissues were subjected to protein extraction and quantification according to Ghisaura et al., 2016 [6]. All protein extracts were then subjected to on-filter reduction, alkylation, and trypsin digestion according to the filter-aided sample preparation (FASP) protocol [7], with some modifications [8]. Peptide mixture concentration was estimated by using the BCA protein assay kit (Thermo Scientific -Rockford, IL).

LC-MS/MS analysis
A Q-TOF hybrid mass spectrometer with a nano lock Z spray source, coupled with a NanoAcquity chromatography system (Waters) on-line, was used for LCeMS/MS analyses as described in Pagnozzi et al., 2014 [9]. LC-MS/MS procedures are fully described in Ghisaura et al., 2019 [4].

Data analysis
Proteome Discoverer software (version 1.4.0.288; Thermo Scientific) was used to analyze the peak lists from the Q-TOF instrument, after conversion into an MGF file. The workflow was as described in Ghisaura et al., 2019 [4]. Gene ontology and protein annotations were retrieved from UniProtKB (http:// www.uniprot.org). The uncharacterized sequences were identified by homology through blasting on NCBI as another non-redundant database (http://blast.ncbi.nlm.nih.gov/Blast.cgi).
Differential protein abundances of different functional categories (Cooling phase; Cold maintenance phase; and Overall changes) were estimated by the Normalized Spectral Abundance Factor (NSAF) according to Zybailov et al., 2006 [10]. The significance threshold RNSAF >0.5 or < À0.5 was applied for analysis. To evaluate the statistical significance of differential protein abundance between logarithmized (normally distributed) NSAF values, a student's t-test (two-sample comparison, p < 0.05) was applied. Logarithmized NSAF values were furthermore corrected by using a false discovery rate (FDR) as a multiple hypothesis testing, with FDR < 0.1 as a threshold limit. The dataset was then deposited in the ProteomeXchange Consortium via the PRIDE partner repository (identifier PXD011059) [1,2,3,11].