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).

Liver proteomics of gilthead sea bream (Sparus aurata) exposed to cold stress.

The gilthead sea bream (Sparus aurata, L.) is very sensitive to low temperatures, which induce fasting and reduced growth performances. There is a strong interest in understanding the impact of cold on fish metabolism to foster the development and optimization of specific aquaculture practices for the winter period. In this study, an 8 week feeding trial was carried out on gilthead sea bream juveniles reared in a Recirculated Aquaculture System (RAS) by applying a temperature ramp in two phases of four weeks each: a cooling phase from 18 °C to 11 °C and a cold maintenance phase at 11 °C. Liver protein profiles were evaluated with a shotgun proteomics workflow based on filter-aided sample preparation (FASP) and liquid chromatography-mass spectrometry (LC-ESI-Q-TOF MS/MS) followed by label-free differential analysis. Along the whole trial, sea breams underwent several changes in liver protein abundance. These occurred mostly during the cooling phase when catabolic processes were mainly observed, including protein and lipid degradation, together with a reduction in protein synthesis and amino acid metabolism. A decrease in protein mediators of oxidative stress protection was also seen. Liver protein profiles changed less during cold maintenance, but pathways such as the methionine cycle and sugar metabolism were significantly affected. These results provide novel insights on the dynamics and extent of the metabolic shift occurring in sea bream liver with decreasing water temperature, supporting future studies on temperature-adapted feed formulations. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD011059.

Development and validation of a Partial Least Squares-Discriminant Analysis (PLS-DA) model based on the determination of ethyl glucuronide (EtG) and fatty acid ethyl esters (FAEEs) in hair for the diagnosis of chronic alcohol abuse.

The chronic intake of an excessive amount of alcohol is currently ascertained by determining the concentration of direct alcohol metabolites in the hair samples of the alleged abusers, including ethyl glucuronide (EtG) and, less frequently, fatty acid ethyl esters (FAEEs). Indirect blood biomarkers of alcohol abuse are still determined to support hair EtG results and diagnose a consequent liver impairment. In the present study, the supporting role of hair FAEEs is compared with indirect blood biomarkers with respect to the contexts in which hair EtG interpretation is uncertain. Receiver Operating Characteristics (ROC) curves and multivariate Principal Component Analysis (PCA) demonstrated much stronger correlation of EtG results with FAEEs than with any single indirect biomarker or their combinations. Partial Least Squares Discriminant Analysis (PLS-DA) models based on hair EtG and FAEEs were developed to maximize the biomarkers information content on a multivariate background. The final PLS-DA model yielded 100% correct classification on a training/evaluation dataset of 155 subjects, including both chronic alcohol abusers and social drinkers. Then, the PLS-DA model was validated on an external dataset of 81 individual providing optimal discrimination ability between chronic alcohol abusers and social drinkers, in terms of specificity and sensitivity. The PLS-DA scores obtained for each subject, with respect to the PLS-DA model threshold that separates the probabilistic distributions for the two classes, furnished a likelihood ratio value, which in turn conveys the strength of the experimental data support to the classification decision, within a Bayesian logic. Typical boundary real cases from daily work are discussed, too.