Protein changes as robust signatures of fish chronic stress: a proteomics approach to fish welfare research

Abstract

Background: Aquaculture is a fast-growing industry and therefore welfare and environmental impact have become of utmost importance. Preventing stress associated to common aquaculture practices and optimizing the fish stress response by quantification of the stress level, are important steps towards the improvement of welfare standards. Stress is characterized by a cascade of physiological responses that, in-turn, induce further changes at the whole-animal level. These can either increase fitness or impair welfare. Nevertheless, monitorization of this dynamic process has, up until now, relied on indicators that are only a snapshot of the stress level experienced. Promising technological tools, such as proteomics, allow an unbiased approach for the discovery of potential biomarkers for stress monitoring. Within this scope, using Gilthead seabream (Sparus aurata) as a model, three chronic stress conditions, namely overcrowding, handling and hypoxia, were employed to evaluate the potential of the fish protein-based adaptations as reliable signatures of chronic stress, in contrast with the commonly used hormonal and metabolic indicators.

Results: A broad spectrum of biological variation regarding cortisol and glucose levels was observed, the values of which rose higher in net-handled fish. In this sense, a potential pattern of stressor-specificity was clear, as the level of response varied markedly between a persistent (crowding) and a repetitive stressor (handling). Gel-based proteomics analysis of the plasma proteome also revealed that net-handled fish had the highest number of differential proteins, compared to the other trials. Mass spectrometric analysis, followed by gene ontology enrichment and protein-protein interaction analyses, characterized those as humoral components of the innate immune system and key elements of the response to stimulus.

Conclusions: Overall, this study represents the first screening of more reliable signatures of physiological adaptation to chronic stress in fish, allowing the future development of novel biomarker models to monitor fish welfare.

Keywords: Aquaculture; Biomarkers; Cortisol; Glucose; Mass-spectrometry; Proteome.

Fig. 1

Violin plots showing the distributions of plasma cortisol (ng/ml), glucose (mg/dl) and lactate (mg/dl) levels of gilthead seabream (Sparus aurata) submitted to different chronic stressors (a – overcrowding, b – net handling, c – hypoxia), in two intensities, and unstressed fish (control) (n = 18). The box-plot inside includes observations from the 25th to the 75th percentiles as determined by R software; the horizontal line indicates the median value. Whiskers extend 1.5 times the interquartile range. Single data points are outlying data. *P < 0.05; **P < 0.01; ***P < 0.001; **** P < 0.0001. NS (not significant) indicates a P-value greater than 0.05

Fig. 2

Post-mortem changes in muscle pH and rigor mortis of gilthead seabream (Sparus aurata) submitted to different chronic stressors (a – overcrowding, b – net handling, c – hypoxia), in two intensities, and unstressed fish (control), stored in ice for 72 h. Data points are the mean ± S.D. of n = 9 for each sampling time. Means labelled * are different at P < 0.05

Fig. 3

Representative pattern of gilthead seabream (Sparus aurata) blood plasma on a 12.5% polyacrylamide 2D gel. Black circles represent the 107 proteins identified by MALDI-TOF/TOF MS with significant differences in abundance in NET groups and black squares the 2 proteins with significant differences in abundance in HYP groups (P < 0.05)

Fig.4

a – Volcano plots of the entire set of plasma proteins detected by DIGE analysis on the NET trial samples. Each point represents the difference in abundance (fold-change) between stressed fish (NET2 on the left; NET4 on the right) and control fish plotted against the level of statistical significance. Dotted vertical lines represent a 2-fold variation in abundance, while dotted horizontal line represent the significance level of P < 0.05. Red dots represent proteins significantly up- and down-regulated. b – Principal component analysis performed with the normalized spot volumes of the 107 identified proteins in the plasma samples of gilthead seabream from the NET trial (n = 6). Blue, orange and red dots represent CTRL, NET2 and NET4 groups, respectively. c – Hierarchical clustering of 107 significantly differential proteins identified in the plasma samples of gilthead seabream from net handling (NET) trial. Rows represent expression patterns of individual proteins, while each column corresponds to a biological replicate (fish). Cell colour indicates the normalized Z-scores of the spot volumes

Fig.5

A – Protein-protein interaction network generated with 18 differential proteins identified in the plasma of fish from NET trial. Nodes represent proteins and edges the functional associations between them. STRING annotations are described in Table 1. Red arrows represent up-regulated proteins in both treatments; blue arrows represent down-regulated proteins in both treatments. D – GO Enrichment analysis of the 18 proteins showing significantly differential abundance between control and NET treatments (hypergeometric test, FDR < 0.05)