SEASONAL WARMING AND RANDOM STRESSORS ALTER BIOLOGICAL AGEING- AND THERMAL-RESPONSIVE GENE EXPRESSION IN GILTHEAD SEA BREAM Sparus aurata

Introduction

Advancing sustainable aquaculture requires robust and quantifiable tools to assess fish welfare and physiological resilience under farming conditions (Noble et al., 2026). In this context, biomarkers capable of capturing the cumulative effects of environmental and husbandry-related stressors are becoming increasingly relevant. Among these, metrics of biological ageing have emerged as a promising framework for evaluating organismal condition beyond chronological age, whereas temperature-responsive molecular markers provide key insight into the adaptive capacity of farmed fish under warming scenarios. Using an epigenomic-transcriptomic integrative approach, recent studies in gilthead sea bream identified and validated 10 biological ageing genes (BAGs: atp1a2, bmp1, calcrl, col5a1, psmd1, ramp1, sirt1, smad1, spred2, thrb) and 36 temperature response genes (TRGs: adam8, arrdc3, egr1, hmgcr, igf1, igf1r, jund, mapk1, mfn2, mmp14, nolc1, pcna, setdb1, sirt1, slc27a4, smarcd1, snx17, spred2, stk11, tyms, ucp3, usp47, usp7, cat, cox1, cpt1a, cs, ghr1, ghr2, gpx1, grp170, grp94, hif-1α, igf2, prdx3, sod2) in white skeletal muscle (WSM) (Belenguer et al., 2024; Naya-Català et al., 2025). Building on these findings, the present study investigated the combined effects of water temperature and random stressors on the expression of these marker panels, and examined their relationship with growth performance and circulating biochemical indicators.

Materials and methods

Gilthead sea bream juveniles (~20 g) from the same production batch were acclimated in 3000 L tanks for 21 days. On 26/05/2025 (t0; 21.35 °C), fish were redistributed into duplicate tanks assigned to a control group (CTRL), maintained under standard farming conditions, or to a stress group (STR), exposed to random stressors applied in variable order and timing to avoid habituation. During the first phase (t0–t1; 09/07/2025; 27.6 °C; 80–100 g), STR fish were subjected to a moderate stressor load (16 min/day), including chasing (3 × 1 min, with 3 min between series, after reducing water level), darkness (20–30 min), noise (3 × 10 impacts, with 10 min between series), and confinement (~75% volume reduction for 30–45 min). As no clear growth differences were detected, stress intensity was increased during t1–t2 (27/08/2025; 28.56 °C; ~150 g) to 33 min/day. In this phase, stressors were applied twice daily: chasing was performed with two nets against the swimming direction of the fish (3 × 2 min, with 4 min between series), darkness lasted 30–45 min, noise increased to 3 × 20 impacts with 2 min between series, and confinement was applied for 45–60 min. No further stressors were imposed from t2 to the end of the experiment (t3; 03/10/2025; 24.57 °C). At each sampling point (t0–t3), 12 fish per group were anesthetized and sacrificed by cervical section. Caudal blood was collected to determine total antioxidant capacity, hemoglobin, glucose, and cortisol. Dorsal WSM samples (150–200 mg) were preserved in RNAlater for RNA extraction and automated quantitative PCR analysis. Temporal and between-group differences were assessed using one-way ANOVA and t-tests (p < 0.05). Multivariate analyses included PERMANOVA (FDR < 0.05), PLS-DA (pR2X < 0.05; pQ2 < 0.05), and hierarchical clustering based on individual fold-change profiles, with markers showing VIP ≥ 1 and p < 0.05 selected as discriminants of group separation.

Results and Discussion

Over the course of the trial, fish increased in body weight from ~20 to ~280 g. The clearest effect of the combined stress–temperature challenge was observed at t2, when STR fish showed an 8% reduction in growth, coinciding with intensified random stress exposure and the highest seasonal water temperatures (Figure 1). This was accompanied by a 15% decrease in feed intake and an 18% reduction in hepatosomatic index, indicating a marked energetic and physiological adjustment under cumulative challenge. Blood parameters also showed a strong time-dependent pattern, with the greatest changes at t2: haemoglobin increased from 4.5 to ~10 g/dL, glucose from ~50 to ~150 mg/dL, and cortisol from ~50 to ~150 mg/dL, whereas total antioxidant capacity decreased from ~1200 to ~800 µM Trolox. However, these variables did not discriminate between CTRL and STR fish at t1 or t2, suggesting that seasonal warming was the main driver of the systemic response, potentially masking the specific contribution of the imposed stressors. In contrast, WSM transcriptional profiling was more sensitive in detecting treatment-related effects. The number of differentially expressed genes between CTRL and STR was highest at t2, with 9 DEGs, compared with 3 and 4 at t1 and t3, respectively. Similarly, PERMANOVA of fold-change signatures separated t2 from the remaining time points, and PLS-DA confirmed this pattern, identifying 19 genes (VIP ≥ 1 and/or p < 0.05) as the main contributors to group segregation. In the STR group, 4 BAGs (atp1a2, bmp1, col5a1, and spred2) and 10 TRGs (cat, grp170, hif1a, igf1, igf1r, igf2, mmp14, nolc1, snx17, and spred2) were downregulated, consistent with molecular signatures previously linked to accelerated biological ageing and thermal stress responses. These findings indicate that WSM transcriptomic markers can detect subtle but biologically relevant effects of the interaction between chronic random stress and elevated temperature, even when classical blood indicators fail to do so. After stressor exposure ceased, STR fish showed a 6.8% increase in growth at t3, together with a 50% reduction in circulating cortisol relative to CTRL, suggesting compensatory growth and partial physiological recovery. Overall, these results confirm the relationship between TRGs, BAGs, random stress, and temperature fluctuations, and support the idea that the combination of sustained stress and high temperature may act as an indicator of accelerated ageing and welfare maladaptation, particularly under the environmental pressure of the warm season.

Fig. 1. Graphical abstract of the main results in this study.

Funding: BreamHOLOBIONT (PID2023-146990OB-I00); TOMACUA (CIAICO/2024/281); CSIC-MOMENTUM (MMT24-IATS-01-01).

References

Noble et al., 2026. Rev. Aquac. 18, e70109; Belenguer et al., 2024. IJMS 25, 9836; Naya-Català et al., 2025. EPIMAR2025. Barcelona. Book of Abstracts.