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Uncovering Growth Variability in Gilthead Seabream (Sparus aurata) through Muscle Transcriptomic Profiling
* 1 , 2 , 1 , 1, 2 , 1 , 1, 2
1  S2AquaColab, Olhão 8700-194, Portugal
2  EPPO, IPMA, Olhão 8700-194, Portugal
Academic Editor: Michael Hässig

Abstract:

Introduction

Growth variability in gilthead seabream (Sparus aurata) remains a key challenge for Mediterranean aquaculture, as fish reared under identical conditions often show large differences in growth rate, which has an economic impact on fish farmers. The molecular basis of this variability is still unclear, as most studies thus far have focused on the targeted gene expression analysis of a limited set of genes.

Methods

A trial conducted at EPPO/IPMA investigated the molecular mechanisms behind fish growth disparities. Sparus aurata showing growth differences were classified into slow- and fast-growing groups based on average batch weight, maintaining equal density across tanks. Muscle samples from six fish per group were analyzed using RNA-seq for differential gene expression (DGE).

Results

Fast-growing fish exhibited superior growth and physiological performance. A total of 1,190 genes were differentially expressed between groups, with 524 up-regulated and 666 down-regulated in fast growers. The most up-regulated genes—tmprss9, ctxn3, nlrp12, dpy30, and znf608—were linked to muscle development, cytoskeletal organization, and transcriptional regulation. Among the most down-regulated were mfap4, dhys, and mdn1, primarily involved in immune response, extracellular matrix remodeling, and energy-demanding processes.

Conclusion

These results reveal distinct molecular signatures associated with growth capacity in seabream. The use of RNA-seq on muscle tissue proved to be effective for identifying genes and regulatory networks directly related to growth performance. The identified set of genes offers potential biomarkers for predicting growth rate, thereby improving aquaculture management. This research advances understanding of growth variability in S. aurata, providing a foundation for strategies to enhance production efficiency, such as selective breeding programs.

Acknowledgments

This study was funded by NanoPEIXE (ALG-01-0247-FEDER-070032), INOVAQUA (MAR-021.1.3-FEAMPA-00004), and Interface Mission cofinanced by PRR (Plano de Recuperação e Resiliência), European Union (operation code 01/C05-i02/2022.P148).

Keywords: RNA-seq; Slow-growing; Biomarker; Prediction tool

 
 
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