The skeletal muscles of teleost fish encompass heterogeneous muscle types, termed slow-twitch muscle (SM) and fast-twitch muscle (FM), characterized by distinct morphological, anatomical, histological, biochemical, and physiological attributes, driving different swimming behaviors. Despite the central role of metabolism in regulating skeletal muscle types and functions, comprehensive metabolomics investigations focusing on the metabolic differences between these muscle types are lacking. To reveal the differences in metabolic characteristics between the SM and FM of teleost, we conducted an untargeted metabolomics analysis using Pseudocaranx dentex as a representative model and identified 411 differential metabolites (DFMs), of which 345 exhibited higher contents in SM and 66 in FM. KEGG enrichment analysis showed that these DFMs were enriched in the metabolic processes of lipids, amino acids, carbohydrates, purines, and vitamins, suggesting that there were significant differences between the SM and FM in multiple metabolic pathways, especially in the metabolism of energy substances. Furthermore, an integrative analysis of metabolite contents, enzymatic activity assays, and gene expression levels involved in ATP-PCr phosphate, anaerobic glycolysis, and aerobic oxidative energy systems was performed to explore the potential regulatory mechanisms of energy metabolism differences. The results unveiled a set of differential metabolites, enzymes, and genes between the SM and FM, providing compelling molecular evidence of the FM achieving a higher anaerobic energy supply capacity through the ATP-PCr phosphate and glycolysis energy systems, while the SM obtains greater energy supply capacity via aerobic oxidation. These findings significantly advance our understanding of the metabolic profiles and related regulatory mechanisms of skeletal muscles, thereby expanding the knowledge of metabolic physiology and ecological adaptation in teleost fish.

The golden pompano ranks at the top of production in current China’s marine fish aquaculture; however, there has been long-standing controversy regarding its valid scientific name. Multiple latin names were used simultaneously to refer to golden pompano, such as Trachinotus ovatus, T. blochii, T. mookalee and T. anak. Moreover, two distinct morphological species are regarded as deserving the scientific name T. ovatus. In this study, we employed DNA barcoding to determine which particular species the “golden pompano” represents and to explore the potential synonyms and cryptic species within T. ovatus and its closely related species. We analyzed the DNA barcodes of golden pompano samples from various aquaculture farms in China’s main production regions, as well as most species within the genus Trachinotus. The phylogenetic analyses revealed that all T. ovatus sequences clustered into two divergent clades with a large genetic distance, and the two clades were geographically separated, being from the Indo-west Pacific and the East Atlantic regions, respectively. Based on the type locality information and historical distribution records, we support the validity of the naming of Trachinotus ovatus from the Indo-west Pacific, and the so-called Trachinotus ovatus from the East Atlantic may represent a cryptic species. All the golden pompano samples were clustered into the Indo-west Pacific T. ovatus clade, with a considerably small intragroup genetic distance, which suggests that the golden pompano in China should be identified as the species Trachinotus ovatus. The golden pompano, T. blochii and T. mookalee were completely separated into distinct monophyletic clades in the phylogenetic trees, which indicated that they are different species. The T. anak clustered with the monophyletic clade of Indo-west Pacific T. ovatus and the genetic distance between them was at the intraspecific difference level. This implied that the T. anak might be the junior synonym of T. ovatus. The species delimitations based on the ABGD and bPTP methods are in agreement with the findings from phylogenetic analyses. The above results help to form a consistent viewpoint regarding the naming of the golden pompano and provide new understandings for the taxonomy of the genus Trachinotus.

Suruga fundicola, one of the few known deep-dwelling gobies, is found in Japan, South Korea, and China. Owing to the limited availability of specimens, little is known about its mitogenome characterization, phylogenetic relationship, and adaptive evolution. In this study, we sequenced four complete mitogenomes using the DNBSEQ platform and Sanger sequencing. The mitogenomes in length ranged from 17,138 to 17,352 bp, primarily due to the variation in the number of long tandem repeat (LTR) sequences within variable region 3 (VR3). Although the gene composition and arrangement of the S. fundicola mitogenome are largely consistent with those of other gobies, we identified an expansion of the ND2 gene (78 bp), and an unexpected noncoding region (NC, 35 bp) located between the ND2 and tRNA^trp genes. To further investigate the variation in VR3, we sequenced this region in all nineteen individuals with the Sanger sequencing method. We detected eight distinct LTR types, containing one–three mutation sites, which formed ten different VR3 patterns. Most VR3 patterns (14/19) consisted of a single type of pure LTR, while the remaining five exhibited heterogeneous patterns composed of two different LTRs. Notably, in LTR types T1 and T3, which co-occur in heterogeneous patterns P1 and P9, we found their respective pure patterns (P2–3 and P7). Recombination provides a better, more plausible mechanism for generating the heterogeneity patterns than slipped-strand mispairing, which better explains the homogeneous LTR expansions. These findings provide evidence of recombination in the control region of a vertebrate mitogenome. A phylogenetic analysis confirmed that S. fundicola has a close relationship with Am. hexanema and C. stigmatias. Compared to five shallow-water species of the AcanthogobiusGroup, the deep-dwelling goby S. fundicola was found to be under stronger purifying selection. Within its mitochondrial protein-coding genes (PCGs), ND2 and ND6 genes were subject to stronger purifying selection than the others. Additionally, four genes showed signs of selection sites with high credibility (one in ATP6, ND3, and ND4; eight in ND2). This study provides valuable genomic resources for S. fundicola and enhances our understanding of the phylogenetic relationship, mitogenome recombination, and adaptive evolution of the goby.

Flesh texture is one of the most important quality traits determining consumer satisfaction and perception of fish. Traditional methods for measuring flesh texture characteristics present several limitations. This study developed a rapid, efficient and non-invasive approach based on hyperspectral imaging (HSI) technology for evaluating texture profile analysis (TPA) parameters in live spotted seabass. Three types of surface hyperspectral data (skin with scales, SWS; skin without scales, SOS; and reversed skin without scales, RSOS) were collected from the dorsal region of 300 live fish across 400–1000 nm wavelength range. After applying five spectral pre-processing methods, five machine learning algorithms, including partial least squares regression (PLSR), least square support vector machine regression (LS-SVR), random forest (RF), convolutional neural network (CNN) and back propagation artificial neural network (BP-ANN), were performed to construct optimal prediction models for seven TPA parameters. Gumminess was predicted most effectively, with the RF model achieving a prediction set coefficient of determination (R²_P) of 0.923, a ratio of performance deviation (RPD) of a mean absolute percentage error (MAPE) of 6.378%. The optimal models enabled quantitative prediction of gumminess, chewiness, and hardness (RPD > 3.0 and MAPE<10%). For cohesiveness and springiness, the models performed satisfactory with R²_P values of 0.859 and 0.854, RPD values of 2.890 and 2.613, and MAPE were 6.858% and 3.002%, respectively. However, prediction accuracy for adhesiveness and resilience was lower, with R²_P values of 0.552 and 0.341, and RPD values of 1.494 and 1.134, respectively. Visualization maps generated from the optimal models facilitated the direct assessment of TPA parameters distribution in the muscle. This study demonstrated that HSI combined with machine learning offers an effective and non-invasive method for assessing flesh quality in live spotted seabass, thereby accelerating the selection of germplasm with superior textural attributes.