DNA Methylation and Chromatin Accessibility Impact Subgenome Expression Dominance in the Common Carp (Cyprinus carpio).

DNA methylation and chromatin accessibility play important roles in gene expression, but their function in subgenome expression dominance remains largely unknown. We conducted comprehensive analyses of the transcriptome, DNA methylation, and chromatin accessibility in liver and muscle tissues of allotetraploid common carp, aiming to reveal the function of epigenetic modifications in subgenome expression dominance. A noteworthy overlap in differential expressed genes (DEGs) as well as their functions was observed across the two subgenomes. In the promoter and gene body, the DNA methylation level of the B subgenome was significantly different than that of the A subgenome. Nevertheless, differences in DNA methylation did not align with changes in homoeologous biased expression across liver and muscle tissues. Moreover, the B subgenome exhibited a higher prevalence of open chromatin regions and greater chromatin accessibility, in comparison to the A subgenome. The expression levels of genes located proximally to open chromatin regions were significantly higher than others. Genes with higher chromatin accessibility in the B subgenome exhibited significantly elevated expression levels compared to the A subgenome. Contrastingly, genes without accessibility exhibited similar expression levels in both subgenomes. This study contributes to understanding the regulation of subgenome expression dominance in allotetraploid common carp.

Rapid and Non-Invasive Assessment of Texture Profile Analysis of Common Carp (Cyprinus carpio L.) Using Hyperspectral Imaging and Machine Learning.

Hyperspectral imaging (HSI) has been applied to assess the texture profile analysis (TPA) of processed meat. However, whether the texture profiles of live fish muscle could be assessed using HSI has not been determined. In this study, we evaluated the texture profile of four muscle regions of live common carp by scanning the corresponding skin regions using HSI. We collected skin hyperspectral information from four regions of 387 scaled and live common carp. Eight texture indicators of the muscle corresponding to each skin region were measured. With the skin HSI of live common carp, six machine learning (ML) models were used to predict the muscle texture indicators. Backpropagation artificial neural network (BP-ANN), partial least-square regression (PLSR), and least-square support vector machine (LS-SVM) were identified as the optimal models for predicting the texture parameters of the dorsal (coefficients of determination for prediction (rp) ranged from 0.9191 to 0.9847, and the root-mean-square error for prediction ranged from 0.1070 to 0.3165), pectoral (rp ranged from 0.9033 to 0.9574, and RMSEP ranged from 0.2285 to 0.3930), abdominal (rp ranged from 0.9070 to 0.9776, and RMSEP ranged from 0.1649 to 0.3601), and gluteal (rp ranged from 0.8726 to 0.9768, and RMSEP ranged from 0.1804 to 0.3938) regions. The optimal ML models and skin HSI data were employed to generate visual prediction maps of TPA values in common carp muscles. These results demonstrated that skin HSI and the optimal models can be used to rapidly and accurately determine the texture qualities of different muscle regions in common carp.