DNA Methylation Difference between Female and Male Ussuri Catfish (Pseudobagrus ussuriensis) in Brain and Gonad Tissues

DNA methylation has been found to be involved in sex determination and differentiation in many aquaculture species. The Ussuri catfish (Pseudobagrus ussuriensis) is a popular aquaculture fish in China with high economic value in which male-biased sex dimorphism was observed in terms of body size and body weight. In this study, DNA methylation-sensitive RAD sequencing (Methyl-RAD) was used to explore the epigenetic difference between adult male and female samples in brain and gonad tissues. In brain tissues, 5,442,496 methylated cytosine sites were found and 9.94% of these sites were from symmetric CCGG or CCWGG sites. Among these sites, 321 differential DNA methylation sites (DMSs) in 171 genes were identified, while in gonad tissues, 4,043,053 methylated cytosines sites were found in total and 11.70% of them were from CCGG or CCWGG. Among these sites, 78 differential DNA methylation sites were found which were located in 64 genes. We also found several sex-determination genes among these differential methylated genes, such as amh, gsdf and hsd11b2 in brain tissues and slco3a1, socs2 and trim47 in gonad tissues. These results provided evidence for understanding the function of DNA methylation in the sex differentiation in Pseudobagrus ussuriensis, which further deepens the relationship between gene regulation and epigenetics.

Metrics for evaluating differentially methylated region sets predicted from BS-seq data

Investigating differentially methylated regions (DMRs) presented in different tissues or cell types can help to reveal the mechanisms behind the tissue-specific gene expression. The identified tissue-/disease-specific DMRs also can be used as feature markers for spotting the tissues-of-origins of cell-free DNA (cfDNA) in noninvasive diagnosis. In recent years, many methods have been proposed to detect DMRs. However, due to the lack of benchmark DMRs, it is difficult for researchers to choose proper methods and select desirable DMR sets for downstream studies. The application of DMRs, used as feature markers, can be benefited by the longer length of DMRs containing more CpG sites when a threshold is given for the methylation differences of DMRs. According to this, two metrics ($Qn$ and $Ql$), in which the CpG numbers and lengths of DMRs with different methylation differences are weighted differently, are proposed in this paper to evaluate the DMR sets predicted by different methods on BS-seq data. DMR sets predicted by eight methods on both simulated datasets and real BS-seq datasets are evaluated by the proposed metrics, the benchmark-based metrics, and the enrichment analysis of biological data, including genomic features, transcription factors and histones. The rank correlation analysis shows that the $Qn$ and $Ql$ are highly correlated to the benchmark metrics for simulated datasets and the biological data enrichment analysis for real BS-seq data. Therefore, with no need for additional biological data, the proposed metrics can help researchers selecting a more suitable DMR set on a certain BS-seq dataset.