Search

Enhancers are located upstream or downstream of key deoxyribonucleic acid (DNA) sequences in genes and can adjust the transcription activity of neighboring genes. Identifying enhancers and determining their functions are important for understanding gene regulatory networks and expression regulatory mechanisms. However, traditional enhancer recognition relies on manual feature engineering, which is time-consuming and labor-intensive, making it difficult to perform large-scale recognition analysis. In addition, if the original dataset is too small, there is a risk of overfitting. In recent years, emerging methods, such as deep learning, have provided new insights for enhancing identification. However, these methods also present certain challenges. Deep learning models typically require a large amount of high-quality data, and data acquisition demands considerable time and resources. To address these challenges, in this paper, we propose a data-augmentation method based on generative adversarial networks to solve the problem of small datasets. Moreover, we used regularization methods such as weight decay to improve the generalizability of the model and alleviate overfitting. The Transformer encoder was used as the main component to capture the complex relationships and dependencies in enhancer sequences. The encoding layer was designed based on the principle of k-mers to preserve more information from the original DNA sequence. Compared with existing methods, the proposed approach made significant progress in enhancing the accuracy and strength of enhancer identification and prediction, demonstrating the effectiveness of the proposed method. This paper provides valuable insights for enhancer analysis and is of great significance for understanding gene regulatory mechanisms and studying disease correlations. [ABSTRACT FROM AUTHOR]

Nuclear receptor subfamily 5 group A member 1 (NR5A1) is expressed in the pituitary gonadotrope and regulates their differentiation. Although several regulatory regions were implicated in Nr5a1 gene expression in the pituitary gland, none of these regions have been verified using mouse models. Furthermore, the molecular functions of NR5A1 in the pituitary gonadotrope have not been fully elucidated. In the present study, we generated mice lacking the pituitary enhancer located in the 6th intron of the Nr5a1 gene. These mice showed pituitary gland-specific disappearance of NR5A1, confirming the functional importance of the enhancer. Enhancer-deleted male mice demonstrated no defects at fetal stages. Meanwhile, androgen production decreased markedly in adult, and postnatal development of reproductive organs, such as the seminal vesicle, prostate, and penis was severely impaired. We further performed transcriptomic analyses of the whole pituitary gland of the enhancer-deleted mice and controls, as well as gonadotropes isolated from Ad4BP-BAC-EGFP mice. These analyses identified several genes showing gonadotrope-specific, NR5A1-dependent expressions, such as Spp1, Tgfbr3l, Grem1, and Nr0b2. These factors are thought to function downstream of NR5A1 and play important roles in reproductive organ development through regulation of pituitary gonadotrope functions. [ABSTRACT FROM AUTHOR]