Androgen receptor binding to an androgen-responsive element in the promoter of the Srsf4 gene inhibits its expression in mouse Sertoli cells

Transcriptional regulation of PEBP1 expression by androgen receptor in mouse testes

Androgen and AR are essential for maintaining spermatogenesis and male fertility. Previous studies have shown that the phosphatidyl ethanolamine binding protein 1 (Pebp1) gene is down-regulated in the selective ablation of the AR in the Sertoli cells of mouse testes compared with wild-type mice, indicating that Pebp1 is a candidate target of AR. The ChIP-PCR data and ChIP-sequencing results of this study verified that Pebp1 is a target gene regulated by AR. Real-time PCR, Western blot analysis, and immunofluorescence data showed that Pebp1 is expressed at all stages of testicular development, with an increasing trend from 1 to 8 weeks of postnatal development. PEBP1 was principally located in the cytoplasm, and high-intensity fluorescence revealed PEBP in the lumen of the testicular tubules. Bioinformatics analysis indicated effective androgen-responsive elements (AREs) located in the promotor of Pepb1 gene. Dual fluorescence assay data showed that androgens and AR could bind to the AREs of Pebp1 and induce an increase of gene expression. These data suggest that Pepb1 is a newfound target gene regulated by androgens and AR in mouse Sertoli cells. However, the detailed molecular mechanism of their role in spermatogenesis still needs to be further studied.Abbreviations: AR: androgen receptor; Pebp1: phosphatidyl ethanolamine binding protein 1; ARKO: androgen receptor knockout; WT: wild type; SCARKO: Sertoli cell-selective androgen receptor knockout; ChIP: chromatin immunoprecipitation; RKIP: Raf kinase inhibitory protein; MAPK: mitogen-activated protein kinase; NF-κB: nuclear factor kappa-light-chain-enhancer of activated B cells; GSK-3: glycogen synthase kinase-3; RT-PCR: reverse transcriptase polymerase chain reaction; SEM: standard error of the mean.

EDLMFC: an ensemble deep learning framework with multi-scale features combination for ncRNA-protein interaction prediction

Background

Non-coding RNA (ncRNA) and protein interactions play essential roles in various physiological and pathological processes. The experimental methods used for predicting ncRNA–protein interactions are time-consuming and labor-intensive. Therefore, there is an increasing demand for computational methods to accurately and efficiently predict ncRNA–protein interactions.

Results

In this work, we presented an ensemble deep learning-based method, EDLMFC, to predict ncRNA–protein interactions using the combination of multi-scale features, including primary sequence features, secondary structure sequence features, and tertiary structure features. Conjoint k-mer was used to extract protein/ncRNA sequence features, integrating tertiary structure features, then fed into an ensemble deep learning model, which combined convolutional neural network (CNN) to learn dominating biological information with bi-directional long short-term memory network (BLSTM) to capture long-range dependencies among the features identified by the CNN. Compared with other state-of-the-art methods under five-fold cross-validation, EDLMFC shows the best performance with accuracy of 93.8%, 89.7%, and 86.1% on RPI1807, NPInter v2.0, and RPI488 datasets, respectively. The results of the independent test demonstrated that EDLMFC can effectively predict potential ncRNA–protein interactions from different organisms. Furtherly, EDLMFC is also shown to predict hub ncRNAs and proteins presented in ncRNA–protein networks of Mus musculus successfully.

Conclusions

In general, our proposed method EDLMFC improved the accuracy of ncRNA–protein interaction predictions and anticipated providing some helpful guidance on ncRNA functions research. The source code of EDLMFC and the datasets used in this work are available at https://github.com/JingjingWang-87/EDLMFC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04069-9.