Decoding protein binding landscape on circular RNAs with base-resolution transformer models.

Circular RNAs (circRNAs), a class of endogenous RNA with a covalent loop structure, can regulate gene expression by serving as sponges for microRNAs and RNA-binding proteins (RBPs). To date, most computational methods for predicting RBP binding sites on circRNAs focus on circRNA fragments instead of circRNAs. These methods detect whether a circRNA fragment contains binding sites, but cannot determine where are the binding sites and how many binding sites are on the circRNA transcript. We report a hybrid deep learning-based tool, CircSite, to predict RBP binding sites at single-nucleotide resolution and detect key contributed nucleotides on circRNA transcripts. CircSite takes advantage of convolutional neural networks (CNNs) and Transformer for learning local and global representations of circRNAs binding to RBPs, respectively. We construct 37 datasets of circRNAs interacting with proteins for benchmarking and the experimental results show that CircSite offers accurate predictions of RBP binding nucleotides and detects key subsequences aligning well with known binding motifs. CircSite is an easy-to-use online webserver for predicting RBP binding sites on circRNA transcripts and freely available at http://www.csbio.sjtu.edu.cn/bioinf/CircSite/.

Recognizing binding sites of poorly characterized RNA-binding proteins on circular RNAs using attention Siamese network.

Circular RNAs (circRNAs) interact with RNA-binding proteins (RBPs) to play crucial roles in gene regulation and disease development. Computational approaches have attracted much attention to quickly predict highly potential RBP binding sites on circRNAs using the sequence or structure statistical binding knowledge. Deep learning is one of the popular learning models in this area but usually requires a lot of labeled training data. It would perform unsatisfactorily for the less characterized RBPs with a limited number of known target circRNAs. How to improve the prediction performance for such small-size labeled characterized RBPs is a challenging task for deep learning-based models. In this study, we propose an RBP-specific method iDeepC for predicting RBP binding sites on circRNAs from sequences. It adopts a Siamese neural network consisting of a lightweight attention module and a metric module. We have found that Siamese neural network effectively enhances the network capability of capturing mutual information between circRNAs with pairwise metric learning. To further deal with the small-sample size problem, we have performed the pretraining using available labeled data from other RBPs and also demonstrate the efficacy of this transfer-learning pipeline. We comprehensively evaluated iDeepC on the benchmark datasets of RBP-binding circRNAs, and the results suggest iDeepC achieving promising results on the poorly characterized RBPs. The source code is available at https://github.com/hehew321/iDeepC.

[Genome-wide analysis and functional prediction of the Trihelix transcription factor family in rice].

The Trihelix transcription factor family plays an essential role in plant growth, development and stress response. However, the studies about identification and analysis of this gene family in rice on the genome-wide level have not been reported. In this study, 31 members of the Trihelix family, which contain highly conserved and characteristic trihelix domain through sequence clustering and functional domains analysis, were identified in rice genome database using bioinformatic tools. These members could be classified into 5 subfamilies (I~V) based on the evolutionary relationship and domain characteristics. Clustering analyses of the Trihelix family in rice, Arabidopsis, Brachypodium distachyom and Sorghum bicolor showed that each species contained different members of subfamily although the classification of the Trihelix family were consistent in these four species, which indicated that the differentiation of the Trihelix gene family occur earlier than that of these species. The conserved motifs in the Trihelix family of rice analyzed using the MEME program were highly consistent with the results of clustering analyses. Intraspecific and interspecific chromosomal replication in partial Trihelix family members were found to exist in rice and between rice and other species through chromosome replication analysis. Microarray data analysis revealed diverse expression patterns of Trihelix family genes in different tissues of rice or in response to six different phytohormones. Moreover, 20 members of the Trihelix transcription factor family were found to interact with other proteins in rice using RiceFRIEND online database analysis. Therefore, our results preliminarily identified the evolution, chromosome distribution and replication, expression patterns, phytohormones response of the Trihelix transcription factor family and the interaction between trihelix family proteins and other proteins in rice, which will provide a basis to further reveal the molecular evolution and biological function of the Trihelix transcription factor family.

Inhibition of Notch Signaling Promotes the Adipogenic Differentiation of Mesenchymal Stem Cells Through Autophagy Activation and PTEN-PI3K/AKT/mTOR Pathway.

BACKGROUND: The Notch signaling pathway is implicated in a broad range of developmental processes, including cell fate decisions. This study was designed to determine the role of Notch signaling in adipogenic differentiation of human bone marrow derived MSCs (BM-MSCs). METHODS: The Notch signaling was inhibited by the γ-secretase inhibitor N-[N-(3,5-difluor- ophenacetyl-L-alanyl)]-S-phenylglycine t-butylester (DAPT). The markers involving adipogenic differentiation of MSCs, the relative pathway PTEN-PI3K/Akt/mTOR and autophagy activation were then analyzed. Furthermore, the autophagy inhibitor chloroquine (CQ) and 3-methyladenine (3-MA) were used to study the role of autophagy in the DAPT-induced the adipogenic differentiation of MSCs. RESULTS: We first confirmed the down -regulation of Notch gene expression during MSCs adipocyte differentiation, and showed that the inhibition of Notch signaling significantly enhanced adipogenic differentiation of MSCs. Furthermore, Notch inhibitor DAPT induced early autophagy by acting on PTEN-PI3K/Akt/mTOR pathway. The autophagy inhibitor CQ and 3-MA dramatically abolished the effects of DAPT-induced autophagy and adipogenic differentiation of MSCs. CONCLUSION: Our results indicate that inhibition of Notch signaling could promote MSCs adipogenesis mediated by autophagy involving PTEN-PI3K/Akt/mTOR pathway. Notch signaling could be a novel target for regulating the adipogenic differentiation of MSCs.

Human mesenchymal stem cells possess different biological characteristics but do not change their therapeutic potential when cultured in serum free medium.

INTRODUCTION: Mesenchymal stem cells (MSCs) are widely investigated in clinical researches to treat various diseases. Classic culture medium for MSCs, even for clinical use, contains fetal bovine serum. The serum-containing medium (SCM) seems a major obstacle for MSCs-related therapies due to the risk of contamination of infectious pathogens. Some studies showed that MSCs could be expanded in serum free medium (SFM); however, whether SFM would change the biological characteristics and safety issues of MSCs has not been well answered. METHODS: Human umbilical cord mesenchymal stem cells (hUC-MSCs) were cultured in a chemical defined serum free medium. Growth, multipotency, surface antigen expression, telomerase, immunosuppressive ability, gene expression profile and genomic stability of hUC-MSCs cultured in SFM and SCM were analyzed and compared side by side. RESULTS: hUC-MSCs propagated more slowly and senesce ultimately in SFM. SFM-expanded hUC-MSCs were different from SCM-expanded hUC-MSCs in growth rate, telomerase, gene expression profile. However, SFM-expanded hUC-MSCs maintained multipotency and the profile of surface antigen which were used to define human MSCs. Both SFM- and SCM-expanded hUC-MSCs gained copy number variation (CNV) in long-term in vitro culture. CONCLUSION: hUC-MCSs could be expanded in SFM safely to obtain enough cells for clinical application, meeting the basic criteria for human mesenchymal stem cells. hUC-MSCs cultured in SFM were distinct from hUC-MSCs cultured in SCM, yet they remained therapeutic potentials for future regenerative medicine.