DeepCBA: a deep learning framework for gene expression prediction in maize based on DNA sequence and chromatin interaction.

Chromatin interactions create spatial proximity between distal regulatory elements and target genes in the genome, which has an important impact on gene expression, transcriptional regulation, and phenotypic traits. To date, several methods have been developed for predicting gene expression. However, existing methods do not take into consideration the impact of chromatin interactions on target gene expression, thus potentially reduces the accuracy of gene expression prediction and mining of important regulatory elements. In this study, a highly accurate deep learning-based gene expression prediction model (DeepCBA) based on maize chromatin interaction data was developed. Compared with existing models, DeepCBA exhibits higher accuracy in expression classification and expression value prediction. The average Pearson correlation coefficients (PCC) for predicting gene expression using gene promoter proximal interactions, proximal-distal interactions, and proximal and distal interactions were 0.818, 0.625, and 0.929, respectively, representing an increase of 0.357, 0.16, and 0.469 over the PCC of traditional methods that only use gene proximal sequences. Some important motifs were identified through DeepCBA and were found to be enriched in open chromatin regions and expression quantitative trait loci (eQTL) and have the molecular characteristic of tissue specificity. Importantly, the experimental results of maize flowering-related gene ZmRap2.7 and tillering-related gene ZmTb1 demonstrate the feasibility of DeepCBA in exploring regulatory elements that affect gene expression. Moreover, the promoter editing and verification of two reported genes (ZmCLE7, ZmVTE4) demonstrated new insights of DeepCBA in precise designing of gene expression and even future intelligent breeding. DeepCBA is available at http://www.deepcba.com/ or http://124.220.197.196/.

CircAGFG1 Promotes Osteosarcoma Progression and Stemness by Competing with miR-302a-3p to Upregulate the Expression of LATS2.

This study aimed to investigate the effect of circRNA (circAGFG1) on the proliferation, migration, invasion, and cell stemness of osteosarcoma cells by targeting miR-302a to regulate LATS2. The expression of circAGFG1 in osteosarcoma cells and normal osteoblasts was detected by real-time fluorescent quantitative PCR (RT-qPCR). Cell proliferation, clone formation, and invasion were detected by CCK-8, clone formation, and cell invasion assays. In vivo tumor formation assay was used to detect the effect of circAGFG1 on tumor growth. The expression level of circAGFG1 was upregulated in osteosarcoma cells. The downregulation of circAGFG1 inhibited the proliferation, invasion, and migration of osteosarcoma cells. The overexpression of circAGFG1 enhanced the stemness of osteosarcoma cells. CircAGFG1 was specifically bound to miR-302a to regulate the expression activity of miR-302a. MiR-302a specifically bound to the 3'UTR of LATS2 and inhibited the expression of LATS2. The overexpression of miR-302a reversed the effect of circAGFG1 on the proliferation, invasion, and migration of osteosarcoma cells. CircAGFG1 regulated the expression of LATS2 by miR-302a, thereby regulating the proliferation, migration, and invasion of osteosarcoma cells.