Human retroviruses, including human T cell leukemia virus type 1 (HTLV-1) and HIV type 1 (HIV-1), encode an antisense gene in the negative strand of the provirus. Besides coding for proteins, the messenger RNAs (mRNAs) of retroviral antisense genes have also been found to regulate transcription directly. Thus, it has been proposed that retroviruses likely localize their antisense mRNAs to the nucleus in order to regulate nuclear events; however, this opposes the coding function of retroviral antisense mRNAs that requires a cytoplasmic localization for protein translation. Here, we provide direct evidence that retroviral antisense mRNAs are localized predominantly in the nuclei of infected cells. The retroviral 3′ LTR induces inefficient polyadenylation and nuclear retention of antisense mRNA. We further reveal that retroviral antisense RNAs retained in the nucleus associate with chromatin and have transcriptional regulatory function. While HTLV-1 antisense mRNA is recruited to the promoter of C-C chemokine receptor type 4 (CCR4) and enhances transcription from it to support the proliferation of HTLV-1–infected cells, HIV-1 antisense mRNA is recruited to the viral LTR and inhibits sense mRNA expression to maintain the latency of HIV-1 infection. In summary, retroviral antisense mRNAs are retained in nucleus, act like long noncoding RNAs instead of mRNAs, and contribute to viral persistence.
Different kinds of biological databases publicly available nowadays provide us a goldmine of multidiscipline big data. The Cancer Genome Atlas is a cancer database including detailed information of many patients with cancer. DrugBank is a database including detailed information of approved, investigational and withdrawn drugs, as well as other nutraceutical and metabolite structures. PubChem is a chemical compound database including all commercially available compounds as well as other synthesisable compounds. Protein Data Bank is a crystal structure database including X-ray, cryo-EM and nuclear magnetic resonance protein three-dimensional structures as well as their ligands. On the other hand, artificial intelligence (AI) is playing an important role in the drug discovery progress. The integration of such big data and AI is making a great difference in the discovery of novel targeted drug. In this review, we focus on the currently available advanced methods for the discovery of highly effective lead compounds with great absorption, distribution, metabolism, excretion and toxicity properties.
Esophageal squamous cell carcinoma (ESCC) accounts for over 90% of all esophageal tumors. However, the molecular mechanism underlying ESCC development and prognosis remains unclear, and there are still no effective molecular biomarkers for diagnosing or predicting the clinical outcome of patients with ESCC. Here, using bioinformatics analyses, we attempted to identify potential biomarkers and therapeutic targets for ESCC. Differentially expressed genes (DEGs) between ESCC and normal esophageal tissue samples were obtained through comprehensive analysis of three publicly available gene expression profile datasets from the Gene Expression Omnibus database. The biological roles of the DEGs were identified by Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Moreover, the Cytoscape 3.7.1 platform and subsidiary tools such as Molecular Complex Detection (MCODE) and CytoHubba were used to visualize the protein-protein interaction (PPI) network of the DEGs and identify hub genes. A total of 345 DEGs were identified between normal esophageal and ESCC samples, which were enriched in the KEGG pathways of the cell cycle, endocytosis, pancreatic secretion, and fatty acid metabolism. Two of the highest scoring models were selected from the PPI network using Molecular Complex Detection. Moreover, CytoHubba revealed 21 hub genes with a valuable influence on the progression of ESCC in these patients. Among these, the high expression levels of five genes—SPP1, SPARC, BGN, POSTN, and COL1A2—were associated with poor disease-free survival of ESCC patients, as indicated by survival analysis. Taken together, we identified that elevated expression of five hub genes, including SPP1, is associated with poor prognosis in ESCC patients, which may serve as potential prognostic biomarkers or therapeutic target for ESCC.
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