Dravet syndrome (DS) is an intractable developmental and epileptic encephalopathy caused largely by de novo variants in the SCN1A gene, resulting in haploinsufficiency of the voltage-gated sodium channel α subunit NaV1.1. Here, we used Targeted Augmentation of Nuclear Gene Output (TANGO) technology, which modulates naturally occurring, nonproductive splicing events to increase target gene and protein expression and ameliorate disease phenotype in a mouse model. We identified antisense oligonucleotides (ASOs) that specifically increase the expression of productive Scn1a transcript in human cell lines, as well as in mouse brain. We show that a single intracerebroventricular dose of a lead ASO at postnatal day 2 or 14 reduced the incidence of electrographic seizures and sudden unexpected death in epilepsy (SUDEP) in the F1:129S-Scn1a+/− × C57BL/6J mouse model of DS. Increased expression of productive Scn1a transcript and NaV1.1 protein was confirmed in brains of treated mice. Our results suggest that TANGO may provide a unique, gene-specific approach for the treatment of DS.

While most monogenic diseases are caused by loss or reduction of protein function, the need for technologies that can selectively increase levels of protein in native tissues remains. Here we demonstrate that antisense-mediated modulation of pre-mRNA splicing can increase endogenous expression of full-length protein by preventing naturally occurring non-productive alternative splicing and promoting generation of productive mRNA. Bioinformatics analysis of RNA sequencing data identifies non-productive splicing events in 7,757 protein-coding human genes, of which 1,246 are disease-associated. Antisense oligonucleotides targeting multiple types of non-productive splicing events lead to increases in productive mRNA and protein in a dose-dependent manner in vitro. Moreover, intracerebroventricular injection of two antisense oligonucleotides in wild-type mice leads to a dose-dependent increase in productive mRNA and protein in the brain. The targeting of natural non-productive alternative splicing to upregulate expression from wild-type or hypomorphic alleles provides a unique approach to treating genetic diseases.

Purpose To investigate the molecular mechanism and search for candidate biomarkers in the gene expression profile of patients with diabetic peripheral neuropathy (DPN). Methods Differentially expressed genes (DEGs) of progressive vs non-progressive DPN patients in dataset GSE24290 were screened. Functional enrichment analysis was conducted, and hub genes were extracted from the protein–protein interaction network. The expression level of hub genes in serum samples in another dataset GSE95849 was obtained, followed by the ROC curve analysis. Results A total of 352 DEGs were obtained from dataset GSE24290. They were involved in 14 gene ontology terms and 10 Kyoto Encyclopedia of Genes and Genomes pathways, mainly related to lipid metabolism. Eight hub genes (LEP, APOE, ADIPOQ, FABP4, CD36, GPAM, CIDEC, and PNPLA4) were revealed, and their expression level was obtained in dataset GSE95849. The receiver operating characteristic curve analysis indicated that CIDEC (AUC=1), APOE (AUC=0.833), CD36 (AUC=0.803), and PNPLA4 (AUC=0.861) might be candidate serum biomarkers of DPN. Conclusion Lipid metabolism of Schwann cells might be inhibited in progressive DPN. CIDEC, APOE, CD36, and PNPLA4 might be potential predictive biomarkers in the early DPN diagnosis of patients with DM.

BackgroundHIV and HCV infections have become the leading global public-health threats. Even more remarkable, HIV-HCV co-infection is rapidly emerging as a major cause of morbidity and mortality throughout the world, due to the common rapid mutation characteristics of the two viruses as well as their similar complex influence to immunology system. Although considerable progresses have been made on the study of the infection of HIV and HCV respectively, few researches have been conducted on the investigation of the molecular mechanism of their co-infection and designing of the multi-target co-inhibitors for the two viruses simultaneously.ResultsIn our study, a multi-target Quantitative Structure-Activity Relationship (QSAR) study of the inhibitors for HIV-HCV co-infection were addressed with an in-silico machine learning technique, i.e. multi-task learning, to help to guide the co-inhibitor design. Firstly, an integrated dataset with 3 HIV inhibitor subsets targeted on protease, integrase and reverse transcriptase respectively, together with another 6 subsets of 2 HCV inhibitors targeted on NS3 serine protease and NS5B polymerase respectively were compiled. Secondly, an efficient multi-target QSAR modelling of HIV-HCV co-inhibitors was performed by applying an accelerated gradient method based multi-task learning on the whole 9 datasets. Furthermore, by solving the L-1-infinity regularized optimization, the Drug-like index features for compound description were ranked according to their joint importance in multi-target QSAR modelling of HIV and HCV. Finally, a drug structure-activity simulation for investigating the relationships between compound structures and binding affinities was presented based on our multiple target analysis, which is then providing several novel clues for the design of multi-target HIV-HCV co-inhibitors with increasing likelihood of successful therapies on HIV, HCV and HIV-HCV co-infection.ConclusionsThe framework presented in our study provided an efficient way to identify and design inhibitors that simultaneously and selectively bind to multiple targets from multiple viruses with high affinity, and will definitely shed new lights on the future work of inhibitor synthesis for multi-target HIV, HCV, and HIV-HCV co-infection treatments.

RNA interference via exogenous short interference RNAs (siRNA) is increasingly more widely employed as a tool in gene function studies, drug target discovery and disease treatment. Currently there is a strong need for rational siRNA design to achieve more reliable and specific gene silencing; and to keep up with the increasing needs for a wider range of applications. While progress has been made in the ability to design siRNAs with specific targets, we are clearly at an infancy stage towards achieving rational design of siRNAs with high efficacy. Among the many obstacles to overcome, lack of general understanding of what sequence features of siRNAs may affect their silencing efficacy and of large-scale homogeneous data needed to carry out such association analyses represents two challenges. To address these issues, we investigated a feature-selection based in-silico siRNA design from a novel cross-platform data integration perspective. An integration analysis of 4,482 siRNAs from ten meta-datasets was conducted for ranking siRNA features, according to their possible importance to the silencing efficacy of siRNAs across heterogeneous data sources. Our ranking analysis revealed for the first time the most relevant features based on cross-platform experiments, which compares favorably with the traditional in-silico siRNA feature screening based on the small samples of individual platform data. We believe that our feature ranking analysis can offer more creditable suggestions to help improving the design of siRNA with specific silencing targets. Data and scripts are available at http://csbl.bmb.uga.edu/publications/materials/qiliu/siRNA.html.