Fusion sequencing via terminator-assisted synthesis (FTAS-seq) identifies TMPRSS2 fusion partners in prostate cancer.

Genetic rearrangements that fuse an androgen-regulated promoter area with a protein-coding portion of an originally androgen-unaffected gene are frequent in prostate cancer, with the fusion between transmembrane serine protease 2 (TMPRSS2) and ETS transcription factor ERG (ERG) (TMPRSS2-ERG fusion) being the most prevalent. Conventional hybridization- or amplification-based methods can test for the presence of expected gene fusions, but the exploratory analysis of currently unknown fusion partners is often cost-prohibitive. Here, we developed an innovative next-generation sequencing (NGS)-based approach for gene fusion analysis termed fusion sequencing via terminator-assisted synthesis (FTAS-seq). FTAS-seq can be used to enrich the gene of interest while simultaneously profiling the whole spectrum of its 3'-terminal fusion partners. Using this novel semi-targeted RNA-sequencing technique, we were able to identify 11 previously uncharacterized TMPRSS2 fusion partners and capture a range of TMPRSS2-ERG isoforms. We tested the performance of FTAS-seq with well-characterized prostate cancer cell lines and utilized the technique for the analysis of patient RNA samples. FTAS-seq chemistry combined with appropriate primer panels holds great potential as a tool for biomarker discovery that can support the development of personalized cancer therapies.

Sensitive and accurate analysis of gene expression signatures enabled by oligonucleotide-labelled cDNA.

High-throughput RNA sequencing offers a comprehensive analysis of transcriptome complexity originated from regulatory events, such as differential gene expression, alternative polyadenylation and others, and allows the increase in diagnostic capacity and precision. For gene expression profiling applications that do not specifically require information on alternative splicing events, the mRNA 3' termini counting approach is a cost-effective alternative to whole transcriptome sequencing. Here, we report MTAS-seq (mRNA sequencing via terminator-assisted synthesis) - a novel RNA-seq library preparation method directed towards mRNA 3' termini. We demonstrate the specific enrichment for 3'-terminal regions by simple and quick single-tube protocol with built-in molecular barcoding to enable accurate estimation of transcript abundance. To achieve that, we synthesized oligonucleotide-modified dideoxynucleotides which enable the generation of cDNA libraries at the reverse transcription step. We validated the performance of MTAS-seq on well-characterized reference bulk RNA and further tested it with eukaryotic cell lysates.

Enzymatic Synthesis of Chimeric DNA Oligonucleotides by in Vitro Transcription with dTTP, dCTP, dATP, and 2'-Fluoro Modified dGTP.

Efficient ways to produce single-stranded DNA are of great interest for diverse applications in molecular biology and nanotechnology. In the present study, we selected T7 RNA polymerase mutants with reduced substrate specificity to employ an in vitro transcription reaction for the synthesis of chimeric DNA oligonucleotides, either individually or in pools. We performed in vitro evolution based on fluorescence-activated droplet sorting and identified mutations V783M, V783L, V689Q, and G555L as novel variants leading to relaxed substrate discrimination. Transcribed chimeric oligonucleotides were tested in PCR, and the quality of amplification products as well as fidelity of oligonucleotide synthesis were assessed by NGS. We concluded that enzymatically produced chimeric DNA transcripts contain significantly fewer deletions and insertions compared to chemically synthesized counterparts and can successfully serve as PCR primers, making the evolved enzymes superior for simple and cheap one-pot synthesis of multiple chimeric DNA oligonucleotides in parallel using a plethora of premixed templates.

CLC-Pred: A freely available web-service for in silico prediction of human cell line cytotoxicity for drug-like compounds.

In silico methods of phenotypic screening are necessary to reduce the time and cost of the experimental in vivo screening of anticancer agents through dozens of millions of natural and synthetic chemical compounds. We used the previously developed PASS (Prediction of Activity Spectra for Substances) algorithm to create and validate the classification SAR models for predicting the cytotoxicity of chemicals against different types of human cell lines using ChEMBL experimental data. A training set from 59,882 structures of compounds was created based on the experimental data (IG50, IC50, and % inhibition values) from ChEMBL. The average accuracy of prediction (AUC) calculated by leave-one-out and a 20-fold cross-validation procedure during the training was 0.930 and 0.927 for 278 cancer cell lines, respectively, and 0.948 and 0.947 for cytotoxicity prediction for 27 normal cell lines, respectively. Using the given SAR models, we developed a freely available web-service for cell-line cytotoxicity profile prediction (CLC-Pred: Cell-Line Cytotoxicity Predictor) based on the following structural formula: http://way2drug.com/Cell-line/.

Recent Advances in the Development of Pharmaceutical Agents for Metabolic Disorders: A Computational Perspective.

BACKGROUND: Metabolic disorders comprise a set of different disorders varying from epidemic diseases such as diabetes mellitus to inborn metabolic orphan diseases such as phenylketonuria. Despite considerable evidence showing the importance of the computational methods in discovery and development of new pharmaceuticals, there are no systematic reviews outlining how they are utilized in the field of metabolic disorders. This review aims to discuss the necessity of the development of web-based tools and databases by integration of available information for solving Big Data problems in network pharmacology of metabolic disorders. METHODS: We undertook a structured search of bibliographic databases for peer-reviewed research literature using a focused review question and inclusion/exclusion criteria. The quality of retrieved papers was appraised using standard tools. RESULTS: The alterations in metabolic pathways cause various cardiovascular, hematological, neurological, gastrointestinal, immune disorders and cancer. In this regard, informatics, Big Data and modeling techniques aid in the design of novel therapeutic agents for metabolic diseases by addressing various Big Data problems in the network polypharmacology (drugs/pharmaceutical agents, proteins, genes, diseases, bioassays, ADMET and metabolic pathways), identification of privileged scaffolds, developing new diagnostic biomarkers, understanding the pathophysiology of disease and progress in personalized medicine. CONCLUSION: The recent advances of developing pharmaceutical agents for various metabolic disorders by considering their pathogenesis, mechanisms of action, therapeutic and adverse effects have been summarized. We have highlighted the role of computational techniques, drug repurposing, and network-based polypharmacological approaches in the identification of new/existing medicines with improved drug-likeness properties for the rare metabolic disorders.