Browse
Submit Data
Databases
API
Help

Dataset Information

27 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

RNA-Targeted Therapies and High-Throughput Screening Methods.

ABSTRACT: RNA-binding proteins (RBPs) are involved in regulating all aspects of RNA metabolism, including processing, transport, translation, and degradation. Dysregulation of RNA metabolism is linked to a plethora of diseases, such as cancer, neurodegenerative diseases, and neuromuscular disorders. Recent years have seen a dramatic shift in the knowledge base, with RNA increasingly being recognised as an attractive target for precision medicine therapies. In this article, we are going to review current RNA-targeted therapies. Furthermore, we will scrutinise a range of drug discoveries targeting protein-RNA interactions. In particular, we will focus on the interplay between Lin28 and let-7, splicing regulatory proteins and survival motor neuron (SMN) pre-mRNA, as well as HuR, Musashi, proteins and their RNA targets. We will highlight the mechanisms RBPs utilise to modulate RNA metabolism and discuss current high-throughput screening strategies. This review provides evidence that we are entering a new era of RNA-targeted medicine.

SUBMITTER: Zhu S

PROVIDER: S-EPMC7216119 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

RNA-Targeted Therapies and High-Throughput Screening Methods.

Zhu Siran S Rooney Saul S Michlewski Gracjan G

International journal of molecular sciences 20200423 8

RNA-binding proteins (RBPs) are involved in regulating all aspects of RNA metabolism, including processing, transport, translation, and degradation. Dysregulation of RNA metabolism is linked to a plethora of diseases, such as cancer, neurodegenerative diseases, and neuromuscular disorders. Recent years have seen a dramatic shift in the knowledge base, with RNA increasingly being recognised as an attractive target for precision medicine therapies. In this article, we are going to review current R ...[more]

PMID: 32340368

Similar Datasets

High-throughput in vivo screening of targeted molecular imaging agents.

Project description:The rapid development and translation of targeted molecular imaging agents from bench to bedside is currently a slow process, with a clear bottleneck between the discovery of new compounds and the development of an appropriate molecular imaging agent. The ability to identify promising new molecular imaging agents, as well as failures, much earlier in the development process using high-throughput screening techniques could save significant time and money. This work combines the advantages of combinatorial chemistry, site-specific solid-phase radiolabeling, and in vivo imaging for the rapid screening of molecular imaging agents. A one-bead-one-compound library was prepared and evaluated in vitro, leading to the identification of 42 promising lead peptides. Over 11 consecutive days, these peptides, along with a control peptide, were successfully radiolabeled with 4-[(18)F]fluorobenzoic acid and evaluated in vivo using microPET. Four peptides were radiolabeled per day, followed by simultaneous injection of each individual peptide into 2 animals. As a result, 4 promising new molecular imaging agents were identified that otherwise would not have been selected based solely on in vitro data. This study is the first example of the practical application of a high-throughput screening approach using microPET imaging of [(18)F]-labeled peptides for the rapid in vivo identification of potential new molecular imaging agents.

| S-EPMC2764889 | biostudies-other

High Throughput Screening and Selection Methods for Directed Enzyme Evolution.

Project description:Successful evolutionary enzyme engineering requires a high throughput screening or selection method, which considerably increases the chance of obtaining desired properties and reduces the time and cost. In this review, a series of high throughput screening and selection methods are illustrated with significant and recent examples. These high throughput strategies are also discussed with an emphasis on compatibility with phenotypic analysis during directed enzyme evolution. Lastly, certain limitations of current methods, as well as future developments, are briefly summarized.

| S-EPMC4461044 | biostudies-other

Discovering cancer vulnerabilities using high-throughput micro-RNA screening.

Project description:Micro-RNAs (miRNAs) are potent regulators of gene expression and cellular phenotype. Each miRNA has the potential to target hundreds of transcripts within the cell thus controlling fundamental cellular processes such as survival and proliferation. Here, we exploit this important feature of miRNA networks to discover vulnerabilities in cancer phenotype, and map miRNA-target relationships across different cancer types. More specifically, we report the results of a functional genomics screen of 1280 miRNA mimics and inhibitors in eight cancer cell lines, and its presentation in a sophisticated interactive data portal. This resource represents the most comprehensive survey of miRNA function in oncology, incorporating breast cancer, prostate cancer and neuroblastoma. A user-friendly web portal couples this experimental data with multiple tools for miRNA target prediction, pathway enrichment analysis and visualization. In addition, the database integrates publicly available gene expression and perturbation data enabling tailored and context-specific analysis of miRNA function in a particular disease. As a proof-of-principle, we use the database and its innovative features to uncover novel determinants of the neuroblastoma malignant phenotype.

| S-EPMC5728403 | biostudies-other

Methods for High-throughput Drug Combination Screening and Synergy Scoring.

Project description:Gene products or pathways that are aberrantly activated in cancer but not in normal tissue hold great promises for being effective and safe anticancer therapeutic targets. Many targeted drugs have entered clinical trials but so far showed limited efficacy mostly due to variability in treatment responses and often rapidly emerging resistance. Toward more effective treatment options, we will need multi-targeted drugs or drug combinations, which selectively inhibit the viability and growth of cancer cells and block distinct escape mechanisms for the cells to become resistant. Functional profiling of drug combinations requires careful experimental design and robust data analysis approaches. At the Institute for Molecular Medicine Finland (FIMM), we have developed an experimental-computational pipeline for high-throughput screening of drug combination effects in cancer cells. The integration of automated screening techniques with advanced synergy scoring tools allows for efficient and reliable detection of synergistic drug interactions within a specific window of concentrations, hence accelerating the identification of potential drug combinations for further confirmatory studies.

| S-EPMC6383747 | biostudies-literature

Genotype-selective combination therapies for melanoma identified by high-throughput drug screening.

Project description:Resistance and partial responses to targeted monotherapy are major obstacles in cancer treatment. Systematic approaches to identify efficacious drug combinations for cancer are not well established, especially in the context of genotype. To address this, we have tested pairwise combinations of an array of small-molecule inhibitors on early-passage melanoma cultures using combinatorial drug screening. Results reveal several inhibitor combinations effective for melanomas with activating RAS or BRAF mutations, including mutant BRAF melanomas with intrinsic or acquired resistance to vemurafenib. Inhibition of both EGF receptor and AKT sensitized treatment-resistant BRAF mutant melanoma cultures to vemurafenib. Melanomas with RAS mutations were more resistant to combination therapies relative to BRAF mutants, but were sensitive to combinations of statins and cyclin-dependent kinase inhibitors in vitro and in vivo. These results show the use of combinatorial drug screening for discovering unique treatment regimens that overcome resistance phenotypes of mutant BRAF- and RAS-driven melanomas.We have used drug combinatorial screening to identify effective combinations for mutant BRAF melanomas, including those resistant to vemurafenib, and mutant RAS melanomas that are resistant to many therapies. Mechanisms governing the interactions of the drug combinations are proposed, and in vivo xenografts show the enhanced benefit and tolerability of a mutant RAS -selective combination, which is currently lacking in the clinic.

| S-EPMC3546137 | biostudies-literature

High-throughput screening identification of poliovirus RNA-dependent RNA polymerase inhibitors.

Project description:Viral RNA-dependent RNA polymerase (RdRP) enzymes are essential for the replication of positive-strand RNA viruses and established targets for the development of selective antiviral therapeutics. In this work we have carried out a high-throughput screen of 154,267 compounds to identify poliovirus polymerase inhibitors using a fluorescence based RNA elongation assay. Screening and subsequent validation experiments using kinetic methods and RNA product analysis resulted in the identification of seven inhibitors that affect the RNA binding, initiation, or elongation activity of the polymerase. X-ray crystallography data show clear density for five of the compounds in the active site of the poliovirus polymerase elongation complex. The inhibitors occupy the NTP binding site by stacking on the priming nucleotide and interacting with the templating base, yet competition studies show fairly weak IC50 values in the low ?M range. A comparison with nucleotide bound structures suggests that weak binding is likely due to the lack of a triphosphate group on the inhibitors. Consequently, the inhibitors are primarily effective at blocking polymerase initiation and do not effectively compete with NTP binding during processive elongation. These findings are discussed in the context of the polymerase elongation complex structure and allosteric control of the viral RdRP catalytic cycle.

| S-EPMC3159743 | biostudies-literature

Predicting high-throughput screening results with scalable literature-based discovery methods.

Project description:The identification of new therapeutic uses for existing agents has been proposed as a means to mitigate the escalating cost of drug development. A common approach to such repurposing involves screening libraries of agents for activities against cell lines. In silico methods using knowledge from the biomedical literature have been proposed to constrain the costs of screening by identifying agents that are likely to be effective a priori. However, results obtained with these methods are seldom evaluated empirically. Conversely, screening experiments have been criticized for their inability to reveal the biological basis of their results. In this paper, we evaluate the ability of a scalable literature-based approach, discovery-by-analogy, to identify a small number of active agents within a large library screened for activity against prostate cancer cells. The methods used permit retrieval of the knowledge used to infer their predictions, providing a plausible biological basis for predicted activity.

| S-EPMC4474167 | biostudies-literature

Statistical methods for analysis of high-throughput RNA interference screens.

Project description:RNA interference (RNAi) has become a powerful technique for reverse genetics and drug discovery, and in both of these areas large-scale high-throughput RNAi screens are commonly performed. The statistical techniques used to analyze these screens are frequently borrowed directly from small-molecule screening; however, small-molecule and RNAi data characteristics differ in meaningful ways. We examine the similarities and differences between RNAi and small-molecule screens, highlighting particular characteristics of RNAi screen data that must be addressed during analysis. Additionally, we provide guidance on selection of analysis techniques in the context of a sample workflow.

| S-EPMC2789971 | biostudies-literature

Materials genomics methods for high-throughput construction of COFs and targeted synthesis.

Project description:Materials genomics represents a research mode for materials development, for which reliable methods for efficient materials construction are essential. Here we present a methodology for high-throughput construction of covalent organic frameworks (COFs) based on materials genomics strategy, in which a gene partition method of genetic structural units (GSUs) with reactive sites and quasi-reactive assembly algorithms (QReaxAA) for structure generation were proposed by mimicking the natural growth processes of COFs, leading to a library of 130 GSUs and a database of ~470,000 materials containing structures with 10 unreported topologies as well as the existing COFs. As a proof-of-concept example, two generated 3D-COFs with ffc topology and two 2D-COFs with existing topologies were successfully synthesized. This work not only presents useful genomics methods for developing COFs and largely extended the COF structures, but also will stimulate the switch of materials development mode from trial-and-error to theoretical prediction-experimental validation.

| S-EPMC6288119 | biostudies-literature

Whole Genome Analysis and Targeted Drug Discovery Using Computational Methods and High Throughput Screening Tools for Emerged Novel Coronavirus (2019-nCoV).

Project description:A novel coronavirus designated as SARS-CoV-2 in February 2020 by World Health organization (WHO) was identified as main cause of SARS like pneumonia cases in Wuhan city in Hubei Province of China at the end of 2019. This been recently declared as Global Pandemic by WHO. There is a global emergency to identify potential drugs to treat the SARS-CoV-2. Currently, there is no specific treatment against the new virus. There is a urgency to identifying potential antiviral agents to combat the disease is urgently needed. An effective and quick approach is to test existing antiviral drugs against. Whole genome analysis and alignment carried out using BLASTn, SMART BLAST and WebDSV 2.0 had shown more than 238 ORF's coding for proteins mostly origin from Bat SARS coronavirus and root genomic origin from Archaea. Molecular docking results against protein targets Furin, papain like proteases, RdRp and Spike glycoprotein had shown paritaprevir, ritonavir, entecavir and chloroquine derivatives are the best drugs to inhibit multi targets of coronavirus infection including natural compounds corosolic acid, baicalin and glycyrrhizic acid with minimal inhibitory concentrations. Thus we propose use of paritaprevir, entecavir, ritonavir and chloroquine derivatives as best drug combination along with niacinamide, folic acid and zinc supplements to treat novel coronavirus infection. We also propose use of plant protease inhibitors (PI's) and Anti-IL8, IL-6, IL-2 as future drug models against coronavirus.

| S-EPMC7331973 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data