Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Tissue-specific genes as an underutilized resource in drug discovery.

ABSTRACT: Tissue-specific genes are believed to be good drug targets due to improved safety. Here we show that this intuitive notion is not reflected in phase 1 and 2 clinical trials, despite the historic success of tissue-specific targets and their 2.3-fold overrepresentation among targets of marketed non-oncology drugs. We compare properties of tissue-specific genes and drug targets. We show that tissue-specificity of the target may also be related to efficacy of the drug. The relationship may be indirect (enrichment in Mendelian disease and PTVesc genes) or direct (elevated betweenness centrality scores for tissue-specifically produced enzymes and secreted proteins). Reduced evolutionary conservation of tissue-specific genes may represent a bottleneck for drug projects, prompting development of novel models with smaller evolutionary gap to humans. We show that the opportunities to identify tissue-specific drug targets are not exhausted and discuss potential use cases for tissue-specific genes in drug research.

SUBMITTER: Ryaboshapkina M

PROVIDER: S-EPMC6510781 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Tissue-specific genes as an underutilized resource in drug discovery.

Ryaboshapkina Maria M Hammar Mårten M

Scientific reports 20190510 1

Tissue-specific genes are believed to be good drug targets due to improved safety. Here we show that this intuitive notion is not reflected in phase 1 and 2 clinical trials, despite the historic success of tissue-specific targets and their 2.3-fold overrepresentation among targets of marketed non-oncology drugs. We compare properties of tissue-specific genes and drug targets. We show that tissue-specificity of the target may also be related to efficacy of the drug. The relationship may be indire ...[more]

PMID: 31076736

Similar Datasets

Genome-Wide Tissue-Specific Genes Identification for Novel Tissue-Specific Promoters Discovery in Soybean.

Project description:Promoters play a crucial role in controlling the spatial and temporal expression of genes at transcriptional levels in the process of higher plant growth and development. The spatial, efficient, and correct regulation of exogenous genes expression, as desired, is the key point in plant genetic engineering research. Constitutive promoters widely used in plant genetic transformation are limited because, sometimes, they may cause potential negative effects. This issue can be solved, to a certain extent, by using tissue-specific promoters. Compared with constitutive promoters, a few tissue-specific promoters have been isolated and applied. In this study, based on the transcriptome data, a total of 288 tissue-specific genes were collected, expressed in seven tissues, including the leaves, stems, flowers, pods, seeds, roots, and nodules of soybean (Glycine max). KEGG pathway enrichment analysis was carried out, and 52 metabolites were annotated. A total of 12 tissue-specific genes were selected via the transcription expression level and validated through real-time quantitative PCR, of which 10 genes showed tissue-specific expression. The 3-kb 5' upstream regions of ten genes were obtained as putative promoters. Further analysis showed that all the 10 promoters contained many tissue-specific cis-elements. These results demonstrate that high-throughput transcriptional data can be used as effective tools, providing a guide for high-throughput novel tissue-specific promoter discovery.

| S-EPMC10298445 | biostudies-literature

T-STAG: resource and web-interface for tissue-specific transcripts and genes.

Project description:T-STAG (tissue-specific transcripts and genes) is a resource and web-interface, designated to analyze tissue/tumor-specific expression patterns in human and mouse transcriptomes. It integrates our refined prediction of specific expression patterns both in genes as well as in individual isoforms with man-mouse orthology data. In combination with the features for combining/contrasting the genes expressed in different tissues, T-STAG implicates important biological applications, such as the detection of differentially expressed genes in tumors, the retrieval of orthologs with significant expression in the same tissue etc. Additionally, our refined categorization of expressed sequence tags (ESTs) according to the normalization of cDNA libraries allows searching for putative low-abundant transcripts. The results are tightly linked to our visualization tools, GeneNest (expression patterns of genes) and SpliceNest (gene structure and alternative splicing). The user-friendly interface of T-STAG offers a platform for comprehensive analysis of tissue and/or tumor-specific expression patterns revealed by the EST data. T-STAG is freely accessible at http://tstag.molgen.mpg.de.

| S-EPMC1160111 | biostudies-literature

Rosa26-LSL-dCas9-VPR: a versatile mouse model for tissue specific and simultaneous activation of multiple genes for drug discovery.

Project description:Transgenic animals with increased or abrogated target gene expression are powerful tools for drug discovery research. Here, we developed a CRISPR-based Rosa26-LSL-dCas9-VPR mouse model for targeted induction of endogenous gene expression using different Adeno-associated virus (AAV) capsid variants for tissue-specific gRNAs delivery. To show applicability of the model, we targeted low-density lipoprotein receptor (LDLR) and proprotein convertase subtilisin/kexin type 9 (PCSK9), either individually or together. We induced up to ninefold higher expression of hepatocellular proteins. In consequence of LDLR upregulation, plasma LDL levels almost abolished, whereas upregulation of PCSK9 led to increased plasma LDL and cholesterol levels. Strikingly, simultaneous upregulation of both LDLR and PCSK9 resulted in almost unaltered LDL levels. Additionally, we used our model to achieve expression of all α1-Antitrypsin (AAT) gene paralogues simultaneously. These results show the potential of our model as a versatile tool for optimized targeted gene expression, alone or in combination.

| S-EPMC9649745 | biostudies-literature

PubChem as a public resource for drug discovery.

Project description:PubChem is a public repository of small molecules and their biological properties. Currently, it contains more than 25 million unique chemical structures and 90 million bioactivity outcomes associated with several thousand macromolecular targets. To address the potential utility of this public resource for drug discovery, we systematically summarized the protein targets in PubChem by function, 3D structure and biological pathway. Moreover, we analyzed the potency, selectivity and promiscuity of the bioactive compounds identified for these biological targets, including the chemical probes generated by the NIH Molecular Libraries Program. As a public resource, PubChem lowers the barrier for researchers to advance the development of chemical tools for modulating biological processes and drug candidates for disease treatments.

| S-EPMC3010383 | biostudies-literature

Postmortem brain tissue as an underutilized resource to study the molecular pathology of neuropsychiatric disorders across different ethnic populations.

Project description:In recent years, large scale meta-analysis of genome-wide association studies (GWAS) have reliably identified genetic polymorphisms associated with neuropsychiatric disorders such as schizophrenia (SCZ), bipolar disorder (BPD) and major depressive disorder (MDD). However, the majority of disease-associated single nucleotide polymorphisms (SNPs) appear within functionally ambiguous non-coding genomic regions. Recently, increased emphasis has been placed on identifying the functional relevance of disease-associated variants via correlating risk polymorphisms with gene expression levels in etiologically relevant tissues. For neuropsychiatric disorders, the etiologically relevant tissue is brain, which requires robust postmortem sample sizes from varying genetic backgrounds. While small sample sizes are of decreasing concern, postmortem brain databases are composed almost exclusively of Caucasian samples, which significantly limits study design and result interpretation. In this review, we highlight the importance of gene expression and expression quantitative loci (eQTL) studies in clinically relevant postmortem tissue while addressing the current limitations of existing postmortem brain databases. Finally, we introduce future collaborations to develop postmortem brain databases for neuropsychiatric disorders from Chinese and Asian subpopulations.

| S-EPMC6643003 | biostudies-literature

Machine Learning on Human Muscle Transcriptomic Data for Biomarker Discovery and Tissue-Specific Drug Target Identification.

Project description:For the past several decades, research in understanding the molecular basis of human muscle aging has progressed significantly. However, the development of accessible tissue-specific biomarkers of human muscle aging that may be measured to evaluate the effectiveness of therapeutic interventions is still a major challenge. Here we present a method for tracking age-related changes of human skeletal muscle. We analyzed publicly available gene expression profiles of young and old tissue from healthy donors. Differential gene expression and pathway analysis were performed to compare signatures of young and old muscle tissue and to preprocess the resulting data for a set of machine learning algorithms. Our study confirms the established mechanisms of human skeletal muscle aging, including dysregulation of cytosolic Ca2+ homeostasis, PPAR signaling and neurotransmitter recycling along with IGFR and PI3K-Akt-mTOR signaling. Applying several supervised machine learning techniques, including neural networks, we built a panel of tissue-specific biomarkers of aging. Our predictive model achieved 0.91 Pearson correlation with respect to the actual age values of the muscle tissue samples, and a mean absolute error of 6.19 years on the test set. The performance of models was also evaluated on gene expression samples of the skeletal muscles from the Gene expression Genotype-Tissue Expression (GTEx) project. The best model achieved the accuracy of 0.80 with respect to the actual age bin prediction on the external validation set. Furthermore, we demonstrated that aging biomarkers can be used to identify new molecular targets for tissue-specific anti-aging therapies.

| S-EPMC6052089 | biostudies-literature

Research resource: Tissue-specific transcriptomics and cistromics of nuclear receptor signaling: a web research resource.

Project description:Nuclear receptors (NRs) are ligand-regulated transcription factors that recruit coregulators and other transcription factors to gene promoters to effect regulation of tissue-specific transcriptomes. The prodigious rate at which the NR signaling field has generated high content gene expression and, more recently, genome-wide location analysis datasets has not been matched by a committed effort to archiving this information for routine access by bench and clinical scientists. As a first step towards this goal, we searched the MEDLINE database for studies, which referenced either expression microarray and/or genome-wide location analysis datasets in which a NR or NR ligand was an experimental variable. A total of 1122 studies encompassing 325 unique organs, tissues, primary cells, and cell lines, 35 NRs, and 91 NR ligands were retrieved and annotated. The data were incorporated into a new section of the Nuclear Receptor Signaling Atlas Molecule Pages, Transcriptomics and Cistromics, for which we designed an intuitive, freely accessible user interface to browse the studies. Each study links to an abstract, the MEDLINE record, and, where available, Gene Expression Omnibus and ArrayExpress records. The resource will be updated on a regular basis to provide a current and comprehensive entrez into the sum of transcriptomic and cistromic research in this field.

| S-EPMC2954640 | biostudies-literature

The Haemonchus contortus kinome--a resource for fundamental molecular investigations and drug discovery.

Project description:BackgroundProtein kinases regulate a plethora of essential signalling and other biological pathways in all eukaryotic organisms, but very little is known about them in most parasitic nematodes.MethodsHere, we defined, for the first time, the entire complement of protein kinases (kinome) encoded in the barber's pole worm (Haemonchus contortus) through an integrated analysis of transcriptomic and genomic datasets using an advanced bioinformatic workflow.ResultsWe identified, curated and classified 432 kinases representing ten groups, 103 distinct families and 98 subfamilies. A comparison of the kinomes of H. contortus and Caenorhabditis elegans (a related, free-living nematode) revealed considerable variation in the numbers of casein kinases, tyrosine kinases and Ca(2+)/calmodulin-dependent protein kinases, which likely relate to differences in biology, habitat and life cycle between these worms. Moreover, a suite of kinase genes was selectively transcribed in particular developmental stages of H. contortus, indicating central roles in developmental and reproductive processes. In addition, using a ranking system, drug targets (n = 13) and associated small-molecule effectors (n = 1517) were inferred.ConclusionsThe H. contortus kinome will provide a useful resource for fundamental investigations of kinases and signalling pathways in this nematode, and should assist future anthelmintic discovery efforts; this is particularly important, given current drug resistance problems in parasitic nematodes.

| S-EPMC4672506 | biostudies-literature

BioPhytMol: a drug discovery community resource on anti-mycobacterial phytomolecules and plant extracts.

Project description:BackgroundTuberculosis (TB) is the second leading cause of death from a single infectious organism, demanding attention towards discovery of novel anti-tubercular compounds. Natural products or their derivatives have provided more than 50% of all existing drugs, offering a chemically diverse space for discovery of novel drugs.DescriptionBioPhytMol has been designed to systematically curate and analyze the anti-mycobacterial natural product chemical space. BioPhytMol is developed as a drug-discovery community resource with anti-mycobacterial phytomolecules and plant extracts. Currently, it holds 2582 entries including 188 plant families (692 genera and 808 species) from global flora, manually curated from literature. In total, there are 633 phytomolecules (with structures) curated against 25 target mycobacteria. Multiple analysis approaches have been used to prioritize the library for drug-like compounds, for both whole cell screening and target-based approaches. In order to represent the multidimensional data on chemical diversity, physiochemical properties and biological activity data of the compound library, novel approaches such as the use of circular graphs have been employed.ConclusionBioPhytMol has been designed to systematically represent and search for anti-mycobacterial phytochemical information. Extensive compound analyses can also be performed through web-application for prioritizing drug-like compounds. The resource is freely available online at http://ab-openlab.csir.res.in/biophytmol/. Graphical AbstractBioPhytMol: a drug discovery community resource on anti-mycobacterial phytomolecules and plant extracts generated using Crowdsourcing. The platform comprises of manually curated data on antimycobacterial natural products along with tools to perform structure similarity and visualization. The platform allows for prioritization of drug like natural products for antimycobacterial drug discovery.

| S-EPMC4206768 | biostudies-literature

Engineered cardiac tissue model of restrictive cardiomyopathy for drug discovery.

Project description:Restrictive cardiomyopathy (RCM) is defined as increased myocardial stiffness and impaired diastolic relaxation leading to elevated ventricular filling pressures. Human variants in filamin C (FLNC) are linked to a variety of cardiomyopathies, and in this study, we investigate an in-frame deletion (c.7416_7418delGAA, p.Glu2472_Asn2473delinAsp) in a patient with RCM. Induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) with this variant display impaired relaxation and reduced calcium kinetics in 2D culture when compared with a CRISPR-Cas9-corrected isogenic control line. Similarly, mutant engineered cardiac tissues (ECTs) demonstrate increased passive tension and impaired relaxation velocity compared with isogenic controls. High-throughput small-molecule screening identifies phosphodiesterase 3 (PDE3) inhibition by trequinsin as a potential therapy to improve cardiomyocyte relaxation in this genotype. Together, these data demonstrate an engineered cardiac tissue model of RCM and establish the translational potential of this precision medicine approach to identify therapeutics targeting myocardial relaxation.

| S-EPMC10040415 | 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