Structural Signatures (sGES): A Web Tool for Enriching Gene Expression Signatures With Protein Structural Features

2021 ◽  
Author(s):  
Nicole Zatorski ◽  
D. Stein ◽  
R. Rahman ◽  
R. Iyengar ◽  
A. Schlessinger
Keyword(s):  
Gene Expression ◽  
Structural Features ◽  
Web Tool ◽  
Gene Expression Signatures

scholarly journals Protein structure-based gene expression signatures

2020 ◽  
Author(s):  
R. Rahman ◽  
Y. Xiong ◽  
J. G. C. van Hasselt ◽  
J. Hansen ◽  
E. A. Sobie ◽  
...  
Keyword(s):  
Gene Expression ◽  
Protein Structure ◽  
Mrna Expression ◽  
Expression Patterns ◽  
Drug Effects ◽  
Tissue Expression ◽  
Structural Features ◽  
Biological Phenomena ◽  
Gene Expression Signatures ◽  
Cellular Phenotypes

AbstractGene expression signatures (GES) connect phenotypes to mRNA expression patterns, providing a powerful approach to define cellular identity, function, and the effects of perturbations. However, the use of GES has suffered from vague assessment criteria and limited reproducibility. The structure of proteins defines the functional capability of genes, and hence, we hypothesized that enrichment of structural features could be a generalizable representation of gene sets. We derive structural gene expression signatures (sGES) using features from various levels of protein structure (e.g. domain, fold) encoded by the transcribed genes in GES, to describe cellular phenotypes. Comprehensive analyses of data from the Genotype-Tissue Expression Project (GTEx), ARCHS4, and mRNA expression of drug effects on cardiomyocytes show that structural GES (sGES) are useful for identifying robust signatures of biological phenomena. sGES also enables the characterization of signatures across experimental platforms, facilitates the interoperability of expression datasets, and can describe drug action on cells.

scholarly journals DREIMT: a drug repositioning database and prioritization tool for immunomodulation

2020 ◽  
Author(s):  
Kevin Troulé ◽  
Hugo López-Fernández ◽  
Santiago García-Martín ◽  
Miguel Reboiro-Jato ◽  
Carlos Carretero-Puche ◽  
...  
Keyword(s):  
Gene Expression ◽  
Drug Repositioning ◽  
Supplementary Information ◽  
Immune Gene ◽  
Immunomodulatory Drugs ◽  
Web Tool ◽  
Immune Gene Expression ◽  
Final Output ◽  
Gene Expression Signatures ◽  
New Hypothesis

Abstract Motivation Drug immunomodulation modifies the response of the immune system and can be therapeutically exploited in pathologies such as cancer and autoimmune diseases. Results DREIMT is a new hypothesis-generation web tool, which performs drug prioritization analysis for immunomodulation. DREIMT provides significant immunomodulatory drugs targeting up to 70 immune cells subtypes through a curated database that integrates 4960 drug profiles and ∼2600 immune gene expression signatures. The tool also suggests potential immunomodulatory drugs targeting user-supplied gene expression signatures. Final output includes drug–signature association scores, FDRs and downloadable plots and results tables. Availabilityand implementation http://www.dreimt.org. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals DREIMT: a drug repositioning database and prioritization tool for immunomodulation

2020 ◽  
Author(s):  
Kevin Troulé ◽  
Hugo López-Fernández ◽  
Santiago García-Martín ◽  
Miguel Reboiro-Jato ◽  
Carlos Carretero-Puche ◽  
...  
Keyword(s):  
Gene Expression ◽  
Immune Cells ◽  
Drug Repositioning ◽  
Immune Gene ◽  
Immunomodulatory Drugs ◽  
Web Tool ◽  
Immune Gene Expression ◽  
Final Output ◽  
Gene Expression Signatures ◽  
New Hypothesis

AbstractMotivationDrug immunomodulation modifies the response of the immune system and can be therapeutically exploited in pathologies such as cancer and autoimmune diseases.ResultsDREIMT is a new hypothesis-generation web tool which performs drug prioritization analysis for immunomodulation. DREIMT provides significant immunomodulatory drugs targeting up to 70 immune cells subtypes through a curated database that integrates 4,960 drug profiles and ~2,6K immune gene expression signatures. The tool also suggests potential immunomodulatory drugs targeting user-supplied gene expression signatures. Final output includes drug-signature association scores, FDRs and downloadable plots and results tables.Availabilityhttp://[email protected]; [email protected]

scholarly journals Protein structure–based gene expression signatures

2021 ◽  
Vol 118 (19) ◽  
pp. e2014866118
Author(s):  
Rayees Rahman ◽  
Nicole Zatorski ◽  
Jens Hansen ◽  
Yuguang Xiong ◽  
J. G. Coen van Hasselt ◽  
...  
Keyword(s):  
Gene Expression ◽  
Protein Structure ◽  
Mrna Expression ◽  
Messenger Rna ◽  
Drug Effects ◽  
Tissue Expression ◽  
Structural Features ◽  
Phenotypic Characterization ◽  
Biological Phenomena ◽  
Gene Expression Signatures

Gene expression signatures (GES) connect phenotypes to differential messenger RNA (mRNA) expression of genes, providing a powerful approach to define cellular identity, function, and the effects of perturbations. The use of GES has suffered from vague assessment criteria and limited reproducibility. Because the structure of proteins defines the functional capability of genes, we hypothesized that enrichment of structural features could be a generalizable representation of gene sets. We derive structural gene expression signatures (sGES) using features from multiple levels of protein structure (e.g., domain and fold) encoded by the mRNAs in GES. Comprehensive analyses of data from the Genotype-Tissue Expression Project (GTEx), the all RNA-seq and ChIP-seq sample and signature search (ARCHS4) database, and mRNA expression of drug effects on cardiomyocytes show that sGES are useful for characterizing biological phenomena. sGES enable phenotypic characterization across experimental platforms, facilitates interoperability of expression datasets, and describe drug action on cells.

Post-Transcriptional Regulation of Cardiac Sarcomere Protein Titin Through 3’ Untranslated Regions

2013 ◽  
Vol 9 ◽  
Author(s):  
Tara A Shrout
Keyword(s):  
Gene Expression ◽  
Transcriptional Control ◽  
Striated Muscle ◽  
Binding Capacity ◽  
Structural Features ◽  
Neonatal Rat ◽  
Regulatory Control ◽  
Untranslated Regions ◽  
Luciferase Reporter ◽  
Regulatory Effects

Titin is the largest known protein in the human body, and forms the backbone of all striated muscle sarcomeres. The elastic nature of titin is an important component of muscle compliance and functionality. A significant amount of energy is expended to synthesize titin, thus we postulate that titin gene expression is under strict regulatory control in order to conserve cellular resources. In general, gene expression is mediated in part by post-transcriptional control elements located within the 5’ and 3’ untranslated regions (UTRs) of mature mRNA. The 3’UTR in particular contains structural features that affect binding capacity to other RNA components, such as MicroRNA, which control mRNA localization, translation, and degradation. The degree and significance of the regulatory effects mediated by two determined variants of titin’s 3’ UTR were evaluated in Neonatal Rat Ventricular Myocyte and Human Embryonic Kidney cell lines. Recombinant plasmids to transfect these cells lines were engineered by insertion of the variant titin 3’UTR 431- and 1047-base pairs sequences into luciferase reporter vectors. Expression due to an unaltered reporter vector served as the control. Quantitative changes in luciferase activity due to the recombinants proportionally represented the effect titin’s respective 3’UTR conferred on downstream post-transcriptional expression relative to the control. The effect due to titin’s shorter 3’UTR sequence was inconclusive; however, results illustrated that titin’s longer 3’UTR sequence caused a 35 percent decrease in protein expression. Secondary structural analysis of the two sequences revealed differential folding patterns that affect the stability and degree of MicroRNA-binding within titin’s variant 3’UTR sequences.

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