scholarly journals Master Regulators Connectivity Map: A Transcription Factors-Centered Approach to Drug Repositioning

2018 ◽  
Vol 9 ◽  
Author(s):  
Marco A. De Bastiani ◽  
Bianca Pfaffenseller ◽  
Fabio Klamt
Keyword(s):  
Transcription Factors ◽  
Drug Repositioning ◽  
Connectivity Map ◽  
Master Regulators

scholarly journals Prioritizing Small Molecule as Candidates for Drug Repositioning using Machine Learning

2018 ◽  
Author(s):  
Khader Shameer ◽  
Kipp W. Johnson ◽  
Benjamin S. Glicksberg ◽  
Rachel Hodos ◽  
Ben Readhead ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Small Molecule ◽  
Chemical Space ◽  
Drug Repositioning ◽  
Chemical Properties ◽  
Support Vector ◽  
Feature Engineering ◽  
Connectivity Map ◽  
Molecular Features

ABSTRACTDrug repositioning, i.e. identifying new uses for existing drugs and research compounds, is a cost-effective drug discovery strategy that is continuing to grow in popularity. Prioritizing and identifying drugs capable of being repositioned may improve the productivity and success rate of the drug discovery cycle, especially if the drug has already proven to be safe in humans. In previous work, we have shown that drugs that have been successfully repositioned have different chemical properties than those that have not. Hence, there is an opportunity to use machine learning to prioritize drug-like molecules as candidates for future repositioning studies. We have developed a feature engineering and machine learning that leverages data from publicly available drug discovery resources: RepurposeDB and DrugBank. ChemVec is the chemoinformatics-based feature engineering strategy designed to compile molecular features representing the chemical space of all drug molecules in the study. ChemVec was trained through a variety of supervised classification algorithms (Naïve Bayes, Random Forest, Support Vector Machines and an ensemble model combining the three algorithms). Models were created using various combinations of datasets as Connectivity Map based model, DrugBank Approved compounds based model, and DrugBank full set of compounds; of which RandomForest trained using Connectivity Map based data performed the best (AUC=0.674). Briefly, our study represents a novel approach to evaluate a small molecule for drug repositioning opportunity and may further improve discovery of pleiotropic drugs, or those to treat multiple indications.

scholarly journals Regulating the Regulators: The Control of Transcription Factors in Plant Defense Signaling

2018 ◽  
Vol 19 (12) ◽  
pp. 3737 ◽  
Author(s):  
Danny Ng ◽  
Jayami Abeysinghe ◽  
Maedeh Kamali
Keyword(s):  
Transcription Factors ◽  
Plant Defense ◽  
Signaling Pathways ◽  
Stress Responses ◽  
Crop Production ◽  
Defense Responses ◽  
Sequencing Data ◽  
Basic Leucine Zipper ◽  
Master Regulators ◽  
External Threats

Being sessile, plants rely on intricate signaling pathways to mount an efficient defense against external threats while maintaining the cost balance for growth. Transcription factors (TFs) form a repertoire of master regulators in controlling various processes of plant development and responses against external stimuli. There are about 58 families of TFs in plants and among them, six major TF families (AP2/ERF (APETALA2/ethylene responsive factor), bHLH (basic helix-loop-helix), MYB (myeloblastosis related), NAC (no apical meristem (NAM), Arabidopsis transcription activation factor (ATAF1/2), and cup-shaped cotyledon (CUC2)), WRKY, and bZIP (basic leucine zipper)) are found to be involved in biotic and abiotic stress responses. As master regulators of plant defense, the expression and activities of these TFs are subjected to various transcriptional and post-transcriptional controls, as well as post-translational modifications. Many excellent reviews have discussed the importance of these TFs families in mediating their downstream target signaling pathways in plant defense. In this review, we summarize the molecular regulatory mechanisms determining the expression and activities of these master regulators themselves, providing insights for studying their variation and regulation in crop wild relatives (CWR). With the advance of genome sequencing and the growing collection of re-sequencing data of CWR, now is the time to re-examine and discover CWR for the lost or alternative alleles of TFs. Such approach will facilitate molecular breeding and genetic improvement of domesticated crops, especially in stress tolerance and defense responses, with the aim to address the growing concern of climate change and its impact on agriculture crop production.

scholarly journals DMAP: a connectivity map database to enable identification of novel drug repositioning candidates

2015 ◽  
Vol 16 (S13) ◽  
Author(s):  
Hui Huang ◽  
Thanh Nguyen ◽  
Sara Ibrahim ◽  
Sandeep Shantharam ◽  
Zongliang Yue ◽  
...  
Keyword(s):  
Drug Repositioning ◽  
Connectivity Map ◽  
Novel Drug

Transcription factors that behave as master regulators during mammalian embryogenesis function as molecular rheostats

2008 ◽  
Vol 411 (2) ◽  
pp. e5-e7 ◽  
Author(s):  
Angie Rizzino
Keyword(s):  
Transcription Factors ◽  
Molecular Mechanisms ◽  
Es Cells ◽  
Embryonic Stem ◽  
Special Property ◽  
The Self ◽  
Leukaemia Inhibitory Factor ◽  
Self Renewal ◽  
Master Regulators ◽  
Mammalian Embryogenesis

Three transcription factors, Sox2, Oct-3/4 and Nanog, have been identified as master regulators that orchestrate mammalian embryogenesis as well as the self-renewal and pluripotency of ES (embryonic stem) cells. Efforts to understand how these transcription factors function have shown that they have a special property in common. Small changes in the expression of any one of these factors dramatically alter the self-renewal and pluripotency of ES cells. In this way, each functions as a molecular rheostat to control the behaviour of ES cells. Recent studies have begun to examine the molecular mechanisms that regulate the levels of these transcription factors. In this issue of the Biochemical Journal, Mullin and co-workers report that Nanog can self-associate to form dimers. Importantly, they also show that the domain responsible for dimerization is also needed for Nanog to sustain the self-renewal of ES cells in the absence of the cytokine LIF (leukaemia inhibitory factor). On the basis of their studies, they propose a novel mechanism for regulating the interactions between Nanog and other nuclear proteins.

scholarly journals Master regulators or lineage-specifying? Changing views on CD4+ T cell transcription factors

2012 ◽  
Vol 12 (11) ◽  
pp. 799-804 ◽  
Author(s):  
Kenneth J. Oestreich ◽  
Amy S. Weinmann
Keyword(s):  
Transcription Factors ◽  
T Cell ◽  
Cd4 T Cell ◽  
Master Regulators

SREBP transcription factors: master regulators of lipid homeostasis

Biochimie ◽  
2004 ◽  
Vol 86 (11) ◽  
pp. 839-848 ◽  
Author(s):  
Delphine Eberlé ◽  
Bronwyn Hegarty ◽  
Pascale Bossard ◽  
Pascal Ferré ◽  
Fabienne Foufelle
Keyword(s):  
Transcription Factors ◽  
Lipid Homeostasis ◽  
Master Regulators

scholarly journals Evaluation of Connectivity Map shows limited reproducibility in drug repositioning

2019 ◽  
Author(s):  
Nathaniel Lim ◽  
Paul Pavlidis
Keyword(s):  
Success Rate ◽  
Cell Line ◽  
Differential Expression ◽  
Drug Repositioning ◽  
False Positives ◽  
Data Driven ◽  
Connectivity Map ◽  
Expression Levels ◽  
Compound Concentration

SummaryThe Connectivity Map (CMap) is a popular resource designed for data-driven drug repositioning using a large transcriptomic compendium. However, evaluations of its performance are limited. We used two iterations of CMap (CMap 1 and 2) to assess their comparability and reliability. We queried CMap 2 with CMap 1-derived signatures, expecting CMap 2 would highly prioritize the queried compounds; success rate was 17%. Analysis of previously published prioritizations yielded similar results. Low recall is caused by low differential expression (DE) reproducibility both between CMaps and within each CMap. DE strength was predictive of reproducibility, and is influenced by compound concentration and cell-line responsiveness. Reproducibility of CMap 2 sample expression levels was also lower than expected. We attempted to identify the “better” CMap by comparison with a third dataset, but they were mutually discordant. Our findings have implications for CMap usage and we suggest steps for investigators to limit false positives.

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