Genome-wide mRNA profiling provides a snapshot of the global state of mammalian cells under different experimental conditions such as diseased vs. normal or drug vs. mock treatment cellular states. However, since measurements are in the form of quantitative changes in mRNA levels, such experimental data does not provide direct understanding of the regulatory upstream molecular mechanisms responsible for the observed changes. Identifying potential cell signaling regulatory mechanisms responsible for changes in gene expression under different experimental conditions or in different tissues has been the focus of many computational systems biology efforts. Most popular approaches include gene ontology or pathway enrichment analyses, as well as reverse engineering of networks from mRNA expression data. However, these methods often assume that differentially expressed genes give rise to pathways and functional modules which is not always true in higher eukaryotes. Here we propose an alternative rational approach, called Expression2Kinases, to identify and rank transcription factors, chromatin modifiers, protein complexes, and protein kinases that are likely responsible for observed changes in gene expression. By combining data from ChIP-seq and ChIP-chip experiments, protein-protein interactions reported in publicly available databases, and kinase-protein phosphorylation reactions collected from the literature, we can identify and rank upstream regulators based on genome-wide changes in gene expression. The idea is to infer the transcription-factors and chromatin regulators responsible for changes in gene-expression;then use protein-protein interactions to """"""""connect"""""""" the identified factors to build transcriptional complexes involving the factors;then use kinase-protein phosphorylation reactions to identify and rank candidate protein kinases that most likely regulate the formation of the identified transcriptional complexes. We plan to validate this method with phosphoproteomics data, data from drug perturbations followed by genome-wide gene expression, RNAi screens, as well as through literature-based text-mining approaches. The project will produce several high quality datasets, web-based software, new algorithms, and robust lists of transcription-factors, histone modifiers, and kinase rankings likely responsible fo mammalian cell regulation. The approach will be experimentally tested in several collaborative projects mainly exploring regulation of differentiating stem and iPS cells. The databases, software tools and algorithms developed for this project will advance drug target discovery and help in unraveling drug mechanisms of action.

Public Health Relevance

The project presents a new rational pipeline to identify and rank upstream regulators that are responsible for observed changes in gene expression collected at the genome-wide scale from mammalian cells as well as method to predict how combinations of FDA approved drugs could be used to affect changes in gene expression. The approach has the potential to rapidly advance drug target discovery, suggest drug repositioning strategies, and help in unraveling drug mechanisms of action, and will be mainly applied to study stem cell differentiation and iPS reprogramming through experimental collaborations.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
1R01GM098316-01A1
Application #
8295704
Study Section
Biodata Management and Analysis Study Section (BDMA)
Program Officer
Dunsmore, Sarah
Project Start
2012-07-18
Project End
2016-04-30
Budget Start
2012-07-18
Budget End
2013-04-30
Support Year
1
Fiscal Year
2012
Total Cost
$326,820
Indirect Cost
$132,812
Name
Icahn School of Medicine at Mount Sinai
Department
Pharmacology
Type
Schools of Medicine
DUNS #
078861598
City
New York
State
NY
Country
United States
Zip Code
10029
Hodos, Rachel; Zhang, Ping; Lee, Hao-Chih et al. (2018) Cell-specific prediction and application of drug-induced gene expression profiles. Pac Symp Biocomput 23:32-43
Koplev, Simon; Lin, Katie; Dohlman, Anders B et al. (2018) Integration of pan-cancer transcriptomics with RPPA proteomics reveals mechanisms of epithelial-mesenchymal transition. PLoS Comput Biol 14:e1005911
Ma'ayan, Avi (2017) Complex systems biology. J R Soc Interface 14:
Fernandez, Nicolas F; Gundersen, Gregory W; Rahman, Adeeb et al. (2017) Clustergrammer, a web-based heatmap visualization and analysis tool for high-dimensional biological data. Sci Data 4:170151
Mallipattu, Sandeep K; Guo, Yiqing; Revelo, Monica P et al. (2017) Krüppel-Like Factor 15 Mediates Glucocorticoid-Induced Restoration of Podocyte Differentiation Markers. J Am Soc Nephrol 28:166-184
Kuleshov, Maxim V; Jones, Matthew R; Rouillard, Andrew D et al. (2016) Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res 44:W90-7
Rouillard, Andrew D; Gundersen, Gregory W; Fernandez, Nicolas F et al. (2016) The harmonizome: a collection of processed datasets gathered to serve and mine knowledge about genes and proteins. Database (Oxford) 2016:
Wang, Zichen; Clark, Neil R; Ma'ayan, Avi (2016) Drug-induced adverse events prediction with the LINCS L1000 data. Bioinformatics 32:2338-45
Morgan, David J; Poolman, Toryn M; Williamson, Andrew J K et al. (2016) Glucocorticoid receptor isoforms direct distinct mitochondrial programs to regulate ATP production. Sci Rep 6:26419
Gundersen, Gregory W; Jagodnik, Kathleen M; Woodland, Holly et al. (2016) GEN3VA: aggregation and analysis of gene expression signatures from related studies. BMC Bioinformatics 17:461

Showing the most recent 10 out of 56 publications