Grantee Research Project Results
2015 Progress Report: Pathway Analysis Core
EPA Grant Number: R835737C005Subproject: this is subproject number 005 , established and managed by the Center Director under grant R835737
(EPA does not fund or establish subprojects; EPA awards and manages the overall grant for this center).
Center: Human Models for Analysis of Pathways (H MAPs) Center
Center Director: Murphy, William L
Title: Pathway Analysis Core
Investigators: Roy, Sushmita
Institution: University of Wisconsin - Madison
EPA Project Officer: Aja, Hayley
Project Period: December 1, 2014 through November 30, 2018 (Extended to November 30, 2019)
Project Period Covered by this Report: December 1, 2014 through November 30,2015
RFA: Organotypic Culture Models for Predictive Toxicology Center (2013) RFA Text | Recipients Lists
Research Category: Chemical Safety for Sustainability
Objective:
The overarching goal of our Pathway Analysis Core is to provide an integrated understanding of cellular response to environmental perturbations by developing novel network biology tools that are applicable to a variety of cell types and tissue models. Our specific aims are to develop methods that (1) integrate temporal dynamics in regulatory network model reconstruction and (2) identify subnetworks that are perturbed under exposure to changing environmental stimuli. We will apply these methods to dissect regulatory networks of tissue and cell-type specific responses to small molecules and toxins from the central nervous system and liver hepatocyte development. Specifically, the core is developing methods to analyze data measured by the Brain MAPS and Liver MAPS projects.
Progress Summary:
RNA-seq Data Analysis from BRAIN-MAPs. We began to analyze RNA-seq data from regional NSC samples created by the Brain MAPS project. Principle components analysis (PCA) identified an outlier sample and also revealed that a component that strongly stratifies the samples by time. We performed k-means clustering on that component as a preliminary effort to identify discrete sub-populations in the samples (please see BRAIN MAPs progress report).
Integrative Network-based Analysis of Transcriptomic Profiles. We developed an approach to infer integrated regulatory networks combining a gene expression-based network inference MERLIN, statistical regression, and network information flow methods. We applied our approach to multiple short time-course data sets measuring transcriptomic response to viral pathogenic infections (Fig 1). Our approach is applicable to other types of perturbations as well, namely small molecules such as toxins that interact with the upstream signaling network. We used our approach to prioritize regulators and learn mechanistic physical regulatory programs specifying downstream gene expression programs. In parallel, we have written a review of recent advances in network-based approaches for integrative analysis of omic datasets.
Inference of Dynamic Transcriptional and Chromatin Modules on Cell Lineages and Time Courses. Transcriptional output is governed by an interplay of chromatin state and transcription factor proteins. To understand the role of chromatin state changes to down-stream gene expression patterns, we have developed a new approach called Chromatin Module Inference on Trees (CMINT), which aims to infer modules of genes that exhibit similar combinations of chromatin marks. We have applied this to a cell-fate specification problem, cellular reprogramming of mouse embryonic fibroblasts to induced pluripotent cells and have identified modules that exhibit known and novel combinations of chromatin marks. A key property of the CMINT algorithm is to trace the trajectory of a gene’s chromatin state on the lineage. We intend to apply this approach to examine dynamic transcriptional modules from RNA-seq and ChIP-seq datasets.
Future Activities:
- Application of available and novel dynamic network and module inference methods (CMINT) to BRAIN MAPs data. Identify gene modules with distinct temporal dynamics characteristic of discrete cell types.
- Adapt and apply network information flow methods to examine tissue-specific response to different toxins.
- Start transcriptomic and epigenomic data analysis from LIVER MAPs project.
Journal Articles on this Report : 2 Displayed | Download in RIS Format
| Other subproject views: | All 17 publications | 9 publications in selected types | All 8 journal articles |
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| Other center views: | All 215 publications | 82 publications in selected types | All 81 journal articles |
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Chasman D, Walters KB, Lopes TJ, Eisfeld AJ, Kawaoka Y, Roy S. Integrating transcriptomic and proteomic data using predictive regulatory network models of host response to pathogens. PLoS Computational Biology2016;12(7):e1005013. |
R835737 (2015) R835737 (2016) R835737 (2017) R835737C005 (2015) |
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Chasman D, Fotuhi Siahpirani A, Roy S. Network-based approaches for analysis of complex biological systems. Current Opinion in Biotechnology 2016;39:157-166. |
R835737 (2015) R835737 (2016) R835737 (2017) R835737C005 (2015) |
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Supplemental Keywords:
regulatory networks, transcriptional modules, chromatin state, dynamic networks, temporal networksProgress and Final Reports:
Original AbstractMain Center Abstract and Reports:
R835737 Human Models for Analysis of Pathways (H MAPs) Center Subprojects under this Center: (EPA does not fund or establish subprojects; EPA awards and manages the overall grant for this center).
R835737C001 Liver MAPs
R835737C002 Brain MAPs
R835737C003 Cancer MAPs: A 3D Organotypic Microfluidic Culture System to
Identify Chemicals that Impact Progression and Development of Breast Cancer
R835737C004 Vascular MAPs: Vascular and Neurovascular Tissue Models
R835737C005 Pathway Analysis Core
The perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.