Grantee Research Project Results
2017 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, 2016 through November 30,2017
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 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:
1. Inference of regulatory networks of early neural stem cell differentiation from BRAIN MAPs RNA-seq data.
We have developed a novel computational pipeline to identify regulatory networks and their dynamics from RNA-seq time course data (Fig. 1). Our approach combines publicly available gene expression datasets with the BRAIN MAPs RNA-seq dataset to infer regulatory network dynamics. As part of this pipeline, we have developed a novel computational method, Escarole (Fig. 1A), which is broadly applicable to gene expression time courses to identify genes with interesting dynamics. Escarole is a multi-task clustering-based approach for obtaining discretized gene expression states per cell type on a lineage tree or in a time course. Using our approach, we have inferred a global regulatory network for hindbrain NSCs (Fig. 1B) comprising 6,448 edges connecting 1,325 TFs and 756 signaling proteins to 4,072 target genes at an FDR of 0.01. We have also identified dynamic genes (Fig. 1C) whose changes in expression are likely associated with important cell fate decisions. These dynamic genes include known patterning genes such as the HOX family, and are enriched for genes involved in toxin-gene interaction networks for 8 different neurotoxins (PMID: 26392547) from the Comparative Toxicogenomics Database (PMID: 27651457).
We have prioritized regulators for these genes (Fig. 1D), and have applied literature validation as well as experimental validation of predicted regulatory connections in collaboration with the Ashton lab. In addition, we have compared our results to existing computational methods to examine gene expression dynamics: SegReg (R. Bacher & N. Leng, R package) and DREM (PMID:22897824). Compared to these approaches, our’s identifies both dynamic genes and their regulators, and recapitulates important processes and pathways that are relevant to early neural differentiation. Together with the Ashton lab, we are validating targets of two candidate regulators, HOXA5 and POU3F2, using siRNA of the regulator followed by qPCR of the predicted targets. We have drafted the first version of our manuscript.
2. Analysis of RNA-seq, chromatin mark ChIP-seq and ATAC-seq data from LIVER MAPs.
Analysis of transcriptomics dynamics during liver development and cultured hepatocyte dedifferentiation. To further examine the dynamics and identify key transcription factors involved in maintaining the hepatocyte state, we have applied Escarole (described above, Fig. 2) to two RNA-seq time courses generated by the Thomson lab: one measuring in vitro dedifferentiation from cultured primary hepatocytes to undifferentiated cells and a second measuring in vivo forward differentiation from the embryonic state to mature liver. Escarole application to the forward differentiation time course (Fig. 2A) identified 14 gene sets, 11 of which are enriched for a total of 579 Gene Ontology (GO) terms (PMID: 10802651), 145 curated pathways (PMID:26771021) and 43 transcription factor motifs (PMID: 25215497). The de-differentiation time course (Fig. 2B) has 47 gene sets, 39 of which are enriched for a total of 1123 GO terms, 206 pathways, and 50 TF motifs. We are using the Escarole trajectories and gene sets to prioritize transcription factors that might be important for maintenance of the hepatocyte state.
Analysis of mouse hepatocyte ATAC-seq data. The Thomson lab is generating ATAC-seq in mouse hepatocytes to identify transcription factors that might be responsible for establishing the mature hepatocyte state. We have performed several pre-processing and visualization analyses on these data. Briefly, we visualized the aggregate 3’ and 5’ cut sites from unique and properly paired ATAC-seq reads, centered around Ctcf motif instances. We identified characteristic Ctcf footprints in the Thomson lab data as well as published datasets (Fig. 2C). We also combined the ATAC-seq with motif position weight matrices (PWMs) to identify cell type specific binding using two approaches: (1) by calling ATAC-seq peaks (MACS2, PMID: 18798982) followed by scanning for motif instances (FIMO, PMID: 21330290); (2) by applying the PIQ algorithm (PMID:24441470), which more directly integrates the PWMs and ATAC-seq data. We applied both strategies to mouse hepatocyte ATAC-seq (Thomson lab), DNaseI assays from mouse liver, and, as a control, DNaseI assays from mouse embryonic stem cells. To assess the quality of the called sites, we compared them to TF-specific ChIP-seq peaks for liver TF Hnf4a (PMID: 25209997), ES specific TFs Pou5f1 and Nanog, and a general TF, Ctcf. Cell type specific motif sites of the liver and ES TFs ranked by confidence tend to overlap the corresponding cell type specific ChIP-seq peaks as measured by a precision-recall curve (Fig. 2D). For the general TF Ctcf, motif sites in both liver and ES cells recover ChIP-seq peaks equally well. Both analysis pipelines exhibited this behavior. Furthermore, gene targets of liver TFs (Hnf1a, Hnf1b and Hnf4a) inferred from the liver ATAC-seq analysis are more highly expressed in liver than non-targets.
Principle Investigator Sushmita Roy was named a 2018 Vilas Associate.
Future Activities:
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Finalize and submit manuscript with BRAIN MAPs project.
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Refine Escarole and network inference to examine time series RNA-seq and ATAC-seq data with LIVER MAPS project.
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Prioritize candidate transcription factors involved in maintaining hepatocyte state to share with LIVER MAPS for validation.
Journal Articles on this Report : 4 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, Roy S. Inference of cell type specific regulatory networks on mammalian lineages. Current Opinion in Systems Biology 2017;2:130-139. |
R835737 (2017) R835737C004 (2017) R835737C005 (2016) R835737C005 (2017) |
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Garcia K, Chasman D, Roy S, Ane J-M. Physiological responses and gene co-expression network of mycorrhizal roots under K+ deprivation. Plant Physiology 2017;173(3):1811-1823. |
R835737 (2017) R835737C004 (2017) R835737C005 (2017) |
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Roy S, Sridharan R. Chromatin module inference on cellular trajectories identifies key transition points and poised epigenetic states in diverse developmental processes. Genome Research 2017;27(7):1250-1262. |
R835737C004 (2017) R835737C005 (2017) |
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Siahpirani A, Roy S. A prior-based integrative framework for functional transcriptional regulatory network inference. Nucleic Acids Research 2017;45(4):e21 (22 pp.). |
R835737C005 (2017) |
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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.
Project Research Results
8 journal articles for this subproject
Main Center: R835737
215 publications for this center
81 journal articles for this center