An Atlas of Genome-wide Transcription Factor Binding
Profiles in Human Hematopoietic Stem/Progenitor Cells

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BloodchIP - Diego Chacon

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BloodChIP is a user friendly database that integrates genome-wide binding profiles of haematopoietic transcription factors (TFs) in primary
human CD34 positive stem/progenitor cells as well as Megakarocytes, SKNO-1 and K562 cell types. It also includes histone profiles from the Human Epigenome Atlas and gene expression of cell types and CD34 fractions.

An interactive web interface allows users to query BloodChIP and ascertain the relative expression level of their genes of interest across normal and leukaemic stem/progenitor fractions. Importantly, the user is then able to associate expression levels in these cell fractions with chromatin accessibility and transcription factor binding profiles in primary human HSPCs to gain insights into the transcriptional regulation of these genes. The database supports exploration and selection based on either genes or transcription factors of interest. All queries, as well as the complete database, can be exported by the user for further data analysis.

Authors: Diego Chacon, Dominik Beck, Jason WH Wong & John E Pimanda

Published in: Health & Medicine
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BloodchIP - Diego Chacon

  1. 1. An Atlas of Genome-wide Transcription Factor Binding Profiles in Human Hematopoietic Stem/Progenitor Cells Prince of Wales Clinical School Diego Chacon, Dominik Beck, Jason WH Wong & John E Pimanda Lowy Cancer Research Centre & Prince of Wales Clinical School, University of New South Wales Introduction Background: Haematopoiesis serves as a model system for the multi-lineage differentiation of adult stem cells. The current paradigm of sorting cells based on cell surface markers and performing functional assays to reconstruct the hierarchy of haematopoietic stem cell differentiation is evolving. There is a growing emphasis on transcriptional drivers that assign cell states by altering the genomic landscape. Cell type specific expression of transcription factors, and cell type specific accessibility of enhancer elements, control gene expression profiles that give blood cells of various lineages their distinct identities. A core set of transcription factors work in combination to regulate gene expression in haematopoietic stem/progenitor cells (HSPCs). Knowledge of how these transcription factors cooperate with each other and interact with other lineage specific transcription factors to regulate gene expression during HSPC differentiation is key to improving our understanding of normal blood development and how these processes are corrupted in leukaemia. What is BloodChIP ?: BloodChIP is a user friendly database that integrates genome-wide binding profiles of seven haematopoietic transcription factors (TFs) in primary human CD34 positive stem/progenitor cells with histone profiles from the Human Epigenome Atlas and gene expression in normal and leukaemic stem/progenitor fractions. An interactive web interface allows users to query BloodChIP and ascertain the relative expression level of their genes of interest across normal and leukaemic stem/progenitor fractions. Importantly, the user is then able to associate expression levels in these cell fractions with chromatin accessibility and transcription factor binding profiles in primary human HSPCs to gain insights into the transcriptional regulation of these genes. The database supports exploration and selection based on either genes or transcription factors of interest. All queries, as well as the complete database, can be exported by the user for further data analysis. Aim: Provide integrated resource that brings together gene regulatory and expression datasets for the study of normal blood differentiation and leukaemia. Method Results Querying BloodChIP for a gene of interest. Querying BloodChIP for gene networks regulated by TFs. An example query on An example query on BloodChIP for the HHEX gene. Different combinatorial binding patterns at six loci were found and are shown with three well defined histone marks. HHEX gene expression is also shown. This information can be further visualized and we link to the Expression Atlas to obtain additional information on HHEX. BloodChIP for genes simultaneously bound by seven TFs. Binding information for all target genes is shown alongside with three well defined histone modifications. Gene expression is also shown for a single gene. The target gene network can be visualized and exported for direct integration with other bioinformatics tools. To explore additional evidence of TF-gene interactions we provide integration with the STRING database. Integration and visualization of network data with other bioinformatics tools. Export functions for gene lists facilitate applications such as Pathway Analysis (A) or Gene Set Enrichment Analysis (B). (C) Export functions for TF-gene relationships allow filtering, visualization and downstream analysis using Cytoscape. Gene visualization of TF binding, histone modifications and gene expression. Integration with the UCSC genome browser facilitates an in depth inspection of the gene locus. Information can further be integrated with in-house data and publicly available genome browser tracks. TF ChIP-Seq: Genome-wide binding catalogues for FLI, ERG, GATA2, RUNX1, SCL, LYL1, LMO2 and IgG were previously generated in human CD34+ cells of normal donors using ChIP-Seq (GEO: GSE45144). Pre-processed reads were aligned to the human genome (hg19) using BWA [1]. Three algorithms (MACS [2], HOMER [3], Partek [4]) were used to call peaks against the negative control IgG. Peaks called by at least two algorithms were identified as high confidence binding regions (HCBR) for downstream analysis. Gene expression across major HSPCs and AML fractions. Boxplots allow for first insights into gene expression changes during normal hematopoiesis and in leukemic stem cells. HHEX is highly expressed only in normal early stem cell and leukemia. Genes are linked to the Expression Atlas. Additional gene information and ontologies can be browsed. For example, HHEX was found differentially expressed in 345 experiments, including leukemic datasets that can be further examined. Peak to gene annotations: HCBR were assigned as regulatory regions of at most two genes using annotations from the GREAT analysis package [5]. Network gene expression across major HSPCs and AML fractions. Hierarchical clustering realizes expression patterns from normal hematopoiesis to leukemic stem cells. A network bound by all seven TFs identifies three major clusters (green, blue and orange), clearly stratifying AML samples (orange), normal stem and early HSPCs (blue), and more committed HSPCs (green). Gene networks are linked to the STRING database. Networks are searched for further evidence on interactions. Information on gene neighborhoods, gene fusions, cooccurrences, co-expression, and other evidence from experiments, databases and homology searches are used. Based on this information four sub-networks are discovered (blue, green, red and yellow) which are characterized by key hub-genes (STAT3, PTPN6, PIK3R1, YWHAG). Expression quantification: Genome-wide expression data for different subtractions of human CD34+ cells of normal donors and AML patients were recently generated using expression arrays (GEO: GSE24006). Robust Multi-chip Average approach was used for normalization and expression levels summarized (Matlab 2012b). Future work References Histone ChIP-Seq: Genome-wide binding catalogues for H3K27ac, H3K4me1, H3K4me3 were previously generated in human CD34+ cells of normal donors by the Epigenome Atlas Project. Sequencing reads were downloaded in a pre-filtered and pre-aligned format. Read coverage +/- 1kb around the center of all HCBR was calculated using BEDtools [6]. • Integration of DNase I data to resolve nucleosome positioning in human CD34+ cells • Integration of ChIP-Seq data for the hematopoietic TFs MYB and GFI1B in CD34+ cells. • Integration of ChIP-Seq data from AML CD34+ cells. [1] Li et al, Bioinformatics 2010 [2] Zhang et al, Genome Biology 2008 [3] Heinz et al, Molecular Cell 2010 [4] http://www.partek.com [5] McLean et al, Nature Biotechnology 2010 [6] Quinlan et al, Bioinformatics 2010

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