The transcriptional network that controls growth arrest and differentiation in a human myeloid leukemia cell line

Abstract

Using deep sequencing (deepCAGE), the FANTOM4 study measured the genome-wide dynamics of transcription-start-site usage in the human monocytic cell line THP-1 throughout a time course of growth arrest and differentiation. Modeling the expression dynamics in terms of predicted cis-regulatory sites, we identified the key transcription regulators, their time-dependent activities and target genes. Systematic siRNA knockdown of 52 transcription factors confirmed the roles of individual factors in the regulatory network. Our results indicate that cellular states are constrained by complex networks involving both positive and negative regulatory interactions among substantial numbers of transcription factors and that no single transcription factor is both necessary and sufficient to drive the differentiation process.

Infiltration of diametrically polarized macrophages predicts overall survival of patients with gastric cancer after surgical resection

“…bone marrow progenitors develop into monocytes, and extravasate into various tissues, where they differentiate into resident macrophages [15]. The monocyte-macrophage lineage is diverse and plastic, attributed to a complex transcriptome [30]. To facilitate immune function and maintain tissue homeostasis, macrophages adopt a wide range of activation states that can be classified within the classic activated M1 and alternative activated M2 models of macrophage polarization by interaction with the tumor-microenvironment milieu [31].…”

Promoter architectures and developmental gene regulation

“…Thus, CAGE provides information on two aspects of the capped transcriptome: 1) genome-wide single basepair resolution map of transcription start sites, and 2) relative levels of transcripts initiated at each CTSS. This information can be used for various analyses, from 5' end centred expression profiling [30,31] to studying promoter architecture [12,25].…”

“…This has led to the discovery of distinct classes of promoters with respect to TSS distribution that correlates with both underlying sequence features and gene function [12], and implies distinct modes of their regulation (reviewed in [34]). Quantitative nature of CAGE has been used to model expression dynamics and to reconstruct the regulatory networks driving the differentiation [30] and maintaining identity of numerous human and mouse cell and tissue types [27], by identifying key transcription factors binding at promoters. Moreover, CAGE signal has been shown to be enriched at enhancers [35] and has been used to construct an atlas of active enhancers over cells and tissues across the whole human body [36].…”

Co-expression of FBN1 with mesenchyme-specific genes in mouse cell lines: implications for phenotypic variability in Marfan syndrome

“…Bioinformatic analysis of motif activity and motif target predictions were performed as described previously. 28 All genes represented on the microarray, which had been allocated a RefSeq (12752 in total), were classified as being either among the 205 genes of the Fbn1-associated cluster or not within the set. The proportion of genes with a z(p, m) score of greater than 1 for each transcription factor binding motif m was calculated for the two groups and a z-value for this difference was determined.…”

“…28 Table 2 lists the 15 transcription factors that had the highest positive correlations with the expression pattern of cluster genes. Comparison was also carried out between genes within the cluster and the remaining genes of the data set.…”