Uncovering a Macrophage Transcriptional Program by Integrating Evidence from Motif Scanning and Expression Dynamics

Abstract

Macrophages are versatile immune cells that can detect a variety of pathogen-associated molecular patterns through their Toll-like receptors (TLRs). In response to microbial challenge, the TLR-stimulated macrophage undergoes an activation program controlled by a dynamically inducible transcriptional regulatory network. Mapping a complex mammalian transcriptional network poses significant challenges and requires the integration of multiple experimental data types. In this work, we inferred a transcriptional network underlying TLR-stimulated murine macrophage activation. Microarray-based expression profiling and transcription factor binding site motif scanning were used to infer a network of associations between transcription factor genes and clusters of co-expressed target genes. The time-lagged correlation was used to analyze temporal expression data in order to identify potential causal influences in the network. A novel statistical test was developed to assess the significance of the time-lagged correlation. Several associations in the resulting inferred network were validated using targeted ChIP-on-chip experiments. The network incorporates known regulators and gives insight into the transcriptional control of macrophage activation. Our analysis identified a novel regulator (TGIF1) that may have a role in macrophage activation.

Data integration for identification of important transcription factors of STAT6-mediated cell fate decisions

“…Recently, Subramanian et al (2015) summarized the potential of data integration and network analysis for uncovering physiological processes of immune cells. For instance, in macrophages, gene expression dynamics and scanning for TF-binding sequence motifs have been used to elucidate transcriptional networks on a large scale (Ramsey et al, 2008;Litvak et al, 2012), while a combination of genome-wide mRNA expression data and network perturbation using methods such as RNAi knockdown was applied to identify useful intervention strategies in infections (König et al, 2010). In T helper (Th) cells, Ciofani et al (2012) demonstrated the power of data integration to construct a regulatory network for Th17 cell differentiation: they applied an integrative approach to delineate the Th17 cell global transcriptional regulatory network using meta-analysis of genome occupancy of multiple TFs, RNA-seq data of TF-deficient T cells, and immune cell transcriptome data.…”

Rapid Temporal Dynamics of Transcription, Protein Synthesis, and Secretion during Macrophage Activation

“…For instance, Ramsey et al combined mRNA expression analysis using microarrays with motif scanning of transcription factor binding sites, inferring a network of asso-ciations between transcription factor genes and clusters of co-expressed target genes (8). Similarly, transcriptome analyses revealed crosstalk between pathways downstream of TLR4 (9) and among the three transcription factors NF-B, C/EBPdelta, and ATF3 that discriminates between transient and persistent TLR4-induced signals (10).…”

Merck Ad5/HIV induces broad innate immune activation that predicts CD8 + T-cell responses but is attenuated by preexisting Ad5 immunity

“…Crosstalk and feedback can be elucidated between immune signaling pathways and gene regulatory networks operating on multiple spatial and temporal scales. We have previously applied systems analysis to identify gene and signaling networks that coordinately amplify and attenuate Toll-like receptor (TLR)-mediated responses underlying innate immune cell activation (14)(15)(16)(17). Recent systems analyses of responses to vaccination with the highly efficacious YF-17D yellow fever vaccine (18,19) and seasonal influenza vaccine (20) have yielded novel insights about their mechanisms of action.…”