The availability of large amounts of high-throughput genomic, transcriptomic and epigenomic data has provided opportunity to understand regulation of the cellular transcriptome with an unprecedented level of detail. As a result, research has advanced from identifying gene expression patterns associated with particular conditions to elucidating signalling pathways that regulate expression. There are over 1,000 transcription factors (TFs) in vertebrates that play a role in this regulation. Determining which of these are likely to be controlling a set of genes can be assisted by computational prediction, utilising experimentally verified binding site motifs. Here we present CiiiDER, an integrated computational toolkit for transcription factor binding analysis, written in the Java programming language, to make it independent of computer operating system. It is operated through an intuitive graphical user interface with interactive, high-quality visual outputs, making it accessible to all researchers. CiiiDER predicts transcription factor binding sites (TFBSs) across regulatory regions of interest, such as promoters and enhancers derived from any species. It can perform an enrichment analysis to identify TFs that are significantly over- or under-represented in comparison to a bespoke background set and thereby elucidate pathways regulating sets of genes of pathophysiological importance.

Activation of the Toll-like receptor (TLR) family of innate immune sensors stimulates multiple signal transduction pathways. Previous studies have suggested that TLR2, TLR4 and TLR9 induce serine phosphorylation of Signal Transducers and Activators of Transcription-1 (STAT1) at residue 727 (S727), although its role in TLR signaling was unclear. We report here that STAT1 rapidly undergoes phosphorylation following TLR4 challenge with lipopolysaccharide (LPS) in a model of LPS hypersensitivity in vivo. Importantly, genetic ablation of STAT1 protected against LPS-induced lethality suggesting that STAT1 may have a key role in TLR-induced inflammation. We have found that multiple TLRs induce Ser727 phosphorylation of STAT1, which is dependent on MyD88 and TRIF signaling, but independent of interferon (IFN) regulatory factor (IRF)-3, IRF7 and the IFN receptor complex, suggesting that activation is a direct TLR response rather than autocrine activation via IFN. Importantly, we found that STAT1 interacts with tumor necrosis factor (TNF) receptor-associated factor-6 (TRAF6), a key mediator of TLR signaling after TLR challenge and that following activation, STAT1 translocates to the nucleus. Critically, macrophages generated from mice in which the S727 residue was replaced with alanine (STAT1 S727A mice) display significantly reduced TNFα protein production, but not reduced interleukin-6 or RANTES protein in response to multiple TLR challenges, as compared with wild-type macrophages. These results clearly demonstrate cross-talk between the TLR and JAK/STAT signaling pathways with direct recruitment of STAT1 by TRAF6 and that the direct activation of STAT1 by TLR signaling suggests a crucial role for STAT1 in TLR-induced inflammation.