Innate immune detection of the type III secretion apparatus through the NLRC4 inflammasome.

The mammalian innate immune system uses Toll-like receptors (TLRs) and Nod-LRRs (NLRs) to detect microbial components during infection. Often these molecules work in concert; for example, the TLRs can stimulate the production of the proforms of the cytokines IL-1beta and IL-18, whereas certain NLRs trigger their subsequent proteolytic processing via caspase 1. Gram-negative bacteria use type III secretion systems (T3SS) to deliver virulence factors to the cytosol of host cells, where they modulate cell physiology to favor the pathogen. We show here that NLRC4/Ipaf detects the basal body rod component of the T3SS apparatus (rod protein) from S. typhimurium (PrgJ), Burkholderia pseudomallei (BsaK), Escherichia coli (EprJ and EscI), Shigella flexneri (MxiI), and Pseudomonas aeruginosa (PscI). These rod proteins share a sequence motif that is essential for detection by NLRC4; a similar motif is found in flagellin that is also detected by NLRC4. S. typhimurium has two T3SS: Salmonella pathogenicity island-1 (SPI1), which encodes the rod protein PrgJ, and SPI2, which encodes the rod protein SsaI. Although PrgJ is detected by NLRC4, SsaI is not, and this evasion is required for virulence in mice. The detection of a conserved component of the T3SS apparatus enables innate immune responses to virulent bacteria through a single pathway, a strategy that is divergent from that used by plants in which multiple NB-LRR proteins are used to detect T3SS effectors or their effects on cells. Furthermore, the specific detection of the virulence machinery permits the discrimination between pathogenic and nonpathogenic bacteria.

Uncovering a macrophage transcriptional program by integrating evidence from motif scanning and expression dynamics.

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.