Analysis of Innate Immune Signaling Networks

先天免疫系统的细胞不断评估宿主粘膜表面和外周组织的感染或损伤的迹象。宿主必须在对有益微生物和少量非病理性微生物的耐受与对更严重感染的强大免疫反应的发展之间找到平衡。新出现的证据表明，这一决定是由细胞根据其与微生物和内源性刺激接触所收到的组合信号做出的。这些信号主要由各种类型的模式识别受体（PRR）感知，虽然在表征通过这些受体诱导的单个信号通路方面取得了显著进展，但相对较少的研究涉及免疫细胞如何整合组合的PRR输入以及这些信号与可溶性宿主源物质（如细胞因子、脂质和补体成分）产生的其他信号的组合。该项目旨在定义在这种情况下决定信号输入和输出函数之间复杂关系的控制原则，并最终生成定量计算模型，以描述与传染病相关的情况下的细胞行为。使用巨噬细胞作为模型系统，我们最初通过测量各种读数来表征细胞对PRR配体和完整病原体的反应，例如；信号蛋白磷酸化、细胞内运输、病原体复制、转录和免疫介质的产生。

Cells of the innate immune system constantly evaluate host mucosal surfaces and peripheral tissues for signs of infection or injury. The host must find a balance between tolerance of beneficial microorganisms and minor non-pathological microbial encounter vs. the development of a robust immune response to more serious infections. Emerging evidence suggests that this decision is made by the cell based on the combinatorial signals it receives from its engagement with microorganisms and endogenous stimuli. These signals are sensed primarily by various classes of pattern recognition receptors (PRR), and while there has been remarkable progress in characterizing the individual signaling pathways induced through these receptors, relatively few studies have addressed how immune cells integrate combined PRR inputs and the combination of these signals with others arising from soluble host derived substances such as cytokines, lipids, and complement components. This project seeks to define the control principles that determine the complex relationship between signal input and output function in this scenario, and ultimately to generate quantitative computational models to describe cellular behavior in circumstances relevant to infectious disease. Using macrophages as a model system, we are initially characterizing the cellular response to PRR ligands and intact pathogens by measurement of a variety of readouts such as; signaling protein phosphorylation, intracellular trafficking, pathogen replication, transcription and production of immune mediators. We have profiled the response of macrophage cells to a group of toll-like receptor (TLR) ligands (LPS, Pam2CSK4, Pam3CSK4, Resiquimod 848 and Poly I:C). Analysis of the response to combined stimuli (mimicking what would occur with an intact pathogen) shows non-additive levels of activation of downstream signaling pathways. This year, we have generated comparative transcriptional profiles following stimulation of RAW cells and BMDM with single or combined TLR ligands. These data suggest that the non-additivity in signaling outputs and cytokines is reflected at the transcriptional level. In ligand combinations that signal exclusively through the MyD88 adapter, all induced genes show less than additive responses. However, in ligand combinations including a TRIF-activating ligand, we see a select subset of genes induced to greater than additive levels. This selective outcome for combined MyD88+TRIF activation is likely used by the host as a detection mechanism for either simultaneous exposure to viral and bacterial pathogens, or to a significant infection with intracellular pathogen, and it leads to the increased production of cytokines that serve to drive a robust adaptive immune response. We seek to identify the basis of the greater than additive release of two key cytokines, IL-6 and IL-12, from macrophages in response to ligands which induce the TRIF and MyD88-dependent pathways. Identifying the cellular mechanism underlying this non-linear response will have important implications both for modeling of PRR pathway crosstalk in macrophages and also for identifying therapeutic targets for inflammatory disorders. This year, we generated transcriptional profiles from BMDMs challenged with poly I:C (I) and R848 (R) and the combination (IR) over a broad time course. We identified several hundred genes with expression characteristics that could implicate them as synergy factors underlying the enhanced production of IL6 and IL-12. These genes have been targeted in an siRNA screen to determine if they affect the macrophages ability to induce high levels of these cytokines in response to the combined IR stimulus. Dynamic modeling of TLR pathway crosstalk will ultimately require computational implementation of the signaling network for each TLR. This year, we began the development of a model for early events in the LPS/TLR4 response, as the presence of both MyD88-dependent and TRIF-dependent branches in this pathway provides the most comprehensive prototype network for TLR signaling, and it has obvious relevance for our genome wide siRNA screening projects using LPS (AI001106). We are using the LSB Computational Biology Units (CBU) Simmune package for this modeling effort. We are using published parameters for the initial reactions in the pathway whenever possible, however currently available data are insufficient to support implementation of a comprehensive dynamic simulation of the entire TLR4 signaling pathway. We have created a set of fluorescently tagged expression constructs to provide CFP and YFP fusions of the TLR receptors, signaling adaptors, and kinases involved in proximal signaling. We are using these to develop a TIRF/FRET-based assay to determine the spatio-temporal characteristics of the early events post ligand binding for input to our model simulation. These imaging experiments are being done in collaboration with the LSB T-Cell Biophysics Unit (TBU) taking advantage of their sophisticated TIRF imaging platform. To evaluate the TLR signal integration that occurs in the context of a real infection, we previously initiated a study of the macrophage response to Burkholderia cenocepacia (Bcc), an opportunistic bacteria particularly problematic in cystic fibrosis and chronic granulomatous disease patients, and closely related to the category A select agents B. mallei and pseudomallei. Macrophages are likely to play a key role in Bcc-induced pulmonary infections, but very little is known about the mechanism of Bcc infection and replication in these cells. This year, we have made significant progress in characterizing the intracellular life cycle of B. cenocepacia and its interaction with the autophagy pathway in human macrophages. Electron and confocal microscopy analysis demonstrates that the invading bacteria interact transiently with the endocytic pathway before escaping to the cytosol. This escape triggers the selective autophagy pathway, and the recruitment of ubiquitin, the ubiquitin-binding adaptors p62 and NDP52 and the autophagosome membrane-associated protein LC3B, to the bacterial vicinity. However, despite recruitment of all the key autophagy initiation components, B. cenocepacia blocks autophagosome completion and replicates in the host cytosol. We find that a pre-infection increase in cellular autophagy flux can significantly inhibit B. cenocepacia replication and that lower autophagy flux in macrophages from immunocompromised CGD patients could contribute to increased B. cenocepacia susceptibility, identifying autophagy manipulation as a potential therapeutic approach to reduce bacterial burden in B. cenocepacia infections. These data are currently under review for publication. Ongoing studies are evaluating how the host autophagy and PRR-driven responses to the bacteria are integrated to determine infection outcome. In addressing how non-TLR inputs can influence the macrophage response to bacterial signals, we previously identified a cellular mechanism underlying the suppressive effect of PGE2 on LPS-induced TNFα production in macrophages. This PGE2 effect involves cAMP-dependent PKA activation leading to inhibitory phosphorylation of a key component of the NF-κB family. This highlighted an important function for PGE2 in regulating the host response during infection to avoid damaging inflammation. This year, we began exploring collaborative opportunities with Yasmine Belkaids lab to build on these prior findings, as they have identified a key role for macrophage/monocyte-derived PGE2 in controlling the inflammatory output from recruited neutrophils during parasitic infection. This collaboration led to the recent publication of a study demonstrating a central role for PGE2 in a commensal-driven regulatory loop required to control inflammatory host-commensal interactions.