Activated Toll-like receptors (TLRs) cluster in lipid rafts and induce pro-and anti-tumor responses. The organization of the assembly is critical to the understanding of how these key receptors control major signaling pathways in the cell. Although several models for individual interactions were proposed, the entire TIR-domain signalosome architecture has not been worked out, possibly due to its complexity. We employ a powerful algorithm, crystal structures and experimental data to model the TLR4 and its cluster. The architecture that we obtain with 8 MyD88 molecules provides the structural basis for the MyD88-templated myddosome helical assembly and receptor clustering; it also provides clues to pro-and anti-inflammatory signaling pathways branching at the signalosome level to Mal/MyD88 and TRAM/TRIF pro-and anti-inflammatory pathways. The assembly of MyD88 death domain (DD) with TRAF3 (anti-viral/anti-inflammatory) and TRAF6 (pro-inflammatory) suggest that TRAF3/TRAF6 binding sites on MyD88 DD partially overlap, as do IRAK4 and FADD. Significantly, the organization illuminates mechanisms of oncogenic mutations, demonstrates that almost all TLR4 parallel pathways are competitive and clarifies decisions at pathway branching points. The architectures are compatible with the currently-available experimental data and provide compelling insights into signaling in cancer and inflammation pathways.

BackgroundInflammation has significant roles in all phases of tumor development, including initiation, progression and metastasis. Interleukin-10 (IL-10) is a well-known immuno-modulatory cytokine with an anti-inflammatory activity. Lack of IL-10 allows induction of pro-inflammatory cytokines and hinders anti-tumor immunity, thereby favoring tumor growth. The IL-10 network is among the most important paths linking cancer and inflammation. The simple node-and-edge network representation is useful, but limited, hampering the understanding of the mechanistic details of signaling pathways. Structural networks complete the missing parts, and provide details. The IL-10 structural network may shed light on the mechanisms through which disease-related mutations work and the pathogenesis of malignancies.ResultsUsing PRISM (a PRotein Interactions by Structural Matching tool), we constructed the structural network of IL-10, which includes its first and second degree protein neighbor interactions. We predicted the structures of complexes involved in these interactions, thereby enriching the available structural data. In order to reveal the significance of the interactions, we exploited mutations identified in cancer patients, mapping them onto key proteins of this network. We analyzed the effect of these mutations on the interactions, and demonstrated a relation between these and inflammation and cancer. Our results suggest that mutations that disrupt the interactions of IL-10 with its receptors (IL-10RA and IL-10RB) and α2-macroglobulin (A2M) may enhance inflammation and modulate anti-tumor immunity. Likewise, mutations that weaken the A2M-APP (amyloid precursor protein) association may increase the proliferative effect of APP through preventing β-amyloid degradation by the A2M receptor, and mutations that abolish the A2M-Kallikrein-13 (KLK13) interaction may lead to cell proliferation and metastasis through the destructive effect of KLK13 on the extracellular matrix.ConclusionsPrediction of protein-protein interactions through structural matching can enrich the available cellular pathways. In addition, the structural data of protein complexes suggest how oncogenic mutations influence the interactions and explain their potential impact on IL-10 signaling in cancer and inflammation.

Even though the Toll-like receptor (TLR) pathway is integral to inflammatory defense mechanisms, its excessive signaling may be devastating. Cells have acquired a cascade of strategies to regulate TLR signaling by targeting protein-protein interactions, or ubiquitin chains, but the details of the inhibition mechanisms are still unclear. Here, we provide the structural basis for the regulation of TLR signaling by constructing architectures of protein-protein interactions. Structural data suggest that 1) Toll/IL-1R (TIR) domain-containing regulators (BCAP, SIGIRR, and ST2) interfere with TIR domain signalosome formation; 2) major deubiquitinases such as A20, CYLD, and DUBA prevent association of TRAF6 and TRAF3 with their partners, in addition to removing K63-linked ubiquitin chains that serve as a docking platform for downstream effectors; 3) alternative downstream pathways of TLRs also restrict signaling by competing to bind common partners through shared binding sites. We also performed in silico mutagenesis analysis to characterize the effects of oncogenic mutations on the negative regulators and to observe the cellular outcome (whether there is/is not inflammation). Missense mutations that fall on interfaces and nonsense/frameshift mutations that result in truncated negative regulators disrupt the interactions with the targets, thereby enabling constitutive activation of the nuclear factor-kappa B, and contributing to chronic inflammation, autoimmune diseases, and oncogenesis.

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Hundreds of different species colonize multicellular organisms making them “metaorganisms”. A growing body of data supports the role of microbiota in health and in disease. Grasping the principles of host-microbiota interactions (HMIs) at the molecular level is important since it may provide insights into the mechanisms of infections. The crosstalk between the host and the microbiota may help resolve puzzling questions such as how a microorganism can contribute to both health and disease. Integrated superorganism networks that consider host and microbiota as a whole–may uncover their code, clarifying perhaps the most fundamental question: how they modulate immune surveillance. Within this framework, structural HMI networks can uniquely identify potential microbial effectors that target distinct host nodes or interfere with endogenous host interactions, as well as how mutations on either host or microbial proteins affect the interaction. Furthermore, structural HMIs can help identify master host cell regulator nodes and modules whose tweaking by the microbes promote aberrant activity. Collectively, these data can delineate pathogenic mechanisms and thereby help maximize beneficial therapeutics. To date, challenges in experimental techniques limit large-scale characterization of HMIs. Here we highlight an area in its infancy which we believe will increasingly engage the computational community: predicting interactions across kingdoms, and mapping these on the host cellular networks to figure out how commensal and pathogenic microbiota modulate the host signaling and broadly cross-species consequences.