Abstract Inflammation, the first line of defense against pathogens can contribute to all phases of tumorigenesis, including tumor initiation, promotion and metastasis. Within this framework, the ...Toll-like receptor (TLR) pathway plays a central role in inflammation and cancer. Although extremely useful, the classical representation of this, and other pathways in the cellular network in terms of nodes (proteins) and edges (interactions) is incomplete. Structural pathways can help complete missing parts of such diagrams: they demonstrate in detail how signals coming from different upstream pathways merge and propagate downstream, how parallel pathways compensate each other in drug resistant mutants, how multi-subunit signaling complexes form and in particular why they are needed and how they work, how allosteric events can control these proteins and their pathways, and intricate details of feedback loops and how kick in. They can also explain the mechanisms of some oncogenic SNP mutations. Constructing structural pathways is a challenging task. Here, our goal is to provide an overview of inflammation and cancer from the structural standpoint, focusing on the TLR pathway. We use the powerful PRISM (PRotein Interactions by Structural Matching) tool to reveal important structural information of interactions in and within key orchestrators of the TLR pathway, such as MyD88.
Members of the p53 family of transcription factors—p53, p63, and p73—share a high degree of homology; however, members can be activated in response to different stimuli, perform distinct (sometimes ...opposing) roles and are expressed in different tissues. The level of complexity is increased further by the transcription of multiple isoforms of each homolog, which may interact or interfere with each other and can impact cellular outcome. Proteins perform their functions through interacting with other proteins (and/or with nucleic acids). Therefore, identification of the interactors of a protein and how they interact in 3D is essential to fully comprehend their roles. By utilizing an in silico protein–protein interaction prediction method—HMI‐PRED—we predicted interaction partners of p53 family members and modeled 3D structures of these protein interaction complexes. This method recovered experimentally known interactions while identifying many novel candidate partners. We analyzed the similarities and differences observed among the interaction partners to elucidate distinct functions of p53 family members and provide examples of how this information may yield mechanistic insight to explain their overlapping versus distinct/opposing outcomes in certain contexts. While some interaction partners are common to p53, p63, and p73, the majority are unique to each member. Nevertheless, most of the enriched pathways associated with these partners are common to all members, indicating that the members target the same biological pathways but through unique mediators. p63 and p73 have more common enriched pathways compared to p53, supporting their similar developmental roles in different tissues.
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.
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.
Microbes, commensals, and pathogens, control the numerous functions in the host cells. They can alter host signaling and modulate immune surveillance by interacting with the host proteins. For ...shedding light on the contribution of microbes to health and disease, it is vital to discern how microbial proteins rewire host signaling and through which host proteins they do this. Host-Microbe Interaction PREDictor (HMI-PRED) is a user-friendly web server for structural prediction of protein-protein interactions (PPIs) between the host and a microbial species, including bacteria, viruses, fungi, and protozoa. HMI-PRED relies on “interface mimicry” through which the microbial proteins hijack host binding surfaces. Given the structure of a microbial protein of interest, HMI-PRED will return structural models of potential host-microbe interaction (HMI) complexes, the list of host endogenous and exogenous PPIs that can be disrupted, and tissue expression of the microbe-targeted host proteins. The server also allows users to upload homology models of microbial proteins. Broadly, it aims at large-scale, efficient identification of HMIs. The prediction results are stored in a repository for community access. HMI-PRED is free and available at https://interactome.ku.edu.tr/hmi.
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•HMI-PRED is a user-friendly web server for host-microbe interaction prediction.•HMI-PRED relies on interface mimicry—the hijacking of host protein binding surfaces.•Any microbial proteins, both pathogenic and commensal proteins, can be analyzed.
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.
Oncoviruses rewire host pathways to subvert host immunity and promote their survival and proliferation. However, exactly how is challenging to understand. Here, by employing the first and to date ...only interface-based host-microbe interaction (HMI) prediction method, we explore a pivotal strategy oncoviruses use to drive cancer: mimicking binding surfaces-interfaces-of human proteins. We show that oncoviruses can target key human network proteins and transform cells by acquisition of cancer hallmarks. Experimental large-scale mapping of HMIs is difficult and individual HMIs do not permit in-depth grasp of tumorigenic virulence mechanisms. Our computational approach is tractable and 3D structural HMI models can help elucidate pathogenesis mechanisms and facilitate drug design. We observe that many host proteins are unique targets for certain oncoviruses, whereas others are common to several, suggesting similar infectious strategies. A rough estimation of our false discovery rate based on the tissue expression of oncovirus-targeted human proteins is 25%.
There is a strong correlation between some pathogens and certain cancer types. One example is Helicobacter pylori and gastric cancer. Exactly how they contribute to host tumorigenesis is, however, a ...mystery. Pathogens often interact with the host through proteins. To subvert defense, they may mimic host proteins at the sequence, structure, motif, or interface levels. Interface similarity permits pathogen proteins to compete with those of the host for a target protein and thereby alter the host signaling. Detection of host–pathogen interactions (HPIs) and mapping the re-wired superorganism HPI network—with structural details—can provide unprecedented clues to the underlying mechanisms and help therapeutics. Here, we describe the first computational approach exploiting solely interface mimicry to model potential HPIs. Interface mimicry can identify more HPIs than sequence or complete structural similarity since it appears more common than the other mimicry types. We illustrate the usefulness of this concept by modeling HPIs of H. pylori to understand how they modulate host immunity, persist lifelong, and contribute to tumorigenesis. H. pylori proteins interfere with multiple host pathways as they target several host hub proteins. Our results help illuminate the structural basis of resistance to apoptosis, immune evasion, and loss of cell junctions seen in H. pylori-infected host cells.
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•Detection of HPIs can clarify mechanisms of pathogen-driven cancers.•Pathogens mimic host protein–protein interfaces to rewire host interactions.•We present a computational approach that exploits solely interface mimicry to predict HPIs.•We apply this concept to H. pylori to see how they modulate host immunity and contribute to oncogenesis.•H. pylori proteins interfere with multiple host pathways as they target several host hub proteins.
Autophagy is biological mechanism allowing recycling of long-lived proteins, abnormal protein aggregates, and damaged organelles under cellular stress conditions. Following sequestration in double- ...or multimembrane autophagic vesicles, the cargo is delivered to lysosomes for degradation. ATG5 is a key component of an E3-like ATG12-ATG5-ATG16 protein complex that catalyzes conjugation of the MAP1LC3 protein to lipids, thus controlling autophagic vesicle formation and expansion. Accumulating data indicate that ATG5 is a convergence point for autophagy regulation. Here, we describe the scaffold protein RACK1 (receptor activated C-kinase 1, GNB2L1) as a novel ATG5 interactor and an autophagy protein. Using several independent techniques, we showed that RACK1 interacted with ATG5. Importantly, classical autophagy inducers (starvation or mammalian target of rapamycin blockage) stimulated RACK1-ATG5 interaction. Knockdown of RACK1 or prevention of its binding to ATG5 using mutagenesis blocked autophagy activation. Therefore, the scaffold protein RACK1 is a new ATG5-interacting protein and an important and novel component of the autophagy pathways.
Inflammation 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.
Using 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.
Prediction 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.
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