HIV-1 Activates Macrophages Independent of Toll-Like Receptors

Background

Macrophages provide an interface between innate and adaptive immunity and are important long-lived reservoirs for Human Immunodeficiency Virus Type-1 (HIV-1). Multiple genetic networks involved in regulating signal transduction cascades and immune responses in macrophages are coordinately modulated by HIV-1 infection.

Methodology/Principal Findings

To evaluate complex interrelated processes and to assemble an integrated view of activated signaling networks, a systems biology strategy was applied to genomic and proteomic responses by primary human macrophages over the course of HIV-1 infection. Macrophage responses, including cell cycle, calcium, apoptosis, mitogen-activated protein kinases (MAPK), and cytokines/chemokines, to HIV-1 were temporally regulated, in the absence of cell proliferation. In contrast, Toll-like receptor (TLR) pathways remained unaltered by HIV-1, although TLRs 3, 4, 7, and 8 were expressed and responded to ligand stimulation in macrophages. HIV-1 failed to activate phosphorylation of IRAK-1 or IRF-3, modulate intracellular protein levels of Mx1, an interferon-stimulated gene, or stimulate secretion of TNF, IL-1β, or IL-6. Activation of pathways other than TLR was inadequate to stimulate, via cross-talk mechanisms through molecular hubs, the production of proinflammatory cytokines typical of a TLR response. HIV-1 sensitized macrophage responses to TLR ligands, and the magnitude of viral priming was related to virus replication.

Conclusions/Significance

HIV-1 induced a primed, proinflammatory state, M1_HIV, which increased the responsiveness of macrophages to TLR ligands. HIV-1 might passively evade pattern recognition, actively inhibit or suppress recognition and signaling, or require dynamic interactions between macrophages and other cells, such as lymphocytes or endothelial cells. HIV-1 evasion of TLR recognition and simultaneous priming of macrophages may represent a strategy for viral survival, contribute to immune pathogenesis, and provide important targets for therapeutic approaches.     

TLR3 can directly trigger apoptosis in human cancer cells

TLRs function as molecular sensors to detect pathogen-derived products and trigger protective responses ranging from secretion of cytokines that increase the resistance of infected cells and chemokines that recruit immune cells to cell death that limits microbe spreading. Viral dsRNA participate in virus-infected cell apoptosis, but the signaling pathway involved remains unclear. In this study we show that synthetic dsRNA induces apoptosis of human breast cancer cells in a TLR3-dependent manner, which involves the molecular adaptor Toll/IL-1R domain-containing adapter inducing IFN-beta and type I IFN autocrine signaling, but occurs independently of the dsRNA-activated kinase. Moreover, detailed molecular analysis of dsRNA-induced cell death established the proapoptotic role of IL-1R-associated kinase-4 and NF-kappaB downstream of TLR3 as well as the activation of the extrinsic caspases. The direct proapoptotic activity of endogenous human TLR3 expressed by cancerous cells reveals a novel aspect of the multiple-faced TLR biology, which may open new clinical prospects for using TLR3 agonists as cytotoxic agents in selected cancers.     

Molecular insights into the interaction between human nicotinamide phosphoribosyltransferase and Toll-like receptor 4

The secreted form of the enzyme nicotinamide phosphoribosyltransferase (NAMPT), which catalyzes a key reaction in intracellular NAD biosynthesis, acts as a damage-associated molecular pattern triggering Toll-like receptor 4 (TLR4)-mediated inflammatory responses. However, the precise mechanism of interaction is unclear. Using an integrated approach combining bioinformatics and functional and structural analyses, we investigated the interaction between NAMPT and TLR4 at the molecular level. Starting from previous evidence that the bacterial ortholog of NAMPT cannot elicit the inflammatory response, despite a high degree of structural conservation, two positively charged areas unique to the human enzyme (the α1-α2 and β1-β2 loops) were identified as likely candidates for TLR4 binding. However, alanine substitution of the positively charged residues within these loops did not affect either the oligomeric state or the catalytic efficiency of the enzyme. The kinetics of the binding of wildtype and mutated NAMPT to biosensor-tethered TLR4 was analyzed. We found that mutations in the α1-α2 loop strongly decreased the association rate, increasing the K_D value from 18 nM, as determined for the wildtype, to 1.3 μM. In addition, mutations in the β1-β2 loop or its deletion increased the dissociation rate, yielding K_D values of 0.63 and 0.22 μM, respectively. Mutations also impaired the ability of NAMPT to trigger the NF-κB inflammatory signaling pathway in human cultured macrophages. Finally, the involvement of the two loops in receptor binding was supported by NAMPT-TLR4 docking simulations. This study paves the way for future development of compounds that selectively target eNAMPT/TLR4 signaling in inflammatory disorders.     

Transcriptional Profiling in Rat Hair Follicles following Simulated Blast Insult: A New Diagnostic Tool for Traumatic Brain Injury

With wide adoption of explosive-dependent weaponry during military activities, Blast-induced neurotrauma (BINT)-induced traumatic brain injury (TBI) has become a significant medical issue. Therefore, a robust and accessible biomarker system is in demand for effective and efficient TBI diagnosis. Such systems will also be beneficial to studies of TBI pathology. Here we propose the mammalian hair follicles as a potential candidate. An Advanced Blast Simulator (ABS) was developed to generate shock waves simulating traumatic conditions on brains of rat model. Microarray analysis was performed in hair follicles to identify the gene expression profiles that are associated with shock waves. Gene set enrichment analysis (GSEA) and sub-network enrichment analysis (SNEA) were used to identify cell processes and molecular signaling cascades affected by simulated bomb blasts. Enrichment analyses indicated that genes with altered expression levels were involved in central nervous system (CNS)/peripheral nervous system (PNS) responses as well as signal transduction including Ca²⁺, K⁺-transportation-dependent signaling, Toll-Like Receptor (TLR) signaling and Mitogen Activated Protein Kinase (MAPK) signaling cascades. Many of the pathways identified as affected by shock waves in the hair follicles have been previously reported to be TBI responsive in other organs such as brain and blood. The results suggest that the hair follicle has some common TBI responsive molecular signatures to other tissues. Moreover, various TBI-associated diseases were identified as preferentially affected using a gene network approach, indicating that the hair follicle may be capable of reflecting comprehensive responses to TBI conditions. Accordingly, the present study demonstrates that the hair follicle is a potentially viable system for rapid and non-invasive TBI diagnosis.     

Integrative subtype discovery in glioblastoma using iCluster

Large-scale cancer genome projects, such as the Cancer Genome Atlas (TCGA) project, are comprehensive molecular characterization efforts to accelerate our understanding of cancer biology and the discovery of new therapeutic targets. The accumulating wealth of multidimensional data provides a new paradigm for important research problems including cancer subtype discovery. The current standard approach relies on separate clustering analyses followed by manual integration. Results can be highly data type dependent, restricting the ability to discover new insights from multidimensional data. In this study, we present an integrative subtype analysis of the TCGA glioblastoma (GBM) data set. Our analysis revealed new insights through integrated subtype characterization. We found three distinct integrated tumor subtypes. Subtype 1 lacks the classical GBM events of chr 7 gain and chr 10 loss. This subclass is enriched for the G-CIMP phenotype and shows hypermethylation of genes involved in brain development and neuronal differentiation. The tumors in this subclass display a Proneural expression profile. Subtype 2 is characterized by a near complete association with EGFR amplification, overrepresentation of promoter methylation of homeobox and G-protein signaling genes, and a Classical expression profile. Subtype 3 is characterized by NF1 and PTEN alterations and exhibits a Mesenchymal-like expression profile. The data analysis workflow we propose provides a unified and computationally scalable framework to harness the full potential of large-scale integrated cancer genomic data for integrative subtype discovery.     

Dynamic cross-talk analysis among TNF-R,TLR-4 and IL-1R signalings in TNFα-induced inflammatory responses

Background

Development in systems biology research has accelerated in recent years, and the reconstructions for molecular networks can provide a global view to enable in-depth investigation on numerous system properties in biology. However, we still lack a systematic approach to reconstruct the dynamic protein-protein association networks at different time stages from high-throughput data to further analyze the possible cross-talks among different signaling/regulatory pathways.

Methods

In this study we integrated protein-protein interactions from different databases to construct the rough protein-protein association networks (PPANs) during TNFα-induced inflammation. Next, the gene expression profiles of TNFα-induced HUVEC and a stochastic dynamic model were used to rebuild the significant PPANs at different time stages, reflecting the development and progression of endothelium inflammatory responses. A new cross-talk ranking method was used to evaluate the potential core elements in the related signaling pathways of toll-like receptor 4 (TLR-4) as well as receptors for tumor necrosis factor (TNF-R) and interleukin-1 (IL-1R).

Results

The highly ranked cross-talks which are functionally relevant to the TNFα pathway were identified. A bow-tie structure was extracted from these cross-talk pathways, suggesting the robustness of network structure, the coordination of signal transduction and feedback control for efficient inflammatory responses to different stimuli. Further, several characteristics of signal transduction and feedback control were analyzed.

Conclusions

A systematic approach based on a stochastic dynamic model is proposed to generate insight into the underlying defense mechanisms of inflammation via the construction of corresponding signaling networks upon specific stimuli. In addition, this systematic approach can be applied to other signaling networks under different conditions in different species. The algorithm and method proposed in this study could expedite prospective systems biology research when better experimental techniques for protein expression detection and microarray data with multiple sampling points become available in the future.

     

Trafficking motifs as the basis for two-compartment signaling systems to form multiple stable states

Transport of molecules in cells is a central part of cell biology. Frequently such trafficking is not just for material transport, but also for information propagation, and serves to couple signaling circuits across cellular compartments. Here, I show that trafficking transforms simple local signaling pathways into self-organizing systems that span compartments and confer distinct states and identities to these compartments. I find that three motifs encapsulate the responses of most single-compartment signaling pathways in the context of trafficking. These motifs combine with different trafficking reactions to generate a diverse set of cellular functions. For example, trafficked bistable switches can oscillate or become quad- or tristable, depending on trafficking mechanisms and rates. Furthermore, the analysis shows how compartments participating in traffic can settle to distinct molecular compositions characteristic of distinct organelle identities. This general framework shows how the interplay between molecular movement and local reactions can generate many system functions, and give distinct identities to different parts of the cell.     

Metabolic modeling of microbial strains in silico

The large volume of genome-scale data that is being produced and made available in databases on the World Wide Web is demanding the development of integrated mathematical models of cellular processes. The analysis of reconstructed metabolic networks as systems leads to the development of an in silico or computer representation of collections of cellular metabolic constituents, their interactions and their integrated function as a whole. The use of quantitative analysis methods to generate testable hypotheses and drive experimentation at a whole-genome level signals the advent of a systemic modeling approach to cellular and molecular biology.     

Integrating 'omic' information: a bridge between genomics and systems biology

The availability of genome sequences for several organisms, including humans, and the resulting first-approximation lists of genes, have allowed a transition from molecular biology to 'modular biology'. In modular biology, biological processes of interest, or modules, are studied as complex systems of functionally interacting macromolecules. Functional genomic and proteomic ('omic') approaches can be helpful to accelerate the identification of the genes and gene products involved in particular modules, and to describe the functional relationships between them. However, the data emerging from individual omic approaches should be viewed with caution because of the occurrence of false-negative and false-positive results and because single annotations are not sufficient for an understanding of gene function. To increase the reliability of gene function annotation, multiple independent datasets need to be integrated. Here, we review the recent development of strategies for such integration and we argue that these will be important for a systems approach to modular biology.     

Toll-Like Receptor 11 (TLR11) Interacts with Flagellin and Profilin through Disparate Mechanisms

Toll-like receptors (TLRs) are innate immune receptors that sense a variety of pathogen-associated molecular patterns (PAMPs) by interacting with them and subsequently initiating signal transduction cascades that elicit immune responses. TLR11 has been shown to interact with two known protein PAMPs: Salmonella and E. coli flagellin FliC and Toxoplasma gondii profilin-like protein. Given the highly divergent biology of these pathogens recognized by TLR11, it is unclear whether common mechanisms are used to recognize these distinct protein PAMPs. Here we show that TLR11 interacts with these two PAMPs using different receptor domains. Furthermore, TLR11 binding to flagellin and profilin exhibits differential dependency on pH and receptor ectodomain cleavage.     

Avian toll-like receptors

Analysis of the genomes of two distantly related bird species, chicken and zebra finch (divergence of about 100 million years), indicate that there are ten avian toll-like receptors and that five of these, TLR2a, 2b, 3, 4, 5 and 7, are clear orthologs to TLRs found in mammals. Duplication of genes has led to TLR1La and 1Lb, TLR2a and 2b, and two TLR7s in the zebra finch. Avian TLR21 may be orthologous to TLR21 found in fish and amphibians, and avian TLR15, which is phylogenetically related to the TLR2 family, appears to be unique to avian species. While TLR2 is conserved between mammalian and avian species, the other TLR2 family members evolved independently. Dimerization between either of the two avian TLR2 species and TLR1La or 1Lb permits recognition of the same broad range of molecules as recognized by mammalian TLR2 dimerized with either TLR1, 6 and 10. Similarly, while TLR9 has been lost from the avian genome, DNA high in unmethylated CpG motifs is immunostimulatory through avian TLR21 which is absent in mammals. Thus, while some TLR members were commonly retained in both mammals and birds, others were separately lost or gained, or diverged independently; but broadly speaking the TLRs of the two classes of vertebrates evolved to recognize very similar spectra of microbial products. Components of downstream TLR signaling are also mostly conserved but with some losses in avian species; notably, TRAM is absent in avian genomes and, hence, the TRIF/TRAM-dependent signaling pathway utilized by mammals in LPS activation appears to be absent in birds.     

Systems biology of lipid metabolism: From yeast to human

Lipid metabolism is highly relevant as it plays a central role in a number of human diseases. Due to the highly interactive structure of lipid metabolism and its regulation, it is necessary to apply a holistic approach, and systems biology is therefore well suited for integrated analysis of lipid metabolism. In this paper it is demonstrated that the yeast Saccharomyces cerevisiae serves as an excellent model organism for studying the regulation of lipid metabolism in eukaryotes as most of the regulatory structures in this part of the metabolism are conserved between yeast and mammals. Hereby yeast systems biology can assist to improve our understanding of how lipid metabolism is regulated.     

Metabolomics in the fight against malaria

Metabolomics uses high-resolution mass spectrometry to provide a chemical fingerprint of thousands of metabolites present in cells, tissues or body fluids. Such metabolic phenotyping has been successfully used to study various biologic processes and disease states. High-resolution metabolomics can shed new light on the intricacies of host-parasite interactions in each stage of the Plasmodium life cycle and the downstream ramifications on the host’s metabolism, pathogenesis and disease. Such data can become integrated with other large datasets generated using top-down systems biology approaches and be utilised by computational biologists to develop and enhance models of malaria pathogenesis relevant for identifying new drug targets or intervention strategies. Here, we focus on the promise of metabolomics to complement systems biology approaches in the quest for novel interventions in the fight against malaria. We introduce the Malaria Host-Pathogen Interaction Center (MaHPIC), a new systems biology research coalition. A primary goal of the MaHPIC is to generate systems biology datasets relating to human and non-human primate (NHP) malaria parasites and their hosts making these openly available from an online relational database. Metabolomic data from NHP infections and clinical malaria infections from around the world will comprise a unique global resource.     

Biochemical and functional analysis of TIR domain containing protein from Brucella melitensis

Toll/interleukin-1 like receptors are evolutionarily conserved proteins in eukaryotes that play crucial role in pathogen recognition and innate immune responses. Brucella are facultative intracellular bacterial pathogens causing brucellosis in animal and human hosts. Brucella behave as a stealthy pathogen by evading the immune recognition or suppressing the TLR signaling cascades. Brucella encode a TIR domain containing protein, TcpB, which suppresses NF-κB activation as well as pro-inflammatory cytokine secretion mediated by TLR2 and TLR4 receptors. TcpB targets the TIRAP mediated pathway to suppress TLR signaling. With the objective of detailed characterization, we have over expressed and purified TcpB from Brucella melitensis in native condition. The purified protein exhibited lipid-binding properties and cell permeability. NF-κB inhibition property of endogenous TcpB has also been demonstrated. The data provide insight into the mechanism of action of TcpB in the intracellular niche of Brucella.