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Light-mediated discovery of surfaceome nanoscale organization and intercellular receptor interaction networks.

ABSTRACT: The molecular nanoscale organization of the surfaceome is a fundamental regulator of cellular signaling in health and disease. Technologies for mapping the spatial relationships of cell surface receptors and their extracellular signaling synapses would unlock theranostic opportunities to target protein communities and the possibility to engineer extracellular signaling. Here, we develop an optoproteomic technology termed LUX-MS that enables the targeted elucidation of acute protein interactions on and in between living cells using light-controlled singlet oxygen generators (SOG). By using SOG-coupled antibodies, small molecule drugs, biologics and intact viral particles, we demonstrate the ability of LUX-MS to decode ligand receptor interactions across organisms and to discover surfaceome receptor nanoscale organization with direct implications for drug action. Furthermore, by coupling SOG to antigens we achieved light-controlled molecular mapping of intercellular signaling within functional immune synapses between antigen-presenting cells and CD8⁺ T cells providing insights into T cell activation with spatiotemporal specificity. LUX-MS based decoding of surfaceome signaling architectures thereby provides a molecular framework for the rational development of theranostic strategies.

SUBMITTER: Muller M

PROVIDER: S-EPMC8639842 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Light-mediated discovery of surfaceome nanoscale organization and intercellular receptor interaction networks

Project description:Delineating the extracellular interaction network of the cell surface proteotype (the surfaceome) is fundamental to understanding cellular signaling function in health and disease. Here, we developed LUX-MS, an optoproteomic technology that enables the spatiotemporal and proteome-wide study of surfaceome signaling architectures at nanoscale level without the need for genetic manipulation. A tunable, light-triggered singlet oxygen generator (SOG) based mechanism mediates in-situ proximity-tagging for subsequent mass spectrometry-based identification of acute protein interactions in their native cellular context. We applied LUX-MS to the characterization of surfaceome signaling structures engaged by antibodies, small molecule drugs, biologics and intact bacteriophages across organisms and in complex environments. Cell-type resolved dissection of intercellular communication using LUX-MS thereby revealed the molecular architecture of functional immunological signaling synapses in unprecedented detail. Altogether, LUX-MS enables facebooking of the social protein networks within surfaceome signaling architectures and provides an unprecedented molecular framework for the rational design of biomedical intervention strategies.

2021-06-14 | PXD020481 | Pride

Literature-based discovery of IFN-gamma and vaccine-mediated gene interaction networks.

Project description:Interferon-gamma (IFN-gamma) regulates various immune responses that are often critical for vaccine-induced protection. In order to annotate the IFN-gamma-related gene interaction network from a large amount of IFN-gamma research reported in the literature, a literature-based discovery approach was applied with a combination of natural language processing (NLP) and network centrality analysis. The interaction network of human IFN-gamma (Gene symbol: IFNG) and its vaccine-specific subnetwork were automatically extracted using abstracts from all articles in PubMed. Four network centrality metrics were further calculated to rank the genes in the constructed networks. The resulting generic IFNG network contains 1060 genes and 26313 interactions among these genes. The vaccine-specific subnetwork contains 102 genes and 154 interactions. Fifty six genes such as TNF, NFKB1, IL2, IL6, and MAPK8 were ranked among the top 25 by at least one of the centrality methods in one or both networks. Gene enrichment analysis indicated that these genes were classified in various immune mechanisms such as response to extracellular stimulus, lymphocyte activation, and regulation of apoptosis. Literature evidence was manually curated for the IFN-gamma relatedness of 56 genes and vaccine development relatedness for 52 genes. This study also generated many new hypotheses worth further experimental studies.

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Light-Fueled Nanoscale Surface Waving in Chiral Liquid Crystal Networks.

Project description:Nano- and micro-actuating systems are promising for application in microfluidics, haptics, tunable optics, and soft robotics. Surfaces capable to change their topography at the nano- and microscale on demand would allow control over wettability, friction, and surface-driven particle motility. Here, we show that light-responsive cholesteric liquid crystal (LC) networks undergo a waving motion of their surface topography upon irradiation with light. These dynamic surfaces are fabricated with a maskless one-step procedure, relying on the liquid crystal alignment in periodic structures upon application of a weak electric field. The geometrical features of the surfaces are controlled by tuning the pitch of the liquid crystal. Pitch control by confinement allows engineering one-dimensional (1D) and two-dimensional (2D) structures that wave upon light exposure. This work demonstrates the potential that self-organizing systems might have for engineering dynamic materials, and harnessing the functionality of molecules to form dynamic surfaces, with nanoscale precision over their waving motion.

| S-EPMC7844818 | biostudies-literature

Discovery of protein interaction networks shared by diseases.

Project description:The study of protein-protein interactions is essential to define the molecular networks that contribute to maintain homeostasis of an organism's body functions. Disruptions in protein interaction networks have been shown to result in diseases in both humans and animals. Monogenic diseases disrupting biochemical pathways such as hereditary coagulopathies (e.g. hemophilia), provided a deep insight in the biochemical pathways of acquired coagulopathies of complex diseases. Indeed, a variety of complex liver diseases can lead to decreased synthesis of the same set of coagulation factors as in hemophilia. Similarly, more complex diseases such as different cancers have been shown to result from malfunctions of common proteins pathways. In order to discover, in high throughput, the molecular underpinnings of poorly characterized diseases, we present a statistical method to identify shared protein interaction network(s) between diseases. Integrating (i) a protein interaction network with (ii) disease to protein relationships derived from mining Gene Ontology annotations and the biomedical literature with natural language understanding (PhenoGO), we identified protein-protein interactions that were associated with pairs of diseases and calculated the statistical significance of the occurrence of interactions in the protein interaction knowledgebase. Significant correlations between diseases and shared protein networks were identified and evaluated in this study, demonstrating the high precision of the approach and correct non-trivial predictions, signifying the potential for discovery. In conclusion, we demonstrate that the associations between diseases are directly correlated to their underlying protein-protein interaction networks, possibly providing insight into the underlying molecular mechanisms of phenotypes and biological processes disrupted in related diseases.

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Module organization and variance in protein-protein interaction networks.

Project description:A module is a group of closely related proteins that act in concert to perform specific biological functions through protein-protein interactions (PPIs) that occur in time and space. However, the underlying module organization and variance remain unclear. In this study, we collected module templates to infer respective module families, including 58,041 homologous modules in 1,678 species, and PPI families using searches of complete genomic database. We then derived PPI evolution scores and interface evolution scores to describe the module elements, including core and ring components. Functions of core components were highly correlated with those of essential genes. In comparison with ring components, core proteins/PPIs were conserved across multiple species. Subsequently, protein/module variance of PPI networks confirmed that core components form dynamic network hubs and play key roles in various biological functions. Based on the analyses of gene essentiality, module variance, and gene co-expression, we summarize the observations of module organization and variance as follows: 1) a module consists of core and ring components; 2) core components perform major biological functions and collaborate with ring components to execute certain functions in some cases; 3) core components are more conserved and essential during organizational changes in different biological states or conditions.

| S-EPMC4369690 | biostudies-literature

Competing Protein-RNA Interaction Networks Control Multiphase Intracellular Organization.

Project description:Liquid-liquid phase separation (LLPS) mediates formation of membraneless condensates such as those associated with RNA processing, but the rules that dictate their assembly, substructure, and coexistence with other liquid-like compartments remain elusive. Here, we address the biophysical mechanism of this multiphase organization using quantitative reconstitution of cytoplasmic stress granules (SGs) with attached P-bodies in human cells. Protein-interaction networks can be viewed as interconnected complexes (nodes) of RNA-binding domains (RBDs), whose integrated RNA-binding capacity determines whether LLPS occurs upon RNA influx. Surprisingly, both RBD-RNA specificity and disordered segments of key proteins are non-essential, but modulate multiphase condensation. Instead, stoichiometry-dependent competition between protein networks for connecting nodes determines SG and P-body composition and miscibility, while competitive binding of unconnected proteins disengages networks and prevents LLPS. Inspired by patchy colloid theory, we propose a general framework by which competing networks give rise to compositionally specific and tunable condensates, while relative linkage between nodes underlies multiphase organization.

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The hierarchical organization of natural protein interaction networks confers self-organization properties on pseudocells.

Project description:BackgroundCell organization is governed and maintained via specific interactions among its constituent macromolecules. Comparison of the experimentally determined protein interaction networks in different model organisms has revealed little conservation of the specific edges linking ortholog proteins. Nevertheless, some topological characteristics of the graphs representing the networks--namely non-random degree distribution and high clustering coefficient--are shared by networks of distantly related organisms. Here we investigate the role of the topological features of the protein interaction network in promoting cell organization.MethodsWe have used a stochastic model, dubbed ProtNet representing a computer stylized cell to answer questions about the dynamic consequences of the topological properties of the static graphs representing protein interaction networks.ResultsBy using a novel metrics of cell organization, we show that natural networks, differently from random networks, can promote cell self-organization. Furthermore the ensemble of protein complexes that forms in pseudocells, which self-organize according to the interaction rules of natural networks, are more robust to perturbations.ConclusionsThe analysis of the dynamic properties of networks with a variety of topological characteristics lead us to conclude that self organization is a consequence of the high clustering coefficient, whereas the scale free degree distribution has little influence on this property.

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Large-scale functional organization of long-range chromatin interaction networks.

Project description:Chromatin interactions play important roles in transcription regulation. To better understand the underlying evolutionary and functional constraints of these interactions, we implemented a systems approach to examine RNA polymerase-II-associated chromatin interactions in human cells. We found that 40% of the total genomic elements involved in chromatin interactions converged to a giant, scale-free-like, hierarchical network organized into chromatin communities. The communities were enriched in specific functions and were syntenic through evolution. Disease-associated SNPs from genome-wide association studies were enriched among the nodes with fewer interactions, implying their selection against deleterious interactions by limiting the total number of interactions, a model that we further reconciled using somatic and germline cancer mutation data. The hubs lacked disease-associated SNPs, constituted a nonrandomly interconnected core of key cellular functions, and exhibited lethality in mouse mutants, supporting an evolutionary selection that favored the nonrandom spatial clustering of the least-evolving key genomic domains against random genetic or transcriptional errors in the genome. Altogether, our analyses reveal a systems-level evolutionary framework that shapes functionally compartmentalized and error-tolerant transcriptional regulation of human genome in three dimensions.

| S-EPMC4181841 | biostudies-literature

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Project description:Genes act in concert via specific networks to drive various biological processes, including progression of diseases such as cancer. Under different phenotypes, different subsets of the gene members of a network participate in a biological process. Single gene analyses are less effective in identifying such core gene members (subnetworks) within a gene set/network, as compared to gene set/network-based analyses. Hence, it is useful to identify a discriminative classifier by focusing on the subnetworks that correspond to different phenotypes. Here we present a novel algorithm to automatically discover the important subnetworks of closely interacting molecules to differentiate between two phenotypes (context) using gene expression profiles. We name it COSSY (COntext-Specific Subnetwork discoverY). It is a non-greedy algorithm and thus unlikely to have local optima problems. COSSY works for any interaction network regardless of the network topology. One added benefit of COSSY is that it can also be used as a highly accurate classification platform which can produce a set of interpretable features.

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Project description:Receptor-regulated cellular signaling often is mediated by formation of transient, heterogeneous protein complexes of undefined structure. We used single and two-color photoactivated localization microscopy to study complexes downstream of the T cell antigen receptor (TCR) in single-molecule detail at the plasma membrane of intact T cells. The kinase ZAP-70 distributed completely with the TCR? chain and both partially mixed with the adaptor LAT in activated cells, thus showing localized activation of LAT by TCR-coupled ZAP-70. In resting and activated cells, LAT primarily resided in nanoscale clusters as small as dimers whose formation depended on protein-protein and protein-lipid interactions. Surprisingly, the adaptor SLP-76 localized to the periphery of LAT clusters. This nanoscale structure depended on polymerized actin and its disruption affected TCR-dependent cell function. These results extend our understanding of the mechanism of T cell activation and the formation and organization of TCR-mediated signaling complexes, findings also relevant to other receptor systems.

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