Dataset Information

Lectin-Glycan Interaction Network-Based Identification of Host Receptors of Microbial Pathogenic Adhesins.

ABSTRACT: UNLABELLED:The first step in the infection of humans by microbial pathogens is their adherence to host tissue cells, which is frequently based on the binding of carbohydrate-binding proteins (lectin-like adhesins) to human cell receptors that expose glycans. In only a few cases have the human receptors of pathogenic adhesins been described. A novel strategy-based on the construction of a lectin-glycan interaction (LGI) network-to identify the potential human binding receptors for pathogenic adhesins with lectin activity was developed. The new approach is based on linking glycan array screening results of these adhesins to a human glycoprotein database via the construction of an LGI network. This strategy was used to detect human receptors for virulent Escherichia coli (FimH adhesin), and the fungal pathogens Candida albicans (Als1p and Als3p adhesins) and C. glabrata (Epa1, Epa6, and Epa7 adhesins), which cause candidiasis. This LGI network strategy allows the profiling of potential adhesin binding receptors in the host with prioritization, based on experimental binding data, of the most relevant interactions. New potential targets for the selected adhesins were predicted and experimentally confirmed. This methodology was also used to predict lectin interactions with envelope glycoproteins of human-pathogenic viruses. It was shown that this strategy was successful in revealing that the FimH adhesin has anti-HIV activity. IMPORTANCE:Microbial pathogens may express a wide range of carbohydrate-specific adhesion proteins that mediate adherence to host tissues. Pathogen attachment to host cells is achieved through the binding of these lectin-like adhesins to glycans on human glycoproteins. In only a few cases have the human receptors of pathogenic adhesins been described. We developed a new strategy to predict these interacting receptors. Therefore, we developed a novel LGI network that would allow the mapping of potential adhesin binding receptors in the host with prioritization, based on the experimental binding data, of the most relevant interactions. New potential targets for the selected adhesins (bacterial uroepithelial FimH from E. coli and fungal Epa and Als adhesins from C. glabrata and C. albicans) were predicted and experimentally confirmed. This methodology was also used to predict lectin interactions with human-pathogenic viruses and to discover whether FimH adhesin has anti-HIV activity.

SUBMITTER: Ielasi FS

PROVIDER: S-EPMC4958244 | biostudies-literature | 2016 Jul

REPOSITORIES: biostudies-literature

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Lectin-Glycan Interaction Network-Based Identification of Host Receptors of Microbial Pathogenic Adhesins.

Ielasi Francesco S FS Alioscha-Perez Mitchel M Donohue Dagmara D Claes Sandra S Sahli Hichem H Schols Dominique D Willaert Ronnie G RG

mBio 20160712 4

<h4>Unlabelled</h4>The first step in the infection of humans by microbial pathogens is their adherence to host tissue cells, which is frequently based on the binding of carbohydrate-binding proteins (lectin-like adhesins) to human cell receptors that expose glycans. In only a few cases have the human receptors of pathogenic adhesins been described. A novel strategy-based on the construction of a lectin-glycan interaction (LGI) network-to identify the potential human binding receptors for pathoge ...[more]

PMID: 27406561

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Project description:Multivalent lectin-glycan interactions (MLGIs) are widespread and vital for biology, making them attractive therapeutic targets. Unfortunately, the structural and biophysical mechanisms of several key MLGIs remain poorly understood, limiting our ability to design spatially matched glycoconjugates as potential therapeutics against specific MLGIs. We have recently demonstrated that natural oligomannose-coated nanoparticles are powerful probes for MLGIs. They can provide not only quantitative affinity and binding thermodynamic data but also key structural information (e.g, binding site orientation and mode) useful for designing glycoconjugate therapeutics against specific MLGIs. Despite success, how designing parameters (e.g., glycan type, density, and scaffold size) control their MLGI biophysical and antiviral properties remains to be elucidated. A synthetic pseudodimannose (psDiMan) ligand has been shown to selectively bind to a dendritic cell surface tetrameric lectin, DC-SIGN, over some other multimeric lectins sharing monovalent mannose specificity but having distinct cellular functions. Herein, we display psDiMan polyvalently onto gold nanoparticles (GNPs) of varying sizes (e.g., ∼5 and ∼13 nm, denoted as G5- and G13 psDiMan hereafter) to probe how the scaffold size and glycan display control their MLGI properties with DC-SIGN and the closely related lectin DC-SIGNR. We show that G5/13 psDiMan binds strongly to DC-SIGN, with sub-nM K ds, with affinity being enhanced with increasing scaffold size, whereas they show apparently no or only weak binding to DC-SIGNR. Interestingly, there is a minimal, GNP-size-dependent, glycan density threshold for forming strong binding with DC-SIGN. By combining temperature-dependent affinity and Van't Hoff analyses, we have developed a new GNP fluorescence quenching assay for MLGI thermodynamics, revealing that DC-SIGN-Gx-psDiMan binding is enthalpy-driven, with a standard binding ΔH 0 of ∼ -95 kJ mol-1, which is ∼4-fold that of the monovalent binding and is comparable to that measured by isothermal titration calorimetry. We further reveal that the enhanced DC-SIGN affinity with Gx-psDiMan with increasing GNP scaffold size is due to reduced binding entropy penalty and not due to enhanced favorable binding enthalpy. We further show that DC-SIGN binds tetravalently to a single Gx-psDiMan, irrespective of the GNP size, whereas DC-SIGNR binding is dependent on GNP size, with no apparent binding with G5, and weak cross-linking with G13. Finally, we show that Gx-psDiMans potently inhibit DC-SIGN-dependent augmentation of cellular entry of Ebola pseudoviruses with sub-nM EC50 values, whereas they exhibit no significant (for G5) or weak (for G13) inhibition against DC-SIGNR-augmented viral entry, consistent to their MLGI properties with DC-SIGNR in solution. These results have established Gx-psDiMan as a versatile new tool for probing MLGI affinity, selectivity, and thermodynamics, as well as GNP-glycan antiviral properties.

Project description:BackgroundThroughout their lifespans, humans continually interact with the microbial world, including those organisms which live in and on the human body. Research in this domain has revealed the extensive links between the human-associated microbiota and health. In particular, the microbiota of the human gut plays essential roles in digestion, nutrient metabolism, immune maturation and homeostasis, neurological signaling, and endocrine regulation. Microbial interaction networks are frequently estimated from data and are an indispensable tool for representing and understanding the conditional correlation between the microbes. In this high-dimensional setting, zero-inflation and unit-sum constraint for relative abundance data pose challenges to the reliable estimation of microbial interaction networks.Methods and resultsTo identify the microbial interaction network, the zero-inflated latent Ising (ZILI) model is proposed which assumes the distribution of relative abundance relies only on finite latent states and provides a novel way to solve issues induced by the unit-sum and zero-inflation constrains. A two-step algorithm is proposed for the model selection of ZILI. ZILI is evaluated through simulated data and subsequently applied to an infant gut microbiota dataset from New Hampshire Birth Cohort Study. The results are compared with results from Gaussian graphical model (GGM) and dichotomous Ising model (DIS). Providing ZILI is the true data-generating model, the simulation studies show that the two-step algorithm can identify the graphical structure effectively and is robust to a range of parameter settings. For the infant gut microbiota dataset, the final estimated networks from GGM and ZILI turn out to have significant overlap in which the ZILI tends to select the sparser network than those from GGM. From the shared subnetwork, a hub taxon Lachnospiraceae is identified whose involvement in human disease development has been discovered recently in literature.ConclusionsConstrains induced by relative abundance of microbiota such as zero inflation and unit sum render the conditional correlation analysis unreliable for conventional methods such as GGM. The proposed optimal categoricalization based ZILI model provides an alternative yet elegant way to deal with these difficulties. The results from ZILI have reasonable biological interpretation. This model can also be used to study the microbial interaction in other body parts.

Dataset Information

Lectin-Glycan Interaction Network-Based Identification of Host Receptors of Microbial Pathogenic Adhesins.

Publications

Lectin-Glycan Interaction Network-Based Identification of Host Receptors of Microbial Pathogenic Adhesins.

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