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We show that SARS-CoV-2 spike protein interacts with both cellular heparan sulfate and angiotensin-converting enzyme 2 (ACE2) through its receptor-binding domain (RBD). Docking studies suggest a heparin/heparan sulfate-binding site adjacent to the ACE2-binding site. Both ACE2 and heparin can bind independently to spike protein in vitro, and a ternary complex can be generated using heparin as a scaffold. Electron micrographs of spike protein suggests that heparin enhances the open conformation of the RBD that binds ACE2. On cells, spike protein binding depends on both heparan sulfate and ACE2. Unfractionated heparin, non-anticoagulant heparin, heparin lyases, and lung heparan sulfate potently block spike protein binding and/or infection by pseudotyped virus and authentic SARS-CoV-2 virus. We suggest a model in which viral attachment and infection involves heparan sulfate-dependent enhancement of binding to ACE2. Manipulation of heparan sulfate or inhibition of viral adhesion by exogenous heparin presents new therapeutic opportunities.
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Betacoronavirus/fisiología , Heparitina Sulfato/metabolismo , Peptidil-Dipeptidasa A/metabolismo , Glicoproteína de la Espiga del Coronavirus/metabolismo , Secuencia de Aminoácidos , Enzima Convertidora de Angiotensina 2 , Betacoronavirus/aislamiento & purificación , Sitios de Unión , COVID-19 , Línea Celular , Infecciones por Coronavirus/patología , Infecciones por Coronavirus/virología , Heparina/química , Heparina/metabolismo , Heparitina Sulfato/química , Humanos , Riñón/metabolismo , Pulmón/metabolismo , Simulación de Dinámica Molecular , Pandemias , Peptidil-Dipeptidasa A/química , Neumonía Viral/patología , Neumonía Viral/virología , Unión Proteica , Dominios Proteicos , Proteínas Recombinantes/biosíntesis , Proteínas Recombinantes/química , Proteínas Recombinantes/aislamiento & purificación , SARS-CoV-2 , Glicoproteína de la Espiga del Coronavirus/química , Glicoproteína de la Espiga del Coronavirus/genética , Internalización del VirusRESUMEN
Characteristically, cells must sense and respond to environmental cues. Despite the importance of cell-cell communication, our understanding remains limited and often lacks glycans. Glycans decorate proteins and cell membranes at the cell-environment interface, and modulate intercellular communication, from development to pathogenesis. Providing further challenges, glycan biosynthesis and cellular behavior are co-regulating systems. Here, we discuss how glycosylation contributes to extracellular responses and signaling. We further organize approaches for disentangling the roles of glycans in multicellular interactions using newly available datasets and tools, including glycan biosynthesis models, omics datasets, and systems-level analyses. Thus, emerging tools in big data analytics and systems biology are facilitating novel insights on glycans and their relationship with multicellular behavior.
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Glicómica , Polisacáridos , GlicosilaciónRESUMEN
Heparin is the most widely prescribed biopharmaceutical in production globally. Its potent anticoagulant activity and safety makes it the drug of choice for preventing deep vein thrombosis and pulmonary embolism. In 2008, adulterated material was introduced into the heparin supply chain, resulting in several hundred deaths and demonstrating the need for alternate sources of heparin. Heparin is a fractionated form of heparan sulfate derived from animal sources, predominantly from connective tissue mast cells in pig mucosa. While the enzymes involved in heparin biosynthesis are identical to those for heparan sulfate, the factors regulating these enzymes are not understood. Examination of the promoter regions of all genes involved in heparin/heparan sulfate assembly uncovered a transcription factor-binding motif for ZNF263, a C2H2 zinc finger protein. CRISPR-mediated targeting and siRNA knockdown of ZNF263 in mammalian cell lines and human primary cells led to dramatically increased expression levels of HS3ST1, a key enzyme involved in imparting anticoagulant activity to heparin, and HS3ST3A1, another glucosaminyl 3-O-sulfotransferase expressed in cells. Enhanced 3-O-sulfation increased binding to antithrombin, which enhanced Factor Xa inhibition, and binding of neuropilin-1. Analysis of transcriptomics data showed distinctively low expression of ZNF263 in mast cells compared with other (non-heparin-producing) immune cells. These findings demonstrate a novel regulatory factor in heparan sulfate modification that could further advance the possibility of bioengineering anticoagulant heparin in cultured cells.
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Proteínas de Unión al ADN/metabolismo , Heparina/metabolismo , Heparitina Sulfato/biosíntesis , Animales , Anticoagulantes , Línea Celular , Células Cultivadas , Cromatografía Líquida de Alta Presión , Regulación de la Expresión Génica/genética , Células HeLa , Heparina/biosíntesis , Heparina/genética , Heparitina Sulfato/genética , Heparitina Sulfato/metabolismo , Humanos , Mastocitos/metabolismo , Sulfotransferasas/metabolismo , Porcinos , Factores de TranscripciónRESUMEN
Vertebrate sialic acids (Sias) display much diversity in modifications, linkages, and underlying glycans. Slide microarrays allow high-throughput explorations of sialoglycan-protein interactions. A microarray presenting ~150 structurally defined sialyltrisaccharides with various Sias linkages and modifications still poses challenges in planning, data sorting, visualization, and analysis. To address these issues, we devised a simple 9-digit code for sialyltrisaccharides with terminal Sias and underlying two monosaccharides assigned from the nonreducing end, with 3 digits assigning a monosaccharide, its modifications, and linkage. Calculations based on the encoding system reveal >113,000 likely linear sialyltrisaccharides in nature. Notably, a biantennary N-glycan with 2 terminal sialyltrisaccharides could thus have >1010 potential combinations and a triantennary N-glycan with 3 terminal sequences, >1015 potential combinations. While all possibilities likely do not exist in nature, sialoglycans encode enormous diversity. While glycomic approaches are used to probe such diverse sialomes, naturally occurring bacterial AB5 toxin B subunits are simpler tools to track the dynamic sialome in biological systems. Sialoglycan microarray was utilized to compare sialoglycan-recognizing bacterial toxin B subunits. Unlike the poor correlation between B subunits and species phylogeny, there is stronger correlation with Sia-epitope preferences. Further supporting this pattern, we report a B subunit (YenB) from Yersinia enterocolitica (broad host range) recognizing almost all sialoglycans in the microarray, including 4-O-acetylated-Sias not recognized by a Yersinia pestis orthologue (YpeB). Differential Sia-binding patterns were also observed with phylogenetically related B subunits from Escherichia coli (SubB), Salmonella Typhi (PltB), Salmonella Typhimurium (ArtB), extra-intestinal E.coli (EcPltB), Vibrio cholera (CtxB), and cholera family homologue of E. coli (EcxB).
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Toxinas Bacterianas , Escherichia coli , Salmonella typhi/química , Ácidos Siálicos , Toxinas Bacterianas/química , Polisacáridos , Toxina del CóleraRESUMEN
Heparin is an essential anticoagulant used for treating and preventing thrombosis. However, the complexity of heparin has hindered the development of a recombinant source, making its supply dependent on a vulnerable animal population. In nature, heparin is produced exclusively in mast cells, which are not suitable for commercial production, but mastocytoma cells are readily grown in culture and make heparan sulfate, a closely related glycosaminoglycan that lacks anticoagulant activity. Using gene expression profiling of mast cells as a guide, a multiplex genome engineering strategy was devised to produce heparan sulfate with high anticoagulant potency and to eliminate contaminating chondroitin sulfate from mastocytoma cells. The heparan sulfate purified from engineered cells grown in chemically defined medium has anticoagulant potency that exceeds porcine-derived heparin and confers anticoagulant activity to the blood of healthy mice. This work demonstrates the feasibility of producing recombinant heparin from mammalian cell culture as an alternative to animal sources.
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Edición Génica , Heparina , Animales , Anticoagulantes , Heparitina Sulfato/metabolismo , Ratones , PorcinosRESUMEN
BACKGROUND: Both RNA-Seq and sample freeze-thaw are ubiquitous. However, knowledge about the impact of freeze-thaw on downstream analyses is limited. The lack of common quality metrics that are sufficiently sensitive to freeze-thaw and RNA degradation, e.g. the RNA Integrity Score, makes such assessments challenging. RESULTS: Here we quantify the impact of repeated freeze-thaw cycles on the reliability of RNA-Seq by examining poly(A)-enriched and ribosomal RNA depleted RNA-seq from frozen leukocytes drawn from a toddler Autism cohort. To do so, we estimate the relative noise, or percentage of random counts, separating technical replicates. Using this approach we measured noise associated with RIN and freeze-thaw cycles. As expected, RIN does not fully capture sample degradation due to freeze-thaw. We further examined differential expression results and found that three freeze-thaws should extinguish the differential expression reproducibility of similar experiments. Freeze-thaw also resulted in a 3' shift in the read coverage distribution along the gene body of poly(A)-enriched samples compared to ribosomal RNA depleted samples, suggesting that library preparation may exacerbate freeze-thaw-induced sample degradation. CONCLUSION: The use of poly(A)-enrichment for RNA sequencing is pervasive in library preparation of frozen tissue, and thus, it is important during experimental design and data analysis to consider the impact of repeated freeze-thaw cycles on reproducibility.
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Criopreservación , ARN , Congelación , Humanos , Reproducibilidad de los Resultados , Análisis de Secuencia de ARNRESUMEN
Cancer immunotherapy has revolutionized treatment and led to an unprecedented wave of immuno-oncology research during the past two decades. In 2018, two pioneer immunotherapy innovators, Tasuku Honjo and James P. Allison, were awarded the Nobel Prize for their landmark cancer immunotherapy work regarding "cancer therapy by inhibition of negative immune regulation" -CTLA4 and PD-1 immune checkpoints. However, the challenge in the coming decade is to develop cancer immunotherapies that can more consistently treat various patients and cancer types. Overcoming this challenge requires a systemic understanding of the underlying interactions between immune cells, tumor cells, and immunotherapeutics. The role of aberrant glycosylation in this process, and how it influences tumor immunity and immunotherapy is beginning to emerge. Herein, we review current knowledge of miRNA-mediated regulatory mechanisms of glycosylation machinery, and how these carbohydrate moieties impact immune cell and tumor cell interactions. We discuss these insights in the context of clinical findings and provide an outlook on modulating the regulation of glycosylation to offer new therapeutic opportunities. Finally, in the coming age of systems glycobiology, we highlight how emerging technologies in systems glycobiology are enabling deeper insights into cancer immuno-oncology, helping identify novel drug targets and key biomarkers of cancer, and facilitating the rational design of glyco-immunotherapies. These hold great promise clinically in the immuno-oncology field.
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Biomarcadores , Sistemas de Liberación de Medicamentos/métodos , Glicómica/métodos , Inmunoterapia/métodos , MicroARNs/metabolismoRESUMEN
Systems glycobiology aims to provide models and analysis tools that account for the biosynthesis, regulation, and interactions with glycoconjugates. To facilitate these methods, there is a need for a clear glycan representation accessible to both computers and humans. Linear Code, a linearized and readily parsable glycan structure representation, is such a language. For this reason, Linear Code was adapted to represent reaction rules, but the syntax has drifted from its original description to accommodate new and originally unforeseen challenges. Here, we delineate the consensuses and inconsistencies that have arisen through this adaptation. We recommend options for a consensus-based extension of Linear Code that can be used for reaction rule specification going forward. Through this extension and specification of Linear Code to reaction rules, we aim to minimize inconsistent symbology thereby making glycan database queries easier. With a clear guide for generating reaction rule descriptions, glycan synthesis models will be more interoperable and reproducible thereby moving glycoinformatics closer to compliance with FAIR standards. Here, we present Linear Code for Reaction Rules (LiCoRR), version 1.0, an unambiguous representation for describing glycosylation reactions in both literature and code.
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OBJECTIVE: Necrotising enterocolitis (NEC) is one of the most common and often fatal intestinal disorders in preterm infants. Markers to identify at-risk infants as well as therapies to prevent and treat NEC are limited and urgently needed. NEC incidence is significantly lower in breast-fed compared with formula-fed infants. Infant formula lacks human milk oligosaccharides (HMO), such as disialyllacto-N-tetraose (DSLNT), which prevents NEC in neonatal rats. However, it is unknown if DSLNT also protects human preterm infants. DESIGN: We conducted a multicentre clinical cohort study and recruited 200 mothers and their very low birthweight infants that were predominantly human milk-fed. We analysed HMO composition in breast milk fed to infants over the first 28â days post partum, matched each NEC case with five controls and used logistic regression and generalised estimating equation to test the hypothesis that infants who develop NEC receive milk with less DSLNT than infants who do not develop NEC. RESULTS: Eight infants in the cohort developed NEC (Bell stage 2 or 3). DSLNT concentrations were significantly lower in almost all milk samples in NEC cases compared with controls, and its abundance could identify NEC cases prior to onset. Aggregate assessment of DSLNT over multiple days enhanced the separation of NEC cases and control subjects. CONCLUSIONS: DSLNT content in breast milk is a potential non-invasive marker to identify infants at risk of developing NEC, and screen high-risk donor milk. In addition, DSLNT could serve as a natural template to develop novel therapeutics against this devastating disorder.
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Enterocolitis Necrotizante/etiología , Leche Humana/química , Oligosacáridos/análisis , Lactancia Materna , Estudios de Casos y Controles , Estudios de Cohortes , Femenino , Humanos , Lactante , Recién Nacido , Recien Nacido Prematuro , Recién Nacido de muy Bajo Peso , Masculino , Estudios Prospectivos , Riesgo , Medición de RiesgoRESUMEN
With the continued emergence of variants of concern, the global threat of COVID-19 persists, particularly in low- and middle-income countries with limited vaccine access. Protein-based vaccines, such as SCB-2019, can be produced on a large scale at a low cost while antigen design and adjuvant use can modulate efficacy and safety. While effective humoral immunity against SARS-CoV-2 variants has been shown to depend on both neutralization and Fc-mediated immunity, data on the effectiveness of protein-based vaccines with enhanced Fc-mediated immunity is limited. Here, we assess the humoral profile, including antibody isotypes, subclasses, and Fc receptor binding generated by a boosting with a recombinant trimer-tag protein vaccine SCB-2019. Individuals who were primed with 2 doses of the ChAdOx1 vaccine were equally divided into 4 groups and boosted with following formulations: Group 1: 9 µg SCB-2019 and Alhydrogel; Group 2: 9 µg SCB-2019, CpG 1018, and Alhydrogel; Group 3: 30 µg SCB-2019, CpG 1018, and Alhydrogel; Group 4: ChAdOx1. Group 3 showed enhanced antibody FcγR binding against wild-type and variants compared to Groups 1 and 2, showing a dose-dependent enhancement of immunity conferred by the SCB-2019 vaccine. Moreover, from day 15 after vaccination, Group 3 exhibited higher IgG3 and FcγR binding across variants of concerns, including Omicron and its subvariants, compared to the ChAdOx1-boosted individuals. Overall, this highlights the potential of SCB-2019 as a cost-efficient boosting regimen effective across variants of concerns.
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Glycosylation is described as a non-templated biosynthesis. Yet, the template-free premise is antithetical to the observation that different N-glycans are consistently placed at specific sites. It has been proposed that glycosite-proximal protein structures could constrain glycosylation and explain the observed microheterogeneity. Using site-specific glycosylation data, we trained a hybrid neural network to parse glycosites (recurrent neural network) and match them to feasible N-glycosylation events (graph neural network). From glycosite-flanking sequences, the algorithm predicts most human N-glycosylation events documented in the GlyConnect database and proposed structures corresponding to observed monosaccharide composition of the glycans at these sites. The algorithm also recapitulated glycosylation in Enhanced Aromatic Sequons, SARS-CoV-2 spike, and IgG3 variants, thus demonstrating the ability of the algorithm to predict both glycan structure and abundance. Thus, protein structure constrains glycosylation, and the neural network enables predictive in silico glycosylation of uncharacterized or novel protein sequences and genetic variants.
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GlyCompareCT is a portable command-line tool to facilitate downstream glycomic data analyses, by addressing data inherent sparsity and non-independence. Inputting glycan abundances, users can run GlyCompareCT with one line of code to obtain the abundances of a minimal substructure set, named glycomotif, thereby quantifying hidden biosynthetic relationships between measured glycans. Optional parameters tuning and annotation are supported for personal preference. For complete details on the use and execution of this protocol, please refer to Bao et al. (2021).1.
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Understanding cellular metabolism is important across biotechnology and biomedical research and has critical implications in a broad range of normal and pathological conditions. Here, we introduce the user-friendly web-based platform ImmCellFie, which allows the comprehensive analysis of metabolic functions inferred from transcriptomic or proteomic data. We explain how to set up a run using publicly available omics data and how to visualize the results. The ImmCellFie algorithm pushes beyond conventional statistical enrichment and incorporates complex biological mechanisms to quantify cell activity. For complete details on the use and execution of this protocol, please refer to Richelle et al. (2021).1.
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Biología Computacional , Proteómica , Proteómica/métodos , Biología Computacional/métodos , Algoritmos , InternetRESUMEN
Beyond the acute illness caused by severe acute respiratory coronavirus 2 (SARS-CoV-2) infection, about one-fifth of infections result in long-term persistence of symptoms despite the apparent clearance of infection. Insights into the mechanisms that underlie postacute sequelae of COVID-19 (PASC) will be critical for the prevention and clinical management of long-term complications of COVID-19. Several hypotheses have been proposed that may account for the development of PASC, including persistence of virus and dysregulation of immune responses. Among the immunological changes noted in PASC, alterations in humoral immunity have been observed in some patient subsets. To begin to determine whether SARS-CoV-2- or other pathogen-specific humoral immune responses evolve uniquely in PASC, we performed comprehensive antibody profiling against SARS-CoV-2, a panel of endemic pathogens, and a panel of routine vaccine antigens using systems serology in two cohorts of patients with preexisting systemic autoimmune rheumatic disease (SARD) who either developed or did not develop PASC. A distinct qualitative shift observed in Fcγ receptor (FcγR) binding was observed in individuals with PASC. Specifically, individuals with PASC harbored weaker FcγR-binding anti-SARS-CoV-2 antibodies and stronger FcγR-binding antibody responses against the endemic coronavirus OC43. Individuals with PASC developed an OC43 S2-specific antibody response with stronger FcγR binding, linked to cross-reactivity across SARS-CoV-2 and common coronaviruses. These findings identify previous coronavirus imprinting as a potential marker for the development of PASC in individuals with SARDs.
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Inmunidad Humoral , Síndrome Post Agudo de COVID-19 , Enfermedades Reumáticas , SARS-CoV-2 , Enfermedades Reumáticas/complicaciones , Enfermedades Reumáticas/inmunología , SARS-CoV-2/inmunología , Humanos , Masculino , Femenino , Persona de Mediana Edad , Anciano , Síndrome Post Agudo de COVID-19/complicaciones , Síndrome Post Agudo de COVID-19/inmunología , Enfermedades Endémicas , Receptores Fc/metabolismo , Anticuerpos Antivirales/sangre , Anticuerpos Antivirales/inmunología , Glicoproteína de la Espiga del Coronavirus/inmunologíaRESUMEN
Human Milk Oligosaccharides (HMOs) are abundant carbohydrates fundamental to infant health and development. Although these oligosaccharides were discovered more than half a century ago, their biosynthesis in the mammary gland remains largely uncharacterized. Here, we use a systems biology framework that integrates glycan and RNA expression data to construct an HMO biosynthetic network and predict glycosyltransferases involved. To accomplish this, we construct models describing the most likely pathways for the synthesis of the oligosaccharides accounting for >95% of the HMO content in human milk. Through our models, we propose candidate genes for elongation, branching, fucosylation, and sialylation of HMOs. Our model aggregation approach recovers 2 of 2 previously known gene-enzyme relations and 2 of 3 empirically confirmed gene-enzyme relations. The top genes we propose for the remaining 5 linkage reactions are consistent with previously published literature. These results provide the molecular basis of HMO biosynthesis necessary to guide progress in HMO research and application with the goal of understanding and improving infant health and development.
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Leche Humana , Oligosacáridos , Glicosiltransferasas/genética , Glicosiltransferasas/metabolismo , Humanos , Lactante , Leche Humana/metabolismo , Oligosacáridos/metabolismoRESUMEN
Glycans are fundamental cellular building blocks, involved in many organismal functions. Advances in glycomics are elucidating the essential roles of glycans. Still, it remains challenging to properly analyze large glycomics datasets, since the abundance of each glycan is dependent on many other glycans that share many intermediate biosynthetic steps. Furthermore, the overlap of measured glycans can be low across samples. We address these challenges with GlyCompare, a glycomic data analysis approach that accounts for shared biosynthetic steps for all measured glycans to correct for sparsity and non-independence in glycomics, which enables direct comparison of different glycoprofiles and increases statistical power. Using GlyCompare, we study diverse N-glycan profiles from glycoengineered erythropoietin. We obtain biologically meaningful clustering of mutant cell glycoprofiles and identify knockout-specific effects of fucosyltransferase mutants on tetra-antennary structures. We further analyze human milk oligosaccharide profiles and find mother's fucosyltransferase-dependent secretor-status indirectly impact the sialylation. Finally, we apply our method on mucin-type O-glycans, gangliosides, and site-specific compositional glycosylation data to reveal tissues and disease-specific glycan presentations. Our substructure-oriented approach will enable researchers to take full advantage of the growing power and size of glycomics data.
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Vías Biosintéticas , Glicómica , Polisacáridos/biosíntesis , Transporte Biológico , Vías Biosintéticas/genética , Análisis por Conglomerados , Análisis de Datos , Eritropoyetina/metabolismo , Fucosiltransferasas/genética , Gangliósidos , Técnicas de Inactivación de Genes , Glicosilación , Humanos , MucinasRESUMEN
Omics experiments are ubiquitous in biological studies, leading to a deluge of data. However, it is still challenging to connect changes in these data to changes in cell functions because of complex interdependencies between genes, proteins, and metabolites. Here, we present a framework allowing researchers to infer how metabolic functions change on the basis of omics data. To enable this, we curated and standardized lists of metabolic tasks that mammalian cells can accomplish. Genome-scale metabolic networks were used to define gene sets associated with each metabolic task. We further developed a framework to overlay omics data on these sets and predict pathway usage for each metabolic task. We demonstrated how this approach can be used to quantify metabolic functions of diverse biological samples from the single cell to whole tissues and organs by using multiple transcriptomic datasets. To facilitate its adoption, we integrated the approach into GenePattern (www.genepattern.org-CellFie).
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Genoma , Redes y Vías Metabólicas , Animales , Redes y Vías Metabólicas/genética , Fenómenos Fisiológicos Celulares , Perfilación de la Expresión Génica , Transcriptoma/genética , Mamíferos/genéticaRESUMEN
Glycosylated biopharmaceuticals are important in the global pharmaceutical market. Despite the importance of their glycan structures, our limited knowledge of the glycosylation machinery still hinders controllability of this critical quality attribute. To facilitate discovery of glycosyltransferase specificity and predict glycoengineering efforts, here we extend the approach to model N-linked protein glycosylation as a Markov process. Our model leverages putative glycosyltransferase (GT) specificity to define the biosynthetic pathways for all measured glycans, and the Markov chain modelling is used to learn glycosyltransferase isoform activities and predict glycosylation following glycosyltransferase knock-in/knockout. We apply our methodology to four different glycoengineered therapeutics (i.e., Rituximab, erythropoietin, Enbrel, and alpha-1 antitrypsin) produced in CHO cells. Our model accurately predicted N-linked glycosylation following glycoengineering and further quantified the impact of glycosyltransferase mutations on reactions catalyzed by other glycosyltransferases. By applying these learned GT-GT interaction rules identified from single glycosyltransferase mutants, our model further predicts the outcome of multi-gene glycosyltransferase mutations on the diverse biotherapeutics. Thus, this modeling approach enables rational glycoengineering and the elucidation of relationships between glycosyltransferases, thereby facilitating biopharmaceutical research and aiding the broader study of glycosylation to elucidate the genetic basis of complex changes in glycosylation.
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The SARS-CoV-2 betacoronavirus uses its highly glycosylated trimeric Spike protein to bind to the cell surface receptor angiotensin converting enzyme 2 (ACE2) glycoprotein and facilitate host cell entry. We utilized glycomics-informed glycoproteomics to characterize site-specific microheterogeneity of glycosylation for a recombinant trimer Spike mimetic immunogen and for a soluble version of human ACE2. We combined this information with bioinformatics analyses of natural variants and with existing 3D structures of both glycoproteins to generate molecular dynamics simulations of each glycoprotein both alone and interacting with one another. Our results highlight roles for glycans in sterically masking polypeptide epitopes and directly modulating Spike-ACE2 interactions. Furthermore, our results illustrate the impact of viral evolution and divergence on Spike glycosylation, as well as the influence of natural variants on ACE2 receptor glycosylation. Taken together, these data can facilitate immunogen design to achieve antibody neutralization and inform therapeutic strategies to inhibit viral infection.
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Betacoronavirus/metabolismo , Infecciones por Coronavirus/enzimología , Infecciones por Coronavirus/virología , Peptidil-Dipeptidasa A/metabolismo , Neumonía Viral/enzimología , Neumonía Viral/virología , Glicoproteína de la Espiga del Coronavirus/metabolismo , Enzima Convertidora de Angiotensina 2 , COVID-19 , Glicosilación , Células HEK293 , Humanos , Simulación de Dinámica Molecular , Pandemias , Peptidil-Dipeptidasa A/química , Dominios Proteicos , Dominios y Motivos de Interacción de Proteínas , Receptores Virales/química , Receptores Virales/metabolismo , SARS-CoV-2 , Glicoproteína de la Espiga del Coronavirus/química , Internalización del VirusRESUMEN
The current COVID-19 pandemic is caused by the SARS-CoV-2 betacoronavirus, which utilizes its highly glycosylated trimeric Spike protein to bind to the cell surface receptor ACE2 glycoprotein and facilitate host cell entry. We utilized glycomics-informed glycoproteomics to characterize site-specific microheterogeneity of glycosylation for a recombinant trimer Spike mimetic immunogen and for a soluble version of human ACE2. We combined this information with bioinformatic analyses of natural variants and with existing 3D-structures of both glycoproteins to generate molecular dynamics simulations of each glycoprotein alone and interacting with one another. Our results highlight roles for glycans in sterically masking polypeptide epitopes and directly modulating Spike-ACE2 interactions. Furthermore, our results illustrate the impact of viral evolution and divergence on Spike glycosylation, as well as the influence of natural variants on ACE2 receptor glycosylation that, taken together, can facilitate immunogen design to achieve antibody neutralization and inform therapeutic strategies to inhibit viral infection.