Biology subjects

Neelamegham, S.

Publications and source records attributed to Neelamegham, S..

14 recordsLinked to original sources

bioRxiv2025

GlycoEnzDB: A database of enzymes involved in human glycosylation

The glycan distribution on cells is governed by the stochastic activity of different families of enzymes that are together called glycoEnzymes. These include ~400 gene products or 2% of the proteome, that have recently been curated in an ontology called GlycoEnzOnto. With the goal of making this ontology more accessible to the larger biomedical and biotechnology community, we organized a web resource called GlycoEnzDB, presenting this enzyme classification both in terms of enzyme function and the pathways that they participate in. This information is linked to i) Figures from the "Essentials of Glycobiology" textbook, ii) General gene, enzyme and pathway data appearing in external databases, iii) Manual and generative-artificial intelligence (AI) based text describing the function and pathways regulated by these entities, iv) Single-cell expression data across cell lines, normal human cell-types and tissue, and v) CRISPR-knockout/activation/inactivation and Transcription factor activity predictions. Whereas these data are curated for human glycoEnzymes, the knowledge framework may be extended to other species also. The user-friendly web interface is accessible at www.virtualglycome.org/glycoenzdb.

biochemistry↗

bioRxiv2025

Transformer-based Deep Learning for Glycan Structure Inference from Tandem Mass Spectrometry

Glycans play critical roles in diverse biological processes, but their structural analysis by tandem mass spectrometry (MS/MS) remains a major challenge due to their branched structure and stereochemistry. Traditional computational methods, such as database searching, are constrained by the scope of existing libraries and can be computationally intensive. While recent deep learning models have advanced the field, they often struggle to capture the complex, long-range dependencies within MS/MS spectra required for accurate inference. To address these challenges, we present GlycoBERT and GlycoBART, novel transformer-based models for glycan structure prediction from MS/MS data. GlycoBERT, a sequence classifier, achieves 95.1% structural accuracy on test data, surpassing the current state-of-the-art deep learning model, CandyCrunch. However, classification-based methods are inherently limited to predicting structures present in the training data. To overcome this, we developed GlycoBART, a generative sequence-to-sequence model capable of de novo glycan inference. On independent validation datasets, both models demonstrate robust performance. Critically, when applied to an MS/MS dataset from human embryonic kidney cells, GlycoBART generated two de novo glycan structures absent from the training set, one of which is a novel structure not catalogued in major glycan databases. Together, GlycoBERT and GlycoBART establish a new benchmark for glycan analysis, offering a powerful framework that enables more accurate and comprehensive exploration of glycan diversity and discovery.

bioinformatics↗

bioRxiv2024

Leucine zipper-based SAIM imaging identifies therapeutic agents to disrupt the cancer cell glycocalyx for enhanced immunotherapy

The abnormally thick glycocalyx of cancer cells can physically limit immune-cell engagement and reduce the efficacy of cellular immunotherapies, but strategies for identifying pharmacological agents that relieve this barrier remain limited. Here, we establish glycocalyx thickness as a pharmacologically actionable physical phenotype. Disruption of mucin-type O-glycosylation reduced glycocalyx thickness and increased NK- and CAR-NK-mediated killing, demonstrating that pharmacological glycocalyx remodeling can enhance innate and CAR-directed cytotoxicity. To screen this phenotype directly, we developed a scanning angle interference microscopy (SAIM) platform using complementary leucine zipper pairs as a stable extracellular plasma-membrane reference. A focused screen of glycosylation and metabolic inhibitors resolved compound- and cell-type-specific nanoscale changes in glycocalyx thickness and identified several thinning agents. Castanospermine, an inhibitor of early N-glycan processing, was among the strongest hits and reduced mucin-probe binding in an independent mucin-rich gastric cancer model. Castanospermine-treated gastric cancer cells retained MUC17 antibody recognition and showed increased physical avidity for MUC17 CAR-T cells, while castanospermine enhanced MUC17 CAR-T-mediated tumor control in real-time co-culture assays. These findings establish direct glycocalyx-thickness measurement as a physical-phenotype-guided screening strategy and identify castanospermine as a candidate modulator for combination with MUC17 CAR-T cells.

bioengineering↗

bioRxiv2024

Conserved role of spike S2 domain N-glycosylation across beta-coronavirus family

Besides acting as an immunological shield, the N-glycans of SARS-CoV-2 are also critical for viral life cycle. As the S2 subunit of spike is highly conserved across beta-coronaviruses, we determined the functional significance of the five stem N-glycans located in S2 between N1098-N1194. Studies were performed with 31 Asn-to-Gln mutants, beta-coronavirus virus-like particles and single-cycle viral replicons. Deletions of stem N-glycans enhanced S1 shedding from trimeric spike, reduced ACE2 binding and abolished syncytia formation. When three or more N-glycans were deleted, spike expression on cell surface and incorporation into virions was both reduced. Viral entry function was progressively lost upon deleting the N1098 glycan in combination with additional glycosite modifications. In addition to SARS-CoV-2, deleting stem N-glycans in SARS-CoV and MERS-CoV spike also prevented viral entry into target cells. These data suggest multiple functional roles for the stem N-glycans, and evolutionarily conserved properties for these complex carbohydrates across human beta-coronaviruses. Author SummaryPrevious work shows that the N-linked glycans of SARS-CoV-2 are essential for viral life cycle. Few natural mutations have been observed in the S2-subunit of the viral spike glycoprotein in GISAID data, and mutations are absent in the five stem N-glycans located between N1098-N1194. In the post-fusion spike structure these glycans lie equidistant, ~4 nm apart, suggesting functional significance. Upon testing the hypothesis that these glycans are critical for SARS-CoV-2 function, we noted multiple roles for the complex carbohydrates including regulation of S1-subunit shedding, spike expression on cells and virions, syncytial formation/cell-cell fusion and viral entry. Besides SARS-CoV-2, these glycans were also critical for other human beta-coronaviruses. Thus, these carbohydrates represent targets for the development of countermeasures against future outbreaks.

microbiology↗

bioRxiv2024

A detailed protocol for generating spike trans-complemented SARS-CoV-2 replicons

Multiple approaches have been implemented for basic science studies that attempt to investigate SARS-CoV-2 biology or virology in biosafety level-2 setting. These include pseudotyped-virus based on lentivirus and vesicular stomatitis virus, virus-like particles that only contain the SARS-CoV-2 structural proteins, and single-cycle replicons. Among these, the single-cycle replicons most closely resemble the authentic virus as they essentially include the full viral genome, except for essential elements required for active virus multiplication. In this regard, we previously developed a SARS-CoV-2 replicon system where a Gaussia Dura luciferase-P2A-mNeonGreen reporter cassette replaced viral spike. In this short paper, we present an optimized protocol for the use of this reagent that overcomes previous technical limitations. We demonstrate that co-transfection of this bacmid along with spike plasmid, using the improved protocol, yields high-quality spike bearing SARS-CoV-2 virus particles with single-cycle infectivity. Due to the nature of bacmid construction, this approach is particularly useful for studying the impact of spike mutagenesis on virus evolution in BSL-2 setting.

microbiology↗

bioRxiv2023

Cell and tissue-specific glycosylation pathways and transcriptional regulation informed by single-cell transcriptomics

While single cell studies have made significant impacts in various subfields of biology, they lag in the Glycosciences. To address this gap, we analyzed single-cell glycogene expressions in the Tabula Sapiens dataset of human tissues and cell types using a recent glycosylation-specific gene ontology (GlycoEnzOnto). At the median sequencing (count) depth, [~]40-50 out of 400 glycogenes were detected in individual cells. Upon increasing the sequencing depth, the number of detectable glycogenes saturates at [~]200 glycogenes, suggesting that the average human cell expresses about half of the glycogene repertoire. Hierarchies in glycogene and glycopathway expressions emerged from our analysis: nucleotide-sugar synthesis and transport exhibited the highest gene expressions, followed by genes for core enzymes, glycan modification and extensions, and finally terminal modifications. Interestingly, the same cell types showed variable glycopathway expressions based on their organ or tissue origin, suggesting nuanced cell- and tissue-specific glycosylation patterns. Probing deeper into the transcription factors (TFs) of glycogenes, we identified distinct groupings of TFs controlling different aspects of glycosylation: core biosynthesis, terminal modifications, etc. We present webtools to explore the interconnections across glycogenes, glycopathways, and TFs regulating glycosylation in human cell/tissue types. Overall, the study presents an overview of glycosylation across multiple human organ systems.

bioinformatics↗

bioRxiv2023

Knockout studies using CD34+ hematopoietic cells suggest that CD44 is a physiological human neutrophil E-selectin ligand

The recruitment of peripheral blood neutrophils at sites of inflammation involves a multistep cascade, starting with E- and P-selectin expressed on the inflamed vascular endothelium binding sialofucosylated glycans on leukocytes. As the glycoconjugate biosynthesis pathways in different cells are distinct, the precise carbohydrate ligands of selectins varies both across species, and between different immune cell populations in a given species. To study this aspect in human neutrophils, we developed a protocol to perform CRISPR/Cas9 gene-editing on CD34+ hHSCs (human hematopoietic stem/progenitor cells) as they are differentiated towards neutrophil lineage. This protocol initially uses a cocktail of SCF (stem-cell factor), IL-3 (interleukin-3) and FLT-3L (FMS-like tyrosine kinase 3 ligand) to expand the stem/progenitor cells followed by directed differentiation to neutrophils using G-CSF (granulocyte colony-stimulating factor). Microfluidics based assays were performed on a confocal microscope platform to characterize the rolling phenotype of each edited cell type in mixed populations. These studies demonstrated that CD44, but not CD43, is a major E-selectin ligand on human neutrophils. The loss of function results were validated by developing sialofucosylated recombinant CD44. This glycosylated protein supported both robust E-selectin binding in a cell-free assay, and it competitively blocked neutrophil adhesion to E-selectin on inflamed endothelial cells. Together, the study establishes important methods to study human neutrophil biology and determines that sialoflucosylated-CD44 is a physiological human E-selectin ligand. KEY POINTSO_LIA CRISPR-Cas9 based protocol was optimized to knockout genes in primary human neutrophils derived from CD34+ hHSCs C_LIO_LIUnder physiological fluid shear conditions, a sialofucosylated form of CD44 expressed on human neutrophils is a major E-selectin ligand C_LI VISUAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC="FIGDIR/small/553923v1_ufig1.gif" ALT="Figure 1"> View larger version (21K): org.highwire.dtl.DTLVardef@1891653org.highwire.dtl.DTLVardef@118e074org.highwire.dtl.DTLVardef@38052aorg.highwire.dtl.DTLVardef@1db0882_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2022

Avian and Human Influenza Viruses Exhibit Distinct Glycoconjugate Receptor Specificities in Human Lung Cells

IAV utilize sialic acid (Sia) containing cell surface glycoconjugates for host cell infection, and IAV strains from different host species show preferences for structurally distinct Sia at the termini of glycoconjugates. Various types of cell surface glycoconjugates (N-glycans, O-glycans, glycolipids) display significant diversity in both structure and carbohydrate composition. To define the types of glycoconjugates that facilitate IAV infection, we utilized the CRISPR/Cas9 technique to truncate different types of glycoconjugates, either individually or in combination, by targeting glycosyltransferases essential to glycan biosynthesis in a human lung epithelial cell line. Our studies show that both human and avian IAV strains do not display strict preferences for a specific type of glycoconjugate. Interestingly, truncation of all three types of glycoconjugates significantly decreased the replication of human IAV strains, yet did not impact the replication of avian IAV strains. Taken together, our studies demonstrate that avian IAV strains utilize a broader repertoire of glycoconjugates for host cell infection as compared to human IAV strains. Author SummaryIt is well known that influenza A viruses (IAV) initiate host cell infection by binding to sialic acid, a sugar molecule present at the ends of various sugar chains called glycoconjugates. These glycoconjugates can vary in chain length, structure, and composition. However, it remains unknown if IAV strains preferentially bind to sialic acid on specific glycoconjugates for host cell infection. Here, we utilized CRISPR gene editing to abolish sialic acid on different glycoconjugate types in human lung cells, and evaluated human versus avian IAV infections. Our studies show that both human and avian IAV strains can infect human lung cells by utilizing any of the three major sialic acid-containing glycoconjugate types, specifically N-glycans, O-glycans, and glycolipids. Interestingly, simultaneous elimination of sialic acid on all three glycoconjugate types in human lung cells dramatically decreased human IAV infection, yet had little effect on avian IAV infection. Our studies indicate that avian IAV strains can utilize a wide variety of glycoconjugates for infection, whereas human IAV strains display restrictions in glycoconjugate type usage. These novel studies show distinct differences in host glycoconjugate preferences between human and avian IAV strains.

microbiology↗

bioRxiv2022

GlycoEnzOnto: A GlycoEnzyme Pathway and Molecular Function Ontology

The glycoEnzymes include a set of proteins having related enzymatic, metabolic, transport, structural and cofactor functions. Current there is no established ontology to describe glycoEnzyme properties and to relate them to glycan biosynthesis pathways. We present GlycoEnzOnto, an ontology describing 386 human glycoEnzymes curated along 135 glycosylation pathways, 134 molecular functions and 22 cellular compartments. The pathways described regulate nucleotide-sugar metabolism, glycosyl-substrate/donor transport, glycan biosynthesis, and degradation. The role of each enzyme in the glycosylation initiation, elongation/branching, and capping/termination phases is described. IUPAC linear strings present systematic human/machine readable descriptions of individual reaction steps and enable automated knowledge-based curation of biochemical networks. All GlycoEnzOnto knowledge is integrated with the Gene Ontology (GO) biological processes. GlycoEnzOnto enables improved transcript overrepresentation analyses and glycosylation pathway identification compared to other available schema, e.g. KEGG and Reactome. Overall, GlycoEnzOnto represents a holistic glycoinformatics resource for systems-level analyses. Availabilityhttps://github.com/neel-lab/GlycoEnzOnto

bioinformatics↗

bioRxiv2021

Community Evaluation of Glycoproteomics Informatics Solutions Reveals High-Performance Search Strategies of Glycopeptide Data

Glycoproteome profiling (glycoproteomics) is a powerful yet analytically challenging research tool. The complex tandem mass spectra generated from glycopeptide mixtures require sophisticated analysis pipelines for structural determination. Diverse software aiding the process have appeared, but their relative performance remains untested. Conducted through the HUPO Human Proteome Project - Human Glycoproteomics Initiative, this community study, comprising both developers and users of glycoproteomics software, evaluates the performance of informatics solutions for system-wide glycopeptide analysis. Mass spectrometry-based glycoproteomics datasets from human serum were shared with all teams. The relative team performance for N- and O-glycopeptide data analysis was comprehensively established and validated through orthogonal performance tests. Excitingly, several high-performance glycoproteomics informatics solutions were identified. While the study illustrated that significant informatics challenges remain, as indicated by a high discordance between annotated glycopeptides, lists of high-confidence (consensus) glycopeptides were compiled from the standardised team reports. Deep analysis of the performance data revealed key performance-associated search variables and led to recommendations for improved "high coverage" and "high accuracy" glycoproteomics search strategies. This study concludes that diverse software for comprehensive glycopeptide data analysis exist, points to several high-performance search strategies, and specifies key variables that may guide future software developments and assist informatics decision-making in glycoproteomics.

biochemistry↗

bioRxiv2020

Efficient Inhibition of O-glycan biosynthesis using the hexosamine analog Ac5GalNTGc

There is a critical need to develop small molecule inhibitors of mucin-type O-linked glycosylation. The best known reagent currently is peracetylated benzyl-GalNAc, but it is only effective at millimolar concentrations. This manuscript demonstrates that Ac5GalNTGc, a peracetylated C-2 sulfhydryl substituted GalNAc, fulfills this unmet need. When added to cultured leukocytes, breast and prostate cells, Ac5GalNTGc increased cell surface VVA-binding by ~10-fold, indicating truncation of O-glycan biosynthesis. Cytometry, mass spectrometry and Western blot analysis of HL-60 promyelocytes demonstrate that 50-80M Ac5GalNTGc prevented elaboration of 30-60% of the O-glycans beyond the Tn-antigen (GalNAc1-Ser/Thr) stage. The effect of the compound on N-glycans and glycosphingolipids was small. Glycan inhibition induced by Ac5GalNTGc resulted in 50-80% reduction in leukocyte sialyl-Lewis-X expression, and L-/P-selectin mediated rolling under flow. Ac5GalNTGc was pharmacologically active in mouse. It reduced neutrophil infiltration to sites of inflammation by ~60%. Overall, Ac5GalNTGc may find diverse applications as a potent inhibitor of O-glycosylation.

biochemistry↗

bioRxiv2020

Inhibition of SARS-CoV-2 viral entry in vitro upon blocking N- and O-glycan elaboration

The Spike protein of SARS-CoV-2, its receptor binding domain (RBD), and its primary receptor ACE2 are extensively glycosylated. The impact of this post-translational modification on viral entry is yet unestablished. We expressed different glycoforms of the Spike-protein and ACE2 in CRISPR-Cas9 glycoengineered cells, and developed corresponding SARS-CoV-2 pseudovirus. We observed that N- and O-glycans had only minor contribution to Spike-ACE2 binding. However, these carbohydrates played a major role in regulating viral entry. Blocking N-glycan biosynthesis at the oligomannose stage using both genetic approaches and the small molecule kifunensine dramatically reduced viral entry into ACE2 expressing HEK293T cells. Blocking O-glycan elaboration also partially blocked viral entry. Mechanistic studies suggest multiple roles for glycans during viral entry. Among them, inhibition of N-glycan biosynthesis enhanced Spike-protein proteolysis. This could reduce RBD presentation on virus, lowering binding to host ACE2 and decreasing viral entry. Overall, chemical inhibitors of glycosylation may be evaluated for COVID-19.

biochemistry↗

bioRxiv2020

A systems based framework to deduce transcription factors and signaling pathways regulating glycan biosynthesis

Glycosylation is a common post-translational modification, and glycan biosynthesis is regulated by a set of glycogenes. The role of transcription factors (TFs) in regulating the glycogenes and related glycosylation pathways is largely unknown. This manuscript presents a multi-omics data-mining framework to computationally predict putative, tissue-specific TF regulators of glycosylation. It combines existing ChIP-Seq (Chromatin Immunoprecipitation Sequencing) and RNA-Seq data to suggest 22,519 potentially significant TF-glycogene relationships. This includes interactions involving 524 unique TFs and 341 glycogenes that span 29 TCGA (The Cancer Genome Atlas) cancer types. Here, TF-glycogene interactions appeared in clusters or communities, suggesting that changes in single TF expression during both health and disease may affect multiple carbohydrate structures. Upon applying the Fishers exact test along with glycogene pathway classification, we identify TFs that may specifically regulate the biosynthesis of individual glycan types. Integration with knowledge from the Reactome database provided an avenue to relate cell-signaling pathways to TFs and cellular glycosylation state. Whereas analysis results are presented for all 29 cancer types, specific focus is placed on human luminal and basal breast cancer disease progression. Overall, the computational predictions in this manuscript present a starting point for systems-wide validation of TF-glycogene relationships.

systems biology↗

bioRxiv2020

A consensus-based and readable extension of Linear Code for Reaction Rules (LiCoRR)

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. Reaction rule-extended Linear Code is an unambiguous representation for describing glycosylation reactions in both literature and code.

systems biology↗