Biology subjects

Bojar, D.

Publications and source records attributed to Bojar, D..

At least 19 recordsLinked to original sources

bioRxiv2026

Expressing glycan motifs as a containment order increases sensitivity and uncovers motif redistributions

Glycan motifs are the standard readout of comparative glycomics, and pipelines treat them as independent features. Here, we show they are not. Motifs are partially ordered by substructure containment, so a parent motif's abundance dominates each of its children. We make that order explicit as a directed acyclic graph, which stratifies the multiple-testing family correctly and, together with an empirical-Bayes variance prior taken from each motif's containment neighborhood, raises estimated true positives across 45 glycomics datasets from 300 to 442 (+47%, p = 0.0001) after controlling for permutation-null false positives. Because a parent's children and its residual form a genuine sub-composition, their logratio balances need no reference frame and no scale model, and they separate a motif's own change from one inherited from its contexts. Of 337 significant parent motifs, 150 (45%) thus carry no signal of their own, while 138 motifs move only in the decomposition. We present case studies for both glycomics and glycoproteomics, including a recurrent reapportioning of core-1 sialylation at the immune-inhibitory disialyl-T antigen across eight human O-glycomes, and a colorectal fucosylation shift confined to the antenna, neither of which any marginal analysis reports.

bioinformatics↗

bioRxiv2026

Enzyme-Linked Cycloaddition Assay (ELCA) for rapid, ultra-sensitive monitoring of secreted sialoglycoproteins

Glycosylation of proteins is central to cell signaling, immune function, and pathogen interactions, yet existing methods for monitoring glycan changes require specialized instrumentation and primarily report on membrane-anchored, rather than secreted, glycoproteins, with a slow turnover. Here, we present the Enzyme-Linked Cycloaddition Assay (ELCA), a click chemistry-based platform for ultra-sensitive detection and semi-quantitative analysis of secreted sialoglycoproteins. By metabolically incorporating an azide-modified sialic acid into newly synthesized glycoproteins and capturing labeled material via strain-promoted cycloaddition, ELCA quantifies aggregate sialylation using a microplate reader-compatible, ELISA-like workflow. We demonstrate that the secreted glycoproteome responds rapidly to pharmacological perturbation, with changes detectable within hours. Benchmarking against common glycosylation inhibitors and profiling cytokine-driven macrophage polarization further establishes ELCAs sensitivity and temporal resolution. Compatible with serum-containing conditions and requiring no specialized instrumentation, ELCA provides a broadly accessible tool for rapid, cost-effective monitoring of secreted glycoprotein dynamics.

biochemistry↗

bioRxiv2026

Biosynthetic glycan modeling reveals metabolic shifts in disease

Glycans are biosynthesized by partly overlapping cascades of enzymatic reactions that can be conceptualized as biosynthetic networks. These networks latently contain reaction rates, substrate specificities, and metabolic states of the underlying cells. Yet extracting this information, across tissues, species, and hundreds of datasets, is challenging with existing methods. Here, we present a new structure-aware algorithm for glycan biosynthetic network construction that is easily scalable to any number of glycomics datasets and results in more interpretable networks. We further show how these networks then can be used to elucidate substrate preferences of enzymes, quantify metabolic shifts in conditions, such as cancer, and provide hypotheses for not yet elucidated structures via physiological network extension. We also show that tumorigenesis shifts glycome regulation from a carrier-expression-dominated regime toward being governed by biosynthetic network structure, which generalizes across six independent cancer cohorts. We envision that these improvements to glycan biosynthetic modeling will aid in understanding glycan regulation and observed glycomic shifts in health and disease.

bioinformatics↗

bioRxiv2026

GlycoForge generates realistic glycomics data under known ground truth for rigorous method benchmarking

Quantifying all complex carbohydrates in a sample produces glycomics data, which constitutes compositional data and is stymied by biosynthetic dependencies between glycans, requiring dedicated analytic workflows. Properly assessing such methods frequently requires simulated data with known ground truths and injectable effects. However, simulating glycomics data, especially with control over effects and biases, is still unsolved. Here, we present GlycoForge, a feature-complete solution for simulating comparative glycomics data. GlycoForge supports simulating fully synthetic glycomics data and templated simulations based on real-world data, with specified motif-level effects, based on Gaussian copulas and estimated covariances. We further support injection of batch effects, both mean and variance shifts, via center-log ratio transformations to maintain compositional closure, and realistic missing data simulation. We showcase the utility of GlycoForge by evaluating batch effect correction algorithms for glycomics data, with automated guidelines for when to use such methods on real-world data. GlycoForge is available as an open-access Python package at https://github.com/BojarLab/GlycoForge.

bioinformatics↗

bioRxiv2025

Extended nuclear glycosylation is a common post-translational modification

In eukaryotes, glycans modify proteins in the secretory pathway and the extracellular space. Aside from nucleocytoplasmic O-GlcNAc, glycosylation is not considered a relevant post-translational modification in other cellular compartments. Here, we challenge this long-standing paradigm by showing that extended O-glycans are commonly found on intranuclear proteins. Through comprehensive genetic and biochemical analyses, we conclusively demonstrate that these O-glycans stem from the secretory pathway, yet are found on nuclear proteins across mammalian cell lines and primary cells. Using knock-out cell lines, we show mechanistically that nuclear glycans are shuttled to the nucleus via active vesicular transport. We identify several of these intranuclear glycoproteins as RNA-binding proteins, including KHSRP/FUBP2, RBM12, and RPP30. Lastly, we show that site-specific glycosylation of RPP30 is crucial for effective tRNA processing. Overall, our findings suggest a much broader role for glycosylation in regulating cellular functions and open up investigation into the role of glycans in more biological processes.

molecular biology↗

bioRxiv2025

GlyContact reveals structure-function relationships in glycans

Glycans are branched, structurally diverse, and highly flexible biomolecules. These characteristics make glycoanalytics and glycan structural characterization challenging, resulting in often unclear structure-to-function relationships. The creation of GlycoShape, currently the largest open-access (OA) database of glycan 3D structures from molecular dynamics (MD) simulations, provides an opportunity to fill this information gap. Here, we present GlyContact, an open-source Python package designed and developed to retrieve, process, and analyze glycan 3D structures, from MD, NMR, or X-ray crystallography. We demonstrate that this platform can (i) unveil the impact of sequence context on glycan motif structure, (ii) yield a predictive understanding of motif flexibility and surface accessibility on lectin-glycan binding, which improves state-of-the-art AI models for this task, and (iii) accurately predict torsion angles of multiple conformers via new AI models. We envision that GlyContact will allow researchers to explore glycan structures within their 3D space, obtaining new insights into their biological functions. GlyContact is available OA at https://github.com/lthomes/glycontact.

bioinformatics↗

bioRxiv2025

Bridging Worlds: Connecting Glycan Representations with Glycoinformatics via Universal Input and a Canonicalized Nomenclature

As the field of glycobiology has developed, so too have different glycan nomenclature systems, reflecting diverse cognitive and practical needs of different scientific uses. While each system serves specific purposes, this multiplicity creates challenges for usability, data integration, and knowledge sharing. Here, we present a practical framework for automated nomenclature conversion, taking any nomenclature as input, without having to declare the specific language, and using a canonicalized IUPAC-condensed format as a standardized output representation. Our implementation handles (i) all common nomenclatures, including common typos, (ii) complex cases including structural ambiguities, modifications, and uncertainty in linkage information, and (iii) different compositional representations. This Universal Input framework can translate more than 10 nomenclatures in less than 1 ms, tested on over 50,000 sequences with 95-100% coverage, enabling seamless integration of existing glycan databases and tools while maintaining the specific advantages of each representation system.

bioinformatics↗

bioRxiv2025

PyCycleBio: modelling non-sinusoidal-oscillator systems in temporal biology

Protein, mRNA, and metabolite abundances can exhibit rhythmic dynamics, such as during the day/night cycle. Leading bioinformatics platforms for identifying biological rhythms often utilise single-component models of the harmonic oscillator equation, or multi-component models based upon the Cosinor framework. These approaches offer distinct advantages: modelling either temporally-resolved regulatory behaviour via the extended harmonic oscillator equation, or complex rhythmic patterns in the case of Cosinor. Here, we have developed a new platform to combine the advantages of these two approaches. PyCycleBio utilises bounded-multi-component models and modulus operators alongside the harmonic oscillator equation, to model a diverse and interpretable array of rhythmic behaviours, including the regulation of temporal dynamics via amplitude coefficients. We demonstrate increased sensitivity and functionality of PyCycleBio compared to other analytical frameworks, and uncover new relationships between data modalities or sampling conditions with the qualities of rhythmic behaviours from biological datasets-- including transcriptomics, proteomics, and metabolomics. We envision that this new approach for disentangling complicated temporal regulation of biomolecules will advance chronobiology and our understanding of physiology. PyCycleBio is available at: https://github.com/Glycocalex/PyCycleBio, and the Python package is available to install at: https://pypi.org/project/pycyclebio/. PyCycleBio can also be used at https://colab.research.google.com/github/Glycocalex/PyCycleBio/blob/main/PyCycleBio.ipynb with no installations necessary.

bioinformatics↗

bioRxiv2025

Seal milk oligosaccharides rival human milk complexity and exhibit functional dynamics during lactation

Breast milk oligosaccharides are crucial for neonatal development and health. Yet most milk research focuses on humans, or domesticated mammals that are historically poor in milk oligosaccharide complexity. Here, we perform an exhaustive mass spectrometry-driven structural characterization of milk oligosaccharides in a wild mammal, Atlantic grey seals (Halichoerus grypus), throughout their lactation period. Characterizing and quantifying 332 milk oligosaccharides, including 166 novel structures, we reveal seals to rival human milk in complexity, with seal free oligosaccharides reaching unprecedented 28 monosaccharides in size. Glycomics and metabolomics time course analysis establishes a concerted regulatory process reshaping the seal milk glycome throughout lactation,similar as in human milk. Functional analysis of herein newly characterized structures reveals anti-biofilm effects and immunomodulatory functions of seal milk oligosaccharides. We envision these findings to overturn long-held assumptions about milk complexity of non-human mammals and enable insights into the functional relevance of complex carbohydrates in breast milk.

biochemistry↗

bioRxiv2024

O-glycosylation contributes to mammalian glycoRNA biogenesis

There is an increasing appreciation for the role of cell surface glycans in modulating interactions with extracellular ligands and participating in intercellular communication. We recently reported the existence of sialoglycoRNAs, where mammalian small RNAs are covalently linked to N-glycans through the modified base acp3U and trafficked to the cell surface. However, little is currently known about the role for O-glycosylation, another major class of carbohydrate polymer modifications. Here, we use parallel genetic, enzymatic, and mass spectrometry approaches to demonstrate that O-linked glycan biosynthesis is responsible for the majority of sialoglycoRNA levels. By examining the O-glycans associated with RNA from cell lines and colon organoids we find known and previously unreported O-linked glycan structures. Further, we find that O-linked glycans released from small RNA from organoids derived from ulcerative colitis patients exhibit higher levels of sialylation than glycans from healthy organoids. Together, our work provides flexible tools to interrogate O-linked glycoRNAs (O-glycoRNA) and suggests that they may be modulated in human disease.

molecular biology↗

bioRxiv2024

Navigating the Maze of Mass Spectra: A Machine-Learning Guide to Identifying Diagnostic Ions in O-Glycan Analysis

Structural details of oligosaccharides, or glycans, often carry biological relevance, which is why they are typically elucidated using tandem mass spectrometry. Common approaches to distinguish isomers rely on diagnostic glycan fragments for annotating topologies or linkages. Diagnostic fragments are often only known informally among practitioners or stem from individual studies, with unclear validity or generalizability, causing annotation heterogeneity and hampering new analysts. Drawing on a curated set of 237,000 O-glycomics spectra, we here present a rule-based machine learning workflow to uncover quantifiably valid and generalizable diagnostic fragments. This results in fragmentation rules to robustly distinguish common O-glycan isomers. We envision this resource to improve glycan annotation accuracy and concomitantly make annotations more transparent and homogeneous across analysts.

bioinformatics↗

bioRxiv2024

Ratios in Disguise, Truths Arise: Glycomics Meets Compositional Data Analysis

Comparative glycomics data are an instance of compositional data defined by the Aitchison simplex, where measured glycans are parts of a whole, indicated by relative abundances, which are then compared between conditions. Applying traditional statistical analyses to this type of data often results in misleading conclusions, such as spurious "decreases" of glycans between conditions when other structures sharply increase in abundance, or routine false-positive rates of >25% for differential abundance. Our work introduces a compositional data analysis framework, specifically tailored to comparative glycomics, to account for these data dependencies. We employ center log-ratio (CLR) and additive log-ratio (ALR) transformations, augmented with a model incorporating scale uncertainty/information, to introduce the most robust and sensitive glycomics data analysis pipeline. Applied to many publicly available comparative glycomics datasets, we show that this model controls false-positive rates and results in new biological findings. Additionally, we present new modalities to analyze comparative glycomics data with this framework. Alpha- and beta-diversity enable exploration of glycan distributions within and between biological samples, while cross-class glycan correlations shed light on complex and previously undetected interdependencies. These new approaches have revealed deeper insights into glycome variations that are critical to understanding the roles of glycans in health and disease.

bioinformatics↗

bioRxiv2024

Decoding glycosylation potential from protein structure across human glycoproteins with a multi-view recurrent neural network

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.

bioinformatics↗

bioRxiv2024

Syntactic Sugars: Crafting a Regular Expression Framework for Glycan Structures

SummaryStructural analysis of glycans pose significant challenges in glycobiology due to their complex sequences. Research questions such as analyzing the sequence content of the 1-6 branch in N- glycans, are biologically meaningful yet can be hard to automate. Here, we introduce a regular expression system, designed for glycans, feature-complete, and closely aligned with regular expression formatting. We use this to annotate glycan motifs of arbitrary complexity, perform differential expression analysis on designated sequence stretches, or elucidate branch-specific binding specificities of lectins in an automated manner. We are confident that glycan regular expressions will empower computational analyses of these sequences. Availability and implementationOur regular expression framework for glycans is implemented in Python and is incorporated into the open-source glycowork package (version 1.1+). Code and documentation are available at https://github.com/BojarLab/glycowork/blob/master/glycowork/motif/regex.py. Contactdaniel.bojar@gu.se

bioinformatics↗

bioRxiv2023

Elucidating the Glycan-Binding Specificity and Structure of Cucumis melo Agglutinin, a New R-Type Lectin

Plant lectins have garnered attention for their roles as laboratory probes and potential therapeutics. Here, we report the discovery and characterization of Cucumis melo agglutinin (CMA1), a new R-type lectin from melon. Our findings reveal CMA1s unique glycan-binding profile, mechanistically explained by its 3D structure, augmenting our understanding of R-type lectins. We expressed CMA1 recombinantly and assessed its binding specificity using multiple glycan arrays, covering 1,046 unique sequences. This resulted in a complex binding profile, strongly preferring C2-substituted, beta-linked galactose (both GalNAc and Fuca1-2Gal), which we contrasted with the established R-type RCA1 lectin. We also report binding of specific glycosaminoglycan subtypes and a general enhancement of binding by sulfation. Further validation using agglutination, thermal shift assays, and surface plasmon resonance confirmed and quantified this binding specificity in solution. Finally, we solved the high-resolution structure of the CMA1 N-terminal domain using X-ray crystallography, supporting our functional findings at the molecular level. Our study provides a comprehensive understanding of CMA1, laying the groundwork for further exploration of its biological and therapeutic potential.

biochemistry↗

bioRxiv2023

Decoding Glycomics: Differential Expression Reimagined

Glycomics, the comprehensive study of all glycan structures in a sample, is a rapidly expanding field with substantial relevance for understanding physiology and disease mechanisms. However, the complexity of glycan structures and glycomics data interpretation present significant challenges, especially when it comes to differential expression analysis. Here, we present a novel computational framework for differential glycomics expression analysis. Our methodology encompasses specialized and domain-informed methods for data normalization and imputation, glycan motif extraction and quantification, differential expression analysis, motif enrichment analysis, time series analysis, and meta-analytic capabilities, allowing for synthesizing results across multiple studies. All methods are integrated into our open-source glycowork package, facilitating performant workflows and user-friendly access. We demonstrate these methods using dedicated simulations and various glycomics datasets. Our rigorous approach allows for more robust, reliable, and comprehensive differential expression analyses in glycomics, contributing to the advancement of glycomics research and its translation to clinical and diagnostic applications.

bioinformatics↗

bioRxiv2023

Predicting glycan structure from tandem mass spectrometry via deep learning

Glycans constitute the most complicated post-translational modification, modulating protein activity in health and disease. However, structural annotation from tandem mass spectrometry data is a bottleneck in glycomics, preventing high-throughput endeavors and relegating glycomics to a few experts. Trained on a newly curated set of 300,000 annotated MS/MS spectra, we present CandyCrunch, a dilated residual neural network predicting glycan structure from raw LC-MS/MS data in seconds (Top1 Accuracy: 87.7%). We developed an open-access Python-based workflow of raw data conversion and prediction, followed by automated curation and fragment annotation, with predictions recapitulating and extending expert annotation. We demonstrate that this can be used for de novo annotation, diagnostic fragment identification, and high-throughput glycomics. For maximum impact, this entire pipeline is tightly interlaced with our glycowork platform and can be easily tested at https://colab.research.google.com/github/BojarLab/CandyCrunch/blob/main/CandyCru nch.ipynb. We envision CandyCrunch to democratize structural glycomics and the elucidation of biological roles of glycans.

bioinformatics↗

bioRxiv2023

GlycoDraw: A Python Implementation for Generating High-Quality Glycan Figures

Glycans are essential to all scales of biology, with their intricate structures being crucial for their biological functions. The structural complexity of glycans is communicated through simplified and unified visual representations according to the Symbol Nomenclature for Glycans (SNFG) guidelines adopted by the community. Here, we introduce GlycoDraw, a Python-native implementation for high-throughput generation of high-quality, SNFG-compliant glycan figures with flexible display options. GlycoDraw is released as part of our glycan analysis ecosystem, glycowork, facilitating integration into existing workflows by enabling fully automated annotation of glycan-related figures and thus assisting the analysis of e.g., differential abundance data or glycomics mass spectra.

bioinformatics↗