Biology subjects

Lundstrom, J.

Publications and source records attributed to Lundstrom, J..

12 recordsLinked to original sources

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↗

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

Distinct and Combined Interferon-α/β-receptor-1 Loss in Neurons and Astrocytes Disrupt Brain Energy Metabolism and Drive Parkinsonian Dementia

Dysregulated interferon-alpha/beta-receptor 1 (IFNAR1) signaling was recently identified to contribute to the development of sporadic Parkinsons Disease (PD) into PD with Dementia (PDD). The molecular, cellular, and phenotypic impacts of brain IFNAR1 loss in aging have not been explored in vivo, which may reveal novel disease mechanisms and therapeutic targets. Here it is shown that baseline IFNAR1 expression varies in the major brain cell types, including neurons and astrocytes, and is differentially affected in PD and Lewy Body Dementia patients compared to unaffected controls. Neuron- and astrocyte-specific transcriptomic and proteomic alterations in Ifnar1-/- mice implicate mitochondrial defects and synergistic dysfunctional neurotransmission upon IFNAR1 loss, leading to glucose hypermetabolism measured by functional metabolic analysis. Consequently, Ifnar1-/- mice exhibited PDD-like pathogenesis, including dopaminergic cell loss in the substantia nigra, cortical neurodegeneration, Lewy-body-like inclusions, neuroinflammation, and progressive PDD-like behavior deficits. Brain cell-specific IFNAR1 loss examined in vivo revealed delayed but distinct development of PDD-like phenotypes, where neuropathology, motor, and cognitive behavior deficits were specifically recapitulated only in mice lacking neuronal IFNAR1, and behavior resembling neuropsychiatric abnormalities recapitulated only in mice lacking astrocytic IFNAR1. This work supports a crucial role of IFNAR1 in brain homeostasis and emphasizes a need for understanding neurodegenerative pathophysiology in cell-specific contexts. HighlightsO_LIIFNAR1 and related type-I IFN genes are differentially expressed among major brain cell types in Parkinsons Disease, Lewy Body Dementia, and unaffected controls C_LIO_LIEarly molecular alterations in Ifnar1-/- mice show lack of immunomodulation contributing to neuroinflammation, mitochondrial defects, and dysregulated energy metabolism C_LIO_LIIfnar1-/- mice develop a progressive Parkinsonian-like disease phenotype, including dopaminergic cell loss in substantia nigra, cortical neurodegeneration, phosphorylated (p)alpha-synuclein+ and pTau+ Lewy-body-like inclusions, neuroinflammation, and progressive motor, cognitive, and neuropsychiatric disturbance-like behavior deficits C_LIO_LINeuropathologies, motor, and cognitive deficits are recapitulated in mice lacking neuronal IFNAR1 (Syn1Cre;Ifnar1fl/fl) whereas neuropsychiatric abnormalities are recapitulated in mice lacking astrocytic IFNAR1 (GFAPCre;Ifnar1fl/fl) C_LI

neuroscience↗

bioRxiv2025

The olfactory bulb reflects structural plasticity within a genetically stable olfactory network

The olfactory bulb (OB), the first central relay of the olfactory pathway, plays a critical role in odor perception and exhibits remarkable structural plasticity shaped by environmental influences. This raises a fundamental question about the extent to which the structure of the OB is genetically determined. While both OB volume and function have been implicated in a range of neurodegenerative and neuropsychiatric disorders, such as Parkinsons disease, schizophrenia, and depression, all of which involve known genetic risk factors, the heritability of the OB structure remains poorly understood. Here, we investigated the heritability of OB volume and the broader olfactory network architecture in a large sample of healthy young adults (n = 941; aged 22-35 years), including monozygotic and dizygotic twin pairs. Using a deep learning-based segmentation model, we first automatically segmented OB volumes and then employed a support vector machine framework to classify zygosity based on within-pair morphological similarity. The OB volume alone showed weak classification performance in distinguishing between monozygotic and dizygotic twins, suggesting limited heritability. However, integrating OB volume with morphometric features from olfactory-associated brain regions, including the hippocampus, parahippocampal gyrus, entorhinal cortex, and medial orbitofrontal cortices, substantially improved classification performance. This effect was specific to the olfactory network, underscoring the distributed nature of genetic influence in this system. These findings suggest that the OB, despite being a highly plastic and environmentally responsive structure, is embedded within a genetically coordinated neural network.

neuroscience↗

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

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↗

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

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↗

bioRxiv2023

Mammalian Milk Glycomes: Connecting the Dots between Evolutionary Conservation and Biosynthetic Pathways

Milk oligosaccharides (MOs) are among the most abundant constituents of breast milk and are essential for health and development. Biosynthesized from monosaccharides into complex sequences, MOs differ considerably between taxonomic groups. Even human MO biosynthesis is insufficiently understood, hampering evolutionary and functional analyses. Using a comprehensive resource of all published MOs from >100 mammals, we develop a nonparametric pipeline for generating and analyzing MO biosynthetic networks, which readily generalizes to other glycan classes. We then use evolutionary relationships and inferred intermediates of these networks to discover (i) distributional glycome biases, (ii) biosynthetic restrictions, such as reaction path dependence, and (iii) conserved biosynthetic modules. This allows us to prune and pinpoint biosynthetic pathways despite missing information. Machine learning and network analysis cluster species by their milk glycome, identifying characteristic sequence relationships and evolutionary gains/losses of motifs, MOs, and biosynthetic modules. These resources and analyses will advance our understanding of glycan biosynthesis and the evolution of breast milk.

bioinformatics↗

bioRxiv2023

Breast Milk Oligosaccharides Contain Immunomodulatory Glucuronic Acid and LacdiNAc

Breast milk is abundant with functionalized milk oligosaccharides (MOs), to nourish and protect the neonate. Yet we lack a comprehensive understanding of the repertoire and evolution of MOs across Mammalia. We report [~]400 MO-species associations (>100 novel structures) from milk glycomics of nine mostly understudied species: alpaca, beluga whale, black rhinoceros, bottlenose dolphin, impala, LHoests monkey, pygmy hippopotamus, domestic sheep, and striped dolphin. This revealed the hitherto unknown existence of the LacdiNAc motif (GalNAc{beta}1-4GlcNAc) in MOs of all species except alpaca, sheep, and striped dolphin, indicating widespread occurrence of this potentially antimicrobial motif in MOs. We also characterize glucuronic acid-containing MOs in the milk of impala, dolphins, sheep, and rhinoceros, previously only reported in cows. We demonstrate that these GlcA-MOs exhibit potent immunomodulatory effects. Our study extends the number of known MOs by >15%. Combined with >1,900 curated MO-species associations, we characterize MO motif distributions, presenting an exhaustive overview of MO biodiversity.

biochemistry↗

bioRxiv2021

LectinOracle - A Generalizable Deep Learning Model for Lectin-Glycan Binding Prediction

Ranging from bacterial cell adhesion over viral cell entry to human innate immunity, glycan-binding proteins or lectins abound in nature. Widely used as staining and characterization reagents in cell biology, and crucial for understanding the interactions in biological systems, lectins are a focal point of study in glycobiology. Yet the sheer breadth and depth of specificity for diverse oligosaccharide motifs has made studying lectins a largely piecemeal approach, with few options to generalize. Here, we present LectinOracle, a model combining transformer-based representations for proteins and graph convolutional neural networks for glycans to predict their interaction. Using a curated dataset of 564,647 unique protein-glycan interactions, we show that LectinOracle predictions agree with literature-annotated specificities for a wide range of lectins. We further identify clusters of lectins with related binding specificity that are not clustered based on sequence similarity. Using a range of specialized glycan arrays, we show that LectinOracle predictions generalize to new glycans and lectins, with qualitative and quantitative agreement with experimental data. We further demonstrate that LectinOracle can analyze whole lectomes and their role in host-microbe interactions. We envision that the herein presented platform will advance both the study of lectins and their role in (glyco)biology.

bioinformatics↗