Biology subjects

Burger, B.

Publications and source records attributed to Burger, B..

7 recordsLinked to original sources

bioRxiv2026

Oleic acid suppresses NLRP3 inflammasome activation to prevent dermal macrophage death and limit imiquimod-induced psoriasis

Psoriasis is associated with altered lipid metabolism, including reduced expression of fatty acid metabolism genes and decreased circulating oleic acid. Here we report that oral oleic acid supplementation normalized serum oleic acid levels and protected from skin thickening and epidermal hyperplasia in a imiquimod mice model of psoriasis. These effects were associated with preserved skin-resident macrophage numbers and reduced expression of key psoriasis-associated inflammatory markers. Mechanistically, imiquimod induces dermal macrophage death via NLRP3/caspase-1 inflammasome activation, while adoptive transfer of dermal macrophages attenuates psoriasiform inflammation. Oleic acid rescued macrophages from imiquimod and nigericin-induced death in vitro by suppressing NF-{kappa}B and NLRP3/caspase-1 signaling, lowering IL-1{beta} and reactive oxygen species production, and restoring mitochondrial homeostasis, independently of TLR7. Together, our results position dermal macrophage survival as a previously unrecognized checkpoint in psoriasiform inflammation, and suggest that restoring lipid metabolic fitness, rather than solely targeting downstream inflammatory mediators, may offer an additional mechanism-based strategy to limit tissue- damaging inflammation.

immunology↗

bioRxiv2025

Circuit Mining in Transcriptomics Data

A central goal in neuropharmacological research is to alter brain function by targeting genes whose expression is specific to the corresponding brain circuit. Identifying such genes in large spatially resolved transcriptomics data requires the expertise of bioinformaticians for handling data complexity and to perform statistical tests. This time-consuming process is often decoupled from the routine workflow of neuroscientists, inhibiting fast target discovery. Here we present a visual analytics approach to mining expression data in the context of meso-scale brain circuits for potential target genes tailored to domain experts with limited technical background. We support several workflows for interactive definition and refinement of circuits in the human or mouse brain, and combine spatial indexing with an alternative formulation of sample variance to enable differential gene expression analysis in arbitrary brain circuits at runtime. A user study highlights the usefulness, benefits, and future potential of our work.

bioinformatics↗

bioRxiv2023

Multidimensional analysis of matched primary and recurrent glioblastoma identifies Fcgamma receptors upregulation on microglia as a contributor of tumor recurrence.

O_LIBackground: Glioblastoma (GBM) is a lethal brain tumor without effective treatment options. The aim of this study was to characterize longitudinal tumor immune microenvironment (iTME) changes in order to find potential actionable targets to prevent GBM-induced immune evasion mechanisms. C_LIO_LIMethods: This study included 15 patient-matched treatment-naive WHO grade 4 primary (pGBM) and recurrent (rGBM) tumors. RNA and proteins extracted from fresh frozen tumor samples from matched pGBM and rGBM were profiled via transcriptomics and proteomics, respectively. A tissue microarray containing paired formalin-fixed paraffin-embedded tumor samples was processed for spatial transcriptomics analysis. C_LIO_LIResults: Differentially expressed genes and proteins between pGBM and rGBM were involved in pathways responsible for synapse development and myelination which have been shown to play a role in GBM recurrence. By categorizing patients into short and long time-to-relapse (STTR vs LTTR), we identified genes positively or negatively associated with TTR. Expression of Fc{gamma} receptors and complement system genes such as FCGR1A (CD64), FCGR3A and C3 in rGBM samples were negatively correlated with TTR, whereas expression of DNMT1/3A, and SMARCA4, involved in DNA methylation, were positively correlated with TTR. Spatial transcriptomic analysis of the tumor cell compartment showed enrichment of oligodendrocytes in rGBM, whereas the myeloid cell compartment switched from quiescent to activated microglia, was enriched in B and T cells, specifically in rGBM with STTR. C_LIO_LIConclusions: Our results uncover a role for CD64-expressing activated microglia in GBM recurrence and suggest that interfering with these cells may represent a therapeutic option for hindering GBM relapse. C_LI Key pointsO_LITranscriptomic and proteomic differences exist between patient-paired primary and recurrent GBM tumors C_LIO_LIHigh expression of Fcy receptors genes on activated microglia at tumor recurrence is associated with shorter time to relapse. C_LI Importance of this studyIn glioblastoma (GBM), the tumor recurs in almost all cases after standard treatment such as surgery and chemo-radiotherapy. In this study, we longitudinally evaluated the immune- and neoplastic compartments using transcriptomic, proteomic, and spatial transcriptomics in patient-matched treatment-naive and recurrent tumor samples. By correlating gene expression with time-to-relapse, we identified a geneset associated with treatment resistance and faster tumor recurrence. Moreover, this study highlighted the plasticity of the myeloid compartment during disease progression and an unfavorable role of activated microglia in tumor recurrence.

cancer biology↗

bioRxiv2022

Extending protein interaction networks using proteoforms and small molecules

Biological network analysis is used to interpret modern high-throughput biomedical data sets in terms of biological functions and pathways. However, the results greatly depend on the topological characteristics of the underlying network, commonly composed of nodes representing genes or proteins that are connected by edges when interacting. In this study, we build biological networks accounting for small molecules, protein isoforms and post-translational modifications. We highlight how these change the global structure of the network and how the connectedness of pathway-based networks is altered. Our findings highlight the importance of carefully crafting the networks for network analysis to better represent the reality of biological systems.

bioinformatics↗

bioRxiv2021

Disentangling cortical functional connectivity strength and topography reveals divergent roles of genes and environment

The human brain varies across individuals in its morphology, function, and cognitive capacities. Variability is particularly high in phylogenetically modern regions associated with higher order cognitive abilities, but its relationship to the layout and strength of functional networks is poorly understood. In this study we disentangled the variability of two key aspects of functional connectivity: strength and topography. We then compared the genetic and environmental influences on these two features. Genetic contribution is heterogeneously distributed across the cortex and differs for strength and topography. In heteromodal areas genes predominantly affect the topography of networks, while their connectivity strength is shaped primarily by random environmental influence such as learning. We identified peak areas of genetic control of topography overlapping with parts of the processing stream from primary areas to network hubs in the default mode network, suggesting the coordination of spatial configurations across those processing pathways. These findings provide a detailed map of the diverse contribution of heritability and individual experience to the strength and topography of functional brain architecture.

neuroscience↗

bioRxiv2018

PathwayMatcher: multi-omics pathway mapping and proteoform network generation

BackgroundMapping biomedical data to functional knowledge is an essential task in bioinformatics and can be achieved by querying identifiers, e.g. gene sets, in pathway knowledgebases. However, the isoform and post-translational modification states of proteins are lost when converting input and pathways into gene-centric lists.\n\nFindingsBased on the Reactome knowledgebase, we built a network of protein-protein interactions accounting for the documented isoform and modification statuses of proteins. We then implemented a command line application called PathwayMatcher (github.com/PathwayAnalysisPlatform/PathwayMatcher) to query this network. PathwayMatcher supports multiple types of omics data as input, and outputs the possibly affected biochemical reactions, subnetworks, and pathways.\n\nConclusionsPathwayMatcher enables refining the network-representation of pathways by including isoform and post-translational modifications. The specificity of pathway analyses is hence adapted to different levels of granularity and it becomes possible to distinguish interactions between different forms of the same protein.

bioinformatics↗

bioRxiv2018

Analysing the structure of pathways and its influence on the interpretation of biomedical datasets

Biochemical pathways are commonly used as a reference to conduct functional analysis on biomedical omics datasets, where experimental results are mapped to knowledgebases comprising known molecular interactions collected from the literature. Due to their central role, the content of the functional knowledgebases directly influences the outcome of pathway analyses. In this study, we investigate the structure of the current pathway knowledge, as exemplified by Reactome, discuss the consequences for biological interpretation, and outline possible improvements in the use of pathway knowledgebases. By providing a view of the underlying network structure, we aim to help pathway analysis users manage their expectations and better identify possible artefacts in the results.

bioinformatics↗