Biology subjects

Hagemann, T.

Publications and source records attributed to Hagemann, T..

3 recordsLinked to original sources

bioRxiv2026

Crystal structure of E. coli Nissle 1917 flagellin reveals novel features that modulate bacterial motility but not TLR5 recognition

The probiotic E. coli Nissle 1917 (EcN) strain is known to promote intestinal homeostasis via flagellin, the protomer of its motility apparatus, the flagellum. The flagellin of EcN shows atypical features, namely a hypervariable region (HVR), whose structure and significance have remained elusive. We therefore determined the crystal structure of the E. coli Nissle 1917 flagellin FliC at a resolution of 1.2 [A] which revealed an unusual domain architecture: the canonical D1 domain was found connected by an extended linker to an extensive HVR whose D2, D3 and D4 domains form an outer domain (OD) which surrounds the filament core comprised of conserved domains D0-D1. Using both recombinant proteins and gene-edited EcN strains expressing mutant flagellins, the functional requirement for these unique features was subsequently studied for effects on immune recognition on intestinal epithelial and immune cells, as well as on flagellar protein expression, assembly and bacterial motility. While human and mouse TLR5 immune recognition of flagellar proteins or intact bacteria was only moderately affected by removal of linker or D4, especially linker removal reduced protein stability and bacterial motility in both soft agar and liquid media swimming assays. Interestingly, depending on the environment, D4 or HVR removal had different effects on motility and surface structure. Finally, a site-directed mutagenesis approach highlighted that loss of TLR5 recognition strictly entails loss of motility but not vice versa. Our data indicate that specific HVRs/OD might be relevant for motility of E. coli Nissle 1917 in specialized environments, but not for immune recognition. Moreover, we find mutational tolerance is greater for immune recognition than for motility, providing new insights into bacterial adaptation to the host environment.

microbiology↗

bioRxiv2025

Diet and temperature interactively impact brown adipose tissue gene regulation controlled by DNA methylation

Controlling brown adipose tissue (BAT) plasticity in adulthood holds promising potential for effective new obesity therapies by targeting the mechanisms of adaptive thermogenesis. Recent studies have shown that BAT development and function are under epigenetic control, with DNA methylation linked to the regulation of key thermogenic and metabolic genes. Here we sought to understand how diet and cold exposure interactively shape BAT gene regulation controlled by DNA methylation. Mice (N = 8 per group) were housed under cold exposure (8{degrees}C) or thermoneutrality (30{degrees}C) and fed either chow or high-fat diet (HFD). BAT was isolated for transcriptome (RNAseq) and methylome (RRBS) analyses. We identified differentially methylated and expressed genes (DMEGs) by comparing the effects of cold exposure under chow and high-fat diet, as well as by analyzing the interaction between temperature and diet. Functional pathway enrichment and EpiFactors Database screening were used to assess epigenetic regulators, and candidate gene expression was validated by modulating DNA-methylation in vitro. We identified [~]1,360 differentially expressed genes (DEGs) uniquely affected by the diet-temperature interaction with most downregulated in HFD-fed mice, indicating that obesity limits the transcriptional response of BAT to cold. 65 DMEGs (4% of DEGs) were largely diet-specific in response to cold exposure, suggesting that DNA methylation contributes to a selective layer of gene regulation during BAT adaptation to distinct metabolic states. In HFD-fed mice, DMEGs were enriched in pathways related to mitochondrial dysfunction, altered lipid metabolism, neuroendocrine signaling, and compensatory stress responses, contrasting the adaptive thermogenic profile observed in chow-fed controls. Differentially expressed genes of epigenetic regulators such as Tet2, Dnmt3a and Apobec1 showed diet- and cold-dependent regulation, indicating impaired methylation flexibility under obesogenic conditions. Using cell culture experiments, we confirmed the regulation of gene expression of candidate genes, validating the functional link between DNA methylation and thermogenic gene regulation. This is the first study to demonstrate an epigenetic response to cold exposure that differs by dietary condition, particularly in obesity. Our findings highlight a coordinated transcriptional and epigenetic remodeling of BAT, shaped by both environmental and metabolic signals. These insights may inform targeted epigenetic or nutritional strategies to restore BAT function and would further strengthen our understanding of how these might be used as therapeutic basis to improve metabolic health in obesity. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=145 SRC="FIGDIR/small/672110v2_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@1f99b80org.highwire.dtl.DTLVardef@502d02org.highwire.dtl.DTLVardef@1bb0a93org.highwire.dtl.DTLVardef@b34e4c_HPS_FORMAT_FIGEXP M_FIG C_FIG Mice were housed under cold exposure or thermoneutrality and fed either chow or high-fat diet (HFD). RNA sequencing (RNAseq) and reduced representation bisulfite sequencing (RRBS) was performed on brown adipose tissue (BAT) which provide the fundament for the identification of differentially methylated positions (DMP) and regions (DMR) as well as differentially expressed genes (DEG) in three models: COLDchow, comparing 8{degrees} vs 30{degrees}C mice on chow diet; COLDHFD, comparing 8{degrees} vs 30{degrees}C mice on HFD diet and{Delta} COLDdiet, comparing COLDchow vs COLDHFD. Differentially methylated and expressed genes (DMEGs; circos plot shows a representative example) were identified for all comparisons based on those DMPs which correlated significantly with differentially expressed genes (DEGs; the heatmap shows a representative example). DMEGs were taken forward for pathways enrichment analyses and to identify differentially regulated epigenetic regulators using the EpiFactors Database. The figure was created with BioRender.

genomics↗

bioRxiv2023

sNucConv: A bulk RNA-seq deconvolution method trained on single-nucleus RNA-seq data to estimate cell-type composition of human subcutaneous and visceral adipose tissues

Deconvolution algorithms rely on single-cell RNA-sequencing (scRNA-seq) data applied onto bulk RNA-sequencing (bulk RNA-seq) to extract information on the cell-types composition and proportions comprising a certain tissue. Adipose tissues cellular composition exhibits enormous plasticity in response to weight changes and high variance at different anatomical locations (depots). However, adipocytes - the functionally unique cell type of adipose tissue, are not amenable to scRNA-seq, a challenge recently met by applying single-nucleus RNA-sequencing (snRNA-seq). Here we aimed to develop a deconvolution method to estimate the cellular composition of human visceral and subcutaneous adipose tissues (hVAT and hSAT, respectively) using snRNA-seq to assess the true cell-type proportions. To correlate deconvolution-estimated cell-type proportions to true (snRNA-seq -derived) proportions, we analyzed seven hVAT and 5 hSAT samples by both bulk RNA-seq and snRNA-seq. snRNA-seq uncovered 15 distinct cell types in hVAT and 13 in hSAT. Deconvolution tools - SCDC, MuSiC, and Scaden exhibited low performance in estimating cell-type proportions (median |R|= 0.12 for estimated vs. true correlations). Notably, estimation accuracy somewhat improved by decreasing the number of cell-types groups, which nevertheless remained low (|R|<0.42). We therefore developed sNuConv, a novel method that employs Scaden, a deep-learning tool, trained using snRNA-seq - based data corrected by i. snRNA-seq/bulk RNA-seq highly-correlated genes, ii. corrected estimated cell-type proportions based on individual cell-type regression models. Applying sNuConv on our bulk RNA-seq data resulted in cell-type proportion estimation accuracy with median R=0.93 (range:0.76-0.97) for hVAT, and median R=0.95 (range:0.92-0.98) for hSAT. The resulting model was depot-specific, reflecting depot-differences in gene expression patterns. Thus, we present sNuConv, a novel, AI-based, method to deduce the cellular landscape of hVAT and hSAT from bulk RNA-seq data, providing proof-of-concept for producing validated deconvolution algorithms for tissues un-amenable to single-cell RNA sequencing.

bioinformatics↗