Biology subjects

Thomsen, J.

Publications and source records attributed to Thomsen, J..

5 recordsLinked to original sources

bioRxiv2025

CARM1-mediated methylation controls interactions of ALIX with key partners important for cytokinesis

CARM1 is an arginine methyltransferase with a well-established role in regulating gene expression, but its cytoplasmic functions remain largely uncharacterized. Here, we identified ALIX, a protein acting with the ESCRT-III machinery in numerous membrane remodeling events, as a main cytoplasmic partner and a relevant substrate of CARM1. We demonstrate that CARM1 methylates arginine residues within the proline-rich motif of ALIX. At the molecular level, ALIX methylation impairs SH3-dependent association with CD2AP, CIN85, and endophilin-A2 that are required for cytokinesis. Using a mutant of ALIX that is unable to bind these proteins, we further show that these interactions are essential for ALIX functions during cytokinesis. Altogether, this work highlights the significance of arginine methylation as a regulatory post-translational modification important for the final step of cell division, by modulating interactions between proline-rich domains and their SH3-containing partners.

cell biology↗

bioRxiv2024

Hydrothermal pretreatment renders peat susceptible to enzymatic saccharification

Sphagnum peat bogs store a large fraction of biologically-bound carbon, due to a steady accumulation of plant material over millennia. The resistance of Sphagnum biomass to decay is poorly understood but of high importance for preservation efforts and climate models. It is shown that peat cellulose and other glucose-rich polysaccharides are readily degradable by a commercial enzyme cocktail designed for the industrial saccharification of lignocellulose of vascular plants. However, prior hydrothermal pretreatment of peat was required for the enzymes to gain access to the polysaccharides. The pretreatment itself released monosaccharides and glucose-containing soluble oligosaccharides. The monosaccharide profile released from hydrothermal pretreatment was consistent with the expected hemicellulose content of Sphagnum and was clearly different from that seen for pretreated tissue from vascular plants, such as pretreated wheat straw. Cellulose retained in the insoluble part of peat was cleaved at a similar or higher rate compared to cellulose from vascular plant tissues. Confocal laser scanning microscopy showed that the hydrothermal pretreatment disrupted the cells and relocated lignin-like compounds. Peat contains a high concentration of iron, which likely explains the pronounced acidification observed for the pretreated peat slurry at ambient conditions during saccharification assays. The acidification is due to abiotic oxidative reactions that also inactivate the enzymes. Adding catalase to the reactions alleviated enzyme inactivation and essentially stopped acidification during saccharification. This study confirms the importance of considering those abiotic oxidative reactions that take place in drained peat material.

plant biology↗

bioRxiv2021

Inferring kinetic rate constants from single-molecule FRET trajectories - a blind benchmark of kinetic analysis tools

Single-molecule FRET (smFRET) is a versatile technique to study the dynamics and function of biomolecules since it makes nanoscale movements detectable as fluorescence signals. The powerful ability to infer quantitative kinetic information from smFRET data is, however, complicated by experimental limitations. Diverse analysis tools have been developed to overcome these hurdles but a systematic comparison is lacking. Here, we report the results of a blind benchmark study assessing eleven analysis tools used to infer kinetic rate constants from smFRET trajectories. We tested them against simulated and experimental data containing the most prominent difficulties encountered in analyzing smFRET experiments: different noise levels, varied model complexity, non-equilibrium dynamics, and kinetic heterogeneity. Our results highlight the current strengths and limitations in inferring kinetic information from smFRET trajectories. In addition, we formulate concrete recommendations and identify key targets for future developments, aimed to advance our understanding of biomolecular dynamics through quantitative experiment-derived models.

biophysics↗

bioRxiv2020

Across kingdom biased CYP-mediated metabolism via small-molecule ligands docking on P450 oxidoreductase

Metabolic control is mediated by the dynamic assemblies and function of multiple redox enzymes. A key element in these assemblies, the P450 oxidoreductase (POR), donates electrons and selectively activates numerous (>50 in humans and >300 in plants) cytochromes P450 (CYPs) controlling metabolism of drugs, steroids and xenobiotics in humans and natural product biosynthesis in plants. The mechanisms underlying POR-mediated CYP metabolism remain poorly understood and to date no ligand binding has been described to regulate the specificity of POR. Here, using a combination of computational modeling and functional assays, we identified ligands that dock on POR and bias its specificity towards CYP redox partners. Single molecule FRET studies revealed ligand docking to alter POR conformational sampling, which resulted in biased activation of metabolic cascades in whole cell assays. We propose the model of biased metabolism, a mechanism akin to biased signaling of GPCRs, where ligand docking on POR stabilizes different conformational states that are linked to distinct metabolic outcomes. Biased metabolism may allow designing pathway-specific therapeutics or personalized food suppressing undesired, disease related, metabolic pathways.

biophysics↗

bioRxiv2020

DeepFRET: Rapid and automated single molecule FRET data classification using deep learning

Single molecule Forster Resonance energy transfer (smFRET) is a mature and adaptable method for studying the structure of biomolecules and integrating their dynamics into structural biology. The development of high throughput methodologies and the growth of commercial instrumentation have outpaced the development of rapid, standardized, and fully automated methodologies to objectively analyze the wealth of produced data. Here we present DeepFRET, an automated standalone solution based on deep learning, where the only crucial human intervention in transiting from raw microscope images to histogram of biomolecule behavior, is a user-adjustable quality threshold. Integrating all standard features of smFRET analysis, DeepFRET will consequently output common kinetic information metrics for biomolecules. We validated the utility of DeepFRET by performing quantitative analysis on simulated, ground truth, data and real smFRET data. The accuracy of classification by DeepFRET outperformed human operators and current commonly used hard threshold and reached >95% precision accuracy only requiring a fraction of the time (<1% as compared to human operators) on ground truth data. Its flawless and rapid operation on real data demonstrates its wide applicability. This level of classification was achieved without any preprocessing or parameter setting by human operators, demonstrating DeepFRETs capacity to objectively quantify biomolecular dynamics. The provided a standalone executable based on open source code capitalises on the widespread adaptation of machine learning and may contribute to the effort of benchmarking smFRET for structural biology insights.

biophysics↗