Biology subjects

Thieme, S.

Publications and source records attributed to Thieme, S..

4 recordsLinked to original sources

bioRxiv2025

The VelB intrinsically disordered domain promotes selective heterodimer formation of velvet domain regulatory proteins for fungal development

Fungi possess several transcription factors with a characteristic velvet domain for DNA-binding and homo- or heterodimerization, which is structurally similar to the mammalian NF-B Rel homology domain. Velvet dimers control fungal development, virulence and mycotoxin formation. VelB is the only regulator, which carries an intrinsically disordered domain (IDD) within the velvet domain. The IDD as well as the positioning within VelB is conserved in the fungal kingdom. Intrinsically disordered regions contribute to transcription activation and DNA binding and frequently appear in eukaryotic transcription factors. The VelB IDD provides selective heterodimerization as well as protein stability control. The IDD is not required for the formation of the VelB-VeA heterodimer of Aspergillus nidulans or Verticillium dahliae, but promotes the formation of the VelB-VosA heterodimer. The IDD destabilizes VelB single molecules and also balances its distribution and ratio between both velvet heterodimers. These balances contribute to control appropriate mycotoxin production and sexual development. Herewith, the VelB IDD represents a novel control mechanism of velvet protein stability and heterodimer formation for precise priming of fungal development.

molecular biology↗

bioRxiv2023

ARID1A Mutations Protect Follicular Lymphoma from FAS-dependent Immune Surveillance by Reducing RUNX3/ETS1-Driven FAS-Expression

The cell death receptor FAS and its ligand (FASLG) play crucial roles in the selection of B cells during the germinal center (GC) reaction. Failure to eliminate potentially harmful B cells via FAS can lead to lymphoproliferation and the development B cell malignancies. The classic form of follicular lymphoma (FL) is a prototypic GC-derived B cell malignancy, characterized by the t(14;18) (q32;q21)IGH::BCL2 translocation and overexpression of antiapoptotic BCL2. Additional alterations were shown to be clinically relevant, including mutations in ARID1A. ARID1A is part of the SWI/SNF nucleosome remodeling complex that regulates DNA accessibility ("openness"). However, the mechanism how ARID1A mutations contribute to FL pathogenesis remains unclear. We analyzed 151 FL biopsies of patients with advanced stage disease at initial diagnosis and found that ARID1A mutations were recurrent and mainly disruptive, with an overall frequency of 18%. Additionally, we observed that ARID1A mutant FL showed significantly lower FAS protein expression in the FL tumor cell population. Functional experiments in BCL2-translocated lymphoma cells demonstrated that ARID1A is directly involved in the regulation of FAS, and ARID1A loss leads to decreased FAS protein and gene expression. However, ARID1A loss did not affect FAS promotor openness. Instead, we identified and experimentally validated a previously unknown co-transcriptional complex consisting of RUNX3 and ETS1 that regulates FAS expression, and ARID1A loss leads to reduced RUNX3 promotor openness and gene expression. The reduced FAS levels induced by ARID1A loss rendered lymphoma cells resistant to both soluble and T cell membrane-anchored FASLG-induced apoptosis. In summary, we have identified a functionally and clinically relevant mechanism how FL cells can escape FAS-dependent immune surveillance, which may also impact the efficacy of T cell-based therapies, including bispecific antibodies and CAR T cells.

cancer biology↗

bioRxiv2021

Biclique extension as an effective approach to predict novel interaction partners in metabolic compound-protein interaction networks.

MotivationMetabolic networks are complex systems of connected chemical reactions with physical interactions between metabolites and proteins playing a critical role for both metabolic conversion and regulation. In this study, we aimed to predict previously unknown compound-protein interactions (CPI) by transferring the concept of biclique extension, which was developed in the context of drug-target interaction prediction and that is based on the rationale that interactions that readily extend an existing biclique are real, to metabolic CPI networks. ResultsWe developed and tested a workflow to predict CPIs based on the concept of extending existing bicliques and applied it to E. coli and human using their respective known CPI network as input. Depending on the chosen biclique size, for the E. coli network we reached a sensitivity of 39% with an associated precision of 59%. For the larger human CPI network, a sensitivity of 78% with a false-positive rate of less than 5% and an associated precision of 75% was obtained. At more stringent settings, a precision as high as 95% was attainable at the expense of a lowered recall. Prediction performance significantly exceeded that obtained using randomized networks as input. Predicted novel interactions were tested for biomolecular function involvement, with TCA-cycle and ribosomal processes found associated with particularly pronounced statistical significance. As we demonstrate, our approach holds great potential to increase efficiency of experimental testing of CPIs and can readily be transferred to other species of interest. Availability and implementationThe R code and datasets are available at https://github.com/SandraThieme/BiPredict.

bioinformatics↗

bioRxiv2017

Bipartite Boolean modelling - a method for mechanistic simulation and validation of large-scale signal transduction networks

The metabolic modelling community has established the gold standard for bottom-up systems biology with reconstruction, validation and simulation of mechanistic genome-scale models. Similar methods have not been established for signal transduction networks, where the representation of internal states leads to scalability issues in both model formulation and execution. While rule- and agent-based methods allow efficient model definition and execution, respectively, model parametrisation introduces an additional layer of uncertainty due to the sparsity of reliably measured parameters. Here, we present a scalable method for parameter-free simulation of mechanistic signal transduction networks. It is based on rxncon and uses a bipartite Boolean logic with separate update rules for reactions and states. Using two generic update rules, we enable translation of any rxncon model into a unique Boolean model, which can be used for network validation and simulation - allowing the prediction of system level function directly from molecular mechanistic data. Through scalable model definition and simulation, and the independence of quantitative parameters, it opens up for simulation and validation of mechanistic genome-scale models of signal transduction networks.

systems biology↗