Biology subjects

Thedieck, K.

Publications and source records attributed to Thedieck, K..

9 recordsLinked to original sources

bioRxiv2025

Collective parameter estimation of related models with an initial stability constraint

Parameterisation of dynamic biochemical network models is a challenging aspect of systems biology. Especially when the parameter space is large and data is semi quantitive but comparable across different experimental conditions. Here, we present a set of command line tools utilising Pycotools (COPASI) that leverages the power of high-performance computing to facilitate parameter estimation of large models with many unknown parameters. In particular, we expand upon the abilities of Pycotools to address two particular issues. Firstly, the difficulty of constraining a models parameterisation to assume the system begins in a steady state (prior to a perturbative stimulation). And secondly, parameterising against relative quantitative time series data that have no absolute scale. Our software operates on the SLURM workload manager system and can be applied to any parameter estimation against time series data produced by applying a single perturbation at time zero to an equilibrated system. We validate that our technique can produce a parameterised model of the MTOR (mechanistic target of rapamycin) network based on semi-quantitative time-series data from 2 breast cancer cell lines, stimulated with insulin and amino acids. We also show our model can make reasonable predictions on distinct signaling dynamics in one breast cancer cell line based on the other by adjusting the initial protein quantities only. In conclusion, models should fit both the initial steady state and the dynamics following stimulation, given that stabilising systems prior to stimulation is a common experimental protocol in signaling research. By expanding standard tools, commonly used in the field, we have developed a widely applicable method, which can easily be evaluated and is amenable to wide general use.

systems biology↗

bioRxiv2025

Increased activity of PRMT5-MEP50 complex improves survival of chromosomally unstable cancer cells by increasing tolerance to protein aggregation and proteotoxicity

Most cancers display chromosomal instability (CIN), a condition that leads to increased rates of chromosome missegregation and thus yields aneuploidy. CIN and aneuploidy are detrimental to healthy cells and therefore, aneuploid cells rely on aneuploidy-tolerating mechanisms to adopt a malignant fate. We previously found PRMT5 to be frequently amplified in a mouse model for aneuploid T cell lymphoblastic lymphoma. In this study, we investigated a possible role of PRMT5 as an aneuploidy tolerating gene. We report that PRMT5 is prone to aggregation when expressed at supra-stoichiometric levels compared to its obligate partner protein MEP50 (methylosome protein 50, WDR77). Intriguingly, we also find that protein aggregation, induced by CIN, is mitigated by jointly increased expression of PRMT5 and MEP50. Accordingly, concomitant PRMT5:MEP50 expression renders cancer cells less sensitive to proteasome inhibitors and CIN while inhibition sensitizes cells to CIN. Our findings provide a possible explanation for why PRMT5 and MEP50 display increased expression, particularly in aneuploid cancers and might reveal a targetable vulnerability of aneuploid cancer.

cancer biology↗

bioRxiv2024

TXNIP mediates LAT1/SLC7A5 endocytosis to reduce amino acid uptake in cells entering quiescence

Entry and exit from cellular quiescence require dynamic adjustments in nutrient acquisition, yet the mechanisms by which quiescent cells downregulate amino acid (AA) transport remain poorly understood. Here, we demonstrate that cells entering quiescence select plasma membrane-resident AA transporters for endocytosis and lysosomal degradation, to match AA uptake with reduced translation. We identify the -arrestin TXNIP as a key regulator of AA uptake during quiescence, since it mediates the endocytosis of the SLC7A5-SLC3A2 (LAT1-4F2hc) transporter complex in response to reduced AKT signaling. Mechanistically, TXNIP interacts with HECT-type ubiquitin ligases to facilitate transporter ubiquitination. Loss of TXNIP disrupts this regulation, resulting in dysregulated AA uptake, sustained mTORC1 signaling, and accelerated quiescence exit. A novel TXNIP loss-of-function mutation in a patient with severe metabolic disease further supports its role in nutrient homeostasis and human health. These findings highlight TXNIPs role in controlling SLC7A5-SLC3A2 mediated AA acquisition with implications for quiescence biology and disease.

cell biology↗

bioRxiv2024

GEMCAT - A new algorithm for gene expression-based prediction of metabolic alterations

The conclusive interpretation of multi-omics datasets obtained from high throughput approaches is an important prerequisite to understand disease-related physiological changes and to predict biomarkers in body fluids. We here present a Gene Expression-based Metabolite Centrality Analysis Tool, GEMCAT, a new genome scale metabolic modelling algorithm. GEMCAT enables integration of transcriptomics or proteomics data to predict changes in metabolite concentrations which can be verified by targeted metabolomics. In addition, GEMCAT allows to trace measured and predicted metabolic changes back to the underlying alterations in gene expression or proteomics and thus enables functional interpretation and integration of multi-omics data. We demonstrate the predictive capacity of GEMCAT on two datasets, one using RNA sequencing data and metabolomics from an engineered human cell line with a functional deletion of the mitochondrial NAD-transporter and another using proteomics and metabolomics measurements from patients with inflammatory bowel disease.

bioinformatics↗

bioRxiv2024

Automated liquid handling extraction and rapid quantification of underivatized amino acids and tryptophan metabolites from human serum and plasma using dual-column U(H)PLC-MRM-MS and its application to prostate cancer study.

Free amino acids (AAs) and their metabolites are important building blocks, energy sources and signaling molecules associated with various pathological phenotypes. The quantification of AA and tryptophan (TRP) metabolites in human serum and plasma is therefore of great diagnostic interest. Robust and reproducible sample extraction and processing workflows as well as rapid, sensitive absolute quantification of AA and TRP metabolites are required to identify candidate biomarkers and to improve current screening methods. We developed a validated semi-automated extraction and sample processing workflow using a robotic liquid handling platform and a rapid method for the absolute quantification of 20 free, underivatized AAs and 6 TRP metabolites using dual-column U(H)PLC-MRM-MS. The automated extraction and sample preparation workflow is designed for use in a 96-well plate format, allowing robust and reproducible high sample throughput without the need for further SPE, evaporation and/or buffer exchange. Samples extracted from serum and/or plasma in 96-well plates can be transferred directly to the U(H)PLC autosampler. The dual-column U(H)PLC-MRM-MS method, using a mixed- mode reversed-phase anion exchange column with formic acid as mobile phase modifier and a high- strength silica reversed-phase column with difluoroacetic acid as mobile phase additive, provided absolute quantification with nanomolar lower limits of quantification (LLOQ) for all metabolites except glycine (LLOQ: 2.46 {micro}M) in only 7.9 minutes. The semi-automated extraction workflow and dual-column U(H)PLC-MRM-MS method was applied to a human prostate cancer study and was shown to discriminate between treatment regimens and to identify amino acids responsible for the statistical separation between healthy controls and prostate cancer patients on active surveillance.

biochemistry↗

bioRxiv2022

LRBA balances antigen presentation and T-cell responses by facilitating autophagy through the binding to PIK3R4 and FYCO1

Reduced autophagy is associated with the aberrant humoral response observed in lipopolysaccharide-responsive beige-like anchor protein (LRBA) deficiency; however, the exact molecular mechanism and its impact on T-cell responses remain unknown. We identified two novel LRBA interactors, phosphoinositide 3-kinase regulatory subunit 4 (PIK3R4) and FYVE And Coiled-Coil Domain Autophagy Adaptor 1 (FYCO1). Both proteins play essential roles in different stages of autophagy. PIK3R4 facilitates the production of phosphatidylinositol-3 phosphate (PI(3)P) required for autophagosome formation and autophagosome-lysosome fusion, whereas FYCO1 allows autophagosome movement. LRBA-KO cells showed an impaired PI(3)P production, a delayed autophagosome-lysosome fusion, an accumulation of enlarged autophagosomes, and an atypical lysosomal positioning. These abnormalities led to decreased cargo material degradation and prolonged antigen presentation to T-cells via autophagy, resulting in increased production of proinflammatory cytokines, as autophagy is a major intracellular degradation system for major histocompatibility class II complex (MHCII) loading. Aberrant autophagosome formation, cargo degradation and antigen presentation were rescued by ectopic expression of WT-LRBA. In summary, we identified a novel function of LRBA that is crucial for T-cell-driven response through the interaction with two proteins of the autophagy machinery. These observations may contribute to the exacerbated T-cell dysregulation observed in LRBA-deficient patients.

immunology↗

bioRxiv2022

Proteome coverage after simultaneous proteo-metabolome liquid-liquid extraction

Proteo-metabolomics is essential in systems biology and simultaneous proteo-metabolome extraction by liquid-liquid extraction (SPM-LLE) allows extraction of the metabolome and proteome from the same sample. Since the proteome is present as a pellet in SPM-LLE it must be solubilized for quantitative proteomics. Solubilization and proteome extraction is a critical factor in the information that can be obtained at the proteome level. In this study, we investigated the performance of two surfactants (sodium deoxycholate (SDC), sodium dodecyl sulfate (SDS)) and urea with respect to proteome coverage and extraction efficiency of an interphase proteome pellet generated by methanol-chloroform based SPM-LLE. We also investigated the extent to which the performance differs when the proteome is extracted from the interphase pellet or by direct cell lysis. Our study reveals that the proteome coverages between the two surfactants and urea for the SPM-LLE interphase pellet were very similar, but the extraction efficiencies differed significantly. While SDS led to enrichment of basic proteins, which were mainly ribosomal and ribonuclear proteins, urea was the most efficient extraction agent for simultaneous proteo-metabolome analysis. The results of our study also show that the performance of surfactants (SDC, SDS) for quantitative proteomics is better when the proteome was extracted by direct cell lysis and not from an interphase pellet. In contrast, the performance of urea for quantitative proteomics was significantly better when the proteome was extracted from an interphase pellet and by direct cell lysis.

systems biology↗

bioRxiv2022

Comparative assessment of quantification methods for tumor tissue phosphoproteomics

With increasing sensitivity and accuracy in mass spectrometry, the tumor phosphoproteome is getting into reach. However, the selection of quantitation techniques best-suited to the biomedical question and diagnostic requirements remains a trial and error decision as no study has directly compared their performance for tumor tissue phosphoproteomics. We compared label-free quantification (LFQ), spike-in-SILAC (stable isotope labeling by amino acids in cell culture) and TMT isobaric tandem mass tags technology for quantitative phosphosite profiling in tumor tissue. TMT offered the lowest accuracy and the highest precision and robustness towards different phosphosite abundances and matrices. Spike-in-SILAC offered the best compromise between these features but suffered from a low phosphosite coverage. LFQ offered the lowest precision but the highest number of identifications. Both spike-in-SILAC and LFQ presented susceptibility to matrix effects. Match between run (MBR)-based analysis enhanced the phosphosite coverage across technical replicates in LFQ and spike-in-SILAC but further reduced the precision and robustness of quantification. The choice of quantitative methodology is critical for both study design such as sample size in sample groups and quantified phosphosites, and comparison of published cancer phosphoproteomes. Using ovarian cancer tissue as an example, our study builds a resource for the design and analysis of quantitative phosphoproteomic studies in cancer research and diagnostics.

biochemistry↗

bioRxiv2020

G3BP1 tethers the TSC complex to lysosomes and suppresses mTORC1 in the absence of stress granules

G3BP1 (Ras GTPase-activating protein-binding protein 1) is widely recognized as a core component of stress granules (SG), non-membranous RNA-protein-assemblies required for cellular survival under stress. We report that in the absence of SG, G3BP1 acts as lysosomal anchor of the Tuberous Sclerosis Complex (TSC) protein complex. By tethering the TSC complex to lysosomes, G3BP1 suppresses signaling through the metabolic master regulator mTORC1 (mechanistic target of rapamycin complex 1). Like the known TSC complex subunits, G3BP1 suppresses phenotypes related to mTORC1 hyperactivity in the context of tumors and neuronal dysfunction. Thus, G3BP1 is not only a core component of SG but also a key element of lysosomal TSC-mTORC1 signaling. HighlightsThe bona fide stress granule component G3BP1 O_LIis a key element of the TSC-mTORC1 signaling axis. C_LIO_LItethers the TSC complex to lysosomes. C_LIO_LIprevents mTORC1 hyperactivation by metabolic stimuli. C_LIO_LIsuppresses mTORC1-driven cancer cell motility and epileptiform activity. C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/044081v3_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@77cb4forg.highwire.dtl.DTLVardef@c4d1a0org.highwire.dtl.DTLVardef@19e83acorg.highwire.dtl.DTLVardef@1d6a378_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗