Biomedical subjects

Thomas Sauter

Publications and source records attributed to Thomas Sauter.

6 recordsLinked to original sources

PubMed2026

Crosstalk between S-nitrosylation and glycation defines a metabolic vulnerability in liver and renal cancers.

Metabolic reprogramming is a defining feature of cancer; however, how it contributes to therapeutic resistance remains incompletely understood. Here we show that loss of aldo-ketoreductase 1A1 (AKR1A1) in renal cell carcinoma (RCC) and hepatocellular carcinoma (HCC) disrupts terminal glycolytic flux and lactate production through S-nitrosylation-mediated inhibition of pyruvate kinase, resulting in the accumulation of methylglyoxal (MGO). In multiple AKR1A1-deficient models, but not in those endogenously expressing the C423/424 A mutant of pyruvate kinase M2, elevated MGO triggers autophagic degradation of Kelch-like ECH-associated protein 1, leading to Nuclear factor erythroid 2-Related Factor 2 (NRF2) activation and transcriptional reprogramming. This NRF2-driven response enhances chemoresistance and promotes tumor cell migration, two hallmarks of aggressive cancer. Therapeutically, we demonstrate that pharmacological inhibition of the glyoxalase system-the major pathway for MGO detoxification-restores drug sensitivity in patient-derived cells and xenograft models, revealing a context-dependent metabolic vulnerability in AKR1A1 loss conditions. These findings identify AKR1A1 as a metabolic tumor suppressor and uncover crosstalk between S-nitrosylation and glycation as a key regulatory axis linking metabolic reprogramming to NRF2-driven therapy resistance, offering glyoxalase inhibition as a potential precision treatment strategy for RCC and HCC.

Humans↗

PubMed2026

WILDkCAT: extract, retrieve, and predict enzyme turnover numbers of constraint-based metabolic models.

SUMMARY: Accurate enzyme turnover numbers are essential for building enzyme-constrained genome-scale metabolic models. However, collecting and curating these parameters remains a major bottleneck. Indeed, kcat values are scattered across multiple databases, reported under varying experimental conditions, and often missing for many enzymes. To address this challenge, we present WILDkCAT, a Python-based pipeline that enables the retrieval of kcat values from wild-type enzyme measured under user-specified pH and temperature ranges for a given metabolic model. The application to Escherichia coli (iML1515) and Homo sapiens (Human-GEM) models demonstrated the ability of WILDkCAT to retrieve substantial kcat coverage and its applicability across diverse genome-scale models. AVAILABILITY AND IMPLEMENTATION: WILDkCAT is available at https://github.com/sysbiolux/WILDkCAT and from PyPI. WILDkCAT works on all major operating systems and computer architectures. The documentation is available at https://sysbiolux.github.io/WILDkCAT.

Software↗

PubMed2006

A domain-oriented approach to the reduction of combinatorial complexity in signal transduction networks.

BACKGROUND: Receptors and scaffold proteins possess a number of distinct domains and bind multiple partners. A common problem in modeling signaling systems arises from a combinatorial explosion of different states generated by feasible molecular species. The number of possible species grows exponentially with the number of different docking sites and can easily reach several millions. Models accounting for this combinatorial variety become impractical for many applications. RESULTS: Our results show that under realistic assumptions on domain interactions, the dynamics of signaling pathways can be exactly described by reduced, hierarchically structured models. The method presented here provides a rigorous way to model a large class of signaling networks using macro-states (macroscopic quantities such as the levels of occupancy of the binding domains) instead of micro-states (concentrations of individual species). The method is described using generic multidomain proteins and is applied to the molecule LAT. CONCLUSION: The presented method is a systematic and powerful tool to derive reduced model structures describing the dynamics of multiprotein complex formation accurately.

Binding Sites↗

PubMed2005

Modeling the VPAC2-activated cAMP/PKA signaling pathway: from receptor to circadian clock gene induction.

Increasing evidence suggests an important role for VPAC2-activated signal transduction pathways in maintaining a synchronized biological clock in the suprachiasmatic nucleus (SCN). Activation of the VPAC2 signaling pathway induces per1 gene expression in the SCN and phase-shifts the circadian clock. Mice without the VPAC2 receptor lack an overt, coherent circadian rhythm in clock gene expression, SCN neuron firing rate, and locomotor behavior. Using a systems approach, we have developed a kinetic model integrating VPAC2 signaling mediated by the cyclic AMP (cAMP)/protein kinase A (PKA) pathway and leading to induced circadian clock gene expression. We fit the model to experimental data from the literature for cAMP accumulation, PKA activation, cAMP-response element binding protein phosphorylation, and per1 induction. By linking the VPAC2 model to a published circadian clock model, we also simulated clock phase shifts induced by vasoactive intestinal polypeptide (VIP) and matched experimental data for the VIP response. The simulated phase response curve resembled the hamster response to a related neuropeptide, GRP1-27, and light. Simulations using pulses of VIP revealed that the system response is extraordinarily robust to input signal duration, a result with physiologically relevant consequences. Lastly, simulations using varied receptor levels matched literature experimental data from animals overexpressing VPAC2 receptors.

Animals↗

PubMed2005

A quantitative approach to catabolite repression in Escherichia coli.

A dynamic mathematical model was developed to describe the uptake of various carbohydrates (glucose, lactose, glycerol, sucrose, and galactose) in Escherichia coli. For validation a number of isogenic strains with defined mutations were used. By considering metabolic reactions as well as signal transduction processes influencing the relevant pathways, we were able to describe quantitatively the phenomenon of catabolite repression in E. coli. We verified model predictions by measuring time courses of several extra- and intracellular components such as glycolytic intermediates, EII-ACrr phosphorylation level, both LacZ and PtsG concentrations, and total cAMP concentrations under various growth conditions. The entire data base consists of 18 experiments performed with nine different strains. The model describes the expression of 17 key enzymes, 38 enzymatic reactions, and the dynamic behavior of more than 50 metabolites. The different phenomena affecting the phosphorylation level of EIIACrr, the key regulation molecule for inducer exclusion and catabolite repression in enteric bacteria, can now be explained quantitatively.

Carbohydrate Metabolism↗

PubMed2004

A benchmark for methods in reverse engineering and model discrimination: problem formulation and solutions.

A benchmark problem is described for the reconstruction and analysis of biochemical networks given sampled experimental data. The growth of the organisms is described in a bioreactor in which one substrate is fed into the reactor with a given feed rate and feed concentration. Measurements for some intracellular components are provided representing a small biochemical network. Problems of reverse engineering, parameter estimation, and identifiability are addressed. The contribution mainly focuses on the problem of model discrimination. If two or more model variants describe the available experimental data, a new experiment must be designed to discriminate between the hypothetical models. For the problem presented, the feed rate and feed concentration of a bioreactor system are available as control inputs. To verify calculated input profiles an interactive Web site (http://www.sysbio.de/projects/benchmark/) is provided. Several solutions based on linear and nonlinear models are discussed.

Algorithms↗