Biology subjects

Klipp, E.

Publications and source records attributed to Klipp, E..

17 recordsLinked to original sources

bioRxiv2025

Regulation of the branched-chain amino acid pathway and its crosstalk to carbon metabolism in yeast

In Saccharomyces cerevisiae, the TORC1 pathway regulates the transition from rapid fermentative growth to respiration during the diauxic shift, by tightly coordinating energy- and biomass-producing pathways. Although leucine and other components of the branched-chain amino acid (BCAA) pathway are known TORC1 regulators, how the BCAA pathway is controlled across this transition and influences the crosstalk between central carbon and amino acid metabolism remains unclear. By integrating high-throughput flow cytometry and untargeted LC-MS metabolomics into a thermodynamically curated genome-scale model, we profiled protein levels, metabolite dynamics, and cellular context during the diauxic shift of a GFP-tagged strain library of the BCAA pathway in wild-type and mutants lacking non-essential genes for BCAA biosynthesis. This analysis revealed that the BCAA pathway operates in two branches, with only the leucine-committed branch exhibiting a fermentative signature aligned with TORC1 activity. We further identified key regulatory elements and showed that dysregulation of the BCAA pathway disrupts distant metabolic circuits, including central carbon metabolism. Our findings highlight the dynamic role of the BCAA pathway in metabolic network integration during the diauxic shift.

systems biology↗

bioRxiv2025

Thermodynamically Explicit Kinetics with Potential for Genome-wide Application

Metabolic processes are inherently dynamic, yet genome-scale models are often limited to steady-state analysis, which cannot capture time-dependent responses crucial for understanding complex diseases like cancer. This work introduces a Thermodynamically Explicit (TDE) kinetic framework to bridge this gap by constructing dynamic metabolic models grounded in fundamental thermodynamic principles. Our approach uses standard chemical potentials to derive simplified rate laws for metabolic reactions, reducing the kinetic complexity of each reaction to a single, biochemically determined prefactor ({lambda}), which we will name kinetic capacity factor. The number of parameters in the suggested TDE kinetics is indeed minimal in the sense that it uses the lowest possible number of free parameters required to define a reaction rate that is both dynamic and thermodynamically consistent. We demonstrate the validity and practical application of this framework by re-parameterizing a well-established kinetic model of glycolysis. The resulting TDE model successfully reproduces the dynamic behavior of the original, more complex model in simulations. By streamlining the parameterization process, the TDE kinetic framework offers a powerful and scalable tool for building genome-wide dynamic metabolic models. This approach paves the way for more accurate and predictive simulations of metabolic behavior, with significant potential for applications in systems biology, medicine, and biotechnology.

biophysics↗

bioRxiv2025

Constitutively active RAS prolongs Cdc42 signalling while attenuating MAPK signalling during fission yeast mating

The small GTPase RAS is a signalling hub activating multiple pathways, which may respond differently to a constitutively active RAS mutation. We explored this issue using fission yeast, where RAS-mediated pheromone signalling (PS) activates the MAPKSpk1 and Cdc42 pathways. We observed that in cells with the yeast RAS mutant, ras1.G17V, the MAPKSpk1 activation was attenuated similarly to that in wildtype cells, whereas Cdc42 activation was prolonged. We built a mathematical model implementing PS negative-feedback circuits and competition between the two Ras1 effectors, MAPKKKByr2 and Cdc42-GEFScd1. The model robustly predicted the MAPKSpk1 activation dynamics of an additional 20 PS mutants. In support of the model, a recombinant Cdc42-GEFScd1 competed with MAPKKKByr2 for Ras1 binding and overexpression of the Ras binding domain of either Cdc42-GEFScd1 or MAPKKKByr2 inhibited both downstream pathways. Our study has established that the constitutively active RAS signalling propagates differently to downstream pathways where the system prevents MAPK overactivation.

cell biology↗

bioRxiv2024

Plant cold acclimation and its impact on sensitivity of carbohydrate metabolism

The ability to acclimate to changing environmental conditions is essential for the fitness and survival of plants. Not only are seasonal differences challenging for plants growing in different habitats but, facing climate change, the likelihood of encountering extreme weather events increases. In order to better assess and respond to associated future challenges and risks it is important to understand the processes happening during acclimation. Previous studies of acclimation processes of Arabidopsis thaliana to changes in temperature and light conditions have revealed a multigenic trait comprising and affecting multiple layers of molecular organization. Here, a combination of experimental and computational methods was applied to study the effects of changing light intensities during cold acclimation on the central carbon metabolism of Arabidopsis thaliana leaves. Mathematical modeling, simulation and sensitivity analysis predicted an important role of hexose phosphate balance for stabilization of photosynthetic CO2 fixation. Experimental validation revealed a profound effect of temperature on the sensitivity of carbohydrate metabolism.

plant biology↗

bioRxiv2021

TCAIM controls effector T cell generation by preventing Mitochondria-Endoplasmic Reticulum Contact Site-initiated Cholesterol Biosynthesis

T cells need to adapt their cellular metabolism for effector cell differentiation. This relies on alterations in mitochondrial physiology. Which signals and molecules regulate those alterations remains unclear. We recently reported, that the mitochondrial protein TCAIM inhibits activation-induced changes in mitochondrial morphology and function and thus, CD4+ effector T cell formation. Using conditional TCAIM knock-in (KI) and knockout (KO) mice, we now show that it also applies to CD8+ T cells and more importantly, delineate the molecular processes in mitochondria by which TCAIM controls effector cell differentiation. TCAIM KI resulted in reduced activation-induced HIF1 protein expression. Metabolomics and transcriptional data in combination with mathematical flux modeling revealed an impaired induction of anabolic pathways, especially of the mevalonate pathway and cholesterol biosynthesis in TCAIM KI CD8+ T cells. Addition of cholesterol completely rescued HIF1 protein expression, activation and proliferation of TCAIM KI CD8+ T cells. At the molecular level, TCAIM delayed activation-induced mitochondria-ER contact (MERC) formation by binding to MERC promoting proteins such as RMD3 and VDAC2. In summary, we demonstrate that TCAIM suppresses effector cell differentiation by inhibiting MERC formation, which induce HIF1-mediated increase in cellular metabolism and cholesterol biosynthesis.

immunology↗

bioRxiv2020

Sperm migration in the genital tract - in silico experiments identify key factors for reproductive success

Sperm migration in the female genital tract controls sperm selection and, therefore, reproductive success as male gametes are conditioned for fertilization while their number is dramatically reduced. Mechanisms underlying sperm migration are mostly unknown, since in vivo investigations are mostly unfeasible for ethical or practical reasons. By presenting a spatio-temporal model of the mammalian female genital tract combined with agent-based description of sperm motion and interaction as well as parameterizing it with bovine data, we offer an alternative possibility for studying sperm migration in silico. The model incorporates genital tract geometry as well as biophysical principles of sperm motion observed in vitro such as positive rheotaxis and thigmotaxis. This model for sperm migration from vagina to oviducts was successfully tested against in vivo data from literature. We found that physical sperm characteristics such as velocity and directional stability as well as sperm-fluid interactions and wall alignment are critical for success, i.e. sperms reaching the oviducts. Therefore, we propose that these identified sperm parameters should be considered in detail for conditioning sperm in artificial selection procedures since the natural processes are normally bypassed in reproductive in vitro technologies. The tremendous impact of mucus flow to support sperm accumulation in the oviduct highlights the importance of a species-specific optimum time window for artificial insemination regarding ovulation. Predictions from our extendable in silico experimental system will improve assisted reproduction in humans, endangered species, and livestock.

biophysics↗

bioRxiv2019

Impact of small RNAs in retrograde signalling pathways in Arabidopsis thaliana

Chloroplast perturbations activate retrograde signalling pathways causing dynamic changes of gene expression. Besides transcriptional control of gene expression different classes of small non-coding RNAs (sRNAs) act in gene expression control, but comprehensive analyses regarding their role in retrograde signalling is lacking. We performed sRNA profiling in response to norflurazon (NF) that provokes retrograde signals in A. thaliana wild type and the two retrograde signalling mutants gun1 and gun5. The RNA samples were also used for mRNA and long non-coding RNA (lncRNA) profiling to link altered sRNA levels to changes of their cognate target RNAs. We identified 122 sRNAs from all known sRNA classes that were responsive to NF in wild type. Strikingly, 140 and 213 sRNAs were found to be differentially regulated in both mutants indicating a retrograde control of these sRNAs. Concomitant with the changes in sRNA expression we detected about 1500 differentially expressed mRNAs in the NF treated wild type and around 900 and 1400 mRNAs that were differentially regulated in the gun1 and gun5 mutant with a high proportion (~30%) of genes encoding plastid proteins. Furthermore, around 20% of predicted miRNA targets code for plastid localised proteins. The analyses of sRNA-target pairs identified pairs with an anticorrelated expression as well pairs showing other expressional relations pointing to a role of sRNAs in balancing transcriptional changes upon retrograde signals. Based on the comprehensive changes in sRNA expression we assume a considerable impact of sRNAs in retrograde-dependent transcriptional changes to adjust plastidic and nuclear gene expression. Significance statementPerturbations of plastid functions trigger retrograde signalling to adjust plastidic and nuclear gene expression, however, the role of small non-coding RNAs acting as regulators in these pathways is not well understood. We analysed small non-coding RNA expression in response to retrograde signals in A. thaliana wild type and two retrograde signalling mutants and identified members of all known small non-coding RNA classes pointing to a functional role of these RNA classes in retrograde pathways.

plant biology↗

bioRxiv2019

Entropic regulation of dynamical metabolic processes

Life depends on the input of energy, either directly provided by sunlight or in form of high-energy matter. The rules and conditions for the conversion of chemical or electromagnetic energy into living structure and all the processes related with life are governed by the laws of thermodynamics. Hence, to understand the potential and the limitations of cell growth and metabolism, it is unavoidable to take these laws into account. During the last years, systems biology has developed many mathematical models aiming to describe steady states and dynamic behavior of cellular processes in qualitative and quantitative terms. The validity of the model predictions depends strongly on whether the model formulation is in agreement with the laws of physics, chemistry, and, specifically, thermodynamics.\n\nHere, we review basic principles of thermodynamics for equilibrium and non-equilibrium processes as well as for closed and open systems as far as they concern metabolic processes, especially in their dynamics. We illustrate the application of thermodynamic laws for some practical cases that are currently intensively studied in systems and computational biology. Specifically, we will discuss the concept of entropy production and energy dissipation for isolated and open systems and its interpretation for the feasibility of biological processes, especially metabolism. We demonstrate that steady states of metabolic systems cannot show energy dissipation, while in dynamical modes entropy of the system can be both increased or decreased, depending on the type of perturbation and the kinetics of the reaction system. These findings are very important for biotechnological processes where energy dissipation should be limited, but also for analysis of healthy and diseased cellular metabolism.

biophysics↗

bioRxiv2019

Probability-based detection of phosphoproteomic uncertainty reveals rare signaling events driven by oncogenic kinase gene fusion

We describe a novel Bayesian method for estimating protein concentration and phosphorylation site occupancy ratios from mass spectrometry experiments. Our variance model assigns standard deviations to all quantitative ratios, even when only a single peptide is observed, increasing the number of quantifiable observations in a sample compared to conventional methods. We further demonstrate the application of this method using a dataset investigating the impact of the PRKAR1A-RET gene fusion in immortalized thyroid cells.

systems biology↗

bioRxiv2019

Osmolyte homeostasis controls single-cell growth rate and maximum cell size of Saccharomyces cerevisiae

Cell growth is well described at the population level, but precisely how nutrient and water uptake and cell wall expansion drive the growth of single cells is poorly understood. Supported by measurements of single-cell growth trajectories and cell wall elasticity, we present a single-cell growth model for yeast. The model links the thermodynamic quantities turgor pressure, osmolarity, cell wall elasto-plasticity, and cell size, using concepts from rheology and thin shell theory. It reproduces cell size dynamics during single-cell growth, budding, and hyper- or hypoosmotic stress. We find that single-cell growth rate and final size are primarily governed by osmolyte uptake and consumption, while bud expansion depends additionally on different cell wall extensibilities of mother and bud. Based on first principles the model provides a more accurate description of size dynamics than previous attempts and its analytical simplification allows for easy combination with models for other cell processes.

systems biology↗

bioRxiv2018

Constitutively active RAS in S. pombe causes persistent Cdc42 signalling but only transient MAPK activation

The small GTPase RAS is a signalling hub for many pathways and oncogenic human RAS mutations are assumed to over-activate all of its downstream pathways. We tested this assumption in fission yeast, where, RAS-mediated pheromone signalling (PS) activates the MAPKSpk1 and Cdc42 pathways. Unexpectedly, we found that constitutively active Ras1.G17V induced immediate but only transient MAPKSpk1 activation, whilst Cdc42 activation persisted. Immediate but transient MAPKSpk1 activation was also seen in the deletion mutant of Cdc42-GEFScd1, a Cdc42 activator. We built a mathematical model using PS negative-feedback circuits and competition between the two Ras1 effectors, MAPKKKByr2 and Cdc42-GEFScd1. The model robustly predicted the MAPKSpk1 activation dynamics of an additional 21 PS mutants. Supporting the model, we showed that a recombinant Cdc42-GEFScd1 fragment competes with MAPKKKByr2 for Ras1 binding. Our study has established a concept that the constitutively active RAS propagates differently to downstream pathways where the system prevents MAPK overactivation. HighlightsO_LIConstitutively active Ras1.GV prolongs Cdc42 activation in S. pombe pheromone signalling C_LIO_LIRas1.GV results in an immediate but only transient MAPKSpk1 activation C_LIO_LIThe RAS effector pathways MAPKSpk1 and Cdc42 compete with each other for active Ras1 C_LIO_LIPredictive modelling explains MAPKSpk1 activation dynamics in 24 signaling-mutants C_LI eTOC BlurbS. pombe Ras1 activates the MAPKSpk1 and Cdc42 pathways. Kelsall et al. report that the constitutively active Ras1.G17V mutation, which causes morphological anomalies, induces prolonged Cdc42 activation but only a transient MAPKSpk1 activation followed by attenuation. Mathematical modelling and biochemical data suggest a competition between the MAPKSpk1 and Cdc42 pathways for active Ras1.

molecular biology↗

bioRxiv2018

Memote: A community-driven effort towards a standardized genome-scale metabolic model test suite

Several studies have shown that neither the formal representation nor the functional requirements of genome-scale metabolic models (GEMs) are precisely defined. Without a consistent standard, comparability, reproducibility, and interoperability of models across groups and software tools cannot be guaranteed.\n\nHere, we present memote (https://github.com/opencobra/memote) an open-source software containing a community-maintained, standardized set of metabolic model tests. The tests cover a range of aspects from annotations to conceptual integrity and can be extended to include experimental datasets for automatic model validation. In addition to testing a model once, memote can be configured to do so automatically, i.e., while building a GEM. A comprehensive report displays the models performance parameters, which supports informed model development and facilitates error detection.\n\nMemote provides a measure for model quality that is consistent across reconstruction platforms and analysis software and simplifies collaboration within the community by establishing workflows for publicly hosted and version controlled models.

systems biology↗

bioRxiv2018

A comprehensive, mechanistically detailed, and executable model of the Cell Division Cycle in Saccharomyces cerevisiae

Understanding how cellular functions emerge from the underlying molecular mechanisms is a key challenge in biology. This will require computational models, whose predictive power is expected to increase with coverage and precision of formulation. Genome-scale models revolutionised the metabolic field and made the first whole-cell model possible. However, the lack of genome-scale models of signalling networks blocks the development of eukaryotic whole-cell models. Here, we present a comprehensive mechanistic model of the molecular network that controls the cell division cycle in Saccharomyces cerevisiae. We use rxncon, the reaction-contingency language, to neutralise the scalability issues preventing formulation, visualisation and simulation of signalling networks at the genome-scale. We use parameter-free modelling to validate the network and to predict genotype-to-phenotype relationships down to residue resolution. This mechanistic genome-scale model offers a new perspective on eukaryotic cell cycle control, and opens up for similar models - and eventually whole-cell models - of human cells.

systems biology↗

bioRxiv2018

Roles of G1 cyclins in the temporal organization of yeast cell cycle - a transcriptome-wide analysis

Oscillating gene expression is crucial for correct timing and progression through cell cycle. In Saccharomyces cerevisiae, G1 cyclins Cln1-3 are essential drivers of the cell cycle and have an important role for temporal fine-tuning. We measured time-resolved transcriptome-wide gene expression for wild type and cyclin single and double knockouts over cell cycle with and without osmotic stress. Clustering of expression profiles, peak-time detection of oscillating genes, integration with transcription factor network dynamics, and assignment to cell cycle phases allowed us to quantify the effect of genetic or stress perturbations on the duration of cell cycle phases. Cln1 and Cln2 showed functional differences, especially affecting later phases. Deletion of Cln3 led to a delay of START followed by normal progression through later phases. Our data and network analysis suggest mutual effects of cyclins with the transcriptional regulators SBF and MBF.

systems biology↗

bioRxiv2018

Two parallel pathways implement robust propionate catabolism and detoxification in mycobacteria

Tuberculosis remains a major global health threat with over 1.5 million deaths each year. Mycobacterium tuberculosis success story is related to a flexible metabolism, allowing growth despite restrictive conditions within the human host.\n\nHost lipids stores are a major carbon source in vivo. Their catabolism yields propionyl-CoA, which is processed by two parallel pathways, the methylmalonyl CoA pathway and the methylcitrate pathway. Both pathways are considered potential drug targets. The methylcitrate pathway is upregulated in the pathological context. However, intermediates of this pathway can be cytotoxic and Mtbs preference for its usage remains unclear.\n\nWe combine thermodynamic kinetic modeling, quantitative proteomics and time-resolved metabolomics to characterize the interplay between the two pathways and to show their functionalities in an efficient and fast propionate catabolism.\n\nWe find that the methylcitrate pathway acts as a transcriptionally regulated, high capacity catabolic pathway due to its favorable thermodynamics and metabolic control distribution. In contrast, the methylmalonyl pathway is constitutively fulfilling biosynthetic tasks and can quickly detoxify propionate pulses, but is thermodynamically restricted to lower capacity.

systems biology↗

bioRxiv2017

Spatial modeling of the membrane-cytosolic interface in protein kinase signal transduction

The spatial architecture of signaling pathways and the inter-action with cell size and morphology are complex but little understood. With the advances of single cell imaging and single cell biology it becomes crucial to understand intracel-lular processes in time and space. Activation of cell surface receptors often triggers a signaling cascade including the activation of membrane-attached and cytosolic signaling components, which eventually transmit the signal to the cell nucleus. Signaling proteins can form steep gradients in the cytosol, which cause strong cell size dependence. We show that the kinetics at the membrane-cytosolic interface and the ratio of cell membrane area to the enclosed cytosolic volume change the behavior of signaling cascades significantly. We present a mathematical analysis of signal transduction in time and space by providing analytical solutions for different spatial arrangements of linear signaling cascades. These investigations are complemented by numerical simulations of non-linear cascades and asymmetric cell shapes.

systems biology↗

bioRxiv2017

Estimation of immune cell content in tumour tissue using single-cell RNA-seq data

As interactions between the immune system and tumour cells are governed by a complex network of cell-cell interactions, knowing the specific immune cell composition of a solid tumour may be essential to predict a patients response to immunotherapy. Here, we analyse in depth how to derive the cellular composition of a solid tumour from bulk gene expression data by mathematical deconvolution, using indication- and cell type-specific reference gene expression profiles (RGEPs) from tumour-derived single-cell RNA sequencing data. We demonstrate that tumour-derived RGEPs are essential for the successful deconvolution and that RGEPs from peripheral blood are insufficient. We distinguish nine major cell types as well as three T cell subtypes. As the ratios of CD4+, CD8+ and regulatory T cells have been shown to predict overall survival, we extended our analysis to include the estimation of prognostic ratios that may enable the application in a clinical setting. Using the tumour derived RGEPs, we can estimate, for the first time, the content of cancer associated fibroblasts, endothelial cells and the malignant cells in a patient sample by a deconvolution approach. In addition, improved tumour cell gene expression profiles can be obtained by this method by computationally removing contamination from non-malignant cells. Given the difficulty around sample preparation and storage to obtain high quality single-cell RNA-seq data in the clinical context, the presented method represents a computational solution to derive the cellular composition of a tissue sample.

bioinformatics↗