Biology subjects

Henning, P.

Publications and source records attributed to Henning, P..

6 recordsLinked to original sources

bioRxiv2025

Illuminating the Role of Asymmetric Mitochondrial Fission on Beta-Cell Health

Mitochondrial dynamics play a critical role in the development of aging-related diseases such as type 2 diabetes mellitus. To investigate how mitochondrial dynamics influence cellular behavior in pancreatic beta-cells, we developed a rule-based, multi-level simulation model of insulin secretion. The pancreatic beta cell model encompasses metabolic pathways (glycolysis and oxidative phosphorylation), compartmental processes (mitochondrial fusion and fission), and cellular processes (insulin secretion), allowing for the investigation of their interplay. The rule-based simulation model captures the high plasticity of these organelles and integrates and builds upon insights from various experimental studies and previous simulation models. Its rule-based specification facilitates the exploration of new hypotheses, the integration of new knowledge and data, and the successive extension of the model. The results of our simulation experiments underscore the importance of peripheral, sorted mitochondrial fission in maintaining mitochondrial health. Downregulation of the fission-associated anchor proteins Fis1 and MFF impacts mitochondrial structure and function differently, highlighting their distinct roles in maintaining mitochondrial health and cellular biogenesis, respectively. With respect to insulin secretion, Drp1 suppression shows that beta cells become unresponsive to glucose, whereas Fis1 downregulation only attenuates the cellular response. The simulation model and simulation results corroborates experimental findings and contribute to a deeper understanding of the mechanisms involved in mitochondrial dynamics of pancreatic beta cells and their relation to metabolic dysregulation in type 2 diabetes mellitus. Author summaryMitochondria, often described as the powerhouses of the cell, undergo constant changes through fusion and fission processes. These dynamics are essential for maintaining cell health and proper functioning. In type 2 diabetes mellitus, this balance can be disrupted. In this work, we developed a multi-level, rule-based simulation model to analyze processes of mitochondrial dynamics and their impact on insulin secretion within pancreatic beta cells. Our model captures diverse biological processes that operate at different but interconnected organizational levels, including energy metabolism, mitochondrial dynamics, and insulin secretion. We found that peripheral fission plays a crucial role in determining whether cells secrete insulin properly. In addition, downregulation of the fission-associated proteins MFF and Fis1 reveals a distinct impact on the structure and function of the mitochondrial network, as well as on insulin secretion in pancreatic beta cells. The simulation model and results provide insights into how mitochondrial dynamics affect beta cell metabolism and insulin release. It enables the study of dynamics at different organizational levels, and its rule-based approach facilitates the integration of new knowledge (e.g., by updating or adding specific rules) and experimental data.

systems biology↗

bioRxiv2025

SIMPROV : Provenance capturing for simulation studies

Improving interpretability and reusability has become paramount for modeling and simulation studies. Provenance, which encompasses information about the entities, activities, and agents involved in producing a model, experiment, or data, is pivotal in achieving this goal. However, capturing provenance in simulation studies presents a tremendous challenge due to the diverse software systems employed by modelers and the various entities and activities to be considered. Existing methods only automatically capture partial provenance from individual software systems, leaving gaps in the overall story of a simulation study. To address this limitation, we introduce a lightweight method that can record the provenance of complete simulation studies by monitoring the modeler in their familiar yet heterogeneous work environment, posing as few restrictions as possible. The approach emphasizes a clear separation of concerns between provenance capturers, which collect data from the diverse software systems used, and a provenance builder, which assembles this information into a coherent provenance graph. Furthermore, we provide a web interface that enables modelers to enhance and explore their provenance graphs. We showcase the practicality of SIMPROV through two cell biological case studies. Author summaryWith the importance of simulation studies in understanding and managing complex dynamic systems, the need to support the interpretation and (re-)use of their results increases. Provenance documents how the products of a simulation study were created and what other products, agents, and activities have been involved in this process. For example, the information based on which data from which cell line a simulation model has been calibrated and validated is central to interpreting the results and assessing how the results can be reused. Therefore, some software tools offer to record provenance information. However, for complete provenance information, the tool must offer all functionalities required for a simulation study. In practice, various tools are typically used. To accommodate this situation, we propose a flexible, decentralized approach: SIMPROV. A provenance capturer - a small piece of software designed to record the modelers actions within a software tool - observes each tool used by the modeler. A central provenance builder then combines the recorded information from all captures. A capturer has to be programmed only once for each software tool used in systems biology, and modelers can work as before with minimal effort needed to record the provenance of their simulation studies automatically.

bioinformatics↗

bioRxiv2024

Expressive modeling and fast simulation for dynamic compartments

Compartmentalization is vital for cell biological processes. The field of rule-based stochastic simulation has acknowledged this, and many tools and methods have capabilities for compartmentalization. However, mostly, this is limited to a static compartmental hierarchy and does not integrate compartmental changes. Integrating compartmental dynamics is challenging for the design of the modeling language and the simulation engine. The language should support a concise yet flexible modeling of compartmental dynamics. Our work is based on ML-Rules, a rule-based language for multi-level cell biological modeling that supports a wide variety of compartmental dynamics, whose syntax we slightly adapt. To develop an efficient simulation engine for compartmental dynamics, we combine specific data structures and new and existing algorithms and implement them in the Rust programming language. We evaluate the concept and implementation using two case studies from existing cell-biological models. The execution of these models outperforms previous simulations of ML-Rules by two orders of magnitude. Finally, we present a prototype of a WebAssembly-based implementation to allow for a low barrier of entry when exploring the language and associated models without the need for local installation. Author summaryBiochemical dynamics are constrained by and influence the dynamics of cellular compartments. Basic constraints are considered by many modeling and simulation tools, e.g., certain reactions may only occur in specific cellular compartments and at a speed influenced by the compartmental volume. However, to capture the functioning of complex compartmental dynamics such as cell proliferation or the fission or fusion of mitochondria, additional efforts are required from tool designers. These refer to how the modeler can specify these dynamics succinctly and unambiguously and how the resulting model can be executed efficiently. For modeling, we rely on ML-Rules, an expressive, formal rule-based language for modeling biochemical systems, which ships with the required features and which we only slightly adapt in our re-implementation. We design a new simulation engine that combines efficient data structures and various algorithms for efficient simulation. The achieved efficiency will enable thorough analysis, calibration, and validation of compartmental dynamics and, thus, allow the "in-silico" pursuit of research questions for which compartmental dynamics are essential. To further facilitate exploring the interplay of compartmental and non-compartmental dynamics, we exploit recent advances in web technology so that ML-Rules models can be run efficiently in the web browser.

systems biology↗

bioRxiv2022

An auto-inhibited state of protein kinase G and implications for selective activation

Cyclic GMP-dependent protein kinases (PKGs) are key mediators of the nitric oxide/cGMP signaling pathway that regulates biological functions as diverse as smooth muscle contraction, cardiac function, and axon guidance. Campaigns targeting nitric oxide synthases and cyclic nucleotide phosphodiesterases in this signaling axis suggest that understanding how cGMP differentially triggers mammalian PKG isoforms could lead to new therapeutics that inhibit or activate PKGs. Alternate splicing of PRKG1 transcripts confers distinct leucine zippers, linkers, and auto-inhibitory pseudo-substrate sequences to PKG I and I{beta} that result in isoform-specific activation properties, but the mechanism of enzyme auto-inhibition and its alleviation by cGMP is still not well understood. Here we present a crystal structure of PKG I{beta} in which the auto-inhibitory sequence and the cyclic nucleotide binding domains are bound to the catalytic domain, providing a snapshot of the auto-inhibited state. Specific contacts between the PKG I{beta} auto-inhibitory sequence and the enzyme active site help explain isoform-specific activation constants and the effects of phosphorylation in the linker. We also present a crystal structure of a PKG I cyclic nucleotide binding domain with an activating mutation linked to Thoracic Aortic Aneurysms and Dissections. Similarity of this structure to wild type cGMP-bound domains and differences with the auto-inhibited enzyme provide a mechanistic basis for constitutive activation. We show that PKG I{beta} auto-inhibition is mediated by contacts within each monomer of the native full-length dimeric protein, and using the available structural and biochemical data we develop a model for the regulation and activation of PKGs.

biochemistry↗

bioRxiv2020

The Roles of Space and Stochasticity in Computational Simulations of Cellular Biochemistry: Quantitative Analysis and Qualitative Insights

Most of the fascinating phenomena studied in cell biology emerge from interactions among highly organized multi-molecular structures and rapidly propagating molecular signals embedded into complex and frequently dynamic cellular morphologies. For the exploration of such systems, computational simulation has proved to be an invaluable tool, and many researchers in this field have developed sophisticated computational models for application to specific cell biological questions. However it is often difficult to reconcile conflicting computational results that use different simulation approaches (for example partial differential equations versus particle-based stochastic methods) to describe the same phenomenon. Moreover, the details of the computational implementation of any particular algorithm may give rise to quantitatively or even qualitatively different results for the same set of starting assumptions and parameters. In an effort to address this issue systematically, we have defined a series of computational test cases ranging from very simple (bimolecular binding in solution) to moderately complex (spatial and temporal oscillations generated by proteins binding to membranes) that represent building blocks for comprehensive three-dimensional models of cellular function. Having used two or more distinct computational approaches to solve each of these test cases with consistent parameter sets, we generally find modest but measurable differences in the solutions of the same problem, and a few cases where significant deviations arise. We discuss the strengths and limitations of commonly used computational approaches for exploring cell biological questions and provide a framework for decision-making by researchers wishing to develop new models for cell biology. As computational power and speed continue to increase at a remarkable rate, the dream of a fully comprehensive computational model of a living cell may be drawing closer to reality, but our analysis demonstrates that it will be crucial to evaluate the accuracy of such models critically and systematically.

systems biology↗

bioRxiv2018

Genome-wide mapping identifies beta-1,4-N-acetyl-galactosaminyl-transferase as a novel determinant of sclerostin levels and bone mineral density

In bone, sclerostin is mainly osteocyte-derived and plays an important local role in adaptive responses to mechanical loading. Whether circulating levels of sclerostin also play a functional role is currently unclear, which we aimed to examine by two sample Mendelian Randomisation (MR). A genetic instrument for circulating sclerostin, derived from a genome wide association study (GWAS) meta-analysis of serum sclerostin in 10,584 European-descent individuals, was examined in relation to femoral neck bone mineral density (BMD; n= 32,744) in GEFOS, and estimated BMD by heel ultrasound (eBMD; n=426,824), and fracture risk (n=426,795), in UK Biobank. Our GWAS identified two novel serum sclerostin loci, B4GALNT3 (standard deviation (SD)) change in sclerostin per A allele ({beta}=0.20, P=4.6x10-49), and GALNT1 ({beta}=0.11 per G allele, P=4.4x10-11). B4GALNT3 is an N-acetyl-galactosaminyltransferase, adding a terminal LacdiNAc disaccharide to target glycocoproteins, found to be predominantly expressed in kidney, whereas GALNT1 is an enzyme causing mucin-type O-linked glycosylation. Using these two SNPs as genetic instruments, MR revealed an inverse causal relationship between serum sclerostin and femoral neck BMD ({beta}= -0.12, 95%CI= -0.20 to -0.05) and eBMD ({beta}= -0.12, 95%CI= -0.14 to -0.10), and a positive relationship with fracture risk ({beta}= 0.11, 95%CI= 0.01 to 0.21). Colocalization analysis demonstrated common genetic signals within the B4GALNT3 locus for higher sclerostin, lower eBMD, and greater B4GALNT3 expression in arterial tissue (Probability>99%). Our findings suggest that higher sclerostin levels are causally related to lower BMD and greater fracture risk. Hence, strategies for reducing circulating sclerostin, for example by targeting glycosylation enzymes as suggested by our GWAS results, may prove valuable in treating osteoporosis.

genetics↗