Search bioRxiv⌕ Search

bioRxiv · 10.1101/2025.11.25.690461

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

Abstract

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.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Henning, P., Schultz, J., Baltrusch, S., Uhrmacher, A. M.. 2025-11-28. Illuminating the Role of Asymmetric Mitochondrial Fission on Beta-Cell Health. https://doi.org/10.1101/2025.11.25.690461

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Interpretable machine learning coupled to gene regulatory networks uncovers subcircuits underlying cell fate decisions

Gene regulatory networks (GRNs) model causal linkages that control cell fate decisions and differentiation transitions. Prioritizing regulatory subnetworks underlying cell state differences is of critical importance, but current methods including those reliant on topological metrics introduce circularity as the metrics prioritizing TFs are computed from the same networks whose assumptions they inherit. Separately, interpretable machine learning methods can identify latent factors (LFs) that discriminate cellular states with formal statistical guarantees but do not model regulatory linkages. Here, we present FOCAL (Factor-Outcome Coupling for Assessment of Linkages), a paradigm to prioritize regulatory subnetworks by coupling state-specific and dynamic GRNs with outcome-supervised LFs learned using interpretable machine learning without reference to network topology. This shifts GRN focus from macroscopic TF nodes to state-specific and dynamic TF-gene linkages. In B and T cells, FOCAL identified GIFs (GRNs coupled to Interpretable latent Factors), prioritized regulatory subnetworks underlying established states as well as transient regulatory episodes preceding them. By coupling LFs learnt from perturbation experiments of lineage-defining TFs, FOCAL identified transcriptional predisposition to alternative fates within progenitor cell populations before overt differentiation. This uncovered a novel NFATC2-IRF8 interplay in activated B cells, that was validated by in-vitro and in-vivo genetic perturbations. The two transcription factors act cooperatively to restrain extrafollicular plasmablast differentiation and promote germinal center B cell fate.

systems biology↗

Comprehensive in silico analysis reveals candidate regulatory mechanisms underlying selective cerebellar vulnerability in pontocerebellar hypoplasia

Pontocerebellar hypoplasia (PCH) is a group of ultrarare, neurodegenerative disorders characterized by cerebellar and pontine hypoplasia. Genetic analysis over the last two decades has revealed an increasing number of pathogenic variants in a wide range of broadly expressed genes functioning in RNA processing, tRNA metabolism, and translation. However, the mechanisms linking these ubiquitous processes to brain region-specific vulnerability are unknown. Here, we established a multi-level variant-to-function in silico framework to predict the molecular consequences of PCH-associated variants in TSEN complex genes. These variants were predicted to have heterogeneous effects on diverse protein properties, including stability, subcellular localization, and degradation, supporting variant-specific rather than uniform disease mechanisms. Complementary transcriptomic analyses showed that PCH-associated genes were not globally enriched in the prenatal cerebellum. Instead, their expression was coordinated in a stage- and cell type-specific manner during cerebellar development. We therefore hypothesize that multiple PCH-associated genes are regulated by a common set of transcription factors, providing an explanation of the selective vulnerability of the cerebellum and to the phenotypic convergence of genetically diverse PCH subtypes. In summary, this study prioritizes candidate variants for biochemical, cellular, and in vivo validation, and identifies regulatory programs, cell lineages, and developmental windows for targeted, mechanistically informed disease modelling.

systems biology↗

Limit-pushing overexpression reveals constraints on protein abundance

Proteins are often classified as toxic or non-toxic without measuring the abundance reached, leaving constraints on tolerable protein abundance unresolved. We established a limit-pushing approach in Saccharomyces cerevisiae combining strong inducible expression with gTOW-mediated high-copy selection to counteract copy-number compensation while measuring protein abundance and growth. Nearly all of approximately 80 chromosome I proteins severely inhibited growth or reduced viability at sufficiently high abundance. We established IE50, the expression level associated with a 50% reduction in growth rate, to quantify their widely varying overexpression tolerance. IE50 was positively associated with predicted structural order and cytoplasmic localization propensity and negatively associated with sulphur content. Single-cell imaging linked higher tolerance to proteins remaining cytoplasmic without becoming aggregation-positive and revealed abundance-dependent changes in localization and organelle morphology. At extreme abundance, Fun12, Nup60, and Pex22 generated distinct large-scale intracellular states through specific sequence regions. These findings establish overexpression toxicity as a quantitative property linked to protein characteristics and reveal both constraints on tolerable abundance and sequence-dependent capacities for intracellular organization.

systems biology↗