bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/269860

A systematic approach for identifying shared mechanisms in epilepsy and its comorbidities

Hoyt, C. T.·Domingo-Fernandez, D.·Balzer (Filep), N. R.·Gueldenpfennig, A.·Hofmann-Apitius, M.
View original source ↗Export metadata ↓

Abstract

Cross-sectional epidemiological studies have shown that the incidence of several nervous system diseases is more frequent in epilepsy patients than in the general population. Some comorbidities (e.g., Alzheimers disease and Parkinsons disease) are also risk factors for the development of seizures; suggesting they may share pathophysiological mechanisms with epilepsy.\n\nA literature-based approach was used to identify gene overlap between epilepsy and its comorbidities as a proxy for a shared genetic basis for disease, or genetic pleiotropy, as a first effort to identify shared mechanisms. While the results identified neurological disorders as the group of diseases with the highest gene overlap, this analysis was insufficient for identifying putative common mechanisms shared across epilepsy and its comorbidities. This motivated the use of a dedicated literature mining and knowledge assembly approach in which a cause-and-effect model of epilepsy was captured with Biological Expression Language.\n\nAfter enriching the knowledge assembly with information surrounding epilepsy, its risk factors, its comorbidities, and antiepileptic drugs, a novel comparative mechanism enrichment approach was used to propose several downstream effectors (including the GABA receptor, GABAergic pathways, etc.) that could explain the therapeutic effects carbamazepine in both the contexts of epilepsy and AD.\n\nWe have made the Epilepsy Knowledge Assembly available at https://www.scai.fraunhofer.de/content/dam/scai/de/downloads/bioinformatik/epilepsy.bel and queryable through NeuroMMSig at http://neurommsig.scai.fraunhofer.de. The source code used for analysis and tutorials for reproduction are available on GitHub at https://github.com/cthoyt/epicom.

Source connections

Explore related subjects

systems biologyPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Hoyt, C. T., Domingo-Fernandez, D., Balzer (Filep), N. R., Gueldenpfennig, A., Hofmann-Apitius, M.. 2018-02-22. A systematic approach for identifying shared mechanisms in epilepsy and its comorbidities. https://doi.org/10.1101/269860

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

KEEP EXPLORING

Related preprints

bioRxiv2026

Enzyme-Constrained Metabolic Modeling Reveals CO2-Dependent Growth Control in Picochlorum celeri

Picochlorum celeri grows 11.6 times faster at elevated CO2 than in air, yet the abundance of its metabolic enzymes is only 17% higher. To explain this, we reconstructed iPiccel954, a genome-scale metabolic model with 3525 reactions, 2973 metabolites and 954 genes, and eciPiccel954, its enzyme-constrained version limited by the measured enzyme abundance in air and at elevated CO2. Calibrating the enzyme-constrained models against the measured proteome showed that a similar protein budget supports very different growth depending on how it is allocated. With urea as the nitrogen source, urea carboxylase was the only step whose relaxation alone raised growth, by 627% in air and 78% at elevated CO2. Bottleneck analysis showed that growth is limited by many enzymes spread across the network in both conditions, that the limiting enzymes differ between the two, and that control over starch metabolism seen in air disappears at elevated CO2. Measured proteomes agreed with these predictions, since the predicted bottleneck enzymes were more abundant than other enzymes. Rapid growth in P. celeri therefore reflects how the protein budget is allocated more than how large it is. Therefore, eciPiccel954 provides a quantitative basis for engineering this alga for biotechnological application.

systems biology↗
bioRxiv2026

Organelle Aging Predicts Human Disease

Aging affects cellular organelles differently, but whether subcellular aging predicts human disease remains unknown. Here, we integrated organelle-enriched proteins in plasma and cerebrospinal fluid proteomes from 61,931 individuals to model aging of 10 organelles across the human lifespan. Organelle aging was heterogeneous with lysosomes, plasma membranes, and Golgi compartments accelerating rapidly after age 60. Individuals with four or more prematurely aged compartments exhibited up to 22-fold higher kidney disease risk and up to 8-fold higher mortality risk. Major diseases are characterized by distinct organelle aging signatures and disease risk increased with the number of accelerated organelles. APOE-{varepsilon}4 dosage was associated with lysosomal and mitochondrial aging and reduced PDP1 levels, implicating altered energy metabolism. Decomposition of organelle signature coefficients into biological programs further showed that diseases are associated with distinct subcellular patterns. These findings establish organelle-resolved aging as a measurable dimension of human biology, revealing subcellular mechanisms underlying disease susceptibility and resilience.

systems biology↗
bioRxiv2026

Simulating the dynamics of gene regulatory systems at cellular scale

The temporal nature of eukaryotic gene expression is the result of a complex, non-linear, dynamical system of regulatory processes. So far, models of such systems trade realism for tractability by using strict simplifying assumptions. These simplified models are often unfaithful to our biological understanding; however, without such simplifications, analytical solutions become more challenging and these dynamical systems must then be studied through simulation. Here, we explore the question of how this complex web of gene regulatory connections can be faithfully simulated for all tens of thousands of genes that usually define a eukaryotic organism. We present a proof-of-principle model and simulator, GeneRegulatorySystems.jl which is based on a biophysical chemical master equation that overcomes three key restrictions. First, it allows an exact stochastic simulation of fully non-linear gene expression dynamics, including feedback control, to parsimoniously capture cause and effect relationships of gene regulation. Further, it includes a general framework for implementing diverse mechanisms of gene control such as protein mediated degradation, RNAi, and protein complex formation. Finally, it scales realistically to tens of thousands of genes. We achieve this by introducing a domain-specific programming language to aid the design of experimental protocols from small-scale, well-studied, scenarios of gene regulation inspired by dynamical systems biology up to large-scale, single cell omics experiments with interventions. In this way, our framework provides a grammar of in silico experimental design that can capture single-cell gene expression dynamics for entire genomes.

systems biology↗