A kinetic model of phospholipase C-γ1 linking structure-based insights to dynamics of enzyme autoinhibition and activation

bnglViz: online visualization of rule-based models

MOTIVATION

Rule-based modeling is a powerful method to describe and simulate interactions among multi-site molecules and multi-molecular species, accounting for the internal connectivity of molecules in chemical species. This modeling technique is implemented in BioNetGen software that is used by various tools and software frameworks, such as BioNetGen stand-alone software, NFSim simulation engine, Virtual Cell simulation and modeling framework, SmolDyn and PySB software tools. These tools exchange models using BioNetGen scripting language (BNGL). Until now, there was no online visualization of such rule-based models. Modelers and researchers reading the manuscripts describing rule-based models had to learn BNGL scripting or master one of these tools to understand the models.

RESULTS

Here, we introduce bnglViz, an online platform for visualizing BNGL files as graphical cartoons, empowering researchers to grasp the nuances of rule-based models swiftly and efficiently, and making the exploration of complex biological systems more accessible than ever before. The produced visualizations can be used as supplemental figures in publications or as a way to annotate BNGL models on web repositories.

AVAILABILITY AND IMPLEMENTATION

Available at https://bnglviz.github.io/.

De novo variants in PLCG1 are associated with hearing impairment, ocular pathology, and cardiac defects

Phospholipase C isozymes (PLCs) hydrolyze phosphatidylinositol 4,5-bisphosphate into inositol 1,4,5-trisphosphate and diacylglycerol, important signaling molecules involved in many cellular processes. PLCG1 encodes the PLCγ1 isozyme that is broadly expressed. Hyperactive somatic mutations of PLCG1 are observed in multiple cancers, but only one germline variant has been reported. Here we describe three unrelated individuals with de novo heterozygous missense variants in PLCG1 (p.Asp1019Gly, p.His380Arg, and p.Asp1165Gly) who exhibit variable phenotypes including hearing loss, ocular pathology and cardiac septal defects. To model these variants in vivo, we generated the analogous variants in the Drosophila ortholog, small wing (sl). We created a null allele slT2A and assessed the expression pattern. sl is broadly expressed, including in wing discs, eye discs, and a subset of neurons and glia. Loss of sl causes wing size reductions, ectopic wing veins and supernumerary photoreceptors. We document that mutant flies exhibit a reduced lifespan and age-dependent locomotor defects. Expressing wild-type sl in slT2A mutant rescues the loss-of-function phenotypes whereas expressing the variants causes lethality. Ubiquitous overexpression of the variants also reduces viability, suggesting that the variants are toxic. Ectopic expression of an established hyperactive PLCG1 variant (p.Asp1165His) in the wing pouch causes severe wing phenotypes, resembling those observed with overexpression of the p.Asp1019Gly or p.Asp1165Gly variants, further arguing that these two are gain-of-function variants. However, the wing phenotypes associated with p.His380Arg overexpression are mild. Our data suggest that the PLCG1 de novo heterozygous missense variants are pathogenic and contribute to the features observed in the probands.

Comparison of rule- and ordinary differential equation-based dynamic model of DARPP-32 signalling network

Dynamic modelling has considerably improved our understanding of complex molecular mechanisms. Ordinary differential equations (ODEs) are the most detailed and popular approach to modelling the dynamics of molecular systems. However, their application in signalling networks, characterised by multi-state molecular complexes, can be prohibitive. Contemporary modelling methods, such as rule- based (RB) modelling, have addressed these issues. The advantages of RB modelling over ODEs have been presented and discussed in numerous reviews. In this study, we conduct a direct comparison of the time courses of a molecular system founded on the same reaction network but encoded in the two frameworks. To make such a comparison, a set of reactions that underlie an ODE model was manually encoded in the Kappa language, one of the RB implementations. A comparison of the models was performed at the level of model specification and dynamics, acquired through model simulations. In line with previous reports, we confirm that the Kappa model recapitulates the general dynamics of its ODE counterpart with minor differences. These occur when molecules have multiple sites binding the same interactor. Furthermore, activation of these molecules in the RB model is slower than in the ODE one. As reported for other molecular systems, we find that, also for the DARPP-32 reaction network, the RB representation offers a more expressive and flexible syntax that facilitates access to fine details of the model, easing model reuse. In parallel with these analyses, we report a refactored model of the DARPP-32 interaction network that can serve as a canvas for the development of more complex dynamic models to study this important molecular system.