An Integrative Approach to Computational Modelling of the Gene Regulatory Network Controlling Clostridium botulinum Type A1 Toxin Production

Abstract

Clostridium botulinum produces botulinum neurotoxins (BoNTs), highly potent substances responsible for botulism. Currently, mathematical models of C. botulinum growth and toxigenesis are largely aimed at risk assessment and do not include explicit genetic information beyond group level but integrate many component processes, such as signalling, membrane permeability and metabolic activity. In this paper we present a scheme for modelling neurotoxin production in C. botulinum Group I type A1, based on the integration of diverse information coming from experimental results available in the literature. Experiments show that production of BoNTs depends on the growth-phase and is under the control of positive and negative regulatory elements at the intracellular level. Toxins are released as large protein complexes and are associated with non-toxic components. Here, we systematically review and integrate those regulatory elements previously described in the literature for C. botulinum Group I type A1 into a population dynamics model, to build the very first computational model of toxin production at the molecular level. We conduct a validation of our model against several items of published experimental data for different wild type and mutant strains of C. botulinum Group I type A1. The result of this process underscores the potential of mathematical modelling at the cellular level, as a means of creating opportunities in developing new strategies that could be used to prevent botulism; and potentially contribute to improved methods for the production of toxin that is used for therapeutics.

Regulatory Networks Controlling Neurotoxin Synthesis in Clostridium botulinum and Clostridium tetani

“…Thus, the regulatory quorum-sensing pathways in C. botulinum remain to be defined. A computational model of group I C. botulinum A growth and toxin production based on nutrient availability, cell density, and quorumsensing signaling has been proposed in agreement with experimental data [127].…”

Impact of CodY protein on metabolism, sporulation and virulence in Clostridioides difficile ribotype 027

“…Thus, the virulence of multiple species is restrained to a significant extent under conditions where CodY is active. In Bacillus anthracis [28], Bacillus cereus [35], Listeria monocytogenes [36], Clostridium botulinum [43, 89] and several Streptococcal species [33, 90, 91], however, CodY appears to activate virulence. Moreover, in Clostridium perfringens , the impact of CodY on virulence varies from strain to strain.…”

Machine learning for integrating data in biology and medicine: Principles, practice, and opportunities

“…Along similar lines, [213,214,22] used Bayesian latent factor models and combined gene expression, copy number variation (CNV), and methylation data to predict protein functions. As a final example, many approaches aim to understand protein functions by combining data from different tissues [22,215,23,216] or different species [217,218,219,220,221,222]. For example, OhmNet [23] organizes 107 human tissues in a multi-layer network, in which each layer represents a tissue-specific proteinprotein interaction network.…”