Bacillus cytotoxicus-A potentially virulent food-associated microbe.

Bacillus cytotoxicus is a member of the Bacillus cereus group with the ability to grow at high temperatures (up to 52℃) and to synthesize cytotoxin K-1, a diarrhoeagenic cytotoxin, which appears to be unique to this species and more cytotoxic than the cytotoxin K-2 produced by other members of this group. Only a few isolates of this species have been characterized with regard to their cytotoxic effects, and the role of cytotoxin K-1 as a causative agent of food poisoning remains largely unclear. Bacillus cytotoxicus was initially isolated from a food-borne outbreak, which led to three deaths, and the organism has since been linked to other outbreaks all involving plant-based food matrices. Other studies, as well as food-borne incidents reported to the UK Food Standards Agency, detected B. cytotoxicus in insect-related products and in dried food products. With insect-related food becoming increasingly popular, the association with this pathogen is concerning, requiring further investigation and evidence to protect public health. This review summarizes the current knowledge around B. cytotoxicus and highlights gaps in the literature from a food safety perspective.

Inferring Gene Regulatory Networks from Multiple Datasets.

Gaussian process dynamical systems (GPDS) represent Bayesian nonparametric approaches to inference of nonlinear dynamical systems, and provide a principled framework for the learning of biological networks from multiple perturbed time series measurements of gene or protein expression. Such approaches are able to capture the full richness of complex ODE models, and can be scaled for inference in moderately large systems containing hundreds of genes. Related hierarchical approaches allow for inference from multiple datasets in which the underlying generative networks are assumed to have been rewired, either by context-dependent changes in network structure, evolutionary processes, or synthetic manipulation. These approaches can also be used to leverage experimentally determined network structures from one species into another where the network structure is unknown. Collectively, these methods provide a comprehensive and flexible platform for inference from a diverse range of data, with applications in systems and synthetic biology, as well as spatiotemporal modelling of embryo development. In this chapter we provide an overview of GPDS approaches and highlight their applications in the biological sciences, with accompanying tutorials available as a Jupyter notebook from https://github.com/cap76/GPDS .

A Framework for Engineering Stress Resilient Plants Using Genetic Feedback Control and Regulatory Network Rewiring.

Crop disease leads to significant waste worldwide, both pre- and postharvest, with subsequent economic and sustainability consequences. Disease outcome is determined both by the plants' response to the pathogen and by the ability of the pathogen to suppress defense responses and manipulate the plant to enhance colonization. The defense response of a plant is characterized by significant transcriptional reprogramming mediated by underlying gene regulatory networks, and components of these networks are often targeted by attacking pathogens. Here, using gene expression data from Botrytis cinerea-infected Arabidopsis plants, we develop a systematic approach for mitigating the effects of pathogen-induced network perturbations, using the tools of synthetic biology. We employ network inference and system identification techniques to build an accurate model of an Arabidopsis defense subnetwork that contains key genes determining susceptibility of the plant to the pathogen attack. Once validated against time-series data, we use this model to design and test perturbation mitigation strategies based on the use of genetic feedback control. We show how a synthetic feedback controller can be designed to attenuate the effect of external perturbations on the transcription factor CHE in our subnetwork. We investigate and compare two approaches for implementing such a controller biologically-direct implementation of the genetic feedback controller, and rewiring the regulatory regions of multiple genes-to achieve the network motif required to implement the controller. Our results highlight the potential of combining feedback control theory with synthetic biology for engineering plants with enhanced resilience to environmental stress.