Natural Genome Diversity of AI-2 Quorum Sensing in Escherichia coli: Conserved Signal Production but Labile Signal Reception

Quorum sensing (QS) regulates the onset of bacterial social responses in function to cell density having an important impact in virulence. Autoinducer-2 (AI-2) is a signal that has the peculiarity of mediating both intra- and interspecies bacterial QS. We analyzed the diversity of all components of AI-2 QS across 44 complete genomes of Escherichia coli and Shigella strains. We used phylogenetic tools to study its evolution and determined the phenotypes of single-deletion mutants to predict phenotypes of natural strains. Our analysis revealed many likely adaptive polymorphisms both in gene content and in nucleotide sequence. We show that all natural strains possess the signal emitter (the luxS gene), but many lack a functional signal receptor (complete lsr operon) and the ability to regulate extracellular signal concentrations. This result is in striking contrast with the canonical species-specific QS systems where one often finds orphan receptors, without a cognate synthase, but not orphan emitters. Our analysis indicates that selection actively maintains a balanced polymorphism for the presence/absence of a functional lsr operon suggesting diversifying selection on the regulation of signal accumulation and recognition. These results can be explained either by niche-specific adaptation or by selection for a coercive behavior where signal-blind emitters benefit from forcing other individuals in the population to haste in cooperative behaviors.International Early Career Scientist grant from the Howard Hughes Medical Institute: (HHMI 55007436), Institut Pasteur, the CNRS, FCT award: (SFRH/BPD/26852/2006), salary support of LAO/ITQB & FCT

Rapid simulation of spatial epidemics: A spectral method

Spatial structure and hence the spatial position of host populations plays a vital role in the spread of infection. In the majority of situations, it is only possible to predict the spatial spread of infection using simulation models, which can be computationally demanding especially for large population sizes. Here we develop an approximation method that vastly reduces this computational burden. We assume that the transmission rates between individuals or sub-populations are determined by a spatial transmission kernel. This kernel is assumed to be isotropic, such that the transmission rate is simply a function of the distance between susceptible and infectious individuals; as such this provides the ideal mechanism for modelling localised transmission in a spatial environment. We show that the spatial force of infection acting on all susceptibles can be represented as a spatial convolution between the transmission kernel and a spatially extended ‘image’ of the infection state. This representation allows the rapid calculation of stochastic rates of infection using fast-Fourier transform (FFT) routines, which greatly improves the computational efficiency of spatial simulations. We demonstrate the efficiency and accuracy of this fast spectral rate recalculation (FSR) method with two examples: an idealised scenario simulating an SIR-type epidemic outbreak amongst N habitats distributed across a two-dimensional plane; the spread of infection between US cattle farms, illustrating that the FSR method makes continental-scale outbreak forecasting feasible with desktop processing power. The latter model demonstrates which areas of the US are at consistently high risk for cattle-infections, although predictions of epidemic size are highly dependent on assumptions about the tail of the transmission kernel

A review of spatial causal inference methods for environmental and epidemiological applications

The scientific rigor and computational methods of causal inference have had great impacts on many disciplines, but have only recently begun to take hold in spatial applications. Spatial casual inference poses analytic challenges due to complex correlation structures and interference between the treatment at one location and the outcomes at others. In this paper, we review the current literature on spatial causal inference and identify areas of future work. We first discuss methods that exploit spatial structure to account for unmeasured confounding variables. We then discuss causal analysis in the presence of spatial interference including several common assumptions used to reduce the complexity of the interference patterns under consideration. These methods are extended to the spatiotemporal case where we compare and contrast the potential outcomes framework with Granger causality, and to geostatistical analyses involving spatial random fields of treatments and responses. The methods are introduced in the context of observational environmental and epidemiological studies, and are compared using both a simulation study and analysis of the effect of ambient air pollution on COVID-19 mortality rate. Code to implement many of the methods using the popular Bayesian software OpenBUGS is provided