Lateral Gene Transfer (LGT) between Archaea and Escherichia coli is a contributor to the emergence of novel infectious disease

BACKGROUND: Lateral gene transfer is the major mechanism for acquisition of new virulence genes in pathogens. Recent whole genome analyses have suggested massive gene transfer between widely divergent organisms. PRESENTATION OF THE HYPOTHESIS: Archeal-like genes acting as virulence genes are present in several pathogens and genomes contain a number of archaeal-like genes of unknown function. Archaea, by virtue of their very different evolutionary history and different environment, provide a pool of potential virulence genes to bacterial pathogens. TESTING THE HYPOTHESIS: We can test this hypothesis by 1)identifying genes likely to have been transferred (directly or indirectly) to E. coli O157:H7 from archaea; 2)investigating the distribution of similar genes in pathogens and non-pathogens and 3)performing rigorous phylogenetic analyses on putative transfers. IMPLICATIONS OF THE HYPOTHESIS: Although this hypothesis focuses on archaea and E. coli, it will serve as a model having broad applicability to a number of pathogenic systems. Since no archaea are known vertebrate pathogens, archaeal-like transferred genes that are associated with virulence in bacteria represent a clear model for the emergence of virulence genes

Interactions between social learning and technological learning in electric vehicle futures

The transition to electric vehicles is an important strategy for reducing greenhouse gas emissions from passenger cars. Modelling transition pathways helps identify critical drivers and uncertainties. Global integrated assessment models (IAMs) have been used extensively to analyse climate mitigation policy. IAMs emphasise technological change processes but are largely silent on important social and behavioural dimensions to technological transitions. Here, we develop a novel conceptual framing and empirical evidence base on social learning processes relevant for vehicle adoption. We then implement this formulation of social learning in IMAGE, a widely-used global IAM. We apply this new modelling approach to analyse how technological learning and social learning interact to influence electric vehicle transition dynamics. We find that technological learning and social learning processes can be mutually reinforcing. Increased electric vehicle market shares can induce technological learning which reduces technology costs while social learning stimulates diffusion from early adopters to more risk-averse adopter groups. In this way, both types of learning process interact to stimulate each other. In the absence of social learning, however, the perceived risks of electric vehicle adoption among later adopting groups remains prohibitively high. In the absence of technological learning, electric vehicles remain relatively expensive and therefore only for early adopters an attractive choice. This first-of-its-kind model formulation of both social and technological learning is a significant contribution to improving the behavioural realism of global IAMs. Applying this new modelling approach emphasises the importance of market heterogeneity, real-world consumer decision-making, and social dynamics as well as technology parameters, to understand climate mitigation potentials