AviCeD · Human and avian Pattern Recognition Receptor activation of cell death pathways: impact on the host inflammatory response to zoonotic infections.

Zoonotic infections, particularly with viruses or bacteria, continue to be important threats to humans globally. These infections can cause severe inflammatory disease in humans, but may be tolerated by their animal hosts although how this occurs is unclear. Many human diseases have underlying inflammatory aetiologies so determining how diversity in host-pathogen responses occurs across animal species provides unique opportunities not only to identify fundamental inflammatory mechanisms, but also to find new anti-inflammatory therapeutic targets. Previously we found how the zoonotic bacterium Salmonella is sensed by Pattern Recognition Receptors (PRRs) to form macromolecular signalling platforms that trigger interconnected, caspase-dependent cell death pathways critical for inflammation and infection control. How these signalling platforms form is unclear. Our cross-species analysis identified major differences in key constituents of these signalling platforms which, we hypothesise, will alter the host inflammatory response to zoonotic pathogens. Our recent discovery of a novel caspase with defective activity against Salmonella in Carnivora spp supports this idea. AviCeD will leverage our new suite of techniques where we endogenously label, mutate or remove innate immune proteins in cell-lines from any species to determine how PRR-driven cell death platforms form in cells in response to infection. We will compare cells from chicken and ducks infected with Salmonella or Influenza virus, as reservoirs species with differential severity in their host inflammatory responses to these pathogens, performing proteomic analysis alongside evolutionary analysis and functional validation to identify key regulatory proteins. We will then analyse the physiological relevance of differential cell death pathway wiring on cell extrinsic inflammatory consequences in two-cell infection models.