Dissecting the functional role of polyketide synthases in Dictyostelium discoideum

Dictyostelium discoideum exhibits the largest repository of polyketide synthase (PKS) proteins of all known genomes. However, the functional relevance of these proteins in the biology of this organism remains largely obscure. On the basis of computational, biochemical, and gene expression studies, we propose that the multifunctional Dictyostelium PKS (DiPKS) protein DiPKS1 could be involved in the biosynthesis of the differentiation regulating factor 4-methyl-5-pentylbenzene-1,3-diol (MPBD). Our cell-free reconstitution studies of a novel acyl carrier protein Type III PKS didomain from DiPKS1 revealed a crucial role of protein-protein interactions in determining the final biosynthetic product. Whereas the Type III PKS domain by itself primarily produces acyl pyrones, the presence of the interacting acyl carrier protein domain modulates the catalytic activity to produce the alkyl resorcinol scaffold of MPBD. Furthermore, we have characterized an O-methyltransferase (OMT12) from Dictyostelium with the capability to modify this resorcinol ring to synthesize a variant of MPBD. We propose that such a modification in vivo could in fact provide subtle variations in biological function and specificity. In addition, we have performed systematic computational analysis of 45 multidomain PKSs, which revealed several unique features in DiPKS proteins. Our studies provide a new perspective in understanding mechanisms by which metabolic diversity could be generated by combining existing functional scaffolds

Predicting disease risk areas through co-production of spatial models: the example of Kyasanur Forest Disease in India’s forest landscapes

Zoonotic diseases affect resource-poor tropical communities disproportionately, and are linked to human use and modification of ecosystems. Disentangling the socio-ecological mechanisms by which ecosystem change precipitates impacts of pathogens is critical for predicting disease risk and designing effective intervention strategies. Despite the global “One Health” initiative, predictive models for tropical zoonotic diseases often focus on narrow ranges of risk factors and are rarely scaled to intervention programs and ecosystem use. This study uses a participatory, co-production approach to address this disconnect between science, policy and implementation, by developing more informative disease models for a fatal tick-borne viral haemorrhagic disease, Kyasanur Forest Disease (KFD), that is spreading across degraded forest ecosystems in India. We integrated knowledge across disciplines to identify key risk factors and needs with actors and beneficiaries across the relevant policy sectors, to understand disease patterns and develop decision support tools. Human case locations (2014–2018) and spatial machine learning quantified the relative role of risk factors, including forest cover and loss, host densities and public health access, in driving landscape-scale disease patterns in a long-affected district (Shivamogga, Karnataka State). Models combining forest metrics, livestock densities and elevation accurately predicted spatial patterns in human KFD cases (2014–2018). Consistent with suggestions that KFD is an “ecotonal” disease, landscapes at higher risk for human KFD contained diverse forest-plantation mosaics with high coverage of moist evergreen forest and plantation, high indigenous cattle density, and low coverage of dry deciduous forest. Models predicted new hotspots of outbreaks in 2019, indicating their value for spatial targeting of intervention. Co-production was vital for: gathering outbreak data that reflected locations of exposure in the landscape; better understanding contextual socio-ecological risk factors; and tailoring the spatial grain and outputs to the scale of forest use, and public health interventions. We argue this inter-disciplinary approach to risk prediction is applicable across zoonotic diseases in tropical settings