Spatial, seasonal and climatic predictive models of Rift Valley fever disease across Africa

Understanding the emergence and subsequent spread of human infectious diseases is a critical global challenge, especially for high-impact zoonotic and vector-borne diseases. Global climate and land-use change are likely to alter host and vector distributions, but understanding the impact of these changes on the burden of infectious diseases is difficult. Here, we use a Bayesian spatial model to investigate environmental drivers of one of the most important diseases in Africa, Rift Valley fever (RVF). The model uses a hierarchical approach to determine how environmental drivers vary both spatially and seasonally, and incorporates the effects of key climatic oscillations, to produce a continental risk map of RVF in livestock (as a proxy for human RVF risk). We find RVF risk has a distinct seasonal spatial pattern influenced by climatic variation, with the majority of cases occurring in South Africa and Kenya in the first half of an El Niño year. Irrigation, rainfall and human population density were the main drivers of RVF cases, independent of seasonal, climatic or spatial variation. By accounting more subtly for the patterns in RVF data, we better determine the importance of underlying environmental drivers, and also make space- and time-sensitive predictions to better direct future surveillance resources

Data from: Pathogeography: leveraging the biogeography of human infectious diseases for global health management

Biogeography is an implicit and fundamental component of almost every dimension of modern biology, from natural selection and speciation to invasive species and biodiversity management. However, biogeography has rarely been integrated into human or veterinary medicine nor routinely leveraged for global health management. Here we review the theory and application of biogeography to the research and management of human infectious diseases, an integration we refer to as ‘pathogeography’. Pathogeography represents a promising framework for understanding and decomposing the spatial distributions, diversity patterns and emergence risks of human infectious diseases into interpretable components of dynamic socio-ecological systems. Analytical tools from biogeography are already helping to improve our understanding of individual infectious disease distributions and the processes that shape them in space and time. At higher levels of organization, biogeographical studies of diseases are rarer but increasing, improving our ability to describe and explain patterns that emerge at the level of disease communities (e.g., co-occurrence, diversity patterns, biogeographic regionalisation). Even in a highly globalized world most human infectious diseases remain constrained in their geographic distributions by ecological barriers to the dispersal or establishment of their causal pathogens, reservoir hosts and/or vectors. These same processes underpin the spatial arrangement of other taxa, such as mammalian biodiversity, providing a strong empirical ‘prior’ with which to assess the potential distributions of infectious diseases when data on their occurrence is unavailable or limited. In the absence of quality data, generalized biogeographic patterns could provide the earliest (and in some cases the only) insights into the potential distributions of many poorly known or emerging, or as-yet-unknown, infectious disease risks. Encouraging more community ecologists and biogeographers to collaborate with health professionals (and vice versa) has the potential to improve our understanding of infectious disease systems and identify novel management strategies to improve local, global and planetary health

Data_supplement_Murray_Pathgeography

Data underpinning analyses presented Boxe

Supplementary tables and figures from Spatial, seasonal and climatic predictive models of Rift Valley fever disease across Africa

Table S1: Details of confirmed RVF outbreaks from EMPRES-i database from 2004-2016; Table S2: Details of all environmental and habitat geo-spatial variables used for modelling; Figure S1: Posterior distributions from an INLA Bayesian additive regression for all regression slopes estimates; Figure S2: Histogram of Conditional Predictive Ordinate (CPO) values from a leave-one-out cross validation of all 976 data points in the INLA Bayesian model; Figure S3: Plot of Mean component of Gaussian Random Field

Megakaryocyte-restricted MYH9 inactivation dramatically affects hemostasis while preserving platelet aggregation and secretion.: Role of myosin IIA in hemostasis and thrombosis

Mutations in the MYH9 gene encoding the nonmuscle myosin heavy chain IIA result in bleeding disorders characterized by a macrothrombocytopenia. To understand the role of myosin in normal platelet functions and in pathology, we generated mice with disruption of MYH9 in megakaryocytes. MYH9Delta mice displayed macrothrombocytopenia with a strong increase in bleeding time and absence of clot retraction. However, platelet aggregation and secretion in response to any agonist were near normal despite absence of initial platelet contraction. By contrast, integrin outside-in signaling was impaired, as observed by a decrease in integrin beta3 phosphorylation and PtdIns(3,4)P(2) accumulation following stimulation. Upon adhesion on a fibrinogen-coated surface, MYH9Delta platelets were still able to extend lamellipodia but without stress fiber-like formation. As a consequence, thrombus growth and organization, investigated under flow by perfusing whole blood over collagen, were strongly impaired. Thrombus stability was also decreased in vivo in a model of FeCl(3)-induced injury of carotid arteries. Overall, these results demonstrate that while myosin seems dispensable for aggregation and secretion in suspension, it plays a key role in platelet contractile phenomena and outside-in signaling. These roles of myosin in platelet functions, in addition to thrombocytopenia, account for the strong hemostatic defects observed in MYH9Delta mice

GWAS and colocalization analyses implicate carotid intima-media thickness and carotid plaque loci in cardiovascular outcomes

Carotid artery intima media thickness (cIMT) and carotid plaque are measures of subclinical atherosclerosis associated with ischemic stroke and coronary heart disease (CHD). Here, we undertake meta-analyses of genome-wide association studies (GWAS) in 71,128 individuals for cIMT, and 48,434 individuals for carotid plaque traits. We identify eight novel susceptibility loci for cIMT, one independent association at the previously-identified PINX1 locus, and one novel locus for carotid plaque. Colocalization analysis with nearby vascular expression quantitative loci (cis-eQTLs) derived from arterial wall and metabolic tissues obtained from patients with CHD identifies candidate genes at two potentially additional loci, ADAMTS9 and LOXL4. LD score regression reveals significant genetic correlations between cIMT and plaque traits, and both cIMT and plaque with CHD, any stroke subtype and ischemic stroke. Our study provides insights into genes and tissue-specific regulatory mechanisms linking atherosclerosis both to its functional genomic origins and its clinical consequences in humans

GWAS and colocalization analyses implicate carotid intima-media thickness and carotid plaque loci in cardiovascular outcomes.

Carotid artery intima media thickness (cIMT) and carotid plaque are measures of subclinical atherosclerosis associated with ischemic stroke and coronary heart disease (CHD). Here, we undertake meta-analyses of genome-wide association studies (GWAS) in 71,128 individuals for cIMT, and 48,434 individuals for carotid plaque traits. We identify eight novel susceptibility loci for cIMT, one independent association at the previously-identified PINX1 locus, and one novel locus for carotid plaque. Colocalization analysis with nearby vascular expression quantitative loci (cis-eQTLs) derived from arterial wall and metabolic tissues obtained from patients with CHD identifies candidate genes at two potentially additional loci, ADAMTS9 and LOXL4. LD score regression reveals significant genetic correlations between cIMT and plaque traits, and both cIMT and plaque with CHD, any stroke subtype and ischemic stroke. Our study provides insights into genes and tissue-specific regulatory mechanisms linking atherosclerosis both to its functional genomic origins and its clinical consequences in humans