A Two-Headed Monster to Avert Disaster: HBS1/SKI7 Is Alternatively Spliced to Build Eukaryotic RNA Surveillance Complexes

The cytosolic RNA exosome, a 3′→5′ exoribonuclease complex, contributes to mRNA degradation in eukaryotes, limiting the accumulation of poorly-translated, improperly translated, or aberrant mRNA species. Disruption of cytosolic RNA exosome activity allows aberrant RNA species to accumulate, which can then be detected by host antiviral immune systems as a signature of pathogen infection, activating antiviral defenses. SKI7 is a critical component of the cytosolic RNA exosome in yeast, bridging the catalytic exoribonuclease core with the SKI2/SKI3/SKI8 adaptor complex that guides aberrant RNA substrates into the exosome. The ortholog of SKI7 was only recently identified in humans as an alternative splice form of the HBS1 gene, which encodes a decoding factor translational GTPase that rescues stalled ribosomes. Here, we identify the plant orthologs of HBS1/SKI7. We found that HBS1 and SKI7 are typically encoded by alternative splice forms of a single locus, although some plant lineages have evolved subfunctionalized genes that apparently encode only HBS1 or only SKI7. In all plant lineages examined, the SKI7 gene is subject to regulation by alternative splicing that can yield unproductive transcripts, either by removing deeply conserved SKI7 coding sequences, or by introducing premature stop codons that render SKI7 susceptible to nonsense-mediated decay. Taking a comparative, evolutionary approach, we define crucial features of the SKI7 protein shared by all eukaryotes, and use these deeply conserved features to identify SKI7 proteins in invertebrate lineages. We conclude that SKI7 is a conserved cytosolic RNA exosome subunit across eukaryotic lineages, and that SKI7 is consistently regulated by alternative splicing, suggesting broad coordination of nuclear and cytosolic RNA metabolism

The effects of weather and climate change on dengue

There is much uncertainty about the future impact of climate change on vector-borne diseases. Such uncertainty reflects the difficulties in modelling the complex interactions between disease, climatic and socioeconomic determinants. We used a comprehensive panel dataset from Mexico covering 23 years of province-specific dengue reports across nine climatic regions to estimate the impact of weather on dengue, accounting for the effects of non-climatic factors

Light regulates alternative splicing outcomes via the TOR kinase pathway

For plants, light is the source of energy and the most relevant regulator of growth and adaptations to the environment by inducing changes in gene expression at various levels, including alternative splicing. Light-triggered chloroplast retrograde signals control alternative splicing in Arabidopsis thaliana. Here, we provide evidence that light regulates the expression of a core set of splicing-related factors in roots. Alternative splicing responses in roots are not directly caused by light but are instead most likely triggered by photosynthesized sugars. The target of rapamycin (TOR) kinase plays a key role in this shoot-to-root signaling pathway. Knocking down TOR expression or pharmacologically inhibiting TOR activity disrupts the alternative splicing responses to light and exogenous sugars in roots. Consistently, splicing decisions are modulated by mitochondrial activity in roots. In conclusion, by activating the TOR pathway, sugars act as mobile signals to coordinate alternative splicing responses to light throughout the whole plant.Fil: Riegler, Stefan. Universitat Fur Bodenkultur Wien; AustriaFil: Servi, Lucas. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Fisiología, Biología Molecular y Neurociencias. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Fisiología, Biología Molecular y Neurociencias; ArgentinaFil: Scarpin, Maria Regina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. University of California; Estados UnidosFil: Godoy Herz, Micaela Amalia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Fisiología, Biología Molecular y Neurociencias. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Fisiología, Biología Molecular y Neurociencias; ArgentinaFil: Kubaczka Zoppi, María Guillermina Jazmín. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Fisiología, Biología Molecular y Neurociencias. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Fisiología, Biología Molecular y Neurociencias; ArgentinaFil: Venhuizen, Peter. Universitat Fur Bodenkultur Wien; AustriaFil: Meyer, Christian. Universite Paris-Saclay; FranciaFil: Brunkard, Jacob O.. University of California; Estados UnidosFil: Kalyna, Maria. Universitat Fur Bodenkultur Wien; AustriaFil: Barta, Andrea. Medizinische Universitat Wien; AustriaFil: Petrillo, Ezequiel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Fisiología, Biología Molecular y Neurociencias. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Fisiología, Biología Molecular y Neurociencias; Argentin

Ears of the Armadillo: Global Health Research and Neglected Diseases in Texas

Neglected tropical diseases (NTDs) have\ud been recently identified as significant public\ud health problems in Texas and elsewhere in\ud the American South. A one-day forum on the\ud landscape of research and development and\ud the hidden burden of NTDs in Texas\ud explored the next steps to coordinate advocacy,\ud public health, and research into a\ud cogent health policy framework for the\ud American NTDs. It also highlighted how\ud U.S.-funded global health research can serve\ud to combat these health disparities in the\ud United States, in addition to benefiting\ud communities abroad

Spatio-temporal modelling of climate-sensitive disease risk: Towards an early warning system for dengue in Brazil

NOTICE: this is the author’s version of a work that was accepted for publication in Computers and Geosciences. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computers and Geosciences, Vol. 37, Issue 3, (2011), DOI: 10.1016/j.cageo.2010.01.008This paper considers the potential for using seasonal climate forecasts in developing an early warning system for dengue fever epidemics in Brazil. In the first instance, a generalised linear model (GLM) is used to select climate and other covariates which are both readily available and prove significant in prediction of confirmed monthly dengue cases based on data collected across the whole of Brazil for the period January 2001 to December 2008 at the microregion level (typically consisting of one large city and several smaller municipalities). The covariates explored include temperature and precipitation data on a 2.5°×2.5°2.5°×2.5° longitude–latitude grid with time lags relevant to dengue transmission, an El Niño Southern Oscillation index and other relevant socio-economic and environmental variables. A negative binomial model formulation is adopted in this model selection to allow for extra-Poisson variation (overdispersion) in the observed dengue counts caused by unknown/unobserved confounding factors and possible correlations in these effects in both time and space. Subsequently, the selected global model is refined in the context of the South East region of Brazil, where dengue predominates, by reverting to a Poisson framework and explicitly modelling the overdispersion through a combination of unstructured and spatio-temporal structured random effects. The resulting spatio-temporal hierarchical model (or GLMM—generalised linear mixed model) is implemented via a Bayesian framework using Markov Chain Monte Carlo (MCMC). Dengue predictions are found to be enhanced both spatially and temporally when using the GLMM and the Bayesian framework allows posterior predictive distributions for dengue cases to be derived, which can be useful for developing a dengue alert system. Using this model, we conclude that seasonal climate forecasts could have potential value in helping to predict dengue incidence months in advance of an epidemic in South East Brazil

Impacts of El Niño Southern Oscillation and Indian Ocean Dipole on dengue incidence in Bangladesh

Dengue dynamics are driven by complex interactions between hosts, vectors and viruses that are influenced by environmental and climatic factors. Several studies examined the role of El Niño Southern Oscillation (ENSO) in dengue incidence. However, the role of Indian Ocean Dipole (IOD), a coupled ocean atmosphere phenomenon in the Indian Ocean, which controls the summer monsoon rainfall in the Indian region, remains unexplored. Here, we examined the effects of ENSO and IOD on dengue incidence in Bangladesh. According to the wavelet coherence analysis, there was a very weak association between ENSO, IOD and dengue incidence, but a highly significant coherence between dengue incidence and local climate variables (temperature and rainfall). However, a distributed lag nonlinear model (DLNM) revealed that the association between dengue incidence and ENSO or IOD were comparatively stronger after adjustment for local climate variables, seasonality and trend. The estimated effects were nonlinear for both ENSO and IOD with higher relative risks at higher ENSO and IOD. The weak association between ENSO, IOD and dengue incidence might be driven by the stronger effects of local climate variables such as temperature and rainfall. Further research is required to disentangle these effects

Time series analysis of dengue fever and weather in Guangzhou, China

<p>Abstract</p> <p>Background</p> <p>Monitoring and predicting dengue incidence facilitates early public health responses to minimize morbidity and mortality. Weather variables are potential predictors of dengue incidence. This study explored the impact of weather variability on the transmission of dengue fever in the subtropical city of Guangzhou, China.</p> <p>Methods</p> <p>Time series Poisson regression analysis was performed using data on monthly weather variables and monthly notified cases of dengue fever in Guangzhou, China for the period of 2001-2006. Estimates of the Poisson model parameters was implemented using the Generalized Estimating Equation (GEE) approach; the quasi-likelihood based information criterion (QICu) was used to select the most parsimonious model.</p> <p>Results</p> <p>Two best fitting models, with the smallest QICu values, are selected to characterize the relationship between monthly dengue incidence and weather variables. Minimum temperature and wind velocity are significant predictors of dengue incidence. Further inclusion of minimum humidity in the model provides a better fit.</p> <p>Conclusion</p> <p>Minimum temperature and minimum humidity, at a lag of one month, are positively associated with dengue incidence in the subtropical city of Guangzhou, China. Wind velocity is inversely associated with dengue incidence of the same month. These findings should be considered in the prediction of future patterns of dengue transmission.</p

Waterborne Outbreak of Gastroenteritis: Effects on Sick Leaves and Cost of Lost Workdays

We examined the acute and cumulative effects of this incidence on sick leaves among public sector employees residing in the clean and contaminated areas, and the additional costs of lost workdays due to the incidence.Daily information on sick leaves of 1789 Finnish Public Sector Study participants was obtained from employers' registers. Global Positioning System-coordinates were used for linking participants to the clean and contaminated areas. Prevalence ratios (PR) for weekly sickness absences were calculated using binomial regression analysis. Calculations for the costs were based on prior studies.Among those living in the contaminated areas, the prevalence of participants on sick leave was 3.54 (95% confidence interval (CI) 2.97–4.22) times higher on the week following the incidence compared to the reference period. Those living and working in the clean area were basically not affected, the corresponding PR for sick leaves was 1.12, 95% CI 0.73–1.73. No cumulative effects on sick leaves were observed among the exposed. The estimated additional costs of lost workdays due to the incidence were 1.8–2.1 million euros.The prevalence of sickness absences among public sector employees residing in affected areas increased shortly after drinking water distribution system was contaminated, but no long-term effects were observed. The estimated costs of lost workdays were remarkable, thus, the cost-benefits of better monitoring systems for the water distribution systems should be evaluated

A New Approach to Monitoring Dengue Activity

Discusses informal surveillance tools for monitoring dengue activity, such as ProMED, GPHIN HealthMap and BioCaster