Highly Pathogenic H5N1 Influenza Viruses Carry Virulence Determinants beyond the Polybasic Hemagglutinin Cleavage Site

Highly pathogenic avian influenza viruses (HPAIV) originate from avirulent precursors but differ from all other influenza viruses by the presence of a polybasic cleavage site in their hemagglutinins (HA) of subtype H5 or H7. In this study, we investigated the ability of a low-pathogenic avian H5N1 strain to transform into an HPAIV. Using reverse genetics, we replaced the monobasic HA cleavage site of the low-pathogenic strain A/Teal/Germany/Wv632/2005 (H5N1) (TG05) by a polybasic motif from an HPAIV (TG05poly). To elucidate the virulence potential of all viral genes of HPAIV, we generated two reassortants carrying the HA from the HPAIV A/Swan/Germany/R65/06 (H5N1) (R65) plus the remaining genes from TG05 (TG05-HAR65) or in reversed composition the mutated TG05 HA plus the R65 genes (R65-HATG05poly). In vitro, TG05poly and both reassortants were able to replicate without the addition of trypsin, which is characteristic for HPAIV. Moreover, in contrast to avirulent TG05, the variants TG05poly, TG05-HAR65, and R65-HATG05poly are pathogenic in chicken to an increasing degree. Whereas the HA cleavage site mutant TG05poly led to temporary non-lethal disease in all animals, the reassortant TG05-HAR65 caused death in 3 of 10 animals. Furthermore, the reassortant R65-HATG05poly displayed the highest lethality as 8 of 10 chickens died, resembling “natural” HPAIV strains. Taken together, acquisition of a polybasic HA cleavage site is only one necessary step for evolution of low-pathogenic H5N1 strains into HPAIV. However, these low-pathogenic strains may already have cryptic virulence potential. Moreover, besides the polybasic cleavage site, the additional virulence determinants of H5N1 HPAIV are located within the HA itself and in other viral proteins

Choosing and Using a Plant DNA Barcode

The main aim of DNA barcoding is to establish a shared community resource of DNA sequences that can be used for organismal identification and taxonomic clarification. This approach was successfully pioneered in animals using a portion of the cytochrome oxidase 1 (CO1) mitochondrial gene. In plants, establishing a standardized DNA barcoding system has been more challenging. In this paper, we review the process of selecting and refining a plant barcode; evaluate the factors which influence the discriminatory power of the approach; describe some early applications of plant barcoding and summarise major emerging projects; and outline tool development that will be necessary for plant DNA barcoding to advance

The construction of causal networks to estimate coral bleaching intensity

Current metrics for predicting bleaching episodes, e.g. NOAA's Coral Reef Watch Program, do not seem to apply well to Brazil's marginal reefs located in Bahia state and alternative predictive approaches must be sought for effective long term management. Bleaching occurrences at Abrolhos have been observed since the 1990s but with a much lower frequency/extent than for other reef systems worldwide. We constructed a Bayesian Belief Network (BN) to back-predict the intensity of bleaching events and learn how local and regional scale forcing factors interact to enhance or alleviate coral bleaching specific to Abrolhos. Bleaching intensity data were collected for several reef sites across Bahia state coast (~12°-20°S; 37°-40°W) during the austral summer 1994-2005 and compared to environmental data: sea surface temperature (SST), diffuse light attenuation coefficient at 490 nm (K490), rain precipitation, wind velocities, and El Niño Southern Oscillation (ENSO) proxies. Conditional independence tests were calculated to produce four specialized BNs, each with specific factors that likely regulate bleaching intensity. All specialized BNs identified that a five-day accumulated SST proxy (SSTAc5d) was the exclusive parent node for coral bleaching producing a total predictive rate of 88% based on SSTAc5d state. When SSTAc5d was simulated as unknown, the Thermal-Eolic Resultant BN kept the total predictive rate of 88%. Our approach has produced initial means to predict beaching intensity at Abrolhos. However, the robustness of the model required for management purposes must be further (and regularly) operationally tested with new in situ and remote sensing data. © 2013 Elsevier Ltd