Persistent domestic circulation of African swine fever virus in Tanzania, 2015–2017

Background African swine fever (ASF) is a highly fatal viral hemorrhagic disease of domestic pigs that threatens livelihoods and food security. In Africa, ASF virus (ASFV) circulates in sylvatic (transmission between warthogs and soft argasid ticks) and domestic (transmission between domestic pigs) cycles, with outbreaks resulting from ASFV spill-over from sylvatic cycle. A number of outbreaks were reported in different parts of Tanzania between 2015 and 2017. The present study investigated ASFV transmission patterns through viral DNA sequencing and phylogenetic analysis. A total of 3120 tissue samples were collected from 2396 domestic pigs during outbreaks at different locations in Tanzania between 2015 and 2017. Partial sequencing of theB646L(p72) gene was conducted for diagnostic confirmation and molecular characterization of ASFV. Phylogenetic analysis to study the relatedness of current ASFV with those that caused previous outbreaks in Tanzania and representatives of all known 24 ASFV was performed using the Maximum Composite Likelihood model with 1000 bootstrap replications in MEGA 6.0. Results ASFV was confirmed to cause disease in sampled domestic pigs. ASFV genotypes II, IX, and X were detected from reported outbreaks in 2015-2017. The current ASFV isolates were similar to those recently documented in the previous studies in Tanzania. The similarities of these isolates suggests for continuous circulation of ASFV with virus maintenance within the domestic pigs. Conclusions Genetic analysis confirmed the circulation of ASFV genotypes II, IX, and X by partialB646L(p72) gene sequencing. The similarities of current isolates to previously isolated Tanzanian isolates and pattern of disease spread suggest for continuous circulation of ASF with virus' maintenance in the domestic pigs. Although certain viral genotypes seem to be geographically restricted into certain zones within Tanzania, genotype II seems to expand its geographical range northwards with the likelihood of spreading to other states of the East African Community. The spread of ASFV is due to breach of quarantine and transportation of infected pigs via major highways. Appropriate control measures including zoosanitary measures and quarantine enforcement are recommended to prevent ASF domestic circulation in Tanzania

Research, development and innovation in Flanders 2004.

Research and Development; Innovation; Flanders;

Targeting mitochondrial 18 kDa translocator protein (TSPO) regulates macrophage cholesterol efflux and lipid phenotype

Abstract The aim of the present study was to establish mitochondrial cholesterol trafficking 18 kDa translocator protein (TSPO) as a potential therapeutic target, capable of increasing macrophage cholesterol efflux to (apo)lipoprotein acceptors. Expression and activity of TSPO in human (THP-1) macrophages were manipulated genetically and by the use of selective TSPO ligands

Scientific, Technical and Economic Committee for Fisheries (STECF) - Report of the STECF Study Group on the Evaluation of Fishery Multi-annual Plans (SGMOS 09-02)

SG-MOS 09-02 was held in IPIMAR, Lisbon, (Portugal), on 23-27 November 2009. The aim of the workshop was to provide Evaluations of three multiannual fisheries management plans:- R(EC) No 388/2006 ¿ multi-annual plan for sole in the Bay of Biscay; R(EC) No 209/2007 ¿ multi-annual plan for sole in the Western Channel R(EC) No676/2007 ¿ multi-annual plan for sole and plaice in the North Sea. STECF reviewed the report during its Plenary meeting on 26-30 April 2010.JRC.DG.G.4-Maritime affair

Antisense artifacts in transcriptome microarray experiments are resolved by actinomycin D

Recent transcription profiling studies have revealed an unanticipatedly large proportion of antisense transcription across eukaryotic and bacterial genomes. However, the extent and significance of antisense transcripts is controversial partly because experimental artifacts are suspected. Here, we present a method to generate clean genome-wide transcriptome profiles, using actinomycin D (ActD) during reverse transcription. We show that antisense artifacts appear to be triggered by spurious synthesis of second-strand cDNA during reverse transcription reactions. Strand-specific hybridization signals obtained from Saccharomyces cerevisiae tiling arrays were compared between samples prepared with and without ActD. Use of ActD removed about half of the detectable antisense transcripts, consistent with their being artifacts, while sense expression levels and about 200 antisense transcripts were not affected. Our findings thus facilitate a more accurate assessment of the extent and position of antisense transcription, towards a better understanding of its role in cells

Highlights from the 6th International Society for Computational Biology Student Council Symposium at the 18th Annual International Conference on Intelligent Systems for Molecular Biology

This meeting report gives an overview of the keynote lectures and a selection of the student oral and poster presentations at the 6th International Society for Computational Biology Student Council Symposium that was held as a precursor event to the annual international conference on Intelligent Systems for Molecular Biology (ISMB). The symposium was held in Boston, MA, USA on July 9th, 2010

Rosetta FlexPepDock ab-initio: Simultaneous Folding, Docking and Refinement of Peptides onto Their Receptors

Flexible peptides that fold upon binding to another protein molecule mediate a large number of regulatory interactions in the living cell and may provide highly specific recognition modules. We present Rosetta FlexPepDock ab-initio, a protocol for simultaneous docking and de-novo folding of peptides, starting from an approximate specification of the peptide binding site. Using the Rosetta fragments library and a coarse-grained structural representation of the peptide and the receptor, FlexPepDock ab-initio samples efficiently and simultaneously the space of possible peptide backbone conformations and rigid-body orientations over the receptor surface of a given binding site. The subsequent all-atom refinement of the coarse-grained models includes full side-chain modeling of both the receptor and the peptide, resulting in high-resolution models in which key side-chain interactions are recapitulated. The protocol was applied to a benchmark in which peptides were modeled over receptors in either their bound backbone conformations or in their free, unbound form. Near-native peptide conformations were identified in 18/26 of the bound cases and 7/14 of the unbound cases. The protocol performs well on peptides from various classes of secondary structures, including coiled peptides with unusual turns and kinks. The results presented here significantly extend the scope of state-of-the-art methods for high-resolution peptide modeling, which can now be applied to a wide variety of peptide-protein interactions where no prior information about the peptide backbone conformation is available, enabling detailed structure-based studies and manipulation of those interactions

Learning a peptide-protein binding affinity predictor with kernel ridge regression

We propose a specialized string kernel for small bio-molecules, peptides and pseudo-sequences of binding interfaces. The kernel incorporates physico-chemical properties of amino acids and elegantly generalize eight kernels, such as the Oligo, the Weighted Degree, the Blended Spectrum, and the Radial Basis Function. We provide a low complexity dynamic programming algorithm for the exact computation of the kernel and a linear time algorithm for it's approximation. Combined with kernel ridge regression and SupCK, a novel binding pocket kernel, the proposed kernel yields biologically relevant and good prediction accuracy on the PepX database. For the first time, a machine learning predictor is capable of accurately predicting the binding affinity of any peptide to any protein. The method was also applied to both single-target and pan-specific Major Histocompatibility Complex class II benchmark datasets and three Quantitative Structure Affinity Model benchmark datasets. On all benchmarks, our method significantly (p-value < 0.057) outperforms the current state-of-the-art methods at predicting peptide-protein binding affinities. The proposed approach is flexible and can be applied to predict any quantitative biological activity. The method should be of value to a large segment of the research community with the potential to accelerate peptide-based drug and vaccine development.Comment: 22 pages, 4 figures, 5 table