Genome-Wide Association Analysis Identifies a Mutation in the Thiamine Transporter 2 (SLC19A3) Gene Associated with Alaskan Husky Encephalopathy

Alaskan Husky Encephalopathy (AHE) has been previously proposed as a mitochondrial encephalopathy based on neuropathological similarities with human Leigh Syndrome (LS). We studied 11 Alaskan Husky dogs with AHE, but found no abnormalities in respiratory chain enzyme activities in muscle and liver, or mutations in mitochondrial or nuclear genes that cause LS in people. A genome wide association study was performed using eight of the affected dogs and 20 related but unaffected control AHs using the Illumina canine HD array. SLC19A3 was identified as a positional candidate gene. This gene controls the uptake of thiamine in the CNS via expression of the thiamine transporter protein THTR2. Dogs have two copies of this gene located within the candidate interval (SLC19A3.2 – 43.36–43.38 Mb and SLC19A3.1 – 43.411–43.419 Mb) on chromosome 25. Expression analysis in a normal dog revealed that one of the paralogs, SLC19A3.1, was expressed in the brain and spinal cord while the other was not. Subsequent exon sequencing of SLC19A3.1 revealed a 4bp insertion and SNP in the second exon that is predicted to result in a functional protein truncation of 279 amino acids (c.624 insTTGC, c.625 C>A). All dogs with AHE were homozygous for this mutation, 15/41 healthy AH control dogs were heterozygous carriers while 26/41 normal healthy AH dogs were wild type. Furthermore, this mutation was not detected in another 187 dogs of different breeds. These results suggest that this mutation in SLC19A3.1, encoding a thiamine transporter protein, plays a critical role in the pathogenesis of AHE.University of California, Davis. School of Veterinary Medicine. Center for Companion Animal Healt

Diversity of Antibiotic-Active Bacteria Associated with the Brown Alga Laminaria saccharina from the Baltic Sea

Bacteria associated with the marine macroalga Laminaria saccharina, collected from the Kiel Fjord (Baltic Sea, Germany), were isolated and tested for antimicrobial activity. From a total of 210 isolates, 103 strains inhibited the growth of at least one microorganism from the test panel including Gram-negative and Gram-positive bacteria as well as a yeast. Most common profiles were the inhibition of Bacillus subtilis only (30%), B. subtilis and Staphylococcus lentus (25%), and B. subtilis, S. lentus, and Candida albicans (11%). In summary, the antibiotic-active isolates covered 15 different activity patterns suggesting various modes of action. On the basis of 16S rRNA gene sequence similarities >99%, 45 phylotypes were defined, which were classified into 21 genera belonging to Alphaproteobacteria, Betaproteobacteria, Gammaproteobacteria, Bacteroidetes, Firmicutes, and Actinobacteria. Phylogenetic analysis of 16S rRNA gene sequences revealed that four isolates possibly represent novel species or even genera. In conclusion, L. saccharina represents a promising source for the isolation of new bacterial taxa and antimicrobially active bacteria

Host Cell Factors in HIV Replication: Meta-Analysis of Genome-Wide Studies

We have analyzed host cell genes linked to HIV replication that were identified in nine genome-wide studies, including three independent siRNA screens. Overlaps among the siRNA screens were very modest (<7% for any pairwise combination), and similarly, only modest overlaps were seen in pairwise comparisons with other types of genome-wide studies. Combining all genes from the genome-wide studies together with genes reported in the literature to affect HIV yields 2,410 protein-coding genes, or fully 9.5% of all human genes (though of course some of these are false positive calls). Here we report an “encyclopedia” of all overlaps between studies (available at http://www.hostpathogen.org), which yielded a more extensively corroborated set of host factors assisting HIV replication. We used these genes to calculate refined networks that specify cellular subsystems recruited by HIV to assist in replication, and present additional analysis specifying host cell genes that are attractive as potential therapeutic targets

Chromosomal organization at the level of gene complexes

Metazoan genomes primarily consist of non-coding DNA in comparison to coding regions. Non-coding fraction of the genome contains cis-regulatory elements, which ensure that the genetic code is read properly at the right time and space during development. Regulatory elements and their target genes define functional landscapes within the genome, and some developmentally important genes evolve by keeping the genes involved in specification of common organs/tissues in clusters and are termed gene complex. The clustering of genes involved in a common function may help in robust spatio-temporal gene expression. Gene complexes are often found to be evolutionarily conserved, and the classic example is the hox complex. The evolutionary constraints seen among gene complexes provide an ideal model system to understand cis and trans-regulation of gene function. This review will discuss the various characteristics of gene regulatory modules found within gene complexes and how they can be characterized

Prognostic model to predict postoperative acute kidney injury in patients undergoing major gastrointestinal surgery based on a national prospective observational cohort study.

Background: Acute illness, existing co-morbidities and surgical stress response can all contribute to postoperative acute kidney injury (AKI) in patients undergoing major gastrointestinal surgery. The aim of this study was prospectively to develop a pragmatic prognostic model to stratify patients according to risk of developing AKI after major gastrointestinal surgery. Methods: This prospective multicentre cohort study included consecutive adults undergoing elective or emergency gastrointestinal resection, liver resection or stoma reversal in 2-week blocks over a continuous 3-month period. The primary outcome was the rate of AKI within 7 days of surgery. Bootstrap stability was used to select clinically plausible risk factors into the model. Internal model validation was carried out by bootstrap validation. Results: A total of 4544 patients were included across 173 centres in the UK and Ireland. The overall rate of AKI was 14·2 per cent (646 of 4544) and the 30-day mortality rate was 1·8 per cent (84 of 4544). Stage 1 AKI was significantly associated with 30-day mortality (unadjusted odds ratio 7·61, 95 per cent c.i. 4·49 to 12·90; P < 0·001), with increasing odds of death with each AKI stage. Six variables were selected for inclusion in the prognostic model: age, sex, ASA grade, preoperative estimated glomerular filtration rate, planned open surgery and preoperative use of either an angiotensin-converting enzyme inhibitor or an angiotensin receptor blocker. Internal validation demonstrated good model discrimination (c-statistic 0·65). Discussion: Following major gastrointestinal surgery, AKI occurred in one in seven patients. This preoperative prognostic model identified patients at high risk of postoperative AKI. Validation in an independent data set is required to ensure generalizability

“Late-stage” deforestation enhances storm trends in coastal West Africa

Deforestation affects local and regional hydroclimate through changes in heating and moistening of the atmosphere. In the tropics, deforestation leads to warming, but its impact on rainfall is more complex, as it depends on spatial scale and synoptic forcing. Most studies have focused on Amazonia, highlighting that forest edges locally enhance convective rainfall, whereas rainfall decreases over drier, more extensive, deforested regions. Here, we examine Southern West Africa (SWA), an example of “late-stage” deforestation, ongoing since 1900 within a 300-km coastal belt. From three decades of satellite data, we demonstrate that the upward trend in convective activity is strongly modulated by deforestation patterns. The frequency of afternoon storms is enhanced over and downstream of deforested patches on length scales from 16 to 196 km, with greater increases for larger patches. The results are consistent with the triggering of storms by mesoscale circulations due to landscape heterogeneity. Near the coast, where sea breeze convection dominates the diurnal cycle, storm frequency has doubled in deforested areas, attributable to enhanced land–sea thermal contrast. These areas include fast-growing cities such as Freetown and Monrovia, where enhanced storm frequency coincides with high vulnerability to flash flooding. The proximity of the ocean likely explains why ongoing deforestation across SWA continues to increase storminess, as it favors the impact of mesoscale dynamics over moisture availability. The coastal location of deforestation in SWA is typical of many tropical deforestation hotspots, and the processes highlighted here are likely to be of wider global relevance

Newton’s Gravitational Law for Link Prediction in Social Networks

Link prediction is an important research area in network science due to a wide range of real-world application. There are a number of link prediction methods. In the area of social networks, these methods are mostly inspired by social theory, such as having more mutual friends between two people in a social network platform entails higher probability of those two people becoming friends in the future. In this paper we take our inspiration from a different area, which is Newton’s law of universal gravitation. Although this law deals with physical bodies, based on our intuition and empirical results we found that this could also work in networks, and especially in social networks. In order to apply this law, we had to endow nodes with the notion of mass and distance. While node importance could be considered as mass, the shortest path, path count, or inverse similarity (AdamicAdar, Katz score etc.) could be considered as distance. In our analysis, we have primarily used degree centrality to denote the mass of the nodes, while the lengths of shortest paths between them have been used as distances. In this study we compare the proposed link prediction approach to 7 other methods on 4 datasets from various domains. To this end, we use the ROC curves and the AUC measure to compare the methods. As the results show that our approach outperforms the other 7 methods on 2 out of the 4 datasets, we also discuss the potential reasons of the observed behaviour