Transcriptomes from German shepherd dogs reveal differences in immune activity between atopic dermatitis affected and control skin

Canine atopic dermatitis (CAD) is an inflammatory and pruritic allergic skin disease with both genetic and environmental risk factors described. We performed mRNA sequencing of non-lesional axillary skin biopsies from nine German shepherd dogs. Obtained RNA sequences were mapped to the dog genome (CanFam3.1) and a high-quality skin transcriptome was generated with 23,510 expressed gene transcripts. Differentially expressed genes (DEGs) were defined by comparing three controls to five treated CAD cases. Using a leave-one-out analysis, we identified seven DEGs: five known to encode proteins with functions related to an activated immune system (CD209, CLEC4G, LOC102156842 (lipopolysaccharide-binding protein-like), LOC480601 (regakine-1-like), LOC479668 (haptoglobin-like)), one (OBP) encoding an odorant-binding protein potentially connected to rhinitis, and the last (LOC607095) encoding a novel long non-coding RNA. Furthermore, high mRNA expression of inflammatory genes was found in axillary skin from an untreated mild CAD case compared with healthy skin. In conclusion, we define genes with different expression patterns in CAD case skin helping us understand post-treatment atopic skin. Further studies in larger sample sets are warranted to confirm and to transfer these results into clinical practice

MAPfastR: Quantitative Trait Loci Mapping in Outbred Line Crosses

MAPfastR is a software package developed to analyze quantitative trait loci data from inbred and outbred line-crosses. The package includes a number of modules for fast and accurate quantitative trait loci analyses. It has been developed in the R language for fast and comprehensive analyses of large datasets. MAPfastR is freely available at: http://www.computationalgenetics.se/?page_id=7.Swedish Foundation for Strategic Research (Future Research Leader program), European Science Foundation (EURYI Award)

Genomic insights into the conservation status of the world’s last remaining Sumatran rhinoceros populations

Highly endangered species like the Sumatran rhinoceros are at risk from inbreeding. Five historical and 16 modern genomes from across the species range show mutational load, but little evidence for local adaptation, suggesting that future inbreeding depression could be mitigated by assisted gene flow among populations. Small populations are often exposed to high inbreeding and mutational load that can increase the risk of extinction. The Sumatran rhinoceros was widespread in Southeast Asia, but is now restricted to small and isolated populations on Sumatra and Borneo, and most likely extinct on the Malay Peninsula. Here, we analyse 5 historical and 16 modern genomes from these populations to investigate the genomic consequences of the recent decline, such as increased inbreeding and mutational load. We find that the Malay Peninsula population experienced increased inbreeding shortly before extirpation, which possibly was accompanied by purging. The populations on Sumatra and Borneo instead show low inbreeding, but high mutational load. The currently small population sizes may thus in the near future lead to inbreeding depression. Moreover, we find little evidence for differences in local adaptation among populations, suggesting that future inbreeding depression could potentially be mitigated by assisted gene flow among populations

Genetic barriers to historical gene flow between cryptic species of alpine bumblebees revealed by comparative population genomics

Evidence is accumulating that gene flow commonly occurs between recently diverged species, despite the existence of barriers to gene flow in their genomes. However, we still know little about what regions of the genome become barriers to gene flow and how such barriers form. Here, we compare genetic differentiation across the genomes of bumblebee species living in sympatry and allopatry to reveal the potential impact of gene flow during species divergence and uncover genetic barrier loci. We first compared the genomes of the alpine bumblebee Bombus sylvicola and a previously unidentified sister species living in sympatry in the Rocky Mountains, revealing prominent islands of elevated genetic divergence in the genome that colocalize with centromeres and regions of low recombination. This same pattern is observed between the genomes of another pair of closely related species living in allopatry (B. bifarius and B. vancouverensis). Strikingly however, the genomic islands exhibit significantly elevated absolute divergence (dXY) in the sympatric, but not the allopatric, comparison indicating that they contain loci that have acted as barriers to historical gene flow in sympatry. Our results suggest that intrinsic barriers to gene flow between species may often accumulate in regions of low recombination and near centromeres through processes such as genetic hitchhiking, and that divergence in these regions is accentuated in the presence of gene flow

Machine learning on normalized protein sequences

<p>Abstract</p> <p>Background</p> <p>Machine learning techniques have been widely applied to biological sequences, e.g. to predict drug resistance in HIV-1 from sequences of drug target proteins and protein functional classes. As deletions and insertions are frequent in biological sequences, a major limitation of current methods is the inability to handle varying sequence lengths.</p> <p>Findings</p> <p>We propose to normalize sequences to uniform length. To this end, we tested one linear and four different non-linear interpolation methods for the normalization of sequence lengths of 19 classification datasets. Classification tasks included prediction of HIV-1 drug resistance from drug target sequences and sequence-based prediction of protein function. We applied random forests to the classification of sequences into "positive" and "negative" samples. Statistical tests showed that the linear interpolation outperforms the non-linear interpolation methods in most of the analyzed datasets, while in a few cases non-linear methods had a small but significant advantage. Compared to other published methods, our prediction scheme leads to an improvement in prediction accuracy by up to 14%.</p> <p>Conclusions</p> <p>We found that machine learning on sequences normalized by simple linear interpolation gave better or at least competitive results compared to state-of-the-art procedures, and thus, is a promising alternative to existing methods, especially for protein sequences of variable length.</p

Improved Bevirimat resistance prediction by combination of structural and sequence-based classifiers

<p>Abstract</p> <p>Background</p> <p>Maturation inhibitors such as Bevirimat are a new class of antiretroviral drugs that hamper the cleavage of HIV-1 proteins into their functional active forms. They bind to these preproteins and inhibit their cleavage by the HIV-1 protease, resulting in non-functional virus particles. Nevertheless, there exist mutations in this region leading to resistance against Bevirimat. Highly specific and accurate tools to predict resistance to maturation inhibitors can help to identify patients, who might benefit from the usage of these new drugs.</p> <p>Results</p> <p>We tested several methods to improve Bevirimat resistance prediction in HIV-1. It turned out that combining structural and sequence-based information in classifier ensembles led to accurate and reliable predictions. Moreover, we were able to identify the most crucial regions for Bevirimat resistance computationally, which are in line with experimental results from other studies.</p> <p>Conclusions</p> <p>Our analysis demonstrated the use of machine learning techniques to predict HIV-1 resistance against maturation inhibitors such as Bevirimat. New maturation inhibitors are already under development and might enlarge the arsenal of antiretroviral drugs in the future. Thus, accurate prediction tools are very useful to enable a personalized therapy.</p

Effect of Breed on Plasma Endothelin-1 Concentration, Plasma Renin Activity, and Serum Cortisol Concentration in Healthy Dogs

BackgroundThere are breed differences in several blood variables in healthy dogs. ObjectiveInvestigate breed variation in plasma endothelin-1 (ET-1) concentration, plasma renin activity, and serum cortisol concentration. AnimalsFive-hundred and thirty-one healthy dogs of 9 breeds examined at 5 centers (2-4 breeds/center). MethodsProspective observational study. Circulating concentrations of ET-1 and cortisol, and renin activity, were measured using commercially available assays. Absence of organ-related or systemic disease was ensured by thorough clinical investigations, including blood pressure measurement, echocardiography, ECG, blood and urine analysis. ResultsMedian ET-1 concentration was 1.29 (interquartile range [IQR], 0.97-1.82) pg/mL, median cortisol concentration 46.0 (IQR, 29.0-80.8) nmol/L, and median renin activity 0.73 (IQR, 0.48-1.10) ng/mL/h in all dogs. Overall, breed differences were found in ET-1 and cortisol concentrations, and renin activity (P <.0001 for all). Pair-wise comparisons between breeds differed in 67% of comparisons for ET-1, 22% for cortisol, and 19% for renin activity, respectively. Within centers, breed differences were found at 5/5 centers for ET-1, 4/5 centers for cortisol, and 2/5 centers for renin activity. Newfoundlands had highest median ET-1 concentration, 3 times higher than Cavalier King Charles Spaniels, Doberman Pinschers, and Dachshunds. Median renin activity was highest in Dachshunds, twice the median value in Newfoundlands and Boxers. Median cortisol concentration was highest in Finnish Lapphunds, almost 3 times higher than in Boxers. Conclusions and Clinical ImportanceBreed variation might be important to take into consideration when interpreting test results in clinical studies.Peer reviewe

Geochemical assessment of metal transfer from rock and soil to water in serpentine areas of Sabah (Malaysia)

The mobility of metals in ultramafic rock–soil systems and metal contamination in serpentine soils were investigated from the Ranau area in Sabah, East Malaysia. Metal concentrations were analysed after division into seven operationally defined fractions by selective sequential extraction (SSE). Geochemical studies showed that the soils are exceptionally high in Cr (95%) residing in refractory residual fractions. Metal speciation studies will shed further light on toxicities in the Malaysian ultramafic tropical environment, reconciled against elemental metal tenure, adopted by common standards