Strategic Focus on 3R Principles Reveals Major Reductions in the Use of Animals in Pharmaceutical Toxicity Testing

The principles of the 3Rs, Replacement, Reduction and Refinement, are being increasingly incorporated into legislations, guidelines and practice of animal experiments in order to safeguard animal welfare. In the present study we have studied the systematic application of 3R principles to toxicological research in the pharmaceutical industry, with particular focus on achieving reductions in animal numbers used in regulatory and investigatory in vivo studies. The work also details major factors influencing these reductions including the conception of ideas, cross-departmental working and acceptance into the work process. Data from 36 reduction projects were collected retrospectively from work between 2006 and 2010. Substantial reduction in animal use was achieved by different strategies, including improved study design, method development and project coordination. Major animal savings were shown in both regulatory and investigative safety studies. If a similar (i.e. 53%) reduction had been achieved simultaneously within the twelve largest pharmaceutical companies, the equivalent reduction world-wide would be about 150,000 rats annually. The results point at the importance of a strong 3R culture, with scientific engagement, collaboration and a responsive management being vital components. A strong commitment in leadership for the 3R is recommended to be translated into cross-department and inter-profession involvement in projects for innovation, validation and implementation. Synergies between all the three Rs are observed and conclude that in silico-, in vitro- and in vivo-methods all hold the potential for applying the reduction R and should be consequently coordinated at a strategic level.     

Andes Hantavirus-Infection of a 3D Human Lung Tissue Model Reveals a Late Peak in Progeny Virus Production Followed by Increased Levels of Proinflammatory Cytokines and VEGF-A

Andes virus (ANDV) causes hantavirus pulmonary syndrome (HPS), a severe acute disease with a 40% case fatality rate. Humans are infected via inhalation, and the lungs are severely affected during HPS, but little is known regarding the effects of ANDV-infection of the lung. Using a 3-dimensional air-exposed organotypic human lung tissue model, we analyzed progeny virus production and cytokine-responses after ANDV-infection. After a 7–10 day period of low progeny virus production, a sudden peak in progeny virus levels was observed during approximately one week. This peak in ANDV-production coincided in time with activation of innate immune responses, as shown by induction of type I and III interferons and ISG56. After the peak in ANDV production a low, but stable, level of ANDV progeny was observed until 39 days after infection. Compared to uninfected models, ANDV caused long-term elevated levels of eotaxin-1, IL-6, IL-8, IP-10, and VEGF-A that peaked 20–25 days after infection, i.e., after the observed peak in progeny virus production. Notably, eotaxin-1 was only detected in supernatants from infected models. In conclusion, these findings suggest that ANDV replication in lung tissue elicits a late proinflammatory immune response with possible long-term effects on the local lung cytokine milieu. The change from an innate to a proinflammatory response might be important for the transition from initial asymptomatic infection to severe clinical disease, HPS.     

Clumpak: a program for identifying clustering modes and packaging population structure inferences across K

The identification of the genetic structure of populations from multilocus genotype data has become a central component of modern population‐genetic data analysis. Application of model‐based clustering programs often entails a number of steps, in which the user considers different modelling assumptions, compares results across different predetermined values of the number of assumed clusters (a parameter typically denoted K), examines multiple independent runs for each fixed value of K, and distinguishes among runs belonging to substantially distinct clustering solutions. Here, we present Clumpak (Cluster Markov Packager Across K), a method that automates the postprocessing of results of model‐based population structure analyses. For analysing multiple independent runs at a single K value, Clumpak identifies sets of highly similar runs, separating distinct groups of runs that represent distinct modes in the space of possible solutions. This procedure, which generates a consensus solution for each distinct mode, is performed by the use of a Markov clustering algorithm that relies on a similarity matrix between replicate runs, as computed by the software Clumpp . Next, Clumpak identifies an optimal alignment of inferred clusters across different values of K, extending a similar approach implemented for a fixed K in Clumpp and simplifying the comparison of clustering results across different K values. Clumpak incorporates additional features, such as implementations of methods for choosing K and comparing solutions obtained by different programs, models, or data subsets. Clumpak , available at http://clumpak.tau.ac.il , simplifies the use of model‐based analyses of population structure in population genetics and molecular ecology.     

Antibacterial activity of the contact and complement systems is blocked by SIC, a protein secreted by Streptococcus pyogenes

Recent studies have shown that activation of complement and contact systems results in the generation of antibacterial peptides. Streptococcus pyogenes, a major bacterial pathogen in humans, exists in >100 different serotypes due to sequence variation in the surface-associated M protein. Cases of invasive and life-threatening S. pyogenes infections are commonly associated with isolates of the M1 serotype, and in contrast to the large majority of M serotypes, M1 isolates all secrete the SIC protein. Here, we show that SIC interferes with the activation of the contact system and blocks the activity of antibacterial peptides generated through complement and contact activation. This effect promotes the growth of S. pyogenes in human plasma, and in a mouse model of S. pyogenes sepsis, SIC enhances bacterial dissemination, results which help explain the high frequency of severe S. pyogenes infections caused by isolates of the M1 serotype.     

Evaluation of microarray data normalization procedures using spike-in experiments

Background  

Recently, a large number of methods for the analysis of microarray data have been proposed but there are few comparisons of their relative performances. By using so-called spike-in experiments, it is possible to characterize the analyzed data and thereby enable comparisons of different analysis methods.     

Joint analysis of demography and selection in population genetics: where do we stand and where could we go?

Teasing apart the effects of selection and demography on genetic polymorphism remains one of the major challenges in the analysis of population genomic data. The traditional approach has been to assume that demography would leave a genome-wide signature, whereas the effect of selection would be local. In the light of recent genomic surveys of sequence polymorphism, several authors have argued that this approach is questionable based on the evidence of the pervasive role of positive selection and that new approaches are needed. In the first part of this review, we give a few empirical and theoretical examples illustrating the difficulty in teasing apart the effects of selection and demography on genomic polymorphism patterns. In the second part, we review recent efforts to detect recent positive selection. Most available methods still rely on an a priori classification of sites in the genome but there are many promising new approaches. These new methods make use of the latest developments in statistics, explore aspects of the data that had been neglected hitherto or take advantage of the emerging population genomic data. A current and promising approach is based on first estimating demographic and genetic parameters, using, e.g., a likelihood or approximate Bayesian computation framework, focusing on extreme outlier regions, and then using an independent method to confirm these. Finally, especially for species where evidence of natural selection has been limited, more experimental and versatile approaches that contrast populations under varied environmental constraints might be more successful compared with species-wide genome scans in search of specific signatures.