I was busy, so I did not have time to take a look until now at what is new this week in PLoS Genetics, Computational Biology, Pathogens and Neglected Tropical Diseases. Go take a look at these papers and others:
A cell receives signals both from its internal and external environment and responds by changing the expression of genes. In this manner the cell adjusts to heat, osmotic pressures and other circumstances during its lifetime. Over long timescales, the network of interacting genes and its regulatory actions also undergo evolutionary adaptation. Yet how do such networks evolve and become adapted?
In this paper we describe the study of a simple model of gene regulatory networks, focusing solely on evolutionary adaptation. We let a population of individuals evolve, while the external environment changes through time. To ensure evolution is the only source of adaptation, we do not provide the individuals with a sensor to the environment. We show that the interplay between the long-term process of evolution and short-term gene regulation dynamics leads to a striking increase in the efficiency of creating well-adapted offspring. Beneficial mutations become more frequent, nevertheless robustness to the majority of mutations is maintained. Thus we demonstrate a clear example of the evolution of evolvability.
Highly pathogenic avian influenza A viruses of H5N1 subtype have spread through Eurasia and Africa with continuing cases of human infection, suggesting the potential to become a pandemic influenza virus. Pigs which are susceptible to infection with both human and avian influenza A viruses are one of the natural hosts where a pandemic virus could be produced. In this study, we characterized in a pig model the infection caused by four H5N1 virus strains isolated from humans, poultry and wild birds. We demonstrated that exposure of pigs through the nose with H5N1 viruses or consumption of meat from infected chickens resulted in infection with mild weight loss. In contrast to mouse and ferret animal models where some of viruses were highly pathogenic and replicated in multiple organs, replication of H5N1 viruses in pigs was restricted to the respiratory tract, mainly to the lungs, and tonsils. Mild to moderate bronchiolitis and pneumonia were observed in the lungs of infected animals. Our results demonstrated that domestic pigs had low susceptibility to infection and disease with highly pathogenic H5N1 influenza A viruses.
To understand chemical-induced biological responses/effects, it is important to have large-scale and rapid capacity to investigate gene expression changes caused by chemical compounds at genome-wide scale in an adult vertebrate model; this capability is essential for drug development and toxicology. Small aquarium fish with vast genomic resources, such as zebrafish, will probably be the only vertebrate models that allow for cost-effective, large-scale, genome-wide determination of gene expression net changes in the entire adult organism in response to a chemical compound. Presently, such a whole adult organism approach is only feasible in invertebrate models such as the worm and fly, and not in rodent models, hence the usefulness of such an approach has not been demonstrated in a vertebrate. By using two classes of chemicals with wide implications to human health, we showed that capturing net changes of gene expression at a genome-wide scale in an entire adult zebrafish is useful for predicting toxicity and chemical classes, for discovering biomarkers and major signaling pathways, as well as for inferring human health risk and new biological insights. Our study provides a new approach for genome-wide investigation of chemical-induced biological responses/effects in a whole adult vertebrate that can benefit the drug discovery process and chemical toxicity testing for environmental health risk inference.
Networks play a crucial role in biology and are often used as a way to represent experimental results. Yet, their analysis and representation is still an open problem. Recent experimental and computational progress yields networks of increased size and complexity. There are, for example, small- and large-scale interaction networks, regulatory networks, genetic networks, protein-ligand interaction networks, and homology networks analyzed and published regularly. A common way to access the information in a network is though direct visualization, but this fails as it often just results in “fur balls” from which little insight can be gathered. On the other hand, clustering techniques manage to avoid the problems caused by the large number of nodes and even larger number of edges by coarse-graining the networks and thus abstracting details. But these also fail, since, in fact, much of the biology lies in the details. This work presents a novel methodology for analyzing and representing networks. Power Graphs are a lossless representation of networks, which reduces network complexity by explicitly representing re-occurring network motifs. Moreover, power graphs can be clearly visualized: they compress up to 90% of the edges in biological networks and are applicable to all types of networks such as protein interaction, regulatory networks, or homology networks.