So, let's see what's new in PLoS Genetics, PLoS Computational Biology, PLoS Pathogens and PLoS Neglected Tropical Diseases this week. As always, you should rate the articles, post notes and comments and send trackbacks when you blog about the papers. Here are my own picks for the week - you go and look for your own favourites:
Is Mate Choice in Humans MHC-Dependent?:
There has been a longstanding hypothesis that selection may have led to mating patterns that encourage heterozygosity at Major Histocompatibility Complex (MHC) loci because of improved immune response to pathogens in the offspring of such matings, and, indeed, this has been observed in several model systems. However, in humans, previous studies regarding the role of the MHC in mate choice or preference, both directly in couples and also indirectly in "sweaty T-shirts" experiments, have reported conflicting results. Here, by using genome-wide genotype data and HLA types in African and European American couples, we test whether humans tend to choose MHC-dissimilar mates. This approach allows us to distinguish MHC-specific effects from genome-wide effects. In the African sample, the patterns at MHC loci is confounded by genome-wide effects, possibly resulting from demographic processes relating to the social organization of this population, and no tendency to choose MHC-dissimilar mates is detected. On the other hand, the sampled European Americans appear to have favoured MHC-dissimilar mates, supporting the hypothesis that MHC influences mate choice in some human populations. Thus, this study suggests that, in some cases, humans may rely on biological factors, in addition to social factors, when choosing a mate.
Evolution of a New Function by Degenerative Mutation in Cephalochordate Steroid Receptors:
Most genes evolved by duplication of more ancient genes. Under existing models of this process, mutations that compromise one copy have no effect on the other; as long as one copy remains intact, such "degenerative" mutations are shielded from selection. Because degenerative mutations are common, most duplicates are expected to be disabled before new functions can evolve. The great functional diversity of genes is therefore somewhat puzzling. Here, we reconstruct how simple degenerative mutations produced a new function in the steroid hormone receptors (SRs), a gene family crucial to reproduction and development. We characterized the two SRs of B. floridae, a cephalochordate that diverged from vertebrates ~500 million years ago, just after the ancestral SR duplicated. One retained the ancestral gene's estrogen receptor-like functions, while the other evolved a new function as a competitive repressor of the first. Either of two historical mutations is sufficient to recapitulate evolution of this function by disabling the receptor's response to estrogen, but leaving its DNA-binding capacity intact. Our results suggest that, for the many genes that function by specifically interacting with other molecules, simple mutations can yield novel functions that, beneficial or deleterious, are exposed to selection.
Sizing Up Allometric Scaling Theory:
The rate at which an organism produces energy to live increases with body mass to the 3/4 power. Ten years ago West, Brown, and Enquist posited that this empirical relationship arises from the structure and dynamics of resource distribution networks such as the cardiovascular system. Using assumptions that capture physical and biological constraints, they defined a vascular network model that predicts a 3/4 scaling exponent. In our paper we clarify that this model generates the 3/4 exponent only in the limit of infinitely large organisms. Our calculations indicate that in the finite-size version of the model metabolic rate and body mass are not related by a pure power law, which we show is consistent with available data. We also show that this causes the model to produce scaling exponents significantly larger than the observed 3/4. We investigate how changes in certain assumptions about network structure affect the scaling exponent, leading us to identify discrepancies between available data and the predictions of the finite-size model. This suggests that the model, the data, or both, need reassessment. The challenge lies in pinpointing the physiological and evolutionary factors that constrain the shape of networks driving metabolic scaling.
Top-Down Analysis of Temporal Hierarchy in Biochemical Reaction Networks:
Cellular metabolism describes the complex web of biochemical transformations that are necessary to build the structural components, to convert nutrients into "usable energy" by the cell, and to degrade or excrete the by-products. A critical aspect toward understanding metabolism is the set of dynamic interactions between metabolites, some of which occur very quickly while others occur more slowly. To develop a "systems" understanding of how networks operate dynamically we need to identify the different processes that occur on different time scales. When one moves from very fast time scales to slower ones, certain components in the network move in concert and pool together. We develop a method to elucidate the time scale hierarchy of a network and to simplify its structure by identifying these pools. This is applied to dynamic models of metabolism for the human red blood cell, human folate metabolism, and yeast glycolysis. It was possible to simplify the structure of these networks into biologically meaningful groups of variables. Because dynamics play important roles in normal and abnormal function in biology, it is expected that this work will contribute to an area of great relevance for human disease and engineering applications.
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