Modelling how ribavirin improves interferon response rates in hepatitis C virus infection

Nearly 200 million individuals worldwide are currently infected with hepatitis C virus (HCV). Combination therapy with pegylated interferon and ribavirin, the latest treatment for HCV infection, elicits long-term responses in only about 50% of patients treated. No effective alternative treatments exist for non-responders. Consequently, significant efforts are continuing to maximize response to combination therapy. However, rational therapy optimization is precluded by the poor understanding of the mechanism(s) of ribavirin action against HCV. Ribavirin alone induces either a transient early decline or no decrease in HCV viral load, but in combination with interferon it significantly improves long-term response rates. Here we present a model of HCV dynamics in which, on the basis of growing evidence, we assume that ribavirin decreases HCV infectivity in an infected individual in a dose-dependent manner. The model quantitatively predicts long-term response rates to interferon monotherapy and combination therapy, fits observed patterns of HCV RNA decline in patients undergoing therapy, reconciles conflicting observations of the influence of ribavirin on HCV RNA decline, provides key insights into the mechanism of ribavirin action against HCV, and establishes a framework for rational therapy optimization.     

Mutations in a member of the Ras superfamily of small GTP-binding proteins causes Bardet-Biedl syndrome

RAB, ADP-ribosylation factors (ARFs) and ARF-like (ARL) proteins belong to the Ras superfamily of small GTP-binding proteins and are essential for various membrane-associated intracellular trafficking processes. None of the approximately 50 known members of this family are linked to human disease. Using a bioinformatic screen for ciliary genes in combination with mutational analyses, we identified ARL6 as the gene underlying Bardet-Biedl syndrome type 3, a multisystemic disorder characterized by obesity, blindness, polydactyly, renal abnormalities and cognitive impairment. We uncovered four different homozygous substitutions in ARL6 in four unrelated families affected with Bardet-Biedl syndrome, two of which disrupt a threonine residue important for GTP binding and function of several related small GTP-binding proteins. Analysis of the Caenorhabditis elegans ARL6 homolog indicates that it is specifically expressed in ciliated cells, and that, in addition to the postulated cytoplasmic functions of ARL proteins, it undergoes intraflagellar transport. These findings implicate a small GTP-binding protein in ciliary transport and the pathogenesis of a pleiotropic disorder.     

Convergence across biomes to a common rain-use efficiency

Water availability limits plant growth and production in almost all terrestrial ecosystems. However, biomes differ substantially in sensitivity of aboveground net primary production (ANPP) to between-year variation in precipitation. Average rain-use efficiency (RUE; ANPP/precipitation) also varies between biomes, supposedly because of differences in vegetation structure and/or biogeochemical constraints. Here we show that RUE decreases across biomes as mean annual precipitation increases. However, during the driest years at each site, there is convergence to a common maximum RUE (RUE(max)) that is typical of arid ecosystems. RUE(max) was also identified by experimentally altering the degree of limitation by water and other resources. Thus, in years when water is most limiting, deserts, grasslands and forests all exhibit the same rate of biomass production per unit rainfall, despite differences in physiognomy and site-level RUE. Global climate models predict increased between-year variability in precipitation, more frequent extreme drought events, and changes in temperature. Forecasts of future ecosystem behaviour should take into account this convergent feature of terrestrial biomes.     

爱尔兰软件产业

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Late Pliocene Greenland glaciation controlled by a decline in atmospheric CO2 levels

It is thought that the Northern Hemisphere experienced only ephemeral glaciations from the Late Eocene to the Early Pliocene epochs (about 38 to 4 million years ago), and that the onset of extensive glaciations did not occur until about 3 million years ago. Several hypotheses have been proposed to explain this increase in Northern Hemisphere glaciation during the Late Pliocene. Here we use a fully coupled atmosphere-ocean general circulation model and an ice-sheet model to assess the impact of the proposed driving mechanisms for glaciation and the influence of orbital variations on the development of the Greenland ice sheet in particular. We find that Greenland glaciation is mainly controlled by a decrease in atmospheric carbon dioxide during the Late Pliocene. By contrast, our model results suggest that climatic shifts associated with the tectonically driven closure of the Panama seaway, with the termination of a permanent El Ni?o state or with tectonic uplift are not large enough to contribute significantly to the growth of the Greenland ice sheet; moreover, we find that none of these processes acted as a priming mechanism for glacial inception triggered by variations in the Earth's orbit.     

Extinction risk depends strongly on factors contributing to stochasticity

Extinction risk in natural populations depends on stochastic factors that affect individuals, and is estimated by incorporating such factors into stochastic models. Stochasticity can be divided into four categories, which include the probabilistic nature of birth and death at the level of individuals (demographic stochasticity), variation in population-level birth and death rates among times or locations (environmental stochasticity), the sex of individuals and variation in vital rates among individuals within a population (demographic heterogeneity). Mechanistic stochastic models that include all of these factors have not previously been developed to examine their combined effects on extinction risk. Here we derive a family of stochastic Ricker models using different combinations of all these stochastic factors, and show that extinction risk depends strongly on the combination of factors that contribute to stochasticity. Furthermore, we show that only with the full stochastic model can the relative importance of environmental and demographic variability, and therefore extinction risk, be correctly determined. Using the full model, we find that demographic sources of stochasticity are the prominent cause of variability in a laboratory population of Tribolium castaneum (red flour beetle), whereas using only the standard simpler models would lead to the erroneous conclusion that environmental variability dominates. Our results demonstrate that current estimates of extinction risk for natural populations could be greatly underestimated because variability has been mistakenly attributed to the environment rather than the demographic factors described here that entail much higher extinction risk for the same variability level.