The synergistic effects of stochasticity and dispersal on population densities

Using laboratory experiments, simulation models, and analytical techniques, we examined the impact of dispersal on the mean densities of patchily distributed populations. Even when dispersal leads to no net additions or removals of individuals from a population, it may nonetheless increase mean population densities if the net immigration rate is positive when populations are growing and negative when they are declining. As a model system for exploring this phenomenon, we used the yeastlike fungus Aureobasidium pullulans. In a laboratory experiment, we showed that dispersal can both ensure persistence and increase mean population densities even when dispersal among populations causes no direct addition or loss of fungal cells. From the laboratory data, we constructed a plausible model of A. pullulans dynamics among apple leaves within an orchard. This simulation model demonstrated that the effect of dispersal on mean densities is enhanced by three factors: weak density dependence of the dynamics within populations, high environmental variability affecting population growth rates, and lack of synchrony among the fluctuations of populations. Using an analytical model, we showed that the underlying mechanisms for this phenomenon are general, suggesting that a large effect of dispersal on mean population densities is possible in many natural systems.     

Multiple paternity in a philopatric rodent: the interaction of competition and choice

Paternity confusion is often suggested as the benefit that femalemammals accrue by mating with multiple males, but genetic advantagesare also possible. Microsatellite-based parentage analyses demonstratethat female banner-tailed kangaroo rats (Dipodomys spectabilis)commonly mate with more than one male; we asked how male andfemale behaviors interact to influence the characteristics ofmales that sire offspring. Specifically, we compared attributes(age, weight, mobility, relatedness, proximity) of the fathersof 229 known-maternity offspring with those of the other malesaccessible to the mothers. Adult males living adjacent to eachfemale attempt to monopolize access to her, and the nearestmale sires a plurality of offspring, but most mothers' youngare fathered by more than one male and littermates are usuallyhalf-sibs. Male proximity and mobility, but not size, influencethe probability of paternity, suggesting a role for competitivemate searching. Females significantly reduce the inbreedingcoefficient of their offspring by mating with males other than(or in addition to) the nearest male. Fathers are less closelyrelated to the mother in years of high density when unrelatedmales are more accessible to the female. Our results favor thegenetic "bet-hedging" hypothesis, whereby females actively butunselectively seek matings with additional males when the malemost likely to win in mate competition is costly to her (inthis case, genetically less compatible). We anticipate thatgenetic bet hedging will be common in species whose femalesare defendable, especially if they are also philopatric.     

Thermodynamic analysis of water molecules at the surface of proteins and applications to binding site prediction and characterization

Water plays an essential role in determining the structure and function of all biological systems. Recent methodological advances allow for an accurate and efficient estimation of the thermodynamic properties of water molecules at the surface of proteins. In this work, we characterize these thermodynamic properties and relate them to various structural and functional characteristics of the protein. We find that high-energy hydration sites often exist near protein motifs typically characterized as hydrophilic, such as backbone amide groups. We also find that waters around alpha helices and beta sheets tend to be less stable than waters around loops. Furthermore, we find no significant correlation between the hydration site-free energy and the solvent accessible surface area of the site. In addition, we find that the distribution of high-energy hydration sites on the protein surface can be used to identify the location of binding sites and that binding sites of druggable targets tend to have a greater density of thermodynamically unstable hydration sites. Using this information, we characterize the FKBP12 protein and show good agreement between fragment screening hit rates from NMR spectroscopy and hydration site energetics. Finally, we show that water molecules observed in crystal structures are less stable on average than bulk water as a consequence of the high degree of spatial localization, thereby resulting in a significant loss in entropy. These findings should help to better understand the characteristics of waters at the surface of proteins and are expected to lead to insights that can guide structure-based drug design efforts.     

Multivariate classification analysis of metabolomic data for candidate biomarker discovery in type 2 diabetes mellitus

Recent advances in genomics, metabolomics and proteomics have made it possible to interrogate disease pathophysiology and drug response on a systems level. The analysis and interpretation of the complex data obtained using these techniques is potentially fertile but equally challenging. We conducted a small clinical trial to explore the application of metabolomics data in candidate biomarker discovery. Specifically, serum and urine samples from patients with type 2 diabetes mellitus (T2DM) were profiled on metabolomics platforms before and after 8 weeks of treatment with one of three commonly used oral antidiabetic agents, the sulfonyurea glyburide, the biguanide metformin, or the thiazolidinedione rosiglitazone. Multivariate classification techniques were used to detect serum or urine analytes, obtained at baseline (pre-treatment) that could predict a significant treatment response after 8 weeks. Using this approach, we identified three analytes, measured at baseline, that were associated with response to a thiazolidinedione after 8 weeks of treatment. Although larger and longer-term studies are required to validate any of the candidate biomarkers, pharmacometabolomic profiling, in combination with multivariate classification, is worthy of further exploration as an adjunct to clinical decision making regarding treatment selection and for patient stratification within clinical trials. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Influenza H5N1 virus infection of polarized human alveolar epithelial cells and lung microvascular endothelial cells

Background

Highly pathogenic avian influenza (HPAI) H5N1 virus is entrenched in poultry in Asia and Africa and continues to infect humans zoonotically causing acute respiratory disease syndrome and death. There is evidence that the virus may sometimes spread beyond respiratory tract to cause disseminated infection. The primary target cell for HPAI H5N1 virus in human lung is the alveolar epithelial cell. Alveolar epithelium and its adjacent lung microvascular endothelium form host barriers to the initiation of infection and dissemination of influenza H5N1 infection in humans. These are polarized cells and the polarity of influenza virus entry and egress as well as the secretion of cytokines and chemokines from the virus infected cells are likely to be central to the pathogenesis of human H5N1 disease.

Aim

To study influenza A (H5N1) virus replication and host innate immune responses in polarized primary human alveolar epithelial cells and lung microvascular endothelial cells and its relevance to the pathogenesis of human H5N1 disease.

Methods

We use an in vitro model of polarized primary human alveolar epithelial cells and lung microvascular endothelial cells grown in transwell culture inserts to compare infection with influenza A subtype H1N1 and H5N1 viruses via the apical or basolateral surfaces.

Results

We demonstrate that both influenza H1N1 and H5N1 viruses efficiently infect alveolar epithelial cells from both apical and basolateral surface of the epithelium but release of newly formed virus is mainly from the apical side of the epithelium. In contrast, influenza H5N1 virus, but not H1N1 virus, efficiently infected polarized microvascular endothelial cells from both apical and basolateral aspects. This provides a mechanistic explanation for how H5N1 virus may infect the lung from systemic circulation. Epidemiological evidence has implicated ingestion of virus-contaminated foods as the source of infection in some instances and our data suggests that viremia, secondary to, for example, gastro-intestinal infection, can potentially lead to infection of the lung. HPAI H5N1 virus was a more potent inducer of cytokines (e.g. IP-10, RANTES, IL-6) in comparison to H1N1 virus in alveolar epithelial cells, and these virus-induced chemokines were secreted onto both the apical and basolateral aspects of the polarized alveolar epithelium.

Conclusion

The predilection of viruses for different routes of entry and egress from the infected cell is important in understanding the pathogenesis of influenza H5N1 infection and may help unravel the pathogenesis of human H5N1 disease.     

Comparison of small n statistical tests of differential expression applied to microarrays

Background  

DNA microarrays provide data for genome wide patterns of expression between observation classes. Microarray studies often have small samples sizes, however, due to cost constraints or specimen availability. This can lead to poor random error estimates and inaccurate statistical tests of differential expression. We compare the performance of the standard t-test, fold change, and four small n statistical test methods designed to circumvent these problems. We report results of various normalization methods for empirical microarray data and of various random error models for simulated data.     

Physics‐based enzyme design: Predicting binding affinity and catalytic activity

Computational enzyme design is an emerging field that has yielded promising success stories, but where numerous challenges remain. Accurate methods to rapidly evaluate possible enzyme design variants could provide significant value when combined with experimental efforts by reducing the number of variants needed to be synthesized and speeding the time to reach the desired endpoint of the design. To that end, extending our computational methods to model the fundamental physical–chemical principles that regulate activity in a protocol that is automated and accessible to a broad population of enzyme design researchers is essential. Here, we apply a physics‐based implicit solvent MM‐GBSA scoring approach to enzyme design and benchmark the computational predictions against experimentally determined activities. Specifically, we evaluate the ability of MM‐GBSA to predict changes in affinity for a steroid binder protein, catalytic turnover for a Kemp eliminase, and catalytic activity for α‐Gliadin peptidase variants. Using the enzyme design framework developed here, we accurately rank the most experimentally active enzyme variants, suggesting that this approach could provide enrichment of active variants in real‐world enzyme design applications. Proteins 2014; 82:3397–3409. © 2014 Wiley Periodicals, Inc.