Stochastic exits from dormancy give rise to heavy‐tailed distributions of descendants in bacterial populations

Variance in reproductive success is a major determinant of the degree of genetic drift in a population. While many plants and animals exhibit high variance in their number of progeny, far less is known about these distributions for microorganisms. Here, we used a strain barcoding approach to quantify variability in offspring number among replicate bacterial populations and developed a Bayesian method to infer the distribution of descendants from this variability. We applied our approach to measure the offspring distributions for five strains of bacteria from the genus Streptomyces after germination and growth in a homogenous laboratory environment. The distributions of descendants were heavy‐tailed, with a few cells effectively ‘winning the jackpot’ to become a disproportionately large fraction of the population. This extreme variability in reproductive success largely traced back to initial populations of spores stochastically exiting dormancy, which provided early‐germinating spores with an exponential advantage. In simulations with multiple dormancy cycles, heavy‐tailed distributions of descendants decreased the effective population size by many orders of magnitude and led to allele dynamics differing substantially from classical population genetics models with matching effective population size. Collectively, these results demonstrate that extreme variability in reproductive success can occur even in growth conditions that are far more homogeneous than the natural environment. Thus, extreme variability in reproductive success might be an important factor shaping microbial population dynamics with implications for predicting the fate of beneficial mutations, interpreting sequence variability within populations and explaining variability in infection outcomes across patients.     

Prevalence, whole genome characterization and phylogenetic analysis of hepatitis B virus in captive orangutan and gibbon

Background Hepatitis B virus (HBV) is a public health problem worldwide and apart from infecting humans, HBV has been found in non‐human primates. Methods We subjected 93 non‐human primates comprising 12 species to ELISA screening for the serological markers HBsAg, antiHBs and antiHBc. Subsequently, we detected HBV DNA, sequenced the whole HBV genome and performed phylogenetic analysis. Results HBV infection was detected in gibbon (4/15) and orangutan (7/53). HBV DNA isolates from two gibbons and seven orangutans were chosen for complete genome amplification. We aligned the Pre‐S/S, Pre‐C/C and entire genomes with HBV sequences and performed phylogenetic analysis. The gibbon and orangutan viruses clustered within their respective groups. Conclusions Both geographic location and host species influence which HBV variants are found in gibbons and orangutans. Hence, HBV transmission between humans and non‐human primates might be a distinct possibility and additional studies will be required to further investigate this potential risk.     

The formation of titan cells in Cryptococcus neoformans depends on the mouse strain and correlates with induction of Th2‐type responses

Cryptococcus neoformans is a pathogenic yeast that can form titan cells in the lungs, which are fungal cells of abnormal enlarged size. Little is known about the factors that trigger titan cells. In particular, it is not known how the host environment influences this transition. In this work, we describe the formation of titan cells in two mouse strains, CD1 and C57BL/6J. We found that the proportion of C. neoformans titan cells was significantly higher in C57BL/6J mice than in CD1. This higher proportion of titan cells was associated with a higher dissemination of the yeasts to the brain. Histology sections demonstrated eosinophilia in infected animals, although it was significantly lower in the CD1 mice which presented infiltration of lymphocytes. Both mouse strains presented infiltration of granulocytes, but the amount of eosinophils was higher in C57BL/6J. CD1 mice showed a significant accumulation of IFN‐γ, TNF‐α and IL17, while C57BL/BL mice had an increase in the anti‐inflammatory cytokine IL‐4. IgM antibodies to the polysaccharide capsule and total IgE were more abundant in the sera from C57BL/6J, confirming that these animals present a Th2‐type response. We conclude that titan cell formation in C. neoformans depends, not only on microbe factors, but also on the host environment.     

Construct validity of the Short Form-36 Health Survey and its relationship with BMI in obese outpatients

Objective: To investigate the construct validity of the Short Form‐36 (SF‐36) Health Survey questionnaire in obese patients. Research Methods and Procedures: Our series consisted of 1735 obese patients (age, 44.7 ± 11.0 years; 1346 women) consecutively enrolled in the QUOVADIS study, an observational multicenter study of obese treatment‐seeking outpatients. The construct validity of the SF‐36 was assessed by main component analysis. Age‐, gender‐, and education‐adjusted general linear models were used to investigate the relationship between BMI and SF‐36 domains or factors identified by main component analysis. Results: BMI was significantly associated with poor health‐related quality of life in all eight SF‐36 domains, and the strongest association was observed with physical activity. Main components analysis generated a six‐factor solution explaining 59% of the observed variance. BMI was strongly associated with factors based on the loading of items regarding the physical activity domain and factors based on role‐physical and role‐emotional items or general health and bodily pain items. In contrast, mental health‐, vitality‐, and social functioning‐based factors were not related to BMI. Discussion: In obese treatment‐seeking outpatients, the clustering of SF‐36 items in main components is not significantly different from the domain‐based approach generally used, thus confirming the robustness of such a generic questionnaire in this specific condition. However, the peculiar clustering of some SF‐36 items and their relationship with BMI suggest that the health‐related quality of life profile of subjects belonging to that population may be better described with alternative aggregations of the SF‐36 items or with disease‐tailored questionnaires.     

Validating and improving elastic network models with molecular dynamics simulations

Elastic network models (ENMs) are a class of simple models intended to represent the collective motions of proteins. In contrast to all‐atom molecular dynamics simulations, the low computational investment required to use an ENM makes them ideal for speculative hypothesis‐testing situations. Historically, ENMs have been validated via comparison to crystallographic B‐factors, but this comparison is relatively low‐resolution and only tests the predictions of relative flexibility. In this work, we systematically validate and optimize a number of ENM‐type models by quantitatively comparing their predictions to microsecond‐scale all‐atom simulations of three different G protein coupled receptors. We show that, despite their apparent simplicity, well‐optimized ENMs perform remarkably well, reproducing the protein fluctuations with an accuracy comparable to what one would expect from all‐atom simulations run for several hundred nanoseconds. Proteins 2010. © 2010 Wiley‐Liss, Inc.     

Prenatal data impacts common bottlenose dolphin (Tursiops truncatus) growth parameters estimated by length‐at‐age curves

Compilation of marine mammal demographic data is central to management efforts. However, marine mammal length‐at‐age growth curves demonstrate limitations. Physiological growth parameters of terrestrial mammals are typically estimated using curvilinear models fit to size‐at‐age data along a time series from conception to senescence. The difficulty of collecting and aging prenatal cetaceans is addressed here, and growth parameters of common bottlenose dolphins (Tursiops truncatus) along coastal Texas were estimated using length‐at‐age information from a broader scope of age classes, including late‐term fetuses. A Gompertz growth curve fit to pre‐ and postnatal data underestimated size parameters, but demonstrated similar growth rate constants (k) to an exclusively postnatal model. However, when growth parameters were broken out, the absolute growth rate (G) and rate of growth decay (g) decreased (0.44 from 0.27 and 0.55 from 0.39, respectively), which underscores the importance of reporting k in its expanded form (G/g). Although the Gompertz fits most age classes well, it cannot explain growth in all age classes. We argue that a novel sigmoidal model would be more useful for inference.