Host-Parasite Interactions and Purifying Selection in a Microsporidian Parasite of Honey Bees

To clarify the mechanisms of Nosema ceranae parasitism, we deep-sequenced both honey bee host and parasite mRNAs throughout a complete 6-day infection cycle. By time-series analysis, 1122 parasite genes were significantly differently expressed during the reproduction cycle, clustering into 4 expression patterns. We found reactive mitochondrial oxygen species modulator 1 of the host to be significantly down regulated during the entire infection period. Our data support the hypothesis that apoptosis of honey bee cells was suppressed during infection. We further analyzed genome-wide genetic diversity of this parasite by comparing samples collected from the same site in 2007 and 2013. The number of SNP positions per gene and the proportion of non-synonymous substitutions per gene were significantly reduced over this time period, suggesting purifying selection on the parasite genome and supporting the hypothesis that a subset of N. ceranae strains might be dominating infection.     

Phenotype Classification of Zebrafish Embryos by Supervised Learning

Zebrafish is increasingly used to assess biological properties of chemical substances and thus is becoming a specific tool for toxicological and pharmacological studies. The effects of chemical substances on embryo survival and development are generally evaluated manually through microscopic observation by an expert and documented by several typical photographs. Here, we present a methodology to automatically classify brightfield images of wildtype zebrafish embryos according to their defects by using an image analysis approach based on supervised machine learning. We show that, compared to manual classification, automatic classification results in 90 to 100% agreement with consensus voting of biological experts in nine out of eleven considered defects in 3 days old zebrafish larvae. Automation of the analysis and classification of zebrafish embryo pictures reduces the workload and time required for the biological expert and increases the reproducibility and objectivity of this classification.     

Can Newts Cope with the Heat? Disparate Thermoregulatory Strategies of Two Sympatric Species in Water

Many ectotherms effectively reduce their exposure to low or high environmental temperatures using behavioral thermoregulation. In terrestrial ectotherms, thermoregulatory strategies range from accurate thermoregulation to thermoconformity according to the costs and limits of thermoregulation, while in aquatic taxa the quantification of behavioral thermoregulation have received limited attention. We examined thermoregulation in two sympatric newt species, Ichthyosaura alpestris and Lissotriton vulgaris, exposed to elevated water temperatures under semi-natural conditions. According to a recent theory, we predicted that species for which elevated water temperatures pose a lower thermal quality habitat, would thermoregulate more effectively than species in thermally benign conditions. In the laboratory thermal gradient, L. vulgaris maintained higher body temperatures than I. alpestris. Semi-natural thermal conditions provided better thermal quality of habitat for L. vulgaris than for I. alpestris. Thermoregulatory indices indicated that I. alpestris actively thermoregulated its body temperature, whereas L. vulgaris remained passive to the thermal heterogeneity of aquatic environment. In the face of elevated water temperatures, sympatric newt species employed disparate thermoregulatory strategies according to the species-specific quality of the thermal habitat. Both strategies reduced newt exposure to suboptimal water temperatures with the same accuracy but with or without the costs of thermoregulation. The quantification of behavioral thermoregulation proves to be an important conceptual and methodological tool for thermal ecology studies not only in terrestrial but also in aquatic ectotherms.     

Evidence for Inbreeding and Genetic Differentiation among Geographic Populations of the Saprophytic Mushroom Trogia venenata from Southwestern China

During the past 40 years, more than 400 Sudden Unexplained Deaths (SUDs) have occurred in Yunnan, southwestern China. Epidemiological and toxicological analyses suggested that a newly discovered mushroom called Trogia venenata was the leading culprit for SUDs. At present, relatively little is known about the genetics and natural history of this mushroom. In this study, we analyzed the sequence variation at four DNA fragments among 232 fruiting bodies of T. venenata collected from seven locations. Our ITS sequence analyses confirmed that all the isolates belonged to the same species. The widespread presence of sequence heterozygosity within many strains at each of three protein-coding genes suggested that the fruiting bodies were diploid, dikaryotic or heterokaryotic. Within individual geographic populations, we found significant deviations of genotype frequencies from Hardy-Weinberg expectations, with the overall observed heterozygosity lower than that expected under random mating, consistent with prevalent inbreeding within local populations. The geographic populations were overall genetically differentiated. Interestingly, while a positive correlation was found between population genetic distance and geographic distance, there was little correlation between genetic distance and barium concentration difference for the geographic populations. Our results suggest frequent inbreeding, geographic structuring, and limited gene flow among geographic populations of T. venenata from southwestern China.     

Metabolites in Blood for Prediction of Bacteremic Sepsis in the Emergency Room

A metabolomics approach for prediction of bacteremic sepsis in patients in the emergency room (ER) was investigated. In a prospective study, whole blood samples from 65 patients with bacteremic sepsis and 49 ER controls were compared. The blood samples were analyzed using gas chromatography coupled to time-of-flight mass spectrometry. Multivariate and logistic regression modeling using metabolites identified by chromatography or using conventional laboratory parameters and clinical scores of infection were employed. A predictive model of bacteremic sepsis with 107 metabolites was developed and validated. The number of metabolites was reduced stepwise until identifying a set of 6 predictive metabolites. A 6-metabolite predictive logistic regression model showed a sensitivity of 0.91(95% CI 0.69–0.99) and a specificity 0.84 (95% CI 0.58–0.94) with an AUC of 0.93 (95% CI 0.89–1.01). Myristic acid was the single most predictive metabolite, with a sensitivity of 1.00 (95% CI 0.85–1.00) and specificity of 0.95 (95% CI 0.74–0.99), and performed better than various combinations of conventional laboratory and clinical parameters. We found that a metabolomics approach for analysis of acute blood samples was useful for identification of patients with bacteremic sepsis. Metabolomics should be further evaluated as a new tool for infection diagnostics.     

Subcellular Localization of p44/WDR77 Determines Proliferation and Differentiation of Prostate Epithelial Cells

The molecular mechanism that controls the proliferation and differentiation of prostate epithelial cells is currently unknown. We previously identified a 44-kDa protein (p44/wdr77) as an androgen receptor-interacting protein that regulates a set of androgen receptor target genes in prostate epithelial cells and prostate cancer. In this study, we found that p44 localizes in the cytoplasm of prostate epithelial cells at the early stage of prostate development when cells are proliferating, and its nuclear translocation is associated with cellular and functional differentiation in adult prostate tissue. We further demonstrated that cytoplasmic p44 protein is essential for proliferation of prostate epithelial cells, whereas nuclear p44 is required for cell differentiation and prostate- specific protein secretion. These studies suggest a novel mechanism by which proliferation and differentiation of prostate epithelial cells are controlled by p44’s location in the cell.