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.     

Can We Accurately Characterize Wildlife Resource Use When Telemetry Data Are Imprecise?

ABSTRACT Telemetry data have been widely used to quantify wildlife habitat relationships despite the fact that these data are inherently imprecise. All telemetry data have positional error, and failure to account for that error can lead to incorrect predictions of wildlife resource use. Several techniques have been used to account for positional error in wildlife studies. These techniques have been described in the literature, but their ability to accurately characterize wildlife resource use has never been tested. We evaluated the performance of techniques commonly used for incorporating telemetry error into studies of wildlife resource use. Our evaluation was based on imprecise telemetry data (mean telemetry error = 174 m, SD = 130 m) typical of field-based studies. We tested 5 techniques in 10 virtual environments and in one real-world environment for categorical (i.e., habitat types) and continuous (i.e., distances or elevations) rasters. Technique accuracy varied by patch size for the categorical rasters, with higher accuracy as patch size increased. At the smallest patch size (1 ha), the technique that ignores error performed best on categorical data (0.31 and 0.30 accuracy for virtual and real data, respectively); however, as patch size increased the bivariate-weighted technique performed better (0.56 accuracy at patch sizes >31 ha) and achieved complete accuracy (i.e., 1.00 accuracy) at smaller patch sizes (472 ha and 1,522 ha for virtual and real data, respectively) than any other technique. We quantified the accuracy of the continuous covariates using the mean absolute difference (MAD) in covariate value between true and estimated locations. We found that average MAD varied between 104 m (ignore telemetry error) and 140 m (rescale the covariate data) for our continuous covariate surfaces across virtual and real data sets. Techniques that rescale continuous covariate data or use a zonal mean on values within a telemetry error polygon were significantly less accurate than other techniques. Although the technique that ignored telemetry error performed best on categorical rasters with smaller average patch sizes (i.e., ≤31 ha) and on continuous rasters in our study, accuracy was so low that the utility of using point-based approaches for quantifying resource use is questionable when telemetry data are imprecise, particularly for small-patch habitat relationships.     

Localization of the glucose phosphotransferase to a cytoplasmically accessible site on intracellular membranes

UDP-glucose:glycoprotein glucose-1-phosphotransferase (Glc-phosphotransferase) catalyzes the transfer of alpha Glc-1-P from UDP-Glc to mannose residues on acceptor glycoproteins. The predominant acceptor for this transfer in rat liver is a glycoprotein of 62 kDa. This acceptor was labeled in liver homogenates through incubation with the 35S-labeled phosphorothioate analogue of UDP-Glc, and its distribution following differential centrifugation was compared to that of the glycoproteins labeled by CMP-[3H]N-acetylneuraminic acid. Whereas 94% of the 3H-labeled macromolecules fractionated to the microsomal pellet, 85% of the 35S-labeled 62-kDa glycoprotein was found in the high-speed supernatant. The distribution of the Glc-phosphotransferase was also examined following differential centrifugation, and the bulk of the activity was found in the 100,000 x g pellet. In contrast to results obtained with the lumenal microsomal markers 4 beta-galactosyltransferase and mannose-6-phosphatase, however, optimal activity of the Glc-phosphotransferase was not dependent on the disruption of microsomal vesicles by detergent. In addition, Glc-phosphotransferase was degraded by exogenous proteases in the absence of detergent, whereas the lumenal markers were not. We conclude, therefore, that the 62-kDa acceptor glycoprotein is cytoplasmic and is glycosylated by the Glc-phosphotransferase at a site accessible to the cytoplasm. This may prove to be a model for the topography of glycosylation of other cytoplasmic glycoproteins as well.     

Analysis of the nuclear envelope polypeptides by isoelectric focusing and electrophoresis

The more insoluble polypeptides of the avian erythrocyte nuclear envelope have been characterized by a two-dimensional electrophoretic procedure. Most of the polypeptides occur in two classes with isoelectric points of approximately 6.4 and 5.7 respectively. The more acidic class contains two polypeptides, P71 and one which contributes to an electrophoretic band previously identified as P55. The more basic class includes P75, P68, P61 and two or more polypeptides from the P55 band. There are four to six isoelectric point variants of each polypeptide in the more basic class, and the relative stain intensities for the variants are similar for the different polypeptides. These similarities in ionic properties suggest a chemical relationship between the polypeptides. These results are discussed in relation to the in vitro conversion of P75 to polypeptides of the same molecular weight as P68, P61 and P55.