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.     

Evidence for Structural Dissimilarity in the Neurotransmitter Binding Region of Purified Acetylcholine Receptors from Human Muscle and Torpedo Electric Organ

Acetylcholine receptor (AChR) purified from human skeletal muscle affinity-alkylated with bromoacetyl[methyl-3H]choline bromide ([3H]BAC) in mildly reducing conditions to yield a specifically radiolabeled polypeptide, Mr 44,000, the alpha-subunit. The binding of [125I]alpha-bungarotoxin to AChR was completely inhibited by affinity-alkylation, indicating that the human AChR's binding site for alpha-bungarotoxin is closely associated with the alpha-subunit's acetylcholine binding site. Structures in the vicinity of the alpha-bungarotoxin binding sites of AChRs from human muscle and Torpedo electric organ were compared by varying the conditions of alkylation. Under optimal conditions of reduction and alkylation, both human and Torpedo AChR incorporated BAC in equivalence to the number of alpha-bungarotoxin binding sites. However, with limited conditions of reduction but sufficient BAC to alkylate 100% of the alpha-bungarotoxin binding sites of human AChR, only 71% of the Torpedo AChR's binding sites were alkylated. In optimal conditions of reduction but with the minimal concentration of BAC that permitted 100% alkylation of the human AChR's alpha-bungarotoxin sites, only 74% of the Torpedo AChR's binding sites were alkylated. These data suggest that the neurotransmitter binding region of human muscle AChR is structurally dissimilar from that of Torpedo electric organ, having a higher binding affinity for BAC and an adjacent disulfide bond that is more readily accessible to reducing agents.     

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.