The Revised Classification of Eukaryotes

This revision of the classification of eukaryotes, which updates that of Adl et al. [J. Eukaryot. Microbiol. 52 (2005) 399], retains an emphasis on the protists and incorporates changes since 2005 that have resolved nodes and branches in phylogenetic trees. Whereas the previous revision was successful in re‐introducing name stability to the classification, this revision provides a classification for lineages that were then still unresolved. The supergroups have withstood phylogenetic hypothesis testing with some modifications, but despite some progress, problematic nodes at the base of the eukaryotic tree still remain to be statistically resolved. Looking forward, subsequent transformations to our understanding of the diversity of life will be from the discovery of novel lineages in previously under‐sampled areas and from environmental genomic information.     

An Emerging Approach for Parallel Quantification of Intracellular Protozoan Parasites and Host Cell Characterization Using TissueFAXS Cytometry

Characterization of host-pathogen interactions is a fundamental approach in microbiological and immunological oriented disciplines. It is commonly accepted that host cells start to change their phenotype after engulfing pathogens. Techniques such as real time PCR or ELISA were used to characterize the genes encoding proteins that are associated either with pathogen elimination or immune escape mechanisms. Most of such studies were performed in vitro using primary host cells or cell lines. Consequently, the data generated with such approaches reflect the global RNA expression or protein amount recovered from all cells in culture. This is justified when all host cells harbor an equal amount of pathogens under experimental conditions. However, the uptake of pathogens by phagocytic cells is not synchronized. Consequently, there are host cells incorporating different amounts of pathogens that might result in distinct pathogen-induced protein biosynthesis. Therefore, we established a technique able to detect and quantify the number of pathogens in the corresponding host cells using immunofluorescence-based high throughput analysis. Paired with multicolor staining of molecules of interest it is now possible to analyze the infection profile of host cell populations and the corresponding phenotype of the host cells as a result of parasite load.     

Computer simulation and interpretation of45Ca efflux profile patterns

Summary Stimulations or inhibitions by various agents of⁴⁵Ca efflux from prelabeled cells or tissues display distinct and reproducible profile patterns when the results are plotted against time as fractional efflux ratios (FER). FER is the fractional efflux of⁴⁵Ca from stimulated cells divided by the fractional efflux from a control unstimulated group. These profile patterns fall into three categories: peak patterns, exponential patterns, and mixed patterns. Each category can be positive (stimulation) or negative (inhibition). The interpretation of these profiles is difficult because⁴⁵Ca efflux depends on three variables: the rate of calcium transport out of the cell, the specific activity of the cell compartment from which the calcium originates, and the concentration of free calcium in this compartment. A computer model based on data obtained by kinetic analyses of⁴⁵Ca desaturation curves and consisting of two distinct intracellular pools was designed to follow the concentration of the traced substance (⁴⁰Ca), the tracer (⁴⁵Ca), and the specific activity of each compartment before, during, and after the stimulation or the inhibition of calcium fluxes at various pool boundaries. The computer model can reproduce all the FER profiles obtained experimentally and bring information which may be helpful to the interpretation of this type of data. Some predictions of the model were tested experimentally, and the results support the views that a peak pattern may reflect a sustained change in calcium transport across the plasma membrane, that an exponential pattern arises from calcium mobilization from an internal subcellular pool, and that a mixed pattern may be caused by a simultaneous change in calcium fluxes at both compartment boundaries.     

Genetic determination of the mitochondrial adenine nucleotide translocation system and ITS role in the eukaryotic cell

On integrating experimental data published previously, the following picture of the mitochondrial adenine nucleotide (AdN) translocation system is being presented: 1. The AdN translocation system serves not only to transport ATP synthesized within mitochondria into the cytosol but also to transport cytosolic ATP into the mitochondria when oxidative phosphorylation is not functioning. 2. The AdN translocator is coded for by nuclear genes and the mitochondrial protein synthesis is not involved in its formation. 3. The AdN translocation system must be preserved and functioning even in cells which could dispense with oxidative phosphorylation. It assures appropriate concentrations of intramitochondrial ATP. 4. The intramitochondrial ATP is required for normal replication of mitochondrial DNA. Tis supports the view that the mitochondrion is a self-replicating semi-autonomous organelle. 5. The appropriate concentration of ATP must be present in mitochondria to make possible cell growth or multiplication. This points to a direct or indirect role of mitochondria in the control of cell proliferation.     

Comparing genomic prediction accuracy from purebred,crossbred and combined purebred and crossbred reference populations in sheep

Background

The accuracy of genomic prediction depends largely on the number of animals with phenotypes and genotypes. In some industries, such as sheep and beef cattle, data are often available from a mixture of breeds, multiple strains within a breed or from crossbred animals. The objective of this study was to compare the accuracy of genomic prediction for several economically important traits in sheep when using data from purebreds, crossbreds or a combination of those in a reference population.

Methods

The reference populations were purebred Merinos, crossbreds of Border Leicester (BL), Poll Dorset (PD) or White Suffolk (WS) with Merinos and combinations of purebred and crossbred animals. Genomic breeding values (GBV) were calculated based on genomic best linear unbiased prediction (GBLUP), using a genomic relationship matrix calculated based on 48 599 Ovine SNP (single nucleotide polymorphisms) genotypes. The accuracy of GBV was assessed in a group of purebred industry sires based on the correlation coefficient between GBV and accurate estimated breeding values based on progeny records.

Results

The accuracy of GBV for Merino sires increased with a larger purebred Merino reference population, but decreased when a large purebred Merino reference population was augmented with records from crossbred animals. The GBV accuracy for BL, PD and WS breeds based on crossbred data was the same or tended to decrease when more purebred Merinos were added to the crossbred reference population. The prediction accuracy for a particular breed was close to zero when the reference population did not contain any haplotypes of the target breed, except for some low accuracies that were obtained when predicting PD from WS and vice versa.

Conclusions

This study demonstrates that crossbred animals can be used for genomic prediction of purebred animals using 50 k SNP marker density and GBLUP, but crossbred data provided lower accuracy than purebred data. Including data from distant breeds in a reference population had a neutral to slightly negative effect on the accuracy of genomic prediction. Accounting for differences in marker allele frequencies between breeds had only a small effect on the accuracy of genomic prediction from crossbred or combined crossbred and purebred reference populations.