Phagocytosis of bacteria by polymorphonuclear leukocytes: a freeze-fracture, scanning electron microscope, and thin-section investigation of membrane structure

The changes in membrane structure of rabbit polymorphonuclear (PMN) leukocytes during bacterial phagocytosis was investigated with scanning electron microscope (SEM), thin-section, and freeze-fracture techniques. SEM observations of bacterial attachment sites showed the involvement of limited areas of PMN membrane surface (0.01-0.25μm(2)). Frequently, these areas of attachment were located on membrane extensions. The membrane extensions were present before, during, and after the engulfment of bacteria, but were diminished in size after bacterial engulfment. In general, the results obtained with SEM and thin-section techniques aided in the interpretation of the three-dimensional freeze-fracture replicas. Freeze-fracture results revealed the PMN leukocytes had two fracture faces as determined by the relative density of intramembranous particles (IMP). Membranous extensions of the plasma membrane, lysosomes, and phagocytic vacuoles contained IMP's with a distribution and density similar to those of the plasma membrane. During phagocytosis, IMPs within the plasma membrane did not undergo a massive aggregation. In fact, structural changes within the membranes were infrequent and localized to regions such as the attachment sites of bacteria, the fusion sites on the plasma membrane, and small scale changes in the phagocytic vacuole membrane during membrane fusion. During the formation of the phagocytic vacuole, the IMPs of the plasma membrane appeared to move in with the lipid bilayer while maintaining a distribution and density of IMPs similar to those of the plasma membranes. Occasionally, IMPs were aligned to linear arrays within phagocytic vacuole membranes. This alignment might be due to an interaction with linearly arranged motile structures on the side of the phagocytic vacuole membranes. IMP-free regions were observed after fusion of lysosomes with the phagocytic vacuoles or plasma membrane. These IMP-free areas probably represent sites where membrane fusion occurred between lysosomal membrane and phagocytic vacuole membrane or plasma membrane. Highly symmetrical patterns of IMPs were not observed during lysosomal membrane fusion.     

Effects of data structure on the estimation of covariance functions to describe genotype by environment interactions in a reaction norm model

Covariance functions have been proposed to predict breeding values and genetic (co)variances as a function of phenotypic within herd-year averages (environmental parameters) to include genotype by environment interaction. The objective of this paper was to investigate the influence of definition of environmental parameters and non-random use of sires on expected breeding values and estimated genetic variances across environments. Breeding values were simulated as a linear function of simulated herd effects. The definition of environmental parameters hardly influenced the results. In situations with random use of sires, estimated genetic correlations between the trait expressed in different environments were 0.93, 0.93 and 0.97 while simulated at 0.89 and estimated genetic variances deviated up to 30% from the simulated values. Non random use of sires, poor genetic connectedness and small herd size had a large impact on the estimated covariance functions, expected breeding values and calculated environmental parameters. Estimated genetic correlations between a trait expressed in different environments were biased upwards and breeding values were more biased when genetic connectedness became poorer and herd composition more diverse. The best possible solution at this stage is to use environmental parameters combining large numbers of animals per herd, while losing some information on genotype by environment interaction in the data.     

Earthworms promote greater richness and abundance in the emergence of plant species across a grassland-forest ecotone

Aims Chalk grasslands are subject to vegetation dynamics that range from species-rich open grasslands to tall and encroached grasslands, and woods and forests. In grasslands, earthworms impact plant communities and ecosystem functioning through the modification of soil physical, chemical and microbiological properties, but also through their selective ingestion and vertical transportation of seeds from the soil seed bank. Laboratory experiments showed that seed–earthworm interactions are species specific, but little is known on the impact of seed–earthworm interactions in the field. The overall aim of this study was to better understand seed–earthworm interactions and their impact on the plant community. First we analyzed the composition of seedlings emerging from casts after earthworm ingestion. Then we compared seedling composition in casts to the plant composition of emerging seedlings from the soil and of the aboveground vegetation along four stages of the secondary succession of chalk grasslands.Methods Four stages of the secondary succession of a chalk grassland—from open sward to woods—were sampled in Upper Normandy, France, in February 2010. Within each successional stage (×3 replicates), we sampled the standing vegetation, soil seed bank at three soil depths (0–2, 2–5 and 5–10cm) and earthworm surface casts along transects. Soil and cast samples were water sieved before samples were spread onto trays and placed into a greenhouse. Emerging seedlings were counted and identified. Effect of successional stage and origin of samples on mean and variability of abundance and species richness of seedlings emerging from casts and soil seed banks were analyzed. Plant compositions were compared between all sample types. We used generalized mixed-effect models and a distance-based redundancy multivariate analysis.Important findings Seedling abundance was always higher in earthworm casts than in the soil seed bank and increased up to 5-fold, 4-fold and 3.5-fold, respectively, in the tall grassland, woods and encroached grassland compared to the soil surface layer. Species richness was also higher in earthworm casts than in the soil seed bank in all successional stages, with a 4-fold increase in the encroached grassland. The plant composition of the standing vegetation was more similar to that of seedlings from casts than to that of seedlings from the soil seed bank. Seedlings diversity emerging from casts in the tall and encroached grasslands tended toward the diversity found in woods. Our results indicate that earthworms may promote the emergence of seedlings. We also suggest that the loss of some plant species in the seed bank and the tall grass vegetation in intermediary successional stages modify the local conditions and prevent the further establishment of early-successional plant species.     

Latest European coelacanth shows Gondwanan affinities

The last European fossil occurrence of a coelacanth is from the Mid-Cretaceous of the English Chalk (Turonian, 90 million years ago). Here, we report the discovery of a coelacanth from Late Cretaceous non-marine rocks in southern France. It consists of a left angular bone showing structures that imply close phylogenetic affinities with some extinct Mawsoniidae. The closest relatives are otherwise known from Cretaceous continental deposits of southern continents and suggest that the dispersal of freshwater organisms from Africa to Europe occurred in the Late Cretaceous.     

Accuracy of imputation to whole-genome sequence data in Holstein Friesian cattle

Background

The use of whole-genome sequence data can lead to higher accuracy in genome-wide association studies and genomic predictions. However, to benefit from whole-genome sequence data, a large dataset of sequenced individuals is needed. Imputation from SNP panels, such as the Illumina BovineSNP50 BeadChip and Illumina BovineHD BeadChip, to whole-genome sequence data is an attractive and less expensive approach to obtain whole-genome sequence genotypes for a large number of individuals than sequencing all individuals. Our objective was to investigate accuracy of imputation from lower density SNP panels to whole-genome sequence data in a typical dataset for cattle.

Methods

Whole-genome sequence data of chromosome 1 (1737 471 SNPs) for 114 Holstein Friesian bulls were used. Beagle software was used for imputation from the BovineSNP50 (3132 SNPs) and BovineHD (40 492 SNPs) beadchips. Accuracy was calculated as the correlation between observed and imputed genotypes and assessed by five-fold cross-validation. Three scenarios S40, S60 and S80 with respectively 40%, 60%, and 80% of the individuals as reference individuals were investigated.

Results

Mean accuracies of imputation per SNP from the BovineHD panel to sequence data and from the BovineSNP50 panel to sequence data for scenarios S40 and S80 ranged from 0.77 to 0.83 and from 0.37 to 0.46, respectively. Stepwise imputation from the BovineSNP50 to BovineHD panel and then to sequence data for scenario S40 improved accuracy per SNP to 0.65 but it varied considerably between SNPs.

Conclusions

Accuracy of imputation to whole-genome sequence data was generally high for imputation from the BovineHD beadchip, but was low from the BovineSNP50 beadchip. Stepwise imputation from the BovineSNP50 to the BovineHD beadchip and then to sequence data substantially improved accuracy of imputation. SNPs with a low minor allele frequency were more difficult to impute correctly and the reliability of imputation varied more. Linkage disequilibrium between an imputed SNP and the SNP on the lower density panel, minor allele frequency of the imputed SNP and size of the reference group affected imputation reliability.     

Right-hand-side updating for fast computing of genomic breeding values

Background

Since both the number of SNPs (single nucleotide polymorphisms) used in genomic prediction and the number of individuals used in training datasets are rapidly increasing, there is an increasing need to improve the efficiency of genomic prediction models in terms of computing time and memory (RAM) required.

Methods

In this paper, two alternative algorithms for genomic prediction are presented that replace the originally suggested residual updating algorithm, without affecting the estimates. The first alternative algorithm continues to use residual updating, but takes advantage of the characteristic that the predictor variables in the model (i.e. the SNP genotypes) take only three different values, and is therefore termed “improved residual updating”. The second alternative algorithm, here termed “right-hand-side updating” (RHS-updating), extends the idea of improved residual updating across multiple SNPs. The alternative algorithms can be implemented for a range of different genomic predictions models, including random regression BLUP (best linear unbiased prediction) and most Bayesian genomic prediction models. To test the required computing time and RAM, both alternative algorithms were implemented in a Bayesian stochastic search variable selection model.

Results

Compared to the original algorithm, the improved residual updating algorithm reduced CPU time by 35.3 to 43.3%, without changing memory requirements. The RHS-updating algorithm reduced CPU time by 74.5 to 93.0% and memory requirements by 13.1 to 66.4% compared to the original algorithm.

Conclusions

The presented RHS-updating algorithm provides an interesting alternative to reduce both computing time and memory requirements for a range of genomic prediction models.     

A comparison of principal component regression and genomic REML for genomic prediction across populations

Background

Genomic prediction faces two main statistical problems: multicollinearity and n ≪ p (many fewer observations than predictor variables). Principal component (PC) analysis is a multivariate statistical method that is often used to address these problems. The objective of this study was to compare the performance of PC regression (PCR) for genomic prediction with that of a commonly used REML model with a genomic relationship matrix (GREML) and to investigate the full potential of PCR for genomic prediction.

Methods

The PCR model used either a common or a semi-supervised approach, where PC were selected based either on their eigenvalues (i.e. proportion of variance explained by SNP (single nucleotide polymorphism) genotypes) or on their association with phenotypic variance in the reference population (i.e. the regression sum of squares contribution). Cross-validation within the reference population was used to select the optimum PCR model that minimizes mean squared error. Pre-corrected average daily milk, fat and protein yields of 1609 first lactation Holstein heifers, from Ireland, UK, the Netherlands and Sweden, which were genotyped with 50 k SNPs, were analysed. Each testing subset included animals from only one country, or from only one selection line for the UK.

Results

In general, accuracies of GREML and PCR were similar but GREML slightly outperformed PCR. Inclusion of genotyping information of validation animals into model training (semi-supervised PCR), did not result in more accurate genomic predictions. The highest achievable PCR accuracies were obtained across a wide range of numbers of PC fitted in the regression (from one to more than 1000), across test populations and traits. Using cross-validation within the reference population to derive the number of PC, yielded substantially lower accuracies than the highest achievable accuracies obtained across all possible numbers of PC.

Conclusions

On average, PCR performed only slightly less well than GREML. When the optimal number of PC was determined based on realized accuracy in the testing population, PCR showed a higher potential in terms of achievable accuracy that was not capitalized when PC selection was based on cross-validation. A standard approach for selecting the optimal set of PC in PCR remains a challenge.

Electronic supplementary material

The online version of this article (doi:10.1186/s12711-014-0060-x) contains supplementary material, which is available to authorized users.