Ectopic Expression of MmKin17 Protein Inhibits Cell Proliferation of Human Tumor-Derived Cells

To characterize the biological role of Kin17 protein, a mammalian nuclear protein which participates in the response to UV and ionizing radiation and binds to curved DNA, EBV-derived vectors carrying (Mm)Kin17 cDNA were constructed and transfected in tumorigenic cells harboring different p53 profiles (HeLa, H1299, and HCT116) and in immortalized HEK 293 cells. (Mm)Kin17 protein expression induced a tremendous decrease in cell proliferation of the three tumorigenic cell lines 2 weeks after transfection. Transfection of HEK 293 cells with an pEBVCMV(Mm)Kin17 plasmid gave rise to numerous (Mm)Kin17-expressing cells which constantly disappeared with time, preventing the establishment of (Mm)Kin17-expressing cells. Several independent clones were isolated from HEK 293 cells carrying a pEBVMT(Mm)Kin17 vector. The two clones described here (B223.1 and B223.2) exhibited different (Mm)Kin17 protein levels and displayed a gradual decrease in their proliferative capacities. In B223.1 cells, the basal expression of (Mm)Kin17 greatly reduced plating efficiency and cell growth. B223.1 cell morphology was altered, with numerous round-shaped cells whose spreading on the culture support was hampered. We observed giant multinucleated cells or cells containing micronuclei-like structures and/or multilobed nuclei. To conclude, (Mm)Kin17 overexpression reduced the proliferation of tumorigenic cells independently of their p53 status and modified cell growth and cell morphology of established HEK 293 cells producing (Mm)Kin17 protein. It is likely that (Mm)Kin17 may interfere with DNA replication.     

Empirical and deterministic accuracies of across-population genomic prediction

Background

Differences in linkage disequilibrium and in allele substitution effects of QTL (quantitative trait loci) may hinder genomic prediction across populations. Our objective was to develop a deterministic formula to estimate the accuracy of across-population genomic prediction, for which reference individuals and selection candidates are from different populations, and to investigate the impact of differences in allele substitution effects across populations and of the number of QTL underlying a trait on the accuracy.

Methods

A deterministic formula to estimate the accuracy of across-population genomic prediction was derived based on selection index theory. Moreover, accuracies were deterministically predicted using a formula based on population parameters and empirically calculated using simulated phenotypes and a GBLUP (genomic best linear unbiased prediction) model. Phenotypes of 1033 Holstein-Friesian, 105 Groninger White Headed and 147 Meuse-Rhine-Yssel cows were simulated by sampling 3000, 300, 30 or 3 QTL from the available high-density SNP (single nucleotide polymorphism) information of three chromosomes, assuming a correlation of 1.0, 0.8, 0.6, 0.4, or 0.2 between allele substitution effects across breeds. The simulated heritability was set to 0.95 to resemble the heritability of deregressed proofs of bulls.

Results

Accuracies estimated with the deterministic formula based on selection index theory were similar to empirical accuracies for all scenarios, while accuracies predicted with the formula based on population parameters overestimated empirical accuracies by ~25 to 30%. When the between-breed genetic correlation differed from 1, i.e. allele substitution effects differed across breeds, empirical and deterministic accuracies decreased in proportion to the genetic correlation. Using a multi-trait model, it was possible to accurately estimate the genetic correlation between the breeds based on phenotypes and high-density genotypes. The number of QTL underlying the simulated trait did not affect the accuracy.

Conclusions

The deterministic formula based on selection index theory estimated the accuracy of across-population genomic predictions well. The deterministic formula using population parameters overestimated the across-population genomic accuracy, but may still be useful because of its simplicity. Both formulas could accommodate for genetic correlations between populations lower than 1. The number of QTL underlying a trait did not affect the accuracy of across-population genomic prediction using a GBLUP method.     

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.     

Long-term response to genomic selection: effects of estimation method and reference population structure for different genetic architectures

Background

Genomic selection has become an important tool in the genetic improvement of animals and plants. The objective of this study was to investigate the impacts of breeding value estimation method, reference population structure, and trait genetic architecture, on long-term response to genomic selection without updating marker effects.

Methods

Three methods were used to estimate genomic breeding values: a BLUP method with relationships estimated from genome-wide markers (GBLUP), a Bayesian method, and a partial least squares regression method (PLSR). A shallow (individuals from one generation) or deep reference population (individuals from five generations) was used with each method. The effects of the different selection approaches were compared under four different genetic architectures for the trait under selection. Selection was based on one of the three genomic breeding values, on pedigree BLUP breeding values, or performed at random. Selection continued for ten generations.

Results

Differences in long-term selection response were small. For a genetic architecture with a very small number of three to four quantitative trait loci (QTL), the Bayesian method achieved a response that was 0.05 to 0.1 genetic standard deviation higher than other methods in generation 10. For genetic architectures with approximately 30 to 300 QTL, PLSR (shallow reference) or GBLUP (deep reference) had an average advantage of 0.2 genetic standard deviation over the Bayesian method in generation 10. GBLUP resulted in 0.6% and 0.9% less inbreeding than PLSR and BM and on average a one third smaller reduction of genetic variance. Responses in early generations were greater with the shallow reference population while long-term response was not affected by reference population structure.

Conclusions

The ranking of estimation methods was different with than without selection. Under selection, applying GBLUP led to lower inbreeding and a smaller reduction of genetic variance while a similar response to selection was achieved. The reference population structure had a limited effect on long-term accuracy and response. Use of a shallow reference population, most closely related to the selection candidates, gave early benefits while in later generations, when marker effects were not updated, the estimation of marker effects based on a deeper reference population did not pay off.     

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.     

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.