Use of chromomycin A3 staining in bovine sperm cells for detection of protamine deficiency

Sperm chromatin integrity is essential for accurate transmission of male genetic information, and normal sperm chromatin structure is important for fertilization. Protamine is a nuclear protein that plays a key role in sperm DNA integrity, because it is responsible for sperm DNA stability and packing until the paternal genome is delivered into the oocyte during fertilization. Our aim was to investigate protamine deficiency in sperm cells of Bos indicus bulls (Nelore) using chromomycin A₃ (CMA₃) staining. Frozen semen from 14 bulls were thawed, then fixed in Carnoy's solution. Smears were prepared and analyzed by microscopy. As a positive control of CMA₃ staining, sperm from one bull was subjected to deprotamination of nuclei. The percentage of CMA₃-positive bovine sperm did not vary among batches. Only two bulls showed a higher percentage of CMA₃-positive sperm cells compared to the others. CMA₃ is a simple and useful tool for detecting sperm protamine deficiency in bulls.     

RNAi-mediated resistance to diverse isolates belonging to two virus species involved in Cassava brown streak disease

Cassava brown streak disease (CBSD) is emerging as one of the most important viral diseases of cassava (Manihot esculenta) and is considered today as the biggest threat to cassava cultivation in East Africa. The disease is caused by isolates of at least two phylogenetically distinct species of single-stranded RNA viruses belonging to the family Potyviridae, genus Ipomovirus. The two species are present predominantly in the coastal lowland [Cassava brown streak virus (CBSV); Tanzania and Mozambique] and highland [Cassava brown streak Uganda virus (CBSUV); Lake Victoria Basin, Uganda, Kenya and Malawi] in East Africa. In this study, we demonstrate that CBSD can be efficiently controlled using RNA interference (RNAi). Three RNAi constructs targeting the highland species were generated, consisting of the full-length (FL; 894 nucleotides), 397-nucleotide N-terminal and 491-nucleotide C-terminal portions of the coat protein (CP) gene of a Ugandan isolate of CBSUV (CBSUV-[UG:Nam:04]), and expressed constitutively in Nicotiana benthamiana. After challenge with CBSUV-[UG:Nam:04], plants homozygous for FL-CP showed the highest resistance, followed by the N-terminal and C-terminal lines with similar resistance. In the case of FL, approximately 85% of the transgenic plant lines produced were completely resistant. Some transgenic lines were also challenged with six distinct isolates representing both species: CBSV and CBSUV. In addition to nearly complete resistance to the homologous virus, two FL plant lines showed 100% resistance and two C-terminal lines expressed 50-100% resistance, whereas the N-terminal lines succumbed to the nonhomologous CBSV isolates. Northern blotting revealed a positive correlation between the level of transgene-specific small interfering RNAs detected in transgenic plants and the level of virus resistance. This is the first demonstration of RNAi-mediated resistance to CBSD and protection across very distant isolates (more than 25% in nucleotide sequence) belonging to two different species: Cassava brown streak virus and Cassava brown streak Uganda virus.     

Reference genes for quantitative reverse transcription-polymerase chain reaction expression studies in wild and cultivated peanut

Background

Molecular genetic studies on rare tumour entities, such as bone tumours, often require the use of decalcified, formalin-fixed, paraffin-embedded tissue (dFFPE) samples. Regardless of which decalcification procedure is used, this introduces a vast breakdown of DNA that precludes the possibility of further molecular genetic testing. We set out to establish a robust protocol that would overcome these intrinsic hurdles for bone tumour research.

Findings

The goal of our study was to establish a protocol, using a modified DNA isolation procedure and quality controls, to select decalcified samples suitable for array-CGH testing. Archival paraffin blocks were obtained from 9 different pathology departments throughout Europe, using different fixation, embedding and decalcification procedures, in order to preclude a bias for certain lab protocols. Isolated DNA samples were subjected to direct chemical labelling and enzymatic labelling systems and were hybridised on a high resolution oligonucleotide chip containing 44,000 reporter elements. Genomic alterations (gains and losses) were readily detected in most of the samples analysed. For example, both homozygous deletions of 0.6 Mb and high level of amplifications of 0.7 Mb were identified.

Conclusions

We established a robust protocol for molecular genetic testing of dFFPE derived DNA, irrespective of fixation, decalcification or sample type used. This approach may greatly facilitate further genetic testing on rare tumour entities where archival decalcified, formalin fixed samples are the only source.     

Genomic selection of purebred animals for crossbred performance in the presence of dominant gene action

Background

Genomic selection is an appealing method to select purebreds for crossbred performance. In the case of crossbred records, single nucleotide polymorphism (SNP) effects can be estimated using an additive model or a breed-specific allele model. In most studies, additive gene action is assumed. However, dominance is the likely genetic basis of heterosis. Advantages of incorporating dominance in genomic selection were investigated in a two-way crossbreeding program for a trait with different magnitudes of dominance. Training was carried out only once in the simulation.

Results

When the dominance variance and heterosis were large and overdominance was present, a dominance model including both additive and dominance SNP effects gave substantially greater cumulative response to selection than the additive model. Extra response was the result of an increase in heterosis but at a cost of reduced purebred performance. When the dominance variance and heterosis were realistic but with overdominance, the advantage of the dominance model decreased but was still significant. When overdominance was absent, the dominance model was slightly favored over the additive model, but the difference in response between the models increased as the number of quantitative trait loci increased. This reveals the importance of exploiting dominance even in the absence of overdominance. When there was no dominance, response to selection for the dominance model was as high as for the additive model, indicating robustness of the dominance model. The breed-specific allele model was inferior to the dominance model in all cases and to the additive model except when the dominance variance and heterosis were large and with overdominance. However, the advantage of the dominance model over the breed-specific allele model may decrease as differences in linkage disequilibrium between the breeds increase. Retraining is expected to reduce the advantage of the dominance model over the alternatives, because in general, the advantage becomes important only after five or six generations post-training.

Conclusion

Under dominance and without retraining, genomic selection based on the dominance model is superior to the additive model and the breed-specific allele model to maximize crossbred performance through purebred selection.     

Suppressors of RNA silencing encoded by the components of the cotton leaf curl begomovirus-betasatellite complex

Begomoviruses (family Geminiviridae) are single-stranded DNA viruses transmitted by the whitefly Bemisia tabaci. Many economically important diseases in crops are caused by begomoviruses, particularly in tropical and subtropical environments. These include the betasatellite-associated begomoviruses causing cotton leaf curl disease (CLCuD) that causes significant losses to a mainstay of the economy of Pakistan, cotton. RNA interference (RNAi) or gene silencing is a natural defense response of plants against invading viruses. In counter-defense, viruses encode suppressors of gene silencing that allow them to effectively invade plants. Here, we have analyzed the ability of the begomovirus Cotton leaf curl Multan virus (CLCuMV) and its associated betasatellite, Cotton leaf curl Multan β-satellite (CLCuMB) which, together, cause CLCuD, and the nonessential alphasatellite (Cotton leaf curl Multan alphasatellite [CLCuMA]) for their ability to suppress gene silencing in Nicotiana benthamiana. The results showed that CLCuMV by itself was unable to efficiently block silencing. However, in the presence of the betasatellite, gene silencing was entirely suppressed. Silencing was not affected in any way when infections included CLCuMA, although the alphasatellite was, for the first time, shown to be a target of RNA silencing, inducing the production in planta of specific small interfering RNAs, the effectors of silencing. Subsequently, using a quantitative real-time polymerase chain reaction assay and Northern blot analysis, the ability of all proteins encoded by CLCuMV and CLCuMB were assessed for their ability to suppress RNAi and the relative strengths of their suppression activity were compared. The analysis showed that the V2, C2, C4, and βC1 proteins exhibited suppressor activity, with the V2 showing the strongest activity. In addition, V2, C4, and βC1 were examined for their ability to bind RNA and shown to have distinct specificities. Although each of these proteins has, for other begomoviruses or betasatellites, been previously shown to have suppressor activity, this is the first time all proteins encoded by a geminiviruses (or begomovirus-betasatellite complex) have been examined and also the first for which four separate suppressors have been identified.     

Accuracies of genomic breeding values in American Angus beef cattle using K-means clustering for cross-validation

Background

Genomic selection is a recently developed technology that is beginning to revolutionize animal breeding. The objective of this study was to estimate marker effects to derive prediction equations for direct genomic values for 16 routinely recorded traits of American Angus beef cattle and quantify corresponding accuracies of prediction.

Methods

Deregressed estimated breeding values were used as observations in a weighted analysis to derive direct genomic values for 3570 sires genotyped using the Illumina BovineSNP50 BeadChip. These bulls were clustered into five groups using K-means clustering on pedigree estimates of additive genetic relationships between animals, with the aim of increasing within-group and decreasing between-group relationships. All five combinations of four groups were used for model training, with cross-validation performed in the group not used in training. Bivariate animal models were used for each trait to estimate the genetic correlation between deregressed estimated breeding values and direct genomic values.

Results

Accuracies of direct genomic values ranged from 0.22 to 0.69 for the studied traits, with an average of 0.44. Predictions were more accurate when animals within the validation group were more closely related to animals in the training set. When training and validation sets were formed by random allocation, the accuracies of direct genomic values ranged from 0.38 to 0.85, with an average of 0.65, reflecting the greater relationship between animals in training and validation. The accuracies of direct genomic values obtained from training on older animals and validating in younger animals were intermediate to the accuracies obtained from K-means clustering and random clustering for most traits. The genetic correlation between deregressed estimated breeding values and direct genomic values ranged from 0.15 to 0.80 for the traits studied.

Conclusions

These results suggest that genomic estimates of genetic merit can be produced in beef cattle at a young age but the recurrent inclusion of genotyped sires in retraining analyses will be necessary to routinely produce for the industry the direct genomic values with the highest accuracy.     

Breeding value prediction for production traits in layer chickens using pedigree or genomic relationships in a reduced animal model

Background

Genomic selection involves breeding value estimation of selection candidates based on high-density SNP genotypes. To quantify the potential benefit of genomic selection, accuracies of estimated breeding values (EBV) obtained with different methods using pedigree or high-density SNP genotypes were evaluated and compared in a commercial layer chicken breeding line.

Methods

The following traits were analyzed: egg production, egg weight, egg color, shell strength, age at sexual maturity, body weight, albumen height, and yolk weight. Predictions appropriate for early or late selection were compared. A total of 2,708 birds were genotyped for 23,356 segregating SNP, including 1,563 females with records. Phenotypes on relatives without genotypes were incorporated in the analysis (in total 13,049 production records).The data were analyzed with a Reduced Animal Model using a relationship matrix based on pedigree data or on marker genotypes and with a Bayesian method using model averaging. Using a validation set that consisted of individuals from the generation following training, these methods were compared by correlating EBV with phenotypes corrected for fixed effects, selecting the top 30 individuals based on EBV and evaluating their mean phenotype, and by regressing phenotypes on EBV.

Results

Using high-density SNP genotypes increased accuracies of EBV up to two-fold for selection at an early age and by up to 88% for selection at a later age. Accuracy increases at an early age can be mostly attributed to improved estimates of parental EBV for shell quality and egg production, while for other egg quality traits it is mostly due to improved estimates of Mendelian sampling effects. A relatively small number of markers was sufficient to explain most of the genetic variation for egg weight and body weight.