Spermatogenic cells of the prepuberal mouse: isolation and morphological characterization

A procedure is described which permits the isolation from the prepuberal mouse testis of highly purified populations of primitive type A spermatogonia, type A spermatogonia, type B spermatogonia, preleptotene primary spermatocytes, leptotene and zygotene primary spermatocytes, pachytene primary spermatocytes and Sertoli cells. The successful isolation of these prepuberal cell types was accomplished by: (a) defining distinctive morphological characteristics of the cells, (b) determining the temporal appearance of spermatogenic cells during prepuberal development, (c) isolating purified seminiferous cords, after dissociation of the testis with collagenase, (d) separating the trypsin-dispersed seminiferous cells by sedimentation velocity at unit gravity, and (e) assessing the identity and purity of the isolated cell types by microscopy. The seminiferous epithelium from day 6 animals contains only primitive type A spermatogonia and Sertoli cells. Type A and type B spermatogonia are present by day 8. At day 10, meiotic prophase is initiated, with the germ cells reaching the early and late pachytene stages by 14 and 18, respectively. Secondary spermatocytes and haploid spermatids appear throughout this developmental period. The purity and optimum day for the recovery of specific cell types are as follows: day 6, Sertoli cells (purity>99 percent) and primitive type A spermatogonia (90 percent); day 8, type A spermatogonia (91 percent) and type B spermatogonia (76 percent); day 18, preleptotene spermatocytes (93 percent), leptotene/zygotene spermatocytes (52 percent), and pachytene spermatocytes (89 percent), leptotene/zygotene spermatocytes (52 percent), and pachytene spermatocytes (89 percent).     

Multilevel selection 2: Estimating the genetic parameters determining inheritance and response to selection

Interactions among individuals are universal, both in animals and in plants and in natural as well as domestic populations. Understanding the consequences of these interactions for the evolution of populations by either natural or artificial selection requires knowledge of the heritable components underlying them. Here we present statistical methodology to estimate the genetic parameters determining response to multilevel selection of traits affected by interactions among individuals in general populations. We apply these methods to obtain estimates of genetic parameters for survival days in a population of layer chickens with high mortality due to pecking behavior. We find that heritable variation is threefold greater than that obtained from classical analyses, meaning that two-thirds of the full heritable variation is hidden to classical analysis due to social interactions. As a consequence, predicted responses to multilevel selection applied to this population are threefold greater than classical predictions. This work, combined with the quantitative genetic theory for response to multilevel selection presented in an accompanying article in this issue, enables the design of selection programs to effectively reduce competitive interactions in livestock and plants and the prediction of the effects of social interactions on evolution in natural populations undergoing multilevel selection.     

Multilevel selection 1: Quantitative genetics of inheritance and response to selection

Interaction among individuals is universal, both in animals and in plants, and substantially affects evolution of natural populations and responses to artificial selection in agriculture. Although quantitative genetics has successfully been applied to many traits, it does not provide a general theory accounting for interaction among individuals and selection acting on multiple levels. Consequently, current quantitative genetic theory fails to explain why some traits do not respond to selection among individuals, but respond greatly to selection among groups. Understanding the full impacts of heritable interactions on the outcomes of selection requires a quantitative genetic framework including all levels of selection and relatedness. Here we present such a framework and provide expressions for the response to selection. Results show that interaction among individuals may create substantial heritable variation, which is hidden to classical analyses. Selection acting on higher levels of organization captures this hidden variation and therefore always yields positive response, whereas individual selection may yield response in the opposite direction. Our work provides testable predictions of response to multilevel selection and reduces to classical theory in the absence of interaction. Statistical methodology provided elsewhere enables empirical application of our work to both natural and domestic populations.     

Conceptual-level workflow modeling of scientific experiments using NMR as a case study

Background  

Scientific workflows improve the process of scientific experiments by making computations explicit, underscoring data flow, and emphasizing the participation of humans in the process when intuition and human reasoning are required. Workflows for experiments also highlight transitions among experimental phases, allowing intermediate results to be verified and supporting the proper handling of semantic mismatches and different file formats among the various tools used in the scientific process. Thus, scientific workflows are important for the modeling and subsequent capture of bioinformatics-related data. While much research has been conducted on the implementation of scientific workflows, the initial process of actually designing and generating the workflow at the conceptual level has received little consideration.     

Acute effects of prolonged intermittent low-intensity isometric warm-up schemes on jump,sprint, and agility performance in collegiate soccer players

The aim of the present study was to compare the effects of different warm-up interventions on jump, sprint and agility performance in collegiate soccer players. Twenty-one healthy male college soccer players (age: 20.14 ± 1.65 years; body height: 179.9 ± 8.34 cm; body mass: 74.4 ± 13.0 kg; % body fat: 9.45 ± 4.8) participated in the study. Subjects underwent four different randomized warm-up protocols separated by at least 48 hours. The warm-up schemes were: 1. no conditioning contraction protocol (NCC); 2. dynamic stretching (DS); 3. prolonged intermittent low-intensity isometric exercise (ST); and, 4. ST with an additional external load equal to 30% of body weight (ST + 30% BW). All interventions were preceded by a general warm-up. Results from one-way repeated measures ANOVA demonstrated a significant difference in countermovement jump (CMJ) at F(3,60) = 10.2, ηρ² = 0.337, p < 0.01. Post hoc analysis revealed a significant difference in CMJ performance in DS when compared to NCC and ST + 30% BW. No significant difference in CMJ was observed between DS and ST. CMJ scores in NCC, ST, and ST + 30% BW were non-significant. There was a significant difference in speed; F(3, 60) = 6.61, ηρ² = 0.248, p < 0.01. Post hoc analysis revealed significantly better time in DS than NCC and ST. However, no difference in speed was observed between DS and ST + 30% BW. Similarly, speed was similar in NCC, ST and ST + 30% BW. A significant difference in agility performance was also observed; F(3, 60) = 24.1, ηρ²= 0.546, p < 0.01. Post hoc analysis revealed significantly greater performance gains in DS than NCC. No significant difference in agility was observed in DS, ST and ST + 30% BW. In conclusion, a prolonged intermittent low-intensity isometric protocol using bodyweight only showed similar benefits with dynamic stretching in countermovement jump performance. When the same isometric condition with additional load equal to 30% of bodyweight was applied, effects in speed and agility were similar to dynamic stretching.     

Reliability of pedigree-based and genomic evaluations in selected populations

Background

Reliability is an important parameter in breeding. It measures the precision of estimated breeding values (EBV) and, thus, potential response to selection on those EBV. The precision of EBV is commonly measured by relating the prediction error variance (PEV) of EBV to the base population additive genetic variance (base PEV reliability), while the potential for response to selection is commonly measured by the squared correlation between the EBV and breeding values (BV) on selection candidates (reliability of selection). While these two measures are equivalent for unselected populations, they are not equivalent for selected populations. The aim of this study was to quantify the effect of selection on these two measures of reliability and to show how this affects comparison of breeding programs using pedigree-based or genomic evaluations.

Methods

Two scenarios with random and best linear unbiased prediction (BLUP) selection were simulated, where the EBV of selection candidates were estimated using only pedigree, pedigree and phenotype, genome-wide marker genotypes and phenotype, or only genome-wide marker genotypes. The base PEV reliabilities of these EBV were compared to the corresponding reliabilities of selection. Realized genetic selection intensity was evaluated to quantify the potential of selection on the different types of EBV and, thus, to validate differences in reliabilities. Finally, the contribution of different underlying processes to changes in additive genetic variance and reliabilities was quantified.

Results

The simulations showed that, for selected populations, the base PEV reliability substantially overestimates the reliability of selection of EBV that are mainly based on old information from the parental generation, as is the case with pedigree-based prediction. Selection on such EBV gave very low realized genetic selection intensities, confirming the overestimation and importance of genotyping both male and female selection candidates. The two measures of reliability matched when the reductions in additive genetic variance due to the Bulmer effect, selection, and inbreeding were taken into account.

Conclusions

For populations under selection, EBV based on genome-wide information are more valuable than suggested by the comparison of the base PEV reliabilities between the different types of EBV. This implies that genome-wide marker information is undervalued for selected populations and that genotyping un-phenotyped female selection candidates should be reconsidered.

Electronic supplementary material

The online version of this article (doi:10.1186/s12711-015-0145-1) contains supplementary material, which is available to authorized users.     

Predicting heterosis for egg production traits in crossbred offspring of individual White Leghorn sires using genome-wide SNP data

Background

The development of a reliable method to predict heterosis would greatly improve the efficiency of commercial crossbreeding schemes. Extending heterosis prediction from the line level to the individual sire level would take advantage of variation between sires from the same pure line, and further increase the use of heterosis in crossbreeding schemes. We aimed at deriving the theoretical expectation for heterosis due to dominance in the crossbred offspring of individual sires, and investigating how much extra variance in heterosis can be explained by predicting heterosis at the individual sire level rather than at the line level. We used 53 421 SNP (single nucleotide polymorphism) genotypes of 3427 White Leghorn sires, allele frequencies of six White Leghorn dam-lines and cage-based records on egg number and egg weight of ~210 000 crossbred hens.

Results

We derived the expected heterosis for the offspring of individual sires as the between- and within-line genome-wide heterozygosity excess in the offspring of a sire relative to the mean heterozygosity of the pure lines. Next, we predicted heterosis by regressing offspring performance on the heterozygosity excess. Predicted heterosis ranged from 7.6 to 16.7 for egg number, and from 1.1 to 2.3 grams for egg weight. Between-line differences accounted for 99.0% of the total variance in predicted heterosis, while within-line differences among sires accounted for 0.7%.

Conclusions

We show that it is possible to predict heterosis at the sire level, thus to distinguish between sires within the same pure line with offspring that show different levels of heterosis. However, based on our data, variation in genome-wide predicted heterosis between sires from the same pure line was small; most differences were observed between lines. We hypothesise that this method may work better if predictions are based on SNPs with identified dominance effects.