Eicosanoids in breast cancer patients before and after mastectomy.

In 19 patients with a malignant breast tumor, tumor tissue and blood were taken to determine the eicosanoid profile and platelet aggregation. Values were compared with those of patients with benign tumors (n = 4), or undergoing a mammary reduction (n = 7). Postoperatively, blood was taken as well in order to compare pre- and postoperative values. Eicosanoids were measured in peripheral blood monocytes and mammary tissue by means of HPLC; furthermore, TXA2, 6-keto-PGF1 alpha, and PGE2 were determined by RIA. Differences in pre- and postoperative values of cancer patients were seen in plasma RIA values: PGE2 and 6-k-PGF1 alpha were significantly higher preoperatively when compared with postoperatively, however, such differences were seen in the control groups as well. Compared to benign tumor or mammary reduction test material the eicosanoid profile of tissue obtained from malignant mammary tumors showed important differences. Except for PGF2 alpha, HHT and 15-HETE no detectable quantities of eicosanoids were found in the non-tumor material, whereas in the malignant tumor material substantial quantities of a number of eicosanoid metabolites were present. Statistically significant correlations could be established between patient/histopathology data and the results of the platelet aggregation assays, e.g. between menopausal status and ADP aggregation; oestrogen receptor (+/-) and collagen and arachidonic acid aggregation, inflammatory cell infiltration score and arachidonic acid aggregation and fibrosis score and ADP aggregation. The results show that eicosanoid synthesis in material from mammary cancer patients is different from that in benign mammary tissue. The implications, in particular, in relation to future prognosis of the patient, remain obscure.     

Drug resistance in rat colon cancer cell lines is associated with minor changes in susceptibility to cytotoxic cells

The development of resistance to anticancer drugs urges the search for different treatment modalities. Several investigators have reported the concomitant development of drug resistance and resistance to natural killer (NK), lymphokine-activated killer (LAK) or monocyte/macrophage cell lysis, while others described unchanged or even increased susceptibility. We investigated this subject in the rat colon carcinoma cell line, CC531-PAR, which is intrinsically multidrug-resistant (MDR), and in three sublines derived from this parental cell line: a cell line with an increased MDR phenotype (CC531-COL), a revertant line from CC531-COL (CC531-REV), which demonstrates enhanced sensitivity to anticancer drugs of the MDR phenotype, and an independently developed cisplatin-resistant line (CC531-CIS). In a 4-h⁵¹Cr-release assay we found no difference in susceptibility to NK cell lysis. No significant differences in lysability by adherent LAK (aLAK) cells were observed in a 4-h assay. In a prolonged 20-h⁵¹Cr-release assay an enhanced sensitivity to aLAK-cell-mediated lysis was observed in the revertant, P-glycoprotein-negative cell line and in the cisplatin-resistant cell line (CC531-CIS). None of the cell lines was completely resistant to lysis by aLAK cells. Therefore, a role for immunotherapy in the treatment of drug-resistant tumors remains a realistic option.     

Minimizing inbreeding by managing genetic contributions across generations

Here we present the strategy that achieves the lowest possible rate of inbreeding (DeltaF) for a population with unequal numbers of sires and dams with random mating. This new strategy results in a DeltaF as much as 10% lower than previously achieved. A simple and efficient approach to reducing inbreeding in small populations with sexes of unequal census number is to impose a breeding structure where parental success is controlled in each generation. This approach led to the development of strategies for selecting replacements each generation that were based upon parentage, e.g., a son replacing its sire. This study extends these strategies to a multigeneration round robin scheme where genetic contributions of ancestors to descendants are managed to remove all uncertainties about breeding roles over generations; i.e., male descendants are distributed as equally as possible among dams. In doing so, the sampling variance of genetic contributions within each breeding category is eliminated and consequently DeltaF is minimized. Using the concept of long-term genetic contributions, the asymptotic DeltaF of the new strategy for random mating, M sires and d dams per sire, is phi/(12M), where phi = [1 + 2((1)/(4))(d)]. Predictions were validated using Monte Carlo simulations. The scheme was shown to achieve the lowest possible DeltaF using pedigree alone and showed that further reductions in DeltaF below that obtained from random mating arise from preferential mating of relatives and not from their avoidance.     

Why genetic selection to reduce the prevalence of infectious diseases is way more promising than currently believed

Genetic selection for improved disease resistance is an important part of strategies to combat infectious diseases in agriculture. Quantitative genetic analyses of binary disease status, however, indicate low heritability for most diseases, which restricts the rate of genetic reduction in disease prevalence. Moreover, the common liability threshold model suggests that eradication of an infectious disease via genetic selection is impossible because the observed-scale heritability goes to zero when the prevalence approaches zero. From infectious disease epidemiology, however, we know that eradication of infectious diseases is possible, both in theory and practice, because of positive feedback mechanisms leading to the phenomenon known as herd immunity. The common quantitative genetic models, however, ignore these feedback mechanisms. Here, we integrate quantitative genetic analysis of binary disease status with epidemiological models of transmission, aiming to identify the potential response to selection for reducing the prevalence of endemic infectious diseases. The results show that typical heritability values of binary disease status correspond to a very substantial genetic variation in disease susceptibility among individuals. Moreover, our results show that eradication of infectious diseases by genetic selection is possible in principle. These findings strongly disagree with predictions based on common quantitative genetic models, which ignore the positive feedback effects that occur when reducing the transmission of infectious diseases. Those feedback effects are a specific kind of Indirect Genetic Effects; they contribute substantially to the response to selection and the development of herd immunity (i.e., an effective reproduction ratio less than one).     

Breeding Value and Variance Component Estimation from Data Containing Inbred Individuals: Application to Gynogenetic Families in Common Carp (Cyprinus Carpio L.)

Under gynogenetic reproduction, offspring receive genes only from their dams and completely homozygous offspring are produced within one generation. When gynogenetic reproduction is applied to fully inbred individuals, homozygous clone lines are produced. A mixed model method was developed for breeding value and variance component estimation in gynogenetic families, which requires the inverse of the numerator relationship matrix. A general method for creating the inverse for a population with unusual relationships between animals is presented, which reduces to simple rules as is illustrated for gynogenetic populations. The presence of clones in gynogenetic populations causes singularity of the numerator relationship matrix. However, clones can be regarded as repeated observations of the same genotype, which can be accommodated by modifying the incidence matrix, and by considering only unique genotypes in the estimation procedure. Optimum gynogenetic sib family sizes for estimating heritabilities and estimates of their accuracy were derived and compared to those for conventional full-sib designs. This was done by means of a deterministic derivation and by stochastic simulation using Gibbs sampling. Optimum family sizes were smallest for gynogenetic families. Only for low heritabilities, there was a small advantage in accuracy under the gynogenetic design.     

A general procedure for predicting rates of inbreeding in populations undergoing mass selection

Predictions of rates of inbreeding (DeltaF), based on the concept of long-term genetic contributions assuming the infinitesimal model, are developed for populations with discrete or overlapping generations undergoing mass selection. Phenotypes of individuals are assumed to be recorded prior to reproductive age and to remain constant over time. The prediction method accounts for inheritance of selective advantage both within and between age classes and for changing selection intensities with age. Terms corresponding to previous methods that assume constant selection intensity with age are identified. Predictions are accurate (relative errors < or =8%), except for cases with extreme selection intensities in females in combination with high heritability. With overlapping generations DeltaF reaches a maximum when parents are equally distributed over age classes, which is mainly due to selection of the same individuals in consecutive years. DeltaF/year decreases much more slowly compared to DeltaF/generation as the number of younger individuals increases, whereas the decrease is more similar as the number of older individuals increases. The minimum DeltaF (per year or per generation) is obtained when most parents were in the later age classes, which is mainly due to an increased number of parents per generation. With overlapping generations, the relationship between heritability and DeltaF is dependent on the age structure of the population.     

Response to genomic selection: The Bulmer effect and the potential of genomic selection when the number of phenotypic records is limiting

Background

Over the last ten years, genomic selection has developed enormously. Simulations and results on real data suggest that breeding values can be predicted with high accuracy using genetic markers alone. However, to reach high accuracies, large reference populations are needed. In many livestock populations or even species, such populations cannot be established when traits are difficult or expensive to record, or when the population size is small. The value of genomic selection is then questionable.

Methods

In this study, we compare traditional breeding schemes based on own performance or progeny information to genomic selection schemes, for which the number of phenotypic records is limiting. Deterministic simulations were performed using selection index theory. Our focus was on the equilibrium response obtained after a few generations of selection. Therefore, we first investigated the magnitude of the Bulmer effect with genomic selection.

Results

Results showed that the reduction in response due to the Bulmer effect is the same for genomic selection as for selection based on traditional BLUP estimated breeding values, and is independent of the accuracy of selection. The reduction in response with genomic selection is greater than with selection based directly on phenotypes without the use of pedigree information, such as mass selection. To maximize the accuracy of genomic estimated breeding values when the number of phenotypic records is limiting, the same individuals should be phenotyped and genotyped, rather than genotyping parents and phenotyping their progeny. When the generation interval cannot be reduced with genomic selection, large reference populations are required to obtain a similar response to that with selection based on BLUP estimated breeding values based on own performance or progeny information. However, when a genomic selection scheme has a moderate decrease in generation interval, relatively small reference population sizes are needed to obtain a similar response to that with selection on traditional BLUP estimated breeding values.

Conclusions

When the trait of interest cannot be recorded on the selection candidate, genomic selection schemes are very attractive even when the number of phenotypic records is limited, because traditional breeding requires progeny testing schemes with long generation intervals in those cases.     

Using pooled data to estimate variance components and breeding values for traits affected by social interactions

Background

Through social interactions, individuals affect one another’s phenotype. In such cases, an individual’s phenotype is affected by the direct (genetic) effect of the individual itself and the indirect (genetic) effects of the group mates. Using data on individual phenotypes, direct and indirect genetic (co)variances can be estimated. Together, they compose the total genetic variance that determines a population’s potential to respond to selection. However, it can be difficult or expensive to obtain individual phenotypes. Phenotypes on traits such as egg production and feed intake are, therefore, often collected on group level. In this study, we investigated whether direct, indirect and total genetic variances, and breeding values can be estimated from pooled data (pooled by group). In addition, we determined the optimal group composition, i.e. the optimal number of families represented in a group to minimise the standard error of the estimates.

Methods

This study was performed in three steps. First, all research questions were answered by theoretical derivations. Second, a simulation study was conducted to investigate the estimation of variance components and optimal group composition. Third, individual and pooled survival records on 12 944 purebred laying hens were analysed to investigate the estimation of breeding values and response to selection.

Results

Through theoretical derivations and simulations, we showed that the total genetic variance can be estimated from pooled data, but the underlying direct and indirect genetic (co)variances cannot. Moreover, we showed that the most accurate estimates are obtained when group members belong to the same family. Additional theoretical derivations and data analyses on survival records showed that the total genetic variance and breeding values can be estimated from pooled data. Moreover, the correlation between the estimated total breeding values obtained from individual and pooled data was surprisingly close to one. This indicates that, for survival in purebred laying hens, loss in response to selection will be small when using pooled instead of individual data.

Conclusions

Using pooled data, the total genetic variance and breeding values can be estimated, but the underlying genetic components cannot. The most accurate estimates are obtained when group members belong to the same family.     

The joint effects of kin, multilevel selection and indirect genetic effects on response to genetic selection

Kin and levels-of-selection models are common approaches for modelling social evolution. Indirect genetic effect (IGE) models represent a different approach, specifying social effects on trait values rather than fitness. We investigate the joint effect of relatedness, multilevel selection and IGEs on response to selection. We present a measure for the degree of multilevel selection, which is the natural partner of relatedness in expressions for response. Response depends on both relatedness and the degree of multilevel selection, rather than only one or the other factor. Moreover, response is symmetric in relatedness and the degree of multilevel selection, indicating that both factors have exactly the same effect. Without IGEs, the key parameter is the product of relatedness and the degree of multilevel selection. With IGEs, however, multilevel selection without relatedness can explain evolution of social traits. Thus, next to relatedness and multilevel selection, IGEs are a key element in the genetical theory of social evolution.     

Protein interaction verification and functional annotation by integrated analysis of genome-scale data

Assays capable of determining the properties of thousands of genes in parallel present challenges with regard to accurate data processing and functional annotation. Collections of microarray expression data are applied here to assess the quality of different high-throughput protein interaction data sets. Significant differences are found. Confidence in 973 out of 5342 putative two-hybrid interactions from S. cerevisiae is increased. Besides verification, integration of expression and interaction data is employed to provide functional annotation for over 300 previously uncharacterized genes. The robustness of these approaches is demonstrated by experiments that test the in silico predictions made. This study shows how integration improves the utility of different types of functional genomic data and how well this contributes to functional annotation.