Correction to: Lentinula edodes as a Source of Bioelements Released Into Artificial Digestive Juices and Potential Anti-inflammatory Material

Biological Trace Element Research - The original version of this article unfortunately contained a mistake.     

Identification of candidate genes and mutations in QTL regions for immune responses in chicken

There are two categories of immune responses – innate and adaptive immunity – both having polygenic backgrounds and a significant environmental component. In our study, adaptive immunity was represented by the specific antibody response toward keyhole limpet hemocyanin (KLH); innate immunity was represented by natural antibodies toward lipopolysaccharide (LPS) and lipoteichoic acid (LTA). Defining genetic bases of immune responses leads from defining quantitative trait loci (QTL) toward a single mutation responsible for variation in the phenotypic trait. The goal of the reported study was to define candidate genes and mutations for the immune traits of interest in chicken by performing an association study of SNPs located in candidate genes defined in QTL regions. Candidate genes and SNPs in QTL regions were selected in silico. SNP association was based on a custom SNP panel, GoldenGate genotyping assay (Illumina) and two statistical models: random mixed model and CAR score. The most significant SNP for immune response toward KLH was located in the JMJD6 gene located on GGA18. Four SNPs in candidate genes FOXJ1 (GGA18), EPHB1 (GGA9), PTGER4 (GGAZ) and PRKCB (GGA14) showed association with natural antibodies for LPS. A single SNP in ITGB4 (GGA18) was associated with natural antibodies for LTA. All associated SNPs mentioned above showed additive effects.     

Statistical modelling of growth using a mixed model with orthogonal polynomials

In statistical modelling, the effects of single-nucleotide polymorphisms (SNPs) are often regarded as time-independent. However, for traits recorded repeatedly, it is very interesting to investigate the behaviour of gene effects over time. In the analysis, simulated data from the 13th QTL-MAS Workshop (Wageningen, The Netherlands, April 2009) was used and the major goal was the modelling of genetic effects as time-dependent. For this purpose, a mixed model which describes each effect using the third-order Legendre orthogonal polynomials, in order to account for the correlation between consecutive measurements, is fitted. In this model, SNPs are modelled as fixed, while the environment is modelled as random effects. The maximum likelihood estimates of model parameters are obtained by the expectation–maximisation (EM) algorithm and the significance of the additive SNP effects is based on the likelihood ratio test, with p-values corrected for multiple testing. For each significant SNP, the percentage of the total variance contributed by this SNP is calculated. Moreover, by using a model which simultaneously incorporates effects of all of the SNPs, the prediction of future yields is conducted. As a result, 179 from the total of 453 SNPs covering 16 out of 18 true quantitative trait loci (QTL) were selected. The correlation between predicted and true breeding values was 0.73 for the data set with all SNPs and 0.84 for the data set with selected SNPs. In conclusion, we showed that a longitudinal approach allows for estimating changes of the variance contributed by each SNP over time and demonstrated that, for prediction, the pre-selection of SNPs plays an important role.     

Assessing the degree of stratification between closely related Holstein-Friesian populations

Genomic information is an important part of the routine evaluation of dairy cattle and provides the wide availability of animals genotyped using single nucleotide polymorphism (SNP) microarrays. We analyzed 2243 Polish and 2294 German Holstein-Friesian bulls genotyped using the Illumina BovineSNP50 BeadChip. For each bull, estimated breeding values (EBVs) calculated from national routine genetic evaluation were available for production traits and for somatic cell score (SCS). Separately for each population, we estimated SNP haplotypes, pairwise linkage disequilibrium (LD), and SNP effects. The SNP genetic covariance between both populations was estimated using a bivariate mixed model. The average LD was lower in the Polish than in the German population and, with increasing genomic distance, LD decays 1.7 times more rapidly in German than in Polish cattle. The comparison of SNP allele frequencies for base populations estimated separately using Polish and German data revealed a very good agreement. The comparison of genetic effects corresponding to various window lengths defined in bp emerged a systematic pattern: regardless of the length of the compared region, few significant differences were found for production traits, while many were observed for SCS. For each trait, the German population had much higher SNP variances than the Polish population and the genetic covariance estimates were all positive. Depending on traits’ inheritance mode, the additive genetic variation can be stored in many genes following the infinitesimal model (like for SCS) or distributed between genes with high effects and the polygenic “background” (like for production traits). Accounting for those differences has implications on the prospective international genomic evaluation.     

Fitting and validating the genomic evaluation model to Polish Holstein-Friesian cattle

The aim of the study was to fit the genomic evaluation model to Polish Holstein-Friesian dairy cattle. A training data set for the estimation of additive effects of single nucleotide polymorphisms (SNPs) consisted of 1227 Polish Holstein-Friesian bulls. Genotypes were obtained by the use of Illumina BovineSNP50 Genotyping BeadChip. Altogether 29 traits were considered: milk-, fat- and protein- yields, somatic cell score, four female fertility traits, and 21 traits describing conformation. The prediction of direct genomic values was based on a mixed model containing deregressed national proofs as a dependent variable and random SNP effects as independent variables. The correlations between direct genomic values and conventional estimated breeding values estimated for the whole data set were overall very high and varied between 0.98 for production traits and 0.78 for non return rates for cows. For the validation data set of 232 bulls the corresponding correlations were 0.38 for milk-, 0.37 for protein-, and 0.32 for fat yields, while the correlations between genomic enhanced breeding values and conventional estimated breeding values for the four traits were: 0.43, 0.44, 0.31, and 0.35. This model was able to pass the interbull validation criteria for genomic selection, which indicates that it is realistic to implement genomic selection in Polish Holstein-Friesian cattle.     

Modelling QTL effect on BTA06 using random regression test day models

In statistical models, a quantitative trait locus (QTL) effect has been incorporated either as a fixed or as a random term, but, up to now, it has been mainly considered as a time-independent variable. However, for traits recorded repeatedly, it is very interesting to investigate the variation of QTL over time. The major goal of this study was to estimate the position and effect of QTL for milk, fat, protein yields and for somatic cell score based on test day records, while testing whether the effects are constant or variable throughout lactation. The analysed data consisted of 23 paternal half-sib families (716 daughters of 23 sires) of Chinese Holstein-Friesian cattle genotyped at 14 microsatellites located in the area of the casein loci on BTA6. A sequence of three models was used: (i) a lactation model, (ii) a random regression model with a QTL constant in time and (iii) a random regression model with a QTL variable in time. The results showed that, for each production trait, at least one significant QTL exists. For milk and protein yields, the QTL effect was variable in time, while for fat yield, each of the three models resulted in a significant QTL effect. When a QTL is incorporated into a model as a constant over time, its effect is averaged over lactation stages and may, thereby, be difficult or even impossible to be detected. Our results showed that, in such a situation, only a longitudinal model is able to identify loci significantly influencing trait variation.     

Stromal myofibroblasts in breast cancer: relations between their occurrence, tumor grade and expression of some tumour markers

It is suggested that tumour stromal myofibroblasts exert an unfavourable effect on the biology of breast cancer. We are aware of only a single study which examined relationships between manifestation of myofibroblasts in the stroma of breast cancer and clinicopathological data of the patients. The present study was aimed at estimation of the effect exerted by myofibroblasts present in the tumour stroma on principal pathological parameters and on expression of Ki67, P53 and HER-2 proteins in the group of the most frequent breast cancers, the ductal cancers. In paraffin sections of 60 ductal breast cancers (20 cases in G1, 20 in G2 and 20 in G3), immunohistochemical reactions were performed to detect expression of smooth muscle actin (SMA) in order to visualize myofibroblasts, Ki67, P53 and HER-2. The studies demonstrated that the most numerous myofibroblasts were present in G3 cases and they were the least frequent in G1 cases (P = 0.02). Positive correlations were observed between the presence of myofibroblasts in tumour stroma and expression of Ki67 and HER-2 in breast cancer cells in the entire group (P < 0.001 and P = 0.001, respectively), in G2 cases (P = 0.003 and P = 0.03) and in G3 cases (P = 0.01 and P = 0.03). Considering that the higher grade, Ki67 and HER-2 are thought to represent unfavourable prognostic factors, the elevated content of myofibroblasts in tumour stroma is probably typical for cases with worse prognosis.     

Decreased expression of p16 in ovarian cancers represents an unfavourable prognostic factor

Decreased expression of p16 may result from hypermethylation of the promoter or from deletion of the gene. It can lead to intensified proliferation of neoplastic cells and to cytostatic drug resistance. The study was aimed at the examination of prognostic value of p16 expression in relation to Ki67 and caspase-3 in ovarian cancers using immunohistochemistry. The immunohistochemical studies were performed on 73 paraffin-embedded samples of ovarian cancers from 43 patients and samples from 6 healthy ovaries. We have used monoclonal antibodies against p16. ABC method and DAB were used for antigens visualisation. The intensity of the immunohistochemical reactions was appraised using the semi-quantitative IRS scale. In healthy ovaries we have shown strong reaction in the nuclei of surface epithelium. In the case of studied ovarian cancers, the reaction of a nuclear and cytoplasmic localization was obtained. The mean overall immunoreactivity score of nuclear p16 expression amounted to 5.30+/-3.44 SD in primary laparotomy material and 6.61+/-4.34 SD in secondary cytoreduction material. Statistical analysis demonstrated that lower p16 expression was typical of the younger patients and the patients who died. Kaplan-Meier's analysis proved that lower expression of p16 was characteristic of cases with shorter overall survival. In the present study we have demonstrated that lowered p16 expression represented an unfavourable prognostic index in ovarian cancer. Lowered p16 expression was also typical for chemotherapy-resistant ceases (cases of lower caspase-3 and higher Ki67 at secondary cytoreduction expression).