Antonios Kominakis

A preliminary study of the application of artificial neural networks to prediction of milk yield in dairy sheep

The aim of this study was to test the usefulness of artificial neural networks (ANNs) for predicting lactation as well as test-day milk yield(s) in Chios dairy sheep on the basis of a few (2–4) available test-day records at the beginning of a lactation period. The ANN employed was a neural network-like system with some advantages over other ANNs. No selection of learning coefficients, of the number of hidden layers or of the number of neurons in the layers was required. The effect on the networks predictive ability of the number of records used in the training phase, the number of input variables (i.e. test-day records) and data preprocessing was investigated. Input variables were the county, herd, lactation, lambing month, litter size, milk yield recorder, test day and days in milk (after lambing) when the first milk sample was obtained. Various criteria of goodness of prediction of lactation as well as of test-day yields were used, including Pearson and rank correlations between observed and predicted yields; the average difference between observed and predicted yields; the difference between their standard deviations; the standard deviation of differences between observed and predicted yields, and the ratio between it and the observed mean value. The average difference between observed and predicted yields was generally statistically non-significant (P<0.05) while predicted standard deviations were underestimated. Values of Pearson and rank correlations between observed and predicted lactation yields ranged from 0.87 to 0.97. In prediction of test-day yields, correlation estimates were generally lower than those obtained in lactation yields and declined as the interval between yields increased. Better predictions were obtained as the number of records used for training increased from 500 to 1000, the number of test-day records increased from 2 to 4, and data preprocessing (i.e. encoding of data) was employed. Training the network for low prediction error of a specific parameter did not improve its overall performance. In contrast, network specialization (i.e. using training data for specific parameters prediction) improved the predictive ability of the parameter in question. Results illustrated the potential effectiveness of ANNs in predicting milk yield in dairy sheep and appeared to justify further pursuit of this research.

Glucocorticoid receptor, nuclear factor kappaB, activator protein-1 and C-jun N-terminal kinase in systemic lupus erythematosus patients.

Objective: Due to the crucial role of the glucocorticoid receptor (GR), nuclear factor ĸB (NFĸB), activator protein-1 (AP-1) and c-jun N-terminal kinase (JNK) in regulating inflammatory mediators and immune responses, we investigated their potential role in systemic lupus erythematosus (SLE). Patients and Methods: Whole cell and nuclear extracts from peripheral blood lymphocytes, isolated from 25 SLE patients and 25 controls, were immunoblotted using GR, p65/NFĸB, c-fos and JNK1 antibodies. The electrophoretic mobility shift assay (EMSA) assessed GR, NFĸB and AP-1-DNA binding in nuclear aliquots. Associations with the disease state and the doses of corticosteroids administered were studied. Results: (i) SLE patients had lower GR-DNA binding (p < 0.001), NFĸB-DNA binding (p < 0.001) and whole cell c-fos (p < 0.01) but higher nuclear NFĸB (p < 0.01). (ii) SLE patients and controls had similar AP-1-DNA binding, nuclear c-fos, GR and JNK, whole cell GR, NFĸB and JNK. (iii) No differences were detected between active and non-active SLE or high- and low-dose corticosteroid patients. (iv) In SLE, increases in GR-DNA binding were associated with increases in NFĸB-DNA binding (p < 0.0001), and increases in nuclear JNK were associated with increases in AP-1-DNA binding (p < 0.01). (v) In controls, increases in GR-DNA binding were associated with increases in AP-1-DNA binding (p < 0.001). Conclusion: We suggest disturbed GR, NFĸB, AP-1 and JNK signaling in SLE, characterized by a reduced GR- and NFĸB-DNA binding, a significant association between GR-mediated and NFĸB-driven pathways, and a significant correlation between nuclear JNK- and AP-1-driven pathways. These disturbances may contribute to abnormal cytokine production and the etiopathogenesis of SLE.

Genetic modelling of test day records in dairy sheep using orthogonal Legendre polynomials

Test day milk yields of three lactations in Sfakia sheep were analyzed fitting a random regression (RR) model, regressing on orthogonal polynomials of the stage of the lactation period, i.e. days in milk. Univariate (UV) and multivariate (MV) analyses were also performed for four stages of the lactation period, represented by average days in milk, i.e. 15, 45, 70 and 105 days, to compare estimates obtained from RR models with estimates from UV and MV analyses. The total number of test day records were 790, 1314 and 1041 obtained from 214, 342 and 303 ewes in the first, second and third lactation, respectively. Error variances and covariances between regression coefficients were estimated by restricted maximum likelihood. Models were compared using likelihood ratio tests (LRTs). Log likelihoods were not significantly reduced when the rank of the orthogonal Legendre polynomials (LPs) of lactation stage was reduced from 4 to 2 and homogenous variances for lactation stages within lactations were considered. Mean weighted heritability estimates with RR models were 0.19, 0.09 and 0.08 for first, second and third lactation, respectively. The respective estimates obtained from UV analyses were 0.14, 0.12 and 0.08, respectively. Mean permanent environmental variance, as a proportion of the total, was high at all stages and lactations ranging from 0.54 to 0.71. Within lactations, genetic and permanent environmental correlations between lactation stages were in the range from 0.36 to 0.99 and 0.76 to 0.99, respectively. Genetic parameters for additive genetic and permanent environmental effects obtained from RR models were different from those obtained from UV and MV analyses.

Combined GWAS and ‘guilt by association’-based prioritization analysis identifies functional candidate genes for body size in sheep

BackgroundBody size in sheep is an important indicator of productivity, growth and health as well as of environmental adaptation. It is a composite quantitative trait that has been studied with high-throughput genomic methods, i.e. genome-wide association studies (GWAS) in various mammalian species. Several genomic markers have been associated with body size traits and genes have been identified as causative candidates in humans, dog and cattle. A limited number of related GWAS have been performed in various sheep breeds and have identified genomic regions and candidate genes that partly account for body size variability. Here, we conducted a GWAS in Frizarta dairy sheep with phenotypic data from 10 body size measurements and genotypic data (from Illumina ovineSNP50 BeadChip) for 459 ewes.ResultsThe 10 body size measurements were subjected to principal component analysis and three independent principal components (PC) were constructed, interpretable as width, height and length dimensions, respectively. The GWAS performed for each PC identified 11 significant SNPs, at the chromosome level, one on each of the chromosomes 3, 8, 9, 10, 11, 12, 19, 20, 23 and two on chromosome 25. Nine out of the 11 SNPs were located on previously identified quantitative trait loci for sheep meat, production or reproduction. One hundred and ninety-seven positional candidate genes within a 1-Mb distance from each significant SNP were found. A guilt-by-association-based (GBA) prioritization analysis (PA) was performed to identify the most plausible functional candidate genes. GBA-based PA identified 39 genes that were significantly associated with gene networks relevant to body size traits. Prioritized genes were identified in the vicinity of all significant SNPs except for those on chromosomes 10 and 12. The top five ranking genes were TP53, BMPR1A, PIK3R5, RPL26 and PRKDC.ConclusionsThe results of this GWAS provide evidence for 39 causative candidate genes across nine chromosomal regions for body size traits, some of which are novel and some are previously identified candidates from other studies (e.g. TP53, NTN1 and ZNF521). GBA-based PA has proved to be a useful tool to identify genes with increased biological relevance but it is subjected to certain limitations.

Sequencing analysis of the human glucocorticoid receptor (NR3C1) gene in multiple sclerosis patients

Various specific human glucocorticoid receptor (NR3C1) gene polymorphisms have been described in multiple sclerosis (MS) patients and correlated with disease progression, susceptibility and aggressiveness. Herein, we investigated the presence of gene alterations in the entire coding region of the NR3C1 in MS patients of variable clinical status (CIS, RRMS and SPMS) and the association(s) of these alterations with severity of disease (EDSS), response to glucocorticoid (GC) treatment and clinical improvement. Sixty Caucasian Greek MS patients were included. Sequencing the coding sequences and intron-exon boundaries of the NR3C1 did not reveal the presence of mutation(s) in any of the MS patients. Three previously described polymorphisms were detected: p.N363S (rs6195), p.N766N (rs6196) and c.1469-16G>T (rs6188). None of the identified alleles/genotypes were found to be associated with the severity of disease, response to glucocorticoids and disease subtypes. Known polymorphism, such as ER22/23EK that has been previously detected in MS patients, was not detected. There is a considerable ethnicity-related variation in the frequency of the NR3C1 polymorphisms. Although a genetic basis of the glucocorticoid sensitivity exists in healthy population, in the presence of chronic inflammation and abundance of cytokines--such in MS patients--other factors appear to play a more important role in GC sensitivity.