Copy number variations affecting growth curve parameters in a crossbred chicken population.

Copy number variations (CNVs) are key structural variations in the genome and may contribute to phenotypic differences. In this study, we used a F2 chicken population created from reciprocal crossing between fast-growing Arian broiler line and Urmia native chickens. The chickens were genotyped by 60 K SNP BeadChip, and PennCNV algorithm was used to detect genome-wide CNVs. The growth curve parameters of W0, k, L, Wf, Wi, ti and average GR were used as phenotypic data. The association between CNV and growth curve parameters was carried out using the CNVRanger R/Bioconductor package. Five CNV regions (CNVRs) were chosen for the validation experiment using qPCR. Gene enrichment analysis was done using WebGestalt. The STRING database was used to search for significant pathways. The results identified 966 CNVs and 600 CNVRs including 468 gains, 67 losses, and 65 both events on autosomal chromosomes. Validation of the CNVRs obtained from the qPCR assay were 79 % consistent with the prediction by PennCNV. A total of 43 significant CNVs were obtained for the seven growth curve parameters. The 416 genes annotated for significant CNVs. Six genes out of 416 genes were most related to growth curve parameters. These genes were LCP2, Dock2, CD80, CYFIP1, NIPA1 and NIPA2. Some of these genes in their biological process were associated with the growth, reproduction and development of cells or organs that ultimately lead to the growth of the body. The results of the study could pave the way for better understanding the molecular process of CNVs and growth curve parameters in birds.

Genetic variants in the TAC1 transcriptional regulatory region affect on trainability and excitability levels in Belgian Malinois dogs.

BACKGROUND: Trainability in dogs is affected by learning aptitude and memory capacity. While this trait has a heritable basis in canines, the specific genetic loci responsible for it remain unknown. Our previous results suggested that the BDNF, CCK and TAC1 genes are associated with learning and memory in canines. Experimental validation is crucial to confirm the effects of these candidate genes on trainability. Understanding the genetic foundation of this trait would offer insight into the inheritance pattern of complex behavioural characteristics. OBJECTIVE: This study aimed to assess the genetic variations within candidate genes and explore their potential associations with behavioural phenotypes in dogs. METHODS: The behavioural characteristics of 123 male Belgian Malinois dogs were assessed using a customised questionnaire. Target regions of candidate genes were screened for genetic variation by single-strand conformational polymorphism (SSCP). Following that, SSCP banding patterns were sequenced, and putative transcription factor binding sites were predicted using bioinformatics tools. Quantitative association analysis between identified genetic variants and behavioural trait scores was performed using the general linear model (GLM). RESULTS: Sequencing the coding and flanking regions revealed three mutations (c.-89C>T, c.-162G>C and c.*33T>A) in the dog TAC1 gene. Bioinformatics analysis predicted two single nucleotide polymorphisms (SNPs) were located within the putative TAC1 promoter and could disrupt transcription factor binding sites. Statistical tests revealed that the c.-89C>T was significantly associated with excitability (p < 0.01), while the c.-162G>C was significantly associated with trainability level (p < 0.05). CONCLUSION: In summary, we identified two regulatory SNPs in the 5'-UTR promoter region of the TAC1 gene that are associated with excitability and trainability in Belgian Malinois dogs. These genetic variations have the potential to alter the binding sites of transcription factors NRF1 and OTX1, consequently influencing TAC1 expression and related behavioural characteristics. Our findings implicate TAC1 polymorphisms as candidates influencing breed-specific behavioural characteristics in canines. Further studies on diverse breeds of dogs are necessary to validate these SNPs' effects.

Assessing the nutritional equivalency of DL-methionine and L-methionine in broiler chickens: a meta-analytical study.

The purpose of this study was to perform a meta-analysis comparing the biological efficiency of DL-methionine and L-methionine (DL-Met and L-Met) in broiler nutrition. This analysis was based on a dataset comprising experiment results published in peer-reviewed papers since 2007. All experiments investigated the response of broilers (aged 0-21 d) to different dietary concentrations of DL- and L-Met, achieved by supplementing crystalline Met. A graphical analysis was conducted on the database using ellipses coverage and nonparametric density techniques. Two methods of linear and nonlinear exponential meta-regression analysis were used to determine relative bioavailability based on average daily gain (ADG) and feed conversion ratio (FCR) responses to dietary Met sources. The analyses of ADG and FCR obtained from both linear and exponential models showed a relative difference between the tested methionine sources. The results showed that both ADG and FCR were improved in L-Met compared to DL-Met supplemented diets. Linear regression analysis revealed that DL-Met was 94.97% (for ADG) and 95.63% (for FCR) as efficacious as L-Met (P < 0.01). In contrast, the analysis of the fitted nonlinear exponential model showed that the biological efficiencies of DL-Met were 91.33% and 76.57% of the values for L-Met for ADG (P = 0.01) and FCR (P = 0.09), respectively. Based on the meta-analytical results, an equivalence of relative biological efficiency of DL-Met in comparison with L-Met for young broilers could not be confirmed.

Trade-off between feed efficiency and immunity in specialized high-performing chickens.

Based on resource allocation theory, ignoring importance of immunity, and focus on growth and feed efficiency (FE) traits in breeding plans may lead to serious weakness in immune system performance. However, in poultry the adverse effects of selection for FE on the immune system are unclear. Therefore, an experiment was conducted to study the trade-off between FE and immunity using a total of 180 high-performing specialized male chickens from a commercial broiler line which were selected over 30 generations for growth (body weight gain, BWG) and FE (residual feed intake, RFI). Birds were reared for 42 d and 5 FE-related traits of the birds in the last week were considered including daily feed intake (DFI), feed conversion ratio (FCR), residual feed intake (RFI), residual BW gain (RG), and residual intake and gain (RIG). For all 180 chickens, immune system performance including humoral immune response, cell-mediated immunity (CMI), and the activity of lysozyme enzyme (L. activity) as innate immunity was measured. After ascending sort of each FE records, 10% of higher records (H-FE: N = 18) and 10% of lower records (L-FE: N = 18) were determined, and immunity between L-FE and H-FE groups were compared. Moreover, L-BWG and H-BWG were analyzed because BWG is one of components in the FE formula. Performance of the immune system was not statistically different for CMI in none of the studied FE groups. Moreover, high and low groups for DFI and BWG were not different regarding the immunity of the birds. Antibody titers against Newcastle disease virus (NDV) were different between low and high groups of FCR, RG, and RIG. Likewise, SRBC-derived antibodies were significantly different between RFI groups. Rather than humoral immunity, RIG had adversely effect on the innate immunity. Results of the present study showed that although RIG is a more appropriate indicator for FE, choosing for high RIG can weaken the performance of the both humoral and innate immune systems, while RFI had fewer adverse effects.

Phylogeny and evolution of the genus Cervus (Cervidae, Mammalia) as revealed by complete mitochondrial genomes.

Mitochondrial DNA (mtDNA) lineages are recognized as important components of intra- and interspecific biodiversity, and allow to reveal colonization routes and phylogeographic structure of many taxa. Among these is the genus Cervus that is widely distributed across the Holarctic. We obtained sequences of complete mitochondrial genomes from 13 Cervus taxa and included them in global phylogenetic analyses of 71 Cervinae mitogenomes. The well-resolved phylogenetic trees confirmed Cervus to be monophyletic. Molecular dating based on several fossil calibration points revealed that ca. 2.6 Mya two main mitochondrial lineages of Cervus separated in Central Asia, the Western (including C. hanglu and C. elaphus) and the Eastern (comprising C. albirostris, C. canadensis and C. nippon). We also observed convergent changes in the composition of some mitochondrial genes in C. hanglu of the Western lineage and representatives of the Eastern lineage. Several subspecies of C. nippon and C. hanglu have accumulated a large portion of deleterious substitutions in their mitochondrial protein-coding genes, probably due to drift in the wake of decreasing population size. In contrast to previous studies, we found that the relic haplogroup B of C. elaphus was sister to all other red deer lineages and that the Middle-Eastern haplogroup E shared a common ancestor with the Balkan haplogroup C. Comparison of the mtDNA phylogenetic tree with a published nuclear genome tree may imply ancient introgressions of mtDNA between different Cervus species as well as from the common ancestor of South Asian deer, Rusa timorensis and R. unicolor, to the Cervus clade.

Identification of differentially expressed long noncoding RNAs in the ovarian tissue of Shal and Sangsari ewes using RNA-seq.

BACKGROUND: The ovary has an important role in reproductive function. Animal reproduction is dominated by numerous coding genes and noncoding elements. Although long noncoding RNAs (LncRNAs) are important in biological activity, little is known about their role in the ovary and fertility. METHODS: Three adult Shal ewes and three adult Sangsari ewes were used in this investigation. LncRNAs in ovarian tissue from two breeds were identified using bioinformatics analyses, and then target genes of LncRNAs were discovered. Target genes were annotated using the DAVID database, and their interactions were examined using the STRING database and Cytoscape software. The expression levels of seven LncRNAs with their target genes were assessed by real-time PCR to confirm the RNA-seq. RESULTS: Among all the identified LncRNAs, 124 LncRNAs were detected with different expression levels between the two breeds (FDR < 0.05). According to the DAVID database, target genes were discovered to be engaged in one biological process, one cellular component, and 21 KEGG pathways (FDR < 0.05). The PES1, RPS9, EF-1, Plectin, SURF6, CYC1, PRKACA MAPK1, ITGB2 and BRD2 genes were some of the most crucial target genes (hub genes) in the ovary. CONCLUSION: These results could pave the way for future efforts to address sheep prolificacy barriers.

Comparison of different response variables in genomic prediction using GBLUP and ssGBLUP methods in Iranian Holstein cattle.

We compared the reliability and bias of genomic evaluation of Holstein bulls for milk, fat, and protein yield with two methods of genomic best linear unbiased prediction (GBLUP) and single-step GBLUP (ssGBLUP). Four response variables of estimated breeding value (EBV), daughter yield deviation (DYD), de-regressed proofs based on Garrick (DRPGR) and VanRaden (DRPVR) were used as dependent variables. The effects of three weighting methods for diagonal elements of the incidence matrix associated with residuals were also explored. The reliability and the absolute deviation from 1 of the regression coefficient of the response variable on genomic prediction (Dev) using GBLUP and ssGBLUP methods were estimated in the validation population. In the ssGBLUP method, the genomic prediction reliability and Dev from un-weighted DRPGR method for milk yield were 0.44 and 0.002, respectively. In the GBLUP method, the corresponding measurements from un-weighted EBV for fat were 0.52 and 0.008, respectively. Moreover, the un-weighted DRPGR performed well in ssGBLUP with fat yield values for reliability and Dev of 0.49 and 0.001, respectively, compared to equivalent protein yield values of 0.38 and 0.056, respectively. In general, the results from ssGBLUP of the un-weighted DRPGR for milk and fat yield and weighted DRPGR for protein yield outperformed other models. The average reliability of genomic predictions for three traits from ssGBLUP was 0.39 which was 0.98% higher than the average reliability from GBLUP. Likewise, the Dev of genomic predictions was lower in ssGBLUP than GBLUP. The average Dev of predictions for three traits from ssGBLUP and GBLUP were 0.110 and 0.144, respectively. In conclusion, genomic prediction using ssGBLUP outperformed GBLUP both in terms of reliability and bias.