Use of Bacillus probiotics for immune responses and intestinal microflora of white leg shrimp Litopenaeus vannamei (Boone, 1931) post larvae

The effect of dietary containing of Bacillus subtilis and Bacillus licheniformis in three groups including commercial, commercial-indigenous and indigenous was investigated on the immune parameters (glucose, albumin, total protein, lysozyme, cortisol, immunoglobulin M (IgM)) and the intestinal flora of white leg shrimp (Litopenaeus vannamei) post larvae. The shrimp were fed for 60 days with four different diets: control (without probiotics), diet T1 supplemented with 1.5×106 CFU g-1 commercial probiotic, diet T2 with 1.5×106 CFU g-1 commercial-indigenous probiotic, diet T3 with 1.5×106 CFU g-1 indigenous probiotic. At the end of experimental period, the levels of biochemical parameters (glucose, total protein, lysozyme, cortisol, IgM) of shrimp fed probiotic diets were significantly higher than in those shrimps fed the control diet for 60 days. However, albumin concentrations showed no significant difference between the experimental treatments and the control, but increased by 1.19, 1.15 and 1.14 after 60 days of feeding with diets T1, T2 and T3, respectively. Likewise, population density of Bacillus bacteria counted in digestive tract of shrimps treated with probiotic were significantly higher than the control group. Results of this study indicated that the addition of probiotic bacilli can improve immune parameters and modulates intestinal microbiota of shrimp (L. vannamei) post larvae

Differential contribution of genomic regions to marked genetic variation and prediction of quantitative traits in broiler chickens

Background: Genome-wide association studies in humans have found enrichment of trait-associated single nucleotide polymorphisms (SNPs) in coding regions of the genome and depletion of these in intergenic regions. However, a recent release of the ENCyclopedia of DNA elements showed that ~80 % of the human genome has a biochemical function. Similar studies on the chicken genome are lacking, thus assessing the relative contribution of its genic and non-genic regions to variation is relevant for biological studies and genetic improvement of chicken populations. Methods: A dataset including 1351 birds that were genotyped with the 600K Affymetrix platform was used. We partitioned SNPs according to genome annotation data into six classes to characterize the relative contribution of genic and non-genic regions to genetic variation as well as their predictive power using all available quality-filtered SNPs. Target traits were body weight, ultrasound measurement of breast muscle and hen house egg production in broiler chickens. Six genomic regions were considered: intergenic regions, introns, missense, synonymous, 5′ and 3′ untranslated regions, and regions that are located 5 kb upstream and downstream of coding genes. Genomic relationship matrices were constructed for each genomic region and fitted in the models, separately or simultaneously. Kernelbased ridge regression was used to estimate variance components and assess predictive ability. Contribution of each class of genomic regions to dominance variance was also considered. Results: Variance component estimates indicated that all genomic regions contributed to marked additive genetic variation and that the class of synonymous regions tended to have the greatest contribution. The marked dominance genetic variation explained by each class of genomic regions was similar and negligible (~0.05). In terms of prediction mean-square error, the whole-genome approach showed the best predictive ability. Conclusions: All genic and non-genic regions contributed to phenotypic variation for the three traits studied. Overall, the contribution of additive genetic variance to the total genetic variance was much greater than that of dominance variance. Our results show that all genomic regions are important for the prediction of the targeted traits, and the whole-genome approach was reaffirmed as the best tool for genome-enabled prediction of quantitative traits

Synthesis and characterization of Sn‑doped TiO2 flm for antibacterial applications

Simple sol–gel method has been exploited to deposit Sn-doped TiO2 thin flms on glass substrates. The resultant coatings were characterized by X-ray difraction (XRD), UV–visible techniques (UV–Vis), Fourier transform infrared spectroscopy (FTIR), and photoluminescence analysis (PL). The XRD pattern reveals an increase in crystallite size of the prepared samples with the increasing doping concentration. A decrease in doping concentrating resulted in the decrease in bandgap values. The diferent chemical bonds on these flms were identifed from their FTIR spectra. The photoluminescence analysis shows an increase in the emission peak intensity with increasing dopant concentration, and this can be attributed to the efect created due to surface states. The prepared samples were tested as antibacterial agent toward both Gram-positive and Gram-negative bacteria like S.aureus (Staphylococcus aureus) and E.coli (Escherichia coli), respectively. The size of the inhibition zones indicates that the sample shows maximum inhibitory property toward E.coli when compared to S.aureus

Estimates of genomic heritability and genome-wide association study for fatty acids profile in Santa Inês sheep

Background: Despite the health concerns and nutritional importance of fatty acids, there is a relative paucity of studies in the literature that report genetic or genomic parameters, especially in the case of sheep populations. To investigate the genetic architecture of fatty acid composition of sheep, we conducted genome-wide association studies (GWAS) and estimated genomic heritabilities for fatty acid profile in Longissimus dorsi muscle of 216 male sheep. Results: Genomic heritability estimates for fatty acid content ranged from 0.25 to 0.46, indicating that substantial genetic variation exists for the evaluated traits. Therefore, it is possible to alter fatty acid profiles through selection. Twenty-seven genomic regions of 10 adjacent SNPs associated with fatty acids composition were identified on chromosomes 1, 2, 3, 5, 8, 12, 14, 15, 16, 17, and 18, each explaining ≥0.30% of the additive genetic variance. Twenty-three genes supporting the understanding of genetic mechanisms of fat composition in sheep were identified in these regions, such as DGAT2, TRHDE, TPH2, ME1, C6, C7, UBE3D, PARP14, and MRPS30. Conclusions: Estimates of genomic heritabilities and elucidating important genomic regions can contribute to a better understanding of the genetic control of fatty acid deposition and improve the selection strategies to enhance meat quality and health attributes

A predictive assessment of genetic correlations between traits in chickens using markers

International audienceAbstractBackgroundGenomic selection has been successfully implemented in plant and animal breeding programs to shorten generation intervals and accelerate genetic progress per unit of time. In practice, genomic selection can be used to improve several correlated traits simultaneously via multiple-trait prediction, which exploits correlations between traits. However, few studies have explored multiple-trait genomic selection. Our aim was to infer genetic correlations between three traits measured in broiler chickens by exploring kinship matrices based on a linear combination of measures of pedigree and marker-based relatedness. A predictive assessment was used to gauge genetic correlations.MethodsA multivariate genomic best linear unbiased prediction model was designed to combine information from pedigree and genome-wide markers in order to assess genetic correlations between three complex traits in chickens, i.e. body weight at 35 days of age (BW), ultrasound area of breast meat (BM) and hen-house egg production (HHP). A dataset with 1351 birds that were genotyped with the 600 K Affymetrix platform was used. A kinship kernel (K) was constructed as K = λG + (1 − λ)A, where A is the numerator relationship matrix, measuring pedigree-based relatedness, and G is a genomic relationship matrix. The weight (λ) assigned to each source of information varied over the grid λ = (0, 0.2, 0.4, 0.6, 0.8, 1). Maximum likelihood estimates of heritability and genetic correlations were obtained at each λ, and the “optimum” λ was determined using cross-validation.ResultsEstimates of genetic correlations were affected by the weight placed on the source of information used to build K. For example, the genetic correlation between BW–HHP and BM–HHP changed markedly when λ varied from 0 (only A used for measuring relatedness) to 1 (only genomic information used). As λ increased, predictive correlations (correlation between observed phenotypes and predicted breeding values) increased and mean-squared predictive error decreased. However, the improvement in predictive ability was not monotonic, with an optimum found at some 0 < λ < 1, i.e., when both sources of information were used together.ConclusionsOur findings indicate that multiple-trait prediction may benefit from combining pedigree and marker information. Also, it appeared that expected correlated responses to selection computed from standard theory may differ from realized responses. The predictive assessment provided a metric for performance evaluation as well as a means for expressing uncertainty of outcomes of multiple-trait selection