Molecular analysis of the bovine coronavirus S1 gene by direct sequencing of diarrheic fecal specimens

Bovine coronavirus (BCoV) causes severe diarrhea in newborn calves, is associated with winter dysentery in adult cattle and respiratory infections in calves and feedlot cattle. The BCoV S protein plays a fundamental role in viral attachment and entry into the host cell, and is cleaved into two subunits termed S1 (amino terminal) and S2 (carboxy terminal). The present study describes a strategy for the sequencing of the BCoV S1 gene directly from fecal diarrheic specimens that were previously identified as BCoV positive by RT-PCR assay for N gene detection. A consensus sequence of 2681 nucleotides was obtained through direct sequencing of seven overlapping PCR fragments of the S gene. The samples did not undergo cell culture passage prior to PCR amplification and sequencing. The structural analysis was based on the genomic differences between Brazilian strains and other known BCoV from different geographical regions. The phylogenetic analysis of the entire S1 gene showed that the BCoV Brazilian strains were more distant from the Mebus strain (97.8% identity for nucleotides and 96.8% identity for amino acids) and more similar to the BCoV-ENT strain (98.7% for nucleotides and 98.7% for amino acids). Based on the phylogenetic analysis of the hypervariable region of the S1 subunit, these strains clustered with the American (BCoV-ENT, 182NS) and Canadian (BCQ20, BCQ2070, BCQ9, BCQ571, BCQ1523) calf diarrhea and the Canadian winter dysentery (BCQ7373, BCQ2590) strains, but clustered on a separate branch of the Korean and respiratory BCoV strains. The BCoV strains of the present study were not clustered in the same branch of previously published Brazilian strains (AY606193, AY606194). These data agree with the genealogical construction and suggest that at least two different BCoV strains are circulating in Brazil.

Pretranslational regulation of the expression of the lipoprotein lipase (EC 3.1, l.34) gene by dietary fatty acids in the rat

Although there have been a number of studies of effects of diet and hormones on lipoprotein lipase (EC 3.1.1.34; LPL) activity and levels of LPL mRNA (Raynolds et al. 1990), there have been no studies which have investigated effects of different dietary fatty acids on LPL gene expression. In the present study male Wistar Albino rats were pair-fed diets containing 50 g fat/kg of different fatty acid composition for 2 weeks. The diets fed were (1) a mixed oil (450 g saturated fatty acids, 420 g monounsaturated fatty acids, 130 g polyunsaturated fatty acids/kg; n 8), (2) maize oil (n 8), or (3) fish oil (n 8). Animals were killed, RNA was extracted from liver and perirenal and epididymal fat pads, and analysed by ‘Northern methodology’. Samples were hybridized to a human cDNA probe for LPL (Gotoda et al. 1989). Two transcripts were identified in epididymai and perirenal adipose tissue which were approximately 3·7 and 1·7 kb in size. The results suggested that (1) fish oil-fed animals had significantly greater production of LPL mRNA in epididymai adipose tissue compared with maize oil-fed animals (P < 0·05), (2) maize oil-fed animals had significantly greater production of LPL mRNA in perirenal fat compared with the other dietary groups (P < 0·05), (3) expression in the liver was not significant. Rats fed on a fish oil diet had significantly reduced plasma triacylglycerol concentrations compared with the mixed-oil group (P < 0·05), but there were no significant differences in plasma cholesterol. The differences in LPL could not be explained directly by the changes in plasma immunoreactive-insulin and glucose-dependent insulinotrophic polypeptide levels in the three groups.

Development of personalised functional foods needs metabolic profiling

Purpose of review There is growing interest in applying metabolic profiling technologies to food science as this approach is now embedded into the foodomics toolbox. This review aims at exploring how metabolic profiling can be applied to the development of functional foods. Recent findings One of the biggest challenges of modern nutrition is to propose a healthy diet to populations worldwide that must suit high inter-individual variability driven by complex gene-nutrient-environment interactions. Although a number of functional foods are now proposed in support of a healthy diet, a one-size-fits-all approach to nutrition is inappropriate and new personalised functional foods are necessary. Metabolic profiling technologies can assist at various levels of the development of functional foods, from screening for food composition to identification of new biomarkers of food intake to support diet intervention and epidemiological studies. Summary Modern ‘omics’ technologies, including metabolic profiling, will support the development of new personalised functional foods of high relevance to twenty-first-century medical challenges such as controlling the worldwide spread of metabolic disorders and ensuring healthy ageing.

Genotype by environment interaction for birth and weaning weights of composite beef cattle in different regions of Brazil

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Cloning, sequencing and expression analysis of the equine hepcidin gene by real-time PCR

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Inactivation of COX-2, HMLH1 and CDKN2A Gene by Promoter Methylation in Gastric Cancer: Relationship with Histological Subtype, Tumor Location and Helicobacter pylori Genotype

Objective: We aimed to evaluate the inactivation of COX-2, HMLH1 and CDKN2A by promoter methylation and its relationship with the infection by different Helicobacter pylori strains in gastric cancer. Methods: DNA extracted from 76 H. pylori-positive gastric tumor samples was available for promoter methylation identification by methylation-specific PCR and H. pylori subtyping by PCR. Immunohistochemistry was used to determine COX-2, p16(INK4A) and HMLH1 expression. Results: A strong negative correlation was found between the expression of these markers and the presence of promoter methylation in their genes. Among cardia tumors, negativity of p16(INK4A) was a significant finding. on the other hand, in noncardia tumors, the histological subtypes had different gene expression patterns. In the intestinal subtype, a significant finding was HMLH1 inactivation by methylation, while in the diffuse subtype, CDKN2A inactivation by methylation was the significant finding. Tumors with methylated COX-2 and HMLH1 genes were associated with H. pylori vac A s1 (p = 0.025 and 0.047, respectively), and the nonmethylated tumors were associated with the presence of the gene flaA. Conclusions: These data suggest that the inactivation of these genes by methylation occurs by distinct pathways according to the histological subtype and tumor location and depends on the H. pylori genotype. Copyright (C) 2011 S. Karger AG, Basel

Buffalo milk production in brazil and colombia: Genotype by environment interaction

The objective of this study was to determine whether there is a genotype by environment interaction (GxE) for dairy buffaloes in Brazil and Colombia. The (co)variance components were estimated by using a bi-trait repeatability animal model with the REML method. Each trait consisted in the milk yield obtained in both countries. Contemporary group (herd, year and season of parity) and age at parity (linear and quadratic covariate) fixed effects, along with the additive genetic, permanent environment, and the residual random effects were included in the model. Genetic, permanent environmental and residual variance and heritabilities were different for both countries. The genetic correlations for milk yield between Brazil and Colombia were low (between 0.10 and 0.13), indicating a GxE interaction between both countries. Knowing that this interaction influences the genetic progress of buffalo populations in Brazil and Colombia, we recommend choosing sires tested in the country they will be used, along with conducting joint genetic evaluations that consider GxE interaction effects.

Genotype by environment interaction in different birth seasons for weight at 240, 365 and 450 days of age in Tabapuã cattle

The objective of this study was to evaluate the effect of genotype by environment interaction (GEI) on the weight of Tabapuã cattle at 240 (W240), 365 (W365) and 450 (W450) days of age. In total, 35,732 records of 8,458 Tabapuã animalswhich were born in the state of Bahia, Brazil, from 1975 to 2001, from 167 sires and 3,707 dams, were used. Two birth seasons were tested as for the environment effect: the dry (D) and rainy (R) ones. The covariance components were obtainedby a multiple-trait analysis using Bayesian inference, in which each trait was considered as being different in each season. Covariance components were estimated by software gibbs2f90. As for W240, the model was comprised of contemporary groups and cow age (in classes) as fixed effects; animal and maternal genetic additive, maternal permanent environmental and residual were considered as random effects. Concerning W365 and W450, the model included only the contemporary aged cow groups as fixed effects and the genetic additive and residual effects of the animal as the random ones. The GEI was assessed considering the genetic correlation, in which values below 0.80 indicated the presence of GEI. Regarding W365 and W450, the GEI was found in both seasons. As for post-weaning weight (W240), the effect of such interaction was not observed. ©2012 Sociedade Brasileira de Zootecnia.

Genotype by environment interaction for post-weaning weight gain, scrotal circumference, and muscling score of composite beef cattle in different regions of Brazil

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Genotype by environment interaction for birth and weaning weights of composite beef cattle in different regions of Brazil

The objectives of the present study were to characterize and define homogenous production environments of composite beef cattle in Brazil in terms of climatic and geographic variables using multivariate exploratory techniques and to use them to assess the presence of G x E for birth weight (BW) and weaning weight (WW). Data from animals born between 1995 and 2008 on 36 farms located in 27 municipalities of the Brazilian states were used. Fifteen years of climate observations (mean minimum and maximum annual temperature and mean annual rainfall) and geographic (latitude, longitude and altitude) data were obtained for each municipality where the farms were located for characterization of the production environments. Hierarchical and nonhierarchical cluster analysis was used to group farms located in regions with similar environmental variables into clusters. Six clusters of farms were formed. The effect of sire-cluster interaction was tested by single-trait analysis using deviance information criterion (DIC). Genetic parameters were estimated by multi-trait analysis considering the same trait to be different in each cluster. According to the values of DIC, the inclusion of sire-cluster effect did not improve the fit of the genetic evaluation model for BW and WW. Estimates of genetic correlations among clusters ranged from -0.02 to 0.92. The low genetic correlation among the most studied regions permits us to suggest that a separate genetic evaluation for some regions should be undertaken. (C) 2012 Elsevier B.V. All rights reserved.

STATISTICAL METHODS FOR RARE AND COMMON VARIANT ASSOCIATION STUDIES

Complex human diseases are a major challenge for biological research. The goal of my research is to develop effective methods for biostatistics in order to create more opportunities for the prevention and cure of human diseases. This dissertation proposes statistical technologies that have the ability of being adapted to sequencing data in family-based designs, and that account for joint effects as well as gene-gene and gene-environment interactions in the GWA studies. The framework includes statistical methods for rare and common variant association studies. Although next-generation DNA sequencing technologies have made rare variant association studies feasible, the development of powerful statistical methods for rare variant association studies is still underway. Chapter 2 demonstrates two adaptive weighting methods for rare variant association studies based on family data for quantitative traits. The results show that both proposed methods are robust to population stratification, robust to the direction and magnitude of the effects of causal variants, and more powerful than the methods using weights suggested by Madsen and Browning [2009]. In Chapter 3, I extended the previously proposed test for Testing the effect of an Optimally Weighted combination of variants (TOW) [Sha et al., 2012] for unrelated individuals to TOW &ndash F, TOW for Family &ndash based design. Simulation results show that TOW &ndash F can control for population stratification in wide range of population structures including spatially structured populations, is robust to the directions of effect of causal variants, and is relatively robust to percentage of neutral variants. In GWA studies, this dissertation consists of a two &ndash locus joint effect analysis and a two-stage approach accounting for gene &ndash gene and gene &ndash environment interaction. Chapter 4 proposes a novel two &ndash stage approach, which is promising to identify joint effects, especially for monotonic models. The proposed approach outperforms a single &ndash marker method and a regular two &ndash stage analysis based on the two &ndash locus genotypic test. In Chapter 5, I proposed a gene &ndash based two &ndash stage approach to identify gene &ndash gene and gene &ndash environment interactions in GWA studies which can include rare variants. The two &ndash stage approach is applied to the GAW 17 dataset to identify the interaction between KDR gene and smoking status.

Assessing time-by-covariate interactions in Cox proportional hazards regression models using cubic spline functions

This dissertation develops and explores the methodology for the use of cubic spline functions in assessing time-by-covariate interactions in Cox proportional hazards regression models. These interactions indicate violations of the proportional hazards assumption of the Cox model. Use of cubic spline functions allows for the investigation of the shape of a possible covariate time-dependence without having to specify a particular functional form. Cubic spline functions yield both a graphical method and a formal test for the proportional hazards assumption as well as a test of the nonlinearity of the time-by-covariate interaction. Five existing methods for assessing violations of the proportional hazards assumption are reviewed and applied along with cubic splines to three well known two-sample datasets. An additional dataset with three covariates is used to explore the use of cubic spline functions in a more general setting. ^