Effect of Soyabean Isoflavones Exposure on Onset of Puberty, Serum Hormone Concentration and Gene Expression in Hypothalamus, Pituitary Gland and Ovary of Female Bama Miniature Pigs

This study was to investigate the effect of soyabean isoflavones (SIF) on onset of puberty, serum hormone concentration, and gene expression in hypothalamus, pituitary and ovary of female Bama miniature pigs. Fifty five, 35-days old pigs were randomly assigned into 5 treatment groups consisting of 11 pigs per treatment. Results showed that dietary supplementation of varying dosage (0, 250, 500, and 1,250 mg/kg) of SIF induced puberty delay of the pigs with the age of puberty of pigs fed basal diet supplemented with 1,250 mg/kg SIF was significantly higher (p<0.05) compared to control. Supplementation of SIF or estradiol valerate (EV) reduced (p<0.05) serum gonadotrophin releasing hormone and luteinizing hormone concentration, but increased follicle-stimulating hormone concentration in pigs at 4 months of age. The expression of KiSS-1 metastasis-suppressor (KISS1), steroidogenic acute regulatory protein (StAR) and 3-beta-hydroxysteroid dehydrogenase/delta-5-delta-4 isomerase (3β-HSD) was reduced (p<0.01) in SIF-supplemented groups. Expression of gonadotropin-releasing hormone receptor in the pituitary of miniature pigs was reduced (p<0.05) compared to the control when exposed to 250, 1,250 mg/kg SIF and EV. Pigs on 250 mg/kg SIF and EV also showed reduced (p<0.05) expression of cytochrome P450 19A1 compared to the control. Our results indicated that dietary supplementation of SIF induced puberty delay, which may be due to down-regulation of key genes that play vital roles in the synthesis of steroid hormones

Prediction of Drought-Resistant Genes in Arabidopsis thaliana Using SVM-RFE

Background: Identifying genes with essential roles in resisting environmental stress rates high in agronomic importance. Although massive DNA microarray gene expression data have been generated for plants, current computational approaches underutilize these data for studying genotype-trait relationships. Some advanced gene identification methods have been explored for human diseases, but typically these methods have not been converted into publicly available software tools and cannot be applied to plants for identifying genes with agronomic traits. Methodology: In this study, we used 22 sets of Arabidopsis thaliana gene expression data from GEO to predict the key genes involved in water tolerance. We applied an SVM-RFE (Support Vector Machine-Recursive Feature Elimination) feature selection method for the prediction. To address small sample sizes, we developed a modified approach for SVM-RFE by using bootstrapping and leave-one-out cross-validation. We also expanded our study to predict genes involved in water susceptibility. Conclusions: We analyzed the top 10 genes predicted to be involved in water tolerance. Seven of them are connected to known biological processes in drought resistance. We also analyzed the top 100 genes in terms of their biological functions. Our study shows that the SVM-RFE method is a highly promising method in analyzing plant microarray data for studyin

A Bayesian Model for Detection of Highorder Interactions Among Genetic Variants in Genome-Wide Association Studies

Background: A central question for disease studies and crop improvements is how genetics variants drive phenotypes. Genome Wide Association Study (GWAS) provides a powerful tool for characterizing the genotypephenotype relationships in complex traits and diseases. Epistasis (gene-gene interaction), including high-order interaction among more than two genes, often plays important roles in complex traits and diseases, but current GWAS analysis usually just focuses on additive effects of single nucleotide polymorphisms (SNPs). The lack of effective computational modelling of high-order functional interactions often leads to significant under-utilization of GWAS data. Results: We have developed a novel Bayesian computational method with a Markov Chain Monte Carlo (MCMC) search, and implemented the method as a Bayesian High-order Interaction Toolkit (BHIT) for detecting epistatic interactions among SNPs. BHIT first builds a Bayesian model on both continuous data and discrete data, which is capable of detecting high-order interactions in SNPs related to case—control or quantitative phenotypes. We also developed a pipeline that enables users to apply BHIT on different species in different use cases. Conclusions: Using both simulation data and soybean nutritional seed composition studies on oil content and protein content, BHIT effectively detected some high-order interactions associated with phenotypes, and it outperformed a number of other available tools. BHIT is freely available for academic users at http://digbio.missouri.edu/BHIT/

PGen: large-scale genomic variations analysis workflow and browser in SoyKB

Background: With the advances in next-generation sequencing (NGS) technology and significant reductions in sequencing costs, it is now possible to sequence large collections of germplasm in crops for detecting genome-scale genetic variations and to apply the knowledge towards improvements in traits. To efficiently facilitate large-scale NGS resequencing data analysis of genomic variations, we have developed " PGen", an integrated and optimized workflow using the Extreme Science and Engineering Discovery Environment (XSEDE) high-performance computing (HPC) virtual system, iPlant cloud data storage resources and Pegasus workflow management system (Pegasus-WMS). The workflow allows users to identify single nucleotide polymorphisms (SNPs) and insertion-deletions (indels), perform SNP annotations and conduct copy number variation analyses on multiple resequencing datasets in a user-friendly and seamless way. Results: We have developed both a Linux version in GitHub (https:// github. com/ pegasus-isi/ PGen-GenomicVariationsWorkflow) and a web-based implementation of the PGen workflow integrated within the Soybean Knowledge Base (SoyKB), (http:// soykb. org/ Pegasus/ index. php). Using PGen, we identified 10,218,140 single-nucleotide polymorphisms (SNPs) and 1,398,982 indels from analysis of 106 soybean lines sequenced at 15X coverage. 297,245 non-synonymous SNPs and 3330 copy number variation (CNV) regions were identified from this analysis. SNPs identified using PGen from additional soybean resequencing projects adding to 500+ soybean germplasm lines in total have been integrated. These SNPs are being utilized for trait improvement using genotype to phenotype prediction approaches developed in-house. In order to browse and access NGS data easily, we have also developed an NGS resequencing data browser (http:// soykb. org/ NGS_ Resequence/ NGS_ index. php) within SoyKB to provide easy access to SNP and downstream analysis results for soybean researchers. Conclusion: PGen workflow has been optimized for the most efficient analysis of soybean data using thorough testing and validation. This research serves as an example of best practices for development of genomics data analysis workflows by integrating remote HPC resources and efficient data management with ease of use for biological users. PGen workflow can also be easily customized for analysis of data in other species.Missouri Soybean Merchandising Council [368]; United Soybean Board [1320-532-5615]This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Single-index regression for pooled biomarker data

<p>Laboratory assays used to evaluate biomarkers (biological markers) are often prohibitively expensive. As an efficient data collection mechanism to save on testing costs, pooling has become more commonly used in epidemiological research. Useful statistical methods have been proposed to relate pooled biomarker measurements to individual covariate information. However, most of these regression techniques have proceeded under parametric linear assumptions. To relax such assumptions, we propose a semiparametric approach that originates from the context of the single-index model. Unlike with traditional single-index methodologies, we face a challenge in that the observed data are biomarker measurements on pools rather than individual specimens. In this article, we propose a method that addresses this challenge. The asymptotic properties of our estimators are derived. We illustrate the finite sample performance of our estimators through simulation and by applying it to a diabetes data set and a chemokine data set.</p

Strategies and Countermeasures for Ensuring Energy Security in China

Energy security is important for the security system of a country. Affected by global geopolitics and the COVID-19 pandemic, China’s energy security is currently facing severe challenges. Reducing the import scale of oil and gas based on the comprehensive and efficient utilization of domestic energy to ensure energy security remains a topic that requires research for promoting China’s high-quality and sustainable development. This study first reviews the evolution of the energy security concept and summarizes the energy security strategies of typical countries. Then the energy security in China is defined from five dimensions: sustainable development, guaranteed supply, scientific support, economic affordability, and guaranteed system. Moreover, the situation faced by China’s energy security is studied and corresponding strategies are proposed. We propose that China should make efforts to stabilize the production of traditional energies, ensure the supply of imported oil and gas, promote renewable energy consumption to complement the current energy mix, encourage cooperative innovation of energy science and technology, and improve the energy development mechanism. Furthermore, we propose several policy suggestions to ensure China’s energy security from the aspects of top-level plan, integrated development of gas and renewable energies, scientific innovation of renewable energies, and international cooperation