Molecular characteristics and transcriptional regulatory of spermatogenesis-related gene RFX2 in adult Banna mini-pig inbred line (BMI)

RFX2 plays critical roles in mammalian spermatogenesis and cilium maturation. Here, the testes of 12-month-old adult boars of Banna mini-pig inbred line (BMI) were subjected to whole-transcriptome sequencing. The results indicated that the average expression (raw count) of RFX2 gene in BMI testes was 16138.25, and the average expression value of the corresponding transcript ENSSSCT00000043271.2 was 123.1898. The CDS of RFX2 obtained from BMI testes was 2,817 bp (GenBank accession number: OL362242). Gene structure analysis showed that RFX2 was located on chromosome 2 of the pig genome with 19 exons. Protein structure analysis indicated that RFX2 contains 728 amino acids with two conserved domains. Phylogenetic analysis revealed that RFX2 was highly conserved with evolutionary homologies among mammalian species. Other analyses, including PPI networks, KEGG, and GO, indicated that BMI RFX2 had interactions with 43 proteins involving various functions, such as in cell cycle, spermatid development, spermatid differentiation, cilium assembly, and cilium organization, etc. Correlation analysis between these proteins and the transcriptome data implied that RFX2 was significantly associated with FOXJ1, DNAH9, TMEM138, E2F7, and ATR, and particularly showed the highest correlation with ATR, demonstrating the importance of RFX2 and ART in spermatogenesis. Functional annotation implied that RFX2 was involved in 17 GO terms, including three cellular components (CC), six molecular functions (MF), and eight biological processes (BP). The analysis of miRNA-gene targeting indicated that BMI RFX2 was mainly regulated by two miRNAs, among which four lncRNAs and five lncRNAs competitively bound ssc-miR-365-5p and ssc-miR-744 with RFX2, respectively. Further, the dual-luciferase report assay indicated that the ssc-miR-365-5p and ssc-miR-744 significantly reduced luciferase activity of RFX2 3'UTR in the 293T cells, suggesting that these two miRNAs regulated the expression of RFX2. Our results revealed the important role of RFX2 in BMI spermatogenesis, making it an intriguing candidate for follow-up studies.(AU)

Identification and validation of novel EST-SSR markers in olives

The olive (Olea europaea L.) is a leading oil crop in the Mediterranean area. Limited information on the inheritance of agronomic significant traits hinders progress in olive breeding programs, which encourages the development of markers linked to the traits. In this study, we report on the development of 46 olive simple sequence repeat (SSR) markers, obtained from 577,025 expressed sequence tags (ESTs) in developing olive fruits generated in the framework of the Slovenian national olive transcriptome project. Sequences were de novo assembled into 98,924 unigenes, which were then used as a source for microsatellites searching. We identified 923 unigenes that contained 984 SSRs among which dinucleotide SSRs (36 %) were the most abundant, followed by tri- (33 %) and hexa- (21 %) nucleotides. Microsatellite repeat motif GA (37 %) was the most common among dinucleotides, while microsatellite repeat motif GAA was the most abundant trinucleotide SSR motif (16 %). Gene ontology annotations could be assigned to 27 % of the unigenes. A hundred and ten expressed sequence tag-derived-simple sequence repeats (EST-SSRs) with annotated genes were selected for primer designing and finally, 46 (42 %) polymorphic EST-SSRs were successfully amplified and used to validate genetic diversity among 24 olive varieties. The average number of alleles per locus, observed heterozygosity, expected heterozygosity, and polymorphic information content were 4.5, 0.649, 0.604 and 0.539, respectively. Twenty-seven EST-SSRs showed good diversity properties and were recommended for further olive genome investigation.

Identification and validation of novel EST-SSR markers in olives

The olive (Olea europaea L.) is a leading oil crop in the Mediterranean area. Limited information on the inheritance of agronomic significant traits hinders progress in olive breeding programs, which encourages the development of markers linked to the traits. In this study, we report on the development of 46 olive simple sequence repeat (SSR) markers, obtained from 577,025 expressed sequence tags (ESTs) in developing olive fruits generated in the framework of the Slovenian national olive transcriptome project. Sequences were de novo assembled into 98,924 unigenes, which were then used as a source for microsatellites searching. We identified 923 unigenes that contained 984 SSRs among which dinucleotide SSRs (36 %) were the most abundant, followed by tri- (33 %) and hexa- (21 %) nucleotides. Microsatellite repeat motif GA (37 %) was the most common among dinucleotides, while microsatellite repeat motif GAA was the most abundant trinucleotide SSR motif (16 %). Gene ontology annotations could be assigned to 27 % of the unigenes. A hundred and ten expressed sequence tag-derived-simple sequence repeats (EST-SSRs) with annotated genes were selected for primer designing and finally, 46 (42 %) polymorphic EST-SSRs were successfully amplified and used to validate genetic diversity among 24 olive varieties. The average number of alleles per locus, observed heterozygosity, expected heterozygosity, and polymorphic information content were 4.5, 0.649, 0.604 and 0.539, respectively. Twenty-seven EST-SSRs showed good diversity properties and were recommended for further olive genome investigation.(AU)

PREDICTION OF GENOMIC-ENABLED BREEDING VALUES AND GENOME-WIDE ASSOCIATION STUDY FOR FEEDLOT AVERAGE DAILY WEIGHT GAIN IN NELORE CATTLE

Selection for fast growth rates using number of days to achieve specific weights or average weight gain would result in shorter production periods. Maintaining the rate of productivity increasing will demand, among other factors, genetically improved animals in both pasture and feedlot systems. Besides, genomic information could be used to predict genomic-enabled breeding values (GEBVs) earlier in animals' life, which would reduce generation intervals and increase productivity gains. Numerous studies have been conducted in order to identify appropriate methodologies to specific breeds and traits, which will result in more accurate GEBVs. The aim of this study was to compare the prediction accuracy of GEBVs and the ability to identify genomic regions and genes related to average weight daily gain in Nelore cattle, by applying different regression models and genotypes densities datasets. Genomic and phenotypic information of 804 steers born in three season, offspring of 34 bulls, were used to predict GEBVs through three models (Bayesian GBLUP, BayesA and BayesC ), four genotypic densities (Illumina BovineHD BeadChip, TagSNPs, GeneSeek Genomic Profiler High (HDi) and Low (LDi) density indicus) and two adjusted phenotypes. Family structure was accounted by using principal component analysis. Animals were assigned either to training (seasons 1 and 2) or testing (season 3) subsets to perform the cross-validation analysis. Estimates of Pearson correlation, regression coefficients and mean squared errors were used to access accuracy, inflation and bias of the estimated GEBVs, respectively. Genome-wide association study (GWAS) was also performed on above datasets, however, results were compared based on model complexity (pD) and deviance information criterion (DIC). SNP effects greater that upper limit of the 99.9% highest posterior interval were considered significant and genes located 25kb up or downstream were assigned to them. These genes were then, submitted to functional annotation clustering using DAVID database. Overall, accounting for family structure did not affect accuracy, but it seems having an effect on bias and inflation of predictions. Regarding to prediction of GEBVs, BayesC models produced the less biased and inflated results for the considered adjusted phenotypes. Furthermore, results from Bos taurus indicus-based chips were as informative as those from Illumina BovineHD, which could be an alternative in implementing genomic selection at lower costs. In relation to GWAS, BayesC model fitted better to the data, except for TagSNPs. A total of 11, 32 and 236 genes were found for LDi, HDi and 770k datasets, respectively, and the functional annotation assigned 3 genes to 1 cluster (LDi), 4 genes to 1 cluster and 35 genes to 3 (770k) enriched clusters. Five genes in common were found through all datasets: SYT1 (BTA 5), EYA2 (BTA 13), ZFHX4, MATN2 and CPQ (BTA 14). In general, the phenotype is a complex trait controlled by many genes and the model BayesC , which accounts for this performs better. Further investigations should account for the influence of the relationship between training and testing dataset.

A seleção para taxa de crescimento utilizando o número de dias para atingir determinado peso ou ganho de peso médio resultaria em menores ciclos de produção. Manter o aumento da produtividade exige, entre outros fatores, a utilização de animais melhorados, tanto nos sistemas de pastagem quanto de confinamento. Além disso, as informações genômicas podem ser usadas para predizer os valores genéticos genômicos (GEBVs) mais cedo na vida dos animais, o que reduziria os intervalos de geração e aumentaria os ganhos de produtividade. Inúmeros trabalhos tem sido conduzidos para identificar metodologias apropriadas à determinadas raças e características, o que irá resultar em GEBVs mais acurados. Os objetivos deste estudo foram comparar a acurácia de predição dos GEBVs e a habilidade de identificar regiões genômicas e genes relacionados ao ganho de peso médio diário em bovinos da raça Nelore, pela aplicação de diferentes modelos de regressão e densidades genotípicas. Informações genômica e fenotípica de 804 novilhos nascidos em três safras, filhos de 34 touros, foram utilizadas para predizer GEBVs por meio de três modelos (Bayesian GBLUP, BayesA e BayesC ), quatro densidades genotípicas (Illumina BovineHD BeadChip, TagSNPs, GeneSeek indicus de alta (HDi) e baixa (LDi) densidades) e dois fenótipos ajustados. A estrutura de família foi considerada por meio da análise de componentes principais. Os animais foram distribuídos em subconjunto de treinamento (safras 1 e 2) ou validação (safra 3) para realização da análise de validação cruzada. Estimativas de correlação de Pearson, coeficientes de regressão e erro quadrado médio foram usados para avaliar acurácia, inflação e viés dos GEBVs estimados, respectivamente. O estudo de associação ampla do genoma (GWAS) também foi realizado nos mesmos conjuntos de dados, entretanto, os resultados foram comparados com base na complexidade do modelo (pD) e no critério de informação do desvio (DIC). Os SNPs com efeitos maiores que o limite superior do intervalo de maior densidade a posteriori, com 99,9% de probabilidade, foram considerados significativos e, genes distantes 25kb de ambos os lados do SNP, foram relacionados. Esses genes foram submetidos ao agrupamento de anotação funcional no banco de dados DAVID. Em geral, a inclusão de informação de estrutura de família no modelo não afetou a acurácia, mas parece ter tido efeitos no viés e inflação das predições. Em relação a predição dos GEBVs, o modelo BayesC produziu os resultados menos viesados e inflacionados para os fenótipos ajustados. Além disso, os resultados obtidos com painéis baseados em Bos taurus indicus foram tão informativos quanto aqueles provenientes do painel Illumina BovineHD, o que pode ser uma alternativa para implementar a seleção genômica com menores custos de genotipagem. Em relação a GWAS, o modelo BayesC foi o que se ajustou melhor aos dados, com exceção do conjunto de TagSNPs. O total de 11, 32 e 236 genes foram identificados usando os conjuntos de dados LDi, HDi e 770k, respectivamente, e a anotação funcional resultou em 1 grupo com 3 genes (LDi), 1 grupo com 4 genes (HDi) e 35 genes distribuídos em 3 grupos (770k). Foram observados cinco genes em comum entre os três conjuntos de dados: SYT1 (BTA 5), EYA2 (BTA 13), ZFHX4, MATN2 e CPQ (BTA 14). De maneira geral, o fenótipo é uma característica complexa controlada por muitos genes e o modelo BayesC que leva isso em consideração, teve melhores resultados. Estudos futuros poderiam considerar a influência do grau de relacionamento entre os animais das populações de treinamento e teste.