Genome-wide DNA methylation profile of prepubertal porcine testis.

The biological structure and function of the mammalian testis undergo important developmental changes during prepuberty and DNA methylation is dynamically regulated during testis development. In this study, we generated the first genome-wide DNA methylation profile of prepubertal porcine testis using methyl-DNA immunoprecipitation (MeDIP) combined with high-throughput sequencing (MeDIP-seq). Over 190 million high-quality reads were generated, containing 43642 CpG islands. There was an overall downtrend of methylation during development, which was clear in promoter regions but less so in gene-body regions. We also identified thousands of differentially methylated regions (DMRs) among the three prepubertal time points (1 month, T1; 2 months, T2; 3 months, T3), the majority of which showed decreasing methylation levels over time. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses revealed that many genes in the DMRs were linked with cell proliferation and some important pathways in porcine testis development. Our data suggest that DNA methylation plays an important role in prepubertal development of porcine testis, with an obvious downtrend of methylation levels from T1 to T3. Overall, our study provides a foundation for future studies and gives new insights into mammalian testis development.

Identification and positional distribution analysis of transcription factor binding sites for genes from the wheat fl-cDNA sequences.

The binding sites of transcription factors (TFs) in upstream DNA regions are called transcription factor binding sites (TFBSs). TFBSs are important elements for regulating gene expression. To date, there have been few studies on the profiles of TFBSs in plants. In total, 4,873 sequences with 5' upstream regions from 8530 wheat fl-cDNA sequences were used to predict TFBSs. We found 4572 TFBSs for the MADS TF family, which was twice as many as for bHLH (1951), B3 (1951), HB superfamily (1914), ERF (1820), and AP2/ERF (1725) TFs, and was approximately four times higher than the remaining TFBS types. The percentage of TFBSs and TF members showed a distinct distribution in different tissues. Overall, the distribution of TFBSs in the upstream regions of wheat fl-cDNA sequences had significant difference. Meanwhile, high frequencies of some types of TFBSs were found in specific regions in the upstream sequences. Both TFs and fl-cDNA with TFBSs predicted in the same tissues exhibited specific distribution preferences for regulating gene expression. The tissue-specific analysis of TFs and fl-cDNA with TFBSs provides useful information for functional research, and can be used to identify relationships between tissue-specific TFs and fl-cDNA with TFBSs. Moreover, the positional distribution of TFBSs indicates that some types of wheat TFBS have different positional distribution preferences in the upstream regions of genes.

Genome-Wide Association Study for Pre-harvest Sprouting Resistance in a Large Germplasm Collection of Chinese Wheat Landraces.

Pre-harvest sprouting (PHS) is mainly caused by the breaking of seed dormancy in high rainfall regions, which leads to huge economic losses in wheat. In this study, we evaluated 717 Chinese wheat landraces for PHS resistance and carried out genome-wide association studies (GWAS) using to 9,740 DArT-seq and 178,803 SNP markers. Landraces were grown across six environments in China and germination testing of harvest-ripe grain was used to calculate the germination rate (GR) for each accession at each site. GR was highly correlated across all environments. A large number of landraces (194) displayed high levels of PHS resistance (i.e., mean GR < 0.20), which included nine white-grained accessions. Overall, white-grained accessions displayed a significantly higher mean GR (42.7-79.6%) compared to red-grained accessions (19.1-56.0%) across the six environments. Landraces from mesic growing zones in southern China showed higher levels of PHS resistance than those sourced from xeric areas in northern and north-western China. Three main quantitative trait loci (QTL) were detected by GWAS: one on 5D that appeared to be novel and two co-located with the grain color transcription factor Tamyb10 on 3A and 3D. An additional 32 grain color related QTL (GCR-QTL) were detected when the set of red-grained landraces were analyzed separately. GCR-QTL occurred at high frequencies in the red-grained accessions and a strong correlation was observed between the number of GCR-QTL and GR (R2 = 0.62). These additional factors could be critical for maintaining high levels of PHS resistance and represent targets for introgression into white-grained wheat cultivars. Further, investigation of the origin of haplotypes associated with the three main QTL revealed that favorable haplotypes for PHS resistance were more common in accessions from higher rainfall zones in China. Thus, a combination of natural and artificial selection likely resulted in landraces incorporating PHS resistance in China.

SNP Mining in Functional Genes from Nonmodel Species by Next-Generation Sequencing: A Case of Flowering, Pre-Harvest Sprouting, and Dehydration Resistant Genes in Wheat.

As plenty of nonmodel plants are without genomic sequences, the combination of molecular technologies and the next generation sequencing (NGS) platform has led to a new approach to study the genetic variations of these plants. Software GATK, SOAPsnp, samtools, and others are often used to deal with the NGS data. In this study, BLAST was applied to call SNPs from 16 mixed functional gene's sequence data of polyploidy wheat. In total 1.2 million reads were obtained with the average of 7500 reads per genes. To get accurate information, 390,992 pair reads were successfully assembled before aligning to those functional genes. Standalone BLAST tools were used to map assembled sequence to functional genes, respectively. Polynomial fitting was applied to find the suitable minor allele frequency (MAF) threshold at 6% for assembled reads of each functional gene. SNPs accuracy form assembled reads, pretrimmed reads, and original reads were compared, which declared that SNPs mined from the assembled reads were more reliable than others. It was also demonstrated that mixed samples' NGS sequences and then analysis by BLAST were an effective, low-cost, and accurate way to mine SNPs for nonmodel species. Assembled reads and polynomial fitting threshold were recommended for more accurate SNPs target.

Genome-wide characterization of developmental stage- and tissue-specific transcription factors in wheat.

BACKGROUND: Wheat (Triticum aestivum) is one of the most important cereal crops, providing food for humans and feed for other animals. However, its productivity is challenged by various biotic and abiotic stresses such as fungal diseases, insects, drought, salinity, and cold. Transcription factors (TFs) regulate gene expression in different tissues and at various developmental stages in plants and animals, and they can be identified and classified into families according to their structural and specialized DNA-binding domains (DBDs). Transcription factors are important regulatory components of the genome, and are the main targets for engineering stress tolerance. RESULTS: In total, 2407 putative TFs were identified from wheat expressed sequence tags, and then classified into 63 families by using Hmm searches against hidden Markov model (HMM) profiles. In this study, 2407 TFs represented approximately 2.22% of all genes in the wheat genome, a smaller proportion than those reported for other cereals in PlantTFDB V3.0 (3.33%-5.86%) and PlnTFDB (4.30%-6.46%). We assembled information from the various databases for individual TFs, including annotations and details of their developmental stage- and tissue-specific expression patterns. Based on this information, we identified 1257 developmental stage-specific TFs and 1104 tissue-specific TFs, accounting for 52.22% and 45.87% of the 2407 wheat TFs, respectively. We identified 338, 269, 262, 175, 49, and 18 tissue-specific TFs in the flower, seed, root, leaf, stem, and crown, respectively. There were 100, 6, 342, 141, 390, and 278 TFs specifically expressed at the dormant seed, germinating seed, reproductive, ripening, seedling, and vegetative stages, respectively. We constructed a comprehensive database of wheat TFs, designated as WheatTFDB ( http://xms.sicau.edu.cn/wheatTFDB/ ). CONCLUSIONS: Approximately 2.22% (2407 genes) of all genes in the wheat genome were identified as TFs, and were clustered into 63 TF families. We identified 1257 developmental stage-specific TFs and 1104 tissue-specific TFs, based on information about their developmental- and tissue-specific expression patterns obtained from publicly available gene expression databases. The 2407 wheat TFs and their annotations are summarized in our database, WheatTFDB. These data will be useful identifying target TFs involved in the stress response at a particular stage of development.