Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1087254 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR28371219_1.fastq.gz | Fastqsanger.gz | |||
| SRR28371219_2.fastq.gz | Fastqsanger.gz | |||
| SRR28371220_1.fastq.gz | Fastqsanger.gz | |||
| SRR28371220_2.fastq.gz | Fastqsanger.gz | |||
| SRR28371320_1.fastq.gz | Fastqsanger.gz |
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Project description:Background: MicroRNAs regulate various biological processes in plants. Considerable data are available on miRNAs involved in the development of rice, maize and barley. In contrast, little is known about miRNAs and their functions in the development of wheat. In this study, five small RNA (sRNA) libraries from wheat seedlings, flag leaves, and developing seeds were developed and sequenced to identify miRNAs and understand their functions in wheat development. Results: Twenty-four known miRNAs belonging to 15 miRNA families were identified from 18 MIRNA loci in wheat in the present study, including 15 (9 MIRNA loci) first identified in wheat, 13 miRNA families (16 MIRNA loci) being highly conserved and 2 (2 MIRNAs loci) moderately conserved. In addition, fifty-five novel miRNAs were also identified. The potential target genes for 15 known miRNAs and 37 novel miRNAs were predicted using strict criteria, and these target genes are involved in a wide range of biological functions. Four of the 15 known miRNA families and 22 of the 55 novel miRNAs were preferentially expressed in the developing seeds with logarithm of the fold change of 1.0～7.6, and half of them were seed-specific, suggesting that they participate in regulating wheat seed development and metabolism. From 5 days post-anthesis to 20 days post-anthesis, miR164 and miR160 increased in abundance in developing seeds, whereas miR169 decreased, suggesting their coordinating functions in the different developmental stages of wheat seed. Moreover, eight known miRNA families and 28 novel miRNAs exhibited tissue-biased expression in wheat flag leaves, with the logarithm of the fold changes of 0.5～5.2. The putative targets of these tissue-preferential miRNAs were involved in various metabolism and biological processes, suggesting complexity of the regulatory networks in different tissues. Our data also suggested that wheat flag leaves have more complicated regulatory networks of miRNAs than developing seeds. Conclusions: Our work identified and characterised wheat miRNAs, their targets and expression patterns. This study is the first to elucidate the regulatory networks of miRNAs involved in wheat flag leaves and developing seeds, and provided a foundation for future studies on specific functions of these miRNAs.
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Project description:To study the expression profiles of hexaploid wheat chromosome 3B genes during the life cycle of a wheat plant and establish a transcriptome atlas for this chromosome, deep transcriptome sequencing was conducted in duplicates in 15 wheat samples corresponding to five different organs (leaf, shoot, root, spike, and grain) at three developmental stages each. Strand-non-specific and strand-specific libraries were used to produce 2.52 billion paired-end reads (232 Gb) and 615.3 single-end reads (62 Gb), respectively.
2014-09-08 | E-MTAB-4484 | biostudies-arrayexpress
Project description:Introduction: Pre-harvest Sprouting (PHS) seriously affects wheat quality and yield. However, to date there have been limited reports. It is of great urgency to breed resistance varieties via quantitative trait nucleotides (QTNs) or genes for PHS resistance in white-grained wheat. Methods: 629 Chinese wheat varieties, including 373 local wheat varieties from 70 years ago and 256 improved wheat varieties were phenotyped for spike sprouting (SS) in two environments and genotyped by wheat 660K microarray. These phenotypes were used to associate with 314,548 SNP markers for identifying QTNs for PHS resistance using several multi-locus genome-wide association study (GWAS) methods. Their candidate genes were verified by RNA-seq, and the validated candidate genes were further exploited in wheat breeding. Results: As a result, variation coefficients of 50% and 47% for PHS in 629 wheat varieties, respectively, in 2020-2021 and 2021-2022 indicated large phenotypic variation, in particular, 38 white grain varieties appeared at least medium resistance, such as Baipimai, Fengchan 3, and Jimai 20. In GWAS, 22 significant QTNs, with the sizes of 0.06% ~ 38.11%, for PHS resistance were stably identified by multiple multi-locus methods in two environments, e.g., AX-95124645 (chr3D:571.35Mb), with the sizes of 36.390% and 45.850% in 2020-2021 and 2021-2022, respectively, was detected by several multi-locus methods in two environments. As compared with previous studies, the AX-95124645 was used to develop Kompetitive Allele-Specific PCR marker QSS.TAF9-3D (chr3D:569.17Mb~573.55Mb) for the first time, especially, it is available in white-grain wheat varieties. Around this locus, nine genes were significantly differentially expressed, and two of them (TraesCS3D01G466100 and TraesCS3D01G468500) were found by GO annotation to be related to PHS resistance and determined as candidate genes. Discussion: The QTN and two new candidate genes related to PHS resistance were identified in this study. The QTN can be used to effectively identify the PHS resistance materials, especially, all the white-grained varieties with QSS.TAF9-3D-TT haplotype are resistant to spike sprouting. Thus, this study provides candidate genes, materials, and methodological basis for breeding wheat PHS resistance in the future.
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Project description:Phytohormones are key regulators of plant growth, development, and signalling networks involved in responses to diverse biotic and abiotic stresses. Transcriptional reference maps of hormone responses have been reported for several model plant species such as Arabidopsis thaliana, Oryza sativa, and Brachypodium distachyon. However, because of species differences and the complexity of the wheat genome, these transcriptome data are not appropriate reference material for wheat studies. We comprehensively analysed the transcriptomic responses in wheat spikes to seven phytohormones, including indole acetic acid (IAA), gibberellic acid (GA), abscisic acid (ABA), ethylene (ET), cytokinin (CK), salicylic acid (SA), and methyl jasmonic acid (MeJA). A total of 3386 genes were differentially expressed at 24 h after the hormone treatments. Furthermore, 22.7% of these genes exhibited overlapping transcriptional responses for at least two hormones, implying there is crosstalk among phytohormones. We subsequently identified genes with expression levels that were significantly and differentially induced by a specific phytohormone (i.e., hormone-specific responses). The data for these hormone-responsive genes were then compared with the transcriptome data for wheat spikes exposed to biotic (Fusarium head blight) and abiotic (water deficit) stresses. Our data were used to develop a transcriptional reference map of hormone responses in wheat spikes.
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