RNA-seq analyses of multiple meristems of soybean: novel and alternative transcripts, evolutionary and functional implications

Wang, Lei; Cao, Chenlong; Ma, Qibin; Zeng, Qiaoying; Wang, Haifeng; Cheng, Zhihao; Zhu, Genfeng; Qi, Ji; Mā, Hong; Nian, Hai; Wang, Yingxiang

doi:10.1186/1471-2229-14-169

Cited by 225 publications

(195 citation statements)

References 90 publications

(133 reference statements)

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“…Recent studies have shown that more than half of these genes in rice undergo AS, either induced by stress conditions or developmentally regulated (E et al, 2013). However, detailed analyses of tissuespecific and developmental AS have so far been very limited (Wang et al, 2014) until our present study which provides new insights into AS of OsLpa1 in particular and gene AS in general in plants.…”

Section: Discussionmentioning

confidence: 99%

Tissue-specific expression, developmentally and spatially regulated alternative splicing, and protein subcellular localization of OsLpa1 in rice

Pang

et al. 2016

J. Zhejiang Univ. Sci. B

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Abstract:The OsLpa1 gene (LOC_Os02g57400) was identified to be involved in phytic acid (PA) metabolism because its knockout and missense mutants reduce PA content in rice grain. However, little is known about the molecular characteristics of OsLpa1 in rice and of its homologues in other plants. In the present study, the spatial pattern of OsLpa1 expression was revealed using OsLpa1 promoter::GUS transgenic plants (GUS: β-glucuronidase); GUS histochemical assay showed that OsLpa1 was strongly expressed in stem, leaf, and root tissues, but in floral organ it is expressed mainly and strongly in filaments. In seeds, GUS staining was concentrated in the aleurone layers; a few blue spots were observed in the outer layers of embryo, but no staining was observed in the endosperm. Three OsLpa1 transcripts (OsLpa1.1, OsLpa1.2, OsLpa1.3) are produced due to alternative splicing; quantitative reversetranscriptase polymerase chain reaction (RT-PCR) analysis revealed that the abundance of OsLpa1.3 was negligible compared with OsLpa1.1 and OsLpa1.2 in all tissues. OsLpa1.2 is predominant in germinating seeds (about 5 times that of OsLpa1.1), but its abundance decreases quickly with the development of seedlings and plants, whereas the abundance of OsLpa1.1 rises and falls, reaching its highest level in 45-d-old plants, with abundance greater than that of OsLpa1.2 in both leaves and roots. In seeds, the abundance of OsLpa1 continuously increases with seed growth, being 27.5 and 15 times greater in 28-DAF (day after flowering) seeds than in 7-DAF seeds for OsLpa1.1 and OsLpa1.2, respectively. Transient expression of chimeric genes with green fluorescence protein (GFP) in rice protoplasts demonstrated that all proteins encoded by the three OsLpa1 transcripts are localized to the chloroplast.

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Section: Discussionmentioning

confidence: 99%

Tissue-specific expression, developmentally and spatially regulated alternative splicing, and protein subcellular localization of OsLpa1 in rice

Pang

et al. 2016

J. Zhejiang Univ. Sci. B

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“…Because RNA-seq is not dependent on prior knowledge of gene sequences, the de novo assembly of transcripts in non-model plant species that lack a reference genome is possible . This approach has been widely used to characterize transcriptomes of non-model plants, such as soybean, rye (Secale cereale L.), tea (Camellia sinensis) and garlic (Allium sativum) (Haseneyer et al 2011, Shi et al 2011, Sun et al 2012, Wang et al 2014.…”

Section: Introductionmentioning

confidence: 99%

Comparative transcriptome analyses of drought-resistant and - susceptible Brassica napus L. and development of EST-SSR markers by RNA-Seq

et al. 2015

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Brassica napus is a dicotyledonous plant in the family Brassicaceae. It is an important oil crop which has been widely cultivated over the world. However, drought stress is a very important threatening to B. napus production. In this study, two B. napus strains with different resistance to drought stress were treated with 200 g/L PEG-6000 as drought simulation agent (marked as S, R)and two controls were treated with 1/2 Hoagland medium(marked as ST, RT). With the help of Illumina paired-end RNA-seq technology and de novo assembly by Trinity, we obtained 107,294 Unigenes (integrated four sample results) with an average length of 834 bp and N50 1245. Of these Unigenes, 84,768, 92,545, 54,857, 28,129, 75,278 were assigned to NR, NT, Swiss-Prot, COG and GO database respectively. A total of 46,861 were mapped to 128 pathways by BLAST comparison against the KEGG database. In order to have an overall understanding of the unique genes' expression difference among four samples, we utilized RPKM to calculate Unigene expression, and to identify differentially expressed genes. Besides, 22,414 SSRs were developed in this study.

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“…The normalized expression values of the significantly differentially expressed mRNAs, lncRNAs, and miRNAs were extracted from the TCGA dataset and used for Euclidean distance-based hierarchical clustering analysis using the R package pheatmap (version 1.0.8, https://cran.r-project.org/package = pheatmap) [24]. The results of this analysis were displayed as a heatmap.…”

Section: Data Source and Preprocessingmentioning

confidence: 99%

Identifying Molecular Markers of Cervical Cancer Based on Competing Endogenous RNA Network Analysis

Qin

Gao

Yang

et al. 2019

Gynecol Obstet Invest

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Aim: Recurrence being a major challenge for the treatment of cervical cancer, we aimed at identifying novel molecular markers of cervical cancer to improve recurrence prediction. Methods: Cervical cancer samples were obtained from the Cancer Genome Atlas. Prognosis-associated long non-coding RNAs (lncRNAs), microRNAs (miRNAs), and mRNAs between recurrent and nonrecurrent samples were acquired using expression analysis. Regulatory relationships among these prognosis-associated RNAs were predicted and used to construct a competing endogenous RNA (ceRNA) network. Key prognostic lncRNAs, miRNAs, and mRNAs were identified using the ceRNA network, followed by the Kaplan-Meier survival analysis to reveal the influence of these key prognostic RNAs on prognosis. Results: In total, 15 lncRNAs, 10 miRNAs, and 348 mRNAs were identified as significant prognosis-associated molecules. The cervical cancer-related ceRNA network contained 13 prognosis-associated lncRNAs, 5 prognosis-associated miRNAs, and 120 prognosis-associated mRNAs. Key prognostic lncRNAs, miRNAs, and mRNAs were further identified using the ceRNA network. The key prognostic lncRNAs included H19 and HOTAIR, those for miRNAs included hsa-miR-133b, hsa-miR-138, and hsa-miR-301b, and those for mRNAs included Wnt family member 2, fibroblast growth factor 7, fibronectin 1, synaptic vesicle glycoprotein 2A, and bone morphogenetic protein 7. Conclusion: The key prognostic lncRNAs, miRNAs, and mRNAs may serve as potential molecular markers for recurrence prediction and may contribute to the treatment of cervical cancer.

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RNA-seq analyses of multiple meristems of soybean: novel and alternative transcripts, evolutionary and functional implications

Cited by 225 publications

References 90 publications

Tissue-specific expression, developmentally and spatially regulated alternative splicing, and protein subcellular localization of OsLpa1 in rice

Tissue-specific expression, developmentally and spatially regulated alternative splicing, and protein subcellular localization of OsLpa1 in rice

Comparative transcriptome analyses of drought-resistant and - susceptible Brassica napus L. and development of EST-SSR markers by RNA-Seq

Identifying Molecular Markers of Cervical Cancer Based on Competing Endogenous RNA Network Analysis

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