Allelic Variations in 5' UTR of TaAFP-B Effecting the Seed Dormancy and Other Agronomic Traits in Transgenic Wheat

BackgroundTaAFP (Triticum aestivum L. ABA insensitive five binding protein) is the homology of AFP of Arabidopsis thaliana which was a negative regulator in ABA signaling and regulated embryo germination and seed dormancy. TaABI5 (Triticum aestivum L. ABA insensitive five) gene was seed-specific, and accumulated during wheat grain maturation and dormancy acquisition, which played an important role in seed dormancy. In our previous study, two allelic variants of TaAFP were identified on chromosome 2BS in common wheat, and designated as TaAFP-B1a and TaAFP-B1b. Sequence analysis showed a 4-bp insertion in the 5’UTR region of TaAFP-B1a compared with TaAFP-B1b, which affected the mRNA transcription level, mRNA decay, translation levels of GUS and tdTomatoER, GUS activity, and was significantly associated with seed dormancy in common wheat. ResultsThe results of transgenic wheats showed that: the genotypes of average GI values, plant height, grain weight of hundred and rough of second and third stem node are all significantly more in pUbi-TaAFP-BaS transformed wheat plants than in pUbi-TaAFP-BbS transformed ones, but transcript expression level. ConclusionAbove all dates indicated that the 4-bp insertion in the 5'UTR of TaAFP-B decreased the transcript expression level of TaAFP-B and the PHS resistance, and increased the plant height, grain weight of hundred and lodging resistance in this system of over expression transgenic wheat.

Genome-Wide Analysis of the Expression of Circular RNA Full-Length Transcripts and Construction of the circRNA-miRNA-mRNA Network in Cervical Cancer

Increasing evidence suggests that circular RNA (circRNA) plays an important role in tumorigenesis by regulating gene expression at the transcriptional and post-transcriptional levels. Alternative splicing events permit multiple transcript isoforms of circRNA to be produced; however, changes in the expression of circRNA full-length transcripts in cervical cancer remain unclear. Here, we systematically explored the dysregulation circRNA full-length transcripts and constructed an improved circRNA-miRNA-mRNA regulatory network to provide potential biomarkers and possible treatment targets in cervical cancer. We identified 9359 circular full-length transcripts from RNase R-treated RNA-seq data in cervical cancer, of which 353 circular full-length transcripts were significantly differentially expressed (DE) between the tumor and normal group. A total of 881 DE mRNA transcript isoforms were also identified from total RNA-seq data in cervical cancer, of which 421 (47.8%) transcript isoforms were up-regulated, and 460 (52.2%) transcript isoforms were down-regulated in tumor samples. Two circRNA-miRNA-mRNA competitively regulated networks, including 33 circRNA transcripts, 2 miRNAs, and 189 mRNA transcripts were constructed. Three genes (COPE, RAB3B, and TFPI) in the network were significantly associated with overall survival (P < 0.05), which indicated that these genes could act as prognostic biomarkers for patients with cervical cancer. Our study revealed genome-wide differential expression of full-length circRNA transcripts and constructed a more accurate circRNA-miRNA-mRNA network at the full-length transcript expression level in cervical cancer. CircRNA may thus be involved in the development of cervical cancer by regulating the expression of COPE, RAB3B, and TFPI. However, the specific regulatory mechanism in cervical cancer requires further study.

FilTar: using RNA-Seq data to improve microRNA target prediction accuracy in animals

Motivation MicroRNA (miRNA) target prediction algorithms do not generally consider biological context and therefore generic target prediction based on seed binding can lead to a high level of false-positive predictions. Here, we present FilTar, a method that incorporates RNA-Seq data to make miRNA target prediction specific to a given cell type or tissue of interest. Results We demonstrate that FilTar can be used to: (i) provide sample specific 3′-UTR reannotation; extending or truncating default annotations based on RNA-Seq read evidence and (ii) filter putative miRNA target predictions by transcript expression level, thus removing putative interactions where the target transcript is not expressed in the tissue or cell line of interest. We test the method on a variety of miRNA transfection datasets and demonstrate increased accuracy versus generic miRNA target prediction methods. Availability and implementation FilTar is freely available and can be downloaded from https://github.com/TBradley27/FilTar. The tool is implemented using the Python and R programming languages, and is supported on GNU/Linux operating systems. Supplementary information Supplementary data are available at Bioinformatics online.