Dataset Information

RNA-Seq analysis of differential gene expression in Betula luminifera xylem during the early stages of tension wood formation.

ABSTRACT: Background:Betula luminifera H. Winkler, which is widely distributed in southern China, is an economically important broadleaf tree species. However, little genomic information of B. luminifera is available, and little is known about the molecular mechanisms of wood formation in this species. Meanwhile, few efforts have focused on investigating the early transcriptional changes during tension wood formation in woody plants. Results:A reference transcriptome dataset was first generated containing 45,700 Unigenes, and 35,135 (76.9%) Unigenes were annotated by a BLAST similarity search against four public databases. Then, based on an anatomical investigation, the global gene expression changes during the early stages of tension wood formation were analyzed. Gene expression profiling showed that a total of 13,273 Unigenes were differentially regulated during the early stages of tension wood formation. Most genes involved in cellulose and lignin biosynthesis were highlighted to reveal their biological importance in tension wood formation. In addition, the transcription levels of many genes involved in the auxin response pathway were significantly changed during the early stages of tension wood formation. Furthermore, 18 TFs co-expressed with key enzymes of cellulose synthesis were identified. Conclusions:Our results revealed the transcriptional changes associated with TW formation and identified potential key genes in the regulation of this process. These results will help to dissect the molecular mechanism of wood formation and provide key candidate genes for marker-assisted selection in B. luminifera.

SUBMITTER: Cai M

PROVIDER: S-EPMC6108316 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

RNA-Seq analysis of differential gene expression in <i>Betula luminifera</i> xylem during the early stages of tension wood formation.

Cai Miaomiao M Huang Huahong H Ni Fei F Tong Zaikang Z Lin Erpei E Zhu Muyuan M

PeerJ 20180821

<h4>Background</h4><i>Betula luminifera</i> H. Winkler, which is widely distributed in southern China, is an economically important broadleaf tree species. However, little genomic information of <i>B</i>. <i>luminifera</i> is available, and little is known about the molecular mechanisms of wood formation in this species. Meanwhile, few efforts have focused on investigating the early transcriptional changes during tension wood formation in woody plants.<h4>Results</h4>A reference transcriptome da ...[more]

PMID: 30155351

Similar Datasets

Project description:BackgroundWood formation affects the chemical and physical properties of wood, and thus affects its utility as a building material or a feedstock for biofuels, pulp and paper. To obtain genome-wide insights on the transcriptome changes and regulatory networks in wood formation, we used high-throughput RNA sequencing to characterize cDNA libraries of mature xylem from tension wood (TW), opposite wood (OW), and normal wood (NW), in the industrial tree species Populus tomentosa.ResultsOur sequencing generated 140,978,316 (TW), 128,972,228 (OW), and 117,672,362 (NW) reads, corresponding to 10,127 (TW), 10,129 (OW), and 10,129 (NW) unique genes. Of these, 361 genes were differentially transcribed between TW and OW (log2FC ≥ 1 or ≤ -1, FDR < 0.05), 2,658 differed between OW and NW, and 2,417 differed between TW and NW. This indicates that NW differs significantly from the wood in branches; GO term analysis also indicated that OW experienced more transcriptome remodeling. The differentially expressed genes included 97 encoding transcription factors (TFs), 40 involved in hormone signal transduction, 33 in lignin biosynthesis, 21 in flavonoid biosynthesis, and 43 in cell wall metabolism, including cellulose synthase, sucrose synthase, and COBRA. More than half of the differentially expressed TF showed more than 4-fold lower transcript levels in NW compared with TW or OW, indicating that TF abundances differed dramatically in different wood types and may have important roles in the formation of reaction wood. In addition, transcripts of most of the genes involved in lignin biosynthesis were more abundant in OW compared with TW, consistent with the higher lignin content of OW. We constructed two transcriptomic networks for the regulation of lignin and cellulose biosynthesis, including TFs, based on the co-expression patterns of different genes. Lastly, we used reverse transcription quantitative PCR to validate the differentially expressed genes identified.ConclusionsHere, we identified the global patterns and differences in gene expression among TW, OW, and NW, and constructed two transcriptomic regulatory networks involved in TW formation in P. tomentosa. We also identified candidate genes for molecular breeding of wood quality, and provided a starting point to decipher the molecular mechanisms of wood formation in Populus.

Project description:Plant phenotypic traits play an important role in understanding plant growth dynamics and complex genetic traits. In phenotyping, the segmentation of plant organs, such as leaves and stems, helps in automatically monitoring growth and improving screening efficiency for large-scale genetic breeding. In this paper, we propose an AC-UNet stem and leaf segmentation algorithm based on an improved UNet. This algorithm aims to address the issues of feature edge information loss and sample breakage in the segmentation of plant organs, specifically in Betula luminifera. The method replaces the backbone feature extraction network of UNet with VGG16 to reduce the redundancy of network information. It adds a multi-scale mechanism in the splicing part, an optimized hollow space pyramid pooling module, and a cross-attention mechanism in the expanding network part at the output end to obtain deeper feature information. Additionally, Dice_Boundary is introduced as a loss function in the back-end of the algorithm to circumvent the sample distribution imbalance problem. The PSPNet model achieves mIoU of 58.76%, mPA of 73.24%, and Precision of 66.90%, the DeepLabV3 model achieves mIoU of 82.13%, mPA of 91.47%, and Precision of 87.73%, on the data set. The traditional UNet model achieves mIoU of 84.45%, mPA of 91.11%, and Precision of 90.63%, and the Swin-UNet model achieves . The mIoU is 79.02%, mPA is 85.99%, and Precision is 88.73%. The AC-UNet proposed in this article achieved excellent performance on the Swin-UNet dataset, with mIoU, mPA, and Precision of 87.50%, 92.71%, and 93.69% respectively, which are better than the selected PSPNet, DeepLabV3, traditional UNet, and Swin-UNet. Commonly used semantic segmentation algorithms. Experiments show that the algorithm in this paper can not only achieve efficient segmentation of the stem and leaves of Betula luminifera but also outperforms the existing state-of-the-art algorithms in terms of both speed. This can provide more accurate auxiliary support for the subsequent acquisition of plant phenotypic traits.

Dataset Information

RNA-Seq analysis of differential gene expression in Betula luminifera xylem during the early stages of tension wood formation.

Publications

RNA-Seq analysis of differential gene expression in <i>Betula luminifera</i> xylem during the early stages of tension wood formation.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets