Plant Molecular Biology Reporter

, Volume 33, Issue 3, pp 424–438 | Cite as

Analysis of the Drought Stress-Responsive Transcriptome of Black Cottonwood (Populus trichocarpa) Using Deep RNA Sequencing

  • Sha Tang
  • Yan Dong
  • Dan Liang
  • Zhoujia Zhang
  • Chu-Yu Ye
  • Peng Shuai
  • Xiao Han
  • Ying Zhao
  • Weilun YinEmail author
  • Xinli XiaEmail author
Original Paper


The complete genome of Populus trichocarpa has been available for years, however, relatively little effort has been made to map and quantify the transcriptome for this important model organism. Here, we applied Illumina sequencing to systematically investigate the leaf transcriptomes derived from P. trichocarpa seedlings grown in normal condition and drought stress. On the basis of the available Populus genome, we defined gene structures and identified a number of novel transcripts and upstream open reading frames, which may be informative for understanding drought adaption of woody plants. We obtained 33,044 genes expressed in leaves covering ~80 % of the available P. trichocarpa gene models and 5689 genes were differentially expressed during drought stress. About 38.9 % of the expressed genes show alternative splicing (AS) patterns. Interestingly, the number of AS events and the expression of some AS genes increased when the plants were subjected to drought. At the physiological level, photosynthetic rates, stomatal conductance, and leaf water potential were significantly reduced after water stress. This was accompanied by strong transcriptional remodeling of energy metabolism, cell growth, carbohydrate metabolism, and cellular homeostasis in P. trichocarpa. In addition, genes involved in photosynthesis, cell wall organization, and osmoprotectants metabolism that may specially modulate the drought stress responses of P. trichocarpa are highlighted. Our analysis provides insight into the transcriptional responses of P. trichocarpa to drought stress and is expected to serve as valuable transcriptome resources for woody plants.


Populus trichocarpa Drought stress Transcriptome analysis High-throughput sequencing Alternative splicing 



This research was supported by the Hi-TechResearch and Development Program of China (2013AA102701), the National Natural Science Foundation of China (31270656), Program for Changjiang Scholars and Innovative Research Team in University (IRT13047), Programs for Scientific Research and Graduate Training from BMEC (Stress Resistance Mechanism of Poplar) and 111 Project of Beijing Forestry University (B13007).

Supplementary material

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Supplementary Fig. S1

A brief introduction of the algorithms used to detect alternative splicing events. (A) Intron retention. Junction sites are detected by TopHat with all default parameters. Junction 1 was detected between two known exon, over 90 % of this junction is covered by at least two unique-mapping reads. The coverage depth of the junction is at least 15 % of the coverage depth of the two exon. Five-bp upstream and downstream of this junction’s boundaries is required to be covered by reads. When a junction site was detected and meet the above conditions, there is an IR event between the two known exons. (B) Alternative 5′ splice site. If either Junction 2 or Junction 3, which have the same 3′ but different 5′ splice sites with Junction 1, and Junction 1 are detected, then there is an Alternative 5′ Splice Site event between Exon1 and Exon2. (C) Alternative 3′ splice site. If either Junction 2 or Junction 3, which have the same 5′ but different 3′ splice sites with Junction 1, and Junction 1 are detected, then there is an Alternative 3′ Splice Site event between Exon1 and Exon2. (D) Exon Skipping. The first transcript have a new exon more than the second transcript, the new exon is inclusive exon, the other two exons is constitutive exon. (GIF 132 kb)

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High Resolution Image (TIFF 3091 kb)
11105_2014_759_Fig6_ESM.gif (38 kb)
Supplementary Fig. S2

Distribution of reads along the lengths of gene models. (A) Reads from the well-watered control plants. (B) Reads from the water-limited plants. (GIF 37 kb)

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High Resolution Image (TIFF 2008 kb)
11105_2014_759_Fig7_ESM.gif (216 kb)
Supplementary Fig. S3

The genome distribution of transcribed regions in P. trichocarpa. The distributions of reads on the 19 longest chromosomes of P. trichocarpa are shown. GeneNumber (blue) indicates the number of genes located at the corresponding position of the chromosome. Coverage (red) represents the percentage of the genomic region covered by Illumina sequencing reads. log2ReadsNumber (green) indicates different transcription levels in the genomic region assessed reads count. (GIF 215 kb)

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High Resolution Image (TIFF 8164 kb)
11105_2014_759_Fig8_ESM.gif (108 kb)
Supplementary Fig. S4

Gene coverage statistics. Gene coverage is the percentage of a gene covered by RNA-Seq reads generated from the two cDNA libraries made from (A) well-watered control plants and (B) water-limited plants. The percentages of genes with different gene coverage are indicated in the pie chart. (GIF 108 kb)

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High Resolution Image (TIFF 2636 kb)
11105_2014_759_Fig9_ESM.gif (69 kb)
Supplementary Fig. S5

Comparison of gene expression levels identified by both Cufflinks and our RPKM-based method. (GIF 69 kb)

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High Resolution Image (TIFF 1106 kb)
11105_2014_759_Fig10_ESM.gif (6.4 mb)
Supplementary Fig. S6

GO enrichment analysis of genes that were up-regulated (A) and down-regulated (B) in response to drought, as compared to the entire P. trichocarpa genome. Functional categories were based on biological processes. Enriched GO terms were significant at P < 0.05 (Hypergeometric P value. Bonferroni adjusted). The biological processes were summarized using REViGO. Disc color indicates P value for hypothesis testing as shown in the color bar, while size is proportional to log2 number of genes in category. (GIF 6526 kb)

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11105_2014_759_MOESM7_ESM.doc (60 kb)
Supplementary Table S1 (DOC 60 kb)
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Supplementary Table S2 (DOC 39 kb)
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Supplementary Table S3 (DOC 2622 kb)
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Supplementary Table S4 (DOC 29 kb)
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Supplementary Table S5 (DOC 3643 kb)
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Supplementary Table S6 (DOC 5095 kb)
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Supplementary Table S7 (DOC 300 kb)
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Supplementary Table S8 (DOC 172 kb)
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Supplementary Table S9 (DOC 556 kb)


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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Sha Tang
    • 1
  • Yan Dong
    • 1
  • Dan Liang
    • 1
  • Zhoujia Zhang
    • 1
  • Chu-Yu Ye
    • 1
  • Peng Shuai
    • 1
  • Xiao Han
    • 1
  • Ying Zhao
    • 1
  • Weilun Yin
    • 1
    Email author
  • Xinli Xia
    • 1
    Email author
  1. 1.College of Biological Sciences and Biotechnology, National Engineering Laboratory for Tree BreedingBeijing Forestry UniversityBeijingPeople’s Republic of China

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