Online ISSN 2092-9293 Print ISSN 1976-9571

Genes & Genomics https://doi.org/10.1007/s13258-017-0631-7

RESEARCH ARTICLE

Transcriptome profiling of PeCRY1 transgenic Populus tomentosa Lina Wang1 · Rongling Wu1 · Wenhao Bo1 Received: 21 October 2016 / Accepted: 7 November 2017 © The Genetics Society of Korea and Springer Science+Business Media B.V., part of Springer Nature 2017

Abstract Cryptochromes, a class of blue light photoreceptors, play vital roles in regulating growth and development in higher plants. Despite their control over various important traits, there have been few studies focusing on cryptochromes in forest trees to date. In this study, the Euphrates poplar (Populus euphratica) gene PeCRY1 (cryptochrome 1 of Populus euphratica) was isolated and heterologously expressed in Populus tomentosa. Three biological replicates of each of the PeCRY1 transgenic P. tomentosa (CRY1) and wild-type (WT) plants were processed for transcriptome profiling. We found 34792 commonly expressed transcripts among the 93868 detected unigenes. Using R package DESeq, we identified 357 differentially expressed genes (DEGs), including 132 upregulated and 225 downregulated genes. Gene ontology (GO) enrichment and KEGG pathway enrichment were used to better understand the functions of detected DEGs. Biosynthetic processes, such as starch and sucrose metabolism, which are closely related to growth and development, were highly enriched. Organic cyclic compound biosynthesis was downregulated, whereas carbohydrate metabolism was upregulated. Through KEGG pathway enrichment, we observed that the pentose phosphate pathway, photosynthesis, and circadian rhythm were significantly enriched. Another method of expression analysis based on quantitative reverse transcription polymerase chain reaction (qRT-PCR) validated our RNA sequencing (RNA-seq) results. Keywords  Transcriptome · Populus tomentosa · Euphrates poplar · RNA-seq · Cryptochromes

Introduction Light is one of the most important external factors affecting plant growth and development because photosynthesis provides energy for higher plants. Light factors, such as quality, intensity, and spectrum, are perceived through photoreceptors (Sharrock and Clack 2002). Currently, there are four types of photoreceptors known in plants: cryptochromes, which are blue light receptors, phototropins, which perceive blue and ultraviolet A (UV-A) wavelengths, phytochromes, which mostly absorb the far-red and red wavelengths, and an unnamed photoreceptor, which senses ultraviolet B (UV-B) Electronic supplementary material  The online version of this article (https://doi.org/10.1007/s13258-017-0631-7) contains supplementary material, which is available to authorized users. * Wenhao Bo [email protected] 1



Center for Computational Biology, National Engineering Laboratory for Tree Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China

wavelengths (Jiao et al. 2007). Cryptochromes are a class of flavoprotein and are homologous with photolyase (Chaves et al. 2011). As a crucial receptor perceiving environmental changes, cryptochrome (CRY) is highly sensitive to blue light and near UV. Cryptochrome1 was first isolated from an Arabidopsis thaliana mutant library during research into the blue lightdependent inhibition of hypocotyl elongation (Ahmad and Cashmore 1993). The protein consists of the cryptochrome N-terminal region (CNT), highly homologous with the photolyase-related (PHR) domain in photolyase, and the cryptochrome C-terminal (CCT) region, which is not contained in photolyase (Todo 1999). There are three CRY genes in the A. thaliana genome: Cryptochrome1, Cryptochrome2, and Cryptochrome3 (Wu and Spalding 2007; Yu et al. 2007). The differences among cryptochrome molecules include electron transfer, phosphorylation, and ubiquitination, resulting in the conformational changes necessary to propagate light signals. Two signal transduction pathways have been discovered: cryptochrome-interacting basic-helix-loop-helix 1 (CIB)dependent Cryptochrome2 regulation of transcription and Suppressor of PHYA1/Constitutively Photomorphogenic 1

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(SPA1/COP1)-dependent cryptochrome regulation of proteolysis (Liu et al. 2011). We focused on two effects of light on plants: providing energy though photosynthesis and regulating growth and development through photomorphogenesis. The function of the blue light receptor in plant photomorphogenesis has been discussed previously; it has been found that AtCRY not only mediates blue-light regulation of de-etiolation but also regulates other important traits (Ahmad and Cashmore 1993; Guo et al. 1998). Studies in rice and tobacco have shown that CRYs inhibit elongation of the hypocotyl and coleoptile under blue and UV-A light (Hirose et al. 2006). CRYs also regulate flower timing; located upstream of the photoperiod pathway, CRYs transform an optical signal into a flowering signal by interacting with signaling proteins to regulate downstream genes, such as CO and FT (Jang et al. 2008; Yang et al. 2005). It has been shown in the hy4 mutant (cry1 mutant of A. thaliana) that the CRY1 protein can promote flowering (Bagnall et al. 1996). Meanwhile, Cryptochrome2 participates in the photoperiod-dependent pathway of flowering (Guo et al. 1998). CRYs also mediate circadian rhythm, which has been studied in animals and A. thaliana. In this regard, Cryptochrome1 works in both weak blue light and strong blue light, whereas Cryptochrome2 causes a decline in sensitivity to photoperiod rather than significantly affecting circadian rhythms (Devlin and Kay 2000; Somers et al. 1998; Yanovsky et al. 2001). Additionally, CRYs are involved with photochromes in stomata relying on blue light (Mao et al. 2005a). Magnetic induction apparently enhances Cryptochrome1 activity, and experiments have shown that dependent reactions in cryptochrome signal transduction pathways are sensitive to magnetic fields (Ahmad et al. 2007). Apart from these functions, CRYs regulate many other biological processes. For example, CRYs are necessary for the programmed cell death (PCD) caused by singlet oxygen and provide protection from intense light (Danon et al. 2006). Additionally, CRYs regulate the root elongation of seedlings such that Cryptochrome1 promotes elongation, whereas cryptochrome2 inhibits it; thus they are antagonistic during primary root elongation (Canamero et al. 2006; Usami et al. 2004; Zeng et al. 2010). Moreover, Cryptochrome1 gives rise to anthocyanin accumulation (Ahmad et al. 1995). Other important biological traits regulated by CRYs include plant height (Platten et al. 2005), fruit and ovule size (El-Assal et al. 2004), tropic growth (Tsuchida-Mayama et al. 2010), shade avoidance (Tsuchida-Mayama et al. 2010), and responses to bacterial and viral pathogens (Jeong et al. 2010). Since CRYs were first identified in A. thaliana, they have been detected in other plants, including moss (Imaizumi et al. 2002), fern (Imaizumi et al. 2000), tomato (Giliberto et al. 2005), pea (Platten et al. 2005), wheat (Xu et al. 2009), barley (Barrero et al. 2014), and Chinese sorghum (Xie et al.

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2005); and animals, including Drosophila (Busza et  al. 2004), rats (Fan et al. 2007), and humans (Foley et al. 2011). However, when it comes to forest trees, there is not enough data to clearly explain the CRY functions in transcription networks. The desert species Populus euphratica grows in dry temperate broadleaf and mixed forests and in subtropical dry broadleaf forests at altitudes of up to 4000 m above sea level, such as the region from North Africa, across the Middle East and central Asia to western China. This tree species requires a large amount of light during development (Chen et al. 2014). Whole genome expression data provides an effective means for understanding physiological and developmental processes. Furthermore, new technologies in transcriptional profiling, such as RNA sequencing (RNA-seq), allow more accurate detection of expressed genes (Wilhelm et al. 2008). To detect how the gene PeCRY1 (cryptochrome 1 of P. euphratica) functions in light-regulated networks, we used Populus tomentosa carrying a construct containing the 35s:PeCRY1 promoter sequence for de novo assembly of the P. tomentosa transcriptome.

Materials and methods Plant material and growth conditions The gene PeCRY1 (offered by the Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China) was transferred to P. tomentosa (obtained from the National Engineering Laboratory for Tree Breeding, Beijing Forestry University, Beijing, China). The transgenic P. tomentosa seedlings was given by Dr. Mao Ke(Center for Computational Biology, Beijing Forestry University) (Mao et al. 2014). Aseptic seedlings without any treatment were considered wild-type (WT), and transgenic P. tomentosa were called CRY1. Both WT P. tomentosa seedlings and transgenic lines were grown on MS medium with 0.4 mM IBA under 16 h light/8 h dark conditions at 22 °C. All plant materials were planted in transparent glass bottles.

RNA quantification and qualification Whole plants of both WT and CRY1 were processed using an RNA extraction kit made by Tiangen. RNA purity was monitored through 1.2% agarose gel electrophoresis and a ­NanoPhotometer® spectrophotometer (IMPLEN, CA, USA), quality was measured using an RNA Assay Kit in a Q ­ ubit® 2.0 Flurometer (LifeTechnologies, CA, USA), and integrity was checked with the RNA Nano 6000 Assay Kit on the

Genes & Genomics

Agilent Bioanalyzer 2100 system (Agilent Technologies, CA, USA).

Library preparation for transcriptome sequencing The library was prepared with the ­NEBNext® ­Ultra™ RNA Library Prep Kit for I­ llumina® (NEB, USA). A 3 μg subsample of quantified RNA from each plant was enriched using magnetic beads with oligo (dT) and was broken into fragments with fragmentation buffer. Using mRNA as a template, the first strand of cDNA was synthesized using random hexamer primers and M-MuLV reverse transcriptase (RNaseH-). Second strand cDNA was synthesized following DNA polymerase I and RNase H addition. The library fragments were purified with the AMPure XP system (Beckman Coulter, Beverly, USA) and eluted with EB buffer for terminal repair. After performing end repair and adding polyA, agarose gel electrophoresis was performed and library quality was assessed on the Agilent Bioanalyzer 2100 system. Clustering was carried out by TruSeq PE Cluster Kit v3-cBot-HS (Illumia). Then, the library was sequenced on an Illumina HiSeq 2500 platform.

Quality control Image data obtained from the Illumina Platform were transformed into an equivalent nucleotide sequence. Joints that emerged from library preparation, low-quality sequences of paired-ends, and sequences containing poly-N were removed from the raw data. The remaining high-quality sequences were subjected to de novo splicing by Trinity (Grabherr et al. 2011) to get contigs, which were then assembled into unigenes.

Gene function annotation Sequence alignment was executed on the unigenes by BLAST (Altschul et al. 1997), aligning with nucleotide databases, including Nt (NCBI non-redundant nucleotide sequences), and protein databases, including Nr (NCBI nonredundant protein sequences), Pfam (Protein family) (Bateman et al. 2008) and Swiss-Prot (a manually annotated and reviewed protein sequence database). Based on the NCBI annotation data, GO items were obtained through the Blast 2GO (Götz et al. 2008) program, and all unigenes were classified into three ontologies: biological processes, cellular processes, and metabolic processes by the software WEGO. In addition to KOG/COG (Clusters of Orthologous Groups of proteins) (Moriya et al. 2007), KO (KEGG Ortholog database) and GO (Gene Ontology) analyses were carried out.

Quantification of gene expression levels Reads corresponding to sequence joints from each sample were mapped to the transcripts and spliced by Bowtie. Read counts represented the expression abundance and gene expression level and were evaluated by standard RESM (Li and Dewey 2011). Because the sample had biological replicates, the R package DEGseq (1.10.1) was used to calculate fold change and P-value for hypothesis testing, and the gene was classified as a DEG (differentially expressed gene) when the adjusted P-value was less than 0.05 (Anders and Huber 2010; Wang et al. 2010). The R package MBCluster was used to complete the cluster analysis at each level (Sekula 2015).

GO enrichment and KEGG pathway enrichment analyses To understand which biological functions were relevant to the DEGs, the probability of the GO term was calculated by the R package GOsEq. The function was considered enriched when the corrected P-value was less than 0.5 (Young et al. 2010). To elucidate the main biochemical, metabolic, and signal transduction pathways, KEGG pathway enrichment was performed using KOBAS (Mao et al. 2005b) software with the Benjamini–Hochberg (BH) correction applied to the false discovery rate (FDR) parameter. The pathway was reported as enriched when FDR ≤ 0.05.

Results Transcriptome sequencing PeCRY1 was expressed in the whole plant of seedlings carrying a construct containing the 35s:PeCRY1 promoter sequence. Wild-type (WT) seedlings were cultured in the same manner as CRY1 seedlings, with three biological replicates of each seedling type. The transcriptome library generated from mRNA of six plants (three CRY1 plants and three WT plants) was analyzed on an Illumina platform. High-throughput mRNA sequencing generated 21–28 million raw reads from the samples, and the Q20% score was high enough to ensure accuracy (Table 1). After data cleanup and quality assessment, clean reads were subjected to the following analysis. As the P. tomentosa genome sequence has been shown to be ineffectively assembled, the de novo assembly method Trinity was used to construct the transcriptome. Clean reads were assembled through Trinity, and each assembled transcript was considered to be a unigene. In total, 93,868 unigenes were detected. The unigenes’ length distribution, which is shown in Fig. 1, mostly consists of nucleotide

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Table 1  Statistics for raw data prepared for transcriptome assembly

Genes & Genomics Sample

Raw reads

Clean reads

Clean bases

Error (%)

Q20 (%)

Q30 (%)

GC content (%)

WT-1 WT-2 WT-3 CRY1-1 CRY1-2 CRY1-3

28,581,810 21,738,860 24,239,147 24,845,968 24,319,793 21,514,907

26,496,948 20,058,464 22,775,145 22,677,457 22,329,110 20,260,138

3.31G 2.51G 2.85G 2.83G 2.79G 2.53G

0.03 0.03 0.03 0.03 0.03 0.03

97.36 96.93 97.35 96.76 96.57 96.82

94.62 93.8 94.59 93.43 93.2 93.52

45.2 43.94 44.95 44.21 43.37 44.6

Fig. 1  The length distribution of 93,868 unigenes detected. Sequences between 200 and 500 bp in length comprised the majority of the total reads

sequences between 200 and 1200 bp. RNA correlation demonstrated remarkably high similarity of expression patterns among samples (Additional file 1, Fig. S1).

Quantification of gene expression in Populus tomentosa After mapping clean reads from each sample using RESM software, the read counts of every transcript being mapped were obtained. When FPKM (expected number

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of fragments per kilobase of transcript sequence per million base pairs sequenced) exceeds 0.3, the gene is generally considered to be expressed. For WT plants, 34,068 transcripts were recognized as commonly expressed. The number for CRY1 plants was 34,792 (Additional file 2, Database 1). The two plant types shared 30,941 of 37,919 total transcripts; these were expressed in both WT and CRY1 plants (Fig. 2a–c). Thus, we found that the quantity of expressed genes was similar between the two sample types and among the three biological replicates.

Genes & Genomics

Fig. 2  Identification of transcripts number. Venn diagrams of transcripts in three separate experiments in a CRY1 plants and b WT plants; c comparison of the expressed genes in two plants

Fig. 3  Volcano plots for all genes in this study. The abscissa represents gene expression fold change in each experimental group, and the longitudinal coordinates represent the statistical significance of the change in gene expression. Scatter in the figure represents each gene. Blue dots indicate no significant change, red dots indicate genes that were significantly upregulated and green dots indicate genes that were significantly downregulated. (Color figure online)

Differential gene expression between WT and CRY1 plants

considered to have a role in ion transport, cellular metabolism, and single-organism metabolic processes.

Although the transcript numbers reflected considerable similarity between the two plant types, the transcription level of each gene reflected remarkable differences between them. After gene expression levels were estimated by RESM, differential gene expression analysis was performed using the DESeq R package. Genes with an adjusted P value < 0.05 calculated by DESeq were regarded as differentially expressed genes (DEGs) (Additional file 3, Database 2). In total, 357 DEGs, including 132 upregulated and 225 downregulated genes, were obtained (Fig. 3). To better understand the DEGs, the R package MBCluster was used to perform hybrid-hierarchical clustering for RNA-seq expressions (Additional file 4, Fig. S2) (Additional file 5, Database 3). The change in expression of every DEG between WT and CRY1 was shown by clustering analysis (Fig. 4). Three clusters were categorized as up-regulated and seven clusters as downregulated. Meanwhile, cluster 1, involving 12 genes, and cluster 5, with 7 genes, remained at the reference level. In the annotation list, the functions of these genes were described in detail. For the up-regulated genes, for example, c47991-g1 was predicted to function in the oxidation–reduction process, electron transport, proton transport, and oxidative phosphorylation, with a close connection with the TCA cycle. At the same time, the gene c49618-g1 was annotated as performing positive regulation of flower development, photoperiod, and flowering. As an example of the downregulated genes, c49057-g8 was

Gene ontology (GO) enrichment and KEGG pathway enrichment analysis of DEGs To elucidate detailed gene functions, gene function annotation was performed on seven databases, including Nr, Nt, Pfam, KOG/COG, Swiss-prot, KEGG, and GO. All genes had been successfully categorized into three main GO categories of biological processes, cellular components, and molecular functions. To analyze the function of DEGs, GO enrichment was performed. In total, 235 of the 357 DEGs identified before, including 150 downregulated genes and 85 upregulated genes, were mapped to the GO database. A detailed comparison of the enrichment of primary biological process of the three expression levels was made (Fig. 5). As anticipated, starch biosynthesis, which is tightly related to growth and development, was highly enriched. Organic cyclic compound biosynthesis was downregulated, as represented by aromatic compound biosynthesis. Additionally, carbohydrate metabolic processes were upregulated. Saccharide (including disaccharide, sucrose, glucan, inositol, oligosaccharide, and starch) metabolism was enriched to varying degrees. Glycolysis was not enriched as highly as carbohydrate metabolism, but both processes were upregulated. Thus, when PeCRY1 is expressed in P. tomentosa, organic substances that play crucial roles in plant growth and development, especially glycol metabolism, display differential expression. On the other hand, regulatory processes

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Genes & Genomics

Fig. 4  The change in expression of every DEG between WT and CRY1 by clustering analysis. Three clusters were categorized as upregulated and seven clusters as downregulated. All the gene-ID and

expression of the DEGs in Fig.  4 are listed in database 2 in detail. The specific genes of each cluster are listed in database 3

Fig. 5  GO terms over-represent enriched of genes that are differentially expressed in PeCRY1 transgenic P. tomentosa. It showed the most 22 over-represented GO categories of genes that were differentially expressed in CRY1. Different colors represent different

expression levels, such as up-regulated, downregulated and differential expressed. Abscissa represents the GO terms and the vertical axis represents the P value of the GO enrichment expressed as -log P. (Color figure online)

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showed different enrichment patterns. The processes involved in the regulation of macromolecule biosynthesis, macromolecule metabolism, and flower development were highly enriched, but they were not coherently upregulated or downregulated. Meanwhile, other processes were overrepresented. For example, transcription, DNA-dependent RNA synthesis, response to chemical stimulus, lateral root development, oxidation–reduction processes, and flowering were all downregulated. DEGs were also subjected to KEGG pathway enrichment analysis. The ten most significantly enriched pathways are listed in Table 2. The details of KEGG pathway enrichment are shown in Fig. 6. As expected, the pentose phosphate pathway, glycolysis, gluconeogenesis, and photosynthesis were highly enriched.

Validation of RNA‑Seq by qRT‑PCR Eight genes (Additional file 6, Database 4) selected randomly from ten clusters were used to validate RNA-Seq results by quantitative reverse transcription polymerase chain reaction (qRT-PCR). There was a good correspondence between the two techniques (Fig. 7) with the parameters t = 47.818, df = 6, p-value = 5.608e-09 and sample estimates of Pearson’s correlation cor = 0.9986905.

Discussion More than 90% of the Euphrates poplar in China grows in the Tarim River region, Xinjiang Province. As the oldest woody plant in the genus Populus, Euphrates poplar is considered a precious genetic resource for stress resistance. It is also a dominant species, making up the majority of vegetation in the Tarim River region (Liu 2011). For centuries, Euphrates poplar has played an important role in preventing desertification, maintaining equilibrium in the ecosystem, and guaranteeing productive agriculture and animal husbandry nearby. In recent years, environmental Table 2  The ten most significantly enriched pathways

stress caused by human disturbance has led to reductions in Euphrates poplar populations. Meanwhile, light intensity influences how well Euphrates poplar grows. Previous research has shown how cryptochrome1 regulates important traits in lower plants and herbaceous crops, including plant height, root length, and flowering time. However, the detailed mechanism of cryptochrome function in tree species has not been explained. Our results may indicate the processes and pathways by which PeCRY1 regulates growth and development. According to expression analysis and annotation, the most up-regulated genes encode proteins or enzymes involved in photosynthesis, such as c41177_g1 (betaamylase 3, chloroplastic-like), and photoperiod, such as c49618_g1 (protein HEADING DATE 3A-like) and c47991_g1 (gibberellin 20 oxidase 1-D-like). Based on DEG clustering, the seven genes in cluster 5 had the most strongly downregulated expression, followed by cluster 7. Nucleotide BLAST provided descriptions of sequences that are highly similar to Unigene c59775-g4 (Chlorophyll a-b binding protein 2) from cluster 7. It is hypothesized that unigene c59775-g4 could be a CRY1 downstream gene related to chlorophyll a-b binding, and it was downregulated by CRY1. As previous research on light-regulated transcription networks in higher plants has shown, light signals are perceived by phytochromes (PHYs) and cryptochromes (CRYs) (Shen et al. 2005). It has been shown that phytochromes downregulate phytochrome-interacting factor proteins (PIFs) (Al-Sady et al. 2006), and the family member PIF1 inhibits the accumulation of protochlorophyllide (Monte et al. 2004), the immediate precursor of chlorophyll, which means that PeCRY1 could lead to the same regulatory effect as phytochrome (Jiao et  al. 2007). The biological process pathways in which unigene c59775-g4 is involved are shown in Table 3. These pathways are all downregulated and closely related to photosynthesis. According to previous research in Arabidopsis, the chlorophyll a-b binding protein (CAB) gene encodes a key photosynthetic enzyme (Millar and Kay 1996), and

Pathway

Rich factor

Q value

Gene number type

Pentose phosphate pathway Starch and sucrose metabolism Glycolysis/gluconeogenesis Fructose and mannose metabolism Pentose and glucuronate interconversions Glycine, serine, and threonine metabolism Methane metabolism Photosynthesis—antenna proteins Phenylpropanoid biosynthesis Circadian rhythm—plant

0.041667 0.020833 0.016949 0.026667 0.02381 0.023529 0.021739 0.125 0.046512 0.076923

0.005468 0.005468 0.025365 0.035305 0.035305 0.035305 0.03579 0.037531 0.046153 0.046153

3 4 3 2 2 2 2 3 4 3

Upregulated Upregulated Upregulated Upregulated Upregulated Upregulated Upregulated Downregulated Downregulated Downregulated

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Genes & Genomics

Fig. 6  Pathway diagram. The vertical axis shows the name of the pathway, and the horizontal axis represents the corresponding Rich factor. The Q-value is represented by color, with smaller Q-values

being closer to red. The number of differentially expressed genes contained in each pathway is represented by the size of the dot. (Color figure online)

our results suggest that unigene c59775-g4 is involved in the photosynthesis pathway. Thus, our results are consistent with the light-regulated mechanism. The pathways involving unigene c59775-g4 always contain other unigenes as well, such as c47265-g1 (hypothetical protein) and c47265-g2 (hypothetical protein), which may be an indication of biological process networks. Considering that CRY1 is an important regulator of growth in plants and of the circadian clock in both plants and animals, carbohydrate metabolism, especially glycolysis/gluconeogenesis and the tricarboxylic acid (TCA) cycle, could be influenced by CRY1. In this study, we found that CRY1 up-regulated biological processes, including starch, sucrose, disaccharide and glucan metabolism, whereas processes such as organic cyclic compound biosynthesis and aromatic compound biosynthesis were downregulated.

Although many components of the circadian clock are also involved in other light-regulated processes, the circadian clock mediates the photoperiod response by integrating with developmental pathways (Jiao et al. 2007), such as the pathways enriched in this study. Previously, it has been shown that CRYs mediate circadian rhythm in animals and A. thaliana. In this regard, CRY1 works in both weak blue light and strong blue light, whereas CRY2 causes a decline in sensitivity to photoperiod rather than significantly affecting circadian rhythms. In this study, we focused on enriched KEGG pathways. In particular, the plant circadian rhythm was highly enriched, as shown in Fig. S3, where the green font represents downregulated genes and the red represents up-regulated genes. GI is widely considered to regulate photoperiod by the pathway GI-CO-FT. However, the latest study indicated that specific expression of the GI

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Genes & Genomics Fig. 7  Difference between two techniques: RNA-Seq and quantitative real-time PCR. Eight genes were selected for validation through RT-PCR. Expression in WT plants was assigned a value of 1

Table 3  Pathways of biological processes involving unigene c59775-g4 GO accession

Description

Overrepresented Gene names P value

GO:0009765 GO:0006091

Photosynthesis light harvesting 0.000825 Generation of precursor metabolites 0.01135 and energy

GO:0019684 GO:0015979

Photosynthesis, light reaction Photosynthesis

0.012056 0.032445

GO:0009416

Response to light stimulus

0.047047

gene was found to increase the expression of FT, whereas the expression of CO did not change, the same pattern that we observed in Euphrates poplar. This provided evidence that light signals may be transmitted to FT directly in Euphrates poplar, that is, without mediation by CO in the central position. Depending on test conditions, approximately 5–25% of genes in the Arabidopsis genome change their expression in response to blue light; most of the changes are mediated by CRY1 and CRY2 (Folta et al. 2003; Ma et al. 2001; Ohgishi et al. 2004; Sellaro et al. 2009). In our results with 93,868 unigenes, there were 357 DEGs. That is to say, 0.38% of genes are differentially expressed under standard conditions. After gene function annotation using seven databases, 33.1% of the sequence had no hits with other species. That discrepancy is due to noncoding transcripts and sequences that were not long enough for analysis occupying a portion of the transcriptome. Furthermore, Euphrates poplar genomics and

c47265-g2,c59775-g4,c47265-g1 c61896-g1,c61666-g1,c47265-g1,c35874-g1,c47806-g1,c51973-g2 c51765-g3,c55414-g2,c42232-g1,c47265-g2,c44701-g1,c47991-g1 c59775-g4,c58612-g1 c47265-g1,c59775-g4,c51973-g2,c47265-g2 c59775-g4,c47265-g2,c62161-g1,c57906-g3,c53280-g1,c51973-g2 c49456-g1,c56470-g3,c47265-g1 c55919-g1,c49618-g1,c53481-g1,c59775-g4,c55919-g2

transcriptomics lack the quantity of data necessary for abundant function annotation. Further analysis showed that there are very important upstream genes, such as GI and FT, present; these regulate growth and development in plants. Some uncharacterized genes were significantly changed, such as the downregulated gene c50899_g2 and the upregulated genes c56075_g1 and c47580_g2. Afterward, the detailed functions of both identified and uncharacterized genes would be verified through cloning, heterologous expression, and microarray analysis.

Conclusions Our results indicate that PeCRY1 plays an important role in the regulation of transcription in Euphrates poplar. By gene annotation and pathway enrichment of differentially expressed genes, we found that PeCRY1 participated in the

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regulation of the circadian clock and carbohydrate metabolism. Some genes in this pathway were identified. These regulation networks of PeCRY1 provide a valuable reference for further research on the function of these genes and the interaction between them, which can be verified through transgenic technology and other biological tests. Acknowledgements  This work is supported by Special Fund for Forest Scientific Research in the Public Welfare (201404102), Changjiang Scholars Award and “Thousand-person Plan” Award and the Fundamental Research Funds for the Central Universities (NO.BLX2014-22). Author contributions  RW and WB conceived and designed the experiments. LW performed the experiments, analyzed the data and wrote the paper. WB contributed reagents/materials/analysis tools.

Compliance with ethical standards  Conflict of interest  All of the authors Wenhao Bo, Rongling Wu and Wang Lina declare no competing financial interests. Ethical approval  The subject of this study is the forest tree Euphrates poplar, which grows widely in Northwest China. The transgenic receptor is Populus tomentosa, which is distributed throughout most of China. Neither is an endangered species. The transgenic plants were all cultured in a laboratory setting, and the laboratory is not personally owned. There was no permit required for this study. Research involving human and animal rights  The article does not contain any studies with human participants performed by any of the authors.

References Ahmad M, Cashmore AR (1993) HY4 gene of A. thaliana encodes a protein with characteristics of a blue-light photoreceptor. Nature 366:162–166 Ahmad M, Lin C, Cashmore AR (1995) Mutations throughout an Arabidopsis blue-light photoreceptor impair blue-light-responsive anthocyanin accumulation and inhibition of hypocotyl elongation. Plant J 8:653–658 Ahmad M, Galland P, Ritz T, Wiltschko R, Wiltschko W (2007) Magnetic intensity affects cryptochrome-dependent responses in Arabidopsis thaliana. Planta 225:615–624 Al-Sady B, Ni W, Kircher S, Schäfer E, Quail PH (2006) Photoactivated phytochrome induces rapid PIF3 phosphorylation prior to proteasome-mediated degradation. Mol Cell 23:439–446 Altschul SF, Madden TL, Schäffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucl Acids Res 25:3389–3402 Anders S, Huber W (2010) Differential expression analysis for sequence count data. Genome Biol 11:1 Bagnall DJ, King RW, Hangarter RP (1996) Blue-light promotion of flowering is absent in hy4 mutants of Arabidopsis. Planta 200:278–280 Barrero JM, Downie AB, Xu Q, Gubler F (2014) A role for barley CRYPTOCHROME1 in light regulation of grain dormancy and germination. Plant Cell 26:1094–1104

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Genes & Genomics Bateman A, Coin L, Durbin R, Finn RD, Hollich V, Griffiths-Jones S, Khanna A, Marshall M, Moxon S et al (2008) The Pfam protein families database. Nucl Acids Res 36(264):263–266 Busza A, Emery-Le M, Rosbash M, Emery P (2004) Roles of the two drosophila CRYPTOCHROME structural domains in circadian photoreception. Science 304:1503–1506 Canamero RC, Bakrim N, Bouly J-P, Garay A, Dudkin EE, Habricot Y, Ahmad M (2006) Cryptochrome photoreceptors cry1 and cry2 antagonistically regulate primary root elongation in Arabidopsis thaliana. Planta 224:995–1003 Chaves I, Pokorny R, Byrdin M, Hoang N, Ritz T, Brettel K, Essen L, Horst G, Batschauer A et al (2011) The cryptochromes: blue light photoreceptors in plants and animals. Annu Rev Plant Biol 62:335–364 Chen J, Yin W, Xia X (2014) Transcriptome profiles of Populus euphratica upon heat shock stress. Curr Genomics 15:326–340 Danon A, Coll NS, Apel K (2006) Cryptochrome-1-dependent execution of programmed cell death induced by singlet oxygen in Arabidopsis thaliana. Proc Natl Acad Sci USA 103:17036–17041 Devlin PF, Kay SA (2000) Cryptochromes are required for phytochrome signaling to the circadian clock but not for rhythmicity. Plant Cell 12:2499–2509 El-Assal S, Alonso-Blanco C, Hanhart C, Koornneef M (2004) Pleiotropic effects of the Arabidopsis cryptochrome 2 allelic variation underlie fruit trait-related QTL. Plant Biol 6:370–374 Fan Y, Hida A, Anderson DA, Izumo M, Johnson CH (2007) Cycling of CRYPTOCHROME proteins is not necessary for circadianclock function in mammalian fibroblasts. Curr Biol 17:1091–1100 Foley LE, Gegear RJ, Reppert SM (2011) Human cryptochrome exhibits light-dependent magnetosensitivity. Nat Commun 2:356 Folta KM, Pontin MA, Karlin-Neumann G, Bottini R, Spalding EP (2003) Genomic and physiological studies of early cryptochrome 1 action demonstrate roles for auxin and gibberellin in the control of hypocotyl growth by blue light. Plant J 36:203–214 Giliberto L, Perrotta G, Pallara P, Weller JL, Fraser PD, Bramley PM, Fiore A, Tavazza M, Giuliano G (2005) Manipulation of the blue light photoreceptor cryptochrome 2 in tomato affects vegetative development, flowering time, and fruit antioxidant content. Plant Physiol 137:199–208 Götz S, García-Gómez JM, Terol J, Williams TD, Nagaraj SH, Nueda MJ, Robles M, Talón M, Dopazo J et al (2008) High-throughput functional annotation and data mining with the Blast2GO suite. Nucl Acids Res 36:3420–3435 Grabherr MG, Haas BJ, Yassour M, Levin JZ, Thompson DA, Amit I, Adiconis X, Fan L, Raychowdhury R et al (2011) Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat Biotechnol 29:644–652 Guo H, Yang H, Mockler TC, Lin C (1998) Regulation of flowering time by Arabidopsis photoreceptors. Science 279:1360–1363 Hirose F, Shinomura T, Tanabata T, Shimada H, Takano M (2006) Involvement of rice cryptochromes in de-etiolation responses and flowering. Plant Cell Physiol 47:915–925 Imaizumi T, Kanegae T, Wada M (2000) Cryptochrome nucleocytoplasmic distribution and gene expression are regulated by light quality in the fern Adiantum capillus-veneris. Plant Cell 12:81–95 Imaizumi T, Kadota A, Hasebe M, Wada M (2002) Cryptochrome light signals control development to suppress auxin sensitivity in the moss Physcomitrella patens. Plant Cell 14:373–386 Jang S, Marchal V, Panigrahi KC, Wenkel S, Soppe W, Deng XW, Valverde F, Couplan (2008) Arabidopsis COP1 shapes the temporal pattern of CO accumulation conferring a photoperiodic flowering response. EMBO J 27:1277–1288 Jeong R-D, Chandra-Shekara A, Barman SR, Navarre D, Klessig DF, Kachroo A, Kachroo P (2010) Cryptochrome 2 and phototropin 2 regulate resistance protein-mediated viral defense by negatively

Genes & Genomics regulating an E3 ubiquitin ligase. Proc Natl Acad Sci USA 107:13538–13543 Jiao Y, Lau OS, Deng XW (2007) Light-regulated transcriptional networks in higher plants. Nat Rev Genet 8:217–230 Li B, Dewey CN (2011) RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 12:323 Liu PX (2011) Study on population structure and dynamics of Populus euphratica in the middle and lower reaches of the Shule River Basin Oasis, Hexi Corridor. J Nat Resources 26:429–439 Liu H, Liu B, Zhao C, Pepper M, Lin C (2011) The action mechanisms of plant cryptochromes. Trends Plant Sci 16:684–691 Ma L, Li J, Qu L, Hager J, Chen Z, Zhao H, Deng XW (2001) Light control of Arabidopsis development entails coordinated regulation of genome expression and cellular pathways. Plant Cell 13:2589–2607 Mao J, Zhang Y-C, Sang Y, Li Q-H, Yang H-Q (2005a) A role for Arabidopsis cryptochromes and COP1 in the regulation of stomatal opening. Proc Natl Acad Sci USA 102:12270–12275 Mao X, Cai T, Olyarchuk JG, Wei L (2005b) Automated genome annotation and pathway identification using the KEGG Orthology (KO) as a controlled vocabulary. Bioinformatics 21:3787–3793 Mao K, Jiang L, Bo W, Xu F, Wu R (2014) Cloning of the cryptochrome-encoding PeCRY1 gene from Populus euphratica and functional analysis in Arabidopsis. PLoS ONE 9:e115201 Millar AJ, Kay SA (1996) Integration of circadian and phototransduction pathways in the network controlling CAB gene transcription in Arabidopsis. Proc Natl Acad Sci USA 93:15491–15496 Monte E, Tepperman JM, Al-Sady B, Kaczorowski KA, Alonso JM, Ecker JR, Li X, Zhang Y, Quail PH (2004) The phytochromeinteracting transcription factor, PIF3, acts early, selectively, and positively in light-induced chloroplast development. Proc Natl Acad Sci USA 101:16091–16098 Moriya Y, Itoh M, Okuda S, Yoshizawa AC, Kanehisa M (2007) KAAS: an automatic genome annotation and pathway reconstruction server. Nucl Acids Res 35:W182-W185 Ohgishi M, Saji K, Okada K, Sakai T (2004) Functional analysis of each blue light receptor, cry1, cry2, phot1, and phot2, by using combinatorial multiple mutants in Arabidopsis. Proc Natl Acad Sci USA 101:2223–2228 Platten JD, Foo E, Elliott RC, Hecht V, Reid JB, Weller JL (2005) Cryptochrome 1 contributes to blue-light sensing in pea. Plant Physiol 139:1472–1482 Sekula MN (2015) An R package for determining the optimal clustering algorithm and optimal number of clusters. Bioinformation 13:101–103 Sellaro R, Hoecker U, Yanovsky M, Chory J, Casal JJ (2009) Synergism of red and blue light in the control of Arabidopsis gene expression and development. Curr Biol 19:1216–1220 Sharrock RA, Clack T (2002) Patterns of expression and normalized levels of the five Arabidopsis phytochromes. Plant Physiol 130:442–456

Shen H, Moon J, Huq E (2005) PIF1 is regulated by light-mediated degradation through the ubiquitin-26S proteasome pathway to optimize photomorphogenesis of seedlings in Arabidopsis. Plant J 44:1023–1035 Somers DE, Devlin PF, Kay SA (1998) Phytochromes and cryptochromes in the entrainment of the Arabidopsis circadian clock. Science 282:1488–1490 Todo T (1999) Functional diversity of the DNA photolyase/blue light receptor family. Mutat Res 434:89–97 Tsuchida-Mayama T, Sakai T, Hanada A, Uehara Y, Asami T, Yamaguchi S (2010) Role of the phytochrome and cryptochrome signaling pathways in hypocotyl phototropism. Plant J 62:653–662 Usami T, Mochizuki N, Kondo M, Nishimura M, Nagatani A (2004) Cryptochromes and phytochromes synergistically regulate Arabidopsis root greening under blue light. Plant Cell Physiol 45:1798–1808 Wang L, Feng Z, Wang X, Wang X, Zhang X (2010) DEGseq: an R package for identifying differentially expressed genes from RNAseq data. Bioinformatics 26:136–138 Wilhelm BT et al (2008) Dynamic repertoire of a eukaryotic transcriptome surveyed at single-nucleotide resolution. Nature 453:1239–1243 Wu G, Spalding EP (2007) Separate functions for nuclear and cytoplasmic cryptochrome 1 during photomorphogenesis of Arabidopsis seedlings. P Natl Acad Sci USA 104:18813–18818 Xie XZ, Chen ZP, Wang XJ (2005) Cloning and expression analysis of CRY2 gene in Sorghum bicolor. J Plant Physiol Mol Biol 31:261 Xu P, Xiang Y, Zhu H, Xu H, Zhang Z, Zhang C, Zhang L, Ma Z (2009) Wheat cryptochromes: subcellular localization and involvement in photomorphogenesis and osmotic stress responses. Plant Physiol 149:760–774 Yang J, Lin R, Sullivan J, Hoecker U, Liu B, Xu L, Deng XW, Wang H (2005) Light regulates COP1-mediated degradation of HFR1, a transcription factor essential for light signaling in Arabidopsis. Plant Cell 17:804–821 Yanovsky MJ, Mazzella MA, Whitelam GC, Casal JJ (2001) Resetting of the circadian clock by phytochromes and cryptochromes in Arabidopsis. J Biol Rhythm 16:523–530 Young MD, Wakefield MJ, Smyth GK, Oshlack A (2010) Gene ontology analysis for RNA-seq: accounting for selection bias. Genome Biol 11:1–12 Yu X, Klejnot J, Zhao XY, Shalitin D, Maymon M, Yang HY, Lee J, Liu XM, Lopez J et al (2007) Arabidopsis cryptochrome 2 completes its posttranslational life cycle in the nucleus. Plant Cell 19:3146–3156 Zeng J, Wang Q, Lin J, Deng K, Zhao X, Tang D, Liu X (2010) Arabidopsis cryptochrome-1 restrains lateral roots growth by inhibiting auxin transport. J Plant Physiol 167:670–673

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