Skip to main content


Transcriptome of the inflorescence meristems of the biofuel plant Jatropha curcas treated with cytokinin



Jatropha curcas, whose seed content is approximately 30–40% oil, is an ideal feedstock for producing biodiesel and bio-jet fuels. However, Jatropha plants have a low number of female flowers, which results in low seed yield that cannot meet the needs of the biofuel industry. Thus, increasing the number of female flowers is critical for the improvement of Jatropha seed yield. Our previous findings showed that cytokinin treatment can increase the flower number and female to male ratio and also induce bisexual flowers in Jatropha. The mechanisms underlying the influence of cytokinin on Jatropha flower development and sex determination, however, have not been clarified.


This study examined the transcriptional levels of genes involved in the response to cytokinin in Jatropha inflorescence meristems at different time points after cytokinin treatment by 454 sequencing, which gave rise to a total of 294.6 Mb of transcript sequences. Up-regulated and down-regulated annotated and novel genes were identified, and the expression levels of the genes of interest were confirmed by qRT-PCR. The identified transcripts include those encoding genes involved in the biosynthesis, metabolism, and signaling of cytokinin and other plant hormones, flower development and cell division, which may be related to phenotypic changes of Jatropha in response to cytokinin treatment. Our analysis indicated that Jatropha orthologs of the floral organ identity genes known as ABCE model genes, JcAP1,2, JcPI, JcAG, and JcSEP1,2,3, were all significantly repressed, with an exception of one B-function gene JcAP3 that was shown to be up-regulated by BA treatment, indicating different mechanisms to be involved in the floral organ development of unisexual flowers of Jatropha and bisexual flowers of Arabidopsis. Several cell division-related genes, including JcCycA3;2, JcCycD3;1, JcCycD3;2 and JcTSO1, were up-regulated, which may contribute to the increased flower number after cytokinin treatment.


This study presents the first report of global expression patterns of cytokinin-regulated transcripts in Jatropha inflorescence meristems. This report laid the foundation for further mechanistic studies on Jatropha and other non-model plants responding to cytokinin. Moreover, the identification of functional candidate genes will be useful for generating superior varieties of high-yielding transgenic Jatropha.


The ever-decreasing crude oil reserves are insufficient to satisfy the increasing demand for petroleum as a transportation and heating fuel, and petroleum consumption also pollutes the environment. Liquid biofuels from plants and microalgae may help solve these problems. Jatropha curcas (hereafter referred to as Jatropha), a perennial deciduous shrub belonging to the family Euphorbiaceae whose seed content is approximately 30–40% oil, is an ideal feedstock for producing biodiesel and bio-jet fuels [13]. Because the quality parameters of Jatropha biodiesel are within the European EN 14214 specification and the emission parameters of sulfur and particulate matter are 80% lower than those of mineral diesel [4], Jatropha is emerging as a potential biofuel plant.

However, currently the seed yield of Jatropha is poor and insufficient for the biodiesel industry [5, 6]. Recently, researchers reported that applying plant growth regulators to Jatropha can improve seed yield [79]. Our previous study found that applying benzyladenine (BA, a synthetic cytokinin) to the inflorescence meristems of Jatropha significantly increased the flower number and the female to male flower ratio, which contributed to an increase in seed yield [10]. Cytokinin is involved in plant development and growth and can be used to regulate many aspects of plant development in both practical and theoretical studies [816]. Endogenous levels of cytokinin content increased in Polianthes tuberosa and Litchi chinensis when they began to flower [17, 18]. In Arabidopsis, cytokinin treatment correlated with early flowering [19, 20]. Transgenic Arabidopsis plants overexpressing CYTOKININ OXIDASE (CKX), which degrades cytokinins, flowered late [21]. These studies indicated that cytokinin stimulates flowering in these plants. Additionally, cytokinin increased the flower number in Arabidopsis[13] and induced an aberrant floral phenotype including an increased number of flower organs [12, 22]. Exogenous cytokinin application and accumulation of endogenous cytokinin increased the flower number in several species [10, 13, 2325]. In addition, the flower sex of Vitis vinifera[26, 27], Luffa cylindrical[28], Momordica charantia[29] and Pinus densiflora[30] was also affected by exogenous cytokinins.

It is not economically viable to improve Jatropha seed yield by exogenous application of BA in large scale plantation. Generating transgenic Jatropha plants with increased female and/or bisexual flower number is critical for improving seed yield. The initial step of transgenic Jatropha study is to identify functional genes. Therefore, the identification of genes involved in flower development following BA treatment and the characterization of their expression profiles are two important prerequisites. Currently, next-generation sequencing technologies make it relatively inexpensive to study the transcriptome of a particular organism or tissue to gain insight into biological processes. In Cucumis stativus, the transcriptomes of flowers of different sexes were sequenced to determine the molecular mechanisms of plant sex determination [31]. To study the genetic control of Fagopyrum floral development, the floral transcriptomes of two species that have the ability to self-pollinate, in contrast to the common Fagopyrum, were characterized [32]. The floral transcriptome was also sequenced to evaluate self-incompatibility in Ziziphus celata, a highly endangered plant [33]. Recently, transcriptome analysis was used to investigate global expression patterns of phytohormone-regulated transcripts in tomato leaves and roots [34, 35]. Therefore, transcriptome sequencing is a proven strategy for expression profiling of genes involved in various processes in plants.

Although the Jatropha genome has been sequenced by a combination of the conventional Sanger method and next-generation multiplex sequencing methods [36, 37], most of the molecular studies on Jatropha have focused on lipid metabolism in seeds. Global analysis of gene expression profiles in developing and germinating seeds were performed to assess differential gene expression and to discover genes involved in lipid metabolism [3845]. For high-throughput discovery of novel Jatropha genes, de novo assembly and transcriptome analysis of different tissues of Jatropha were performed [46]. However, molecular studies on flower development and/or flower developmental responses to phytohormone treatment in Jatropha are scarce.

Given that BA increased flower number and the female to male ratio and induced bisexual flowers in Jatropha[10], we conducted a time course study of gene expression profiles in inflorescence meristems of Jatropha exposed to BA. One 454 sequencing run was performed that generated a total of 294.6 Mb of transcript sequences. Differentially expressed genes involved in the biosynthesis, metabolism, and signaling of cytokinin and other plant hormones, flower development and cell division, which may be related to the phenotypic changes of Jatropha in response to cytokinin treatment, were further analyzed.

We expect that characterization of the transcriptome of Jatropha inflorescence meristems treated with BA will contribute not only to genetic engineering and breeding of Jatropha but will also provide insight into the mechanism of how cytokinin affects the flower development of Jatropha and other non-model plants.

Results and discussion

Effects of BA on Jatrophaflower development

To quantitatively apply BA to inflorescence meristems of Jatropha, absorbent cotton containing a BA solution was used rather than hand sprayers as in previous studies [10]. In line with our previous work [10], BA application to the inflorescence meristems of Jatropha resulted in a significant increase in the total flower number and the female flower number of each inflorescence (Figure 1A,B, Additional file 1: Figure S1A). In addition to promoting the number of normal female and male flowers, BA treatment also induced bisexual and asexual flowers (Additional file 1: Figure S1). As the number of female flowers is one of the most important factors affecting fruiting, the increased female flower number and the newly induced bisexual flowers contributed to more fruits than the control (Figure 1C,D, Additional file 1: Figure S1A).

Figure 1

Effects of BA on flower development and fruiting of Jatropha . Inflorescence (A, B) and infructescence (C, D) from control plants (A, C) and BA-treated plants (B, D). Female flowers are marked with red arrows in A and B.

454 sequencing and transcriptome assembly

For 454 transcriptome sequencing of Jatropha inflorescence meristems, four cDNA libraries were constructed from inflorescence meristems collected before BA treatment (Control), BA treatment for 2 hours (T), 4 hours post BA treatment (T +4 H) and 22 hours post BA treatment (T +22 H), respectively. Each cDNA library was used in a one-quarter run on a 454 GS FLX Titanium instrument, which produced a total of 839,205 raw reads (Table 1) that were handled by a 454 GS FLX system to cut off the adapters and low quality bases. Then, all raw reads were subjected to processing using an in-house-developed program to remove the low-quality reads. This procedure yielded 703,755 high-quality reads ranging from 100 bp to 790 bp, with an average length of 364 bp (Table 1). A total of 644,835 reads, corresponding to 91.63% of the high-quality reads, were assembled by the CAP3 program [47] using default parameters with an overlap of >40 bp and an identity of 90%. This analysis gave rise to 23,591 contigs with an average length of 740 bp, and 58,920 reads were identified as singletons (Table 1). Approximately 75.68% of the contigs were assembled from three or more reads. The distribution of the number of reads per contig, the length distribution of the contigs and the singletons are presented in Figure 2.

Table 1 Characteristics of raw data and assembly summary
Figure 2

Distribution of the number of reads per contig and the length distribution of the contigs and singletons. (A) Distribution of the sequences in the contigs; (B) Length distribution of the contigs; (C) Length distribution of the singletons.

Functional annotation

Open reading frames (ORFs) were identified using an in-house developed program based on ‘GetORF’ from EMBOSS, which predicted CDS of 81,736 unigenes (Table 2). The annotations of the unigenes were identified through a BLAST search against the Swiss-Prot and GenBank databases with an E-value cut-off of 1e-3, which resulted in approximately 78.12% (18,429) of the contigs and approximately 48.50% (28,356) of the singletons with significant matches (Table 2). Approximately 10,727 contigs and 12,864 singletons were assigned to clusters of orthologous groups for eukaryotic complete genomes (KOG). Meanwhile, gene ontology (GO) terms were assigned to 8,148 contigs and 11,030 singletons. We also mapped the sequences of all of the unigenes to the Jatropha genome sequences constructed by Sato et al. [36] and upgraded by Hirakawa et al. [37], which gave rise to 18,504 contigs and 28,848 singletons with significant matches. There are still 5,087 contigs (21.56%) and 29,622 singletons (50.66%) with no assigned function, which represent a source for gene discovery (Table 2).

Table 2 Transcriptome annotation summary

Functional annotation of CDSs was performed by searching against NCBI non-redundant protein database and KEGG protein database using BLASTP with E value 1e-3. Then the top hit protein (with the highest bit score) was chosen to count its origin (encoded by what organism). The distribution of sequences from various species closely related to those found in the Jatropha inflorescence meristem transcriptome is shown in Additional file 2: Table S1. More than half (58.4%) of Jatropha sequences are similar to sequences found in the close related castor bean (Ricinus communis) genome, which is consistent with the analysis of whole genome sequences of Jatropha[36, 37]. In contrast, the percentage of Jatropha sequences with top hit to sequences of the model plant Arabidopsis is as low as 0.81% (Additional file 2: Table S1). Given these results, studying the transcriptomes of Jatropha inflorescence meristems with and without BA treatment may shed light on the molecular mechanisms of cytokinin effects on the flower development of castor bean and other non-model plants.

Transcript clustering by expression signatures

To analyze the critical cellular processes in inflorescence meristems of Jatropha following BA treatment, we grouped the transcripts according to their expression patterns across four samples into eight clusters (Figure 3, Additional file 3: Table S2). Cluster I contains 517 transcripts down-regulated by BA treatment at T, T +4 H and T +22 H. Cluster II has 92 transcripts up-regulated by BA treatment at T, T +4 H and T +22 H. Cluster III is composed of 1,007 transcripts that are abundant at T, Cluster V is composed of 717 transcripts that are abundant at T +4 H and Cluster VII is composed of 1767 transcripts that are abundant at T +22 H. Conversely, Cluster IV is composed of 1,223 transcripts that are down-regulated at T, Cluster VI is composed of 2,629 transcripts that are down-regulated at T +4 H and Cluster VIII is composed of 1,370 transcripts that are down-regulated at T +22 H. A number of differentially expressed genes were included in more than one cluster according to their expression profiles. Thus although there are more than 9,000 genes presented in the Figure 3, there are only 5,871 genes in total expressed differentially following cytokinin treatment actually. These results revealed that there are more down-regulated genes than up-regulated genes after BA treatment. Rashotte et al. [48] found that more genes are down-regulated by BA than by zeatin treatment and inferred that some genes may be specifically down-regulated by BA that are not actually regulated by cytokinin. A recent transcriptome analysis of cytokinin responses in tomato leaves also showed that there were more repressed genes than induced genes early (2 h) in the response to BA treatment [34].

Figure 3

Clustering of differentially expressed genes in the transcriptome of Jatropha inflorescence meristems treated with BA. N is the number of transcripts found in each cluster. Cluster I and II contain cytokinin-repressed or induced genes at all three time points, respectively; Cluster III, V and VII contain cytokinin-induced genes at T, T +4 H and T +22 H, respectively; Cluster IV, VI and VIII contain cytokinin-repressed genes at T, T +4 H and T +22 H, respectively. A number of differentially expressed genes were included in more than one cluster according to their expression profiles. The y-axis represents the normalized expression value. The expression value of each gene was Z-score (mean centered, with standard deviations as the unit, p <0.05). T: BA treatment for 2 hours, T +4 H: 4 hours post BA treatment, T +22 H: 22 hours post BA treatment.

Compared to Arabidopsis orthologs, a number of Jatropha genes showed different expression patterns in inflorescence meristems exposed to cytokinin. The difference may result from different treatment methods and/or different concentrations of BA used for treatment. Most treatments of Arabidopsis are on seedlings rather than on inflorescence meristems employed in this study for Jatropha. And the concentration of BA usually was less than 20 μM in treatments of Arabidopsis[49], whereas the concentration of BA used in this study was 1 mM.

Gene ontology enrichment analysis of eight clusters

The gene ontology (GO) enrichment analysis of the genes in each cluster shown in Figure 3 identified the biological processes and molecular functions that characterize each cluster using DAVID (the Database for Annotation, Visualization and Integrated Discovery) [50, 51] (Table 3). This analysis revealed that the transcripts in Cluster I, which were down-regulated by BA treatment at T, T +4 H and T +22 H, were mainly involved in normal cell development, catabolic processes, DNA replication and carbohydrate metabolism. Transcripts up-regulated by BA in all treated samples in Cluster II involved in secondary metabolism (aromatic compounds and glutamine), nitrogen biosynthetic processes, positive regulation of signal transduction and sensory perception of light stimulus. These results coincide with previous findings that cytokinin-regulated genes are involved in glutamine and nitrogen metabolism [52] and that cytokinin-induced transcriptional and morphological effects, which mimic light effects, can be caused by exogenous application or overproduction of endogenous cytokinins [5355]. In addition, Cluster II also showed a significant representation of transcripts related to the activities of receptors and molecular and signal transducers. Previous studies provided evidence that plants perceive and respond to cytokinins through a multistep phosphorelay pathway similar to the bacterial two-component system. The receptors and type-A response regulators are induced by cytokinin [5658]. The transcripts at T, T +4 H and T +22 H (Cluster III, V and VII) are predominantly involved in DNA replication, fertilization, secondary metabolism, regulation of cell size, translation, hormone secretion and transcription initiation. Genes associated with activities of base pairing with mRNA, ion and RNA binding, triplet codon-amino acid adaptors, some peptidases, transferases and translation regulators were also enriched in these clusters. In Arabidopsis, genes respond to cytokinin after 15 or 30 min treatment were considered as early cytokinin-regulated genes, which are categorized into gene groups of secondary metabolism, signal transduction, and transcription factors [52, 59]. In this study, after BA treatment for 2 hours, most of BA-regulated genes are categorized into secondary metabolism and DNA replication (Cluster III, Table 3). Transcripts with low expression at one time point in Clusters IV, VI and VIII were associated with cellular responses to hormone stimulus and signaling, regulation of cell activity, secondary metabolism (carboxylic acid, glucose, arginine, aromatic compounds and nitrogen compounds), and circadian rhythm. Moreover, genes encoding proteins with receptor activity, signal transduction, and mental iron binding were also enriched in these clusters (Table 3).

Table 3 GO enrichment analysis of differential transcripts in eight clusters (p <0.05)

The enriched terms represented critical biological processes in inflorescence meristems treated with BA and indicated the cross-talk of cytokinin signaling with other biological processes, such as circadian rhythms [14, 60], glucose catabolism and nitrogen compound biosynthesis [52, 61].

Metabolic pathway analysis

DAVID analysis also revealed the enriched pathways associated with the transcripts in each cluster (Table 4). Genes that showed a decrease in transcript abundance in response to BA in Clusters I, IV, VI and VIII were associated with apoptosis, which is negatively regulated by cytokinin [62, 63]. Moreover, down-regulated genes were also associated with steroid hormone biosynthesis, amino acid metabolism (arginine, proline, alanine, aspartate and glutamate), pyruvate metabolism, purine metabolism, endocytosis, the citrate cycle (TCA cycle), calcium signaling pathways and the metabolism of xenobiotics by cytochrome P450. Conversely, genes up-regulated by BA treatment in Clusters II, V and VII were associated with the cell cycle, DNA replication, the proteasome, pyrimidine metabolism and steroid hormone biosynthesis. Knowledge of the differential expression of transcripts encoding proteins involved in these pathways upon BA treatment will provide important clues regarding the mechanism of flower development responses to cytokinin in Jatropha.

Table 4 KEGG pathway enrichment analysis of differentially expressed transcripts in eight clusters (p <0.05)

Genes involved in cytokinin biosynthesis, metabolism and signaling

The differentially expressed genes that were annotated to function in cytokinin biosynthesis, metabolism and signaling were further analyzed.

Only one sequence in the transcriptome database, isopentenyl transferase 9 (IPT9), was annotated as functioning in cytokinin biosynthesis. Jatropha IPT9 (JcIPT9) was weakly expressed under both BA-treated and untreated conditions and was not responsive to BA treatment (Figure 4), which is consistent with previous results [52]. Four Jatropha LONELY GUYs (JcLOG3, 7, 8, and 9), which encode cytokinin-activating enzymes that function in the final step of bioactive cytokinin synthesis [64], were identified. JcLOG3, 7 and 8 also displayed weak expression across treated and untreated samples, whereas JcLOG9 was down-regulated in response to BA (Figure 4). Three Jatropha cytokinin oxidases/dehydrogenases (JcCKX1, 4, and 5) were present in the transcriptome database, and only JcCKX5 was up-regulated by BA treatment (Figure 4). The stable induction of CKX5 by cytokinin was confirmed in 9 of 13 microarray studies conducted by different labs [49].

Figure 4

Expression profiles of cytokinin biosynthesis- and metabolism-related genes and two-component elements following BA treatment. Colors indicate the expression values scaled to the standard deviations and centered at the control intensity level (Z-score). Red indicates increased expression and green indicates decreased expression relative to the control condition. Gene expression at time points labeled with an asterisk on the map showed significantly differential expression between treated and control inflorescence meristems (p <0.05), whereas the remaining genes were considered to not respond to BA treatment (p >0.05).

Previous genetic and molecular studies suggested that cytokinin signal transduction occurs through a two-component system [6569]. Transcripts involved in two-component systems were identified in Jatropha, and their expression patterns are shown in Figure 4. Three Jatropha orthologs of histidine kinases (JcHK2, 3 and 4), which act as cytokinin sensors [70], were identified. JcHK2 was slightly induced by BA treatment (Figure 4), the expression of which detected by qRT-PCR was significantly higher than control after BA treatment in this study (Figure 5). The other two receptors, JcHK3 and 4, showed no clear response to BA treatment revealed by both transcriptome and qRT-PCR analysis (Figures 4 and 5). The gene coding for the Jatropha ortholog of histidine phosphotransfer protein 1 (JcHP1) was significantly repressed upon BA treatment, whereas JcHP5 was not differentially regulated under our experimental conditions (Figures 4 and 5). Neither HP1 nor HP5 responded to cytokinin treatment in Arabidopsis[52, 66, 71]. However, repression of tomato HP1 expression after BA treatment was reported by Shi et al. [34], who performed a transcriptome analysis of cytokinin responses in tomato leaves. Four Jatropha orthologs of type-A response regulator (JcRRAs) and two type-B RR genes (JcRRBs) were identified in this study. JcRRA3 and JcRRA5 were up-regulated by BA treatment (Figures 4 and 5), and in previous studies, RRA3 and RRA5 were robustly induced by cytokinin in Arabidopsis[49, 52, 72]. However, the mRNA levels of JcRRA9 and JcRRA17 showed no significant differences between the BA-treated and control samples (Figure 4). In contrast to the 454 transcriptome data, the expression level of JcRRA9 determined by qRT-PCR showed a significant increase at T +22 H compared to control (Figure 5). Moreover, the transcription levels of JcRRB2 and JcRRB18, which are B-type RRs, were not significantly affected by cytokinin (Figures 4 and 5), which agreed with previous studies on the differential expression of B-type response regulators in response to cytokinins [66, 72].

Figure 5

Quantification of the expression of genes involved in cytokinin and other phytohormone signaling by qRT-PCR. Results are shown as the relative expression of genes at different time points before and after BA treatment. Values are means ± standard deviations (n =3). *Statistically significant at the 5% level, **Statistically significant at the 1% level.

Cytokinin-regulated genes involved in the metabolism and signaling of other phytohormones

Signaling pathways of phytohormones in plants are considered to be interconnected in a complex network [73]. Transcriptome studies indicated that cytokinin and auxin mutually regulate their signaling factors and/or their metabolism to control plant growth and development [48, 74]. In this study, the Jatropha ortholog of AUXIN-RESISTANT1 (JcAUX1), which augments auxin’s chemiosmotic influx into cells [75], showed response to cytokinin with a decrease in transcript abundance (Figure 6). Additionally, the Jatropha ortholog of the gene encoding the F-box protein TRANSPORT INHIBITOR RESPONSE1 (JcTIR1), an auxin receptor [76], was also down-regulated by BA treatment at T +22 H (Figure 5). Similarly, the Jatropha ortholog of INDOLEACETIC ACID-INDUCED PROTEIN 14 (JcIAA14), belonging to the AUX/IAA gene family, was also down-regulated (Figures 5 and 6). However, the Jatropha ortholog of another auxin-inducible gene that encodes small auxin up RNA (JcSAUR) [77, 78] was slightly but not significantly up-regulated by BA treatment (Figure 6). The auxin response factors (ARFs), which bind to conserved DNA sequences called auxin-response elements (AuxREs) in the promoter regions of primary auxin response genes, act as transcriptional activators or repressors depending on the nature of their middle domain [7981]. Jatropha orthologs of ARF1 (JcARF1, transcriptional repressor) and ARF5 (JcARF5, transcriptional activator) [82] were identified, and the mRNA level of JcARF1 was increased whereas that of JcARF5 was decreased (Figure 6). Moreover, ARF5 was reported to repress ARR7/15, which negatively regulate cytokinin signaling in the shoot stem cell niche of Arabidopsis[82], indicating that cytokinin-regulated auxin signaling may be realized through the interaction between ARR7/15 and ARF5. The expression changes of genes involved in auxin signal transduction upon BA treatment in Jatropha were more similar to those of tomato [34] than to those of Arabidopsis[52]. Taken together, these results indicate that intricate agonistic and antagonistic interactions exist between cytokinin and auxin in the development of inflorescence meristems in Jatropha. They also indicate differences may exist in the gene responses to cytokinin between Jatropha and the model plant Arabidopsis.

Figure 6

Regulation of other phytohormone signaling genes following BA treatment revealed by DEGseq analysis of the 454 transcripts. The colors indicate the expression values scaled to the standard deviations and centered at the control intensity level (Z-score). Red indicates increased expression and green indicates decreased expression relative to the control condition. Gene expression at time points labeled with an asterisk on the map showed differential expression between treated and control inflorescence meristems (p <0.05), whereas the remainder were considered to not respond to BA treatment (p >0.05).

Following BA treatment, Jatropha orthologs of genes encoding a GA receptor, GA INSENSITIVE DWARF1 (JcGID1), and a DELLA protein, RGA LIKE 2 (JcRGL2), were up-regulated (Figure 6). A previous study of cytokinin response genes in Arabidopsis by Brenner et al. [54] also reported the induction of DELLA protein-encoding genes by cytokinin and reported that cytokinin treatment caused reduced expression of GA20 oxidase. However, in our study, BA did not cause changes in the transcript abundance of a Jatropha ortholog of GA20 oxidase (Figure 6). Earlier studies showed feedback regulation between GA content and GID1 gene expression [83], and the amount of DELLA proteins was found to be inversely related to the amount of bioactive GA [84]. A Jatropha ortholog of another DELLA protein-encoding gene, REPRESSOR OF ga1-1 (JcRGA1), was slightly up-regulated (Figure 6). The induction of these GA signaling genes by cytokinin in Jatropha inflorescence meristems suggests that cytokinin reduces GA activity by up-regulating suppressors of GA signaling genes and supports previous findings that GA and cytokinin exert antagonistic effects on many aspects of plant development [52, 85].

Jatropha orthologs of two ABA biosynthesis genes, ABA DEFICIENT 1 (JcABA1) and JcABA2, were found to not be affected by BA treatment (Figure 6). However, we identified three Jatropha orthologs of ABA receptors, PYR1-LIKE (PYL) [86], JcPYL1, JcPYL4 and JcPYL8, and one of them (JcPYL8) was significantly repressed by BA, whereas mRNA levels of JcPYL1 and JcPYL4 showed no clear response to BA treatment (Figure 6). In addition, a Jatropha ortholog of protein phosphatase 2C (JcPP2C), whose activity was inhibited by PYLs in response to ABA in Arabidopsis[86, 87], responded to BA with an increase of transcript abundance (Figure 6). Conversely, the expression of a Jatropha ortholog of calcium-dependent protein kinase 4 (JcCDPK4), which is an important positive regulator in CDPK/calcium-mediated ABA signaling pathways at the whole-plant level in Arabidopsis[88], was down-regulated significantly by BA treatment (Figure 6). The BA responsiveness of Jatropha orthologs of genes involved in ABA signaling indicated an antagonistic interaction between cytokinin and ABA, which is in line with earlier studies revealing that cytokinins inhibit ABA production [89, 90].

Cytokinin also interacts with ethylene [91]. Decreases were identified in the transcript abundance of the Jatropha orthologs of ethylene receptor 1 (JcETR1) and the gene encoding mitogen-activated protein kinase 6 (JcMPK6) (Figure 6), both of which are negative regulators of ethylene signaling [92, 93]. The Jatropha orthologs of two additional genes, ethylene-responsive transcription factor 1 (JcERF1) and ethylene insensitive protein 3 (JcEIN3), were also repressed by BA treatment (Figures 5 and 6). In Arabidopsis, ethylene-insensitive protein 2 (EIN2) and EIN3-binding F-box 1 (EBF1) are induced by ethylene [94, 95]. The Jatropha orthologs of both of these genes were up-regulated by BA treatment in the present study (Figure 6), indicating that positive regulators of the ethylene signaling pathway were induced by BA treatment. The expression profiles of these genes suggested that cytokinin may act partially by influencing ethylene metabolism and signaling genes [96].

Four Jatropha orthologs of genes involved in brassinosteroid (BR) signaling were identified. The expression levels of two of them, BRASSINOSTEROID-INSENSITIVE 1 (JcBRI1) and BRI1-ASSOCIATED RECEPTOR KINASE 1 (JcBAK1), which work together as a receptor kinase pair initiating the signal transduction cascade [97, 98], were not significantly altered by BA treatment (Figure 6). The Jatropha ortholog of BRASSINOSTEROID-SIGNALING KINASE 8 (JcBSK8), which is in a small family of kinases that activate BR signaling downstream of BRI[99], was induced by BA (Figure 6). Furthermore, BA treatment inhibited the expression of the Jatropha ortholog of BRASSINAZOLE RESISTANT 1 (BZR1) (Figures 5 and 6), which is a transcriptional repressor with dual roles in BR homeostasis and growth responses [100]. These findings indicate positive crosstalk between the cytokinin and BR signaling pathways in Jatropha.

Genes encoding the jasmonate ZIM-domain (JAZ) proteins, which are key regulators of jasmonate signaling [101], have been reported to repress transcription of jasmonate-responsive genes [102]. The transcription level of the Jatropha ortholog of JAZ1 (JcJAZ1) increased upon BA treatment (Figures 5 and 6). The Jatropha ortholog of ENHANCED DISEASE SUSCEPTIBILITY 5 (JcEDS5) was up-regulated, whereas the Jatropha ortholog of NONEXPRESSOR OF PATHOGENESIS-RELATED GENES 4 (JcNPR4) showed no response to BA treatment (Figure 6). Both of EDS5 and NPR4 are involved in salicylate signaling [103]. These results imply that jasmonate and salicylate signaling may involve crosstalk with cytokinin in Jatropha.

Genes involved in flower development

BA treatment profoundly affected Jatropha flower development (Figure 1). The expression profiles of Jatropha orthologs of 23 flowering-related genes identified in Jatropha inflorescence meristems are presented in Figure 7A. And the relative expression of 14 of these genes were further confirmed by qRT-PCR (Figure 7B). The mRNA level of the Jatropha ortholog of CONSTANS-LIKE2 (JcCOL2), which contains a CCT (CONSTANS, CONSTANS-LIKE, and TIMING OF CAB1) domain and is related to flowering in response to the photoperiod [104], was significantly decreased by BA treatment (Figure 7A,B). In contrast, expression of tomato COL (Solyc07g006630) in leaves and Arabidopsis COL (At5g15850) in CKX1-overexpressing plants was induced upon treatment with cytokinin [34, 52]. The transcription level of the Jatropha ortholog of CYP89A5, a member of the P450 gene family, increased after BA treatment (Figure 7). Genes in the P450 gene family in Arabidopsis and tomato were also induced by cytokinin [34, 48, 52], suggesting that CYP89A5 may be involved in Jatropha inflorescence development in response to cytokinin. The transcript annotated to encode the Jatropha ortholog of GIGANTEA (JcGI), a circadian clock-controlled gene that regulates photoperiodic flowering in Arabidopsis[105, 106], was significantly induced at T +4 H (Figure 7A,B). Li et al. [13] reported that LEAFY (LFY), a floral meristem identity gene, was up-regulated by the accumulation of endogenous cytokinin in Arabidopsis. However, in our study, the Jatropha ortholog of LFY (JcLFY) showed no response to BA treatment (Figure 7A,B). The Jatropha ortholog of UNUSUAL FLORAL ORGANS (JcUFO), which encodes an F-box protein required for normal patterning and growth in the floral meristem in Arabidopsis[107], was down-regulated by BA (Figure 7B). AINTEGUMENTA (ANT) has been proposed to act downstream of auxin and involve in floral initiation, growth and patterning in Arabidopsis[108, 109]. After treatment with BA, Jatropha ortholog of ANT (JcANT) was profoundly down-regulated (Figure 7A,B). In Arabidopsis, ant flowers have fewer and smaller floral organs and possess ovules lacking integuments and a functional embryo sac [110, 111]. However, with decreased mRNA level of JcANT, flowers were not shown abnormal phenotypes in Jatropha after exposing to BA. Furthermore, Jatropha orthologs of other flowering-related genes such as CLAVATA1 (JcCLV1), KNOTTED-LIKE HOMEOBOX (JcKNOX), transcriptional corepressor LEUNIG (JcLUG), and serine/threonine protein kinase ABRUPTUS (JcABR) were also down-regulated (Figure 7A).

Figure 7

Expression profiles of genes involved in flower development. (A) Expression profiles of genes involved in flower development obtained by the DEGseq analysis of the 454 transcripts. The colors indicate the expression values scaled to the standard deviations and are centered at the control intensity level (Z-score). Red indicates increased expression and green indicates decreased expression relative to the control condition. Gene expression at time points labeled with an asterisk on the map showed differential expression between the treated and control inflorescence meristems (p <0.05), whereas the remaining genes were considered to not respond to BA treatment (p >0.05). (B) Quantification of gene expression by qRT-PCR. Results are shown as the relative expression of genes at different time points before and after BA treatment. Values are means ± standard deviations (n =3). **Statistically significant at the 1% level.

The ABCE model was formulated from the analysis of floral homeotic mutants with organ identity defects in two adjacent whorls of the flower, and similar classes of mutants were described in both Arabidopsis and Antirrhinum, suggesting that the regulation of organ identity was highly conserved in evolution [112114]. In this study, Jatropha orthologs of A-function genes APETALA 1,2 (JcAP1,2), B-function gene PISTILLATA (JcPI), C-function gene AGAMOUS (JcAG) and E-function genes SEPALLATA1,2,3 (JcSEP1,2,3) were significantly repressed, with an exception of one B-function gene JcAP3 that was shown to be up-regulated by BA treatment (Figure 7A,B). Along with the altered expression of these ABCE model genes, bisexual and asexual flowers were induced, but no abnormal floral organ development was found in Jatropha treated with BA (Additional file 1: Figure S1, [10]). In Arabidopsis, exogenous BA application resulted in abnormal flowers that resemble the phenotypes of mutants, clv1, ap1, ap2, and ap3[22, 115]. However, the increased mRNA levels of AP1, PI and AG in transgenic Arabidopsis, which resulted from the expression of AtIPT4 under control of AP1, also caused abnormal flower and floral organ development [116]. The different phenotypic changes in Arabidopsis[22, 115] and Jatropha occurring upon BA treatment (Additional file 1: Figure S1, [10]) likely resulted from the differential responses of flowering-related genes as revealed in this study and previous studies on Arabidopsis[12]. It is noteworthy that the relative expression of several genes involved in flower development were found to be significantly reduced after BA treatment by qRT-PCR analysis (Figure 7B), whereas their expression did not show significant difference compared to the control in transcriptome sequencing analysis (Figure 7A). This discrepancy probably resulted from the low background expression of these genes and/or the insufficient transcriptome sequencing depth. Normalizing the cDNA library and enhancing the depth of transcriptome sequencing may help to solve this problem.

Cytokinin-regulated genes involved in cell division

It was reported decades ago that cytokinin promotes cell division [117]. More recent work found that an elevated level of endogenous cytokinin in Arabidopsis, achieved by overexpression of ATP/ADP isopentenyltransferase 4 (AtIPT4) [13] or loss-of-function of CKX, promoted cell division of the inflorescence meristem and the flower meristem [118120]. In this study, expression of the S phase marker histone H4[64, 121] was found to be significantly higher in BA-treated (2 h) samples than in controls (Figure 8A). We identified four additional genes related to cell division in the database. Two Jatropha orthologs (Contig14149 and Contig2870) of cyclin D3 (JcCycD3;1 and JcCycD3;2), a D-type plant cyclin gene that plays an important role in the G1-to-S phase transition [121], were significantly up-regulated by BA treatment (Figure 8A,B). In Arabidopsis, cytokinin activates cell division through induction of CycD3[121123]. In addition, the Jatropha ortholog of cyclin A (JcCycA3;2), an A-type plant cyclin gene that was reported to be associated with cell division [124, 125], was also induced following BA treatment (Figure 8A,B). Arabidopsis TSO1 (AtTSO1) encodes a protein with conserved CXC domains known to bind DNA that is homologous to animal proteins that function in chromatin complexes [126, 127]. AtTSO1 is involved in cell division during the development of the inflorescence meristem [128, 129]. Moreover, mutant alleles of AtTSO1 that cause defects in flower and ovule development were identified [130, 131]. Subsequently, Andersen et al. [127] reported that mutations in the AtTSO1 gene disrupt the development of both male and female reproductive tissues. JcTSO1, a Jatropha ortholog of AtTSO1, was highly expressed at T +22 H in Jatropha inflorescence meristems (Figure 8A,B). Based on this finding, we suggest that JcTSO1, together with other BA up-regulated and cell division associated genes (Figure 8), may be involved not only in promoting cell division in inflorescence meristems that resulted in increased total flower number per inflorescence after BA treatment (Figure 1, Additional file 1: Figure S1), but also in flower organ development that resulted in induced bisexual flowers upon BA treatment (Additional file 1: Figure S1, [10]).

Figure 8

Expression profiles of genes involved in cell division. (A) Expression profiles of genes involved in cell division obtained by DEGseq analysis of the 454 transcripts. The colors indicate the expression values scaled to the standard deviations and are centered at the control intensity level (Z-score). Red indicates increased expression and green indicates decreased expression relative to the control condition. Gene expression at time points labeled with an asterisk on the map showed differential expression between treated and control inflorescences (p <0.05). (B) Quantification of gene expression by qRT-PCR. Results are shown as the relative expression of genes at different time points before and after BA treatment. Values are means ± standard deviations (n =3). *Statistically significant at the 5% level, **Statistically significant at the 1% level.


In this study, a time-course experiment was conducted to characterize activated and repressed genes in the inflorescence meristems of Jatropha following cytokinin treatment using transcriptome sequencing by a 454 GS FLX Titanium instrument. This approach produced 703,755 high-quality reads ranging from 100 bp to 790 bp, with an average length of 364 bp (Table 1). Up-regulated and down-regulated annotated and novel genes were identified (Figure 3, Additional file 3: Table S2), resulting in an unprecedented view of the regulatory activities of cytokinin in Jatropha inflorescence meristems.

Abundant physiological data suggest that plant hormones interact with each other to regulate various aspects of development [132135]. In this study, the cytokinin signaling pathway was found to crosstalk with other signals, mainly through pathways converging on or through transcriptional factors or other signaling components. For example, interactions with GA can occur through induction of the negative regulators JcGID1 and JcRGL1. The expression profiles of genes involved in other hormone signaling pathways indicated that: 1) there are multiple agonistic and antagonistic effects between cytokinin and auxin; 2) cytokinin may reduce plant responsiveness to GA and ABA by repressing GA and ABA metabolism- and signaling-related genes and 3) cytokinin may act synergistically with ethylene and BR in controlling Jatropha inflorescence meristem development.

Exogenous BA treatments resulted in different phenotypic changes between Jatropha (Additional file 1: Figure S1, [10]) and Arabidopsis[22, 115]. And the response of ABCE model genes to CK in Jatropha (Figure 7) was also different from that of Arabidopsis[116]. Our analysis also indicates the increased total flower number per inflorescence of Jatropha after BA treatment (Figure 1, Additional file 1: Figure S1) resulted from the enhanced inflorescence branching, which was demonstrated by the occurrence of the fifth order branching in BA-treated inflorescences, whereas only the fourth order branching was found in control inflorescences (Figure 9, Additional file 4: Table S3). Therefore the up-regulation of genes related to cell division, especially JcCycD3 and JcTSO1, likely contributed to the increased flower number per inflorescence after BA treatment in Jatropha. BA-induced expression of JcCycD3 and JcTSO1 caused production of more cells in the inflorescence meristems, which leads to an increase in size of inflorescence meristems and therefore an enhanced inflorescence branching, and results in more flowers. This hypothesis is supported by a recent study showing that the elevated CK levels in the reproductive SAM of rice resulted in increased meristem activity, enhanced panicle branching, and a consequent increase of grain number [136]. The functions of JcCycD3 and JcTSO1 will be further analyzed using transgenic Jatropha plants.

Figure 9

BA treatment enhanced inflorescence branching of Jatropha . (A) Branching of control inflorescence. (B) Branching of BA-treated inflorescence. (C) Diagram of branching of control inflorescence. (D) Diagram of branching of BA-treated inflorescence. To clearly show the pattern of inflorescence branching, the flowers in (A) and (B) were removed. The numbers (1–5) on the branches in (C) and (D) represent different orders of branching. represents a female flower; represents a male flower.

Like most of the published reports of RNA-seq data [31, 32, 137, 138], due to the high cost of 454 sequencing at the commencement of this study, only a single biological replicate was included for transcriptome analysis, which prevented proper statistical testing on identification of differential expressed genes. Nonetheless, different transcript abundance of the annotated genes in control and BA-treated inflorescences revealed by 454 sequencing in this study provide a valuable data source for selecting putative BA-regulated Jatropha genes for further verification by qRT-PCR with sufficient biological replicates. In this study, we did each qRT-PCR experiment with three biological and three technical replicates per biological replicate. Our results, as shown in Figures 4, 5, 6, 7 and 8, indicated a high correlation of expression levels between 454 sequencing data and qRT-PCR, but the congruence of statistically significant differential expression revealed by the two approaches was low. This observation is consistent with previous work showing lack of congruence between 454 sequencing and qRT-PCR results regarding genes predicted as significantly differential expression [139].

To our knowledge, this is the first report on the transcriptional regulation and identification of genes that are differentially expressed in the inflorescence meristems of Jatropha exposed to cytokinin. We have identified a set of cytokinin-regulated genes in Jatropha inflorescence meristems through expression profiling. Some of them correspond to previously identified genes in Arabidopsis, and others show different expression patterns from their Arabidopsis orthologs. Further analyses of these genes with different expression patterns are needed to elucidate their roles in the cytokinin responses of Jatropha inflorescence meristems. Although transcriptional analysis is only an initial step and does not identify functional relevance as it may or may not relate to changes in the level and/or activity of the corresponding proteins, the potential cytokinin-responsive transcripts identified in this study will provide a good starting point for investigations into the molecular mechanisms of Jatropha responses to cytokinin.


Plant material and BA treatment

Cuttings from one Jatropha plant were propagated into individual plants and used as experimental plants. BA treatment was performed when the plants were undergoing the flowering stage in the following year. In total, 230 inflorescence meristems with a diameter of approximately 0.5 cm were selected as experiment subjects. First, each meristem was wrapped around by a piece of absorbent cotton weighing 10 mg. Then, 200 μl of a 1 mM BA solution containing 0.05% Tween-20 was applied to the cotton using a pipette.

Before BA treatment, 50 meristems were collected as control. Two hours after treatment, all cotton pieces wrapped around the inflorescence meristems were removed, and 50 meristems were sampled, which were identified as T (BA treatment for 2 hours). Fifty meristems were sampled at 4 and 22 hours post BA treatment, which were identified as T +4 H and T +22 H, respectively. The remaining 30 meristems were kept for phenotypic analysis. The fifty inflorescence meristems sampled at each time point, which were pooled together as one sample for 454 sequencing, were immediately frozen in liquid nitrogen and stored at −80°C.

To investigate the effect of cytokinin on branching of inflorescence, BA working solution (0.5 mM) with 0.05% (v/v) Tween-20 was sprayed onto each inflorescence meristem (about 0.5 cm in diameter) and the surrounding leaves using a hand sprayer. Control inflorescence meristems were sprayed with distilled water containing 0.05% (v/v) Tween-20. Spraying was conducted once. Thirty inflorescence meristems were used for each treatment.

RNA extraction, cDNA synthesis and 454 sequencing

Each frozen sample was ground in a mortar with liquid nitrogen, and total RNA was isolated using TRIzol reagent (Invitrogen Corp., Carlsbad, CA) following the standard protocol. An Agilent 2100 instrument was used to check the RNA quality (RIN >0.7), and a NanoDrop spectrophotometer (ND-2000C, Thermo Fisher Scientific, USA) was used to quantify RNA concentration. Messenger RNA was further purified using a MicroPoly(A) Purist Kit (Ambion) according to the protocol. Double-stranded cDNA was synthesized from mRNA according to Ng's full-length cDNA synthesis protocol [140] with some modifications [141] and then fragmented to 300–800 bp. The prepared cDNAs were transformed into single-stranded template DNA (sstDNA) libraries using the GS DNA Library Preparation kit (Roche Applied Science). sstDNA libraries were clonally amplified in a bead-immobilized form using the GS emPCR kit (Roche Applied Science) and sequenced on the 454 Genome Sequencer FLX instrument.

Sequence assembly and annotation

The 454 transcriptome sequencing reads were first handled by a 454 GS FLX system, which cut off the adapter and low-quality bases. The reads were then filtered by an in-house-developed program to remove low-quality reads. The qualified reads were then assembled by CAP3 using the default parameters [47]. Open reading frames were identified using an in-house-developed program based on ‘GetORF’ from EMBOSS [142], and the annotation was performed through BLAST searches against the Swiss-Prot and GenBank databases with an E-value cutoff of 1E-3. Gene ontology analysis was performed using GoPipe through BLASTP against the Swiss-Prot and TrEMBL databases using an E-value cutoff of 1E-3 [143]. The metabolic pathway was constructed based on the KEGG database by the BBH (bi-directional best hit) method [144].

Analysis of differentially expressed genes

To estimate gene expression, the read number for each gene was first transformed into RPKM (reads per kilobase per million reads) [145], and differentially expressed genes were identified by the DEGseq (identifying differentially expressed genes from gene expression data) package using the method MARS (MA-plot-based method with random sampling model) [146]. We use p-value <0.05 and the absolute value of log2Ratio >1 as the threshold to judge the significance of contig expression difference.

We perform BLASTX (NT query to AA database) in TAIR WU Blast against TAIR10 Arabidopsis proteins using NO filters to identify the most highly similar genes involved in plant hormone signaling, flower development and cell division (Additional file 5: Table S4). The members of the two-component signaling pathway in Jatropha was named following the nomenclature reported by Heyl et al. [147].

We used Z score transformation [148] to perform the normalization in Figure 3, which are calculated by subtracting the mean RPKM (reads per kilobase per million reads), and dividing that result by the standard deviation (SD) of four time points for each gene, according to the formula:

Zscore= R P K M G mean RPKM /SD

Where G is any gene from the transcriptome database.We used Z score transformation with some modifications to perform the normalization in heat maps of Figures 4, 6, 7 and 8, which are calculated by subtracting the control RPKM, and dividing that result by the standard deviation (SD) of four time points for each gene, according to the formula:

Zscore= R P K M G R P K M control /SD

Where G is any gene from the transcriptome database and RPKMcontrol is the RPKM of control.

We identified enriched GO terms and pathways for the differentially expressed genes using DAVID (the Database for Annotation, Visualization and Integrated Discovery). DAVID is a web-based bioinformatics application that systematically identifies enrichment for biological annotations based on large gene lists derived from high-throughput genomic experiments [50, 51].

Quantitative real-time PCR (qRT-PCR) confirmation

The expression profiles of 15 genes involved in plant hormone signaling, 14 genes associated with flowering and 4 genes involved in cell division were investigated by qRT-PCR to confirm the transcriptome data. The RNA samples used for qRT-PCR were isolated from tissues collected from the experiment used for constructing the 454 libraries, and from inflorescence meristems collected in a replicate experiment. cDNA for each sample was synthesized from total RNA using the PrimeScript RT reagent Kit with gDNA Eraser (Perfect Real Time) (Takara, Japan) according to the instructions. The primers used for qRT-PCR are listed in Additional file 6: Table S5. The PCR products were sent to BGI (Shenzhen, China) for sequencing using specific primers for sequence confirmation. Relative gene expression levels were detected using the SYBR Premix Ex Taq II (Tli RNaseH Plus) (Takara, Japan) according to the manufacturer’s instructions on a LightCycler 480 II (Roche, USA) instrument. The relative expression of each gene was calculated using the 2-ΔΔCT method [149]. All quantitative PCR experiments were repeated with three biological and three technical replicates per biological replicate.

Accession numbers

All of the sequences of the unigenes greater than 200-bp in length obtained from the transcriptome sequencing of inflorescence meristems in Jatropha have been deposited in the Transcriptome Shotgun Assembly (TSA) database, (Accession: PRJNA265802;ID: 265802). Unigenes less than 200-bp in length were listed in Additional file 7: Table S6.


  1. 1.

    Fairless D: Biofuel: the little shrub that could–maybe. Nature. 2007, 449 (7163): 652-655.

  2. 2.

    Li L, Coppola E, Rine J, Miller JL, Walker D: Catalytic hydrothermal conversion of triglycerides to non-ester biofuels. Energy Fuels. 2010, 24 (2): 1305-1315.

  3. 3.

    Bonnet S, Gheewala SH: Potential of Jatropha as an Energy Crop. Jatropha, Challenges for a New Energy Crop. 2012, New York: Springer, 571-582.

  4. 4.

    Makkar H, Maes J, De Greyt W, Becker K: Removal and degradation of phorbol esters during pre-treatment and transesterification of Jatropha curcas oil. J Am Oil Chem Soc. 2009, 86 (2): 173-181.

  5. 5.

    Sanderson K: Wonder weed plans fail to flourish. Nature. 2009, 461 (7262): 328-329.

  6. 6.

    Divakara B, Upadhyaya H, Wani S, Gowda C: Biology and genetic improvement of Jatropha curcas L.: a review. Appl Energy. 2010, 87 (3): 732-742.

  7. 7.

    Ghosh A, Chikara J, Chaudhary D, Prakash AR, Boricha G, Zala A: Paclobutrazol arrests vegetative growth and unveils unexpressed yield potential of Jatropha curcas. J Plant Growth Regul. 2010, 29 (3): 307-315.

  8. 8.

    Xu G, Luo R, Yao Y: Paclobutrazol improved the reproductive growth and the quality of seed oil of Jatropha curcas. J Plant Growth Regul. 2013, 32 (4): 875-883.

  9. 9.

    Abdelgadir H, Jäger A, Johnson S, Van Staden J: Influence of plant growth regulators on flowering, fruiting, seed oil content, and oil quality of Jatropha curcas. S Afr J Bot. 2010, 76 (3): 440-446.

  10. 10.

    Pan BZ, Xu ZF: Benzyladenine treatment significantly increases the seed yield of the biofuel plant Jatropha curcas. J Plant Growth Regul. 2011, 30 (2): 166-174.

  11. 11.

    Sakamoto T, Sakakibara H, Kojima M, Yamamoto Y, Nagasaki H, Inukai Y, Sato Y, Matsuoka M: Ectopic expression of KNOTTED1-like homeobox protein induces expression of cytokinin biosynthesis genes in rice. Plant Physiol. 2006, 142 (1): 54-62.

  12. 12.

    Lindsay DL: Cytokinin-induced gene expression in Arabidopsis. PhD Thesis. 2006, Saskatoon: University of Saskatchewan

  13. 13.

    Li X, Su Y, Zhao X, Li W, Gao X, Zhang X: Cytokinin overproduction-caused alteration of flower development is partially mediated by CUC2 and CUC3 in Arabidopsis. Gene. 2010, 450 (1–2): 109-120.

  14. 14.

    Hanano S, Domagalska MA, Nagy F, Davis SJ: Multiple phytohormones influence distinct parameters of the plant circadian clock. Genes Cells. 2006, 11 (12): 1381-1392.

  15. 15.

    Riefler M, Novak O, Strnad M, Schmulling T: Arabidopsis cytokinin receptor mutants reveal functions in shoot growth, leaf senescence, seed size, germination, root development, and cytokinin metabolism. Plant Cell. 2006, 18 (1): 40-54.

  16. 16.

    Gonzalez-Rizzo S, Crespi M, Frugier F: The Medicago truncatula CRE1 cytokinin receptor regulates lateral root development and early symbiotic interaction with Sinorhizobium meliloti. Plant Cell. 2006, 18 (10): 2680-2693.

  17. 17.

    Chang ST, Chen WS, Hsu CY, Yu HC, Du BS, Huang KL: Changes in cytokinin activities before, during and after floral initiation in Polianthes tuberosa. Plant Physiol Biochem. 1999, 37 (9): 679-684.

  18. 18.

    Chen WS: Changes in cytokinins before and during early flower bud differentiation in lychee (Litchi chinensis Sonn.). Plant Physiol. 1991, 96 (4): 1203-1206.

  19. 19.

    D’Aloia M, Bonhomme D, Bouché F, Tamseddak K, Ormenese S, Torti S, Coupland G, Périlleux C: Cytokinin promotes flowering of Arabidopsis via transcriptional activation of the FT paralogue TSF. Plant J. 2011, 65 (6): 972-979.

  20. 20.

    He YW, Loh CS: Induction of early bolting in Arabidopsis thaliana by triacontanol, cerium and lanthanum is correlated with increased endogenous concentration of isopentenyl adenosine (iPAdos). J Exp Bot. 2002, 53 (368): 505-512.

  21. 21.

    Werner T, Motyka V, Laucou V, Smets R, Van Onckelen H, Schmülling T: Cytokinin-deficient transgenic Arabidopsis plants show multiple developmental alterations indicating opposite functions of cytokinins in the regulation of shoot and root meristem activity. Plant Cell. 2003, 15 (11): 2532-2550.

  22. 22.

    Venglat S, Sawhney VK: Benzylaminopurine induces phenocopies of floral meristem and organ identity mutants in wild-type Arabidopsis plants. Planta. 1996, 198 (3): 480-487.

  23. 23.

    Ohkawa K: Effects of gibberellins and benzylandenine on dormancy and flowering of Lilium speciosum. Sci Hortic. 1979, 10 (3): 255-260.

  24. 24.

    Ravetta D, Palzkill D: The effect of growth regulators and apex removal on branching and flower bud production of jojoba. Ind Crops Prod. 1992, 1 (1): 47-55.

  25. 25.

    Prat L, Botti C, Fichet T: Effect of plant growth regulators on floral differentiation and seed production in Jojoba (Simmondsia chinensis (Link) Schneider). Ind Crops Prod. 2008, 27 (1): 44-49.

  26. 26.

    Negi SS, Olmo HP: Sex conversion in a male Vitis vinifera L. by a Kinin. Science. 1966, 152 (3729): 1624-1625.

  27. 27.

    Negi SS, Olmo HP: Certain embryological and biochemical aspects of cytokinin SD 8339 in converting sex of a male Vitis vinifera (Sylvestris). Am J Bot. 1972, 59 (8): 851-857.

  28. 28.

    Takahashi H, Suge H, Saito T: Sex expression as affected by N6-benzylaminopurine in staminate inflorescence of Luffa cylindrica. Plant Cell Physiol. 1980, 21 (4): 525-536.

  29. 29.

    Ghosh S, Basu P: Effect of some growth regulators on sex expression of Momordica charantia L. Sci Hortic. 1982, 17 (2): 107-112.

  30. 30.

    Wakushima S, Yoshioka H, Sakurai N: Lateral female strobili production in a Japanese red pine (Pinus densiflora Sieb. Et Zucc.) clone by exogenous cytokinin application. J For Res. 1996, 1 (3): 143-148.

  31. 31.

    Guo S, Zheng Y, Joung JG, Liu S, Zhang Z, Crasta OR, Sobral BW, Xu Y, Huang S, Fei Z: Transcriptome sequencing and comparative analysis of cucumber flowers with different sex types. BMC Genomics. 2010, 11 (1): 384-

  32. 32.

    Logacheva MD, Kasianov AS, Vinogradov DV, Samigullin TH, Gelfand MS, Makeev VJ, Penin AA: De novo sequencing and characterization of floral transcriptome in two species of buckwheat (Fagopyrum). BMC Genomics. 2011, 12 (1): 30-

  33. 33.

    Edwards CE, Parchman TL, Weekley CW: Assembly, gene annotation and marker development using 454 floral transcriptome sequences in Ziziphus celata (Rhamnaceae), a highly endangered, Florida endemic plant. DNA Res. 2012, 19 (1): 1-9.

  34. 34.

    Shi X, Gupta S, Lindquist IE, Cameron CT, Mudge J, Rashotte AM: Transcriptome analysis of cytokinin response in tomato leaves. PLoS One. 2013, 8 (1): e55090-

  35. 35.

    Gupta S, Shi X, Lindquist IE, Devitt N, Mudge J, Rashotte AM: Transcriptome profiling of cytokinin and auxin regulation in tomato root. J Exp Bot. 2013, 64 (2): 695-704.

  36. 36.

    Sato S, Hirakawa H, Isobe S, Fukai E, Watanabe A, Kato M, Kawashima K, Minami C, Muraki A, Nakazaki N, Takahashi C, Nakayama S, Kishida Y, Kohara M, Yamada M, Tsuruoka H, Sasamoto S, Tabata S, Aizu T, Toyoda A, Shin-i T, Minakuchi Y, Kohara Y, Fujiyama A, Tsuchimoto S, Kajiyama S, Makigano E, Ohmido N, Shibagaki N, Cartagena J: Sequence analysis of the genome of an oil-bearing tree, Jatropha curcas L. DNA Res. 2011, 18 (1): 65-76.

  37. 37.

    Hirakawa H, Tsuchimoto S, Sakai H, Nakayama S, Fujishiro T, Kishida Y, Kohara M, Watanabe A, Yamada M, Aizu T, Toyoda A, Fujiyama A, Tabata S, Fukui K, Sato S: Upgraded genomic information of Jatropha curcas L. Plant Biotechnol. 2012, 29 (2): 123-130.

  38. 38.

    Jiang H, Wu P, Zhang S, Song C, Chen Y, Li M, Jia Y, Fang X, Chen F, Wu G: Global analysis of gene expression profiles in developing physic nut (Jatropha curcas L.) seeds. PLoS One. 2012, 7 (5): e36522-

  39. 39.

    King AJ, Li Y, Graham IA: Profiling the developing Jatropha curcas L. seed transcriptome by pyrosequencing. BioEnergy Res. 2011, 4 (3): 211-221.

  40. 40.

    Costa GG, Cardoso KC, Del Bem LE, Lima AC, Cunha MA, de Campos-Leite L, Vicentini R, Papes F, Moreira RC, Yunes JA, Campos FA, Silva MJD: Transcriptome analysis of the oil-rich seed of the bioenergy crop Jatropha curcas L. BMC Genomics. 2010, 11 (1): 462-

  41. 41.

    Natarajan P, Kanagasabapathy D, Gunadayalan G, Panchalingam J, Shree N, Sugantham PA, Singh KK, Madasamy P: Gene discovery from Jatropha curcas by sequencing of ESTs from normalized and full-length enriched cDNA library from developing seeds. BMC Genomics. 2010, 11 (1): 606-

  42. 42.

    Chen MS, Wang GJ, Wang RL, Wang J, Song SQ, Xu ZF: Analysis of expressed sequence tags from biodiesel plant Jatropha curcas embryos at different developmental stages. Plant Sci. 2011, 181 (6): 696-700.

  43. 43.

    Gomes K, Almeida T, Gesteira A, Lôbo I, Guimarães A, Miranda AB, Sluys MAV, Cruz RS, Cascardo J, Carels N: ESTs from seeds to assist the selective breeding of Jatropha curcas L. for oil and active compounds. Genomics Insights. 2010, 3 (1): 29-56.

  44. 44.

    Gu K, Chiam H, Tian D, Yin Z: Molecular cloning and expression of heteromeric ACCase subunit genes from Jatropha curcas. Plant Sci. 2011, 180 (4): 642-649.

  45. 45.

    Xu R, Wang R, Liu A: Expression profiles of genes involved in fatty acid and triacylglycerol synthesis in developing seeds of Jatropha (Jatropha curcas L.). Biomass Bioenergy. 2011, 35 (5): 1683-1692.

  46. 46.

    Natarajan P, Parani M: De novo assembly and transcriptome analysis of five major tissues of Jatropha curcas L. using GS FLX titanium platform of 454 pyrosequencing. BMC Genomics. 2011, 12 (1): 191-

  47. 47.

    Huang X, Madan A: CAP3: A DNA sequence assembly program. Genome Res. 1999, 9 (9): 868-877.

  48. 48.

    Rashotte AM, Carson SD, To JP, Kieber JJ: Expression profiling of cytokinin action in Arabidopsis. Plant Physiol. 2003, 132 (4): 1998-2011.

  49. 49.

    Bhargava A, Clabaugh I, To JP, Maxwell BB, Chiang Y-H, Schaller GE, Loraine A, Kieber JJ: Identification of cytokinin-responsive genes using microarray meta-analysis and RNA-seq in Arabidopsis. Plant Physiol. 2013, 162 (1): 272-294.

  50. 50.

    Huang DW, Sherman BT, Lempicki RA: Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 2009, 37 (1): 1-13.

  51. 51.

    Huang DW, Sherman BT, Lempicki RA: Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009, 4 (1): 44-57.

  52. 52.

    Brenner WG, Romanov GA, Köllmer I, Bürkle L, Schmülling T: Immediate‒early and delayed cytokinin response genes of Arabidopsis thaliana identified by genome‒wide expression profiling reveal novel cytokinin‒sensitive processes and suggest cytokinin action through transcriptional cascades. Plant J. 2005, 44 (2): 314-333.

  53. 53.

    Chory J, Reinecke D, Sim S, Washburn T, Brenner M: A role for cytokinins in de-etiolation in Arabidopsis (det mutants have an altered response to cytokinins). Plant Physiol. 1994, 104 (2): 339-347.

  54. 54.

    Chin-Atkins AN, Craig S, Hocart CH, Dennis ES, Chaudhury AM: Increased endogenous cytokinin in the Arabidopsis amp1 mutant corresponds with de-etiolation responses. Planta. 1996, 198 (4): 549-556.

  55. 55.

    Catterou M, Dubois F, Smets R, Vaniet S, Kichey T, Van Onckelen H, Sangwan‒Norreel BS, Sangwan RS: hoc: an Arabidopsis mutant overproducing cytokinins and expressing high in vitro organogenic capacity. Plant J. 2002, 30 (3): 273-287.

  56. 56.

    Hutchison CE, Kieber JJ: Cytokinin signaling in Arabidopsis. Plant Cell. 2002, 14 (Suppl 1): S47-S59.

  57. 57.

    To JP, Kieber JJ: Cytokinin signaling: two-components and more. Trends Plant Sci. 2008, 13 (2): 85-92.

  58. 58.

    Hwang I, Sheen J: Two-component circuitry in Arabidopsis cytokinin signal transduction. Nature. 2001, 413 (6854): 383-389.

  59. 59.

    Lee DJ, Park J-Y, Ku S-J, Ha Y-M, Kim S, Kim MD, Oh M-H, Kim J: Genome-wide expression profiling of ARABIDOPSIS RESPONSE REGULATOR 7 (ARR7) overexpression in cytokinin response. Mol Genet Genomics. 2007, 277 (2): 115-137.

  60. 60.

    Salomé PA, To JPC, Kieber JJ, McClung CR: Arabidopsis response regulators ARR3 and ARR4 play cytokinin-independent roles in the control of circadian period. Plant Cell. 2006, 18 (1): 55-69.

  61. 61.

    Sakakibara H, Takei K, Hirose N: Interactions between nitrogen and cytokinin in the regulation of metabolism and development. Trends Plant Sci. 2006, 11 (9): 440-448.

  62. 62.

    van Doorn WG, Celikel FG, Pak C, Harkema H: Delay of Iris flower senescence by cytokinins and jasmonates. Physiol Plant. 2013, 148 (1): 105-120.

  63. 63.

    Downs CG, Somerfield SD, Davey MC: Cytokinin treatment delays senescence but not sucrose loss in harvested broccoli. Postharvest Biol Tec. 1997, 11 (2): 93-100.

  64. 64.

    Kurakawa T, Ueda N, Maekawa M, Kobayashi K, Kojima M, Nagato Y, Sakakibara H, Kyozuka J: Direct control of shoot meristem activity by a cytokinin-activating enzyme. Nature. 2007, 445 (7128): 652-655.

  65. 65.

    Kakimoto T: CKI1, a histidine kinase homolog implicated in cytokinin signal transduction. Science. 1996, 274 (5289): 982-985.

  66. 66.

    Kiba T, Taniguchi M, Imamura A, Ueguchi C, Mizuno T, Sugiyama T: Differential expression of genes for response regulators in response to cytokinins and nitrate in Arabidopsis thaliana. Plant Cell Physiol. 1999, 40 (7): 767-771.

  67. 67.

    D'Agostino IB, Deruère J, Kieber JJ: Characterization of the response of the Arabidopsis response regulator gene family to cytokinin. Plant Physiol. 2000, 124 (4): 1706-1717.

  68. 68.

    Inoue T, Higuchi M, Hashimoto Y, Seki M, Kobayashi M, Kato T, Tabata S, Shinozaki K, Kakimoto T: Identification of CRE1 as a cytokinin receptor from Arabidopsis. Nature. 2001, 409 (6823): 1060-1063.

  69. 69.

    Sakakibara H, Taniguchi M, Sugiyama T: His-Asp phosphorelay signaling: a communication avenue between plants and their environment. Plant Mol Biol. 2000, 42 (2): 273-278.

  70. 70.

    Higuchi M, Pischke MS, Mahonen AP, Miyawaki K, Hashimoto Y, Seki M, Kobayashi M, Shinozaki K, Kato T, Tabata S, Helariutta Y, Sussman MR, Kakimoto T: In planta functions of the Arabidopsis cytokinin receptor family. Proc Natl Acad Sci U S A. 2004, 101 (23): 8821-8826.

  71. 71.

    Imamura A, Hanaki N, Nakamura A, Suzuki T, Taniguchi M, Kiba T, Ueguchi C, Sugiyama T, Mizuno T: Compilation and characterization of Arabiopsis thaliana response regulators implicated in His-Asp phosphorelay signal transduction. Plant Cell Physiol. 1999, 40 (7): 733-742.

  72. 72.

    Kiba T, Aoki K, Sakakibara H, Mizuno T: Arabidopsis response regulator, ARR22, ectopic expression of which results in phenotypes similar to the wol cytokinin-receptor mutant. Plant Cell Physiol. 2004, 45 (8): 1063-1077.

  73. 73.

    Cui X, Luan S: A new wave of hormone research: crosstalk mechanisms. Mol Plant. 2012, 5 (5): 959-960.

  74. 74.

    Goda H, Sawa S, Asami T, Fujioka S, Shimada Y, Yoshida S: Comprehensive comparison of auxin-regulated and brassinosteroid-regulated genes in Arabidopsis. Plant Physiol. 2004, 134 (4): 1555-1573.

  75. 75.

    Yang Y, Hammes UZ, Taylor CG, Schachtman DP, Nielsen E: High-affinity auxin transport by the AUX1 influx carrier protein. Curr Biol. 2006, 16 (11): 1123-1127.

  76. 76.

    Dharmasiri N, Dharmasiri S, Estelle M: The F-box protein TIR1 is an auxin receptor. Nature. 2005, 435 (7041): 441-445.

  77. 77.

    Abel S, Theologis A: Early genes and auxin action. Plant Physiol. 1996, 111 (1): 9-17.

  78. 78.

    Guilfoyle TJ: Auxin-regulated genes and promoters. New Compr Biochem. 1999, 33: 423-459.

  79. 79.

    Liscum E, Reed JW: Genetics of Aux/IAA and ARF action in plant growth and development. Plant Mol Biol. 2002, 49 (3–4): 387-400.

  80. 80.

    Ulmasov T, Hagen G, Guilfoyle TJ: Activation and repression of transcription by auxin-response factors. Proc Natl Acad Sci U S A. 1999, 96 (10): 5844-5849.

  81. 81.

    Ulmasov T, Hagen G, Guilfoyle TJ: Dimerization and DNA binding of auxin response factors. Plant J. 2002, 19 (3): 309-319.

  82. 82.

    Zhao Z, Andersen SU, Ljung K, Dolezal K, Miotk A, Schultheiss SJ, Lohmann JU: Hormonal control of the shoot stem-cell niche. Nature. 2010, 465 (7301): 1089-1092.

  83. 83.

    Griffiths J, Murase K, Rieu I, Zentella R, Zhang ZL, Powers SJ, Gong F, Phillips AL, Hedden P, Sun TP, Thomas SG: Genetic characterization and functional analysis of the GID1 gibberellin receptors in Arabidopsis. Plant Cell. 2006, 18 (12): 3399-3414.

  84. 84.

    Achard P, Genschik P: Releasing the brakes of plant growth: how GAs shutdown DELLA proteins. J Exp Bot. 2009, 60 (4): 1085-1092.

  85. 85.

    Jasinski S, Piazza P, Craft J, Hay A, Woolley L, Rieu I, Phillips A, Hedden P, Tsiantis M: KNOX action in Arabidopsis is mediated by coordinate regulation of cytokinin and gibberellin activities. Curr Biol. 2005, 15 (17): 1560-1565.

  86. 86.

    Park SY, Fung P, Nishimura N, Jensen DR, Fujii H, Zhao Y, Lumba S, Santiago J, Rodrigues A, Chow TF, Alfred SE, Bonetta D, Finkelstein R, Provart NJ, Desveaux D, Rodriguez PL, McCourt P, Zhu JK, Schroeder JI, Volkman BF, Cutler SR: Abscisic acid inhibits type 2C protein phosphatases via the PYR/PYL family of START proteins. Science. 2009, 324 (5930): 1068-1071.

  87. 87.

    Ma Y, Szostkiewicz I, Korte A, Moes D, Yang Y, Christmann A, Grill E: Regulators of PP2C phosphatase activity function as abscisic acid sensors. Science. 2009, 324 (5930): 1064-1068.

  88. 88.

    Zhu SY, Yu XC, Wang XJ, Zhao R, Li Y, Fan RC, Shang Y, Du SY, Wang XF, Wu FQ, Xu YH, Zhang XY, Zhang DP: Two calcium-dependent protein kinases, CPK4 and CPK11, regulate abscisic acid signal transduction in Arabidopsis. Plant Cell. 2007, 19 (10): 3019-3036.

  89. 89.

    Cowan AK, Cairns ALP, Bartels-Rahm B: Regulation of abscisic acid metabolism: towards a metabolic basis for abscisic acid-cytokinin antagonism. J Exp Bot. 1999, 50 (334): 595-603.

  90. 90.

    Cowan A, Railton I: Cytokinins and ancymidol inhibit abscisic acid biosynthesis in Persea gratissima. J Plant Physiol. 1987, 130 (2): 273-277.

  91. 91.

    El-Showk S, Ruonala R, Helariutta Y: Crossing paths: cytokinin signalling and crosstalk. Development. 2013, 140 (7): 1373-1383.

  92. 92.

    Hua J, Meyerowitz EM: Ethylene responses are negatively regulated by a receptor gene family in Arabidopsis thaliana. Cell. 1998, 94 (2): 261-271.

  93. 93.

    Huang Y, Li H, Hutchison CE, Laskey J, Kieber JJ: Biochemical and functional analysis of CTR1, a protein kinase that negatively regulates ethylene signaling in Arabidopsis. Plant J. 2003, 33 (2): 221-233.

  94. 94.

    Potuschak T, Lechner E, Parmentier Y, Yanagisawa S, Grava S, Koncz C, Genschik P: EIN3-dependent regulation of plant ethylene hormone signaling by two arabidopsis F box proteins: EBF1 and EBF2. Cell. 2003, 115 (6): 679-689.

  95. 95.

    Alonso JM, Hirayama T, Roman G, Nourizadeh S, Ecker JR: EIN2, a bifunctional transducer of ethylene and stress responses in Arabidopsis. Science. 1999, 284 (5423): 2148-2152.

  96. 96.

    Cary AJ, Liu W, Howell SH: Cytokinin action is coupled to ethylene in its effects on the inhibition of root and hypocotyl elongation in Arabidopsis thaliana seedlings. Plant Physiol. 1995, 107 (4): 1075-1082.

  97. 97.

    Nam KH, Li J: BRI1/BAK1, a receptor kinase pair mediating brassinosteroid signaling. Cell. 2002, 110 (2): 203-212.

  98. 98.

    Li J, Wen J, Lease KA, Doke JT, Tax FE, Walker JC: BAK1, an Arabidopsis LRR receptor-like protein kinase, interacts with BRI1 and modulates brassinosteroid signaling. Cell. 2002, 110 (2): 213-222.

  99. 99.

    Tang W, Kim TW, Oses-Prieto JA, Sun Y, Deng Z, Zhu S, Wang R, Burlingame AL, Wang ZY: BSKs mediate signal transduction from the receptor kinase BRI1 in Arabidopsis. Science. 2008, 321 (5888): 557-560.

  100. 100.

    He JX, Gendron JM, Sun Y, Gampala SSL, Gendron N, Sun CQ, Wang ZY: BZR1 is a transcriptional repressor with dual roles in brassinosteroid homeostasis and growth responses. Science. 2005, 307 (5715): 1634-1638.

  101. 101.

    Chini A, Fonseca S, Fernandez G, Adie B, Chico J, Lorenzo O, Garcia-Casado G, Lopez-Vidriero I, Lozano F, Ponce M, Micol JL, Solano R: The JAZ family of repressors is the missing link in jasmonate signalling. Nature. 2007, 448 (7154): 666-671.

  102. 102.

    Thines B, Katsir L, Melotto M, Niu Y, Mandaokar A, Liu G, Nomura K, He SY, Howe GA, Browse J: JAZ repressor proteins are targets of the SCFCOI1 complex during jasmonate signalling. Nature. 2007, 448 (7154): 661-665.

  103. 103.

    Boatwright JL, Pajerowska‒Mukhtar K: Salicylic acid: an old hormone up to new tricks. Mol Plant Pathol. 2013, 14 (6): 623-634.

  104. 104.

    Wenkel S, Turck F, Singer K, Gissot L, Le Gourrierec J, Samach A, Coupland G: CONSTANS and the CCAAT box binding complex share a functionally important domain and interact to regulate flowering of Arabidopsis. Plant Cell. 2006, 18 (11): 2971-2984.

  105. 105.

    Sawa M, Kay SA: GIGANTEA directly activates Flowering Locus T in Arabidopsis thaliana. Proc Natl Acad Sci U S A. 2011, 108 (28): 11698-11703.

  106. 106.

    Fowler S, Lee K, Onouchi H, Samach A, Richardson K, Morris B, Coupland G, Putterill J: GIGANTEA: a circadian clock-controlled gene that regulates photoperiodic flowering in Arabidopsis and encodes a protein with several possible membrane-spanning domains. EMBO J. 1999, 18 (17): 4679-4688.

  107. 107.

    Samach A, Klenz JE, Kohalmi SE, Risseeuw E, Haughn GW, Crosby WL: The UNUSUAL FLORAL ORGANS gene of Arabidopsis thaliana is an F-box protein required for normal patterning and growth in the floral meristem. Plant J. 1999, 20 (4): 433-445.

  108. 108.

    Azhakanandam S, Nole-Wilson S, Bao F, Franks RG: SEUSS and AINTEGUMENTA mediate patterning and ovule initiation during gynoecium medial domain development. Plant Physiol. 2008, 146 (3): 1165-1181.

  109. 109.

    Krizek BA: AINTEGUMENTA and AINTEGUMENTA-LIKE6 act redundantly to regulate Arabidopsis floral growth and patterning. Plant Physiol. 2009, 150 (4): 1916-1929.

  110. 110.

    Baker SC, RobinsonBeers K, Villanueva JM, Gaiser JC, Gasser CS: Interactions among genes regulating ovule development in Arabidopsis thaliana. Genetics. 1997, 145 (4): 1109-1124.

  111. 111.

    Elliott RC, Betzner AS, Huttner E, Oakes MP, Tucker WQJ, Gerentes D, Perez P, Smyth DR: AINTEGUMENTA, an APETALA2-like gene of Arabidopsis with pleiotropic roles in ovule development and floral organ growth. Plant Cell. 1996, 8 (2): 155-168.

  112. 112.

    Coen ES, Meyerowitz EM: The war of the whorls- genetic interactions controlling flower development. Nature. 1991, 353 (6339): 31-37.

  113. 113.

    Causier B, Schwarz-Sommer Z, Davies B: Floral organ identity: 20 years of ABCs. Semin Cell Dev Biol. 2010, 21 (1): 73-79.

  114. 114.

    Whipple CJ, Zanis MJ, Kellogg EA, Schmidt RJ: Conservation of B class gene expression in the second whorl of a basal grass and outgroups links the origin of lodicules and petals. Proc Natl Acad Sci U S A. 2007, 104 (3): 1081-1086.

  115. 115.

    Han YY, Zhang C, Yang HB, Jiao YL: Cytokinin pathway mediates APETALA1 function in the establishment of determinate floral meristems in Arabidopsis. Proc Natl Acad Sci U S A. 2014, 111 (18): 6840-6845.

  116. 116.

    Yu M, Li X, Zhang X: Expression of AtIPT4 gene under the control of APETALA1 promoter results in abnormal flower and floral organ development. Chin Bull Bot. 2009, 44 (1): 59-68.

  117. 117.

    Skoog F, Miller C: Chemical regularion of growth and organ formation in plant fissue cultured. Symp Soc Exp Biol. 1957, 11: 118-131.

  118. 118.

    Bartrina I, Otto E, Strnad M, Werner T, Schmülling T: Cytokinin regulates the activity of reproductive meristems, flower organ size, ovule formation, and thus seed yield in Arabidopsis thaliana. Plant Cell. 2011, 23 (1): 69-80.

  119. 119.

    Motoyuki A, Hitoshi S, Lin S: Cytokinin oxidase regulates rice grain production. Science. 2005, 309 (5735): 741-745.

  120. 120.

    Werner T, Motyka V, Strnad M, Schmülling T: Regulation of plant growth by cytokinin. Proc Natl Acad Sci U S A. 2001, 98 (18): 10487-10492.

  121. 121.

    Riou-Khamlichi C, Huntley R, Jacqmard A, Murray JAH: Cytokinin activation of Arabidopsis cell division through a D-type cyclin. Science. 1999, 283 (5407): 1541-1544.

  122. 122.

    Hu Y, Bao F, Li J: Promotive effect of brassinosteroids on cell division involves a distinct CycD3‒induction pathway in Arabidopsis. Plant J. 2008, 24 (5): 693-701.

  123. 123.

    Dewitte W, Scofield S, Alcasabas AA, Maughan SC, Menges M, Braun N, Collins C, Nieuwland J, Prinsen E, Sundaresan V, Murray JAH: Arabidopsis CYCD3 D-type cyclins link cell proliferation and endocycles and are rate-limiting for cytokinin responses. Proc Natl Acad Sci U S A. 2007, 104 (36): 14537-14542.

  124. 124.

    Imai KK, Ohashi Y, Tsuge T, Yoshizumi T, Matsui M, Oka A, Aoyama T: The A-type cyclin CYCA2;3 is a key regulator of ploidy levels in Arabidopsis endoreduplication. Plant Cell. 2006, 18 (2): 382-396.

  125. 125.

    Vanneste S, Coppens F, Lee E, Donner TJ, Xie Z, Van Isterdael G, Dhondt S, De Winter F, De Rybel B, Vuylsteke M, Veylder LD, Friml J, Inzé D, Grotewold E, Scarpella E, Sack F, Beemster GTS, Beeckman T: Developmental regulation of CYCA2s contributes to tissue-specific proliferation in Arabidopsis. EMBO J. 2011, 30 (16): 3430-3441.

  126. 126.

    Sutou S, Miwa K, Matsuura T, Kawasaki Y, Ohinata Y, Mitsui Y: Native tesmin is a 60-kilodalton protein that undergoes dynamic changes in its localization during spermatogenesis in mice. Biol Reprod. 2003, 68 (5): 1861-1869.

  127. 127.

    Andersen SU, Algreen-Petersen RG, Hoedl M, Jurkiewicz A, Cvitanich C, Braunschweig U, Schauser L, Oh S-A, Twell D, Jensen EØ: The conserved cysteine-rich domain of a tesmin/TSO1-like protein binds zinc in vitro and TSO1 is required for both male and female fertility in Arabidopsis thaliana. J Exp Bot. 2007, 58 (13): 3657-3670.

  128. 128.

    Sijacic P, Wang W, Liu Z: Recessive antimorphic alleles overcome functionally redundant loci to reveal TSO1 function in Arabidopsis flowers and meristems. PLoS Genet. 2011, 7 (11): e1002352-

  129. 129.

    Hauser BA, Villanueva JM, Gasser CS: Arabidopsis TSO1 regulates directional processes in cells during floral organogenesis. Genetics. 1998, 150 (1): 411-423.

  130. 130.

    Song JY, Leung T, Ehler LK, Wang C, Liu Z: Regulation of meristem organization and cell division by TSO1, an Arabidopsis gene with cysteine-rich repeats. Development. 2000, 127 (10): 2207-2217.

  131. 131.

    Hauser BA, He JQ, Park SO, Gasser CS: TSO1 is a novel protein that modulates cytokinesis and cell expansion in Arabidopsis. Development. 2000, 127 (10): 2219-2226.

  132. 132.

    Santner A, Calderon-Villalobos LI, Estelle M: Plant hormones are versatile chemical regulators of plant growth. Nat Chem Biol. 2009, 5 (5): 301-307.

  133. 133.

    Greenboim-Wainberg Y, Maymon I, Borochov R, Alvarez J, Olszewski N, Ori N, Eshed Y, Weiss D: Cross talk between gibberellin and cytokinin: the Arabidopsis GA response inhibitor SPINDLY plays a positive role in cytokinin signaling. Plant Cell. 2005, 17 (1): 92-102.

  134. 134.

    Weiss D, Ori N: Mechanisms of cross talk between gibberellin and other hormones. Plant Physiol. 2007, 144 (3): 1240-1246.

  135. 135.

    Moubayidin L, Di Mambro R, Sabatini S: Cytokinin–auxin crosstalk. Trends Plant Sci. 2009, 14 (10): 557-562.

  136. 136.

    Li S, Zhao B, Yuan D, Duan M, Qian Q, Tang L, Wang B, Liu X, Zhang J, Wang J: Rice zinc finger protein DST enhances grain production through controlling Gn1a/OsCKX2 expression. Proc Natl Acad Sci U S A. 2013, 110 (8): 3167-3172.

  137. 137.

    Rodriguez MCS, Edsgärd D, Hussain SS, Alquezar D, Rasmussen M, Gilbert T, Nielsen BH, Bartels D, Mundy J: Transcriptomes of the desiccation‒tolerant resurrection plant Craterostigma plantagineum. Plant J. 2010, 63 (2): 212-228.

  138. 138.

    Feng J, Meyer CA, Wang Q, Liu JS, Liu XS, Zhang Y: GFOLD: a generalized fold change for ranking differentially expressed genes from RNA-seq data. Bioinformatics. 2012, 28 (21): 2782-2788.

  139. 139.

    Poelchau MF, Reynolds JA, Denlinger DL, Elsik CG, Armbruster PA: A de novo transcriptome of the Asian tiger mosquito, Aedes albopictus, to identify candidate transcripts for diapause preparation. BMC Genomics. 2011, 12 (1): 619-

  140. 140.

    Ng P, Wei CL, Sung WK, Chiu KP, Lipovich L, Ang CC, Gupta S, Shahab A, Ridwan A, Wong CH, Liu ET, Ruan YJ: Gene identification signature (GIS) analysis for transcriptome characterization and genome annotation. Nat Methods. 2005, 2 (2): 105-111.

  141. 141.

    Zhang F, Guo H, Zheng H, Zhou T, Zhou Y, Wang S, Fang R, Qian W, Chen X: Massively parallel pyrosequencing-based transcriptome analyses of small brown planthopper (Laodelphax striatellus), a vector insect transmitting rice stripe virus (RSV). BMC Genomics. 2010, 11 (1): 303-

  142. 142.

    Rice P, Longden I, Bleasby A: EMBOSS: the European Molecular Biology Open Software Suite. Trends Genet. 2000, 16 (6): 276-277.

  143. 143.

    Chen Z, Xue C, Zhu S, Zhou F, Ling XB, Liu G, Chen L: GoPipe: streamlined gene ontology annotation for batch anonymous sequences with statistics. Prog Biochem Biophys. 2005, 32 (2): 187-190.

  144. 144.

    Kanehisa M, Goto S, Furumichi M, Tanabe M, Hirakawa M: KEGG for representation and analysis of molecular networks involving diseases and drugs. Nucleic Acids Res. 2010, 38 (Database issue): D355-D360.

  145. 145.

    Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B: Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods. 2008, 5 (7): 621-628.

  146. 146.

    Wang L, Feng Z, Wang X, Wang X, Zhang X: DEGseq: an R package for identifying differentially expressed genes from RNA-seq data. Bioinformatics. 2010, 26 (1): 136-138.

  147. 147.

    Heyl A, Brault M, Frugier F, Kuderova A, Lindner A-C, Motyka V, Rashotte AM, Schwartzenberg KV, Vankova R, Schaller GE: Nomenclature for members of the two-component signaling pathway of plants. Plant Physiol. 2013, 161 (3): 1063-1065.

  148. 148.

    Cheadle C, Vawter MP, Freed WJ, Becker KG: Analysis of microarray data using Z score transformation. J Mol Diagn. 2003, 5 (2): 73-81.

  149. 149.

    Livak KJ, Schmittgen TD: Analysis of relative gene expression data using real-time quantitative PCR and the 2−ΔΔCT method. Methods. 2001, 25 (4): 402-408.

Download references


We thank Dr. Changning Liu for helping in sequence data submission. This work was supported by funding from the Top Science and Technology Talents Scheme of Yunnan Province (2009CI123), the Natural Science Foundation of Yunnan Province (2011FA034), the National Natural Science Foundation of China (31370595) and the CAS 135 program (XTBG-T02) awarded to Z.-F. Xu. The authors gratefully acknowledge the Central Laboratory of the Xishuangbanna Tropical Botanical Garden for providing research facilities.

Author information

Correspondence to Zeng-Fu Xu.

Additional information

Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

BZP and ZFX conceived the experiments and wrote the manuscript. BZP performed cytokinin treatment, data analysis and qRT-PCR analysis. MSC participated in the experimental design and manuscript preparation. JN conducted part of the qRT-PCR analysis and helped to draft the manuscript. All the authors have read and approved the final manuscript.

Electronic supplementary material

Additional file 1: Figure S1: Effects of BA on flower development and fruiting of Jatropha. (A) Effects of BA treatment on the flower number of various sexes per inflorescence and fruit number per infructescence. (B) Effects of BA treatment on the percentage of flowers of various sexes. The values are means ± standard deviations (n =30 inflorescences). *Statistically significant at the 5% level, **Statistically significant at the 1% level. (PDF 91 KB)

Additional file 2: Table S1: Top-hit species distribution of BLAST matches of Jatropha unigenes with a cutoff of 1e-3. (DOCX 17 KB)

Additional file 3: Table S2: Clustering of differentially expressed genes in the transcriptomes of control and BA-treated inflorescence of Jatropha (p <0.05). (XLSX 1 MB)

Additional file 4: Table S3: BA treatment enhanced inflorescence branching of Jatropha. (DOCX 26 KB)

Additional file 5: Table S4: BLASTX results of genes involved in plant hormone signaling, flower development and cell division against TAIR. (DOCX 30 KB)

Additional file 6: Table S5: Primer sequences and PCR amplicon lengths of the selected genes. (DOCX 18 KB)

Additional file 7: Table S6: Sequences of unigenes less than 200-bp in length obtained from the transcriptome sequencing of inflorescence meristems in Jatropha.(ZIP 12 KB)

Authors’ original submitted files for images

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Pan, B., Chen, M., Ni, J. et al. Transcriptome of the inflorescence meristems of the biofuel plant Jatropha curcas treated with cytokinin. BMC Genomics 15, 974 (2014).

Download citation


  • Biofuel
  • Physic nut
  • Cytokinin
  • 454 Sequencing
  • Flower
  • Cell division