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Transcriptome analysis of two inflorescence branching mutants reveals cytokinin is an important regulator in controlling inflorescence architecture in the woody plant Jatropha curcas

BMC Plant Biology volume 19, Article number: 468 (2019) Cite this article

Abstract

Background

In higher plants, inflorescence architecture is an important agronomic trait directly determining seed yield. However, little information is available on the regulatory mechanism of inflorescence development in perennial woody plants. Based on two inflorescence branching mutants, we investigated the transcriptome differences in inflorescence buds between two mutants and wild-type (WT) plants by RNA-Seq to identify the genes and regulatory networks controlling inflorescence architecture in Jatropha curcas L., a perennial woody plant belonging to Euphorbiaceae.

Results

Two inflorescence branching mutants were identified in germplasm collection of Jatropha. The duo xiao hua (dxh) mutant has a seven-order branch inflorescence, and the gynoecy (g) mutant has a three-order branch inflorescence, while WT Jatropha has predominantly four-order branch inflorescence, occasionally the three- or five-order branch inflorescences in fields. Using weighted gene correlation network analysis (WGCNA), we identified several hub genes involved in the cytokinin metabolic pathway from modules highly associated with inflorescence phenotypes. Among them, Jatropha ADENOSINE KINASE 2 (JcADK2), ADENINE PHOSPHORIBOSYL TRANSFERASE 1 (JcAPT1), CYTOKININ OXIDASE 3 (JcCKX3), ISOPENTENYLTRANSFERASE 5 (JcIPT5), LONELY GUY 3 (JcLOG3) and JcLOG5 may participate in cytokinin metabolic pathway in Jatropha. Consistently, exogenous application of cytokinin (6-benzyladenine, 6-BA) on inflorescence buds induced high-branch inflorescence phenotype in both low-branch inflorescence mutant (g) and WT plants. These results suggested that cytokinin is an important regulator in controlling inflorescence branching in Jatropha. In addition, comparative transcriptome analysis showed that Arabidopsis homologous genes Jatropha AGAMOUS-LIKE 6 (JcAGL6), JcAGL24, FRUITFUL (JcFUL), LEAFY (JcLFY), SEPALLATAs (JcSEPs), TERMINAL FLOWER 1 (JcTFL1), and WUSCHEL-RELATED HOMEOBOX 3 (JcWOX3), were differentially expressed in inflorescence buds between dxh and g mutants and WT plants, indicating that they may participate in inflorescence development in Jatropha. The expression of JcTFL1 was downregulated, while the expression of JcLFY and JcAP1 were upregulated in inflorescences in low-branch g mutant.

Conclusions

Cytokinin is an important regulator in controlling inflorescence branching in Jatropha. The regulation of inflorescence architecture by the genes involved in floral development, including TFL1, LFY and AP1, may be conservative in Jatropha and Arabidopsis. Our results provide helpful information for elucidating the regulatory mechanism of inflorescence architecture in Jatropha.

Background

Inflorescence architecture directly influencing plant reproductive success [1], is a key agronomic factor determining seed yield. In higher plants, inflorescence architectures exhibit remarkable diversity, which is derived from the inflorescence position on the shoot, the flower arrangement within inflorescence and the patterns and timing of flower initiation during reproductive development [2, 3]. Recently, a detailed review has been made on evolutionary developmental biology studies of inflorescence architecture [4]. In inflorescence development, the maintenance of the inflorescence meristem and initiation of the floral meristem are critical to determine inflorescence architecture. In Arabidopsis thaliana, CLAVATA (CLV) pathways regulate meristem maintenance by restricting the expression of WUSCHEL (WUS), which defines the stem cell niche [5, 6]. A mutation in CLAVATA (CLV1, CLV2 or CLV3) results in the accumulation of meristem cells to generate an increased shoot meristem dome [7]. Mutation in WUS causes defective shoot and floral meristems [6]. In maize, thick tassel dwarf1 (td1) and fasciated ear2 (fea2) are orthologs of CLV1 and CLV2, respectively. Loss-of-function mutant td1 exhibits a fascinated ear with extra rows of kernels and a tassel with more dense spikelets [8]. The fea2 mutant exhibits a massive ear inflorescence meristem and increased organ number [9].

In Arabidopsis, the transition time from the inflorescence to floral meristem is critical in the determination of inflorescence architecture; TERMINAL FLOWER1 (TFL1), LEAFY (LFY) and APETALA1 (AP1) participate in this process and regulate inflorescence branching patterns [10, 11]. TFL mutation promotes the conversion of the inflorescence meristem into the floral meristem, causing an abnormal inflorescence with a compound floral structure [12]. By contrast, the ectopic expression of TFL1 in a transgenic plant induces a highly branched inflorescence phenotype in Arabidopsis [13]. Conversely, LFY and AP1 repress the expression of TFL1 in the floral meristem [11, 14]. Moreover, SUPPRESSOR OF OVEREXPRESSION OF CONSTANS 1 (SOC1), SHORT VEGETATIVE PHASE (SVP), AGAMOUS-LIKE 24 (AGL24), and SEPALLATA 4 (SEP4) redundantly regulate inflorescence architecture by directly suppressing the expression of TFL1; a soc1–2 agl24–3 svp-41 sep4–1 quadruple mutant displays a massive inflorescence branching phenotype [15].

Overexpression of rice RCN1 and RCN2 and maize ZCN1-ZCN6, which are homologous to Arabidopsis TFL1, causes highly branched inflorescences through maintaining the indeterminacy of the inflorescence meristem [16, 17]. Rice TAWAWA1 (TAW1) regulates inflorescence architecture by suppressing the specification of the spikelet meristem to maintain the indeterminacy of inflorescence architecture [18]. A gain-of-function mutant, tawawa1-D, exhibits a highly branched inflorescence and increased number of spikelets, whereas a TAW1 knockdown mutant displays a small, reduced branching inflorescence resulting from early termination of inflorescence meristems and the formation of spikelet meristems [18]. In rice, the WEALTHY FARMER’S PANICLE (WFP)/IDEAL PLANT ARCHITECTURE (IPA1) locus is linked to a Squamosa Promoter Binding Protein-Like 14 (OsSPL14) gene that can be suppressed by OsmiR156 [19]. A single nucleotide change in OsSPL14 that relieves the repression of OsmiR156 causes increased panicle branching and grain number [19, 20]. Overexpression of a maize UNBRANCHED3 (UB3), an ortholog of rice OsSPL14, dramatically repressed tillering and panicle branching in rice; however, moderate expression of UB3 slightly suppressed tillering, but promoted panicle branching, resulting in an increased grain number per panicle in rice [21, 22].

In maize, two APETALA2-like genes, indeterminate spikelet1 (ids1) and sister of indeterminate spikelet 1 (SID1), are required for inflorescence branching by regulating the initiation of spikelet meristem and floral meristem, and an ids1 sid1 double mutant shows a reduced branching inflorescence phenotype [23]. Three maize genes, RAMOSA1 (RA1), RA2 and RA3, regulate the fate of the inflorescence meristem, and mutations in these genes cause an increased long-branching inflorescence [24,25,26].

Plant hormones such as auxin and cytokinin are required for the initiation and outgrowth of axillary meristems that generate inflorescence branches and florets during reproductive development [27, 28]. Several auxin biosynthesis and polar transport components, such as YUCCA (YUC), PIN-FORMED 1 (PIN1) and PINOID (PID), are important regulators in inflorescence development [27]. In Arabidopsis, the YUC gene encodes flavin monooxygenases (FMOs) that catalyze a rate-limiting step in tryptophan-dependent auxin biosynthesis; overexpression of YUC promotes auxin levels during development processes [29]. In maize, sparse inflorescence1 (spi1), a homologous Arabidopsis YUC, encodes a FMO, and spi1 mutation reveals defects in the initiation of axillary meristems and lateral organs, causing reduced branch number and floral organs in inflorescences [30]. Auxin transport proteins PIN1 and PID are required for the distribution of auxin; mutation in pin1 or pid causes a pin-like inflorescence with abnormal flowers because of defects in the initiation of the axillary meristem in Arabidopsis [31, 32]. Maize barreninflorescence2 (bif2), an ortholog of PID, is required for the initiation of the axillary meristem and lateral primordia; the bif2 mutant displays a reduced number of branches, spikelets, florets and kernels in inflorescences [33]. Barren inflorescence1 (bif1) mutation causes a similar phenotype to that of the bif2 mutant [34]; the barren stalk1 (ba1) mutant displays an unbranched inflorescence without spikelets because of a defect in auxin signaling [35]. In Setaria viridis, SvAUXIN1 is required for inflorescence development and its loss-of-function mutant sparse panicle1 (spp1) displays reduced and uneven inflorescence branching phenotype [36].

Cytokinin is required for meristem activity and plays a positive role in the shoot meristem [37]. In Arabidopsis, CYTOKININ OXIDASE 3 (CKX3) and CKX5 catalyze the degradation of cytokinin; ckx3 ckx5 double mutant display larger inflorescences and floral meristems because of the accumulation of cytokinin [38]. In rice, a QTL locus, Gn1, encodes an OsCKX2; the reduced expression of OsCKX2 causes higher cytokinin levels in the inflorescence meristem to increase the number of branches and spikelets, leading to enhanced grain yield [39]. A zinc finger transcription factor, DROUGHT AND SALT TOLERANCE (DST), directly regulates the accumulation of cytokinin in the shoot apical meristem (SAM) to promote panicle branching loading to the increase of grain number [40]. The LONELY GUY (LOG) gene encodes an enzyme that participates in the final step of bioactive cytokinin synthesis; mutation in log causes a small inflorescence with reduced branch and spikelet number [37].

At present, the studies on molecular mechanism of inflorescence architecture are focus on the model plants and few in perennial woody plants because of their long reproductive cycle and the difficulty in the establishment of genetic transformation system. To understand the regulatory mechanism of inflorescence architecture, the investigation of the evolutionary changes in developmental morphology and the identification of key factors controlling inflorescence development are crucial in closely related plant lineages that display different inflorescence structures [4], especially, in perennial woody plants.

Jatropha curcas L. has high seed oil content and is considered a potential biofuel plant [41, 42]. The Jatropha inflorescence exhibits a dichasial cyme pattern bearing male and female flowers in the same inflorescence. Few female flowers per inflorescence is considered one of the factors leading to poor seed yield in Jatropha [43]. Our previous research showed that co-suppression of JcLFY delayed flower formation, leading to production of more secondary inflorescence branches [44], but roles of JcTFL1b [44, 45] and JcAP1 [46] in inflorescence branching remain unclear. Paclobutrazol (PAC), an inhibitor of gibberellin biosynthesis, causes compacted inflorescences with short branches, resulting in increased seed yield in Jatropha [47, 48]. Cytokinin treatment on inflorescence buds generated a larger inflorescence with a significantly increased the number of female and total flowers [43, 49, 50], which means a high-branch inflorescence phenotype. These results suggest that cytokinin may play a significant role in the determination of inflorescence architecture in Jatropha. In this study, two Jatropha mutants that exhibit different inflorescence branching traits were used for comparative transcriptome analysis to identify genes and regulation networks that participate in the regulation of inflorescence architecture. Our study will contribute to the understanding of the genetic basis of inflorescence architecture and to the breeding of high-yield Jatropha varieties.

Results

dxh and g mutants have different inflorescence branching phenotypes

In general, wild-type (WT) Jatropha has a four-order branch cyme inflorescence bearing female and male flowers, and three- or five-order branch inflorescence is occurred occasionally in different growth conditions. Here, we reported two inflorescence branching mutants: a high-branch dxh (Chinese for “more florets”) mutant with a seven-order branch inflorescence, which came from a mutagenized population treated with cobalt-60 gamma rays; a low-branch g mutant with a three-order branch inflorescence, which originated from a natural mutation [51] (Fig. 1). In the dxh mutant, the total flower number, female flower number, fruit number, seed number, seed yield and oil content were significantly increased, whereas the female-to-male ratio was decreased, and the 100-seed weight remained constant in dxh mutant plants compared with those in WT plants (Fig. 2). The increase in seed yield and oil content and the constant 100-seed weight in dxh plants indicated that the dxh mutant is an excellent material for breeding high-yield Jatropha varieties. The g mutant had a gynoecious genotype with normal female flowers, whereas male flowers were aborted, indicating that it is also good breeding material.

Fig. 1
figure 1

Phenotypes of inflorescence branching of WT and dxh and g mutants. a-c indicate the branching structure of inflorescences in WT and dxh and g mutants, respectively. Bar = 2.0 cm. d-f indicate the intact inflorescences of WT and dxh and g mutants. In panels a, b, d and e, the blue arrows indicate the fourth branching, and the red arrows denote the seventh branching. Bar = 5.0 cm. g-i indicate the infructescences of WT and dxh and g mutants. Bar = 5.0 cm. j-l indicate the diagrammatic sketch of inflorescence structure of WT and dxh and g mutants

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Fig. 2
figure 2

Comparison of agronomic traits between WT plants and dxh mutants. a Total flower number per inflorescence. b Female flower number per inflorescence. c Percentage of female flowers. d Fruit number per infructescence. e Seed number per infructescence. f Seed yield per infructescence. g 100-seed weight. h Seed oil content. The number of inflorescences/infructescences surveyed is 34–44; values are means ± SD for (a-h). Statistical test analysis was performed using the Welch two sample t-test in R software

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Identification of differentially expressed genes

To investigate the regulatory mechanism underlying inflorescence branching, we carried out RNA-Seq analysis between five group samples to identify DEGs (Additional file 1). These samples contained shoot tips that can generate inflorescence meristem and inflorescence buds from WT plants (hereafter referred to as ckI and ckII), shoot tips and inflorescence buds from the dxh mutant (referred to as dxhI and dxhII), and inflorescence buds from the g mutant (referred to as gII). A multidimensional scaling (MDS) plot showed that ckI, dxhI and gII samples can be well separated from ckII and dxhII based on the biological coefficient of variation (BCV) (Additional file 2). The close relationship between high-branch mutant (dxhII) and WT (ckII) inflorescence samples indicated that they have highly similar gene expression profiles. For simplicity, we hereafter refer to the comparison of ckII vs. ckI as ckII_ckI, dxhII vs. dxhI as dxhII_dxhI, dxhI vs. ckI as dxhI_ckI, dxhII vs. ckII as dxhII_ckII, gII vs. ckII as gII_ckII, and gII vs. dxhII as gII_dxhII. In the six pairs, 3549 DEGs in ckII_ckI, 410 in dxhII_dxhI, 2089 in dxhI_ckI, 329 in dxhII_ckII, 12,413 in gII_ckII and 12,282 in gII_dxhII were identified at an FDR of < 0.05 (Additional files 3 and 4). Among them, we identified 28 DEGs that have opposite expression patterns in inflorescences between pairwise high-branch mutant vs. WT (dxhII_ckII) and low-branch mutant vs. WT (gII_ckII) (Fig. 3 and Additional file 5). Expression of two genes of them were upregulated in inflorescences in pairwise high-branch mutant vs. WT (dxhII_ckII) and downregulated in pairwise low-branch mutant vs. WT (gII_ckII) while 26 genes were downregulated in pairwise gII_ckII, and upregulated in dxhII_ckII (Fig. 3). Interestingly, ten of them were long noncoding RNAs (LncRNAs) [52], suggesting that LncRNA may be involved in the regulation of inflorescence branching in Jatropha.

Fig. 3
figure 3

Hierarchical clustering of differentially expressed genes with opposite expression profiles in inflorescences between pairwise high-branch mutant vs. WT (dxhII_ckII) and low-branch mutant vs. WT (gII_ckII). The pairwise ckII_ckI and dxhII_dxhI indicate the comparisons of inflorescence bud and shoot tip in WT and dxh mutant, respectively; dxhI_ckI indicates the comparison of shoot tips between dxh mutant and WT; dxhII_ckII, gII_ckII and gII_dxhII indicate the comparisons of inflorescence buds between dxh and g mutant and WT, respectively. Genes shown in the figure is listed in Additional file 6. The color bar from red to blue indicates up-regulated and down-regulated expressions of genes in each pair; the dark color represents high fold change and the light color represents low fold change; the number in each color cell indicates corresponding value of fold change

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Differentially expressed genes involved in inflorescence development

According to Arabidopsis and rice genes involved in inflorescence development, we identified 21 homologous genes that may play similar roles in regulating Jatropha inflorescence development from our assembled data set. Nine of them were differentially expressed in inflorescences in pairwise low-branch vs. high-branch mutant (gII_dxhII) (Fold change ≥2.0 and FDR < 0.05) (Additional file 7). Most of them exhibited opposite expression patterns between pairwise high-branch mutant vs. WT (dxhII_ckII) and low-branch mutant vs. WT (gII_ckII) (Fig. 4), which are coincided with their respective phenotypes. Expression of Jatropha AGAMOUS-LIKE 6 (JcAGL6), FRUITFUL (JcFUL), JcLFY, SEPALLATA 1 (JcSEP1), JcSEP2a, JcSEP2b, JcSEP3 and WUSCHEL-RELATED HOMEOBOX 3 (JcWOX3) was upregulated, whereas expression of JcAGL24 and JcTFL1 was downregulated in pairwise low-branch vs. high-branch mutant (gII_dxhII). In Arabidopsis, TFL1 promotes inflorescence branching, whereas LFY and AP1 prevent inflorescence branching by repressing the expression of TFL1 in the floral meristem [7, 8, 10]. Our results showed that JcTFL1, JcLFY and JcAP1 may play similar roles in controlling inflorescence architecture in Jatropha, suggesting the function of these genes may be conservative in Jatropha and Arabidopsis.

Fig. 4
figure 4

Hierarchical clustering of differentially expressed genes involved in inflorescence development. The pairwise ckII_ckI, dxhII_dxhI, dxhI_ckI, dxhII_ckII, gII_ckII and gII_dxhII indicate the same pairs shown in Fig. 3. The genes shown in the figure is listed in Additional file 7. The color bar from red to blue indicates up-regulated and down-regulated expressions of genes in each pair; the dark color represents high fold change and light color represents low fold change; the number in each color cell indicates corresponding value of fold change

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Differentially expressed genes involved in auxin and cytokinin metabolic and signaling pathways

Auxin and cytokinin play important roles in inflorescence branching in Arabidopsis and rice [27, 28]. We identified 22 and 26 homologous genes involved in the auxin and cytokinin metabolic and signaling pathways, respectively, from our transcriptome data set (Additional file 8). Among them, Jatropha ALDEHYDE OXIDASE 1 (JcAAO1), CHALCONE SYNTHASE (JcCHS), cytochrome P450, family 83, subfamily B, polypeptide 1 (JcCYP83B1), IAA carboxyl methyltransferase 1 (JcIAMT1), JcPID, JcPIN1, SMALL AUXIN UP RNA 20 (JcSAUR20), and YUCCA4 (JcYUC4) may be involved in the auxin metabolic and signaling pathways. Jatropha CYTOKININ OXIDASE/DEHYDROGENASE 3 (JcCKX3), JcCKX7, ISOPENTENYLTRANSFERASE 1 (JcIPT1), JcIPT5, JcLOG1, JcLOG3, and JcLOG5 may be involved in the cytokinin metabolic and signaling pathways. All of them were differentially expressed in inflorescences in pairwise high-branch mutant vs. WT (dxhII_ckII) or low-branch mutant vs. WT (gII_ckII) (Fold change ≥2.0 and FDR < 0.05) (Fig. 5). Thus, the auxin and cytokinin metabolic or signaling pathways may participate in regulating inflorescence branching in Jatropha.

Fig. 5
figure 5

Hierarchical clustering of differentially expressed genes involved in auxin and cytokinin metabolic or signaling pathways. The pairwise ckII_ckI, dxhII_dxhI, dxhI_ckI, dxhII_ckII, gII_ckII and gII_dxhII indicate the same pairs shown in Fig. 3. The genes shown in the figure is listed in Additional file 8. The color bar from red to blue indicates up-regulated and down-regulated expressions of genes in every pair; the dark color represents high fold change and light color represents fold change; the number in each color cell indicates corresponding value of fold change

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Confirmation of expression profiles of candidate genes by real-time qPCR

To validate the result of transcriptome analysis, we selected a dozen candidate genes to test their expression patterns across the five group samples using the real-time qPCR method. These genes included JcFUL, JcSEP1, JcSEP2A, JcSEP2B, JcSEP3, JcTFL1, JcCD35911.0, JcCD37832.1, JcCD39325.1, JcCD47611.19, JcCD53029.968 and JcCD53029.1529 (Additional files 9 and 10). Correlation analysis showed that the expression patterns of these genes displayed by RNA-Seq were consistent with that by real-time qPCR (Additional file 11), indicating that the transcriptome results in this study are reliable.

Construction of gene co-expression networks and identification of hub genes that regulate inflorescence branching

We constructed a gene co-expression network using the WGCNA package [53]. In the network, 22 merged “modules” were identified; high correlation coefficients between genes in these modules indicated a high degree of interconnection (Additional file 12). The expression profile of each module was represented by its eigengene. We investigated the relationship between the module eigengenes and phenotype (three different inflorescence branching phenotypes) and tissue traits (shoot tips and inflorescence buds). MEblue and MEblack modules were highly associated with phenotype traits, and MEsalmon and MEdarkgreen modules were highly associated with deferent tissue traits (Fig. 6 and Additional file 13). We focused on the MEblue and MEblack modules because they might play more important roles in regulating inflorescence branching. According to the node connectivity that reflects how frequently a node interacts with others in a biological network and the results of transcriptome analysis, a dozen hub genes were identified from MEblue and MEblack modules (Figs. 7, 8 and Additional file 14). These genes include six Arabidopsis homologous genes, Jatropha ADENOSINE KINASE 2 (JcADK2), JcAPT1, JcCKX3, ISOPENTENYLTRANSFERASE 5 (JcIPT5), LONELY GUY 3 (JcLOG3) and JcLOG5 involved in the cytokinin metabolic pathway, four genes JcAAO1, JcPID, JcPIN1, and JcPIN3 in the auxin biosynthetic and signaling pathways, JcSOC1, SPATULA (JcSPT) and five LncRNA genes. Among them, JcADK2, JcAPT1, JcCKX3, JcIPT5, JcLOG3, JcLOG5, JcPID, JcPIN1 and JcPIN3 were differentially expressed across different phenotypic inflorescences. The results showed that cytokinin and auxin metabolic or signaling pathways may participate in inflorescence development and may play vital roles in controlling inflorescence architecture.

Fig. 6
figure 6

Correlation analysis between module eigengenes (ME) and biological traits (phenotypes and tissues) using weighted gene correlation network analysis (WGCNA). Each color block in left color column indicates a module identified (22 modules). The right color bar indicates the correlation coefficient; the red color represents the positive correlation and green represents the negative correlation. Two middle columns correspond to phenotype and tissues traits; the numbers in each color cell indicate the correlation coefficient and the corresponding P-value (numbers in brackets) between modules and two traits, calculated using the WGCNA package

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Fig. 7
figure 7

Co-expression network of genes in the MEblue module shown in Fig. 6. The red color indicates the differentially expressed genes with opposite expression profiles in inflorescences between pairwise high-branch mutant vs. WT (dxhII_ckII) and low-branch mutant vs. WT (gII_ckII) which are showed in Fig. 3; the green color indicates differentially expressed genes involved in inflorescence development which are showed in Fig. 4; the cyan color indicates differentially expressed genes in auxin metabolic and signaling pathways, and blue color indicates genes in cytokinin metabolic and signaling pathways which are showed in Fig. 5. Detailed information on genes shown in the figure is listed in Additional file 14

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Fig. 8
figure 8

Co-expression network of genes in the MEblack module shown in Fig. 6. The red color indicates the differentially expressed genes with opposite expression profiles in inflorescences between pairwise high-branch mutant vs. WT (dxhII_ckII) and low-branch mutant vs. WT (gII_ckII) which are showed in Fig. 3; the cyan color indicates the differentially expressed genes in auxin metabolic and signaling pathways, and the blue color indicates cytokinin metabolic and signaling pathways which are showed in Fig. 5. Detailed information on the genes shown in the figure is listed in Additional file 14

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Application of 6-benzylaminopurine (6-BA) promoted an increase in inflorescence branches in both g mutant and WT plants

To validate the functions of cytokinin and auxin in inflorescence branching, we applied 6-BA (cytokinin) and 1-naphthaleneacetic acid (NAA, auxin) to the inflorescence buds of low-branch mutant (g) and WT plants, respectively. After 6-BA treatment, more than 70% of inflorescences displayed high-branch phenotype in both g mutant and WT plants, although the growth of some inflorescence buds was arrested (Additional file 15 A, E and C, G). A three-order branch inflorescence became a four-order branch inflorescence in g mutant plants (Additional file 15 B, F and A, E); and a five-order branch inflorescence became a six-order branch inflorescence in WT plants (Additional file 15 D, H and C, G), which is similar to the high-branch inflorescence phenotype of dxh mutant. We postulate that a strong cytokinin activity is present in dxh mutant inflorescence buds than in WT ones, which results in the high-branch inflorescence phenotype while a weak cytokinin activity in g mutant inflorescence buds leads to the low-branch inflorescence phenotype. However, NAA treatment had no effect on inflorescence branching (data not shown). These results indicate that cytokinin is an important regulator in regulating inflorescence branching in Jatropha.

Discussion

In inflorescence development, cytokinin and auxin are essential for the formation and growth of the inflorescence branch meristem in Arabidopsis and rice [27, 28]. A gene co-expression network analysis showed that six homologous genes involved in the cytokinin metabolic pathway, and four genes in the auxin biosynthetic and signaling pathways are hub genes, which are from modules highly associated with inflorescence architecture phenotypes (Figs. 7 and 8), indicating that cytokinin and auxin likely to play important roles in regulating inflorescence branching in Jatropha. In cytokinin metabolic process, ADK catalyzes the phosphorylation of adenosine to AMP and converts cytokinin nucleosides to nucleotides contributing to intracellular CK homeostasis [54, 55]. ATP1 converts active cytokinin to inactive form and loss of ATP1 activity causes high accumulation of cytokinin bases evoking abnormal cytokinin-regulated responses [56]. Isopentenyltransferases (IPTs) catalyze the formation of isopentenyladenosine 5′-monophosphate (iPMP) from AMP and dimethylallylpyrophosphate (DMAPP), which is the first step of the cytokinin biosynthetic pathways [57]. LOG promotes cytokinin activity, and log mutants display a deficient in the maintenance of the shoot meristem in rice [28]. Cytokinin oxidases/dehydrogenases (CKXs) catalyze the irreversible degradation of cytokinins in cytokinin metabolic pathways [58]. In pairwise low-branch vs. high-branch mutant (gII_dxhII) and low-branch mutant vs. WT (gII_ckII) inflorescences, the expression of JcIPT1 and JcIPT5 was downregulated while the expression of JcLOG1, JcLOG3, JcLOG5, JcCKX1, JcCKX3, and JcCKX7 was upregulated (Fig. 5). These results indicated that cytokinin biosynthesis is decreased, but cytokinin activation and degradation are promoted in low-branch mutant (g) inflorescence compared to those in high-branch mutant (dxh) and WT inflorescences. These effects may cause the low-branch inflorescence phenotype of g mutant.

In Arabidopsis, TRYPTOPHANAMINOTRANSFERASE OF ARABIDOPSIS 1 (TAA1) catalyzes the conversion from L-tryptophan (Trp) to indole-3-pyruvic acid (IPA), and YUC FMOs catalyze the oxidative decarboxylation of IPA to generate indole-3-acetic acid (IAA), both of which are the key enzymes in auxin biosynthesis [59, 60]. The expression of JcTAA1 and JcYUC4 was downregulated, indicating that auxin biosynthesis is decreased in low-branch (g) mutant inflorescence compared to that in high-branch (dxh) mutant inflorescence, along with the downregulated expression of JcPIN1 and JcPID whose homologous genes serve as efflux carriers in auxin polar transport in Arabidopsis [61, 62] (Fig. 5). In addition, the expression of JcSAUR20, an auxin responsive gene, was downregulated, and JcIAMT1 and JcCHS were upregulated, confirming that auxin biosynthesis may be decreased in low-branch mutant (g) inflorescence. In Arabidopsis, IAMT1 encodes an IAA carboxyl methyltransferase that converts IAA to methyl-IAA ester (MeIAA); the overexpression of MeIAA causes dramatic hyponastic leaf phenotypes [63]. CHS encodes the first enzyme in flavonoid biosynthesis that is considered an auxin transport inhibitor [64]. However, there is no reasonable explanation for the upregulated expression of JcAAO1 and JcCYP83B1 genes, which are also involved in the auxin biosynthetic signaling pathways [65].

The application of 6-BA to inflorescence buds resulted in increased inflorescence branches both in low-branch mutant (g) and WT plants (Additional file 15). In our previous research, 6-BA treatment on WT inflorescence buds significantly promoted total flower number per inflorescence meaning a high-branch inflorescence phenotype, which is positive correlation to 6-BA concentration [49]. Thidiazuron (TDZ), another synthetic compound with cytokinin activity, was also shown to promote initial inflorescence branching in WT plants, although the final branch number will be decreased because of the abortion of flower buds [66]. These results supported that cytokinin is an important regulator in controlling inflorescence branching, which is in agreement with our results of WGCNA analysis. Mutation of several genes involved in the cytokinin metabolic or signaling pathways causes abnormal inflorescence branching phenotypes in Arabidopsis and rice [37,38,39], suggesting that cytokinin might has a conserved role in regulating inflorescence branching in different species.

Conclusions

Using WGCNA, we identified several hub genes involved in the cytokinin metabolic pathway from modules highly associated with inflorescence architecture phenotypes. The application of cytokinin (6-BA) to inflorescence buds induced high-branch inflorescences both in low-branch mutant (g) and WT plants. These results supported that cytokinin is an important regulator and may play vital role in controlling inflorescence branching in Jatropha. Several Arabidopsis homologous genes involved in inflorescence development is significantly differentially expressed in inflorescence buds between mutants and WT plants, indicating that they participate in the regulation of inflorescence architecture in Jatropha. Based on the above results, we speculate that the change of inflorescence branching phenotype of two mutants may result from mutations at one or more loci in genome regions that contain genes involved in cytokinin metabolism and/or in inflorescence development. Our results will be helpful for elucidating the regulatory mechanism of inflorescence architecture in Jatropha.

Methods

Plant growth conditions and cytokinin (6-benzyladenine, 6-BA) treatment on inflorescence buds

The wild-type (WT), duo xiao hua (dxh) and gynoecy (g) mutant were grown in field in the Xishuangbanna Tropical Botanical Garden (XTBG) of the Chinese Academy of Sciences (21° N, 101° E) located in Mengla County, Yunnan Province, China. WT plant has a four-order branch inflorescence under normal growth conditions, occasionally a three- or five-order branch inflorescence. The dxh mutant has a high-branch inflorescence phenotype, a seven-order branch inflorescence, which was derived from a mutagenized population treated with cobalt-60 gamma rays. Its selfing progeny was selected until the stable high-branch inflorescence phenotype. The g mutant has a low-branch inflorescence phenotype, a three-order branch inflorescence, which was derived from a natural variation [51]. The cutting-propagated plants from single plant with stable phenotype were used for the preparation of experimental materials. Two-year-old cutting-propagated plants were grown in a field at 2 × 2 m per plant at the XTBG.

To confirm the effect of cytokinin on inflorescence branching, middle stage inflorescence buds (about 0.8 cm in diameter) that growth approximately 7–10 days from occurrence of invisible inflorescence bud of low-branch mutant (g) and WT plants were selected for once treatment with 1.0 mM 6-BA solution containing 0.05% Tween-20. The 6-BA and mock solutions were sprayed onto inflorescence buds wetting them to the point of run-off, ten inflorescence buds from three to five plants per treatment. After 2–3 weeks, inflorescence phenotypes were surveyed. Seed oil content was measured by using the minispec mq-one Seed Analyzer (Bruker Optik GmbH, Germany) as described previously [43], three replicates for each sample.

Statistics of traits of high-branch inflorescence mutant (dxh) and WT

During the suitable period, the total flower number, female flower number, ratio of female-to-male flowers, fruit number, seed number, seed yield, weight of 100 seeds, and seed oil content per inflorescence/infructescence were surveyed in high-branch inflorescence mutant (dxh) and WT, respectively. In total 33 inflorescence/infructescence are surveyed in dxh and 44 ones in WT. Statistical test analysis was performed using the Welch two sample t-test in R software (https://cran.r-project.org).

Collection of samples, RNA isolation and library construction

At initial reproductive period, after removed leaves, shoot tips that can generate inflorescence meristem were harvested from dxh and WT plants. Inflorescence buds that growth approximately 3–4 days from occurrence of invisible inflorescence bud (about 0.4 cm in diameter) were harvested from dxh and g mutants and WT plants. Three shoot tips or inflorescence buds were pooled as one biological replicate for RNA isolation, three replicates per sample. Total RNA extraction, library construction and quality control were performed as previously described [51]. Sequencing was performed on an Illumina Hiseq 2500 platform by Novogene Bioinformatics Technology (Beijing, China).

De novo transcriptome assembly and read mapping

Raw reads were treated with the Fastq_clean [67] and assessed with FASTQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc). De novo transcriptome assembly was performed using Trinity (version 2.0.6) with default parameters [68, 69]. In all 122,526 sequences were generated. Bowtie version 1.1.1 (−v 2 -m 10) was used for the mapping of the paired-end reads from each library [70].

Identification of differentially expressed transcripts

The Corset (version 1.03) was used for abundance estimation of transcripts [71]. Differentially expressed transcripts (DEGs) with a false discovery rate (FDR) of < 0.05 were identified by using the edgeR package [72]. The Venny (version 2.1) was used for the generation of venn diagram of DEGs (http://bioinfogp.cnb.csic.es/tools/venny/index.html). Hierarchical clustering of transcripts was performed using the pheatmap R package (version 1.0.7) (https://github.com/cran/pheatmap).

Annotation of transcripts

A total of 16,206 filtered transcripts used for differentially expressed analysis were annotated with BLASTX search against the Ensembl Plants database (http://plants.ensembl.org) with Evalue < 1.0E-05. Among them, 14,680 transcripts were annotated and 1526 were not found (Additional file 16). Ten transcripts displayed in Fig. 3 were defined as LncRNAs because they have not annotation, not coding protein and length > 200 bp [52].

Validation of expression profiles of candidate genes by real-time PCR (qPCR)

RNA samples for qPCR are same as RNA-seq ones. The cDNA was synthesized from total RNA (1.0 μg) using a PrimeScript RT Reagent Kit (Takara, Otsu, Japan), for each sample. qPCR was performed on a LightCycler 480 II (Roche, Penzberg, Germany) using the SYBR green I Kit (Roche), with three independent biological replicates for each sample and three technical replicates. JcGAPDH was as the internal reference. Primers for qPCR were listed in Additional file 10. The relative expression levels of genes were calculated by the 2−ΔΔ CT method. Correlation analysis between RNA-Seq and qPCR expression data of the genes is performed with cor.test in R software (https://cran.r-project.org).

Construction and analysis of weighted gene co-expression networks

The raw count data of differentially expressed genes, which were transformed with Log2(x + 1), from edgeR were applied to construct co-expression networks using the R package weighted gene correlation network analysis (WGCNA) [53]. The soft thresholding power of 6 was chosen based on the criterion of approximate scale-free topology. The minimum module size was 30, and modules were merged with the cutoff value of 0.2. The interaction network was visualized using the Cytoscape software [73].

Availability of data and materials

RNA-Seq data from dxh and WT samples were deposited in NCBI under the accession number SRP122257. RNA-Seq data from three g samples were deposited under accession numbers SRR4473569, SRR4473570 and SRR4473575 [51]. Transcriptome sequences referred to the differential expression analysis of genes were listed in Additional file 17.

Abbreviations

6-BA:

6-Benzyladenine

AAO:

Aldehyde oxidase

ADK:

Adenosine kinase

AGL:

Agamous-like

AMP:

Activated protein kinase

AP1:

Apetala1

APT:

Adenine phosphoribosyl transferase

ATP:

Atairp2 target protein

BA1:

Barren stalk1

BIF:

Barren infloresccence

CHS:

Chalcone synthase

CKX:

Cytokinin oxidase/dehydrogenase

CLV:

Clavata

CPM:

Counts per million mapped reads

CR:

Cox-reid profile-adjusted likelihood

CYP:

Cytochrome P450

DEG:

Differentially expressed genes

DMAPP:

Dimethylallylpyrophosphate

DST:

Drought and salt tolerance

DXH:

Duo xiao hua

FC:

Fold change

FDR:

False discovery rate

FEA2:

Fasciated ear2

FMO:

Flavin monooxygenase

FUL:

Fruitful

G:

Gynoecy

GLM:

Generalized linear model

IAA:

Indol-3-acetic acid

IAMT:

IAA carboxylmethyltransferase

IDS:

Indeterminate spikelet

IPA:

Indole-3-pyruvic acid

iPMP:

Isopentenyladenosine 5′-monophosphate

IPT:

Isopentenyltransferase

LFY:

Leafy

LncRNA:

Long noncoding RNA

LOG:

Lonely guy

MDS:

Multidimensional scaling

MeIAA:

Methyl-IAA ester

MIR56:

Microrna156

NAA:

1-Naphthaleneacetic acid

PID:

Pinoid

PIN:

Pin-formed

qPCR:

Real-time quantitative PCR

QTL:

Quantitative trait locus

RA:

Ramosa

RCN:

Rice terminal flower 1/centroradialis homologs

RNA-Seq:

RNA-sequence

SAM:

Shoot apical meristem

SAUR:

Small auxin up RNA

SEP:

Sepallata

SID:

S ister of indeterminate spikelet

SOC1:

Suppressor of overexpression of constans 1

SPI1:

Sparse inflorescence1

SPL:

Squamosa promoter binding protein-like

SPT:

Spatula

SVP:

Short vegetative phase

TAA:

Tryptophanaminotransferase of Arabidopsis

TAW1:

Tawawa1

TD1:

Tassel dwarf1

TDZ:

Thidiazuron

TFL1:

Terminal flower1

TRP:

Tryptophan

UB3:

Unbranched3

WFP:

Wealthy farmer’s panicle

WGCNA:

Weighted gene correlation network analysis

WOX:

Wuschel-related homeobox

WT:

Wild-type

WUS:

Wuschel

YUC:

Yucca

ZCN:

Zea centroradialis

References

  1. Wyatt R. Inflorescence architecture: how flower number, arrangement, and phenology affect pollination and fruit-set. Am J Bot. 1982;69(4):585–94.

    Article  Google Scholar 

  2. Prenner G, Vergara-Silva F, Rudall PJ. The key role of morphology in modelling inflorescence architecture. Trends Plant Sci. 2009;14(6):302–9.

    CAS  PubMed  Article  Google Scholar 

  3. Endress PK. Disentangling confusions in inflorescence morphology: patterns and diversity of reproductive shoot ramification in angiosperms. J Syst Evol. 2010;48(4):225–39.

    Article  Google Scholar 

  4. Ma Q, Zhang W, Xiang Q-Y. Evolution and developmental genetics of floral display—a review of progress. J Syst Evol. 2017;55(6):487–515.

    Article  Google Scholar 

  5. Brand U, Fletcher JC, Hobe M, Meyerowitz EM, Simon R. Dependence of stem cell fate in Arabidopsis on a feedback loop regulated by CLV3 activity. Science. 2000;289(5479):617–9.

    CAS  PubMed  Article  Google Scholar 

  6. Laux T, Mayer KF, Berger J, Jurgens G. The WUSCHEL gene is required for shoot and floral meristem integrity in Arabidopsis. Development. 1996;122(1):87–96.

    CAS  PubMed  Google Scholar 

  7. Schoof H, Lenhard M, Haecker A, Mayer KF, Jurgens G, Laux T. The stem cell population of Arabidopsis shoot meristems in maintained by a regulatory loop between the CLAVATA and WUSCHEL genes. Cell. 2000;100(6):635–44.

    CAS  Article  PubMed  Google Scholar 

  8. Bommert P, Lunde C, Nardmann J, Vollbrecht E, Running M, Jackson D, Hake S, Werr W. thick tassel dwarf1 encodes a putative maize ortholog of the Arabidopsis CLAVATA1 leucine-rich repeat receptor-like kinase. Development. 2005;132(6):1235–45.

    CAS  PubMed  Article  Google Scholar 

  9. Taguchi-Shiobara F, Yuan Z, Hake S, Jackson D. The fasciated ear2 gene encodes a leucine-rich repeat receptor-like protein that regulates shoot meristem proliferation in maize. Genes Dev. 2001;15(20):2755–66.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  10. Thompson BE, Hake S. Translational biology: from Arabidopsis flowers to grass inflorescence architecture. Plant Physiol. 2009;149(1):38–45.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  11. Ratcliffe OJ, Bradley DJ, Coen ES. Separation of shoot and floral identity in Arabidopsis. Development. 1999;126(6):1109–20.

    CAS  PubMed  Google Scholar 

  12. Shannon S, Meeks-Wagner DR. A mutation in the Arabidopsis TFL1 gene affects inflorescence meristem development. Plant Cell. 1991;3(9):877–92.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  13. Ratcliffe OJ, Amaya I, Vincent CA, Rothstein S, Carpenter R, Coen ES, Bradley DJ. A common mechanism controls the life cycle and architecture of plants. Development. 1998;125(9):1609–15.

    CAS  PubMed  Google Scholar 

  14. Liljegren SJ, Gustafson-Brown C, Pinyopich A, Ditta GS, Yanofsky MF. Interactions among APETALA1, LEAFY, and TERMINAL FLOWER1 specify meristem fate. Plant Cell. 1999;11(6):1007–18.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  15. Liu C, Teo ZW, Bi Y, Song S, Xi W, Yang X, Yin Z, Yu H. A conserved genetic pathway determines inflorescence architecture in Arabidopsis and rice. Dev Cell. 2013;24(6):612–22.

    CAS  PubMed  Article  Google Scholar 

  16. Nakagawa M, Shimamoto K, Kyozuka J. Overexpression of RCN1 and RCN2, rice TERMINAL FLOWER 1/CENTRORADIALIS homologs, confers delay of phase transition and altered panicle morphology in rice. Plant J. 2002;29(6):743–50.

    CAS  PubMed  Article  Google Scholar 

  17. Danilevskaya ON, Meng X, Ananiev EV. Concerted modification of flowering time and inflorescence architecture by ectopic expression of TFL1-like genes in maize. Plant Physiol. 2010;153(1):238–51.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  18. Yoshida A, Sasao M, Yasuno N, Takagi K, Daimon Y, Chen R, Yamazaki R, Tokunaga H, Kitaguchi Y, Sato Y, Nagamura Y, Ushijima T, Kumamaru T, Iida S, Maekawa M, Kyozuka J. TAWAWA1, a regulator of rice inflorescence architecture, functions through the suppression of meristem phase transition. Proc Natl Acad Sci. 2013;110(2):767–72.

    CAS  PubMed  Article  Google Scholar 

  19. Jiao Y, Wang Y, Xue D, Wang J, Yan M, Liu G, Dong G, Zeng D, Lu Z, Zhu X, Qian Q, Li J. Regulation of OsSPL14 by OsmiR156 defines ideal plant architecture in rice. Nat Genet. 2010;42(6):541–4.

    CAS  PubMed  Article  Google Scholar 

  20. Miura K, Ikeda M, Matsubara A, Song XJ, Ito M, Asano K, Matsuoka M, Kitano H, Ashikari M. OsSPL14 promotes panicle branching and higher grain productivity in rice. Nat Genet. 2010;42(6):545–9.

    CAS  PubMed  Article  Google Scholar 

  21. Wang L, Zhang Q. Boosting rice yield by fine-tuning SPL gene expression. Trends Plant Sci. 2017;22(8):643–6.

    CAS  PubMed  Article  Google Scholar 

  22. Du Y, Liu L, Li M, Fang S, Shen X, Chu J, Zhang Z. UNBRANCHED3 regulates branching by modulating cytokinin biosynthesis and signaling in maize and rice. New Phytol. 2017;214(2):721–33.

    CAS  PubMed  Article  Google Scholar 

  23. Chuck G, Meeley R, Hake S. Floral meristem initiation and meristem cell fate are regulated by the maize AP2 genes ids1 and sid1. Development. 2008;135(18):3013–9.

    CAS  PubMed  Article  Google Scholar 

  24. Vollbrecht E, Springer PS, Goh L, Buckler Iv ES, Martienssen R. Architecture of floral branch systems in maize and related grasses. Nature. 2005;436(7054):1119–26.

    CAS  PubMed  Article  Google Scholar 

  25. Satoh-Nagasawa N, Nagasawa N, Malcomber S, Sakai H, Jackson D. A trehalose metabolic enzyme controls inflorescence architecture in maize. Nature. 2006;441(7090):227–30.

    CAS  PubMed  Article  Google Scholar 

  26. Bortiri E, Chuck G, Vollbrecht E, Rocheford T, Martienssen R, Hake S. ramosa2 encodes a LATERAL ORGAN BOUNDARY domain protein that determines the fate of stem cells in branch meristems of maize. Plant Cell. 2006;18(3):574–85.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  27. Barazesh S, McSteen P. Hormonal control of grass inflorescence development. Trends Plant Sci. 2008;13(12):656–62.

    CAS  PubMed  Article  Google Scholar 

  28. Kyozuka J. Control of shoot and root meristem function by cytokinin. Curr Opin Plant Biol. 2007;10(5):442–6.

    CAS  PubMed  Article  Google Scholar 

  29. Zhao Y, Christensen SK, Fankhauser C, Cashman JR, Cohen JD, Weigel D, Chory J. A role for flavin monooxygenase-like enzymes in auxin biosynthesis. Science. 2001;291(5502):306–9.

    CAS  PubMed  Article  Google Scholar 

  30. Gallavotti A, Barazesh S, Malcomber ST, Hall D, Schmidt RJ, McSteen P. sparse inflorescence1 encodes a monocot-specific YUCCA-like gene required for vegetative and reproductive development in maize. Proc Natl Acad Sci. 2008;105(39):15196–201.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  31. Okada K, Ueda J, Komaki MK, Shimura Y. Requirement of the auxin polar transport system in early stages of Arabidopsis floral bud formation. Plant Cell. 1991;3(7):677–84.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  32. Bennett S, Alvarez J, Bossinger G. Morphogenesis in pinoid mutants of Arabidopsis thaliana. Plant J. 1995;8:505–20.

    CAS  Article  Google Scholar 

  33. Hake S. Barren inflorescence2 regulates axillary meristem development in the maize inflorescence. Development. 2001;128(15):2881–91.

    PubMed  Google Scholar 

  34. Barazesh S. barren inflorescence1 functions in organogenesis during vegetative and inflorescence development in maize. Genetics. 2008;179(1):389.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  35. Ritter MK, Padilla CM, Schmidt RJ. The maize mutant barren stalk1 is defective in axillary meristem development. Am J Bot. 2002;89(2):203–10.

    PubMed  Article  Google Scholar 

  36. Huang P, Jiang H, Zhu C, Barry K, Jenkins J, Sandor L, Schmutz J, Box MS, Kellogg EA, Brutnell TP. Sparse panicle1 is required for inflorescence development in Setaria viridis and maize. Nature Plants. 2017;3:17054.

    CAS  PubMed  Article  Google Scholar 

  37. 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):652–5.

    CAS  PubMed  Article  Google Scholar 

  38. Bartrina I, Otto E, Strnad M, Werner T, Schmulling 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.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  39. Ashikari M, Sakakibara H, Lin S, Yamamoto T, Takashi T, Nishimura A, Angeles ER, Qian Q, Kitano H, Matsuoka M. Cytokinin oxidase regulates rice grain production. Science. 2005;309(5735):741–5.

    CAS  PubMed  Article  Google Scholar 

  40. 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. 2013;110(8):3167–72.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  41. 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 JA, et al. Sequence analysis of the genome of an oil-bearing tree, Jatropha curcas L. DNA Res. 2011;18(1):65–76.

    CAS  PubMed  Article  Google Scholar 

  42. Fairless D. Biofuel: the little shrub that could--maybe. Nature. 2007;449(7163):652–5.

    PubMed  Article  Google Scholar 

  43. Pan B-Z, Xu Z-F. Benzyladenine treatment significantly increases the seed yield of the biofuel plant Jatropha curcas. J Plant Growth Regul. 2011;30(2):166–74.

    CAS  Article  Google Scholar 

  44. Li C, Fu Q, Niu L, Luo L, Chen J, Xu Z-F. Three TFL1 homologues regulate floral initiation in the biofuel plant Jatropha curcas. Sci Rep. 2017;7:43090.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  45. Li C, Luo L, Fu Q, Niu L, Xu Z-F. Identification and characterization of the FT/TFL1 gene family in the biofuel plant Jatropha curcas. Plant Mol Biol Report. 2015;33(2):326–33.

    CAS  Article  Google Scholar 

  46. Tang MY, Tao YB, Xu ZF. Ectopic expression of Jatropha curcas APETALA1 (JcAP1) caused early flowering in Arabidopsis, but not in Jatropha. PeerJ. 2016;4:e1969.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  47. Ghosh A, Chikara J, Chaudhary DR, 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):307–15.

    CAS  Article  Google Scholar 

  48. Song J, Chen M-S, Li J, Niu L, Xu Z. Effects of soil-applied paclobutrazol on the vegetative and reproductive growth of biofuel plant Jatropha curcas. Plant Diver Resour. 2013;35(2):173–9.

    Google Scholar 

  49. Chen M-S, Pan B-Z, Wang G-J, Ni J, Niu L, Xu Z-F. Analysis of the transcriptional responses in inflorescence buds of Jatropha curcas exposed to cytokinin treatment. BMC Plant Biol. 2014;14(1):318.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  50. Pan B-Z, Chen M-S, Ni J, Xu Z-F. Transcriptome of the inflorescence meristems of the biofuel plant Jatropha curcas treated with cytokinin. BMC Genomics. 2014;15(1):974.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  51. Chen M-S, Pan B-Z, Fu Q, Tao Y-B, Martínez-Herrera J, Niu L, Ni J, Dong Y, Zhao M-L, Xu Z-F. Comparative transcriptome analysis between gynoecious and monoecious plants identifies regulatory networks controlling sex determination in Jatropha curcas. Front Plant Sci. 2017;7:1953.

    PubMed  PubMed Central  Google Scholar 

  52. Chekanova JA. Long non-coding RNAs and their functions in plants. Curr Opin Plant Biol. 2015;27:207–16.

    CAS  PubMed  Article  Google Scholar 

  53. Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 2008;9(1):559.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  54. Moffatt BA, Wang L, Allen MS, Stevens YY, Qin W, Snider J, von Schwartzenberg K. Adenosine kinase of Arabidopsis. Kinetic properties and gene expression. Plant Physiol. 2000;124(4):1775–85.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  55. Schoor S, Farrow S, Blaschke H, Lee S, Perry G, von Schwartzenberg K, Emery N, Moffatt B. Adenosine kinase contributes to cytokinin interconversion in Arabidopsis. Plant Physiol. 2011;157(2):659–72.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  56. Zhang X, Chen Y, Lin X, Hong X, Zhu Y, Li W, He W, An F, Guo H. Adenine Phosphoribosyl Transferase 1 is a key enzyme catalyzing Cytokinin conversion from Nucleobases to nucleotides in Arabidopsis. Mol Plant. 2013;6(5):1661–72.

    CAS  PubMed  Article  Google Scholar 

  57. Kakimoto T. Identification of plant cytokinin biosynthetic enzymes as dimethylallyl diphosphate: ATP/ADP isopentenyltransferases. Plant Cell Physiol. 2001;42(7):677–85.

    CAS  PubMed  Article  Google Scholar 

  58. Schmülling T, Werner T, Riefler M, Krupková E, Bartrina y Manns I. Structure and function of cytokinin oxidase/dehydrogenase genes of maize, rice, Arabidopsis and other species. J Plant Res. 2003;116(3):241–52.

    PubMed  Article  CAS  Google Scholar 

  59. Stepanova AN, Robertson-Hoyt J, Yun J, Benavente LM, Xie DY, Dolezal K, Schlereth A, Jurgens G, Alonso JM. TAA1-mediated auxin biosynthesis is essential for hormone crosstalk and plant development. Cell. 2008;133(1):177–91.

    CAS  PubMed  Article  Google Scholar 

  60. Mashiguchi K, Tanaka K, Sakai T, Sugawara S, Kawaide H, Natsume M, Hanada A, Yaeno T, Shirasu K, Yao H, McSteen P, Zhao Y, Hayashi K, Kamiya Y, Kasahara H. The main auxin biosynthesis pathway in Arabidopsis. Proc Natl Acad Sci. 2011;108(45):18512–7.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  61. Blilou I, Xu J, Wildwater M, Willemsen V, Paponov I, Friml J, Heidstra R, Aida M, Palme K, Scheres B. The PIN auxin efflux facilitator network controls growth and patterning in Arabidopsis roots. Nature. 2005;433(7021):39–44.

    CAS  PubMed  Article  Google Scholar 

  62. Christensen SK, Dagenais N, Chory J, Weigel D. Regulation of auxin response by the protein kinase PINOID. Cell. 2000;100(4):469–78.

    CAS  PubMed  Article  Google Scholar 

  63. Qin G, Gu H, Zhao Y, Ma Z, Shi G, Yang Y, Pichersky E, Chen H, Liu M, Chen Z. An indole-3-acetic acid carboxyl methyltransferase regulates Arabidopsis leaf development. Plant Cell. 2005;17(10):2693–704.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  64. Brown DE, Rashotte AM, Murphy AS, Normanly J, Tague BW, Peer WA, Taiz L, Muday GK. Flavonoids act as negative regulators of auxin transport in vivo in Arabidopsis. Plant Physiol. 2001;126(2):524–35.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  65. Seo M, Akaba S, Oritani T, Delarue M, Bellini C, Caboche M, Koshiba T. Higher activity of an aldehyde oxidase in the auxin-overproducing superroot1 mutant of Arabidopsis thaliana. Plant Physiol. 1998;116(2):687–93.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  66. Pan B-Z, Luo Y, Song L, Chen M-S, Li J-L, Xu Z-F. Thidiazuron increases fruit number in the biofuel plant Jatropha curcas by promoting pistil development. Ind Crop Prod. 2016;81:202–1.

    CAS  Article  Google Scholar 

  67. Zhang M, Zhan F, Sun H, Gong X, Fei Z, Gao S: Fastq_clean: an optimized pipeline to clean the Illumina sequencing data with quality control. In: Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM 2014). Belfast, UK: 2014;44–48.

  68. Grabherr MG, Haas BJ, Yassour M, Levin JZ, Thompson DA, Amit I, Adiconis X, Fan L, Raychowdhury R, Zeng Q, Chen Z, Mauceli E, Hacohen N, Gnirke A, Rhind N, di Palma F, Birren BW, Nusbaum C, Lindblad-Toh K, Friedman N, Regev A. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat Biotechnol. 2011;29(7):644–52.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  69. Haas BJ, Papanicolaou A, Yassour M, Grabherr M, Blood PD, Bowden J, Couger MB, Eccles D, Li B, Lieber M, MacManes MD, Ott M, Orvis J, Pochet N, Strozzi F, Weeks N, Westerman R, William T, Dewey CN, Henschel R, LeDuc RD, Friedman N, Regev A. De novo transcript sequence reconstruction from RNA-seq using the trinity platform for reference generation and analysis. Nat Protoc. 2013;8(8):1494–512.

    CAS  PubMed  Article  Google Scholar 

  70. Langmead B, Trapnell C, Pop M, Salzberg SL. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 2009;10(3):R25.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  71. Davidson NM, Oshlack A. Corset: enabling differential gene expression analysis for de novo assembled transcriptomes. Genome Biol. 2014;15(7):410.

    PubMed  PubMed Central  Google Scholar 

  72. Robinson MD, McCarthy DJ, Smyth GK. edgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26(1):139–40.

    CAS  Article  PubMed  Google Scholar 

  73. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13(11):2498–504.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

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Acknowledgments

The authors gratefully acknowledge the Central Laboratory of the Xishuangbanna Tropical Botanical Garden for providing high-performance computing and other research facilities.

Funding

This work was supported by funding from the National Natural Science Foundation of China (31670612, 31370595, 31300568, and 31500500), the Program of Chinese Academy of Sciences (kfj-brsn-2018-6-008), and the CAS 135 program (2017XTBG-T02). Funding bodies were not involved in the design of the study, data collection and analysis, or interpretation of the manuscript.

Author information

Authors and Affiliations

  1. CAS Key Laboratory of Tropical Plant Resources and Sustainable Use, Xishuangbanna Tropical Botanical Garden, The Innovative Academy of Seed Design, Chinese Academy of Sciences, Menglun, Mengla, 666303, Yunnan, China

    Mao-Sheng Chen, Mei-Li Zhao, Gui-Juan Wang, Hui-Ying He, Xue Bai, Bang-Zhen Pan, Qian-Tang Fu, Yan-Bin Tao, Ming-Yong Tang & Zeng-Fu Xu

  2. Center of Economic Botany, Core Botanical Gardens, Chinese Academy of Sciences, Menglun, Mengla, 666303, Yunnan, China

    Mao-Sheng Chen, Hui-Ying He, Bang-Zhen Pan, Qian-Tang Fu, Yan-Bin Tao, Ming-Yong Tang & Zeng-Fu Xu

  3. College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China

    Mei-Li Zhao & Xue Bai

  4. Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias, Campo Experimental Huimanguillo, Huimanguillo, Tabasco, Mexico

    Jorge Martínez-Herrera

Authors
  1. Mao-Sheng Chen
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  2. Mei-Li Zhao
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  3. Gui-Juan Wang
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  4. Hui-Ying He
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  5. Xue Bai
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  6. Bang-Zhen Pan
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  7. Qian-Tang Fu
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  8. Yan-Bin Tao
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  9. Ming-Yong Tang
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  10. Jorge Martínez-Herrera
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  11. Zeng-Fu Xu
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Contributions

Z-FX and M-SC designed the experiments and wrote the manuscript. M-SC analyzed the data and drafted the manuscript. M-SC and G-JW mutated Jatropha to obtain the desired mutant. M-SC, M-LZ, H-YH, XB, B-ZP, QF, Y-BT, MT and JMH carried out experiments. All authors reviewed the final manuscript. All authors agree to be accountable for the content of the work.

Corresponding author

Correspondence to Zeng-Fu Xu.

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Supplementary information

Additional file 1.

Sequencing read counts, quality, and alignment statistics for 15 Jatropha samples.

Additional file 2.

Relationships of 15 inflorescence bud samples based on multidimensional scaling (MDS) analysis. The MDS plot is generated using plotMDS function in edgeR package. The distances between samples correspond to biological coefficient of variation (BCV) between those samples. ckI indicates the shoot tips of WT, containing ckI_1, ckI_2 and ckI_3 samples; ckII indicates the inflorescence buds of WT, containing ckII_1, ckII_2 and ckII_3 samples; dxhI indicates the shoot tips of dxh mutant, containing dxhI_1, dxhI_2 and dxhI_3 samples; dxhII indicates the inflorescence buds of dxh mutant, containing dxhII_1, dxhII_2 and dxhII_3 samples; and gII indicates the inflorescence buds of g mutant, containing gII_1, gII_2 and gII_3 samples, respectively.

Additional file 3.

Differentially expressed genes in inflorescences between six pairs in Jatropha. The pairwise ckII_ckI, dxhII_dxhI, dxhI_ckI, dxhII_ckII, gII_ckII and gII_dxhII indicate the same pairs shown in Fig. 3; blue lines indicate genes with a two-fold change; red points indicate genes with significantly different expression at a false discovery rate (FDR) of < 0.05. FC, fold change; CPM, counts per million mapped reads.

Additional file 4.

List of differentially expressed genes identified in inflorescences between six pairs in Jatropha. The pairwise ckII_ckI, dxhII_dxhI, dxhI_ckI, dxhII_ckII, gII_ckII and gII_dxhII indicate the same pairs shown in Fig. 3.

Additional file 5.

The overlap of differentially expressed genes in inflorescences between six pairs in Jatropha. The pairwise ckII_ckI, dxhII_dxhI, dxhI_ckI, dxhII_ckII, gII_ckII and gII_dxhII indicate the same pairs shown in Fig. 3. All differentially expressed transcripts in Additional file 5 were listed in Additional file 4.

Additional file 6.

Differentially expressed genes with opposite expression patterns in inflorescences between pairwise dxhII_ckII and gII_dxhII.

Additional file 7.

Homologous genes involved in inflorescence development in Jatropha.

Additional file 8.

Homologous genes involved in the auxin and cytokinin metabolic and signaling pathways in Jatropha.

Additional file 9.

Validation of the expression profiles of 12 candidate genes by real-time qPCR. ckI and dxhI indicate the shoot tips of WT plants and dxh mutants; ckII, dxhII and gII indicate inflorescence buds of WT, dxh and g mutants, respectively. JcGAPDH was as the internal reference. The error bars represent SD (n = 3).

Additional file 10.

List of primers for qPCR validation.

Additional file 11.

Correlation analysis between RNA-Seq and qPCR expression data of the genes shown in Additional file 9. The correlation analysis is performed with cor.test in R software. FC, fold change.

Additional file 12.

Weighted co-expression network and modules identified by WGCNA analysis. Each leaf in the hierarchical cluster tree represents one gene; each row and column of the heat map plot corresponds to one gene; in the heat map, light color indicate weak co-expression, and dark color indicates strong co-expression; twenty-two modules were labeled by different colors.

Additional file 13.

Correlation analysis of modules and phenotype traits. Each row and column represent one module; the red color represents high adjacency (positive correlation), and blue color represents low adjacency (negative correlation); red squares along the diagonal indicate the modules with similar expression patterns.

Additional file 14.

Gene list in MEblack and MEblue modules.

Additional file 15.

Application of 6-benzylaminopurine (6-BA) promotes inflorescence branching of g mutants and WT plants. (A)-(D) show the complete inflorescence, and (E)-(H) show the dissected inflorescence, respectively. (A) and (E) indicate an increased inflorescence branching treated with 6-BA and (B) and (F) display a normal inflorescence branching of g mutants with mock. (C) and (G) indicate an increase inflorescence branching treated with 6-BA and (D) and (H) display a normal inflorescence branching of WT plants with mock. The numbers in (E)-(H) represent different orders of inflorescence branching. Bar = 5.0 cm.

Additional file 16.

Annotation of transcripts.

Additional file 17.

Transcripts sequences used for the differential expression analysis.

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Chen, MS., Zhao, ML., Wang, GJ. et al. Transcriptome analysis of two inflorescence branching mutants reveals cytokinin is an important regulator in controlling inflorescence architecture in the woody plant Jatropha curcas. BMC Plant Biol 19, 468 (2019). https://doi.org/10.1186/s12870-019-2069-3

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Keywords

  • Branching
  • Cytokinin
  • Inflorescence
  • Physic nut
  • WGCNA analysis

BMC Plant Biology

ISSN: 1471-2229

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