Abstract
Background
Floral transition plays an important role in development, and proper
time is necessary to improve the value of valuable ornamental trees.
The molecular mechanisms of floral transition remain unknown in
perennial woody plants. “Bairihua” is a type of C. bungei that can
undergo floral transition in the first planting year.
Results
Here, we combined short-read next-generation sequencing (NGS) and
single-molecule real-time (SMRT) sequencing to provide a more complete
view of transcriptome regulation during floral transition in C. bungei.
The circadian rhythm-plant pathway may be the critical pathway during
floral transition in early flowering (EF) C. bungei, according to
horizontal and vertical analysis in EF and normal flowering (NF) C.
bungei. SBP and MIKC-MADS-box were seemingly involved in EF during
floral transition. A total of 61 hub genes were associated with floral
transition in the MEturquoise model with Weighted Gene Co-expression
Network Analysis (WGCNA). The results reveal that ten hub genes had a
close connection with the GASA homologue gene (Cbu.gene.18280), and the
ten co-expressed genes belong to five flowering-related pathways.
Furthermore, our study provides new insights into the complexity and
regulation of alternative splicing (AS). The ratio or number of
isoforms of some floral transition-related genes is different in
different periods or in different sub-genomes.
Conclusions
Our results will be a useful reference for the study of floral
transition in other perennial woody plants. Further molecular
investigations are needed to verify our sequencing data.
Keywords: Floral transition, RNA sequencing, WGCNA, Early flowering,
Catalpa bungei
Background
Floral transition is the developmental process by which a plant
transitions from vegetative growth to reproductive growth. During this
process, inflorescence primordia instead of leaf primordia develop from
the shoot apical meristem (SAM) [[45]1–[46]3]. Great progress has been
made in understanding the factors that trigger floral transition
[[47]4]. A set of floral transition-related genes, such as SPL
(Squamosa-promoter binding protein-like) [[48]5–[49]7], TOC (Timing of
cab expression 1) [[50]8], LUX (Luxarrhythmo) [[51]8], PIF (Phytochrome
interacting factor) [[52]9], CO (constans) [[53]10], FRI (Frigida)
[[54]11], GA20ox (GA20oxidases) [[55]7], GA3ox (GA3oxidases) [[56]12],
SOC1 (Suppressor of overexpression of constans 1) [[57]13], have been
detected, in addition to others [[58]14, [59]15]. These genes are
mainly categorized into five major pathways that regulate floral
transition, including the age pathway, photoperiod and circadian clock
pathway, autonomous pathway, vernalisation pathway and GA pathway
[[60]4]. These genes are independent and closely related to each other,
forming sophisticated gene regulatory networks (GRNs) [[61]1, [62]16].
For example, SPL is involved in inducing the expression of flowering
integrator genes, namely, LEAFY (LFY) and APETALA1 (AP1), thereby
triggering flowering [[63]17]. TOC and LUX are the critical genes in
circadian rhythms pathway [[64]18, [65]19]. In term of the feed-back
loop, TOC1 can either directly or indirectly regulate CCA1 and LHY,
which in turn suppress TOC1 expression by binding to its regulatory
region [[66]20, [67]21]. The circadian clock gene LUX affects flowering
by forming the evening complex (EC) with EARLY FLOWERING 3 (ELF3) and
ELF [[68]22]. FRI controls flowering by regulating the expression of
the floral transition of floral repressor FLC, which encodes a MADS-box
protein [[69]11]. CO promotes flowering by directly activating the
expression of its downstream genes including FT and SOC1 [[70]23]. SOC1
is also regulated by active GA in the gibberellin pathway and
positively regulated by SPL in the age pathway [[71]24]. However, most
of these studies were focused on annual herbaceous model plants, such
as Arabidopsis [[72]25] and Rice [[73]26]. In perennial woody plants,
the studies involved in floral transition are still in their infancy
[[74]27, [75]28]. Few studies have been conducted on floral transition
in trees, partly due to the long juvenile phase and the difficulty in
distinguishing vegetative buds from flowering buds at the beginning of
the budding phase of trees. Catalpa bungei. C.A. Mey (C. bungei,
Family: Bignoniaceae) is an important ornamental tree species in China
[[76]29, [77]30]. C. bungei not only has good woody properties but is
also famous for its beautiful flowers. The commercial value of this
species is largely related to its flowering time. The optimum flowering
time greatly affects the quality of C. bungei. C. bungei is a perennial
tree that undergoes its first floral transition in the fifth year or
more of planting. However, an early flowering (EF), the new natural
variety of C. bungei, was found to undergo floral transition in the
first planting year, and almost 100% of its buds were mixed buds, which
is very rare for woody plants ([78]http://www.forestry.gov.cn/). At
present, the research on C. bungei mainly focuses on the development of
wood and flower organs [[79]29–[80]32], and the study of the flowering
of C. bungei is just beginning. The EF variety, which only develops
mixed buds, solves the problem of material selection and provides an
opportunity to evaluate the floral transition process in perennial
ornamental woody plants.
Next-generation sequencing (NGS) technologies have become a powerful
tool for describing gene expression levels. However, NGS is limited by
the necessity of short reads during library construction [[81]33].
Single-molecule real-time (SMRT) sequencing technology overcomes this
limitation by generating kilobase-sized sequencing reads [[82]34]. The
combination of NGS and SMRT approaches not only enables the overall
transcript level of each gene to be analysed but also provides vital
insight into alternative splicing (AS) events [[83]35], which have
fundamental roles in a wide range of plant growth and development
processes [[84]36–[85]41]. In particular, the AS of genes, such as FT,
FLC, and PRR, regulates floral transition [[86]19, [87]20, [88]40,
[89]42–[90]46].
The NGS and SMRT sequencing platform was used to further investigate
the genes involved in floral transition. In this study, we analysed the
data from three perspectives, namely, horizontal analysis, vertical
analysis and WGCNA. A total of 61 hub genes that may be associated with
floral transition in C. bungei were mined. Several potential protein
interactions were found by regulatory network analysis. The complexity
of AS events in the EF and NF varieties was addressed via SMRT
sequencing. More than 50% of the identified genes had multiple
structures. This work provides a guideline for future studies on how
woody plants regulate the expression of key genes during floral
transition.
Results
Grouping of the buds from EF variety and NF variety
An EF variety was used to study floral transition. A NF variety was
used as a control (Fig. [91]1a). The EF buds were subgrouped into three
consecutive differentiation stages, namely, vegetative buds (Vb),
transition buds (Tb), and reproductive buds (Rb), according to their
anatomical structure (Fig. [92]1b). In the Vb, the reproductive shoot
apex was still invisible. In the Tb, the reproductive shoot apexes had
initiated. In the Rb, the development of the reproductive shoot apex
had completed, and the differentiated sepals, petals, pistils, etc.
were observed. The NF buds were always Vb morphologically. However, we
subgrouped them artificially into the three stages according to the
corresponding collection date for the control. Since the molecular
regulation of floral transition begins far before morphological changes
occur, many critical molecular regulations should have already occurred
in the Vb [[93]29, [94]31, [95]47].
Fig. 1.
[96]Fig. 1
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Photos and internal morphology of the EF and NF buds in the first
planting year. a Photos of the EF and NF buds in the first planting
year. Pictures are the EF phenotype (top) and NF phenotype (top). Early
flowering (EF), Normal flowering (NF).b Internal morphology of the EF
and NF buds. Sections of the buds from the EF and NF varieties. EF-Vb,
photo of the vegetative buds from the EF variety; EF-Tb, photo of the
transition buds from the EF variety; EF-Rb, photo of the reproduction
buds from the EF variety; NF-Vb, photo of the vegetative buds from the
NF variety; NF-Tb, photo of the transition buds from the NF variety;
NF-Rb, photo of the reproduction buds from the NF variety. Vegetative
buds (Vb), transition buds (Tb), and reproductive buds (Rb). Early
flowering (EF), Normal flowering (NF)
Illumina-based RNA and SMRT sequencing and assembly
To explore the molecular regulation during floral transition in C.
bungei, we carried out NGS and SMRT sequencing for the stem apical
buds. The stem apical buds (Vb, Tb and Rb) from the EF and NF varieties
were prepared for NGS. Each group had three biological replicates. A
total of 18 mRNA samples were subjected to 2*150 bp paired-end
sequencing using the HiSeq 4000 platform, which produced more than 13G
of clean reads (Table [98]S1). Subsequently, the RNA samples were
pooled according to EF and NF for SMRT sequencing. The full-length
cDNAs of these samples were sequenced and constructed using the PacBio
RS II platform. In total, 13 SMRT cells and 16 SMRT cells were used for
the EF and NF mixed samples, respectively, with three size fractions,
namely, 1–2 kb, 2–3 kb, and > 3 kb. The mean ReadsOfInsert lengths
produced in the EF and NF samples were 2702 bp and 4028 bp,
respectively. ReadsOfInserts were composed of 261,651 full-length
non-chimeric reads and 175,647 non-full-length reads in EF and 122,967
full-length reads and 339,065 non-full-length reads in NF. The average
lengths of the full-length non-chimeric reads were 2592 bp and 2605 bp
in EF and NF, respectively. The non-full-length transcripts and the
full-length transcripts were classified based on the presence of 5′
primers, 3′ primers and poly(A) tails reaching near-saturation of gene
discovery (Table [99]S2, Fig. [100]S1, Fig. [101]S2). The transcript
length distributions generated by these two platforms showed that
approximately 88% of the assembled transcripts from the Illumina
platform and 11% of the transcripts from the SMRT reads were < 600
bases (Fig. [102]S3A)[.] A total of 22,934 annotated genes were
detected by Illumina RNA-seq. In contrast, 14,753 EF and 15,212 NF
annotated genes were detected by SMRT sequencing. Of the annotated
genes, 11,631 genes were found by both Illumina and SMRT. A total of
6628 genes were identified only by Illumina, and 1450 genes were
identified only by SMRT, i.e., 383 EF-specific genes, 489 NF-specific
genes and 578 common genes in both the EF and NF varieties (Fig.
[103]S3B). The high sensitivity of SMRT makes it possible to detect the
alternative polyadenylation (APA) in the transcriptome high-throughput
data. In our experiment, of the 36,935 genes detected by SMRT, 13,843
transcripts had one poly (A) site, while 1962 genes had at least five
poly (A) sites (Fig. [104]S3C). These APAs could increase transcriptome
complexities, subsequently affecting post-transcriptional regulation.
Differential gene expression during floral transition
To characterize the expression profiles of the 14,231 EF DEGs and 7378
NF DEGs, the expression data υ (from Vb to Tb and Tb to Rb) were
normalized to 0, log[2]^(Tb/Vb), and log[2]^(Rb/Vb). In total, all the
DEGs clustered into eight profiles based on STEM analysis (Fig. [105]2a
and Fig. [106]S4A). It was assumed that the DEGs obtained from the
vertical analysis between EF-Vb and EF-Tb were mainly associated with
floral transition. In our data, genes belonging to Profile 3 and
Profile 4 showed no significant difference between EF-Vb and EF-Tb.
Therefore, Profiles 0, 1, 2, 5, 6, and 7 were chosen for subsequent
analyses (Fig. [107]2b). Profiles 0, 1, and 2 were downregulated
between Vb and Tb in the EF buds and contained 427, 568 and 4286 DEGs,
respectively. Profiles 5, 6, and 7 were upregulated between Vb and Tb
in the EF buds and contained 4268, 627 and 272 DEGs, respectively.
Fig. 2.
[108]Fig. 2
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Analysis of differential gene expression during floral transition of
the EF variety. a Venn diagram analysis of the number of DEGs between
EF-Vb vs EF-Tb, EF-Tb vs EF-Rb and EF-Vb vs EF-Rb. EF-Vb, the data of
vegetative buds from the EF variety; EF-Tb, the data of the transition
buds from the EF variety; EF-Rb, the data of the reproduction buds from
the EF variety. b The 8 significant expression profiles during floral
transition of EF. c Partial KEGG pathways associated with floral
transition of EF. The longitudinal axis represents the percent of the
number of genes. The horizontal axis represents the pathway names. The
dark blue rectangle indicates the data were from Profile 0. The red
rectangle indicates the data were from Profile 1. The green rectangle
indicates the data were from Profile 2. The purple rectangle indicates
the data were from Profile 5. The light blue rectangle indicates the
data were from Profile 6. The orange rectangle indicates the data were
from Profile 7
All the DEGs in EF buds that belonged to profiles 0, 1, 2, 5, 6 and 7
were subjected to KEGG pathway enrichment analysis (Table [110]S3). The
KEGG pathways associated with plant floral transition are listed in
Fig. [111]2c. Plant-pathogen interaction (ko04626), plant hormone
signal transduction (ko04075), microbial metabolism in diverse
environments (ko01120), starch and sucrose metabolism (ko00500) and
circadian rhythm-plant (ko04712) were significantly enriched in all six
profiles. Plant-pathogen interaction (ko04626) was significantly
enriched in Profile 5, plant hormone signal transduction was
significantly enriched in Profile 2, starch and sucrose metabolism was
significantly enriched in Profile 7 and circadian rhythm-plant was
significantly enriched in Profile 0. Most of the pathways, such as
photosynthesis (ko00195), brassinosteroid biosynthesis, and anthocyanin
biosynthesis, were not enriched in all six profiles. The photosynthesis
and anthocyanin biosynthesis pathways were obviously enriched obviously
in Profile 7. Brassinosteroid biosynthesis was obviously enriched in
Profile 6. In addition, the photosynthesis-antenna proteins pathway was
only enriched in Profile 2. The high expression of the circadian
rhythm-plant pathway in EF-Vb implied that circadian rhythm-related
genes may promote the activation of related downstream pathways,
eventually leading to early flowering. In addition, the KEGG pathway
enrichment results of DEGs in NF buds were mainly related to
carbohydrate metabolism and energy metabolism, and no related plant
floral transition pathways were found (Fig. [112]S4B).
Gene sets differentially expressed between the EF and NF buds
To investigate the DEGs that might lead to floral transition,
horizontal analysis was performed between EF and NF. In total, 4584
genes exhibited significantly higher expression and 4351 genes
exhibited significantly lower expression at different stages in EF
compared to NF. There were 1905 DEGs between EF-Vb and NF-Vb (including
65 upregulated and 34 downregulated TFs). There were 5438 DEGs between
EF-Tb and NF-Tb (including 217 upregulated and 235 downregulated TFs).
There were 1593 DEGs between EF-Rb and NF-Rb (including 14 upregulated
and 23 downregulated TFs) (Fig. [113]3a).
Fig. 3.
[114]Fig. 3
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Differential gene expression in EF compared to NF at different stages
of floral transition. a The number of upregulated (upper bars) and
downregulated (lower bars) genes at each stage of floral transition in
EF compared to NF is given. I indicates EF-Vb vs NF-Vb; II indicates
EF-Tb vs NF-Tb; III indicates EF-Rb vs NF-Rb. The number of TFs up- or
downregulated at each stage of floral transition is also given. EF-Vb,
the number of genes of vegetative buds from the EF variety; EF-Tb, the
number of genes of the transition buds from the EF variety; EF-Rb, the
number of genes of the reproduction buds from the EF variety; NF-Vb,
the number of genes of vegetative buds from the NF variety; NF-Tb, the
number of genes of the transition buds from the NF variety; NF-Rb, the
number of genes of the reproduction buds from the NF variety. b KEGG
analysis of at different stages of floral transition in down- and
upregulated genes in EF. The colour scale at the bottom represents
significance (corrected P-value). c Partial TF families showing
up-regulation or down-regulation at different stages during floral
transition between EF and NF
TFs are critical for development transition in plants [[116]48,
[117]49]. In our data, 58 TF families were significantly differentially
expressed in EF compared to NF during floral transition (Table
[118]S4). Thirteen of the 58 TF families, such as B3 [[119]50], bHLH
[[120]51], GRAS [[121]52, [122]53], ARF [[123]54], AP2 [[124]55], SBP
[[125]6] have been reported as important developmental regulators (Fig.
[126]3b). GRAS, HSF, NAC and MYB-related genes showed significant
enrichment in EF/NF-Tb-UP. MYB, bHLH, and GATA showed significant
enrichment in EF/NF-Rb-UP. In addition, C3H and SBP showed significant
enrichment in EF/NF-Vb. Furthermore, all SBPs were only enriched via
upregulation in the EF compared to NF in vegetative buds. This implies
that the SBP family might relate with the early floral transition in
EF, similar to the function of SBP in other plants during floral
transition [[127]7, [128]31, [129]56–[130]62].
The DEGs were assigned to 67 KEGG pathways. The top 20 pathways are
presented in (Table [131]S5). Enrichment analysis suggested circadian
rhythm-plant (ko04712) and ubiquitin mediated proteolysis (ko04120)
were significantly enriched in Vb, while photosynthesis-antenna
proteins (ko00196), nitrogen metabolism (ko00910) and plant−pathogen
interaction (ko04626) were significantly enriched in Tb (Fig. [132]3c).
These results combined with data from the vertical analysis, further
supported the idea that the circadian rhythm-plant pathway was critical
during floral transition.
Identification of conserved and/or divergent gene co-expression modules
WGCNA was performed to obtain a comprehensive understanding of genes
expressed in the successive developmental stages of EF and NF and to
identify the genes that might be associated with floral transition.
After filtering out the genes with low expression (FPKM < 0.05), 34,483
genes were retained for WGCNA. Co-expression networks were constructed
on the basis of pair-wise correlations of gene expression across all
samples. Modules were defined as clusters of highly interconnected
genes, and genes within the same cluster had high correlations.
Correlated expression profiles imply that the genes operate in
collaboration or in related pathways and that they contribute together
to a given phenotype [[133]63]. Our analysis identified 11 distinct
modules (labelled with different colours), which are defined by major
tree branches (Fig. [134]S5). The number of genes in the modules ranged
from 81 to 11,700. Four modules were highly expressed in one sample:
MEdarkturquoise was highly associated with EF-Vb; MElightgreen was
highly associated with NF-Vb; MEturquoise was highly associated with
EF-Tb, and MEdarkgrey was highly associated with NF-Rb (Fig. [135]4a).
Fig. 4.
[136]Fig. 4
[137]Open in a new tab
Co-expression networks during floral transition in EF and NF. a
Module-trait associations were evaluated by correlations between MEs,
and traits are shown. The left panel shows the 11 modules and the
number of member genes. The colour scale on the right shows
module-trait correlations from − 1 (Green) to 1 (Blue). EF-Vb
represents the vegetative buds from early flowering, as a trait; EF-Tb
represents the transition buds from early flowering, as a trait; EF-Rb
represents the reproductive buds from early flowering, as a trait;
NF-Vb represents the vegetative buds from natural flowering, as a
trait; NF-Tb represents the transition buds from natural flowering, as
a trait; NF-Rb represents the reproductive buds from natural flowering,
as a trait. A high degree of correlation between a specific module and
the trait is indicated when the module name is highlighted in red. b
Heatmaps showing genes in the modules that were significantly
over-represented in EF-Vb
To explore the significance of the modules, correlations between the
MEs and the three developmental periods were analysed. As the molecular
regulation of floral transition starts before morphology changes occur,
genes should have already changed in the vegetative stage to direct
floral transition. The genes associated with floral transition should
exhibit differential expression in Vb (Fig. [138]4b). Based on this
principle, MEdarkturquoise was considered the main module of interest.
In total, 1223 genes were included in the MEdarkturquoise module, among
which 677 genes were known genes, and 564 genes were new genes (Table
[139]S6). To validate the accuracy of the transcriptome analysis
results, 8 unigenes were selected for qRT-PCR confirmation. The
expression profiles of the candidate unigenes revealed using qRT-PCR
data were consistent with those derived from sequencing (Fig. [140]S6).
To study the relationship between these genes and floral transition
more accurately, the top 10% of the genes were selected according to
the correlation results. Sixty-one of these genes were annotated as hub
genes involved in floral transition (Table [141]2, Table [142]S7). The
61 hub genes were classed into the five floral regulation pathways,
namely, the age pathway (Cbu.gene.9773 and Cbu.gene.16991, SPL
homologous genes), autonomous pathway Cbu.gene.669, FCA homologous
gene; Cbu.gene.14804, FY homologous genes), verbalization pathway
(TCONS_00014487, FRI homologous genes), GA pathway (Cbu.gene.15447,
GA20ox homologous genes; Cbu.gene.1698, GA3ox homologous genes) and
photoperiod and circadian clock pathway (Cbu.gene.21497 PIF homologous
gene; Cbu.gene.12567, LUX homologous genes; Cbu.gene.7628, CO
homologous genes). In addition, several floral integrators, such as
SOC1 and AP2-like, and several hormone relation factors, including
Cbu.gene.26092 and Cbu.gene.26299 (ARF homologous genes) (Fig. [143]5,
Table [144]1), were detected. Subsequently, we analysed the regulatory
network of the 61 hub genes in the MEturquoise module. Thirty-eight TFs
were annotated from the regulatory network. Accordingly, the
MIKC-MADS-box was shown to be highly related to floral transition
[[145]64–[146]67].
Table 2.
Protein interactions predicted with CbuGASA online
Gene ID Annotation Gene ID Annotation Delta G (binding free
energy)/kcal/mol Kd (dissociation constant)/M
Cbu_mRNA.18280 GASA Cbu_mRNA.15447 GA20ox −14.76 1.98E-12
Cbu_mRNA.18280 GASA Cbu_mRNA.9773 SBP −15.85 2.39E-12
Cbu_mRNA.18280 GASA TCONS_00030167 FY −15.40 5.08E-12
Cbu_mRNA.18280 GASA TCONS_00018857 TOC −14.57 2.08E-11
Cbu_mRNA.18280 GASA Cbu_mRNA.12567 LUX −14.56 2.09E-11
Cbu_mRNA.18280 GASA Cbu_mRNA.16991 SBP −13.40 1.48E-10
Cbu_mRNA.18280 GASA Cbu_mRNA.20253 LUX −13.18 2.17E-10
Cbu_mRNA.18280 GASA Cbu_mRNA.669 FCA −12.99 2.99E-10
Cbu_mRNA.18280 GASA Cbu_mRNA.14804 FY −12.66 5.19E-10
Cbu_mRNA.18280 GASA Cbu_mRNA.1698 GA3ox −12.16 1.21E-09
Cbu_mRNA.11030 PIF4 Cbu_mRNA.15447 GA20ox −11.05 7.88E-09
[147]Open in a new tab
Fig. 5.
[148]Fig. 5
[149]Open in a new tab
Correlation networks analysis of the top hub genes in MEdarkturquoise
module. Yellow ovals represent the pathway names and rectangles of
different colours represent individual genes. The genes in the purple
shade were assigned to the photoperiod and circadian clock pathway. The
genes in the green shade may belong to the age pathway. The genes in
the brown shade may belong to the autonomous pathway. The genes in the
grey shade may belong to the GA pathway. The genes in the blue shade
may belong to the vernalisation pathway. The genes in the dark green
shade may belong to the hormone relations factors. The genes in the
yellow shade may belong to the floral integrator pathway
Table 1.
Summary of the 61 hub genes involving floral transition from the
MEdarkturquoise module
Pathway Gene ID
Age pathway TCONS_00052651 TCONS_00057653 TCONS_00027094
Cbu_mRNA.23831 Cbu_mRNA.26743 TCONS_00052052
Cbu_mRNA.9773 Cbu_mRNA.16991 Cbu_mRNA.2336
Cbu_mRNA.8594
Photoperiod and Circadian clock pathway Cbu_mRNA.14926 TCONS_00052455
TCONS_00053697
Cbu_mRNA.7628 Cbu_mRNA.5793 TCONS_00014648
Cbu_mRNA.11030 TCONS_00018857 Cbu_mRNA.21497
TCONS_00044354 TCONS_00030163 Cbu_mRNA.20253
Cbu_mRNA.12567 Cbu_mRNA.22384
Autonomous pathway Cbu_mRNA.14804 TCONS_00016820 TCONS_00031182
TCONS_00003671 TCONS_00034304 TCONS_00042531
Cbu_mRNA.669 TCONS_00030167 Cbu_mRNA.9987
Verbalization pathway TCONS_00014487 TCONS_00042864
Hormome related pathway Cbu_mRNA.1698 Cbu_mRNA.26092 Cbu_mRNA.27590
Cbu_mRNA.15447 Cbu_mRNA.26089 Cbu_mRNA.11067
Cbu_mRNA.13568 Cbu_mRNA.5910 Cbu_mRNA.27401
Cbu_mRNA.14406 TCONS_00007773 TCONS_00038537
TCONS_00039927 Cbu_mRNA.26299 Cbu_mRNA.13803
TCONS_00012445 Cbu_mRNA.25413 TCONS_00012127
Cbu_mRNA.22816
Others Cbu_mRNA.18280 Cbu_mRNA.9758 TCONS_00053100
Cbu_mRNA.13118 TCONS_00020053 TCONS_00038570
TCONS_00012131
[150]Open in a new tab
Interestingly, 10 out of the 61 hub genes had a close connection with
Cbu.gene.18280, which was annotated as a GASA homologous gene (Fig.
[151]S7). According to WGCNA analysis, GASA was predicted to have high
connectivity with CbuSPL (age pathway), CbuFCA and CbuFY (autonomous
pathway), CbuGA3ox and CbuG20ox (GA pathway) and CbuTOC1 and CbuLUX
(photoperiod and circadian clock pathway). In addition, CbuPIF4
(photoperiod pathway) and CbuGA20ox (GA pathway) can affect the floral
transition by promoting the expression of CbuSOC1 (Fig. [152]6).
However, floral transition is a very complicated process in C. bungei
and needs to be further verified.
Fig. 6.
Fig. 6
[153]Open in a new tab
Hypothetical model for the networks of floral transition in C. bungei.
Hypothetical model for the networks of floral transition in C. bungei.
The red dotted lines show the new findings in this study, but
relationships are uncertain and need further experiments
To verify the intersection results of GASA, we performed
protein-protein interaction analysis
([154]http://www.iitm.ac.in/bioinfo/PPA_Pred/prediction.html#). The
dissociation constants (Kd), as well as on- and off-rates (k[on] and
k[off]) less than 10^− 9, were set to predict protein binding. The
protein interaction prediction results were highly consistent with the
WGCNA results (Table [155]2).
To further study the correlation of CbuGASA and the 8 known hub genes
(Table [156]2), we analysed the correlation coefficients of these mRNAs
between the EF and NF samples during three developmental periods. Based
on all trends, CbuGASA with 6 of 8 known hub genes (CbuGA3ox, CbuSPLs,
CbuLUXs, CbuFCA) exhibited a positive correlation (r > 0.75) (Table
[157]S8). During the flowering process, the expression levels of mRNAs
were significantly higher in the EF-Vbs than in the NF-Vbs, and
gradually decreased with age. These results were consistent with the
expression patterns of the homologous genes in Arabidopsis.
Analysis of AS events from hub genes during floral transition development in
C. bungei
Understanding AS events plays an extremely important role in
understanding protein diversity [[158]35, [159]68, [160]69]. On the
basis of obtaining high-quality full-length isoforms, we performed a
systematic analysis of AS in C. bungei. A total of 79,356 AS events
from 25,662 mRNAs and 69,775 AS events from 25,046 mRNAs were detected
in the two pools by SMRT. These AS events could be classified into five
major types, namely, SE, IR, A5, A3, and AE [[161]70]. However, no AE
type was detected by Illumina (Fig. [162]7a). For both datasets, IR
events showed the highest proportion in the EF and NF buds; SE events
were the least frequent AS type in both the EF and NF buds. In
addition, the percentage of the other more complicated AS types was
greater in EF than in NF (Fig. [163]7a, Table [164]S9). The AS event
data from EF were compared with those from NF. A total of 23,851 and
22,936 common AS events from 9521 genes were detected in the EF and the
NF buds. A total of 46,839 AS events associated with 15,526 genes were
identified only in NF, and 55,505 AS events from 16,141 genes were
detected only in EF (Table [165]S10, Table [166]S11). This result
showed that SMRT is more accurate than Illumina for AS detection.
Fig. 7.
[167]Fig. 7
[168]Open in a new tab
Characterization of AS events and validation of isoforms using reverse
transcription polymerase chain reaction (RT)-PCR. a The proportion of
different types of AS events detected by Illumina-seq or SMRT-seq in EF
and NF. Classification of AS events: exon skipping (SE), intron
retention (IR), and alternative exon (AE). b RT-PCR validation of AS
events for five genes. Gel bands in each figure show PCR results in NF
and EF. The transcript structure of each isoform is shown in the right
panel. Boxes show exons in each transcript model. Green boxes show the
isoforms from EF, and yellow boxes show the isoforms from NF. PCR
primers (F, forward and R, reverse) are shown on the first isoform of
each gene. c The number of genes belonging to the three genres. Genre-I
contains the genes that have less isoforms in the EF variety than in
the NF variety; genre-II contains the genes with similar numbers of
isoforms in the EF and NF varieties. Genre-III contains the genes with
less isoforms in the NF variety than in the EF variety. d The number
isoforms of homologous genes in EF and NF. Gene names in the reference
annotation (in parentheses) and corresponding names in the C. bungei
genome are shown
One of the most important features of SMRT is the ability to identify
AS events by directly comparing isoforms of the same gene [[169]33,
[170]68, [171]71–[172]74]. We randomly selected ten genes to evaluate
the accuracy of AS events using RT-PCR (Table [173]S12). The size of
each amplified fragment was consistent with that of the predicted
fragment (Fig. [174]7b, Fig. [175]S8). These amplified fragments were
then cloned for Sanger sequencing, and the amplified sequences were
consistent with the SMRT data. These genes were divided into three
groups according to the number of isoforms in EF and NF. The genes that
had fewer isoforms in the EF than in NF were classified into genre-I,
the genes with no obvious difference in the number of isoforms in EF vs
NF were classified into genre-II, and the genes that had less isoforms
in NF compared to EF were classified into genre-III (Fig. [176]7c).
Further analysis of the AS events during floral transition was
performed based on the SMRT data.
To evaluate the novel isoforms that may be involved in floral
transition, we analysed the AS events of 200 genes (61 hub genes and
their 10% target genes) in EF and NF. Among them, 41 genes were in
genre-I, 36 genes were in genre-II and 28 genes were in genre-III. To
study the specificity of the AS events in different stages, we verified
the isoforms from three developmental periods in EF and NF by RT-PCR.
For example, Cbu.gene.25040 (GRF homologous gene) was found in two
isoforms in EF-Vb, NF-Vb and NF-Rb, and one Cbu.gene.25040 isoform was
found in the rest of the buds. Cbu.gene.7628 (CO homologous gene) had
different numbers of isoforms between EF-Vb and NF-Vb. Two
Cbu.gene.7628 isoforms were found in EF-Vb, and one Cbu.gene.7628
isoform was found in NF-Vb. Cbu.gene.16991 (SPL homologous gene) had
different numbers of isoforms between EF-Vb and NF-Vb. Two
Cbu.gene.16991 isoforms were found in EF-Vb, and one Cbu.gene.16991
isoform was found in NF-Vb. In addition, there were different numbers
of isoforms between Vb and Tb. There were three Cbu.gene.16991 isoforms
in Tb from both EF and NF. (Fig. [177]7d, Fig. [178]S9). The results
showed that the genes in C. bungei exhibit divergent structures of
isoform splicing in different bud stages.
Discussion
Perennial ornamental woody plants have irreplaceable economic value. In
this study, we analysed the data from three perspectives, namely,
horizontal analysis (EF vs NF), vertical analysis (EF buds at different
developmental stages) and WGCNA. We applied this strategy to the EF and
NF samples, thereby enabling the correlation of specific expression
information from transcriptional data to EF vegetative buds that formed
during floral transition. Floral transition is a very complex
regulatory system [[179]4, [180]25, [181]75]. Multiple biological
pathways, such as the age pathway, GA pathway, and photoperiod pathway,
are involved in this process. The circadian rhythm pathway was
significantly enriched in the whole floral transition cycle of EF
compared with NF (Fig. [182]2c and Fig. [183]3b). This suggested that
the circadian pathway might be critical for the early floral transition
of EF. In addition, the photosynthesis pathway was mainly concentrated
in Profile 7 (Fig. [184]2c), showing that this pathway gradually
increased with the completion of floral transition. The circadian
rhythm and photosynthesis pathways are important pathways that affect
the reproductive transformation in Arabidopsis [[185]18, [186]26,
[187]76]. Circadian rhythm genes are regulated by the photosynthesis
pathway and are used in related downstream photosynthesis pathways to
regulate flowering. In the EF variety, the enrichment of circadian
pathway genes may promote the activation of related downstream
pathways, leading to early flowering.
TFs play important roles during development transition. Several TF
families, such as SBP, MIKC type MADS-BOX, ARF, bHLH, and G2-like, were
identified as floral transition-related in our study (Fig. [188]3a,
Table [189]S4). The floral transition process was regulated by multiple
TFs in the EF variety, similar to the case of other plants. In this
study, SBP, ERF, bHLH, C3H and NAC were the top 5 TFs by number in
vegetative buds, and all SBPs were only enriched via upregulation in EF
compared to NF in vegetative buds (Fig. [190]3c). In the EF/NF-Tb-UP
groups, SBP was the top TF by number. Four SBP family members were
identified in the 61 hub genes, which were determined by WGCNA (Fig.
[191]5). SBPs are important floral transition regulators related with
the age pathway [[192]17, [193]77]. In Arabidopsis, SPL, which is
negatively regulated by miR156, promotes floral transition by
activating the expression of several other genes, such as SOC1, miR172,
and LFY [[194]5, [195]78–[196]81]. Combined with the traits of the EF,
the age pathway seems to be an important active pathway in floral
transition, which implies that SBPs are important TFs affecting floral
transition in EF.
A total of 1223 genes were included in the MEdarkturquoise module. To
more preciselyanalyse the genes related to floral transition, we
selected the top 10% of these genes according to connectivity.
Sixty-one of 123 genes were annotated as specifically related to floral
transition. These 61 genes were distributed into five pathways, i.e.,
the age pathway (e.g., CbuSPL), the photoperiod pathway (e.g., CbuPIF,
CbuCO and CbuLUX), the autonomous pathway (e.g., CbuFCA and CbuFY), and
the vernalisation pathway (e.g., CbuFRI), and CbuGA20ox, CbuARF, and
CbuERF are hormone-related genes. These genes were highly expressed in
EF-Vb. With the completion of floral transition, the expression of
these genes decreased gradually, indicating that these genes may
promote floral transition in C. bungei. In addition, several DEGs were
annotated as CbuSOC1 and CbuAP2-LIKE, which are critical genes in
flowering. Most of our RNA-seq results were consistent with those in
Arabidopsis, so the identified genes already had clear pathways in
Arabidopsis. In addition to SBP, which regulates floral transition via
the age pathway, several pathways were reflected in these data
[[197]82]. For example, TOC1 and LUX are important factors in the three
interlocking feedback loops in the circadian pathway, which mainly act
on the upstream of the photoperiod pathway and ultimately regulate
floral transition via the positive regulation of CO [[198]8]. FCA and
FY are autonomous pathway genes that have been studied in depth
[[199]83, [200]84]. FCA can inhibit the accumulation of FLC, which
contains RNA binding proteins containing RNA recognition motifs (RRM)
[[201]8, [202]85]. FLC interacts with FY through the FCA WW domain, and
the FCA/FY complex may negatively regulate FLC at the mRNA level
[[203]86]. GA20ox and GA3ox are critical for the synthesis of active GA
[[204]87]. The GA pathway influences floral transition mainly through
two branches of active GA and DELLA. At the early stage of flower
development, the active GA is involved in promoting the expression of
SOC1 and LFY and then the expression of downstream flowering genes. At
the later stage of flower development, the active GA leads to the
degradation of the DELLA protein and thus relieves the inhibition of
flowering. Therefore, floral transition is guaranteed [[205]87,
[206]88]. Interestingly, several studies have revealed that DELLA
regulates hypocotyl elongation by interacting with PIFs [[207]75],
contributing to floral transition by interacting with SPL [[208]7], FD
[[209]10] and SOC1 [[210]24, [211]62]. SOC1 plays an important role in
regulating floral transition by integrating signals involved in all
pathways, as well as interacting with many other genes to regulate
floral transition [[212]24]. However, all floral transition-related
pathways still need to be further explored in C. bungei. These genes
should be further studied to determine whether they are related to
floral transition in the EF variety.
Furthermore, 41 highly connected gene pairs in the 61 hub genes were
predicted by WGCNA (Fig. [213]5). Among these genes, Cbu.gene.18280
(GASA homologous gene) is particularly interesting. In this study, GASA
had a strong interaction with GA20ox. GA20ox is an important gene for
synthesizing GA3. These predictions were further verified by
protein-protein interaction analysis online (Table [214]2). The GASA
family is named for its GA3-induced expression in Lycopersicon
esculentum [[215]89–[216]91]. In Arabidopsis, GASA4 regulates floral
meristem identity [[217]90] and GASA5 can extend flowering time by
promoting the expression of FLC and FT [[218]91]. GA plays an important
role in the floral transition of plants, but more specific pathways
need to be further studied [[219]92]. We also found that CbuGASA was
correlated with CbuFCA, CbuFY, CbuTOC1, CbuLUX and CbuSPL. The
expression correlations of CbuGASA and its co-expressed genes suggest
that CbuGASA is a positive regulator and involved in flowering
regulation. These proteins/genes are important for floral transition by
their respective pathways. This result provides important information
for our subsequent research. Additional experiments need to be
performed to verify the hypotheses about the role of CbuGASA in floral
transition in C. bungei. To ensure more accurate transcriptome data,
analysis of the genetic differences between the EF and NF varieties is
one of the most important experiments we will undertake in future
research.
In eukaryotes, AS greatly contributes to transcriptional diversity
[[220]33, [221]71, [222]74, [223]93]. AS produces multiple transcripts
from a single gene and gives rise to proteins with various structures,
subcellular localizations, stabilities and functions. AS has
fundamental roles in a wide range of plant growth and development
processes [[224]41, [225]94–[226]96]. Previous reports showed that
isoforms have tissue specificity [[227]93], and the ratio of the
isoforms changes during the different growth periods [[228]44]. For
example, flowering integrator FT is also subjected to AS events in
temperate grasses. The ratio of the two AS evens of FT was
progressively reduced during development, indicating that one of the
two AS events is regulated by endogenous cues rather than an external
cue for flowering. In our study, similar results were found. For
example, the key genes in the floral transition of C. bungei, CbuCO
(Cbu.gene.7628) and CbuSPL (Cbu.gene.16991) are also subjected to AS
during the flowering process. In Vb of EF and NF, the ratio of AS in Vb
(2:1) was higher than that of the other two periods of EF and NF (1:1).
In addition, CbuSPL had more isoforms in Tb than in Vb in both the EF
and NF buds. The above results imply that AS events may have an
important role in floral transition in C. bungei. These observations
indicate that AS greatly increases the complexity of gene transcription
in C. bungei, and more experiments are needed to test this hypothesis.
For example, we should not only consider how genes are transcribed in
development periods but also investigate the functional differences of
homologous genes in C. bungei. The multi-omics data were integrated to
explore the floral transition mechanism in C. bungei.
Conclusions
This study expands our view of the transcriptomes of C. bungei during
floral transition. A number of DEGs were detected in vegetative to
reproductive growth buds following WGCNA analysis. These genes belonged
to pathways that collectively regulate floral transition, and the
results enhance our understanding of gene regulation during floral
transition in perennial woody plants. Furthermore, SMRT analysis
provided the first insights into AS events in C. bungei. Frame usage in
the same transcript further increases the genetic complexity of C.
bungei. These results will facilitate future functional genomics
studies.
Methods
Plant material and experimental procedures
C. bungei is a perennial plant that flowers after 5 years of
forestation ([229]http://www.forestry.gov.cn/). A natural EF variety
that flowers after 1-year forestation was found in Henan province,
China (Fig. [230]1a). This EF has been applied to produce new varieties
of C. bungei, which are named ‘bairihua’. From 28 February to 31 March
2016, we collected the buds from the first round to the axillary buds
of EF and NF varieties at an interval of every 1–2 days. All samples
were collected from 9:00–12:00 and transferred immediately to liquid
nitrogen for SMRT- and Illumina-based RNA sequencing and reverse
transcription-polymerase chain reaction (RT-PCR). Using paraffin
section analysis, vegetative buds (Vb), transition buds (Tb) and
reproduction buds (Rb) were identified for transcriptome sequencing.
Library preparation and PacBio sequencing
To construct libraries for Pacific Biosciences (PacBio) sequencing,
equal amounts of EF and NF buds from each stage (vegetative stage,
transition stage and reproduction stage) were pooled together. Total
RNA from the NF buds for three periods, NF-Vb, NF-Tb and NF-Rb, was
mixed to provide the ‘NF’ sample for comparison with the EF sample. For
EF buds, total RNA from three periods, EF-Vb, EF-Tb and EF-Rb, was
mixed to provide the ‘EF’ sample. The two mixed RNA samples from buds
were reverse transcribed using the SMRT [er]® PCR cDNA Synthesis Kit.
PCR amplification was carried out using the KAPA HiFi PCR Kit. The
product was separated by agarose gel-based size selection into cDNA
fractions 1–2 kb, 2–3 kb and > 3 kb in length. These SMRT libraries
were generated using the PacBio 1.0 Template Preparation Kit (Menlo
Park, CA, USA, part #001–322-716) according to the standard protocol.
The 1–2 kb library was sequenced using five SMRT cells, and the other
two libraries were sequenced using four SMART cells. The cDNA products
were purified for library construction using the SMRTbell Template Prep
Kit 1.0. Libraries were sequenced using P6C4 polymerase (PacBio, P/N
100–372-700) and chemistry on the PacBio RS II platform with 240-min
movie times. Each size fraction for each sample was run through the
Iso-Seq pipeline included in the SMRT analysis software package
individually. First, ROIs (previously known as circular consensus
sequences) were generated using the minimum filtering requirement of 0
or more passes of the insert and a minimum read quality of 75. This
requirement allows for the highest yield going into the subsequent
steps, while creating higher-accuracy consensus sequences when
possible. The pipeline then classified the ROIs as full-length
non-chimeric or non-full-length reads. Full-length reads with lengths
of at least 300 bp were determined by detecting poly(A) tails, 5′
primers and 3′ primers. All full-length reads were aligned to the C.
bungei genome using G[MAP] software.
Illumina RNA sequencing of EF and NF buds
The samples stages were used to construct 18 libraries for
Illumina-based RNA sequencing, which were named NF-Vb, NF-Tb, NF-Rb,
EF-Vb, EF-Tb and EF-Rb. Each stage had three biological replicates.
Total samples were sent to the Beijing Genomics Institute for
strand-specific library construction and sequencing on an Illumina
HiSeq 4000 platform. In total, 13G of 150-bp paired-end reads were
generated. Raw sequence data of the libraries for differentially
expressed gene (DEG) profiling analysis were filtered to remove reads
containing adapters, reads with an unknown nucleotide content exceeding
10% unknown nucleotides, and reads with a low-quality base (value <=5)
content greater than 50%. Clean reads were mapped into the
transcriptome reference database using SOAP software. No more than 2
mismatched bases were permitted, and unique mapped reads were obtained.
Fragments per kilobase of exon per million fragments mapped (FPKM) was
used to obtain the relative expression levels. A differential
expression analysis of the two groups was performed using the DESeq
package. The resulting P-values were adjusted using Benjamini and
Hochberg’s approach for controlling the false discovery rate. DEGs with
a |fold change| > = 1.2 and an FDR < 0.05 were identified between each
comparison. The DEG expression data υ (from Vb to Tb and Tb to Rb) were
normalized to 0, log[2]^(Tb/Vb), and log[2]^(Rb/Vb). DEGs were
clustered using the Short Time-series Expression Miner (STEM). To
analyse gene co-expression patterns based on mRNA profiles in buds from
the EF and NF varieties, WGCNA was performed according to Langfelder
and Horvath (2008) [[231]97]. Here, we chose a power of four so that
the resulting networks exhibited approximate scale-free topology (model
fitting index R2 = 0.80). The resulting gene dendrogram was used for
module detection using the Dynamic Tree Cut method (minimum module
size = 50) [[232]63]. Co-regulated genes are grouped into modules based
on the corresponding genes’ information. In addition, the intra-modular
hub genes refer to highly connected genes in a module. They can be
determined by calculating the Pearson correlation between the
expression level and the module eigengene. In this study, the top 10%
of genes with high correlation were considered as hub genes for a given
module. Finally, the modules of interest were input into Cytoscape to
determine network information [[233]44].
With the Gene Ontology (GO; [234]http://www.geneontology.org) and Kyoto
Encyclopedia of Genes and Genomes (KEGG; [235]https://www.kegg.jp/)
pathway annotation results, we classified mRNAs according to official
classifications, and we also performed GO and pathway functional
enrichment using Phyper, a function of R software. The parameters for
Phyper were set as P-values 0.05 after Bonferroni correction. The
phyper function in R was used to analyse the P-value for each function
theme:
[MATH: P=1‐∑i=0
m−1MiN−Mn−iNn :MATH]
Smaller P-values were associated with greater enrichment of the
candidate genes in a given function theme
([236]https://en.wikipedia.org/wiki/Hypergeometric_distribution). The
generation of Venn diagrams and hierarchical clustering heat maps in
this study were conducted using the gmodels, Venn diagram and Pheatmap
packages in R ([237]https://www.r-project.org/) based on the gene list
and the gene expression levels for each type.
Pipeline for isoform sequencing analysis
To classify AS events, the tool AStalavista was employed using the
raw.gtf file assembled from the Illumina RNA-seq and SMRT sequencing
data. AS analysis was conducted using SpliceGrapher by converting the
detected splice isoforms into splice graphs. Introns fully subsumed by
an exon were labelled as retained. Overlapping exons that differed at
their 5′ or 3′ splice junctions were considered alternative 5′ or 3′
splicing events, respectively. Finally, exons absent in other isoforms
were considered exon skipping events.
MATS was used to call the differentially spliced events between the EF
and NF samples at the three development periods, using the aligned.bam
files as input with default settings. For comparison, the merged.gtf
file derived from the SMRT and new Illumina data was used as a
reference. The examined events included skipped exon (SE), alternative
5′ splice site (A5), alternative 3′ splice site (A3), alternative exons
(AE), and retained intron (IR) events.
RNA extraction, quantification, and RT-PCR
Total RNA was extracted using RNA Reagent (RN38; Aidlab Biotechnology,
Beijing, China) according to the manufacturer’s protocol and treated
with RNase-free DNase I (Takara, Dalian, China) to remove genomic DNA
contamination. First-strand cDNA was generated from 1 μg of total RNA
isolated from buds using the superscript first-strand synthesis system
(Invitrogen, USA). The specific primers were designed using Primer
3plus and synthesized by Majorbiogene Co., Ltd. (Beijing, China). The
melting temperature of the primers was 60 °C, and the amplicon lengths
were 100–200 bp. Real-time qRT-PCR was performed on a Roche LightCycle
480 Real-Time PCR System (Roche Applied Science, Germany) using a SYBR
Premix Ex Taq™ Kit (TaKaRa, Dalian, China) according to the
manufacturer’s instructions. Cbu-actin was used as an internal control,
and each reaction was conducted in triplicate [[238]31]. All the
primers are shown in Table [239]S12. Each reaction was performed in a
total reaction mixture volume of 20 μL containing 2 μL of first-strand
cDNA as template. The amplification program was as follows: 3 min at
95 °C and 30 cycles of 15 s at 95 °C, 30 s at 60 °C, 1 min at 72 °C and
10 min 72 °C. Each reaction was performed with three replicates. The
expression levels of candidate genes were determined by CT values and
calculated by the 2^−△△Ct method. We test the correlation of expression
(CEG) between mRNAs by using the Pearson correlation coefficient. The
Pearson correlation coefficient was calculated by COR() using the
average relative expression of three replicates in R [[240]98].
Transcription factor prediction and protein and protein interaction analysis
We found the ORF of each DEG by using getorf (version: EMBOSS: 6.5.7.0,
parameters: -minsize 150,
//[241]www.bioinformatics.nl/cgi-bin/emboss/help/getorf). We aligned
ORFs to transcription factor (TF) domains from PlntfDB
([242]http://plntfdb.bio.uni-potsdam.de/v3.0/) by using hmmsearch
([243]http://hmmer.org). We used DIAMOND (version: v0.8.31, parameters:
--evalue le-5 –outfmt 6 –max-target-seqs 1 —more-sensitive,
[244]https://github.com/bbuchfink/diamond) to map the DEGs to the
STRING (version: v10, [245]http://string-db.org/) database to obtain
interactions between DEG-encoded proteins using homology with known
proteins.
Supplementary information
[246]12864_2020_6918_MOESM1_ESM.tif^ (7MB, tif)
Additional file 1: Fig. S1. Read length and quality in EF. (A) Read
length distribution in the 1–2 kb library. (B) Read quality
distribution in the 2–3 kb library. (C) Read length distribution in the
above 3 kb library.
[247]12864_2020_6918_MOESM2_ESM.tif^ (6.2MB, tif)
Additional file 2: Fig. S2. Read length and quality in NF. (A) Read
length distribution in the 1–2 kb library. (B) Read quality
distribution in the 2–3 kb library. (C) Read quality distribution in
the 3–6 kb library. (D) Read quality distribution in the above 6 kb
library.
[248]12864_2020_6918_MOESM3_ESM.tif^ (1.2MB, tif)
Additional file 3: Fig. S3. Characterization of the C. bungei
transcriptome by SMRT-seq. (A) Distribution of transcript lengths from
different sequencing platforms. (B) Venn diagram showing the common and
unique annotated genes detected by SMRT and Illumina. (C) Distribution
of the number of APA sites per gene.
[249]12864_2020_6918_MOESM4_ESM.tif^ (1.7MB, tif)
Additional file 4: Fig. S4. Analysis of differential gene expression of
the NF. (A) The 8 significant expression profiles of NF. (B) Partial
KEGG pathways associated with the NF.
[250]12864_2020_6918_MOESM5_ESM.tif^ (8.7MB, tif)
Additional file 5: Fig. S5. Hierarchical cluster tree showing all
modules.
[251]12864_2020_6918_MOESM6_ESM.tif^ (8MB, tif)
Additional file 6: Fig. S6. qRT-PCR validation of mRNA for 14 genes.
[252]12864_2020_6918_MOESM7_ESM.tif^ (3.4MB, tif)
Additional file 7: Fig. S7. qRT-PCR validation of mRNA for 10 genes.
[253]12864_2020_6918_MOESM8_ESM.tif^ (2.2MB, tif)
Additional file 8: Fig. S8. RT-PCR validation of AS events for 5 genes.
This figure is supplemental to Fig. [254]7b.
[255]12864_2020_6918_MOESM9_ESM.tif^ (2.5MB, tif)
Additional file 9: Fig. S9. RT-PCR validation of AS events in three
development periods. This figure is supplemental to Fig. [256]7d.
[257]12864_2020_6918_MOESM10_ESM.xlsx^ (13.8KB, xlsx)
Additional file 10: Table S1. Summary of Illumina short reads.
[258]12864_2020_6918_MOESM11_ESM.docx^ (14.8KB, docx)
Additional file 11: Table S2. Statistics of the SMRT sequencing data.
[259]12864_2020_6918_MOESM12_ESM.xlsx^ (11.2KB, xlsx)
Additional file 12: Table S3. Top 20 KEGG pathways significantly
enriched with DEGs during floral transition in EF.
[260]12864_2020_6918_MOESM13_ESM.xlsx^ (54.2KB, xlsx)
Additional file 13: Table S4. All predicted transcription factors in EF
during floral transition.
[261]12864_2020_6918_MOESM14_ESM.xlsx^ (11.7KB, xlsx)
Additional file 14: Table S5. Top 20 KEGG pathways significantly
enriched with DEGs in three different stages between EF and NF.
[262]12864_2020_6918_MOESM15_ESM.xlsx^ (160.4KB, xlsx)
Additional file 15: Table S6. Summary of all genes from the
MEdarkturquoise module.
[263]12864_2020_6918_MOESM16_ESM.xlsx^ (23.2KB, xlsx)
Additional file 16: Table S7. Summary of DEGs classified into five
pathways.
[264]12864_2020_6918_MOESM17_ESM.xlsx^ (9.3KB, xlsx)
Additional file 17: Table S8. The correlation coefficients of
expression of mRNAs between the EF and NF during three developmental
periods. r is the correlation coefficient of expression of the mRNAs.
[265]12864_2020_6918_MOESM18_ESM.xlsx^ (9.6KB, xlsx)
Additional file 18: Table S9. Summary of identification of splicing
junctions using NGS and SMRT data.
[266]12864_2020_6918_MOESM19_ESM.xlsx^ (84.9KB, xlsx)
Additional file 19: Table S10. Summary of AS events from DEGs in EF.
[267]12864_2020_6918_MOESM20_ESM.xlsx^ (89.9KB, xlsx)
Additional file 20: Table S11. Summary of AS events from DEGs in NF.
[268]12864_2020_6918_MOESM21_ESM.xlsx^ (13.8KB, xlsx)
Additional file 21: Table S12. Summary of primers used in this study.
Acknowledgements