Abstract
Fruit ripening is a genetically programmed process. Transcription
factors (TFs) play key roles in plant development and ripening by
temporarily and spatially regulating the transcription of their target
genes. In this study, a total of 159 TFs were identified from a
spontaneous late-ripening mutant 'Fengwan' (C. sinensis L. Osbeck)
sweet orange (MT) and its wild-type counterpart ('Fengjie 72–1', WT)
along the ripening period via the Transcription Factor Prediction of
PlantTFDB 3.0. Fifty-two differentially expressed TFs were identified
between MT and WT; 92 and 120 differentially expressed TFs were
identified in WT and MT, respectively. The Venn diagram analysis showed
that 16 differentially expressed TFs were identified between MT and WT
and during the ripening of WT and MT. These TFs were primarily assigned
to the families of C2H2, Dof, bHLH, ERF, MYB, NAC and LBD.
Particularly, the number of TFs of the ERF family was the greatest
between MT and WT. According to the results of the WGCNA analysis, a
weighted correlation network analysis tool, several important TFs
correlated to abscisic acid (ABA), citric acid, fructose, glucose and
sucrose were identified, such as RD26, NTT, GATA7 and MYB21/62/77.
Hierarchical cluster analysis and the expression analysis conducted at
five fruit ripening stages further validated the pivotal TFs that
potentially function during orange fruit development and ripening.
Introduction
Transcription factors (TFs) play key roles in plant development and
stress responses through the temporary and spatial regulation of the
transcription of target genes [[28]1]. Many fruits develop from carpels
(true fruit) or other floral-associated tissues (false or accessory
fruit). During fruit development and ripening, TFs act as pivotal
regulators. Several classes of transcription factors have defined
functions in Arabidopsis and tomato carpel and fruit tissues, including
HD-Zip, KNOX, HB, SBP, BHLH, RAVB3, YABBY and AP2/ERF [[29]2, [30]3].
Screens for such regulators of fresh fruit ripening are important, and
additional players remain elusive.
TFs are typically classified into different families based on their
DNA-binding domains (DBDs); generally, TFs belonging to the same family
have similar functions. Recent studies have indicated that an
increasing number of TFs have been identified as having functions
during fruit development and ripening in climacteric and
non-climacteric fruit. For example, the overexpression of VvABF2, a
bZIP family transcription factor, in grape cells resulted in the
up-regulation and/or modification of groups of genes associated with
abscisic acid (ABA) responses and enhanced responses to ABA treatment
and changes in the synthesis of phenolic compounds and cell wall
softening [[31]4]. Silencing of PacMYBA, an R2R3-MYB transcription
factor from red-colored sweet cherry cv. Hong Deng (Prunus avium L.),
resulted in sweet cherry fruit lacking red pigment [[32]5]. Another
R2R3-MYB transcription factor, FaMYB10, has been identified as playing
a major role in the regulation of flavonoid/phenylpropanoid metabolism
during ripening of strawberry fruit [[33]6]. In tomatoes and bananas,
NAC transcription factors, such as NAC1/NAC2, may be involved in fruit
ripening via interactions with ethylene signal components [[34]7,
[35]8].
The AP2/ERF gene family encodes plant-specific transcription factors
that respond to developmental and environmental stimuli, and many of
these factors function downstream of the ethylene, biotic, and abiotic
stress signaling pathways [[36]9]. In tomatoes, LeERF2 is an important
regulator of ethylene biosynthesis [[37]10], and SlAP2a and SlERF6 are
negative regulators of fruit ripening. The RNAi repression of SlAP2a
and SlERF6 results in fruits that over-produce ethylene, ripen early
and modify carotenoid accumulation [[38]11, [39]12].
Cys2/His2 (C2H2)-type zinc finger proteins (ZFPs) are one of the
largest families of transcriptional regulators in plants, which are
important components in the regulation of plant growth, development,
hormone responses, and tolerance to biotic and abiotic stresses
[[40]13, [41]14]. Previous studies have shown that C2H2-type zinc
finger protein ZFP36 is a key regulator involved in abscisic
acid-induced antioxidant defense and oxidative stress tolerance in rice
[[42]15] and that Arabidopsis C2H2 proteins AZF1 and AZF2 function as
transcriptional repressors involved in the expression of abscisic
acid-repressive and auxin-inducible genes under abiotic stress
conditions [[43]16]. The bHLH family has also been implicated in a
range of functions in plants, frequently in conjunction with MYBs; the
MYB-bHLH-WD40/WDR (MBW) regulatory complex is involved in regulating
the biosynthesis of anthocyanins, which are important for coloration
during fruit ripening [[44]17]. The highly conserved N-terminal DOF
region of the plant-specific DNA-binding-with-one-finger (Dof) family
TF acts as a DNA-binding domain and corresponds to a conserved DNA
cis-element (A/T)AAAG or its complementary inverse sequence [[45]18].
Numerous studies have shown that Dof transcription factors are involved
in various biological processes during plant growth and development,
such as carbon and nitrogen metabolism, which can influence sugar
accumulation in fruit [[46]19], the light response, which is a
significant regulatory factor for fruit ripening [[47]20], flower and
pollen development [[48]21], and seed germination and development
[[49]22].
Citrus is one of the most important fruit crops worldwide and has a
non-climacteric fruit maturation character [[50]23]. During the
ripening process of citrus, the expressions of a large number of genes
are changed, up-regulated or down-regulated [[51]24]. As
transcriptional expression regulators, TFs play pivotal roles in this
process. Recently, we examined 'Fengjie 72–1' and 'Fengwan' during the
ripening period at the transcriptomic level [[52]24]. 'Fengwan' sweet
orange (MT) is a spontaneous late-ripening mutant from the ‘Fengjie
72–1’ orange (Citrus sinensis L. Osbeck) (WT) [[53]24]. The mechanisms
involved in the ripening of citrus fruit remain unclear, and only a few
regulators have been reported. 'Fengjie 72–1' and 'Fengwan' have
provided a promising platform to reveal the transcription factors
involved in citrus fruit development and ripening. In this study, we
created a protein sequence database of differential expression genes
(DEGs) including the DEGs between MT and WT and DEGs of MT and WT
during fruit ripening. This database was used to identify TFs in the
Plant Transcription Factor Database v3.0 (PlantTFDB 3.0) [[54]1].
Numerous TFs were identified, and we employed coexpression network
analyses using the R package WGCNA [[55]25] and qRT-PCR to identify the
most credible and relevant TFs for citrus fruit ripening.
Materials and Methods
Plant materials and RNA preparation
Fruit samples of ‘Fengjie 72–1’ orange (C. sinensis L. Osbeck) (WT) and
its spontaneous late-ripening mutant ‘Fengwan’ (MT), which were both
cultivated in the same orchard (N31°03'35", E109°35'25") (Fengjie,
Chongqing City, China), were harvested at 150, 170, 190, 210, and 240 d
after flowering (DAF). Twelve representative fruits were sampled from
each tree at each developmental stage. After separating the pulp from
the peel, the pulp was sliced. The sliced WT pulp samples were combined
(as for the MT samples), rapidly frozen in liquid nitrogen and stored
at -80°C [[56]26, [57]27]. A portion of the samples was used for
extracting total RNA, as described previously [[58]28]. Another aliquot
was used for the determination of ABA, sugar and organic acid
composition and concentration.
Transcription factors isolation, identification and analysis
The WT and MT fruit pulps harvested at 170, 190 and 210 DAF were
subjected to RNA-seq using an Illumina HiSeq^™2000 at the Beijing
Genomics Institute (Shenzhen). The RNA-seq data of these six fruit pulp
samples of MT and WT, obtained in a previous study [[59]24], were used
in the present study, and the data of RNA-seq were submitted to the
Gene Expression Omnibus ([60]www.ncbi.nlm.nih.gov/geo/), accession
number [61]GSE69432. The gene expression levels were calculated using
the RPKM (Reads Per kb per Million reads) method according to Zheng et
al. [[62]29]. Referring to the previous studies [[63]29, [64]30], the
Poisson model provides a natural framework for identifying
differentially expressed genes. Denoting the number of unambiguous
clean reads from a given gene as x, and considering that the expression
of every gene occupies only a small part of the library, p(x) would
closely follow the Poisson distribution,
[MATH: P(x) = e-λ
λxx! :MATH]
(λ is the real transcripts of the gene). A strict algorithm was used to
identify differentially expressed genes between the two samples. The
total clean read number of sample 1 is N1, and the total clean read
number of sample 2 is N2; and gene A holds x reads in sample1 and y
reads in sample2. The probability of gene A expressed equally between
the two samples can be calculated as
[MATH: 2∑i = 0i-yp(i|x)<
/mrow> :MATH]
or
[MATH: 2(1-∑i = 0i-y
p(i|x))
:MATH]
(if
[MATH: ∑i = 0i-y
p(i|x)>0.5),P(yx) = (N2N2)y(x+y)!x!y!(1+N2N1)x+y+
1 :MATH]
. The p-value corresponds to the differential gene expression test. FDR
(False Discovery Rate) is a method used to determine the threshold of
P-value in multiple tests [[65]31]. ‘FDR ≤ 0.001 and the absolute value
of log[2]Ratio ≥ 1’ was used as the thresholds to judge the
significance of differences in gene expression. The values of
fold-change with their respective P-values and FDR values for all genes
were listed in [66]S1 Table. A total of 18879 genes of WT and MT
([67]S2 Table), 628 differential expression genes (DEGs) between MT and
WT, 1036 DEGs between different ripening stages in WT and 1406 DEGs
between different ripening stages in MT were used as original databases
for transcription factor identification [[68]24]. The protein sequences
of these genes were isolated from the citrus genome
([69]http://citrus.hzau.edu.cn/). The protein sequences of identified
TFs were aligned against the GO database and KEGG pathway database
using KOBAS 2.0 ([70]http://kobas.cbi.pku.edu.cn/) [[71]32] to perform
enrichment analysis. The corrected P-value < 0.01 was set as cutoff for
enrichment. REVIGO [[72]33] was used to visualize and summarize the
terms corresponding to biological processes and molecular functions
identified using KOBAS 2.0.
The Arabidopsis TFs database of PlantTFDB 3.0 [[73]1] was used as the
reference TF database. The Transcription Factor Prediction algorithm,
in which HMMER 3.0 [[74]34] was used to identify TFs and assign these
genes to different families [[75]1], was performed to identify TFs. The
best BLAST hits had maximal e-values of 1e-10. A cluster analysis was
performed on the TF cluster of MT vs WT according to Eisen et al.
[[76]35] using Cluster 3.0. The log[2] of RKPM for each TF was used for
hierarchical clustering analysis.
Gene Network Construction
The WGCNA (v1.42) package in R was used to construct coexpression
networks [[77]25]. A total of 18879 genes ([78]S2 Table) with RKPM
higher than 0.3 were used for WGCNA unsigned coexpression network
analysis. The modules were obtained using the automatic network
construction function blockwiseModules with default settings, except
that the maxBlockSize was 19000, the TOMType was unsigned, the
minModuleSize was 30, and the mergeCutHeight was 0.25. Once the network
modules were identified, we validated their membership using a
permutation procedure according to a previous study [[79]36]. When the
modules truly showed statistical and potentially functional relevance,
the average TO (topological overlap) should be higher than that of
random groups of genes of similar size. The eigengene value was
calculated for each module and used to test the association with each
sample. The total connectivity and intramodular connectivity (function
softConnectivity), kME (for modular membership, also known as
eigengene-based connectivity), and kME-P value were calculated for the
18879 genes clustered into 32 modules. The module eigengenes to relate
consensus modules to physiological data and the 16 TFs identified in
all three clusters DEGs of MT, WT and MT vs WT were also performed via
WGCNA. These physiological data were measured in a previous study
[[80]24], which included malic acid, citric acid, quinic acid,
fructose, glucose, sucrose and abscisic acid (ABA). In the present
study, we used the RPKM of these 16 TFs and the physiological data of
three ripening stages (170, 190 and 210 DAF) of WT and MT for the WGCNA
analysis. A correlation coefficient (the absolute value) of more than
0.8 and p-value < 0.05 was used as the cutoff criteria for identifying
the significance between physiological traits/TFs and modules.
RNA Isolation and real-time quantitative PCR analysis
Total RNA were extracted from the samples of MT and WT harvested at
150, 170, 190, 210, and 240 DAF, as previously described [[81]37]. The
sequences of the primer pairs designed using Primer Express 3.0
(Applied Biosystems, Foster City, CA, USA) listed in [82]S3 Table. The
qRT-PCR analysis was conducted using an ABI 7900HT Fast Real-time
system (Applied Biosystems) with the GAPDH gene as the reference
[[83]38], as previously described [[84]24]. Real-time PCR was conducted
with three replicates for each sample, and the data are indicated as
the means ± standard error (SE) (n = 3).
Results
Identification of differentially expressed transcription factors during
citrus fruit ripening
In a previous study [[85]24], the transcriptomes of fruit pulps of MT
and WT at the ripening stages 170, 190 and 210 DAF were analyzed. In
the present study, a total of 18879 genes in these six transcriptomes
were used to identify TFs ([86]S2 Table). A total of 934 TFs were
identified in WT and MT, 922 TFs were identified in MT and 929 TFs were
identified in WT ([87]S4 Table). These 934 TFs were assigned to 57
different families, the top three families of which were bHLH (71 TFs),
NAC (64 TFs) and ERF (58 TFs) ([88]S1 Fig).
We used a stringent value of FDR ≤ 0.001 and P value < 0.05 as the
threshold to judge the significant differences in the gene expressions.
A total of 1036 and 1406 genes differently expressed (≥ 2-fold) in WT
and MT during fruit ripening, respectively. The protein sequences of
these two cluster DEGs were used as the original database for
transcription factor searching. The Arabidopsis TF database of
PlantTFDB 3.0 [[89]1] was used as the reference TF database. The
Transcription Factor Prediction algorithm [[90]1] was performed to
identify TFs. A total of 144 TFs were identified including 92 TFs in
the DEG cluster of WT and 120 TFs in the DEG cluster of MT ([91]S5
Table; [92]Fig 1A). According to the Venn diagram analysis, 68 TFs were
identified in both WT and MT DEG clusters ([93]Fig 1A; [94]S5 Table).
Fig 1. The Venn Diagram analysis (A) and the families assignment of TFs of WT
(B), MT (C) and MT vs WT (D).
[95]Fig 1
[96]Open in a new tab
MT vs WT indicate the TF cluster, which is differentially expressed
between MT and WT.
As shown in [97]Fig 1B and 1C, TFs were assigned to different families:
28 families in WT and 30 families in MT. The top three families of WT,
containing the greatest number of TFs, were C2H2 (10 TFs), ERF (9) and
Dof (8) ([98]Fig 1B), and the top three families of MT were bHLH (14),
C2H2 (10), Dof (9) and MYB (9) ([99]Fig 1C). Notably, the C2H2 and Dof
families were consistently in the top three families in both WT and MT.
Function analysis of TFs identified in both MT and WT during fruit ripening
To gain a better understanding of the role of TFs in fruit ripening,
GO-based term classification and KEGG-based pathway enrichment were
performed. Using a cutoff of corrected P-value < 0.01, 68 TFs, which
were differentially expressed in both MT and WT during fruit ripening,
were enriched to 37 biological processes and 14 molecular functions
after summarizing the GO terms by removing redundant GO terms using
REViGO [[100]33] ([101]S6 Table). In biological processes, several
hubs, including response to gibberellin, gene expression, regulation of
multicellular organismal process, biological regulation, heterocycle
metabolic process, nitrogen compound metabolic process and biosynthetic
process, were significantly enriched ([102]Fig 2A). Nucleic acid
binding transcription factor activity, sequence-specific DNA binding
transcription factor activity, sequence-specific DNA binding, protein
dimerization activity, chromatin binding, and heterocyclic compound
binding were significantly enriched in molecular function ([103]Fig
2B). However, there was only one enrichment KEGG pathway to been
identified (data not shown). One GRAS family transcription factor GAI
(Cs2g16940) and one ARR-B family gene ARR12 (Cs7g06180) were enriched
in the plant hormone signal transduction pathway.
Fig 2. Biological process (A) and molecular function (B) enrichment analysis
of the TFs differentially expressed during fruit ripening in both MT and WT.
[104]Fig 2
[105]Open in a new tab
Bubble color indicates the p-value; plot size indicates the frequency
of the GO term in the underlying GOA database (bubbles of more general
terms are larger).
Plant hormones are important for fruit development and ripening. In the
present study, 8, 9 and 10 TFs were enriched in the biological
processes of ‘response to gibberellin’, ‘response to salicylic acid’
and ‘response to ethylene’, respectively ([106]S6 and [107]S7 Tables).
Some TFs were enriched in different biological processes, for example,
GAI (Cs2g16940) was enriched in ‘response to gibberellin’, ‘response to
salicylic acid’ and ‘response to ethylene’ and MYB77 (Cs3g23950) was
enriched in ‘response to salicylic acid’ and ‘response to ethylene’
([108]S7 Table).
Differentially expressed transcription factors between MT and WT
In a previous study [[109]24], a total of 628 genes were differently
expressed (≥ 2-fold) between MT and WT. The protein sequences of this
cluster DEGs were used as the original database for transcription
factor searching. A total of 52 differentially expressed TFs between MT
and WT were identified, the TF cluster MT vs WT ([110]Fig 1A and
[111]Table 1). MT is a later-ripening bud mutant of WT; therefore, the
extensive analysis of these 52 TFs will identify important TFs involved
in later-ripening trait formation.
Table 1. Differential expression transcription factors (TFs) between MT and
WT.
170, 190 and 210 indicate 170, 190 and 210 DAF, respectively. The
change fold is shown as a log[2] ratio. Module colors were obtained
from the analysis of WGCNA. Clusters were obtained from hierarchical
clustering analysis via Cluster 3.0.
GeneID Family Module color Cluster Fold change (MT/WT) Description
170 190 210
Cs9g16810 ERF lightyellow I -0.18 0.82 1.76 C-repeat-binding factor 4
Cs2g05620 ERF red I 1.18 0.54 1.49 ERF domain protein 9
Cs1g07950 ERF blue I 2.22 0.66 0.91 ethylene responsive element binding
factor 4
Cs3g19420 ERF midnightblue I 1.05 0.92 1.50 Integrase-type DNA-binding
superfamily protein
Cs1g11880 ERF lightgreen II -1.40 -0.32 -0.12 Integrase-type
DNA-binding superfamily protein
Cs9g13610 ERF black II 0.09 -1.12 -0.25 Integrase-type DNA-binding
superfamily protein
Cs5g29870 ERF blue II -2.47 0.57 0.37 ethylene response factor 1
Cs1g23230 Dof turquoise I 1.05 1.01 0.75 OBF binding protein 1
Cs7g03670 Dof red I 1.08 -0.42 0.98 cycling DOF factor 2
orange1.1t01261 Dof purple II -1.22 -1.19 -0.10 Dof-type zinc finger
DNA-binding family protein
Cs5g01740 Dof brown II -0.08 -1.07 -0.83 Dof-type zinc finger
DNA-binding family protein
Cs3g21070 Dof lightgreen II -3.19 -0.26 -0.39 Dof-type zinc finger
DNA-binding family protein
Cs8g18320 Dof brown IV -0.32 -2.13 -10.36 Dof-type zinc finger
DNA-binding family protein
Cs8g17960 C2H2 cyan I 1.04 0.26 0.65 C2H2-type zinc finger family
protein
Cs7g01850 C2H2 turquoise I 0.57 1.03 0.75 C2H2-type zinc finger family
protein
Cs8g04280 C2H2 yellow II -1.23 -0.28 0.15 salt tolerance zinc finger
Cs3g02080 C2H2 blue II -0.48 -1.11 -0.33 indeterminate(ID)-domain 5
Cs7g21900 C2H2 red III -7.18 -2.94 -2.64 C2H2-type zinc finger family
protein
Cs7g19870 bHLH turquoise II -1.27 -0.15 -0.08 bHLH DNA-binding
superfamily protein
Cs9g13930 bHLH turquoise II -0.58 -1.01 -0.23 bHLH DNA-binding
superfamily protein
Cs1g02580 bHLH tan II -1.08 -0.17 -0.05 bHLH DNA-binding superfamily
protein
Cs6g21120 bHLH yellow VI 0.05 10.97 -7.16 bHLH DNA-binding family
protein
Cs6g21530 MYB turquoise I 1.06 0.66 -0.35 myb domain protein 16
Cs2g12700 MYB green II -2.41 0.38 0.00 myb domain protein 62
Cs6g01750 MYB cyan II -2.72 -1.37 -0.68 myb domain protein 61
Cs3g23950 MYB yellow V 0.06 2.07 9.66 myb domain protein 77
Cs8g02020 MYB_related saddlebrown II -0.71 -0.63 -1.19 myb-like HTH
transcriptional regulator family protein
Cs7g31610 MYB_related turquoise II -1.09 0.26 0.15 Duplicated
homeodomain-like superfamily protein
Cs2g27940 MYB_related red III -6.21 -2.84 -2.18 myb domain protein 21
Cs8g14700 NAC red I 1.74 1.49 1.88 NAC domain containing protein 61
Cs1g06760 NAC yellow II -1.23 -0.38 -0.05 NAC (No Apical Meristem)
domain transcriptional regulator superfamily protein
Cs5g29650 NAC green II -1.40 -1.85 -0.23 NAC domain containing protein
74
Cs2g13920 NAC brown II 0.11 -1.94 -2.55 NAC domain containing protein
84
Cs8g04300 LBD blue II -1.34 -1.66 0.42 LOB domain-containing protein 38
Cs7g30620 LBD yellow II -3.12 0.12 0.20 Lateral organ boundaries (LOB)
domain family protein
Cs7g26710 LBD blue II -2.17 -0.03 -1.66 LOB domain-containing protein
41
Cs8g15030 bZIP lightgreen II -1.90 -0.10 -0.13 bZIP transcription
factor family protein
Cs5g32400 ARF yellow II -1.27 -0.12 -0.37 auxin response factor 1
Cs5g26420 G2-like brown IV 0.05 -1.84 -8.70 Homeodomain-like
superfamily protein
Cs5g26470 GATA turquoise I 1.22 1.06 1.04 GATA transcription factor 7
Cs1g23790 GRAS turquoise II 0.12 -1.27 1.26 GRAS family transcription
factor
Cs6g15330 GRF green II -0.25 -1.74 -0.24 growth-regulating factor 4
Cs1g23760 HD-ZIP green II -2.95 -1.11 -0.67 homeobox protein 40
Cs4g13650 HRT-like brown II 0.24 -1.24 -0.64 effector of transcription2
Cs9g07650 HSF turquoise II -1.30 0.09 -0.15 heat shock transcription
factor A6B
Cs4g14590 HSF yellow II -1.16 -0.36 -0.16 heat shock transcription
factor A2
Cs7g11810 MIKC purple I 0.58 1.01 -0.11 K-box region and MADS-box
transcription factor family protein
Cs5g17820 MIKC blue II -2.15 -1.42 -0.69 K-box region and MADS-box
transcription factor family protein
Cs7g10990 SBP turquoise I 1.31 1.09 0.95 Squamosa promoter-binding
protein-like (SBP domain) transcription factor family protein
Cs3g23280 WOX turquoise I 3.54 1.38 0.00 WUSCHEL related homeobox 4
Cs2g02790 WRKY yellow II -1.11 -0.32 0.02 WRKY family transcription
factor
Cs6g21230 ZF-HD turquoise I 1.27 0.17 -0.12 mini zinc finger 2
[112]Open in a new tab
As shown in [113]Fig 1D, these 52 TFs were assigned to 22 different
families. The top three families of the DEG cluster MT vs WT were ERF
(7), Dof (6) and C2H2 (5). Thus, we focused on the ERF family TFs, as
the ERF family contained the greatest number of TFs in the DEG cluster
MT vs WT. The change fold of gene expression, the log[2] ratio, between
the MT and WT were performed with hierarchical cluster analysis using
Cluster 3.0 ([114]Fig 3). As shown in [115]Fig 3, six clusters were
identified. The TFs of cluster I were up-regulated in MT and TFs of
cluster II were down-regulated in MT. The number of down-regulated TFs
was much more than that of up-regulated TFs.
Fig 3. Hierarchical cluster analysis of the TF differential expressed between
MT and WT.
Fig 3
[116]Open in a new tab
In cluster I, NAC61 (Cs8g14700) and GATA7 (Cs5g26470) were up-regulated
more than 2-fold in MT at all three ripening stages; ERF4 (Cs1g07950)
and WOX4 (Cs3g23280) were up-regulated more than 6-fold in MT at 170
DAF ([117]Table 1). In cluster II, there were several TFs
down-regulated more than 6-fold in MT, such as MYB61/62
(Cs6g01750/Cs2g12700), Cs3g21070 (Dof family TF), ERF1 (Cs5g29870) and
HB40 (Cs1g23760) ([118]Table 1). Other clusters were TFs with a
substantial change between MT and WT, such as MYB21/77 (Cs2g27940/
Cs3g23950) and OBP2 (Cs8g18320), which were down/up-regulated in the
range of 0 to hundreds fold ([119]Table 1).
After removing redundant GO terms, these 52 TFs were enriched to 28
biological processes and 9 molecular functions (P-value < 0.01)
([120]S8 Table). According to the result of REViGO [[121]33], in
biological process, most of TFs were assigned to ‘regulation of
transcription, DNA-templated’, ‘response to ethylene’ and ‘nitrogen
compound metabolic process’ ([122]S2A Fig); in molecular function, most
of TFs were assigned to ‘nucleic acid binding transcription factor
activity’, ‘chromatin binding’, ‘sequence-specific DNA binding
transcription factor activity’, ‘transcription regulatory region DNA
binding’ and ‘heterocyclic compound binding’ (dispensability < 0.15)
([123]S2B Fig). Interestingly, some TFs were enriched in
hormone-related processes, such as ‘response to ethylene’ (10 TFs),
‘response to jasmonic acid’ (10), ‘response to gibberellin’ (6),
‘response to auxin’ (9) and ‘ethylene-activated signaling pathway’ (7).
These TFs involved in hormone related processes might be candidate
regulators for the formation of later-ripening trait, which were listed
in [124]S9 Table. Thereinto, MYB16 (Cs6g21530), MYB21 (Cs2g27940) and
ERF4 (Cs1g07950) were assigned to different hormone response processes,
indicating that these TFs might play a wide range of regulatory roles
during citrus fruit ripening. In addition, three TFs were identified to
enrich in plant hormone signal transduction pathway (data not shown).
ERF1 (Cs5g29870), ARF1 (Cs5g32400) and TGA9 (Cs8g15030) were assigned
to ethylene, auxin and salicylic acid signal transduction pathways,
respectively.
Coexpression Network Analysis with WGCNA
TFs can regulate a large number of target genes, as these genes are
characterized based on network regulation. Therefore, a weighted
correlation network analysis tool, WGCNA, was adopted [[125]25]. The
WGCNA R software package is a systems biology approach whose purpose is
to understand networks instead of individual genes. In the present
study, coexpression networks were constructed based on pairwise
correlations between the genes in common expression trends across all
18879 genes in all samples, including all three ripening stage
transcriptomes of MT and WT ([126]S2 Table). The modules are defined as
clusters of highly interconnected genes, and genes within the same
module are highly correlated with one another. The weighted correlation
network analysis resulted in 32 distinct modules, labeled with
different colors ([127]Fig 4A). After validation using a permutation
procedure according to a previous study [[128]36], 24 modules displayed
TO that was higher than what is expected for random groups of
transcripts ([129]S3 Fig); the modules of cyan, darkorange,
darkturquoise, lightcyan, lightyellow, magenta, pink and royalblue had
no truly statistical relevance. As shown in [130]Fig 4A, each tree
branch constitutes a module, and each leaf in the branch is one gene.
Each module contained different numbers of genes. The turquoise module
contained 3985 genes, which was the largest cluster of genes; the
smallest module, violet module, only contained 41 genes ([131]Fig 4B).
The module eigengene is the first principal component of a given module
and can be considered a representative of the gene expression profile
of that module ([132]S4 Fig). The TFs identified in the present study
were assigned to different modules. As shown in [133]S5 Fig, most TFs
were assigned to turquoise, yellow, brown and blue modules. The
turquoise module eigengene exhibited down-regulated expression during
fruit ripening in WT and MT. In contrast, the yellow module eigengene
was up-regulated expression during the fruit ripening of WT and MT.
Interestingly, the expression patterns of brown and blue module
eigengenes were different between WT and MT ([134]S4 Fig).
Fig 4. Hierarchical cluster tree with dissimilarity based on topological
overlap showing coexpression modules identified by WGCNA (A).
[135]Fig 4
[136]Open in a new tab
Each leaf in the tree is one gene. The major tree branches constitute
32 modules labeled by different colors. Module colors were determined
in the single-block analysis. B, Module-physiological traits
association. Each row corresponds to a module. The number of genes in
each module is indicated on the left. Each column corresponds to a
physiological trait. The color of each cell at the row-column
intersection indicates the correlation coefficient between the module
and the physiological trait, and the numbers in each cell indicate
correlation coefficient R and P value, respectively.
In our previous study [[137]24], we measured the content of soluble
sugar, organic acid and abscisic acid (ABA) of WT and MT fruits at
different ripening stages. These physiological trait data were used in
the present study to perform a correlation network analyses with gene
expression trends ([138]Fig 4B). As shown in [139]Fig 4B, malic acid
was highly positively correlated with the greenyellow module (r = 1, p
= 3e-05), and citric acid and quinic acid were all highly positively
correlated with the turquoise module. For soluble sugars, fructose and
glucose were all positively correlated with red, yellow and black
modules, while sucrose was correlated with lightgreen, purple and black
modules; ABA is a significant hormone for citrus fruit ripening, and in
the present study, this hormone was highly positively correlated with
the gray60 module.
Sixteen TFs were identified in all three cluster DEGs, including MT, WT
and MT vs WT ([140]Fig 1A). These 16 TFs may play important roles in
the citrus fruit ripening process. Thus, we conducted a correlation
analysis between these 16 TFs and gene modules ([141]Fig 5). As shown
in [142]Table 2 and [143]Fig 5, MYB77 (Cs3g23950) and MYB62 (Cs2g12700)
belonged to the yellow and green modules, respectively; however, these
genes were all high positively correlated with the gray60 module, which
was positively correlated with ABA. RD26 (Cs1g06760) and WRKY42
(Cs2g02790) belonged to the yellow module and also had the highest
positive correlation with the yellow module, and MYB21 (Cs2g27940) was
highly positively correlated with the red module ([144]Table 1 and
[145]Fig 5). These findings showed that RD26, WRKY42 and MYB21/77 had a
high correlation with fructose and glucose. HAM4 (Cs1g23790), GATA7
(Cs5g26470) and NTT (Cs7g01850) belonged to the turquoise module, which
was high positively correlated with citric acid and quinic acid
([146]Table 2 and [147]Fig 5). Additionally, 4 TFs, ET2 (Cs4g13650),
Dof 4.6 (Cs5g01740), MYR2 (Cs5g26420) and OBP2 (Cs8g18320), were highly
positively correlated with the brown module, which was the largest
cluster in these 16 TFs and had a positive correlation with quinic acid
([148]Fig 5). Two Dof family TFs were in the brown module ([149]Table 2
and [150]Fig 5).
Fig 5. Module-TF association.
[151]Fig 5
[152]Open in a new tab
Each row corresponds to a module. The number of genes in each module is
indicated on the left. Each column corresponds to a TF. The color of
each cell at the row-column intersection indicates the correlation
coefficient between the module and the TF, and the numbers in each cell
indicate correlation coefficient R and P value, respectively.
Table 2. Differential expression transcription factors (TFs) during fruit
ripening of WT, MT and between MT and WT.
170, 190 and 210 indicate 170, 190 and 210 DAF, respectively. RPKM,
reads per kb per million reads. E-value was calculated by BLAST.
Gene ID Gene Name Family moduleColor MT(RPKM) WT(RPKM) A. thaliana
ortholog gene E value
170 190 210 170 190 210
Cs1g06760 RD26 NAC yellow 41.70 100.33 290.49 97.95 130.30 301.33
AT4G27410.2 1.00E-136
Cs2g02790 WRKY42 WRKY yellow 3.05 9.34 26.03 6.56 11.66 25.68
AT4G04450.1 1.00E-166
Cs3g23950 MYB77 MYB yellow 13.82 14.06 0.81 13.23 3.34 - AT3G50060.1
5.00E-58
Cs1g23790 HAM4 GRAS turquoise 9.43 2.54 1.95 8.70 6.11 0.82 AT4G36710.1
1.00E-162
Cs5g26470 GATA7 GATA turquoise 33.37 7.76 2.76 14.32 3.73 1.34
AT4G36240.1 4.00E-36
Cs7g01850 NTT C2H2 turquoise 25.55 10.73 4.68 17.24 5.25 2.77
AT3G57670.1 1.00E-125
Cs7g03670 CDF2 Dof red 12.55 6.24 7.28 5.94 8.32 3.69 AT5G39660.2
1.00E-95
Cs2g27940 MYB21 MYB_related red - 1.02 7.00 0.07 7.32 31.64 AT3G27810.1
9.00E-55
Cs3g19420 DREB26 ERF midnightblue 43.47 40.92 8.62 20.97 21.59 3.06
AT1G21910.1 4.00E-49
Cs2g12700 MYB62 MYB green 0.80 4.80 - 4.25 3.69 - AT1G68320.1 4.00E-97
Cs4g13650 ET2 HRT-like brown 15.29 4.39 1.64 12.92 10.37 2.56
AT5G56780.1 1.00E-93
Cs5g01740 Dof 4.6 Dof brown 12.95 6.02 2.66 13.67 12.64 4.72
AT4G24060.1 7.00E-73
Cs5g26420 MYR2 G2-like brown 4.56 1.56 - 4.41 5.61 0.42 AT3G04030.3
1.00E-165
Cs8g18320 OBP2 Dof brown 4.41 1.07 - 5.50 4.71 1.31 AT1G07640.2
3.00E-63
Cs8g04300 LBD38 LBD blue 5.88 0.96 2.40 14.86 3.04 1.79 AT3G49940.1
7.00E-66
Cs7g26710 LBD41 LBD blue 8.10 2.67 0.68 36.38 2.73 2.17 AT3G02550.1
1.00E-79
[153]Open in a new tab
In addition, to identify TFs with high GS (Gene Significance GS is the
correlation between the gene and the trait) and MM (module membership
MM is the correlation of the module eigengene and the gene expression
profile), we performed intramodular analysis via WGCNA. A correlation
coefficient (the absolute value) of more than 0.8 and P < 0.05 was used
as cutoff for identifying the significance between physiological traits
and modules ([154]Fig 4B). |GS|≥ 0.8 with P < 0.05, |MM| ≥ 0.8 and P <
0.05 were used as cut-off criteria for identifying genes with high GS
and MM, which were listed in [155]S10 Table. As shown in [156]S10
Table, 4 TFs had a high positive correlation with ABA including two MYB
TFs, one ERF TF and one ZIP TF; 16 TFs were correlated with sucrose; 38
TFs were correlated with fructose (because the expression pattern of
fructose was almost the same as that of glucose, these 38 TFs were also
correlated with glucose); 31 TFs were correlated with quinic acid; 49
TFs were correlated with citric acid and 18 TFs were correlated with
malic acid.
Expression analysis of the candidate TFs
In the present study, TFs were identified from the RNA-seq data of MT
and WT at three ripening stages; therefore, we selected candidate TFs
to perform expression analysis at five different ripening stages to
validate the expression of TFs. Fruits harvested at 150, 170, 190, 210,
and 240 DAF were selected. As expected, these 16 TFs were all
differentially expressed between MT and WT. There were six TFs with
up-regulated expression in WT ([157]Fig 6A) and five TFs with
up-regulated expression in MT ([158]Fig 6B) and five TFs
up/down-regulated in MT/WT ([159]Fig 6C). For the differential ABA
accumulation in WT and MT during ripening and the maturation time also
delayed in MT [[160]24], the analysis of ripening-related TFs revealed
differential regulation between both cultivars.
Fig 6. Expression analysis of TFs at five citrus fruit ripening stages.
[161]Fig 6
[162]Open in a new tab
A, B and C indicate three expression patterns between MT and WT. 150,
170, 190, 210 and 240 indicate 150, 170, 190, 210 and 240 DAF,
respectively. A single asterisk (*) represents a statistically
significant difference (P < 0.05). Analyzed using Student's t-test.
Discussion
Fruit ripening is a genetically programmed, highly coordinated, and
irreversible phenomenon in which the physiology, biochemistry, and
structure of the organ are developmentally altered to influence
appearance, texture, flavor, and aroma [[163]39]. Although the ripening
phenomena varies among species, changes typically include color
modification through the alteration of chlorophyll, carotenoid, and/or
flavonoid accumulation; the modification of sugars, acids, and volatile
profiles that affect nutritional quality, flavor, and aroma; and the
modification of textural via alterations of cell wall structure and/or
metabolism [[164]3]. Transcription factors are a group of proteins that
control cellular processes by regulating the expression of downstream
target genes. TFs have been characterized as pivotal regulators in the
ripening of different fresh fruits [[165]4, [166]11, [167]40, [168]41].
In the present study, a total of 159 TFs were identified and assigned
to different families. Some TFs might be significant regulators during
citrus fruit ripening. The systems approach in data mining via WGCNA
was particularly fruitful in identifying physiological traits,
associated modules and genes for future functional studies. The
hierarchical clustering analyses performed on the differentially
expressed TFs between MT and WT was powerful in identifying different
expression pattern TFs.
Identification of candidate TFs involved in the formation of late-ripening
trait
MT is a late-ripening mutant of WT. In the present study, 52
differentially expressed TFs between MT and WT were identified. The ERF
family contained the greatest number of TFs in the DEG cluster MT vs WT
([169]Fig 1D), indicating that the TFs of the ERF family might be key
regulators for the formation of late-ripening trait of MT. The result
of the GO terms and KEGG pathway enrichment analysis revealed several
TFs involved in phytohormone related biological processes ([170]S9
Table). Particularly, the TFs related to ethylene might play much more
important roles. Combining the cluster analysis of gene expression,
some candidate TFs, such as MYB16 (Cs6g21530), MYB21/77 (Cs2g27940/
Cs3g23950), OBP2 (Cs8g18320) and ERF4 (Cs1g07950) were screened
([171]Table 1 and [172]Fig 3). MYB16 (Cs6g21530), MYB21 (Cs2g27940) and
ERF4 (Cs1g07950) were assigned to different hormone response processes
indicating that these TFs might play a wide range of regulatory roles
during citrus fruit ripening ([173]S9 Table). ERF1 (Cs5g29870), ARF1
(Cs5g32400) and TGA9 (Cs8g15030) were assigned to ethylene, auxin and
salicylic acid signal transduction pathways, respectively. Therefore,
these TFs might be important regulators for the formation of
late-ripening trait.
Identification of candidate TFs involved in citrus fruit ripening
In this study, a total of 144 TFs, which were differentially expressed
during citrus fruit ripening, were identified ([174]S5 Table).
According to the analysis of TF family distribution, the TFs of bHLH,
C2H2, Dof, ERF and MYB families might play significant roles during
citrus fruit ripening, particularly those of C2H2 and Dof families,
which were among the top three families identified in both WT and MT.
To gain a better understanding of TF roles in fruit ripening, GO-based
term classification and KEGG-based pathway enrichment were performed.
In the present study, some important biological processes were
identified, such as ‘response to gibberellin’, ‘response to salicylic
acid’ and ‘response to ethylene’ ([175]S6 and [176]S7 Tables). The GAI
(Cs2g16940), a TF of GRAS family, was enriched in ‘response to
gibberellin’, ‘response to salicylic acid’ and ‘response to ethylene’
and this TF was also enriched in gibberellin signal transduction
pathway.
During the citrus ripening process, the ABA signal pathway may act as a
central regulator, functioning in combination with other hormones,
including ethylene and jasmonic acid (JA) [[177]24, [178]42]. ABA is an
important phytohormone involved in fruit ripening and abiotic stress
[[179]43]. In recent years, considerable progress has been made in the
understanding of ABA signal transduction pathways in fruits. However,
only a few TFs have been identified as important for fruit ripening,
associated with the ABA response, such as VvABF2 [[180]4], MYB10
[[181]6], MYB30 [[182]44] and PacMYBA [[183]5]. In the present study,
several TFs, including ABR1 (Cs3g21660), RD26 (Cs1g06760), DREB26
(Cs3g19420), MYB77 (Cs3g23950), MYB61 (Cs6g01750), MYB62 (Cs2g12700),
were implicated as having differential expression during citrus fruit
ripening ([184]Table 1 and [185]S5 Table). MYB77 and MYB62 exhibited
high correlation with the gray60 module, which was highly correlated
with ABA (Figs [186]4 and [187]5). In previous studies, these TFs were
shown to respond to an ABA signal involved in abiotic stress [[188]45],
lateral root growth [[189]46] and stomatal aperture [[190]47]. RD26 is
an activator of ABA signal transduction, and Arabidopsis transgenic
plants overexpressing RD26 were highly sensitive to ABA, and
RD26-repressed plants were insensitive [[191]45]. In the present study,
RD26 was up-regulated in WT during the entire ripening stage. DREB26
can largely influence plant development, for which overexpression in
Arabidopsis resulted in deformed plants [[192]48]. MYB77 could directly
interact with ABA receptor PYL8 and activate auxin signal transduction
involved in lateral root growth [[193]46]; in the present study, it was
up-regulated in MT and showed a differential expression pattern in MT
and WT ([194]Fig 6). However, the functions of these TFs in the fruit
ripening are unknown; therefore, these TFs may also have a similar
function in response to ABA, which are valuable for further study in
fresh fruit. In climacteric fruit, ERF family TFs have been implicated
in hormone biosynthesis, fruit ripening and carotenoid synthesis in
several fruits, such as the tomato [[195]10, [196]11, [197]49], apple
[[198]50, [199]51], kiwifruit [[200]40] and longan [[201]52]. However,
the actual functions of fruit AP2/ERF genes are still poorly
understood, and furthermore, the role for these genes in nonclimacteric
fruit remains unclear. In the present study, ERF family TFs were the
largest cluster genes in MT vs WT ([202]Fig 1D), suggestingthat ERF TFs
may play an important role in the formation of later-ripening traits in
MT and other ripening related processes. JA is another important
phytohormone involved in anthocyanin accumulation [[203]53].
Anthocyanins as one of the flavonoids are biosynthesized through the
flavonoid pathway. The members of MYB-bHLH-WD40/WDR (MBW), an important
regulatory mechanism for modulating anthocyanin accumulation, bHLH and
MYB, have been extensively studied [[204]17, [205]54, [206]55]. In the
present study, several bHLH and MYB TFs have been identified during
fruit ripening, particularly in MT. A total of 14 bHLH TFs were
identified ([207]Fig 1). Although citrus fruit do not accumulate
anthocyanin, these TFs may interact with the JA signal pathway involved
in the biosynthesis of flavonoids or other processes. In the present
study, MYB21 was up-regulated in WT more than 10-fold compared with MT
at 190 DAF ([208]Fig 6), which interacted with jasmonate involved in
stamen filament growth in Arabidopsis [[209]56]. Thus, this gene may
also have other functions in citrus fruit ripening interacting with JA.
In addition, MYB21 was highly correlated with the red module, which was
highly correlated with glucose and fructose (Figs [210]3 and [211]4);
therefore, MYB21 may be involved in sugar metabolism during fruit
ripening. The LBD family TFs act as repressors of anthocyanin synthesis
and affect additional nitrogen responses, which also regulate sectors
of flavonoid biosynthesis [[212]57].
Carbon and nitrogen metabolism, chloroplast development and the light
response pathway are important for plant development and ripening.
Previous studies have shown that Dof, C2H2 and GATA family TFs play
pivotal roles in these metabolic pathways [[213]19, [214]58–[215]61].
Transgenic sweet potato plants overexpressing SRF1 (a Dof TF)
significantly increased the content of storage root dry matter and
starch, while the glucose and fructose content drastically decreased,
and the enzymes involved in sugar metabolism, and soluble acid
invertase showed decreased activity in transgenic plants [[216]58]. In
wheat, the expression of TaDof1 was influenced by the levels of
nitrogen [[217]62]. Themutation of GNC (GATA21) reduces chlorophyll
levels and produces defects in the regulation of genes involved in
sugar metabolism [[218]63]. Plants must respond to several
environmental cues, one of the most important being light. Ward et al.
[[219]20] reported that Dof TF OBP3 was regulated by light in
Arabidopsis thaliana and suggested a model where OBP3 is a component in
both phyB and cry1 signaling pathways, acting as positive and negative
regulators, respectively. The Dof transcription factor can also respond
to photoperiod regulation activating flowering. AtCDFs (CYCLING DOF
FACTOR) are a group of commonly studied Dof TFs in response to the
photoperiod, such as AtCDF1 [[220]64], AtCDF2 [[221]65]. C2H2 TFs
function as part of a large regulatory network that senses and responds
to different environmental stimuli [[222]14]. Transgenic Arabidopsis
plants that constitutively express Zat12 (comprising two C2H2-type zinc
finger domains) are more tolerant to high light and osmotic and
oxidative stresses, and Zat12 antisense and knockout plants are more
sensitive to light, osmotic stress and salinity [[223]66, [224]67]. In
the present study, numerous C2H2 and Dof family TFs and several GATA
family TFs were identified during citrus fruit ripening ([225]Fig 1).
Some C2H2 and Dof TFs were highly correlated with glucose, quinic acid
and citric acid ([226]S10 Table). Some TFs related to light responses,
such as GATA7 [[227]59] and MYR2 [[228]68], showed differential
expressed between MT and WT ([229]Fig 6). However, the functions of
these TFs in fruit development and ripening are not clear; thus, these
TFs may play roles in the regulation of sugar and acid metabolism and
fruit coloration responding to light.
The degradation of organic acids for fruit ripening is also important.
Organic acids and soluble sugars contribute highly to the flavor and
overall quality of citrus fruit. Organic acids play an essential role
in energy generation, response to nutritional shortage [[230]69] and
metal ion stress [[231]70]. Many of the structural genes involved in
the metabolism have been isolated from various fruits
[[232]71–[233]73]. In the present study, many TFs correlated with
citric acid, quinic acid and malic acid during navel orange fruit
ripening have been identified ([234]S10 Table). Hence, TFs may play a
significant role during the degradation of organic acids. In addition,
some of the TFs correlated with organic acids were assigned to the
plant hormone signal transduction pathway, such as Cs8g15030 (TGA9) and
Cs6g16030 (ARF8); therefore, plant hormones may play an important role
in the metabolism of organic acids.
In conclusion, in this study, we have identified numerous important TFs
involved in citrus fruit ripening on the platform of the later-ripening
bud mutant "Fengwan" navel orange and its wild-type "Fengjie" navel
orange. The identified TFs belong to different families and are
primarily assigned to the C2H2, Dof, bHLH, ERF, NAC, MYB and LBD
families. Recently, several TFs have been studied in perennial fruit;
herein, we determined a large cluster of TFs related to fruit ripening
to provide information for the screening of TFs for further functional
analysis.
Supporting Information
S1 Fig. The family distribution of transcription factors identified in
all samples (A), MT (B) and WT (C).
(TIF)
[235]Click here for additional data file.^ (729.2KB, tif)
S2 Fig. Biological process (A) and molecular function (B) enrichment
analysis of the TFs differentially expressed between MT and WT during
fruit ripening.
Bubble color indicates the p-value; plot size indicates the frequency
of the GO term in the underlying GOA database (bubbles of more general
terms are larger).
(TIF)
[236]Click here for additional data file.^ (1.7MB, tif)
S3 Fig. Multidimensional scaling plot of dissimilarities between genes,
based on topological overlap.
(TIF)
[237]Click here for additional data file.^ (476.4KB, tif)
S4 Fig. The eigengenes expression of 32 modules clustered via WGCNA.
(TIF)
[238]Click here for additional data file.^ (870.7KB, tif)
S5 Fig. The module assignment of TFs of WT (A), MT (B), MT vs WT (C)
and the TFs included in these three clusters (D).
The number of TFs in each module is indicated at the right.
(TIF)
[239]Click here for additional data file.^ (172.8KB, tif)
S1 Table. The values of fold-change with their respective p-values and
FDR values for all genes.
(XLS)
[240]Click here for additional data file.^ (23.2MB, xls)
S2 Table. The total genes used for WGCNA analysis.
WT1, WT2 and WT3 indicate 170, 190 and 210 DAF of WT, respectively;
MT1, MT2 and MT3 indicate 170, 190 and 210 DAF of MT, respectively.
RPKM, reads per kb per million reads; GS, gene significance; p.GS, p
value of GS; MM.module and p.MM.module indicate the correlation
coefficient and P value, respectively.
(XLSX)
[241]Click here for additional data file.^ (22.7MB, xlsx)
S3 Table. Primer sequences for real-time PCR.
(XLSX)
[242]Click here for additional data file.^ (11.9KB, xlsx)
S4 Table. Identified transcription factors in WT and MT.
(XLSX)
[243]Click here for additional data file.^ (152.9KB, xlsx)
S5 Table. Identified differential expressed transcription factors of
WT, MT and MT vs WT.
WT1, WT2 and WT3 indicate 170, 190 and 210 DAF of WT, respectively;
MT1, MT2 and MT3 indicate 170, 190 and 210 DAF of MT, respectively.
RPKM, reads per kb per million reads; MM.module and p.MM.module
indicate the correlation coefficient and P value, respectively. MT vs
WT indicate the TF cluster, which is differentially expressed between
MT and WT.
(XLSX)
[244]Click here for additional data file.^ (251.6KB, xlsx)
S6 Table. Gene ontology enrichment analysis (p-value < 0.01) of the TFs
differentially expressed during fruit ripening in both MT and WT.
(XLSX)
[245]Click here for additional data file.^ (12.8KB, xlsx)
S7 Table. Enriched (p-value < 0.01) GO term gene list differentially
expressed during fruit ripening in both MT and WT relative to hormones.
(XLSX)
[246]Click here for additional data file.^ (9.8KB, xlsx)
S8 Table. Gene ontology enrichment analysis (p-value < 0.01) of the TFs
differentially expressed between MT and WT during fruit ripening.
(XLSX)
[247]Click here for additional data file.^ (11.6KB, xlsx)
S9 Table. Enriched (p-value < 0.01) GO term gene list differentially
expressed between MT and WT during fruit ripening relative to hormones.
(XLSX)
[248]Click here for additional data file.^ (11.8KB, xlsx)
S10 Table. Identified TFs with high GS and MM associated with
physiological traits.
GS, gene significance; p.GS, p value of GS; MM, module membership;
p.MM, p value of MM.
(XLS)
[249]Click here for additional data file.^ (248KB, xls)
Acknowledgments