Abstract

   Abelmoschus esculentus (okra or lady’s fingers) is a vegetable with
   high nutritional value, as well as having certain medicinal effects. It
   is widely used as food, in the food industry, and in herbal medicinal
   products, but also as an ornamental, in animal feed, and in other
   commercial sectors. Okra is rich in bioactive compounds, such as
   flavonoids, polysaccharides, polyphenols, caffeine, and pectin. In the
   present study, the concentrations of total flavonoids and
   polysaccharides in five organs of okra were determined and compared.
   Transcriptome sequencing was used to explore the biosynthesis pathways
   associated with the active constituents in okra. Transcriptome
   sequencing of five organs (roots, stem, leaves, flowers, and fruits) of
   okra enabled us to obtain 293,971 unigenes, of which 232,490 were
   annotated. Unigenes related to the enzymes involved in the flavonoid
   biosynthetic pathway or in fructose and mannose metabolism were
   identified, based on Kyoto Encyclopedia of Genes and Genomes (KEGG)
   pathway analysis. All of the transcriptional datasets were uploaded to
   Sequence Read Archive (SRA). In summary, our comprehensive analysis
   provides important information at the molecular level about the
   flavonoid and polysaccharide biosynthesis pathways in okra.

   Keywords: flavonoids, glycosyltransferase, polysaccharide, okra,
   transcriptome

1. Introduction

   Okra (lady’s finger; Abelmoschus esculentus (L.) Moench), a crop which
   is a member of the mallow family, is widely distributed in tropical to
   subtropical regions [[34]1]. Okra is grown as a health protective
   vegetable in China and it has a great deal of medicinal values. Several
   bioactive constituents of okra, including flavones, polysaccharides,
   pectin, alkaloids, and linoleic acid, were identified in previous
   reports [[35]2,[36]3,[37]4]. These bioactives can help digestion, and
   have anti-cancer, anti-tumor, anti-diabetes, antibiosis,
   anti-inflammatory properties, confer liver protection, and other health
   care effects [[38]5,[39]6,[40]7].

   Previous studies centered on the purification and characterization of
   bioactive substances in okra. Water extract of okra pods are very
   thick, due to the high concentration of polysaccharide-based mucilages,
   which are frequently used as thickening agents for soups [[41]8]. Okra
   polysaccharides can also be used instead of egg white as a fat
   substitute for chocolate biscuits and chocolate frozen desserts
   [[42]9,[43]10]. Of polysaccharides from okra mucilage reacted with a
   number of plant-based agglutinins, with the greatest response to
   mistletoe toxic lectin-I (ML-I), as well as precipitating well with
   agglutinins from Abrus precatorius, Momordica charantia, and Ricinus
   communis [[44]11]. It was found that okra polysaccharide was composed
   of rhamnose, galactose, and galacturonic acid, and also contained
   rhamnogalacturonan I, which is an acidic polysaccharide
   [[45]12,[46]13]. The anti-tumor and immune-stimulatory activities of
   natural polysaccharides have attracted much attention, due to their low
   toxicity and minimal side effects [[47]14,[48]15,[49]16]. OFPS11, a
   water-soluble polysaccharide, has been extracted and purified from okra
   flowers [[50]17]. Three purified fractions of okra polysaccharides were
   obtained by water extraction and alcohol precipitation [[51]18]. A new
   flavonol glycoside (5,7,3′,4′-tetrahydroxy-4′′-O-methyl flavonol
   -3-O-β-D- glucopyranoside) was isolated from the fruit of okra
   [[52]19]. Reverse-phase high performance liquid chromatography (RPHPLC)
   was used to determine the content of the three flavonoid glycosides and
   the total flavonoid in different species, as well as in different
   tissues and organs of okra [[53]20]. High performance liquid
   chromatography (HPLC) method was used to determine the contents of the
   five flavonoids (rutin, hyperoside, isoquercitrin, myricetin,
   quercitrin) simultaneously in the Abelmoschus moschatus Medicus capsule
   [[54]21].

   Previous studies on okra have mainly focused on genetic breeding,
   cultivation techniques, nutritional components, and medicinal efficacy
   [[55]22,[56]23,[57]24,[58]25,[59]26]. Most of the molecular biology
   studies on okra were based on molecular markers, such as inter-simple
   sequence repeat (ISSR) and sequence-related amplified polymorphism
   (SRAP) markers, to analyze the genetic diversity of okra germplasm
   resources [[60]27,[61]28]. Because the okra genome has not been
   sequenced, there is little published research on the identification and
   organ-specific expression patterns of the synthesis genes associated
   with the synthesis of bioactive constituents in this crop. In the
   current study, transcription databases from the roots, stems, leaves,
   flowers, and fruits of okra were established. Very large numbers of
   unigenes related to flavonoid and polysaccharide biosynthesis were
   identified. Our transcriptome data are a useful resource to investigate
   expression profiles of the biosynthesis genes of bioactive constituents
   in okra.

2. Materials and Methods

2.1. Plant Materials

   Okra (Abelmoschus esculentus cv. Xianzhi) plants were grown in the farm
   of Zhejiang Academy of Agricultural Sciences in the Zhejiang Province
   of China under natural conditions. Roots, stems, and leaves were
   collected from four-week-old okra seedlings. Flowers were collected
   after opening; fruits were harvested at 5 days after anthesis. Three
   biological replicates were taken for each organ. Samples were
   immediately snap-frozen in liquid N[2], and then stored at −80 °C until
   they were used.

2.2. RNA Isolation and Library Construction

   Each sample from five organs was produced as an independent pool (a
   total of 15 pools). Total RNA was extracted using the RNAiso for
   Polysaccharide-rich Plant Tissue reagent (TAKARA, Dalian, China)
   according to the manufacturer’s protocol, and was then quantified using
   a Nanodrop8000 (ThermoFisher, Waltham, MA, USA) and Agilent 2100
   Bioanalyzer (Agilent, Santa Clara, CA, USA) with RIN number >7.0. The
   sequencing libraries were generated using the VAHTS mRNA-Seq v2 Library
   Prep Kit for Illumina^® (Vazyme, Nanjing, China) following
   manufacturer’s recommendations. Firstly, mRNA was purified from total
   RNA using poly-T oligo-attached magnetic beads. Fragmentation was
   performed using divalent cations under elevated temperature in Vazyme
   Frag/Prime Buffer (Vazyme). The cleaved RNA fragments were copied into
   first strand cDNA using reverse transcriptase and random primers.
   Second strand cDNA synthesis was subsequently performed using buffer,
   dNTPs, DNA polymerase I, and RNase H. Then, the cDNA fragments were end
   repaired with the addition of a single “A” base at the 3′-end of each
   strand, and subsequently ligated with the special sequencing adapters
   (Vazyme). The products were purified, and size selected with VAHTS^TM
   DNA Clean Beads (Vazyme) in order to get appropriate size for
   sequencing. PCR was performed, and aimed products were purified
   finally. PCR was performed with Phusion^® High-Fidelity DNA Polymerase
   (New England Biolabs, Ipswich, MA, USA). The number of cycles was
   adjusted according to the content of cDNA. For example, the amount of
   cDNA is 1 μg, using 6 cycles; when the amount of cDNA is 50 ng, with 10
   cycles. Agilent 2100 Bioanalyzer (Agilent) system was used for library
   quality control.

2.3. Sequencing, Quality Control, and De Novo Assembly

   The raw reads were obtained from an Illumina HiSeq X Ten platform
   (Illumina, San Diego, CA, USA), and then filtered to exclude reads
   containing adaptors, reads with >5% unknown nucleotides, and
   low-quality reads with >20% of bases with a quality value ≤10. The
   obtained clean data was used to calculate the Q20 (proportion of
   nucleotides with quality value larger than 20), Q30 (proportion of
   nucleotides with quality value larger than 20), and GC-content. A de
   novo strategy was employed to assemble the transcriptome of okra based
   on a total of 104.375 Gb of data, using the Trinity assembly program
   with default parameters to form contigs [[62]29]. TIGR Gene Indices
   clustering tool (Tgicl) was used to cluster assembly sequences and
   redundancy removal (setting: -l 40 -c10 -v 25 -O′-repeat_stringency
   0.95 -minmatch 35 -minscore 35′) [[63]30]. The assembly sequences no
   shorter than 200 were abstracted as unigenes for subsequent analysis.
   All of the transcriptional data were released in Sequence Read Archive
   (SRA) (SRP130180).

   To identify transcripts homologous to those in model species, the
   functions of unigenes were annotated by BLAST program (e-value 1 ×
   10^−5) against protein databases, including NCBI (National Center for
   Biotechnology Information) non-redundant protein sequences (Nr),
   Swiss-Prot protein, Gene Ontology (GO), Clusters of Orthologous Groups
   of proteins (COG) and Kyoto Encyclopedia of Genes and Genomes (KEGG)
   databases. Annotation with the associated metabolic pathway was
   achieved by KEGG, and GO annotation was carried out by Blast2GO
   [[64]31]. Gene ontology functions of all unigenes were classified by
   the Web Gene Ontology Annotation Plot (WEGO) program [[65]32].

2.4. Calculation of the Expression Level of the Transcripts and Analysis of
Differentially Expressed Genes

   The technique of fragments per kilobase of transcripts per million
   mapped reads (FPKM) was used to calculate the expression level of each
   unigene. Differentially expressed unigenes were analyzed using DESeq2
   package, based on the negative binomial distribution test [[66]33]. The
   thresholds of significant differentially expressed unigenes in
   comparisons between any two organs were FDR (False discovery rate) ≤
   0.05 and |log2Fold change| ≥ 1. MultiExperiment Viewer [[67]34] (MeV)
   (version 4.9.0) was used to group all differentially expressed genes
   (DEGs) into 25 clusters by a K-means algorithm, based on their log[2]
   values of transcript abundance [[68]35].

2.5. Determination of Total Flavonoids and Polysaccharides in Okra

   Samples of the five organs were collected as described in [69]Section
   2.1, with three replicates. To determine the polysaccharide contents in
   each of five different organs, phenol–sulfuric acid method was applied
   in this study. Briefly, the dried samples were ground into fine
   particles. An aliquot (0.1 g) of powdered sample was defatted with 80%
   (v/v) ethanol under continuous stirring at 90 °C for 3 h, and then
   filtered, and the sample washed with 80% (v/v) ethanol three times. The
   dried residue was extracted with 150 mL of double-distilled water for 3
   h at 90 °C. The residue was washed three times with double-distilled
   water. Each sample produced 200 mL of combined aqueous extracts. An
   aliquot (1 mL) of the aqueous extracts was added to 1 mL phenol
   solution and 5 mL concentrated sulfuric acid solution and incubated for
   30 min in a water bath at 40 °C. After cooling, the absorbance of 490
   nm was determined to quantify polysaccharide concentration. To
   determine the concentration of total flavonoids in each plant organ,
   the Plant Flavonoids test kit (Jiancheng Bioengineering, Nanjing,
   China) was used, according to the manufacturer’s instruction.

3. Results

3.1. Results of De Novo Assembly and Unigene Annotation

   Three replicate samples from each of the five organs, namely roots,
   stems, leaves, flowers, and fruits, were collected from okra for
   paired-end transcriptome sequencing. A total of 716,330,252 raw reads
   were obtained from all the cDNA libraries. After filtering out adaptors
   and low-quality reads, 695,836,110 clean reads were generated,
   containing 104.375 Gbp clean data ([70]Table 1). The number of clean
   reads in the roots was 46,916,458 (97.22% of the raw reads), 47,097,672
   (97.27% of the raw reads) and 46,112,238 (97.24% of the raw reads)
   clean reads for the three replicates. For stems, the values were
   47,316,422 (97.26%), 47,323,464 (97.19%), and 43,132,910 (97.242%)
   clean reads. For leaves, the values were 47,341,802 (97.36%),
   46,142,720 (97.30%), and 49,272,238 (97.34%) clean reads. For flowers,
   the values were 42,777,662 (97.05%), 45,883,906 (97.10%), and
   41,960,012 (96.87%) clean reads. For fruits, the values were 49,905,802
   (96.93%), 48,083,880 (97.26%), and 46,568,924 (97.25%) clean reads.
   Trinity software was used to assemble the clean reads of each sample.
   Finally, a total length of 0.38 Gb unigenes was generated, with the
   mean length of a unigene being 1297 bp (N50: 1885 bp) (N50 value
   defined as the length for which the collection of all sequences of that
   length or longer contains at least half of the sum of the lengths of
   all sequences) ([71]Table S1).

Table 1.

   Sequencing output statistics of 15 samples.
   Samples Total Raw Reads Total Clean Reads Total Clean Nucleotides (nt)
   Q20 Percentage Q30 Percentage N Percentage GC Percentage
   Root1 48,257,634 46,916,458 7,037,468,700 97.80% 94.49% 0.01% 44.72%
   Root2 48,419,454 47,097,672 7,064,650,800 97.92% 94.74% 0.01% 44.86%
   Root3 47,422,632 46,112,238 6,916,835,700 97.79% 94.47% 0.00% 44.82%
   Stem1 48,647,618 47,316,422 7,097,463,300 97.88% 94.62% 0.01% 44.91%
   Stem2 48,693,636 47,323,464 7,098,519,600 97.94% 94.75% 0.01% 44.96%
   Stem3 44,275,350 43,132,910 6,469,936,500 97.21% 93.29% 0.00% 44.61%
   Leaf1 48,625,474 47,341,802 7,101,270,300 98.00% 94.84% 0.01% 45.61%
   Leaf2 47,424,186 46,142,720 6,921,408,000 97.85% 94.52% 0.00% 45.95%
   Leaf3 50,618,708 49,272,238 7,390,835,700 97.84% 94.49% 0.00% 45.78%
   Flower1 44,078,602 42,777,662 6,416,649,300 97.89% 94.64% 0.00% 45.22%
   Flower2 47,743,650 45,883,906 6,882,585,900 97.85% 94.51% 0.00% 45.90%
   Flower3 43,314,640 41,960,012 6,294,001,800 97.85% 94.52% 0.00% 45.59%
   Fruit1 51,485,706 49,905,802 7,485,870,300 97.45% 93.84% 0.00% 45.26%
   Fruit2 49,436,520 48,083,880 7,212,582,000 98.01% 94.90% 0.01% 44.67%
   Fruit3 47,886,442 46,568,924 6,985,338,600 97.92% 94.70% 0.00% 44.83%
   All 716,330,252 695,836,110 104,375 Gb
   [72]Open in a new tab

   Q20 percentage shows the proportion of nucleotides. Q30 percentage
   shows the proportion of nucleotides with quality value larger than 30.
   N: proportion of unknown nucleotides in clean reads.

   The distribution of unigene lengths are shown in [73]Figure 1a, and a
   summary of the sequencing output results and assembly quality
   statistics are listed in [74]Table 1 and [75]Table S1, respectively. To
   determine functions of the assembled unigenes in okra, all of the
   unigene sequences were run against six databases, using BLASTX (with
   e-value as 1 × 10^−5) (BLASTN for Nt database (NCBI non-redundant
   nucleotide sequences), with e-value as 1 × 10^−10). In total, 225,656
   unigenes are annotated in the Nr database, 204,461 in the Nt database,
   154,530 in the SwissProt database, 137,117 in the KEGG database, 94,019
   in the COG database, and 182,512 in the GO database ([76]Figure 1b).
   Based on these annotations, 232,490 of the okra unigenes have at least
   one hit in six databases. As the genome of okra has not been published,
   we blasted the sequences of all unigenes against other species via the
   Nr database. A total of 783 plant species exhibited mRNA sequences
   homologous to okra unigenes. The results showed similarity overlap
   between okra and Ricinus communis (21.55%), Vitis vinifera (20.58%),
   Populus trichocarpa (16.78%), Amygdalus persica (12.85%), Fragaria
   vesca ssp. vesca (4.2%), Glycine max (4.2%), and Gossypium hirsutum
   (3.31%) ([77]Figure 1c).

Figure 1.

   [78]Figure 1
   [79]Open in a new tab

   Characteristics of unigenes generated by Illumina sequencing. (a) The
   length distribution of all assembled unigenes. (b) The number of
   unigenes annotated by different databases, including NR, NT,
   Swiss-Prot, KEGG, COG, and GO. (c) Species distribution of the top
   BLAST hits for all homologous sequences. (d) COG function
   classification of all unigenes. NR: Non-redundant; NT: Nucleotide;
   KEGG: Kyoto Encyclopedia of Genes and Genomes; COG: Clusters of
   Orthologous Groups; GO: Gene Ontology.

3.2. Classification of Enriched Gene Ontology Terms and KEGG Pathways

   The GO database was used to categorize standardized gene functions. GO
   analysis organized the predicted functions of unigenes into three main
   ontology terms: molecular function, cellular component, and biological
   process. Based on the information from the Nr annotation, we obtained
   the GO function annotation by Blast2GO and WEGO software ([80]Figure
   2a) [[81]32]. In the biological process GO term, the most frequent was
   “cellular process”, with 122,914 unigenes. In the cellular component
   ontology, “cell” and “cell part” were the most highly represented
   groups, with 140,964 and 140,713 unigenes, respectively. Lastly, in the
   molecular function category, the largest numbers were found in
   “binding” (92,021) in molecular function category. Furthermore, we
   compared all unigenes against the COG database to predict the possible
   functions of these unigenes. A total of 187,006 unigenes were matched
   and allocated to 25 functional categories ([82]Figure 1d). The three
   most frequent categories were “General function prediction only”
   (33,456 unigenes, 17.89% of the total), “Transcription” (19,635,
   10.50%), and “Signal transduction mechanisms” (17,134, 9.16%). The COG
   analysis helped us to understand the protein function distribution
   characteristics and functional classification statistics in okra.

Figure 2.

   [83]Figure 2
   [84]Open in a new tab

   Gene Ontology and KEGG terms assignment of okra unigenes. (a) A total
   of 182,513 unigenes were assigned to different GO terms. (b) A total of
   137,117 unigenes were assigned to different KEGG terms. Different color
   blocks represent different terms, from top to down, “Organismal
   Systems”, “Genetic Information Processing”, “Environmental Information
   Processing”, “Cellular Processes” and “Metabolism”. Proportion of
   unigenes in each second level term in “Organismal Systems” (c),
   “Genetic Information Processing” (d), “Environmental Information
   Processing” (e), and “Metabolism” (f).

   To identify the biological functions of the okra genes, all of the
   assembled unigenes were classified into KEGG pathways by using BLASTX
   against the KEGG database. A total of 137,117 unigenes were classified
   into five main KEGG functional subcategories, including organismal
   systems, genetic information processing, cellular process,
   environmental information processing, and metabolism ([85]Figure 2b).
   In total, the unigenes were assigned to 128 KEGG pathways. The most
   heavily enriched KEGG pathways were related to metabolic pathways. In
   “genetic information processing” subcategory, the “translation” term
   contained the largest number of unigenes (40.02%) ([86]Figure 2d). In
   the “metabolism” subcategory, the “Global map” term contained the
   largest number of unigenes (41.88%) ([87]Figure 2f).

3.3. Differential Gene Expression Analysis and KEGG Pathway Classification
among Different Organs

   To identify genes which were differentially expressed among the five
   organs, the FPKM values were used. Based on our data, and analyzed
   using a significance threshold of at least a 2-fold difference in
   expression and a FDR < 0.05, a total of 90,601 significant DEGs were
   identified between the fruits and any of the other four organs
   ([88]Figure 3a). To show the major trends in organ-specific expression
   between five organs, all 90,601 differentially expressed unigenes were
   assigned to 25 clusters by MeV software, using hierarchical clustering
   (HCL) and the K-means clustering method. Transcript levels of unigenes
   in cluster 22 were higher in roots than in the other organs; unigenes
   in cluster 9 were highly expressed in stems, while unigenes belonging
   to clusters 5, 6, and 8 were highly expressed in leaves. Unigenes in
   clusters 10, 12, 13, 14, 15, 16, and 20 were highly expressed in
   flowers, while transcript levels of unigenes in cluster 11 and 24 were
   very high in fruits ([89]Figure 3b).

Figure 3.

   [90]Figure 3
   [91]Open in a new tab

   Transcriptional variation between five different organs of okra. (a)
   Expression profiles of the differentially expressed unigenes (DEGs)
   between five different organs are illustrated by a heatmap. (b) Cluster
   analysis by the K-means method from the gene expression profiles. Red
   circles indicated the organ-specific expressed genes.

   We then identified differentially expressed unigenes between fruits and
   any of the other four organs. Based on our analysis, the DEGs in “Fr
   (fruits) vs. R (roots)”, “Fr vs. S (stems)”, “Fr vs. L (leaves)”, and
   “Fr vs. Fl (flowers)” were determined to be 22,841, 9883, 45,397, and
   53,626, respectively ([92]Figure 4a). In “Fr vs. R”, 7493 upregulated
   and 15,348 downregulated genes were identified. In “Fr vs. S”, there
   were 2827 upregulated and 7065 downregulated genes. In fruits compared
   to leaves, 17,276 upregulated and 28,121 downregulated genes were
   identified. The comparison between fruits and flowers showed the
   largest number of differentially expressed genes, with 32,061 genes
   upregulated and 21,585 downregulated in fruits. A Venn diagram was used
   to compare the four datasets ([93]Figure 4b). In detail, 1173 DEGs were
   common to both “Fr vs. R” and “Fr vs. S” comparisons, while 1327 DEGs
   were identified in both “Fr vs. S” and “Fr vs. L” comparisons. A total
   of 13,683 DEGs were common to both “Fr vs. L” and “Fr vs. Fl”
   comparisons, while 497 DEGs were identified in both “Fr vs. Fl” and “Fr
   vs. R” comparisons. There were 1825 DEGs common to all four comparisons
   ([94]Figure 4b). GO classifications of DEGs in the four different
   comparisons were showed in [95]Figure 4c.

Figure 4.

   [96]Figure 4
   [97]Open in a new tab

   Overview of the DEGs among five different organs in okra. (a) DEGs in
   different comparisons, including R vs. Fr, S vs. Fr, L vs. Fr, and Fl
   vs. Fr, are showed by volcanic map. (b) Venn diagrams of the DEGs in
   the four different comparisons. (c) GO classification of DEGs in the
   four different comparisons. From top to down, R vs. Fr comparison, S
   vs. Fr comparison, L vs. Fr comparison and Fl vs. Fr comparison. R:
   root; S: stem; L: leaf; Fl: flower; Fr: Fruit.

   To identify the biological functions of the fruit-specifically
   expressed genes in okra, all DEGs between the fruits and the other
   organs (Fr vs. R, Fr vs. S, Fr vs. L, and Fr vs. Fl) were subjected to
   KEGG pathway enrichment analysis. A total of 13,396 DEGs between Fr and
   R were allocated to 128 pathways, 5,571 DEGs to 121 pathways (Fr vs.
   S), 26,040 DEGs to 127 pathways (Fr vs. L) and 30,415 DEGs to 128
   pathways (Fr vs. Fl) ([98]Tables S2–S5). The ten most highly
   represented pathways in each comparison are shown in [99]Figure 4c,
   according to the biological process GO term analysis. GO term analysis
   indicated that the organ-specific DEGs were involved in various
   biological processes. Interestingly, three pathways, metabolic
   pathways, biosynthesis of secondary metabolites and plant–pathogen
   interaction, were each significantly enriched with respect to DEGs in
   the R vs. FR, and L vs. FR. Plant–pathogen interaction, biosynthesis of
   secondary metabolites, and plant hormone signal transduction, were each
   significantly enriched in DEGs from the S vs. FR comparison, while
   metabolic pathways, biosynthesis of secondary metabolites, and plant
   hormone signal transduction, were each significantly enriched in DEGs
   of the Fl vs. Fr comparison.

3.4. Differentially Expressed Genes Comparison of Total Flavonoids and
Polysaccharides in Five Different Organs of Okra

   The contents of total flavonoids and polysaccharides were determined in
   all five organs, namely R, S, L, Fl, and Fr, with significant
   differences in contents being observed between the different organs for
   both components ([100]Figure 5). Total flavonoids and polysaccharides
   were mainly concentrated in flowers and fruits ([101]Figure 5b,c). The
   highest content of total flavonoids was 18.49 mg g^−1 for fruits, and
   the highest content of polysaccharides was 21.76 mg g^−1 for flowers.
   Using the transcriptome data from the five organs, we explored the
   molecular mechanism behind of the organ-specific distribution in the
   active components in okra.

Figure 5.

   [102]Figure 5
   [103]Open in a new tab

   Determination of total flavonoids and polysaccharide contents in five
   different organs of Abelmoschus esculentus (Linn.) Moench. (a) Samples
   from different organs of okra. (b) Determination of total flavonoids
   contents in five organs, including roots, stems, leaves, flowers, and
   fruits. (c) Determination of polysaccharide contents s in five organs,
   including roots, stems, leaves, flowers, and fruits. Values are ±
   standard deviations of three independent replicates. Means marked with
   different letters indicate significant differences at alpha = 0.05
   using Duncan’s multiple range tests. DW: dry weight.

3.5. Differentially Expressed Genes Related to Flavonoid Biosynthetic Pathway
in Okra

   Most plants contain flavonoids, which play an important role in plant
   growth, development, flowering, fruit production, and plant defense
   against pathogens [[104]20]. Total flavonoids were detected in the
   different parts of the okra ([105]Figure 5b). The biosynthesis pathway
   of flavonoids has been identified in a number of plant species, which
   provides us with the opportunity to identify the flavonoid-related
   enzymes in okra. In total, 931 unigenes encoding 13 flavonoid
   biosynthesis-related enzymes were identified and quantified in okra
   ([106]Figure 6). Of these, three enzymes (flavonoid 3',5'-hydroxylase,
   enzyme code (EC): 1.14.13.88; anthocyanidin reductase, EC: 1.13.1.77;
   trans-cinnamate 4-monooxygenase, EC: 1.14.13.11) were highly expressed
   in flowers, while two enzymes (naringenin-chalcone synthase, EC:
   2.3.1.74; chalcone isomerase, EC: 5.5.1.6) exhibited high expression
   levels in fruits and five enzymes (dihydrokaempferol 4-reductase, EC:
   1.1.1.219; flavanone 4-reductase, EC: 1.1.1.234; flavanone
   3-dioxygenase, EC: 1.14.11.9; flavonoid 30-monooxygenase, EC:
   1.14.13.21; naringenin-chalcone synthase, EC: 2.3.1.74) showed lowest
   expression in stems ([107]Figure 6b). The full names of enzymes by EC
   IDs were provided in [108]Table S6.

Figure 6.

   [109]Figure 6
   [110]Open in a new tab

   Significance analysis of the flavonoid biosynthesis related unigenes
   expressions in okra. (a) Schematic representation of the enzymes
   involved in flavonoid biosynthesis pathway. Red indicates significant
   differentially expressed genes. (b) The average expression levels of
   the unigenes associated with each enzyme were calculated based on FPKM
   values. EC: 1.14.13.36: coumaroylquinate (coumaroylshikimate)
   3′-monooxygenase; EC: 2.1.1.104: caffeoyl-CoA 3-O-methyltransferase;
   EC: 1.14.13.88: flavonoid 3′,5′-hydroxylase; EC: 2.3.1.74:
   naringenin-chalcone synthase; EC: 5.5.1.6: chalcone isomerase; EC:
   2.3.1.133: shikimate O-hydroxycinnamoyltransferase; EC: 1.14.11.9:
   flavanone 3-dioxygenase; EC: 1.14.11.23: flavonol synthase; EC:
   1.14.13.21: flavonoid 3′-monooxygenase; EC: 1.14.13.11: trans-cinnamate
   4-monooxygenase; EC: 1.1.1.219: dihydrokaempferol 4-reductase; EC:
   1.1.1.234: flavanone 4-reductase; EC:1.3.1.77: anthocyanidin reductase.
   The grids with different colors from green to red show the relative
   expression levels to log[2] (FPKM), from −3 to 3. EC: enzyme code.

3.6. Differentially Expressed Genes Related to Fructose and Mannose
Metabolism and Glycosyltransferase Genes in Okra

   Our transcriptome data revealed that 691 unigenes encoded 13 key
   enzymes related to fructose and mannose metabolism. A map of proposed
   pathways for polysaccharide biosynthesis in okra was constructed
   ([111]Figure 7a). Unigenes annotated for each enzyme in fructose and
   mannose metabolism are listed in [112]Table S5. The largest number of
   unigenes (113) was annotated as aldehyde reductase (EC: 1.1.1.21)
   encoding genes. The second largest number of unigenes (94) was
   identified as hexokinase (EC: 2.7.1.1) encoding genes, while 22
   unigenes were mannitol-1-phosphate 5-dehydrogenase (EC: 5.4.2.8)
   encoding gene ([113]Table S7). The expression levels of enzymes were
   calculated based on average FPKM values of the unigenes ([114]Figure
   7a).

Figure 7.

   [115]Figure 7
   [116]Open in a new tab

   Expression patterns of the putative fructose and mannose metabolism and
   polysaccharide biosynthesis unigenes in okra. (A) The pathway of
   fructose and mannose metabolism is based on KEGG analysis. The full
   names of enzymes by EC IDs are provided in [117]Table S7. The average
   expression level of the enzyme encoding unigenes in various organs is
   indicated by a heat map. (B) Analysis of glycosyltransferase unigenes
   in four fruit contained comparisons, including R vs. Fr, S vs. Fr, L
   vs. Fr, and Fl vs. Fr. The grids with different colors from green to
   red show the relative expression levels to log[2](FPKM), from −3 to 3.
   The unigene IDs and the expression level of all glycosyltransferase
   encoding genes are listed in [118]Table S8.

   The genes encoding three enzymes, (guanosine diphosphate) GDP-L-fucose
   synthase (EC: 1.1.1.271), Guanosine diphosphate-mannose 4,6-dehydratase
   (EC: 4.2.1.47), and phosphomannomutase (EC: 5.4.2.8), were more highly
   expressed in fruits than in any other okra organs. The biosynthesis of
   glycan and glycosides requires the participation of glycosyltransferase
   (GTs; EC: 2.4.x.y) enzymes, which are an important and a very
   widespread group of enzymes in higher plants [[119]36]. Based on BLASTX
   [[120]37] comparison of the five transcriptomes, a total of 970
   unigenes were annotated to GT genes, consisting of 44
   fucosyltransferase genes, 184 mannosyltransferase genes, 104
   xylosyltransferase genes, and 638 glucosyltransferase genes. The
   expression patterns of GT genes were exhibited as a heat map performed
   by the MeV tool ([121]Figure 7b). The expression levels of four
   family’s genes were listed in [122]Table S8.

4. Discussion

   Okra is widely grown as a vegetable, and as a source of herbal
   medicines in China [[123]38]. Flavonoids and polysaccharides are two of
   the most important active constituents of okra. In many plants, a large
   number of genes associated with polysaccharide and flavonoid
   biosynthesis have been identified [[124]39,[125]40,[126]41], but little
   is known about the tissue-specific distribution and the molecular basis
   of biosynthesis of these two active components in okra. The aim of the
   present study was to identify genes associated with flavonoid and
   polysaccharide synthesis, and to explain the mechanism of
   organ-specific distribution of flavonoids and polysaccharides in this
   plant.

   High-throughput sequencing is suitable for the study of the genome of a
   non-model plant, which has not been sequenced [[127]42]. Neither the
   genome size or chromosome count for okra has been precisely reported.
   Okra (usually 2n = 130) is probably an allotetraploid, derived from a
   wild species from India (Abelmoschus tuberculatus Pal & H.B. Singh, 2n
   = 58), and a species (possibly Abelmoschus ficulneus (L.) Wight & Arn.
   ex Wight, 2n = 72 [[128]43]). Omics technologies (genomics,
   transcriptomics, proteomics, and metabolomics) were potential tools to
   understand the environmental factors influencing okra growth and
   adaptation [[129]44]. In recent years, some okra transcriptomics data
   have been published. More than 150,000 unigenes were generated and
   assembled from the combined leaf and pod transcriptome of okra by
   Schafleitner’s group [[130]45]. In the present study, 15 transcriptome
   datasets from five organs of okra, composed of 293,971 unique
   sequences, were generated and assembled. The number of unigenes
   generated in our experiments was almost twice that generated by
   Schafleitner, providing a more comprehensive dataset for the functional
   study of this species. The fruits (pods or capsules) of okra are rich
   in dietary fiber, minerals, vitamins, polysaccharides, and flavonoids
   [[131]20,[132]46]. The detection of genes with fruit-selective
   expression is a promising strategy for clarifying how polysaccharides
   and flavonoids accumulate to higher levels in fruits than in other
   organs of okra.

   In okra, the highest concentration of total flavonoids was in the
   flowers and fruits ([133]Figure 5b). The expression patterns of the
   unigenes, annotated as genes related to the flavonoid biosynthesis
   pathway, were analyzed using the transcriptome data. A large number of
   DEGs involved in the flavonoid biosynthesis pathway were identified in
   the present study. Based on the KEGG database, we analyzed the
   expression pattern of 13 groups of enzymes associated with flavonoid
   biosynthesis, and found that most of them were downregulated in the
   stems and leaves, but were upregulated in flowers and fruits
   ([134]Figure 6b). Many studies have shown that flavonoids play an
   important role in the growth and germination of the pollen tube
   [[135]47,[136]48], while anthocyanin accumulation in fruit is an
   important index of maturity and fruit quality [[137]49]. The
   flavonoid/phenylpropanoid pathway is responsible for anthocyanin
   biosynthesis in plants [[138]49,[139]50]. Analysis of the flavonoid
   synthesis pathway enabled us to understand, at the molecular level, the
   mechanism by which flavonoids/anthocyanins accumulate in the okra
   fruit.

   RNA sequencing (“RNA-seq”) would facilitate more detailed studies in
   non-model plants, such as okra. A large number of candidate genes
   involved in the biosynthesis of polysaccharides were identified in
   Polygonatum sibiricum by transcriptome sequencing [[140]40]. In the
   present study, three fructose and mannose metabolism-related enzymes,
   namely GDP-L-fucose synthase, phosphomannomutase, and GDP-mannose
   4,6-dehydratase, were shown to be highly expressed in fruits
   ([141]Figure 7a). GDP-L-fucose synthase (also known as
   GDP-4-keto-6-deoxy-D-mannose epimerase/reductase, GMER), a key enzyme
   converting GDP-4-oxo-6-deoxy-D-mannose into GDP-L-fucose, exists widely
   in animals and plants [[142]51]. Phosphomannomutase (PMM) is a key
   enzyme in mannose metabolism, which catalyzes the interconversion
   between mannose-1-phosphate (M1P) and mannose-6-phosphate (M6P)
   [[143]52]. M1P is the main substrate for the synthesis of GDP-mannose,
   which is essential for biosynthesis of ascorbic acid (AsA) through the
   Smirnoff–Wheeler pathway [[144]53,[145]54,[146]55]. Overexpression of
   the PMM gene increased AsA content and tolerance to oxidative stress
   [[147]53,[148]54], while mild mutations of PMM were observed to
   decrease tolerance to high temperature stress in higher plants
   [[149]55].

   The conversion of GDP-D-mannose to GDP-4-keto-6-deoxy-D-mannose is
   catalyzed by GDP-D-mannose-4, 6-dehydratase (GMD) in a de novo pathway
   [[150]56]. The Arabidopsis MUR1 (GMD2) gene encodes an isoform of
   GDP-D-mannose-4,6-dehydratase, catalyzing the dehydration of
   GDP-D-mannose [[151]15]. It was reported that 95% of N-linked glycans
   lack L-fucose residues in the defective Atmur1 mutant, and L-fucose was
   replaced by a hexose residue in the remaining 5% [[152]57].
   Glycosyltransferases (EC 2.4.x.y) exist widely in plants. They are
   responsible for the catalytic glycosylation from
   nucleotide-diphospho-sugar (NDP-sugars) to a series of small molecular
   compounds, forming glycoside compounds, by controlling the formation of
   glycoside bonds [[153]58,[154]59]. Various NDP-sugars form growing
   polysaccharide chains by the action of GTs in a key downstream step in
   polysaccharide biosynthesis [[155]40]. More than 450 members of the GT
   family were identified in Arabidopsis, and over 600 members in rice
   [[156]36]. In the present study, large numbers of GT-encoding genes
   were identified as DEGs in the four inter-organ comparisons: Fr vs. R,
   Fr vs. S, Fr vs. L, and Fr vs. Fl ([157]Figure 7a). In all, 970
   unigenes encoding GTs were found in okra ([158]Tables S3 abd S6). By
   comparison of the FPKM from the 15 RNA-Seq libraries, the most abundant
   transcript was for glucosyltransferase genes (638), followed by
   mannosyltransferase genes (184). Our results provide several candidate
   genes for regulating polysaccharide synthesis and secondary metabolism
   in okra.

5. Conclusions

   The content of flavonoids and polysaccharides was determined in
   different organs of okra, with fruits (highest flavonoid concentration)
   and flowers (highest polysaccharide concentration) being particularly
   rich in these compounds. We obtained 15 datasets, comprising 293,971
   unigenes, from the okra transcriptome, derived by de novo technology
   using the Illumina platform. Of these, 232,490 unigenes were annotated
   by comparison with the Nr, Nt, Swiss-Prot, KEGG, COG, and GO databases.
   A large number of DEGs were identified in fruits compared with the
   other four organs. The major biological functions of DEGs were grouped
   into different GO categories and KEGG pathways. The expression of genes
   related to the flavonoid biosynthetic pathway, to fructose and mannose
   metabolism, and to glycosyltransferases, was analyzed in five organs of
   okra. Our comprehensive transcriptome analysis provides a useful
   database for further research on this important but little-studied
   crop.

Acknowledgments