Abstract
Background
Taxillus chinensis (DC.) Danser, the official species of parasitic
loranthus that grows by parasitizing other plants, is used in various
traditional Chinese medicine prescriptions. ABA-dependent and
ABA-independent pathways are two major pathways in response to drought
stress for plants and some genes have been reported to play a key role
during the dehydration including dehydration-responsive protein RD22,
late embryogenesis abundant (LEA) proteins, and various transcription
factors (TFs) like MYB and WRKY. However, genes responding to
dehydration are still unknown in loranthus.
Methods and Results
Initially, loranthus seeds were characterized as recalcitrant seeds.
Then, biological replicates of fresh loranthus seeds (CK), and seeds
after being dehydrated for 16 hours (Tac-16) and 36 hours (Tac-36) were
sequenced by RNA-Seq, generating 386,542,846 high quality reads. A
total of 164,546 transcripts corresponding to 114,971 genes were
assembled by Trinity and annotated by mapping them to NCBI
non-redundant (NR), UniProt, GO, KEGG pathway and COG databases.
Transcriptome profiling identified 60,695, 56,027 and 66,389
transcripts (>1 FPKM) in CK, Tac-16 and Tac-36, respectively. Compared
to CK, we obtained 2,102 up-regulated and 1,344 down-regulated
transcripts in Tac-16 and 1,649 up-regulated and 2,135 down-regulated
transcripts in Tac-36 by using edgeR. Among them some have been
reported to function in dehydration process, such as RD22, heat shock
proteins (HSP) and various TFs (MYB, WRKY and ethylene-responsive
transcription factors). Interestingly, transcripts encoding ribosomal
proteins peaked in Tac-16. It is indicated that HSPs and ribosomal
proteins may function in early response to drought stress. Raw
sequencing data can be accessed in NCBI SRA platform under the
accession number SRA309567.
Conclusions
This is the first time to profile transcriptome globally in loranthus
seeds. Our findings provide insights into the gene regulations of
loranthus seeds in response to water loss and expand our current
understanding of drought tolerance and germination of seeds.
Introduction
Taxillus chinensis (DC.) Danser, which is the official name of
parasitic loranthus according to the Pharmacopoeia, is wildly used in
various traditional Chinese medicine prescriptions such as the
treatment of rheumatism, threatened abortion, hypertension, angina
pectoris, stroke, and arrhythmia for many years in China [[44]1–[45]4].
There are a total of 51 species of parasitic loranthus, of which 23 are
distributed in Guangxi of China [[46]5]. It is also called
“Sangjisheng” in China and grows by parasitizing other plants like
Aceraceae, Anacardiaceae, Euphorbiaceae, Fabaceae, Fagaceae,
Juglandaceae, Moraceae, Rosaceae, and Rutaceae [[47]6]. In plants,
developmental processes such as seed germination, seedling development,
leaf development and flowering are always affected by various
environmental stresses, such as drought, high salinity, and high or low
temperatures [[48]7–[49]9]. However, it is still unknown about the
effects of these environmental stresses on loranthaceous.
In plants, drought stress induces various biochemical and physiological
responses, such as stomatal closure, repression of cell growth and
photosynthesis, and activation of respiration [[50]10]. At cellular and
molecular levels, a large number of genes have been reported to respond
to drought stress [[51]11–[52]13]. Large scale profiling methods like
microarray and next-generation sequencing have been demonstrated to
estimate the gene expression changes during dehydration process in
several model species, such as Arabidopsis [[53]14, [54]15], rice
[[55]16–[56]18], soybean [[57]19, [58]20] and other plants
[[59]21–[60]23]. These studies have shown that the plant defense
against drought stress starts with the perception of water loss, which
can trigger the activation of abscisic acid (ABA)-dependent and
ABA–independent regulatory systems [[61]24]. According to their
functions, the gene products induced by drought stress can be divided
into two groups [[62]12, [63]13, [64]24]. The first group includes
proteins directly protecting against the drought stress, such as
chaperones, late embryogenesis abundant (LEA) proteins, mRNA-binding
proteins, water channel proteins, and lipid-transfer proteins. The
second group contains various TFs that probably function in further
regulation of signal transduction and gene expression, protein kinases,
protein phosphatases, and other signaling molecules such as
calmodulin-binding proteins.
RNA-Seq, a next generation sequencing technology, has become a useful
tool for genome-wide gene expression analysis [[65]25]. It enables the
de novo assembly and gene expression analyses for those species, of
which the genome sequences are not available currently [[66]26]. To
study drought stress induced genes, RNA-Seq has been used to assemble
the transcriptome and profiled gene expression in Glycine max [[67]20],
Brassica rapa L. ssp. Pekinensis [[68]21], Bryum argenteum [[69]27],
Brassica napus [[70]28], and Gossypium arboretum [[71]29]. In Jindou21
(drought-tolerant soybean genotype), 518 and 614 genes including genes
ethylene-responsive factors, MYB TFs, and zinc finger proteins are
differentially expressed under water deficit condition in leaves and
roots, respectively [[72]20]. Comparative transcriptome analysis of
Brassica napus has shown that a total of 6,018 and 5,377 genes
including AREB/ABF, NAC, WRKY and MYB/MYC TFs are induced in response
to drought stress [[73]28] in root and leaf, respectively. However,
genes induced by drought stress in loranthus are still unknown.
Due to the importance of loranthus in medical use, it is necessary to
identify drought responsive genes in loranthus seeds. According to the
desiccation tolerance ability, seeds are mainly divided into two types:
orthodox and recalcitrant. Recalcitrant seeds lack the mechanisms of
metabolic “switch-off” and intracellular dedifferentiation, which
contribute significantly to their desiccation sensitivity [[74]30]. On
molecular level, the abundance of LEA protein regulated by ABI3 (B3
domain-containing transcription factor) has been verified to link to
the desiccation tolerance in recalcitrant and orthodox legume seeds
[[75]31]. In this study, we profiled the transcriptome of loranthus
seeds during the dehydration using the Illumina HiSeq 2000 system.
Differential gene expression analysis and annotation for these
transcripts revealed that some gene products have been reported to be
involved in drought tolerance, such as various transcription factors,
dehydration-responsive protein RD22, ABI3, heat shock proteins and zinc
finger proteins. It is interesting that transcripts encoding ribosomal
proteins peaked in at loranthus seeds after 16 hours of dehydration.
Down-regulation of auxin related proteins, RNA binding protiens, and
dehydration-responsive element-binding proteins may be signals of lower
germination rates and cell death. This is the first time to analyze
transcriptome in loranthus species. Our findings will contribute to
understand the drought tolerance mechanism in loranthus and contribute
to the research field of loranthus in breeding programs.
Material and Methods
Ethics statement
No specific permits were required for the described field studies. The
location is not privately-owned or protected in any way, and the field
studies did not involve endangered or protected species.
Seed collection
The seeds of Taxillus chinensis (DC.) Danser were collected from 10
trees of Dracontomelon duperreanum Pierre in Guangxi Province of China
in December of 2014. They were confirmed by senior botanists at
Institute of Medicinal Plant Development, Chinese Academy of Medical
Sciences.
Seed water loss and water content assay
The dehydration and water content assay of mature loranthus seeds were
performed according to the manufacturer’s protocol. In brief, the
aluminum case was dried to constant weight, then 100 clean and fresh
seeds were weighed (W[1]) within the aluminum case and weighed again
after being dried at 100±2°C until constant weight (W[2]). We
replicated four times to obtain the average moisture content (W[0],
shown in %) in fresh seeds using the formula below.
[MATH:
W0=W1<
mo>−W2W1*100%
:MATH]
(1)
Whereas W1 means the average weight of fresh seeds and W2 means the
average of dehydrated seeds. Next, another 200 clean and fresh seeds
were divided into four groups (50 seeds in each group), incubated in
sealed desiccants with silica gel after being weighted and weighted
every 4 hours. So the moisture content in seeds after dehydration can
be calculated by using this formula:
[MATH: M(%)=W
0−W1′−W2′W1′*100% :MATH]
(2)
Here,
[MATH: W1′ :MATH]
and
[MATH: W2′ :MATH]
stand for the average weight of fresh seeds before and after
dehydration, respectively.
Determination of seed viability by staining
The viability of loranthus seeds was assessed by immersing the seeds in
a solution of 1% (w/v) 2,3,5-Triphenyl Tetrazolium Chloride (TTC,
Sigma) according to the protocols [[76]32, [77]33]. Briefly, using a
sterile scalpel 25 seeds were cut for small incisions allowing the TTC
to enter. After an eight-hour incubation in 1% TTC solution at 25°C,
seeds were washed several times by sterile water. If viable, a redox
reaction would change the embryo color from white to reddish brown
during cellular respiration [[78]34]. This experiment was replicated
four times.
Germination experiment
Germination experiment was conducted exactly as described previously
[[79]35]. Briefly, at each dehydration time point 25 seeds were placed
on wetted double layers of Fisher No. 1 filter papers in a dish and
incubated at 25°C under 16 h photoperiod for two weeks, before
germination rates were determined. Germination experiment was
replicated four times for each time point.
RNA extraction
We selected fresh seeds (CK) and seeds after dehydration for 16 hours
(Tac-16) and 36 hours (Tac-36) for deep sequencing. Total RNA was
isolated from the seeds by using TRIzol^® reagent (Invitrogen)
according to the manufacturer’s protocol [[80]36]. Briefly, 10 seeds (~
3–4 g) was mixed with 1 ml of TRIzol^® reagent, homogenized by power
homogenizer and centrifuged at 12,000 ×g for 10 min at 4°C. Then, the
fatty layer was discarded, cleared supernatant was transferred into a
new tube. Next, 0.2 ml of chloroform was added into the tube, following
by shaking the tube for 15 secs, centrifugation at 12,000 ×g for 15 min
at 4°C and moving the aqueous phase into another new tube for RNA
precipitation. We added 10 μg of RNase-free glycogen and 0.5 ml of 100%
isopropanol into the aqueous phase, incubated the samples at room
temperature for 10 min and centrifuged them at 12,000 ×g for 10 min at
4°C. Finally, the RNA pellet was washed by 1 ml of 75% ethanol,
air-dried, suspended in RNase-free water and water-bathed at 60°C for
10 min. The quality of total RNA was evaluated and controlled by
Agilent 2100 Bioanalyzer. For each sample we replicated total RNA
isolation for three times and pooled them for cDNA library construction
and sequencing.
cDNA library construction and sequencing
A total amount of 20 μg RNA was used for transcriptome cDNA library
construction by using TruSeq^TM RNA Sample Preparation Kit v2
(Illumina) and the cDNA library was sequenced on an Illumina HiSeq 2000
platform following the manufacturers’ protocols. In brief, poly(A)
mRNAs were obtained by using Dynal Oligo(dT) beads (Invitrogen). mRNAs
were then chemically fragmented into ~200 nt fragments. mRNA fragments
were next copied into first strand cDNA by using reverse transcriptase
and random primers, followed by the second strand cDNA synthesis using
DNA Polymerase I (Invitrogen) and RNase H (Invitrogen) treatment. After
end repaired by using End Repair Mix (Illumina) reagent, the cDNA
fragments were purified and enriched to create the final cDNA library.
A total of six libraries (each sample has two replicates) were
sequenced by pair-end (2×90 bp) method on an Illumina HiSeq^TM 2000
platform.
De novo assembly of the transcriptome
After adapter sequences and low quality reads were removed, raw
sequencing reads were cleaned and quality controlled by FastQC software
([81]http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Then,
de novo assembly of high quality reads was carried out by Trinity
software (release 2014-07-17) [[82]37], according to the protocol
[[83]38].
Transcriptome annotation
Gene Ontology (GO) and biological pathway annotations for the assembled
transcriptome were performed by mapping them to public databases,
including NCBI non-redundant (NR), UniProt and Kyoto Encyclopedia of
Genes and Genomes (KEGG) databases. First, BLAST software [[84]39] was
used to align all the transcripts to NR and UniProt databases. Matched
transcripts were filtered by using a cut-off of e-value (1 × 10^−5).
Then, BLAST2GO software [[85]40] was used to retrieve associated GO
items describing biological processes (BPs), cellular components (CCs)
and molecular functions (MFs) for the assembled transcripts. The enzyme
commission numbers (EC) for each transcript were also annotated by
BLAST2GO. Using a cut-off of e-value (≤1e-5), the transcripts with
corresponding ECs were obtained and mapped to KEGG metabolic pathway
database. Then, likely protein sequences were extracted from the
assembled transcripts by TransDecoder, which is included in the Trinity
software distribution. Potential signal peptides, transmembrane domains
and rRNA transcripts were predicted by using SignalP [[86]41], TMHMM
Sever v2.0 [[87]42] and RNAMMER [[88]43], respectively. To identify the
proteins distributed in EuKaryotic Orthologous Groups (KOG), Clusters
of Orthologous Groups (COGs), and non-supervised orthologous groups
(NOGs), the likely protein sequences were further used to search
against EggNOG database (v4.1, [89]http://eggnogdb.embl.de) [[90]44].
Protein functional domains were identified by mapping the likely
proteins to Pfam database [[91]45] using HMMER [[92]46] and filtered by
using the cut-off of e-value (≤1e-5).
Reads alignment and transcriptome profile
To profile gene expression in loranthus seeds, Bowtie2 [[93]47] and
RSEM (RNA-Seq by Expectation-Maximization) [[94]48] were used to map
clean reads of CK (CK-1 and CK-2), Tac-16 (Tac-16-1 and Tac-16-2) and
Tac-36 (Tac-36-1 and Tac-36-2) to the assembled transcriptome and
evaluate the abundance of each transcript, respectively. To compare the
expression levels of transcripts in different samples, we used FPKM
(fragments per kilobase of transcript per million mapped reads) for
normalization and to present the expression of transcripts.
Differentially expressed transcripts
Transcripts differentially expressed during the dehydration process
were identified by using egdeR [[95]49]. We used a strict criterial to
select differentially expressed transcripts: normalized expression >1
FPKM, Log2FC (log2 fold change) >1 (up-regulated) or Log2FC <-1
(down-regulated), p-value <0.05 and FDR (false discovery rate) <0.05.
GO and KEGG pathway enrichment analysis
To identify significant GO terms and KEGG pathways enriched by
candidate transcripts, we used p-value (Fisher’s exact test) to show
the significance of enrichment for specific GO term. Then, q-value
[[96]50] was calculated to correct the p-value for each GO term or
pathway and control the false discovery rate. Significant GO items and
KEGG pathways should satisfy the critical: q-value<0.05. GO items and
KEGG pathways not associated with plant bio activities were filtered.
Quantitative real-time PCR
To validate the expression of the assembled transcripts, quantitative
real-time PCR (qRT-PCR) experiment was performed following the
protocols. In brief, total RNA was extracted by using TRIzol^® reagent
(Invitrogen), as described. The quality of total RNA was evaluated and
controlled by NanoDrop 1000 (Thermo Scientific). Forward and reverse
primer sequences for candidate transcripts and control (actin-3) were
predicted by Primer3 ([97]http://bioinfo.ut.ee/primer3-0.4.0/) and
synthesized at BGI-Shenzhen. Then, FastQuant RT Kit (with gDNase,
Tiangen) was used to synthesize cDNA from total RNA (1 μg). The cDNA
samples (100 ng, 2 μl) were next mixed with 10 μl of SuperReal PreMix
(SYBR Green, Tiangen), forward primer (0.8 μl), reverse primer (0.8
μl), 5 × gDNA Buffer (2 μl) and ddH[2]O (6.4 μl) to make the final PCR
reaction mix. The final PCR reaction mix (SYBR Green) was amplified on
a LightCycler480II (Roche) with three steps PCR, including 1 cycle
initial denaturation (95°C for 3 min), 40 cycles of PCR reactions (95°C
for 10 s, 60°C for 15 s and 72°C for 20 s) and a melting/dissociation
curve stage (95°C for 10 s, 65°C for 1 min and a continuous temperature
ramp (0.11°C/s) from 65 to 97°C). For each candidate we replicated
three times of qRT-PCR in every sample. Average of the Ct (cycle
threshold) for each candidate was calculated, ΔCt was used to evaluate
the expression levels of candidate transcripts in each sample and ΔΔCt
method was used to show the different expression of a particular
transcript between two samples [[98]51].
[MATH:
ΔΔCt=(Ctt1−
Ctr
1)−(Ctt0−Ctr0)
:MATH]
(3)
Whereas
[MATH:
Ctt
1 :MATH]
and
[MATH:
Ctt
0 :MATH]
stand for the average Ct values of a candidate transcript detected in
dehydrated seeds and fresh seeds, respectively;
[MATH:
Ctr
1 :MATH]
and
[MATH:
Ctr
0 :MATH]
stand for the average Ct values of actin-3 detected in dehydrated seeds
and fresh seeds, respectively. The relative normalized expression (RNE)
was calculated by using 2^-ΔΔCt method.
Availability of data and material
The raw sequencing files of these six samples (FASTQ formatted files)
can be accessed in the NCBI Sequence Read Archive (SRA) database
([99]http://trace.ncbi.nlm.nih.gov/Traces/sra/) under the accession
number of SRA309567 (CK-1: SRR2902061; CK-2: SRR4067162; Tac-16-1:
SRR2902062; Tac-16-2: SRR4067163; Tac-36-1: SRR2902063; Tac-36-2
SRR4067164). The assembled transcripts can be accessed from NCBI
Transcriptome Shotgun Assembly Sequence (TSA) Database under the
accession number [100]GELW00000000. Scripts for key steps, such as
transcriptome de novo assembly, annotation, expression profile and
differential expression, can be seen in [101]S1 File.
Results and Discussion
Seeds dehydration, viability test and germination experiment
The loranthus seeds were collected from 10 trees of Dracontomelon
duperreanum Pierre in Guangxi Province of China in December of 2014 and
confirmed by senior botanists. Loranthus is wildly used in traditional
Chinese medicine prescriptions and it is still unknown about the
loranthus seed response to environmental stresses. Loranthus seeds
always disperse as fresh seeds in wild by our long-term observations,
so among the environmental stresses we first want to study the gene
expression changes in loranthus seeds in response to water loss.
Initially, we observed that loranthus seeds were recalcitrant because
the viability and germination rate of loranthus seeds dropped quickly
if they were stored in dry environment [[102]52]. Using seed
germination experiment we found after being stored for three days the
germination rate decreased from 86% (germination rate of fresh seeds)
to 40% and after six days the germination rate was only 5%. Then, we
examined the moisture content of fresh loranthus seeds was 50.7% (w/w)
on average ([103]Fig 1A), which is similar to that in soybean
[[104]53]. Next, we performed the dehydration experiment and tested the
viability of loranthus seeds (25 seeds × 4 replicates) using TTC
staining. [105]Fig 1A showed the moisture content, the viability and
germination rate of loranthus seeds (25 seeds × 4 replicates) during
the dehydration process. It is clear that the viability and germination
rate of loranthus seeds were associated with the water content in
seeds. The germination rate of loranthus fresh seeds was 86%
(viability: 99%) and after being dehydrated for 16 hours it was dropped
to 66% (viability: 66%, moisture content: 35.17%). If the seeds were
dehydrated for 36 hours, the moisture content was decreased to 24.93%,
the viability was decreased to 15% and the germination rate was 6%.
After 40 hours’ dehydration the moisture content, viability and
germination rate were examined as 23.47%, 9% and 0, respectively.
Because both viability and germination rate were decreased most after
being dehydrated for 16 hours (Tac-16) and 36 hours (Tac-36), we
collected seeds from these two time points and CK to study the gene
expression changes of loranthus seeds during the dehydration process.
TTC staining of CK, Tac-16 and Tac-36 confirmed that the viability of
loranthus seeds was affected by drought ([106]Fig 1B). Loranthus seeds
were abnormal after 16 hours’ dehydration and non-viable after 36
hours’ dehydration. In fact, moisture content has been experimented to
play a key role in seed germination [[107]54, [108]55], especially in
early seed germination [[109]56]. In Arabidopsis, water in seeds
supplies several biochemical reactions during the seed germination,
such as water up-taken from outside [[110]53, [111]56]. At molecular
level, drought stress induced genes have been characterized in
different plant species including Arabidopsis [[112]14, [113]15], rice
[[114]16–[115]18], soybean [[116]19, [117]20], and other plants
[[118]21–[119]23].
Fig 1. Treatment of loranthus seeds.
[120]Fig 1
[121]Open in a new tab
(A) Moisture content assay, viability test and germination test of
loranthus seeds. (B) TTC test of CK, Tac-16 and Tac-36 show fresh seeds
(CK) are viable, seeds in Tac-16 group are abnormal and seeds in Tac-36
group are non-viable.
Transcriptome de novo assembly
In this study, we conducted six cDNA libraries (in biological
replicates) for CK, Tac-16 and Tac-36 and sequenced them using
paired-end transcriptome sequencing. After the low quality reads were
removed, 386,542,846 high quality reads were obtained and used for de
novo assembly by using Trinity software [[122]37]. A total of 164,546
transcripts corresponding to 114,971 genes ([123]Table 1) were
assembled. The assembly contains 149,031,959 bases (159 M in size),
which was determined to be ‘strong’ and ‘fully consider’ for evaluating
de novo transcriptome [[124]57]. Next, other measures like GC content,
N10, N20 and N50 were used to evaluate the transcriptome. The GC
content, N10, N20 and N50 of the assembled transcriptome were
calculated as 41.12%, 3,972, 3,040 and 1,610, respectively ([125]Table
1). Of them, N50 is a statistical measure of average length of a set of
sequences like genome and transcriptome sequences [[126]58]. In
addition, length distribution of the assembled transcripts ([127]Fig 2)
told us there were a total of 46,869 (28.48%) transcripts longer than
1,000 bp, of them 7,521 (4.57%) transcripts were longer than 3,000 bp.
Except short transcripts (< 400 nt), the numbers of assembled
transcripts, transcripts detected in CK, Tac-16 and Tac-36 were close
to each other. This is the first time to study the loranthus seed
transcriptome, it is hard to evaluate the numbers of transcripts and
genes in loranthus due to the missing information of its genome
sequence and annotation.
Table 1. Overview of the transcriptome de novo assembly.
Type Result
High quality reads 386,542,846
Total Trinity genes 114,971
Total Trinity transcripts 164,546
GC (%) 41.12
N10 3,972
N20 3,040
N50 1,610
Total assembled bases 149,031,959
[128]Open in a new tab
Fig 2. Length distribution of the assembled transcripts and transcripts (>
1FPKM) detected in CK, Tac-16 and Tac-36.
[129]Fig 2
[130]Open in a new tab
Annotation of the assembled loranthus seed transcriptome
We next annotated the assembled transcriptome by mapping it to NCBI
non-redundant (NR), UniProt, GO and KEGG databases and the numbers of
transcripts matched to each database can be seen in [131]Fig 3A. By
using BLAST software [[132]39] and a cut-off of e-value (< 1 × e^-5),
the largest number (67,628, 41.10%) of transcripts were aligned to NR
database, followed by the UniProt database (50,870, 30.92%). We further
explored the loranthus transcripts aligned to species in the NR mapping
results ([133]Fig 3B). There were 19,977 transcripts aligned to Vitis
vinifera, taking 29.54% of all the transcripts aligned to NR database,
followed by Theobroma cacao (3,010, 4.45%), Nelumbo nucifera (2,742,
7.05%) and Ziziphus jujuba (2,504, 3.70%). Using the NR and UniProt
mapping results 38,559 (23.43%) transcripts ([134]Fig 3A) whose
orthologous sequences have GO annotations were divided into three
categories: cellular component, biological process, and molecular
function ([135]S1 Table). GO analysis ([136]Fig 3C) showed 19 GO items
were enriched by more than 10% of the total assembled transcripts. Top
10 of them were “metabolic process” (25,062 transcripts), “cellular
process” (23,847 transcripts), “catalytic activity” (19,620
transcripts), “cell” (17,340 transcripts), “cell part” (17,196
transcripts), “binding” (16,406 transcripts), “single-organism process”
(13,988 transcripts), “membrane” (13,668 transcripts), “organelle”
(11,457 transcripts) and “membrane part” (8,590 transcripts). In
addition, 47,262 transcripts were identified to involve in 362
different KEGG pathways ([137]Fig 3A). According to the numbers of
transcripts, top 10 KEGG pathways can be seen in [138]Fig 3D. The most
significant KEGG pathway was “metabolic pathway” (ko01100), containing
10,609 transcripts. We also identified 1,857 and 1,645 transcripts
playing a key role in the pathways of “plant-pathogen interaction”
(ko04626) and “plant hormone signal transduction” (ko04075),
respectively. The ontologies and pathways annotated for loranthus seed
transcriptome showed their potential functions in seed development
[[139]59] and tolerance of environmental stresses [[140]60].
Interestingly, 9 transcripts ([141]Fig 3A) were predicted to be
ribosomal RNAs by using RNAMMER [[142]43].
Fig 3. Annotation of the assembled transcriptome.
[143]Fig 3
[144]Open in a new tab
(A) Number of transcripts aligned to different databases. (B) Species
aligned by the assembled loranthus seed transcriptome. (C) Gene
Ontology analysis for the assembled loranthus seed transcriptome. (D)
Top 10 significant KEGG pathways. (E) COG annotation.
Next, we extracted likely proteins from the assembled transcripts using
TransDecoder. In total, 49,004 transcripts (29.78% of the total
assembled transcripts) were predicted to encode 61,610 proteins. Among
the likely proteins, we identified 40,846 (24.82%) transcripts
containing Pfam domain sequences, 3,169 (1.93%) with signal peptides
and 10,104 (6.14%) transcripts encoding membrane related proteins
([145]Fig 3A). Then, likely proteins encoded by the assembled
transcripts were mapped to eggNOG database and proteins encoded by
24,580 (14.94%) transcripts were distributed in EuKaryotic Orthologous
Groups (KOG), Clusters of Orthologous Groups (COGs), and non-supervised
orthologous groups (NOGs), see [146]S2 Table. As show in [147]Fig 3E,
4,315 likely proteins were poorly characterized, 2,911 likely proteins
were from COG of signal transduction mechanisms, and 2,138 likely
proteins were from COG of post-translational modification, protein
turnover, and chaperones. Annotations from different perspectives will
give a better understanding of the functions of the assembled
transcripts and help to identify transcripts involved in the
dehydration process. In addition, the reasons of some transcripts
annotated without encoding ability should be further explored
[[148]61].
Transcriptome profile and different expression
The viability and germination rate of loranthus seeds dropped quickly
during dehydration ([149]Fig 1A). In order to identify genes induced by
drought stress and profile them in loranthus seeds, we performed two
biological replicates for CK, Tac-16 and Tac-36 samples and the
expression of transcripts was evaluated separately in each replicate.
Bowtie2 [[150]47] was used to align the high quality reads to the
assembled transcriptome and RSEM [[151]48] tool was used to profile
gene expression in all samples. In total, we obtained 91,666
transcripts (>1 FPKM) and 54,047, 52,579, 48,681, 48,540, 57,436 and
56,811 transcripts (>1 FPKM) distributed in CK-1. CK-2, Tac-16-1,
Tac-16-2, Tac-36-1 and Tac-36-2, respectively. Length distribution of
transcripts detected in CK, Tac-16 and Tac-36 can be found in [152]Fig
2and the distribution of their normalized expression ([153]Fig 4A)
showed 80.53% ~ 82.49% of the total detected transcripts (excluding
transcripts < 1 FPKM) in each sample were less than 10 FPKM. Pearson
correlations for the replicates were above 0.9 in CK, Tac-16 and Tac-36
well ([154]Fig 4B), which indicated the replicates were performed very
well. Venn diagram ([155]Fig 4C) of detected transcripts in CK, Tac-16
and Tac-36 (> 1 FPKM in at least one of the two replicates) revealed
38,513 (42.01% of the total detected transcripts) were commonly
detected and 12,701 (20.93% of transcripts detected in CK), 9,935
(17.73% of transcripts detected in Tac-16) and 16,098 (24.74% of
transcripts detected in Tac-36) transcripts were detected exclusively
in CK, Tac-16 and Tac-36, respectively.
Fig 4. Transcriptome profiling and differential expression.
[156]Fig 4
[157]Open in a new tab
(A) Distribution of normalized expression of transcripts detected in
all samples. (B) Heat map of correlations between replicates. (C) Venn
diagram of transcripts (>1 FPKM) detected in CK, Tac-16 and Tac-36. (D)
Number of up- and down-regulated transcripts in Tac-16 and Tac-36
compared to CK. (E) Venn diagram of differentially expressed
transcripts identified in all comparisons. Numbers in red represent
commonly up- (1091) and down-regulated (955) transcripts in Tac16 and
Tac-36 compared to CK.
To characterize drought induced genes in loranthus seeds, we employed
edgeR [[158]49] to identify transcripts differentially expressed in
Tac-16 and Tac-36 compared to CK and used the criterial as follows:
FPKM >5 in at least one sample, Log2FC >1 or Log2FC <-1, p-value <0.05
and FDR <0.05. Compared to CK, we obtained 2,102 up-regulated and 1,344
down-regulated transcripts in Tac-16 and 1,649 up-regulated and 2,135
down-regulated transcripts in Tac-36 ([159]Fig 4D). We also compared
Tac-16 and Tac-36 and found 1,358 transcripts differentially expressed
including 135 transcripts up-regulated in Tac-36 and 1,223 transcripts
up-regulated in Tac-16 ([160]Fig 4D). In all three comparisons we
obtained a total of 5,349 transcripts differentially expressed in
loranthus seeds during the dehydration. Venn diagram of differentially
expressed transcripts ([161]Fig 4E) showed the numbers of transcripts
commonly and specifically up-regulated or down-regulated in Tac-16 and
Tac-36. It is revealed that 1,091 transcripts were commonly
up-regulated and 955 transcripts were commonly down-regulated in Tac-16
and Tac-36 in comparison of CK. Interestingly, 24 transcripts such as
c36451_g2_i1 (LEGB4_VICFA, legumin type B), c51416_g2_i1 (AB40G_ARATH,
ABC transporter G family member 40) and c59053_g2_i1 (DIR23_ARATH,
dirigent protein 23) kept increasing while 61 transcripts such as
c60985_g1_i1 (12KD_FRAAN, auxin-repressed 12.5 kDa protein),
c67927_g8_i3 (BH094_ARATH, transcription factor bHLH94), c49978_g1_i1
(DRE2D_ARATH, dehydration-responsive element-binding protein 2D) and
c39272_g1_i1 (HS23C_OXYRB, small heat shock protein, chloroplastic)
kept decreasing during the dehydration process. Detailed information of
differentially expressed transcripts in Tac-16 and Tac-36 compared to
CK can be accessed in [162]S3 Table.
Functional analysis of differentially expressed transcripts
Next, we annotated the differentially expressed transcripts using GO
and KEGG pathway databases. Overall, loranthus seed transcripts induced
by drought were involved in metabolic pathways, such as “Metabolic
pathways” (ko01100), “Riboflavin metabolism” (ko00740) and “Terpenoid
backbone biosynthesis” (ko00900), signaling transduction pathway “Plant
hormone signal transduction” (ko04075) and environmental adaption
pathways such as “Plant-pathogen interaction” (ko04626), “Circadian
entrainment” (ko04713) and “Circadian rhythm–plant” (ko04712)
([163]Table 2).In addition, we found several pathways may be associated
with loranthus seeds in response to dehydration at different stages.
For example, there were 107 transcripts involved in “Ribosome”
(ko03010) in Tac-16 (p-value = 2.2E-16, q-value = 6.8E-14) but only 40
in Tac-36 (p-value = 0.683, q-value = 1). And transcripts involved in
“Neuroactive ligand-receptor interaction” (ko04080) decreased from 19
in Tac-16 (p-value = 1.37E-15, q-value = 4.24E-13) to 0 in Tac-36
(p-value = 1, q-value = 1). Although the numbers of transcripts
annotated by KEGG pathway were decreased in Tac-36 compared to Tac-16,
pathways of “Biosynthesis of secondary metabolites” (ko01110),
“Biosynthesis of antibiotics” (ko01130) and “plant hormone signal
transduction” (ko04075) had more differentially expressed transcripts
in Tac-36 ([164]Table 2).
Table 2. KEGG pathway analysis for differentially expressed transcripts.
Group Pathway ID Tac-16[165]^a P-value Q-value Tac-36[166]^b P-value
Q-value
Global and overview maps Metabolic pathways ko01100 426 2.2E-16 6.8E-14
572 2.2E-16 7.11E-14
Global and overview maps Biosynthesis of secondary metabolites ko01110
232 1.07E-13 3.3E-11 391 2.2E-16 7.11E-14
Signal transduction Plant hormone signal transduction ko04075 87
3.03E-12 9.35E-10 118 2.2E-16 7.11E-14
Carbohydrate metabolism Galactose metabolism ko00052 44 4.19E-12
1.29E-09 48 2.16E-12 6.97E-10
Biosynthesis of other secondary metabolites Flavonoid biosynthesis
ko00941 11 0.005321 1 26 9.56E-12 3.09E-09
Carbohydrate metabolism Starch and sucrose metabolism ko00500 68
1.42E-07 4.38E-05 86 2.21E-11 7.14E-09
Biosynthesis of other secondary metabolites Phenylpropanoid
biosynthesis ko00940 27 0.002195 0.678255 48 1.31E-10 4.23E-08
Carbohydrate metabolism Amino sugar and nucleotide sugar metabolism
ko00520 60 9E-12 2.78E-09 61 6.16E-10 1.99E-07
Glycan biosynthesis and metabolism Other glycan degradation ko00511 27
1.29E-05 0.003974 37 3.12E-09 1.01E-06
Metabolism of other amino acids Cyanoamino acid metabolism ko00460 17
0.009255 1 33 4.59E-09 1.48E-06
Global and overview maps Biosynthesis of antibiotics ko01130 95
0.001081 0.334029 132 5.25E-09 1.7E-06
Environmental adaptation Plant-pathogen interaction ko04626 86 2.19E-07
6.77E-05 99 1.85E-08 5.98E-06
Biosynthesis of other secondary metabolites Monobactam biosynthesis
ko00261 5 0.03746 1 14 1.92E-08 6.2E-06
Global and overview maps Microbial metabolism in diverse environments
ko01120 95 7.67E-07 0.000237 111 2.89E-08 9.34E-06
Energy metabolism Photosynthesis—antenna proteins ko00196 0 1 1 9
3.46E-08 1.12E-05
Amino acid metabolism Lysine biosynthesis ko00300 2 0.5836 1 14 4E-08
1.29E-05
Metabolism of terpenoids and polyketides Carotenoid biosynthesis
ko00906 9 0.003549 1 17 7.15E-08 2.31E-05
Environmental adaptation Circadian entrainment ko04713 16 2.07E-06
0.000639 19 1.18E-07 3.8E-05
Metabolism of cofactors and vitamins Porphyrin and chlorophyll
metabolism ko00860 6 0.4873 1 23 1.37E-07 4.43E-05
Xenobiotics biodegradation and metabolism Naphthalene degradation
ko00626 3 0.02358 1 8 2.85E-07 9.19E-05
Xenobiotics biodegradation and metabolism Polycyclic aromatic
hydrocarbon degradation ko00624 15 1.32E-08 4.09E-06 14 4.91E-07
0.000158
Biosynthesis of other secondary metabolites Stilbenoid, diarylheptanoid
and gingerol biosynthesis ko00945 14 3.4E-06 0.00105 16 6.4E-07
0.000207
Metabolism of other amino acids Taurine and hypotaurine metabolism
ko00430 5 0.002268 0.700812 9 7.97E-07 0.000257
Cell growth and death Apoptosis ko04210 66 0.01266 1 94 1.55E-06
0.000499
Environmental adaptation Circadian rhythm—plant ko04712 20 0.000174
0.053828 26 1.94E-06 0.000627
Biosynthesis of other secondary metabolites Anthocyanin biosynthesis
ko00942 1 0.5807 1 8 4.24E-06 0.001368
Excretory system Aldosterone-regulated sodium reabsorption ko04960 0 1
1 10 4.46E-06 0.001442
Biosynthesis of other secondary metabolites Isoflavonoid biosynthesis
ko00943 5 0.0185 1 10 8.61E-06 0.002782
Metabolism of cofactors and vitamins Riboflavin metabolism ko00740 2
0.4556 1 10 8.61E-06 0.002782
Global and overview maps Degradation of aromatic compounds ko01220 5
0.002567 0.793203 8 1.06E-05 0.003424
Lipid metabolism Glycerolipid metabolism ko00561 16 0.09111 1 30
1.38E-05 0.00447
Excretory system Vasopressin-regulated water reabsorption ko04962 8
0.001223 0.377907 11 2.74E-05 0.008863
Carbohydrate metabolism Glycolysis / Gluconeogenesis ko00010 42 2.4E-05
0.00741 46 2.8E-05 0.00905
Digestive system Mineral absorption ko04978 5 0.1158 1 12 3.83E-05
0.012368
Lipid metabolism Fatty acid biosynthesis ko00061 10 0.04153 1 18
4.29E-05 0.013841
Metabolism of terpenoids and polyketides Terpenoid backbone
biosynthesis ko00900 15 0.003843 1 21 4.3E-05 0.013886
Xenobiotics biodegradation and metabolism Dioxin degradation ko00621 3
0.000889 0.274577 4 4.92E-05 0.015888
Metabolism of terpenoids and polyketides Limonene and pinene
degradation ko00903 12 2.78E-05 0.008581 12 9.9E-05 0.031971
Global and overview maps 2-Oxocarboxylic acid metabolism ko01210 10
0.3284 1 23 0.000112 0.036079
Xenobiotics biodegradation and metabolism Bisphenol degradation ko00363
12 2.06E-06 0.000637 10 0.000214 0.06909
Carbohydrate metabolism Citrate cycle (TCA cycle) ko00020 22 1.75E-05
0.00542 21 0.000326 0.105266
Metabolism of other amino acids Glutathione metabolism ko00480 21
3.74E-06 0.001157 17 0.001681 0.542963
Carbohydrate metabolism Ascorbate and aldarate metabolism ko00053 18
1.84E-05 0.00567 15 0.002348 0.758404
Xenobiotics biodegradation and metabolism Aminobenzoate degradation
ko00627 12 5.62E-05 0.017366 10 0.002609 0.842707
Translation Ribosome ko03010 107 2.2E-16 6.8E-14 40 0.683 1
Digestive system Protein digestion and absorption ko04974 25 2.2E-16
6.8E-14 4 0.3388 1
Signaling molecules and interaction Neuroactive ligand-receptor
interaction ko04080 19 1.37E-15 4.24E-13 0 1 1
Sensory system Phototransduction—fly ko04745 20 3.63E-11 1.12E-08 9
0.009196 1
Sensory system Phototransduction ko04744 12 5.86E-08 1.81E-05 6 0.01012
1
[167]Open in a new tab
a. Number of differentially expressed transcripts in Tac-16 compared to
CK.
b. Number of differentially expressed transcripts in Tac-36 compared to
CK.
We performed K-means clustering analysis using MEV software (v4.9) and
found four main groups of transcripts with different expression
patterns in CK, Tac-16 and Tac-36 ([168]Fig 5B). The biggest group
containing 1,857 transcripts that were down-regulated in loranthus
dehydrated seeds compared to CK. They were significantly enriched
(p-value <0.05, q-value <0.05) in biological processes including
“regulation of cellular process” (GO:0050794), “regulation of seed
germination” (GO:0010029), “pollen germination” (GO:0009846), “response
to auxin” (GO:0009733), “plant ovule development” (GO:0048481),
“response to far red light” (GO:0010218), “photoperiodism, flowering”
(GO:0048573) and “response to brassinosteroid” (GO:0009741). The
decreased expression of transcripts involved in seed germination and
cell development, especially the plant ovule development indicated the
germination of loranthus seeds was affected due to the water loss, so
we assume that loranthus seeds are recalcitrant. The second group
contained 1657 transcripts that were up-regulated in loranthus seeds
during the dehydration. GO analysis revealed these transcripts were
involved in the biological processes such as “posttranscriptional
regulation of gene expression” (GO:0010608), “regulation of shoot
system development” (GO:0048831), “response to cyclopentenone”
(GO:0010583), “vesicle fusion” (GO:0006906), “regulation of catalytic
activity” (GO:0050790) and “jasmonic acid metabolic process”
(GO:0009694). The third group contained 1,156 transcripts whose
expression peaked in Tac-16. They were involved in “cellular aromatic
compound metabolic process” (GO:0006725), “GPI anchor biosynthetic
process” (GO:0006506), “protein homooligomerization” (GO:0051260) and
“carbohydrate metabolic process” (GO:0005975). The last group contained
679 transcripts that were down-regulated in loranthus seeds during the
dehydration process. Compared to the biggest group, the transcripts in
this group were down-regulated in Tac-36 compared to Tac-16 and were
involved in the biological processes like “defense response”
(GO:0006952), “cytokinesis by cell plate formation” (GO:0000911),
“response to heat” (GO:0009408), “transcription from RNA polymerase II
promoter” (GO:0006366) and “metabolic process” (GO:0008152). Overall,
in response to drought loranthus seeds actively or passively reduced
the cell developmental activities, metabolism and transcripts involved
in seed germination and plant defense system while transcripts
associated with posttranscriptional regulation and gene silencing by
RNA were up-regulated due to the water loss in loranthus seeds. In
tobacco, reducing the metabolism is expected to play a major role at
the early stage in programmed cell death [[169]62]. Auxin and other
hormones like abscisic acid (ABA), cytokinins and ethylene are well
known to regulate the cell growth in multiple stages including cell
development and cell death [[170]63]. It is interesting that ABA also
has the capacity of delaying the process of programmed cell death in
aleurone cells although the mechanism is unknown [[171]64, [172]65].
Another review says cell death is programmed when the metabolism is
perturbed by abiotic stresses and the cells seem to be ready to die
based on the integration of different signals including auxin,
cytokinins, ethylene, and elicitors [[173]66].
Fig 5. K-means clustering analysis of the differentially expressed
transcripts in dehydrated loranthus seeds in comparison of CK and GO
analysis.
[174]Fig 5
[175]Open in a new tab
Differentially expressed transcripts in response to drought stress
In order to understand the genes induced by drought in loranthus seeds,
we further annotated the differentially expressed transcripts and found
there were several genes whose products have been reported to be
involved in cell development and cell death. It is well known that
ABA-dependent and ABA-independent pathways are two main pathways for
plants to respond the water loss [[176]24, [177]67, [178]68], so we
first examined the expression of ABA associated transcripts. In
loranthus seed transcriptome we detected 49 ABA associated transcripts,
of which 14 were differentially expressed in dehydrated seeds
([179]Table 3, [180]Fig 6, [181]S3 Table). It is notable that ASR1
(Abscisic stress-ripening protein 1), which is induced by water and
salt stress [[182]69], was highly expressed (>200 FPKM) and
up-regulated significantly in dehydrated loranthus seeds, compared to
fresh loranthus seeds.
Table 3. Transcripts encoding different proteins are associated with
dehydration tolerance.
Gene family Detected Differentially expressed Up-regulated (Tac-16/CK)
Down-regulated (Tac-16/CK) Up-regulated (Tac-36/CK) Down-regulated
(Tac-36/CK)
ABA associated protein 49 14 6 1 8 4
Auxin related protein 187 27 6 10 5 17
Binding protein 1544 94 37 28 22 34
Dehydration-responsive element-binding protein 12 5 0 3 0 5
Dehydration-responsive protein RD22 11 2 1 0 1 1
Heat shock protein 163 48 18 28 0 25
Late embryogenesis abundant protein 5 0 0 0 0 0
Ribosomal proteins 1049 88 86 0 0 22
Transcriptional factors 1277 160 80 20 75 63
Zinc finger proteins 654 52 25 11 30 19
[183]Open in a new tab
Fig 6. Differentially expressed transcripts involved in response to drought
stress.
[184]Fig 6
[185]Open in a new tab
The functions of drought induced genes include protecting cells and
regulating genes for signal transduction [[186]70, [187]71]. According
to their functions, these genes are classified into two groups. The
first group of genes encoding proteins probably functions in stress
tolerance, like LEA proteins, mRNA-binding proteins and aquaporins. In
this study, we identified 5 and 55 transcripts encoding LEA proteins
and aquaporins, respectively, but only three transcripts encoding
aquaporins were differentially expressed significantly in dehydrated
loranthus seeds. However, we found a number of binding proteins
differentially expressed ([188]Fig 6) and most of them were
ATP-associated and RNA binding proteins ([189]Table 3, [190]S3 Table).
The second group of gene products may be involved in further regulation
of signal transduction and gene expression, like various transcription
factors. In total, we detected 1,277 transcripts encoding transcription
factors in all samples, of which 160 were differentially expressed
during dehydration process. As shown in [191]Table 3, 80 up-regulated
and 20 down-regulated transcripts encoding TFs were identified in
Tac-16 while 75 up-regulated and 63 down-regulated transcripts encoding
TFs were identified in Tac-36, compared to CK. Differentially expressed
transcripts encoding transcription factors include MYB, WRKY, and some
ethylene-responsive transcription factors ([192]S3 Table). These
transcription factors have been reported to regulate ABA-dependent and
ABA-independent pathways in plants to respond the dehydration stress
and other stresses [[193]19, [194]24, [195]72–[196]75].
Among the ABA-dependent genes, RD22 (dehydration-responsive protein 22)
is induced by drought stress because its promoter region contains a
cis-acting element and can be recognized by MYB and MYC transcription
factors [[197]76]. We found RD22 mRNA was significantly up-regulated in
dehydrated seeds compared to fresh seeds ([198]Fig 6). Another group of
ABA-dependent genes was heat shock protein (HSP). In response to
dehydration, smHSPs (small HSPs) and HSPs are up-regulated in flesh
fly, Sarcophaga crassipalpis [[199]77, [200]78], the collembolan
Folsomia candida [[201]79], the eutardigrade Richtersius coronifer
[[202]80] and Belgica antarctica [[203]81]. In contrast, we found
smHSPs such as HS22C and HS23C and HSPs such as HS17C and HSP7C were
down-regulated under drought stress in loranthus seeds ([204]Table 3,
[205]Fig 6). It is hard to determine the expression patterns of HSPs
under drought stress in plants. In Arabidopsis five transcripts
encoding HSPs were up-regulated while one was down-regulated during
rehydration process after dehydration [[206]15]. In current study, we
found 18 up-regulated and 28 down-regulated HSP transcripts in Tac-16
compared to CK, but they were down-regulated in loranthus seeds after
being dehydrated over 36 hours. In addition, compared to the
down-regulated HSPs, the expression levels of up-regulated HSPs in
Tac-16 were much lower ([207]Fig 6). Considering this we assume that
the up-regulation of HSPs in orthodox seeds maybe one effective way to
increase the tolerance to drought stress.
We also showed in [208]Table 3and [209]Fig 6five down-regulated
transcripts encoding dehydration responsive element binding proteins
(DRE2D and DREB3), which are ABA-independent genes. In Arabidopsis, Zea
mays and Oryza sativa, DREB genes have been demonstrated to improve
tolerance to drought, salt, cold and heat [[210]82–[211]84]. It is
revealed in wheat and barley overexpression of DREB3 can elevate the
expression of various stress responsive genes including CBF/DREB and
LEA/COR/DHN genes, which means the DREB3 is inducible under drought
stress and may help to increase the tolerance of water deficit
[[212]85]. However, both DRE2D and DREB3 were down-regulated in
loranthus seeds during the dehydration process. In [213]Table 3and
[214]Fig 6, we showed some other differentially expressed transcripts
whose products might be associated with drought tolerance, including
auxin related proteins, ribosomal proteins (RP) and zinc finger
proteins (ZFNs). Of the 187 detected auxin related transcripts four
(12KD_FRAAN, AX6B and LAX2) were down-regulated significantly in Tac-36
compared to CK. Although the relationship between 12KD_FRAAN
(auxin-repressed 12.5 kDa protein) and drought is still unknown,
12KD_FRAAN has been reported to be correlated with fruit growth
[[215]86]. The decrease of auxin related proteins might explain the
reduce of cell activities and viability of seeds during dehydration. In
rice, overexpression of ZFNs can enhance drought and salt tolerance
[[216]87, [217]88]. But studies of ZFN gene expression changes in
response of dehydration stresses are controversial [[218]81, [219]89].
It is also hard to tell if ZFNs were up- or down-regulated in Tac-16
compared to CK ([220]Table 3, [221]Fig 6). We identified 25
up-regulated and 11 down-regulated ZFN transcripts in Tac-16, 30
up-regulated and 19 down-regulated ZFN transcripts in Tac-36, and it is
implicated that ZFNs might function in drought tolerance at an early
stage of dehydration in loranthus seeds. It is notable that the
expression of 86 transcripts encoding ribosomal proteins was
up-regulated in Tac-16 but down-regulated in Tac-36. Like ZFNs,
ribosomal proteins function in early response of dehydration in
Arabidopsis [[222]90–[223]92].
Transcripts involved in seed germination
Previous studies have shown several genes are involved in the
regulation of seed germination in Arabidopsis [[224]93–[225]96],
Brassica oleracea [[226]97] and Brassica napus [[227]98].
Interestingly, some of them have been identified to respond to the
drought stress as well. For example, ABI3, controls embryo degreening
through SGR1 (Stay-green 1) and SGR2 (Stay-green 2) and participates in
ABA-regulated gene expression during seed germination [[228]96,
[229]99]. In addition, COR47 (Cold-induced COR47 protein), OLEO1
(Oleosin), CHI (chalcone flavonone isomerase), CHS (chalcone synthase),
DFR (dihydraflavonol-4-reductase), and RAB18 (Ras-related protein 18)
have been characterized to regulate the process of Arabidopsis seed
germination [[230]96]. In rapeseeds, ZFN mRNAs were down regulated
during the seed germination. In soybean seeds, LEA mRNAs are inducible
by maturation or drying [[231]100]. Some of them have been discussed
according to their functions in response to drought stress previously.
After removing lowly expressed transcripts strictly (<10 FRPKM), we
found the transcripts encoding ABI3_ARATH, RAVL1_ARATH, DFRA_VITVI and
RGL2_ARATH were down-regulated significantly in dehydrated loranthus
seeds compared to fresh seeds ([232]Fig 7). Because of water loss in
loranthus seeds, four transcripts encoding CHS2_RUTGR, DFRA_VITVI and
RAVL1_ARATH (encoded by two transcripts) were down-regulated while two
transcripts encoding OLEO1_PRUDU and RAV2_ARATH increased more or less
in Tac-16 and decreased quickly in Tac-36 ([233]S3 Table). The
down-regulation of seed germination key molecules might be related with
the reduced seed viability and low rate of seed germination.
Fig 7. Transcripts associated with seed germination.
[234]Fig 7
[235]Open in a new tab
Different symbols are used show the significance of different
expression: < 0.05 (*), <0.01 (**) and <0.001 (***). Error bar
represents the standard deviation.
Validation by quantitative real-time PCR
To validate the expression of differentially expressed transcripts in
dehydrated and fresh loranthus seeds, quantitative real-time PCR
(qRT-PCR) experiment was performed due to its high throughput,
sensitivity and accuracy. It is widely used to determine the accuracy
of transcripts and their expression identified by RNA-Seq [[236]101,
[237]102]. In view of this, 9 transcripts were randomly selected for
qRT-PCR and actin-3 was used as control. The expected size of target
transcripts ranged from 96 to 200 bp ([238]S4 Table). For each
transcript we performed three times in every sample. After qRT-PCR
amplification ΔCt was calculated. Then, to compare a transcript in
different samples, we used ΔΔCt method [[239]51], shown as RNE.
[240]Fig 8showed the Log[2]FC identified by RNA-Seq (HiSeq2500) and RNE
detected by qRT-PCR. It is clear that qRT-PCR confirmed the
up-regulation of these transcripts.
Fig 8. qRT-PCR validation for 9 candidate transcripts.
[241]Fig 8
[242]Open in a new tab
Log2FC means log2 fold change in RNA-Seq experiment while RNE means
relative normalized expression (2^-ΔΔCt) in qRT-PCR experiment. Error
bar represents the standard deviation.
Conclusions
In conclusion, we assembled the transcriptome of seeds during
dehydration process in Taxillus chinensis (DC.) Danser using Illumina
RNA-Seq system. A total of 164,546 transcripts corresponding to 114,971
genes were assembled from three libraries–fresh seeds (CK), seeds after
16 hours (Tac-16) and 36 hours (Tac-36) dehydration. Gene expression
profiles showed 38,513 transcripts were commonly detected in all
samples and 12,701, 9,935 and 16,098 transcripts were detected
exclusively in CK, Tac-16 and Tac-36, respectively. Compared to CK,
differential expression analysis demonstrated by edgeR characterized
2,102 up-regulated and 1,344 down-regulated transcripts in Tac-16 and
1,649 up-regulated and 2,135 down-regulated transcripts in Tac-36.
K-means clustering analysis divided them into four groups with
different expression patters and functions. Annotation of
differentially expressed transcripts revealed transcripts encoding ABA
associated proteins (ASR1 and ABAH4), dehydration-responsive protein
RD22, zinc finger proteins and some TFs were up-regulated in Tac-16 and
Tac-36. We also found controversial dysregulations of heat shock
proteins in response to drought stress in loranthus seeds.
Interestingly, transcripts encoding ribosomal proteins peaked in
Tac-16, indicating they might be functional in early dehydration
process. This is the first time to report loranthus transcriptome. The
output of this study will contribute understanding the mechanism of
gene regulation in loranthus seeds during dehydration and give new
insights of genes induced by drought stress.
Supporting Information
S1 File. Scripts for key steps used in this study.
(PDF)
[243]Click here for additional data file.^ (252.9KB, pdf)
S1 Table. Gene Ontology annotation for the assembled transcripts.
(XLSX)
[244]Click here for additional data file.^ (6.3MB, xlsx)
S2 Table. COG annotation for the assembled transcripts.
(XLSX)
[245]Click here for additional data file.^ (542.7KB, xlsx)
S3 Table. Differentially expressed transcripts in Tac-36 and Tac-16
compared to CK.
(XLSX)
[246]Click here for additional data file.^ (791.6KB, xlsx)
S4 Table. Primer sequences used in qRT-PCR experiment.
(XLSX)
[247]Click here for additional data file.^ (10.2KB, xlsx)
Acknowledgments