Abstract
Background
Drought stress is the major environmental stress that affects plant
growth and productivity. It triggers a wide range of responses
detectable at molecular, biochemical and physiological levels. At the
molecular level the response to drought stress results in the
differential expression of several metabolic pathways. For this reason,
exploring the subtle differences in gene expression of drought
sensitive and drought tolerant genotypes enables the identification of
drought-related genes that could be used for selection of drought
tolerance traits. Genome-wide RNA-Seq technology was used to compare
the drought response of two sorghum genotypes characterized by
contrasting water use efficiency.
Results
The physiological measurements carried out confirmed the drought
sensitivity of IS20351 and the drought tolerance of IS22330 genotypes,
as previously studied. The expression of drought-related genes was more
abundant in the drought sensitive genotype IS20351 compared to the
tolerant genotype IS22330. Under drought stress Gene Ontology
enrichment highlighted a massive increase in transcript abundance in
the sensitive genotype IS20351 in “response to stress” and “abiotic
stimulus”, as well as for “oxidation-reduction reaction”. “Antioxidant”
and “secondary metabolism”, “photosynthesis and carbon fixation
process”, “lipids” and “carbon metabolism” were the pathways most
affected by drought in the sensitive genotype IS20351. In addition,
genotype IS20351 showed a lower constitutive expression level of
“secondary metabolic process” (GO:0019748) and “glutathione transferase
activity” (GO:000004364) under well-watered conditions.
Conclusions
RNA-Seq analysis proved to be a very useful tool to explore differences
between sensitive and tolerant sorghum genotypes. Transcriptomics
analysis results supported all the physiological measurements and were
essential to clarify the tolerance of the two genotypes studied. The
connection between differential gene expression and physiological
response to drought unequivocally revealed the drought tolerance of
genotype IS22330 and the strategy adopted to cope with drought stress.
Electronic supplementary material
The online version of this article (doi:10.1186/s12870-016-0800-x)
contains supplementary material, which is available to authorized
users.
Keywords: RNA-Seq, Drought stress, Sorghum bicolor, Water Use
Efficiency, Drought tolerance
Background
Drought is the most important abiotic stress in terms of limiting crop
productivity worldwide. Water availability is, therefore, of primary
importance for a non-limiting crop production in the current changing
global climate scenario. The slogan “more crop per drop” [[29]1] was
the track for crop improvement in water limited environments aiming to
address the growing demand for water, food and commodities (such as
energy) of the growing world population [[30]2].
Among the C4 cereals, Sorghum bicolor is the species most suited to
environments that are prone to drought. Its tolerance to drought is a
consequence of morphological and anatomical characteristics (thick leaf
wax, deep root system) and physiological responses (osmotic adjustment,
stay green, quiescence) [[31]3]. The high genetic variability among
sorghum genotypes and the relatively small size of its genome make this
cereal a good model for the identification of drought related genomic
regions and genes valuable to unravel the high complexity of drought
tolerance related traits [[32]4, [33]5]. Several sorghum linkage maps,
including high density maps [[34]6], have been built using different
types of DNA markers [[35]7, [36]8]. Different genomic regions related
to drought tolerance at pre-flowering and post-flowering stage were
identified [[37]9] but it was the availability of the sorghum genome
sequence [[38]4] that has enabled the monitoring of the genome-wide
gene expression profile at a single time in response to several abiotic
stresses through microarray or RNA-Seq analysis [[39]3, [40]10–[41]12].
These studies resulted in the identification of drought stress
responsive genes and their regulatory elements.
Several transcriptomics studies were carried out on sorghum using
RNA-Seq analysis to monitor gene expression in response to osmotic
stress and abscisic acid [[42]3], to provide a S. bicolor expression
atlas on the dynamic genotype-specific expression profiles [[43]13], or
to identify genome-wide SNPs that can potentially enhance genetic
analysis and the application of molecular markers in sorghum genomics
and breeding [[44]14]. In addition to physiologic or agronomic
approaches, genomics offer new opportunities for dissecting
quantitative traits into their single determinants (quantitative trait
loci, QTLs) paving the way to marker-assisted selection (MAS) or direct
gene editing via genetic engineering [[45]15].
Drought stress elicits a wide range of responses in plants [[46]16]. It
increases oxidative damage in chloroplasts [[47]17, [48]18], reduces
photosynthesis [[49]19–[50]21], limits metabolic reactions [[51]22],
triggers sugar catabolism, in order to provide osmotically active
compound and signal molecules [[52]23–[53]25], and modifies cellular
lipid composition [[54]26]. To cope with drought stress, plants have
developed various strategies, such as generation of larger and deeper
root systems [[55]27], regulation of stomatal closure to reduce water
loss [[56]28], accumulation of compatible solutes and protective
proteins [[57]29], and an increase in the level of antioxidants
[[58]30]. Identification of drought resistant traits was frequently
labelled as “complex” although we already know the results of all the
modifications adopted by plants to cope with drought stress [[59]31].
In this study we have furthered extended the knowledge on the drought
response of two sorghum genotypes through transcriptomic analysis
[[60]32]. A massive parallel sequencing of RNA (RNA-Seq) on the
Illumina platform was used to provide a thorough scenario on the whole
sorghum transcriptome in response to drought stress. Several categories
of key genes involved in drought response have been identified.
Results
Physiological responses to drought stress
Twenty sorghum plants (ten per each genotype) were subjected to severe
drought stress by withholding water from 26 DAE (Days After Emergence)
until 34 DAE when 0.2 FTSW (Fraction of Transpirable Soil Water) was
reached in all the stressed plants (Fig. [61]1, solid line, white
dots). Subsequently the stressed plants were kept at 0.2 FTSW by
irrigating daily for nine days, while the control plants were kept at
FTSW values higher than 0.6 for the entire duration of the experiment
(Fig. [62]1, solid line, full dots). The daily transpired water (DTW)
was under 400 gr for the stressed plant, while it was up to 1000 gr for
the control plants (Fig. [63]1, dotted lines).
Fig. 1.
Fig. 1
[64]Open in a new tab
Trend of FTSW and daily transpired water during the dry-down
experiment. On the left axis with circles symbols the trend of FTSW
during the dry-down: with full circles the WW plants and with the empty
circles the DS ones. On the right axis with triangles the daily
transpired water: full triangles for the WW plants and empty triangles
for the DS ones. DAE = days after emergence. Mean of 10 plants ± SE
Leaf area, chlorophyll fluorescence parameters (maximum quantum yield,
Fv/Fm, the photosystem II efficiency, ΦPSII, and non-photochemical
quenching, qNP) and gas exchange measurements (photosynthetic rate, Pn,
and transpiration E) were quantified for the entire duration of the
experiment (data not shown).
The decreased FTSW led to a reduction in RWC (Relative Water Content)
values and these changes were greater in the sensitive genotype IS20351
than in the tolerant genotype IS22330 (Table [65]1). Drought stress
also dramatically reduced chlorophyll fluorescence and photosynthetic
rate. Under stress conditions the tolerant genotype IS22330 showed a
significantly higher value of Fv/Fm than the sensitive genotype IS20351
(Table [66]1). The same trend was observed for ΦPSII: 0.36 and 0.28 for
the tolerant and the sensitive genotype, respectively. In contrast, the
qNP under drought stress was higher in the sensitive genotype IS20351
than in the tolerant genotype IS22330 (Table [67]1).
Table 1.
Physiological responses of sorghum genotypes to drought stress
Genotype Condition FTSW RWC Chlorophyll fluorescence Gas exchange
Fv/Fm ΦPSII qNP Pn E WUE[i] WUE[a]
% % μmol m^−2 s^−1 mmol m^−2 s^−1 μmol mol^−1 g/l
IS20351 WW 0.70 92.7 0.803^a 0.50^a 0.18^a 31.2^a 4.58^b 6.38^a 3.31^b
DS 0.2 78.4 0.779^c 0.28^c 0.15^b 19.8^c 5.56^a 3.56^c 3.26^b
IS22330 WW 0.80 92.9 0.804^a 0.52^a 0.11^b 30.4^a 4.51^b 6.74^a 3.74^ab
DS 0.2 88.4 0.791^b 0.36^b 0.08^c 24.1^b 4.93^b 4.88^b 4.23^a
LSD [(0.05)] 7.76 0.006 0.08 0.002 2.4 0.58 0.28 0.59
P <0.0001 <0.01 <0.0001 <0.05 <0.0001 <0.0001 <0.001 <0.01 <0.05
[68]Open in a new tab
Analysis of relative water content (RWC), chlorophyll fluorescence
(Fv/Fm, FPSII and qNP), gas exchange (Photosynthetic rate, Pn, and
Transpiration, E), intrinsic (WUEi) and agronomic WUE (WUEa) in sorghum
plants in well-watered (WW) and drought stress (DS) conditions at
vegetative stage of 9th leaf. Values followed by the same letter are
not statistically significant at LSD test p < 0.05 performed on the
interaction genotype× irrigation.
Drought stress affected Pn in both the genotypes differently; the
sensitive genotype IS20351 had a greater reduction in Pn (36.5 %) while
the tolerant genotype IS22330 showed a Pn reduction of 20.7 %.
Transpiration (E) did not differ between the WW (Well-Watered) and DS
(Drought-Stressed) plants of the tolerant genotype IS22330, while there
was a statistically significant difference between the WW and DS plants
of the sensitive genotype IS20351. The intrinsic water use efficiency
(WUE[i]) decreased linearly for the DS plants of both genotypes from
the beginning of the experiment (26 DAE) until harvest (42 DAE), while
the WW plants kept their WUE[i] close to 6 μmol mmol^−1 (Fig. [69]2).
WUE[i] of DS plants of the tolerant genotype IS22330 was significantly
higher than that of DS plants belonging to the sensitive genotype
IS20351 during the stress period (p < 0.05) (Fig. [70]2). The agronomic
water use efficiency (WUE[a]), calculated at harvest, was higher for
the tolerant genotype IS22330 (4.23 g/l) than for the sensitive
genotype IS20351 (3.26 g/l), thereby confirming the trend highlighted
by WUE[i].
Fig. 2.
Fig. 2
[71]Open in a new tab
Trend of WUE[i] calculated during the dry down experiment. Circles
represent the sensitive genotype IS20351 and triangles the tolerant
IS22330. For both the genotypes the full symbols represents the WW
plants whilst the empty symbols represent the DS ones. Mean of 10
plants ± SE
Drought stress reveals different intergenic transcripts and novel splice
sites
Transcription profiles of IS20351 and IS22330 under well-watered (WW)
and drought-stressed (DS) conditions were explored using the Illumina
Genome Analyzer deep sequencing. Three biological replicates were
analysed for each condition, resulting in twelve samples. In total,
0.56 billion clean reads, each 100 nucleotides long, were generated,
with approximately 47 million clean reads from each sample. The reads
mapping to the reference genome were categorised into two classes:
uniquely mapped reads, that are reads that map to only one position in
the reference genome, and multi-position match, that are reads mapping
to more than one position in the reference genome (Table [72]2). The
assembled transcripts were mapped on the genome: on average 72 % were
known transcripts, 10 % were novel transcripts and 18 % were intergenic
transcripts (Table [73]3).
Table 2.
Number of reads sequenced and mapped with SOAPaligner/SOAP2
Genotype Treatment Total Reads Total Unmapped Reads Total Mapped Reads
Unique match Multi-position match
IS20351 WW 47090292 11993194 35097098 32702251 2394847
DS 46866452 11969692 34896760 32188700 2708060
IS22330 WW 47504944 11856843 35648101 32660622 2987479
DS 46840269 11086330 35753938 32549143 3204795
[74]Open in a new tab
The numbers of unique mapped reads plus the multi-position match equals
the total number of mapped reads in well-watered (WW) and drought
stress (DS) conditions
Table 3.
Classification of transcript produced in sorghum leaves under
well-watered (WW) and drought stress (DS) conditions
Genotype Treatment Total Mapped Reads Match to known transcripts
Intergenic transcripts Novel Transcripts Alternative Splicing Events
IS20351 WW 75 % 73 % 18 % 9 % 24178
DS 74 % 71 % 20 % 9 % 24367
IS22330 WW 75 % 72 % 18 % 10 % 20498
DS 76 % 72 % 18 % 10 % 24304
[75]Open in a new tab
Percentage of total mapped reads on the reference genome, percentage of
match with known transcripts, with intergenic transcripts and novel
transcripts identified, and alternative splicing events identified
Drought stress induced alternative splicing events (ASE) in the two
genotypes (Table [76]3): in the sensitive genotype IS20351 no
difference in ASE were found, while in the tolerant genotype IS22330
the ASE were increased by 18 %.
Drought stress triggers differential expression of particular genes and GO
classes
Each condition was represented by three biological replicates,
resulting in eighteen pairwise comparisons between control and stressed
plants of the two genotypes. The transcript abundance of each gene was
calculated as reads per kilobase transcriptome per million mapped reads
(RPKM) (Fig. [77]3a). This value was used to determine the differential
expression analysis as Log[2] ratio between DS and WW plants per
genotype and between the two genotypes under WW and DS conditions. Four
comparisons were analysed in this study: i) the genotypes IS20351 and
IS22330 under WW conditions (WW IS22-IS20 in yellow), ii) the genotypes
IS20351 and IS22330 under DS conditions (DS IS22-IS20 in green), iii)
the genotype IS20351 in response to DS conditions (IS20 DS-WW in blue),
iv) the genotype IS22330 in response to DS conditions (IS22 DS-WW in
red).
Fig. 3.
Fig. 3
[78]Open in a new tab
Comparison under study. a Number of DEGs (RPKM) in each pairwise
comparison. Blue and red bar are up- an down-regulated genes
respectively expressed in well-watered (WW) and drought stressed (DS)
conditions in the genotypes IS20351 (IS20) and IS22330 (IS22). b Total
number of DEGs that passed the cut-off of Log[2] FC >2 in each
comparison. In yellow the number of DEGs resulting from the comparison
between IS20351 and IS22330 in well-watered (WW) conditions, in green
the number of DEGs resulting from the comparison between the two
genotypes under drought stress (DS) conditions; in blue the numbers of
DEGs in response to drought stress in IS20351 and in red the number of
DEGs in response to drought stress in IS22330. c Venn diagram showing
the numbers of up- and down- regulated genes resulted from the four
comparison performed. The number of up- or down- regulated genes shared
among the four comparison is represented by overlapping circles
After applying a stringent cut-off (see [79]Methods section), the
comparison of genotypes IS20351 and IS22330 under WW conditions
identified 1643 differentially expressed genes (DEGs), and the
comparison of genotypes IS20351 and IS22330 under DS conditions
identified 1845 DEGs. 1599 genes were differentially expressed in
IS20351 in response to drought stress, whilst only 636 were
differentially expressed in IS22330 (Fig. [80]3b). Venn diagrams
highlight the overlap of DEGs between each pairwise comparison
(Fig. [81]3c).
Comparison between IS22330 and IS20351 under WW conditions (Fig. [82]3c
in yellow) resulted in 1030 up-regulated genes and 613 down-regulated
genes. Only 340 genes were uniquely up- and 160 genes down-regulated in
IS22330 in these conditions. The singular enrichment analysis (SEA),
carried out with AgriGO software
([83]http://bioinfo.cau.edu.cn/agriGO/index.php) on the 340
up-regulated genes, highlighted 34 GO terms significantly enriched:
“aromatic compound biosynthetic process” (GO:0019438), “secondary
metabolic process” (GO:0019748), and “flavonoid biosynthetic process”
(GO:0009812) in the cellular processes category; “glutathione
transferase activity” (GO:0004364), “oxygen binding” (GO:0019825),
“UDP-glucosyltransferase activity” (GO:0035251) in molecular functions
category (Additional file [84]1: Table S1). “Apoptosis” (GO:0006915)
and “oxidoreductase activity” (GO:0016491) were the most enriched GO
terms in the biological processes and molecular function categories
among the 160 uniquely down-regulated genes expressed in WW conditions
in IS22330 (Additional file [85]1: Table S2).
The comparison between the two genotypes under DS conditions resulted
in 1036 up- and 809 down-regulated genes. Among these genes, only 428
and 393 were uniquely up- and down- regulated in the genotype IS22330
in comparison to IS20351. “Regulation of DNA replication” (GO:
0006275), “cell death” (GO:0008219), “regulation of cell growth by
extracellular stimulus” (GO:0001560), “secondary metabolic processes”
(GO:0019748) including “terpenoids biosynthetic process” (GO:0016114),
“glutathione transferase activity” (GO:0004364) and “pre-replicative
complex” (GO:0005656) (Additional file [86]1: Table S3) were the most
enriched GO terms among the 75 identified after SEA of the 428
up-regulated genes. Among the 393 down-regulated genes 24 GO terms were
significantly enriched: “lipid localization” (GO:0010876), “apoptosis”
(GO:0006915), “flavonol biosynthetic process” (GO:0051555), “electron
carrier activity” (GO:0009055) and “heme binding” (GO:0020037)
(Additional file [87]1: Table S4).
The main difference between the two genotypes was in the total number
of genes differentially expressed in response to drought stress: 1599
for the sensitive IS20351 and 636 for the tolerant IS22330. The SEA
analysis, performed on all the 1599 and 636 DEGs expressed in response
to drought in the genotypes IS20351 and IS22330, showed 197
significantly enriched GO terms (p-value <0.05) in the sensitive
genotype IS20351 while 34 in the tolerant IS22330. Twenty GO terms were
enriched in both the genotypes in response to drought stress and are
represented in the heat map (Fig. [88]4). “Response to heat”, “RNA
modification”, “cytosolic part” and “ribosomal subunit” GO terms were
enriched with the same extent in both the genotypes. Different GO
enrichment was recorded between IS203351 and IS22330 for
“oxidation-reduction process”, “response to abiotic stimulus”,
“oxidoreductase activity”, “response to chemical stimulus”, “small
molecule metabolic process”, “response to stress”, “chloroplast”,
“single-organism metabolic process” and “cytoplasm component”. All
these GO terms were more enriched in IS20351 than in IS22330.
Fig. 4.
Fig. 4
[89]Open in a new tab
Heat map showing the 20 common GO terms enriched under drought stress
in sorghum leaves of IS20351 and IS22330. The cluster frequency was
used as a parameter for the parametric analysis of gene enrichment
analysis. The figure was generated using R software, Limma package
Between the two genotypes there were 145 common up-regulated genes in
response to drought stress and 50 common down-regulated genes
(Fig. [90]3c). The SEA performed on these common DEGs highlighted 11
enriched GO terms belonging to biological processes: “response to
abscisic acid stimulus” (GO:0009737), “response to water deprivation”
(GO:0009414), “photosynthesis, light reaction” (GO:0019684) were the
most enriched GO (Additional file [91]1: Table S5).
The SEA analysis performed with AgriGO on the unique up-regulated genes
of IS20351and IS22330 (respectively 559 and 78 genes) highlighted 74
enriched GO terms in IS20351 and and 6 enriched GO terms IS22330. The
cross comparison of SEA
([92]http://bioinfo.cau.edu.cn/agriGO/analysis.php?method=compare)
highlighted 6 common GO terms (Additional file [93]1: Table S6). The
SEA analysis performed on the unique down-regulated genes (602 and 241
for IS20351 and IS22330 respectively) highlighted 166 and 32
significantly enriched GO terms in IS20351 and IS22330 respectively;
after the cross comparison of SEA only 6 resulted as being common to
both genotypes (Additional file [94]1: Table S7).
Drought stress affects different pathways
The KEGG pathway analysis was performed to assign the related
biological pathways in which DEGs were involved. One-hundred and
seventy-one genes, uniquely expressed in response to drought stress in
both the genotypes, were assigned to 112 different KEGG pathways
belonging to 24 clades under five major KEGG categories including
‘organismal system’ (I), ‘cellular process’ (II), ‘environmental
information processing’ (III), ‘genetic information processing’ (IV),
and ‘metabolism’ (V) (Fig. [95]5). Gene-set enrichment analysis showed
that translation, signal transduction and carbon metabolism were the
top three up-regulated pathways represented by the genes uniquely
expressed in response to drought stress; metabolism pathways (V) and
signal transduction were, on the other hand, the most enriched
down-regulated pathways (Fig. [96]5).
Fig. 5.
Fig. 5
[97]Open in a new tab
Number of up- and down-regulated genes in each clade of the KEGG
pathway maps. The 171 unigenes were assigned 112 KEGG pathways within
24 clades under five major categories: “organismal systems” (I),
“cellular processes” (II), “environmental information processing”
(III), “genetic information processing” (IV), “metabolism” (V). Per
each clades are shown the up- (in red) and the down- (in blue)
regulated genes
KEGG pathway analysis was also performed on the genes that were
uniquely up- and down-regulated in response to drought stress in both
genotypes (Fig. [98]6). Transcription factors, ‘environmental
information processing’ pathways, and pathways related to ‘cellular
processes’ and ‘organismal system’ remained unchanged among the
uniquely up-regulated genes (Fig. [99]6 in red). The most striking
differences in the transcriptomic profiles of the two genotypes in
response to drought were mainly in the ‘metabolism’ pathways (that were
up-regulated by 36 % in IS20351 and 22 % in IS22330), in the ‘genetic
information processing’ pathway (that was up-regulated to a greater
extent in IS20351) and in the number of genes not assigned to pathways
(Fig. [100]6 in red). Focusing on the up-regulated ‘metabolism’
pathways, the tolerant genotype IS22330 showed a two-fold (or greater)
enrichment in the metabolism of other amino acids, the nucleotide
metabolism, the glycan biosynthesis metabolism and the lipid metabolism
compared to the sensitive genotypes IS20351 (Fig. [101]6 in red). Amino
acid metabolism, carbohydrate metabolism and energy metabolism were
more enriched in the sensitive genotype IS20351 than in the tolerant
genotype IS22330 (Fig. [102]6 in red).
Fig. 6.
Fig. 6
[103]Open in a new tab
Distribution in KEGG pathways of the unique up- and down-regulated
genes in response to drought for the genotype IS20351 and IS22330. Pie
charts showing the percentage of genes up- (in red) and down- (in blue)
regulated in response to drought stress for the genotypes IS20351 (a)
and IS22330 (b)
The ‘metabolism’ pathways of IS20351 and IS22330 were down-regulated to
the same degree in response to drought stress (Fig. [104]6 in blue).
‘Cellular processes’ pathways represented 4 % of the down-regulated
genes in IS20351 and 2 % in IS22330 (Fig. [105]6 in blue). ‘Organismal
system’ pathways, ‘genetic information processing’ pathways and
transcription factors were down-regulated to a greater extent in the
tolerant genotype IS22330 (Fig. [106]6 in blue). Among the
down-regulated ‘metabolism’ pathways, energy metabolism, nucleotide,
cofactors and vitamins metabolism, glycan biosynthesis and metabolism,
and carbohydrate metabolism pathways were down-regulated with a higher
frequency in the sensitive genotype IS20351 than in the tolerant
IS22330 (Fig. [107]6 in blue).
Drought stress response of sorghum transcriptome
The MapMan software (3.5.1R2) [[108]33] was used to show a pathway
overview of 1599 and 636 DEGs expressed in response to drought stress
and it was selected for its capacity to show statistically significant
drought mediated gene expression data for the sensitive genotype
IS20351 (Fig. [109]7a) and the tolerant genotype IS22330
(Fig. [110]7b). Three main aspects were selected for a deeper
evaluation of drought tolerant traits: the antioxidant and secondary
metabolism pathways, light reaction and carbon fixation pathways, lipid
and carbon metabolism.
Fig. 7.
Fig. 7
[111]Open in a new tab
Distribution of up- (in red) and down- (in blue) regulated genes in
metabolic pathways in response to drought stress for IS20351 and
IS22330. Drought mediated expression changes in the metabolic pathways
in leaves of IS20351 (a) and IS22330 (b). The figure was generated
using MapMan and shows DEGs that passed the cut-off of Log[2] FC >2
Response of antioxidant and secondary metabolism related genes
DEGs related to antioxidant and secondary metabolism were analysed
together because of the strong relationship between the capacity to
scavenge ROS through antioxidant genes and metabolites derived from the
secondary metabolism.
Seventeen DEGs were identified in the sensitive genotype IS20351 in
response to drought: 5 were up-regulated and 12 down-regulated
(Additional file [112]2: Table S1). In the tolerant genotype IS22330,
in the same condition, only 4 DEGs were found and three of them were
up-regulated. The sb09g025730.2 gene showed a peculiar behaviour; it
was up-regulated in the tolerant genotype IS22330 and dramatically
down-regulated in the sensitive IS20351. The sb06g001970.1 gene was
up-regulated in the sensitive genotype IS20351 and remained unchanged
in the tolerant IS22330. In contrast, the sb09g001690.1 gene was
up-regulated in the tolerant IS22330 and its expression remained
unchanged in the sensitive IS20351.
Drought affected the secondary metabolism in both sorghum genotypes.
Fifty DEGs were found in the sensitive genotype IS20351 and 27 in the
tolerant IS22330 (Additional file [113]2: Table S1). In the sensitive
genotype IS20351, about the same number of genes were up- and
down-regulated (25), whilst in the tolerant genotype IS22330 the
down-regulated genes were more than the up- regulated ones; 20 and 7,
respectively (Additional file [114]2: Table S1). Among the
down-regulated genes, the isoprenoids and phenylpropanoids metabolism
was the most affected metabolism, with 20 genes in IS20351 and 10 in
IS22330. The flavonoids pathway showed a peculiar behaviour being
up-regulated by drought in the sensitive genotype IS20351 and
down-regulated in the tolerant genotype IS22330. The changes in the
secondary metabolism expression pattern, for example the change in the
chlorophyll/carotenoids content, was reflected in the fluorescence
parameters recorded.
Response of light reactions and carbon fixation pathways
The photosynthetic pathway was drastically affected by drought in the
sensitive genotype IS20351, with 28 genes differentially expressed in
response to drought: 19 belong to the light reaction pathway and 9 to
the Calvin cycle.
Among the 19 DEGs belonging to the light reaction pathway, 15 genes
were down-regulated in response to drought (Additional file [115]2:
Table S1): 8 code for protein belonging to the light harvesting complex
I or II (LHCI and LHCII), 6 code for protein related to photosystem I
and II (PSI and PSII) and 1 codes for the gamma subunit of the ATP
synthase. Two isoforms of PSII polypeptide subunits were strongly
up-regulated together with the electron carrier ferrodoxin in the
sensitive genotype IS20351 in response to drought (Additional file
[116]2: Table S1). In the tolerant genotype IS22330 the light reaction
pathway was also affected, but to a lower extent. Only three genes
belonging to the light reaction pathway were up-regulated in response
to drought: 2 implicated in PSII and one in photosynthetic electron
transport, the ferrodoxin (Additional file [117]2: Table S1).
9 genes related to the carbon fixation pathway (Calvin cycle) were
differentially expressed in the sensitive genotype IS20351 (Additional
file [118]2: Table S1): 6 were down-regulated by drought and 3 were
up-regulated (Sb01g037510.1, Sb06g004280.1 and Sb05g027880.1). In the
tolerant genotype IS22330 no genes were differentially expressed in
response to drought (Additional file [119]2: Table S1).
Lipid and carbon metabolism in response to drought stress
In terms of DEGs the lipid metabolism was more greatly affected in the
sensitive genotype IS20351 (Additional file [120]2: Table S1). In this
genotype fatty acid synthesis, elongation and lipid degradation via
beta-oxidation cycle were all up-regulated (Additional file [121]2:
Table S1). Phospholipid and sphingolipid syntheses were down-regulated
in response to drought (Additional file [122]2: Table S1). In the
tolerant genotype IS22330 the steroids biosynthesis and phospholipase D
were up-regulated (Additional file [123]2: Table S1).
Also the carbon metabolism was more greatly affected by drought in the
sensitive genotype IS20351 than in the tolerant IS22330. In IS20351
drought highlighted 12 DEGs: 7 genes belonging to the degradation of
starch and sucrose were up-regulated, and 5 genes were down-regulated
(Additional file [124]2: Table S1). In the tolerant genotype IS22330
only 2 genes were down-regulated (Additional file [125]2: Table S1).
Discussion
In plants exposure to drought triggers a wide range of responses,
ranging from molecular expression, biochemical metabolism to ecosystem
level, that involve lots of genes and pathways related to diverse
mechanisms [[126]16]. In this study we evaluated these mechanisms
through RNA-Seq analysis of two sorghum genotypes subjected to the same
extent of drought stress. The responses differed greatly between the
sensitive IS20351 and the tolerant IS22330 genotypes in terms of the
number of genes and pathways involved in drought stress response, but
also in terms of the constitutive expression level of several pathways.
Constitutive drought tolerance trait
The trend of FTSW, together with the value of the daily transpiration
rate, confirmed that the DS plants of both genotypes were subjected to
the same environmental conditions and to the same extent of drought
stress. In addition, transcriptomics analysis provided unequivocal
evidence on RNA modifications triggered by drought stress. “Response to
heat” (GO:0009408) and “RNA modification” (GO:0009451) GO terms were
enriched to the same extent in both genotypes.
Although the drought stress level applied was equal (0.2 FTSW), the two
genotypes responded differently; in IS20351 a significantly higher
number of differentially expressed genes (DEGs) was observed than in
the tolerant genotype IS22330, resulting in a greater enrichment of GO
terms related to drought stress response in IS20351 than in IS22330.
The up-regulation of genes under WW conditions of “secondary metabolic
process” (GO:0019748), and related GO terms, in the genotype IS22330
confirm its intrinsic tolerance, previously only characterized from a
physiological point of view [[127]32]. In this genotype, the
constitutive upper level of flavonoids and secondary metabolites led to
increased drought tolerance traits according to Winkel-Shirley
[[128]34]. Furthermore the “glutathione transferase activity”
(GO:000004364) was up-regulated in the tolerant genotype IS22330
confirming the role of the glutathione-S transferase family in
improving environmental stress resistance in crops [[129]35].
Drought tolerance strategies
Drought stress results in a massive production of reactive oxygen
species (ROS) [[130]17, [131]18] that cause oxidative stress. The
sequence of events that occur in plant tissues in response to oxidative
drought-induced stress was well described by Mano et al. [[132]36]. The
antioxidant enzymes constitute the “first line of defence” against ROS
and oxidative stress generated by different abiotic and biotic injuries
[[133]37, [134]38]. The activity of these enzymes can be enhanced or
repressed depending on the species, genotype, stress duration and
severity [[135]39–[136]41]. In the “response to abiotic stimulus”
(GO:0009628), “oxido-reductase activity” (GO:0016491) and “response to
stress” (GO:0009628) gene ontology categories, genes were more greatly
down-regulated by drought in the sensitive genotype IS20351 than in the
tolerant IS22330, enabling us to speculate that the tolerant IS22330
had a constitutively higher expression of antioxidant genes that is not
affected by drought stress. Experimental evidence showed that the
antioxidant enzyme activity might be depressed in excess-light
conditions, especially when plants are faced with additional stresses
such as drought or temperature [[137]42].
To cope with the oxidative stress caused by drought, genes coding for
secondary metabolites such as phenylpropanoids, phenolic compounds and
flavonoids, are overexpressed [[138]43]. Phenylpropanoids have the
greatest potential to reduce ROS, the polyphenols act as antioxidants
to protect plants against oxidative stress [[139]44], flavonoids play
different molecular functions, including stress protection in plants
[[140]34], and also flavanols were found to be oxidated in response to
severe drought in tea plants, suggesting their involvement in plant
protection [[141]45]. All these compounds are widely synthetized in
response to several abiotic stresses, including drought
[[142]46–[143]50]. In wheat and willow leaves an increase in flavonoid
and phenolic acids content was observed together with an induction of
genes involved in the flavonoid biosynthetic pathway in response to
various stresses, including drought [[144]51, [145]52]. With our study,
we confirm that under drought stress the up-regulation of these genes
in the sensitive genotype IS20351 was higher than in the tolerant
genotype IS22330, whilst a constitutively higher expression of these
genes in the tolerant IS22330 under control conditions led to a lower
synthesis of stress induced compounds. The accumulation of these
compounds and the differential expression of the above mentioned genes
remains genotype dependent [[146]53].
Only in the last decade was it hypothesized that flavonoids might also
play a role as antioxidant in response to severe excess of light
complementing the role of antioxidant enzymes [[147]54–[148]57]. Agati
et al. [[149]42] found that flavonoid genes were up-regulated in
response to drought in the sensitive genotype IS20351 whilst they were
mostly down-regulated in the tolerant IS22330. The biosynthesis of
“antioxidant” flavonoids, in fact, increases more in stress sensitive
species than in stress tolerant ones [[150]42]. The reason for this
lies in the fact that stress sensitive species display a less efficient
“first line” of defence against ROS in conditions of stress and they
are therefore exposed to a more severe oxidative stress [[151]58,
[152]59]. In any case, the relationship between antioxidant enzymes and
flavonoids in response to abiotic and biotic stress it is not yet well
clarified [[153]42].
Drought stress induces a decrease in the chlorophyll content, a
consequential change in the chlorophyll/carotenoid ratio [[154]60] and
an increase in the ratio of violaxanthin-cycle pigment. This results in
a reduction of light absorption centres, an enhancement of
non-photochemical quenching in order to dissipate the excess of light,
and a reduction in photosynthetic rate [[155]19–[156]21]. All these
stress-induced physiological modifications (qNP and Pn) were observed
to a greater extent in the sensitive genotype IS20351. The
physiological response is supported by the observation that a high
number of genes involved in the terpenoids and carotenoids biosynthesis
were down-regulated in IS20351 and not in IS22330, in agreement with
the decreased concentration of some carotenoids under severe drought
stress [[157]17, [158]38, [159]61].
The down-regulation of genes related to carotenoids and chlorophyll
biosynthetic pathways leads to the down-regulation of light reaction
and carbon fixation pathways, that in fact were dramatically affected
by drought in the sensitive genotype IS20351. The decreased expression
pattern mainly involved the light harvesting complex I and II and
polypeptide subunits of the photosystems (I and II). In particular, the
light-harvesting chlorophyll a/b-binding proteins (LHCBs) were
extremely down-regulated in the sensitive genotype IS20351 according to
several studies in which the down-regulation of LHCBs reduces plant
tolerance [[160]62–[161]65]. The LHCBs, complexed with chlorophyll and
xanthophylls, form the antenna complex [[162]66] and play an important
role in adaptation to environmental stress [[163]63–[164]65]. Their
expression is regulated by multiple environmental factors including
light [[165]67], oxidative stress [[166]68, [167]69] and abscisic acid
(ABA) [[168]70]. Also the genes involved in the “carbon fixation” were
more greatly down-regulated in the sensitive genotype IS20351 rather
than in the tolerant one. The up-regulation of Sb03g040610.1 was the
main exception in the expression pattern of this genotype; this gene
codes for the electron carrier ferrodoxin. Comparing the Log2 values of
this gene in the two genotypes, it appears that this gene was more
up-regulated in the sensitive genotype than in the tolerant one (5.2
and 3.4 for IS20351 and IS22330, respectively). This result indicates
that the tolerant genotype IS22330 could better cope with the excess of
light during drought stress. This is further supported at a
physiological level by the low qNP value recorded. Conversely, the
sensitive genotype IS20351 over expressed this gene so that it can
dispose the excess of electrons and consequently waste the excess of
light in non-photochemical reactions.
According to literature, under drought stress starch (inactive
osmotically) content decreases, whilst content of soluble sugars
(osmotically active) increases, assuring the maintenance of leaf water
status and plant growth [[169]23–[170]25]. In the sensitive genotype
IS20351, starch synthases were down-regulated and enzymes involved in
the degradation of starch and sucrose up-regulated. According to Sturm
and Tang [[171]71] invertases play a role in several processes ranging
from phloem loading to response to abiotic and biotic stresses
[[172]23, [173]72]. Exogenous ABA applied in soybean green beans
[[174]73] and maize leaves exposed to drought [[175]74] showed an
increase in invertase activity. Gazarrani and McCourt [[176]75] also
highlighted that hexose-based signals originating from sucrose cleavage
are implicated in the regulation of ABA biosynthetic genes. It is well
known that sucrose plays a crucial role as a key molecule in energy
transduction and as a regulator of cellular metabolism
[[177]76–[178]78]. Furthermore, sucrose and other sugars are energy and
carbon sources required for defence response and are necessary for
plant survival under drought stress conditions [[179]79]. Like
hormones, sucrose can act as primary messenger controlling the
expression of several genes involved in sugar metabolism.
Lipids are important membrane components and, under drought stress,
significant modifications of the lipid membranes occur. For this reason
our investigation also focused on this metabolic pathway. The fatty
acid elongation is considered to be the rate-limiting step in cuticular
wax biosynthesis [[180]80, [181]81]. The accumulation of wax has a key
role in limiting water losses from plants [[182]82]. It is widely
accepted that drought stress can increase the amount of wax in several
species [[183]83–[184]87] and that this increase is associated with an
improved drought tolerance [[185]88]. According to our results, the
sensitive genotype IS20351 up-regulated these genes in response to
drought; on the contrary, the drought tolerant genotype IS22330
remained unchanged. The hypothesis is that the tolerant genotype
IS22330 has a constitutively higher expression level of genes related
to drought tolerance, such as genes involved in cuticular wax synthesis
and fatty acid desaturation. This hypothesis is also confirmed by the
observation that, according to Torres-Martin et al. [[186]89], no
changes in omega-3 desaturase expression were highlighted in response
to drought in the tolerant genotype IS22330. On the contrary, the
omega-3 desaturases were down-regulated in the sensitive genotype
IS20351 [[187]89].
The first evidence of the involvement of sphingolipids in the
signal-transduction pathways in plants, including in response to
drought, was provided by Ng et al. [[188]90]. Until that moment only
the implication of sphingolipids in conferring stability to plant
membranes, contributing to acclimation to drought stress had been
hypothesized [[189]91]. Spiegel and Milstien [[190]92] afterwards
explored the link between the sphingosine-1-phosphate and the drought
hormone abscisic acid in the release of calcium from the vacuole.
RNA-Seq results highlighted the ineffective response of the drought
sensitive genotype IS20351 that down-regulated sphingolipids in
response to drought, except for a ceramidase (sb03g028410.1).
In cowpea leaves a massive breakdown of membrane lipids was observed in
response to drought with a more severe degradation in the sensitive
plants [[191]93]. The main enzyme responsible for the drought-induced
degradation of membrane phospholipids is phospholipase D (PLD)
[[192]94]. According to El Masouf et al. [[193]95], the drought
sensitive genotype IS20351 strongly up-regulated the PLD expression,
whilst in the drought tolerant IS22330 the expression was only slightly
up-regulated. Recently, PLD up-regulation was associated to drought and
salt stress tolerance [[194]96–[195]99] and the product of its
activity, the phosphatidic acid, is involved in ABA signalling in
stomatal movement [[196]100]. PLDa1, in particular, is the most
predominant PLD in plants activated by ABA [[197]101].
Some interesting genes provided insight into the drought tolerance of
the genotypes analysed. The Sb06g014320 gene, encoding for a
glycerophosphodiester phosphodiesterase, found to be up-regulated in
response to drought in sorghum leaves [[198]12], was strongly
down-regulated in response to drought in the sensitive genotype
IS20351. The Sb07g027910 gene, encoding for a
monogalactosyl-diacylglycerol (MGDG) synthase, found to map to a stay
green QTL [[199]102] and to be overexpressed in response to drought in
sorghum leaves, was down regulated in the sensitive genotype IS20351.
Since these genes are involved in drought tolerance related pathways,
the first in choline biosynthesis and the second in
phosphatidylinositol biosynthesis, a down regulation in response to
drought is proof of sensitivity to drought stress for the sensitive
genotype IS20351. A confirmation of the drought tolerance of IS22330
was the overexpression of genes related to the phosphatidylinositol
biosynthesis, such as sb08g016610, sb08g022520 and sb05g026855.
Conclusion
RNA-Seq analysis, performed in this study, proved to be a good method
to investigate complex traits in different genotypes. The sorghum
transcriptome analysed in response to drought conditions revealed
unequivocal traits of sensitivity and tolerance in the two sorghum
genotypes studied.
The first evidence of sensitivity to drought of the genotype IS20351
was represented by the physiological measurements (gas exchange and
chlorophyll fluorescence) that drought dramatically affected. This
evidence was confirmed at a transcriptomic level by the higher number
of differentially expressed genes (DEGs) observed in the sensitive
genotype IS20351 and not in the tolerant genotype IS22330. The
sensitivity to drought of IS20351 was further confirmed by the lower
constitutive expression level of “secondary metabolic process”
(GO:0019748) and “glutathione transferase activity” (GO:000004364)
observed under well-watered conditions in IS20351 in comparison with
the tolerant genotype IS22330. In addition, the enriched GO terms
analysis highlighted the differences existing between the two genotypes
in coping with drought stress and the strategies adopted. The sensitive
genotype hydrolysed carbohydrates and sugars, while the tolerant
IS22330 activated the synthesis of other amino acids (glycinbetaine,
glutathione) to cope with drought stress. In conclusion, we can confirm
that the sensitive genotype IS20351 perceived the drought stress
imposed (0.2 FTSW) to a greater extent than the tolerant genotype
IS22330, showing an overactive genetic response. IS22330, on the other
hand, being generally less affected by drought in all the analysed
pathways, could be used as a genetic donor to further improve the
sorghum germoplasm with drought tolerance traits.
Methods
Plant material, drought stress conditions and physiological measurements
Two sorghum genotypes of the durra race, IS20351 and IS22330, were
cultivated in pots in July 2013 in a dry down experiment in open field
condition in the experimental station of Università Cattolica del Sacro
Cuore, Piacenza, Italy. The genotypes are part of germplasm collection
of CIRAD and were provided by the CRB-T (Centre de Resources
Biologiques Tropicales) CIRAD Montpellier. IS20351 and IS22330 were
previously characterized in 2012 for their contrasting tolerance to
drought [[200]32]. According to Fracasso et al. [[201]32], germination
of seeds was carried out in Petri dishes at 25 °C and in dark
conditions for 3 days. Five germinated seeds were planted in plastic
pots (16 L capacity), filled with a base layer of sand to guarantee
drainage and 8 kg of a soil mixture (24 % clay, 64 % silt, and 12 %
sand), that had been previously sieved, dried and homogenized. At the
4th leaf stage, plants were thinned in order to have one healthy plant
per pot.
The Fraction of Transpirable Soil Water (FTSW) was determined as the
ratio of Available Soil Water Content (ASWC) divided by the Total
Transpirable Soil Water (TTSW) as follows:
[MATH: FTSW=ASWCTTSW=SWC
−WPFC−WP :MATH]
Where ASWC represent the Available Soil Water Content for the plant,
derived from the actual soil water content calculated as difference
between the Soil Water Content (SWC) and the soil water content at
Wilting Point (WP), and TTSW as the difference between the soil water
content at Field Capacity (FC) and the water content at WP. Both FC and
WP were determined in a short previous experiment (data not shown).
Plants were grown under well-water conditions until they reached the
6th leaf stage. At this moment, all the plants were irrigated until FC,
the soil surface was covered by a thin layer of perlite, and the top of
the pot was covered with PVC bags. A little slit was made in the bottom
of the plastic bag to allow the sorghum plant to grow through. The slit
was sealed with adhesive packing tape to minimise water loss by
evaporation. Following the protocol of the dry-down experiment
[[202]32] a decrease of pot weight between two consecutive weight
determinations is only attributed to plant transpiration.
Forty plants were divided in two groups: the well-watered (WW) and the
drought stressed (DS) plants. Irrigation was withheld for half of them
(the DS ones) till the FTSW value reached 0.2. This value was kept
constant for nine days by re-integrating water losses of the DS plants
day by day, while the WW plants were irrigated daily to maintain soil
water content close to 0.7 FTSW. After nine days had passed, the plants
were harvested in order to perform physiological and transcriptomic
analysis.
Leaf area, chlorophyll fluorescence parameters (maximum quantum yield,
Fv/Fm, photosystem II efficiency, ΦPSII, and non photochemical
quenching, qNP), gas exchange measurements (photosynthetic rate, Pn,
and transpiration, E) were measured for the entire duration of the
experiment every two days. Pn and E data were used to calculate the
intrinsic WUE (WUE[i]) as the ratio between photosynthetic activity and
the transpiration rate (μmol mol^−1). At the destructive sampling date
(on the 42nd day after emergence, DAE), leaves samples were collected
in order to perform transcriptomic analysis. At the same moment,
relative water content (RWC) and biomass production were determined in
order to calculate the agronomic water use efficiency (WUE[a]) the
ratio between dry biomass production (g) and the total transpired water
(L) according to Mastrorilli et al. [[203]104].
Measurements of chlorophyll fluorescence parameters Fv/Fm, qNP and
ΦPSII were carried out with a portable chlorophyll fluorometer
(Fluorescence Monitoring System, Hansatech instruments, Norfolk,
England) on the youngest fully expanded leaf. The value of minimal
fluorescence was determined through pre-dawn measurements by applying
weak modulated light (0.4 μmol m^−2 s^−1) and maximal fluorescence (Fm)
was induced by a short pulse (0.7 s) of saturating light
(15300 μmol m^−2 s^−1). The measurements were recorded between 12 and
2 pm for ΦPSII and before dawn for Fv/Fm.
Photosynthetic rate (Pn) was measured on the same leaf used for
chlorophyll fluorescence measurements using a portable infrared gas
analyser (CIRAS-2, PP System, Amesbury, USA): leaf surface area
4,5 cm^2, saturated CO[2] concentration of 400 μmolmol^−1, and PPFD
2000 μmol m^−2 s^−1. Photosynthetic rate (Pn) was recorded between 12
and 2 pm.
Relative Water Content (RWC) was determined at the destructive sampling
time according to the methodology described by Barr and Weatherley
[[204]103]. Twelve leaf disks of 20 mm of diameter were collected from
each plant for the RWC determination. The disks were weighed, then
soaked in distilled water for 24 h at 4 °C in the dark to determine the
turgid weight. The dry weight was determined after drying the leaves
for 72 h at 95 °C. The relative water content was then calculated using
the following equation:
[MATH: RWC=FM−DMTM−DM*100 :MATH]
where FW is the fresh weight, TW the turgid weight after the
rehydration in distilled water and DW the dry weight after drying.
RNA extraction, cDNA library construction and sequencing
Three biological replicates were used for all RNA-Seq experiments from
each genotypes and water treatment. The total RNA from the leaf
meristem was extracted using Trizol reagent (Invitrogen, Carlsbad, CA)
and purified using the RNeasy Plant Mini kit (Qiagen, Valencia, CA). On
column DNase digestion was performed according to the manufacturer’s
protocol (Qiagen, Valencia, CA). RNA quality and integrity was verified
using a 2100 Bioanalyzer RNA Nanochip (Agilent, Santa Clara, CA) and
all three samples had RNA Integrity Number (RIN) value more than 8.5.
The quantification of the total RNA was checked by a NanoDropND-1000
Spectrophotometer (Nano-Drop, Wilmington, DE) and agarose gel
electrophoresis.
Illumina sequencing using the GAII platform was performed at Beijing
Genomics Institute (BGI-Shenzhen, Shenzhen, China
[205]http://www.genomics.cn/en/index) according to the manufacturer’s
instructions (Illumina, San Diego, CA). Briefly, poly-A RNA was
isolated from 20 μg of total RNA using Magnetic Oligo (dT) Beads
(Illumina) and digested in short fragment. First and second strand
synthesis were followed by end repair, and adenosines were added to the
3’ ends. Adapters were ligated to the cDNA and fragments (200 ± 25 bp)
were purified by agarose gel electrophoresis and amplified by PCR.
Finally, after validating on an Agilent Technologies 2100 Bioanalyzer
using the Agilent DNA 1000 chip kit, the cDNA library was sequenced on
a PE flow cell using Illumina Genome Analyzer IIx, and the workflow was
as follows: template hybridization, isothermal amplification,
linearization, blocking, sequencing primer hybridization, and
sequencing on the sequencer for Read 1.
Data processing and analysis
The RNA-seq reads generated by the Illumina Genome Analyzer were
initially processed to remove the adapter sequences, reads in which
unknown bases are more than 10 % and low-quality reads. After
filtering, the remaining reads, so called “clean reads”, were used for
downstream bioinformatics analysis. In the pipeline, clean reads are
aligned to the reference sequence
([206]ftp://ftp.ensemblgenomes.org/pub/plants/release-20/fasta/sorghum_
bicolor/dna/) by using SOAPaligner/SOAP2. No more than 5 mismatches are
allowed in the alignment. A quality control step was performed after
that step and the distribution of reads on reference genes was
analysed. Gene coverage was calculated as the percentage of a gene
covered by reads. This value is equal to the ratio of the base number
in a gene covered by unique mapping reads to the total base number of
coding region in that gene. The expression level was, on the other
hand, calculated using RPKM (Reads per Kilobase transcriptome per
Million mapped reads) method [[207]105], according to the following
formula:
[MATH: RPKM=106
CNL/103
:MATH]
where C is the uniquely mapped counts determined from the high quality
category, L is the cDNA length for the longest splice variant for a
particular gene and N is the number of total mappable reads which was
determined as the sum of the high quality reads and the highly
repetitive reads. This method is able to eliminate the influence of
different gene length and sequencing discrepancy on the calculation of
gene expression. Log[2] transformations of this normalization were
performed.
Screening, expression pattern, gene ontology analysis and pathway enrichment
of DEGs
A strict algorithm was developed to identify differentially expressed
genes between two samples and false positive and false negative errors
are performed using Benjamini and Yekutieli [[208]106] FDR method. We
used FDR ≤0.001and the absolute value of Log[2]Ratio ≥2 as the
threshold to judge the significance of gene expression difference. Gene
Ontology (GO) enrichment was based on AgriGO software [[209]107] with
hypergeometric statistical test and Hocberg (FDR).
Pathway enrichment analysis of DEGs was performed using the Kyoto
Encyclopedia of Genes and Genome (KEGG,
[210]http://www.genome.jp/kegg/). This analysis allows to identify
enriched metabolic pathways or signal transduction pathways in DEGs
comparing with the whole genome background. A strict algorithm was used
for the analysis:
[MATH: P=1−∑i=0
m−1MiN−Mn−iNm
:MATH]
Where N is the number of all genes with KEGG annotation; n is the
number of DEGs in N, M is the number of all genes annotated to specific
pathway. Pathways with Qvalue ≤0.05 are significantly enriched in DEGs.
Novel transcript prediction and alternative splicing analysis
The assembled transcripts were compared with the annotated genomic
transcripts from the reference sequences in order to discover novel
transcribed regions. Three requirements are needed: the transcript must
be at least 200 bp away from annotated gene, the length of the
transcript must be over 180 bp, the sequencing depth must be no less
than 2. The Coding Potential Calculator (CPC:
[211]http://cpc.cbi.pku.edu.cn/ ) was used to assess the protein-coding
potential. TopHat software [[212]108] was used to detect alternative
splicing events (ASE).
Ethics approval and consent to participate
Not applicable.
Consent to publish
Not applicable.
Availability of data and materials
The data sets supporting the results of this article are included
within the article and its additional files.
Availability of supporting data
The data discussed in this publication have been deposited in NCBI’s
Gene Expression Omnibus (Edgar et al., 2002) and are accessible through
GEO Series accession number [213]GSE80699
([214]http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE80699).
Acknowledgements