Abstract
Background
Downy mildew (DM), caused by pathogen Plasmopara viticola (PV) is the
single most damaging disease of grapes (Vitis L.) worldwide. However,
the mechanisms of the disease development in grapes are poorly
understood. A method for estimating gene expression levels using Solexa
sequencing of Type I restriction-endonuclease-generated cDNA fragments
was used for deep sequencing the transcriptomes resulting from PV
infected leaves of Vitis amurensis Rupr. cv. Zuoshan-1. Our goal is to
identify genes that are involved in resistance to grape DM disease.
Results
Approximately 8.5 million (M) 21-nt cDNA tags were sequenced in the
cDNA library derived from PV pathogen-infected leaves, and about 7.5 M
were sequenced from the cDNA library constructed from the control
leaves. When annotated, a total of 15,249 putative genes were
identified from the Solexa sequencing tags for the infection (INF)
library and 14,549 for the control (CON) library. Comparative analysis
between these two cDNA libraries showed about 0.9% of the unique tags
increased by at least five-fold, and about 0.6% of the unique tags
decreased more than five-fold in infected leaves, while 98.5% of the
unique tags showed less than five-fold difference between the two
samples. The expression levels of 12 differentially expressed genes
were confirmed by Real-time RT-PCR and the trends observed agreed well
with the Solexa expression profiles, although the degree of change was
lower in amplitude. After pathway enrichment analysis, a set of
significantly enriched pathways were identified for the differentially
expressed genes (DEGs), which associated with ribosome structure,
photosynthesis, amino acid and sugar metabolism.
Conclusions
This study presented a series of candidate genes and pathways that may
contribute to DM resistance in grapes, and illustrated that the
Solexa-based tag-sequencing approach was a powerful tool for gene
expression comparison between control and treated samples.
Background
Downy mildew of grapes occurs in most parts of the world where grapes
are grown, but favors those regions that experience warm, wet
conditions during the vegetative growth of the vine. A major outbreak
of the disease can cause severe losses in yield and berry quality.
Symptoms of DM are usually first noticed on leaves as yellowish and
later oily lesions on the leaf's upper surface with a 'downy' mass
observed on the corresponding underside of the leaf. It can also cause
deformation of shoots, tendrils, inflorescences and clusters of young
berries. Berries become less susceptible as they mature, however rachis
infection can spread into the older fruit which leads to direct crop
loss by shelling of berries [[34]1].
Downy mildew is caused by the pathogen Plasmopara viticola (PV).
Primary infection begins with the overwintering oospore on infected
leaves or plant litter in the soil that germinates in the spring and
produces a sporangium [[35]2]. When plant parts are covered with a film
of moisture from rain or irrigation, the sporangium releases small
swimming spores (zoospores) that are then spread by splashing water.
The spores can germinate by producing a germ tube that enters the green
tissue (including leaves, inflorescences, bunches and young berries)
through the stomates [[36]3]. Secondary infection, which is the major
source of disease spread, produces spores that may be mobilized by wind
and rain to establish new infection sites. The cycle ends with the
sexual production of over-wintering oospores [[37]2].
Different genotypes of grapes show varying level of resistance to PV,
ranging from susceptible V. vinifera, to the moderately resistant V.
rupestris and V. amurensis, V. cinerea, V. riparia and V. candicans, to
the totally resistant Muscadinia rotundifolia [[38]4-[39]6]. The
world-wide grape industry relies predominantly on V. vinifera, which
requires chemical protection to produce healthy fruits. However, such
chemicals may have negative environmental impacts and/or pose risk to
human health. A promising alternative strategy that could
simultaneously improve grape health and limit chemical use is to
identify the unique genes or mechanisms from resistant species that
could potentially confer resistance to the pathogen or lower
presentation of symptoms. These elements may potentially be introduced
into V. vinifera through long-term breeding efforts or transgenic
methods. With this perspective, it is important to unravel the
molecular basis of natural defense responses in resistant grapevines to
DM challenge, including identification of the genetic processes that
may contribute to resistance.
Responses to PV have been characterized in various resistant species.
Mechanisms of resistance include induction of chemical barriers,
initiation of processes that delay invasive growth of mycelia, or
mechanisms that establish hypersensitive response after inoculation of
PV [[40]7-[41]9]. Genetic and gene expression profiling studies have
concluded that Rpv1, NPR1 homologs, and PR protein encoding genes
contribute to the function of DM resistance in grapevines
[[42]10-[43]12]. Others factors, including the amino acid
beta-aminobutyric acid [[44]13], and the proteins beta-1, 3-Glucanase
[[45]14], stilbene synthase (STS) [[46]15], phenylalanine ammonia lyase
(PAL) [[47]16], thaumatin-like proteins and chitinase [[48]17] may also
play an important role in DM resistance. Many attempts, including
transgenic [[49]18-[50]21] and traditional breeding approaches
[[51]10,[52]22,[53]23], have been undertaken to introgress resistance
into V. vinifera genotypes.
To understand the mechanism(s) of the host resistance at the molecular
level, a critical first step is to identify the transcripts that
accumulate in response to the pathogen attack. In this study,
"Zuoshan-1", a clonal selection from wild V. amurensis with cold
hardiness and high resistance to DM [[54]24], was employed to identify
a set of candidate genes associated with DM resistance using Solexa
sequencing technology. Solexa sequencing is a technology capable of
obtaining novel information for whole-genome-wide transcript expression
without prior sequence knowledge. This report presents the finding of
these tests.
Results
Inoculation and symptom development
The fourth unfolded leaf from the shoot apex of "Zuoshan-1" was
inoculated with PV. No visible symptoms were observed in the first 4
days (Figure [55]1a and [56]1b). The 'downy' mass was obviously
observed on the 6th day (Figure [57]1c) and exacerbated on the 8th day
(Figure [58]1d). Oil spots emerged gradually on the site of pathogen
and the spores did not spread to the other healthy tissues 18 days
after inoculation (Figure [59]1e and [60]1f).
Figure 1.
[61]Figure 1
[62]Open in a new tab
Symptom development on leaf surface of "Zuoshan-1" after PV infection.
The fourth unfolded leaf from the shoot apex of "Zuoshan-1" was
inoculated on (a) day 0. Subsequent images depict the state of
infection and symptom development on (b) day 4, (c) day 6, (d) day 8
and (e and f) 18 d. Panel e shows the upper leaf and panel f shows the
lower leaf surface.
Tag identification and quantification
A total of 8,549,948 and 7,527,499 tags were sequenced in infected
(INF) and control (CON) libraries, respectively (Table [63]1). After
filtering out low quality tags (tags containing 'N' and adaptor
sequences), 8,474,583 and 7,525,307 tags (noted herein as "clean" tags)
remained in INF and CON libraries. To increase the robustness of the
approach, single-copy tags in the two libraries (247,900 in INF and
253,156 in CON library) were excluded from further analysis. As a
result, a total of 8,226,683 and 7,272,151 clean tags remained from the
two libraries, from which 233,653 (INF) and 203,514 (CON) unique tags
were obtained. There were 30,139 more unique tags in the INF than in
the CON library, possibly representing genes related to pathogen
interaction and symptom development. The percentage of unique tags
rapidly declined as copy number increased, indicating only a small
portion of the transcripts were expressed at high level in the
conditions tested.
Table 1.
Solexa tags in the infected (INF) and control (CON) libraries.
INF CON
total tag 8549948 7527499
clean tag 8474583 7525307
clean tag copy number = 1 247900 253156
unique tag 233653 203514
unique tag copy number >5 98318 80345
unique tag copy number >10 63202 51438
unique tag copy number >20 39772 31441
unique tag copy number >50 19776 14804
unique tag copy number >100 10615 7701
[64]Open in a new tab
Depth of sampling
Saturation of the library is determined by identification of unique
tags. Sequencing reaches saturation when no new unique tags are
detected. The results shown in Figure [65]2 indicate that INF and CON
libraries were sequenced to saturation, producing a full representation
of the transcripts in the conditions tested. In both libraries fewer
unique tags were identified as the number of sequencing tags increases,
reaching a plateau shortly after 6 M tags were sequenced. No new unique
tags were identified as the total tag number approached 8.5 M in INF
library and 7.5 M in CON library.
Figure 2.
[66]Figure 2
[67]Open in a new tab
Accumulation of Solexa total tag and unique tag in the two libraries.
New unique tag ("y" axis) of INF (solid line) and CON (broken line)
libraries decreased as the solexa sequencing increased ("x" axis). The
total unique tag was 233,653 in INF and 203,514 in CON library.
Annotation analysis of the unique tag
The unique tags were compared against the genome and gene sequences of
V. vinifera cv. Pinot Noir [[68]25] using blastn. Tags with a complete
match or one base pair mismatch were considered further. The results in
Table [69]2 show that a substantial proportion of tags (81.60% in INF
library and 83.72% in CON library) matched to the "Pinot Noir" genome,
and 91,638 (39.21% of unique tags) and 83,079 (40.82% of unique tags)
in INF and CON library matched to 18,841 (61.91%) and 18,068 (59.37%)
"Pinot Noir" genes. Further analysis revealed that 82,886 unique tags
(35.47%) in INF library and 75,290 (36.99%) in CON library matched to
only one gene sequence in the "Pinot Noir' genome (Table [70]2). These
data indicated that approximately 50% of transcripts predicted in grape
are expressed in the infected or control leaves, with more transcripts
present in the infected sample.
Table 2.
Annotation of "Zuoshan-1" Solexa tags against the "Pinot Noir" genomic
sequence.
INF CON
__________________________________________________________________
match to genome match to gene match to genome match to gene
unique tag 190665 (81.60%)* 91638 (39.21%)* 170380 (83.72%)* 83079
(40.82%)*
matched genes 18841 (61.91%)^# 18068 (59.37%)^#
__________________________________________________________________
unique tag matched to one gene 82886 (35.47%)* 75290 (36.99%)*
matched genes 15249 (50.51%)^# 14549 (47.81%)^#
[71]Open in a new tab
Note: *percentage of matched tags/total tags;^#percentage of matched
genes/total assembled CDs of "Pinot Noir".
Tags with no homology to grape were compared with blastn to the VBI
Microbial Database [[72]26] containing genomic sequence information
from Phytophthora sojae, Phytophthora infestans and Hyaloperonospora
parasitica. There were 251 tags identified in INF library found to be
identical to those of the oomycete during PV infection (additional file
[73]1).
Comparison of gene expression level between the two libraries
Differences of tag frequencies that appeared in the INF and CON
libraries were used for estimating gene expression levels in response
to PV infection. The transcripts detected with at least two-fold
differences in the two libraries are shown in Figure [74]3 (FDR
<0.001). The red dots (3,125) and green dots (1,847) represent
transcripts higher or lower in abundance for more than two fold in INF
library, respectively. The blue dots represent transcripts that
differed less than two fold between the two libraries, which were
arbitrarily designated as "no difference in expression". The DEGs with
five fold or greater differences in accumulation were shown in Figure
[75]4. A total of 513 genes (about 0.9% total unique tags) increased by
at least five fold, and 167 genes (about 0.6% total unique tags) were
decreased by at least five fold in the INF library, while the
expression level of 98.5% unique tags was within five-fold difference
between the two samples.
Figure 3.
[76]Figure 3
[77]Open in a new tab
Comparision of gene expression level between the two libraries. For
comparing gene expression level between the two libraries, each library
was normalized to 1 million tags. Red dots represent transcripts more
prevalent in the infected leaf library, green dots show those present
at a lower frequency in the infected tissue and blue dots indicate
transcripts that did not change significantly. The parameters "FDR
<0.001" and "log2 Ratio ≥ 1" were used as the threshold to judge the
significance of gene expression difference.
Figure 4.
[78]Figure 4
[79]Open in a new tab
Differentially expressed tags in infected (INF) tissue library. The "x"
axis represents fold-change of differentially expressed unique tags in
the INF library. The "y" axis represents the number of unique tags
(log10). Differentially accumulating unique tags with a 5-fold
difference between libraries are shown in the red region (98.49%). The
blue (0.89%) and green (0.61%) regions represent unique tags that are
up- and downregulated for more than 5 fold in the INF library,
respectively.
Of DEGs with differences greater than twenty fold (Table [80]3), 69
genes were present at higher levels in the INF library, 67 of which
were associated with defense (6), transport (3), transcription (11),
signal transduction (14) and metabolism (33). The highest DEG was
phosphate-induced protein gene which was present at 229 fold of control
levels. Among these highly expressed genes, many were associated with
senescence, abiotic and biotic stresses.
Table 3.
List of DEGs changed for 20 fold and more in INF library.
Gene Annotation Stress related function Accession Identity Fold
Upregulated genes
Defence
GSVIVT00025506001 polygalacturonase-inhibiting protein [Vitis labrusca
x Vitis Riparia] inhibits fungal endopolygalacturonases [81]ACS16072.1
333/333 (100%) 60
GSVIVT00001105001 thaumatin-like protein [Vitis vinifera] pathogen
defence; drought and heat combination [82]AAQ10092.1 217/225 (96%) 57
GSVIVT00017370001 harpin-induced
protein-related/HIN1-related/harpin-responsive protein-related
[Arabidopsis thaliana] pathogen defence; senescence [83]NP_565634.1
141/267 (52%) 33
GSVIVT00002965001 TMV response-related protein [Zea mays] Tobacco
Mosaic Virus response [84]ACG48457.1 39/91 (42%) 32
GSVIVT00005362001 glutaredoxin [Populus trichocarpa] senescence
[85]EEE75685.1 91/155 (58%) 29
GSVIVT00024683001 beta-glucosidase [Rosa hybrid cultivar] activation of
phytoanticipins [86]BAG13451.1 382/531 (71%) 21
Transport
GSVIVT00001094001 multidrug resistance pump, putative [Ricinus
communis] fungal resistance [87]EEF51093.1 407/509 (79%) 121
GSVIVT00015121001 mitochondrial dicarboxylate carrier protein, putative
[Ricinus Communis] aluminum tolerance [88]EEF48606.1 271/324 (83%) 38
GSVIVT00030447001 multidrug resistance protein ABC transporter family
protein [Populus Trichocarpa] Senescence; drought and heat combination
[89]EEE80779.1 64/194 (32%) 25
Signal transduction
GSVIVT00030628001 leucine-rich repeat receptor-like protein kinase
[Nicotiana tabacum] senescence [90]AAF66615.1 644/923 (69%) 145
GSVIVT00006178001 FERONIA receptor-like kinase [Arabidopsis thaliana]
defence, stresses [91]ABT18100.1 317/621 (51%) 56
GSVIVT00019504001 MAP3K-like protein kinase [Arabidopsis thaliana]
disease resistance, drought and heat combination [92]CAB16796.1 184/359
(51%) 52
GSVIVT00002706001 calmodulin-binding protein [Arabidopsis thaliana]
senescence [93]NP_565379.1 21/45 (46%) 39
GSVIVT00020989001 calcium-binding EF hand family protein [Arabidopsis
thaliana] defence related; senescence; drought and heat combination
[94]NP_568568.1 81/166 (48%) 35
GSVIVT00029809001 ethylene-regulated transcript 2 (ERT2) [Arabidopsis
thaliana] senescence [95]CAB45883.1 96/204 (47%) 34
GSVIVT00036549001 calmodulin-binding protein [Arabidopsis thaliana]
senescence [96]NP_565379.1 149/366 (40%) 28
GSVIVT00002973001 calmodulin binding protein-like [Elaeis guineensis]
senescence [97]ABP04242.1 89/135 (65%) 27
GSVIVT00025017001 BRASSINOSTEROID INSENSITIVE 1-associated receptor
kinase 1 precursor, putative [Ricinus communis] disease, cell death
[98]EEF29110.1 415/639 (64%) 26
GSVIVT00000612001 nodulin-like protein [Arabidopsis thaliana] drought
and heat combination [99]AAC28987.1 397/550 (72%) 23
GSVIVT00033036001 RING-H2 subgroup RHE protein [Populus tremula x
Populus alba] drought and heat combination [100]AAW33880.1 168/296
(56%) 22
GSVIVT00009150001 PAR-1a [Nicotiana tabacum] potato virus Y, SAR induce
[101]CAA58733.1 127/178 (71%) 22
GSVIVT00027614001 receptor-protein kinase-like protein [Arabidopsis
thaliana] drought and heat combination [102]BAA98098.1 632/849 (74%) 20
GSVIVT00030574001 leucine-rich repeat receptor-like protein kinase
[Arabidopsis thaliana] senescence [103]ACN59244.1 317/611 (51%) 20
Transcription
GSVIVT00014947001 zinc-finger protein 1 [Datisca glomerata] defence,
stresses [104]AAD26942.1 144/246 (58%) 60
GSVIVT00016398001 dehydration-responsive element binding protein 3
[Glycine max] biotic and abiotic stresses [105]ABB36646.1 116/187 (62%)
52
GSVIVT00007409001 DRE-binding protein 3b [Gossypium hirsutum] drought
and heat combination [106]ABB45861.1 134/237 (56%) 22
GSVIVT00020131001 basic helix-loop-helix protein [Nicotiana tabacum]
senescence [107]BAF30984.1 105/228 (46%) 33
GSVIVT00001092001 Dehydration-responsive element-binding protein 1F,
putative [Ricinus communis] phytohormone, pathogen and environmental
stresses [108]EEF51090.1 143/242 (59%) 30
GSVIVT00007410001 CBF4 transcription factor [Vitis vinifera] cold
stress [109]ABE96792.1 218/218 (100%) 30
GSVIVT00016403001 jasmonate ZIM domain 1 [Catharanthus roseus]
wounding; herbivory; salinity [110]ACM89457.1 131/275 (47%) 27
GSVIVT00028041001 AP2 domain class transcription factor [Malus x
domestica] senescence; drought and heat combination [111]ADE41117.1
172/327 (52%) 26
GSVIVT00027444001 GRAS family transcription factor [Populus
trichocarpa] chitin response [112]EEE95719.1 446/586 (76%) 26
GSVIVT00006790001 basic helix-loop-helix (bHLH) family protein
[Arabidopsis thaliana] fugal resistance related; senescence
[113]NP_568850.1 152/239 (63%) 21
GSVIVT00002446001 WRKY transcription factor 21 [Populus tomentosa x P.
bolleana] senescence,stresses [114]ACV92023.1 196/364 (53%) 21
Metabolism
GSVIVT00015203001 putative phosphate-induced protein [Nicotiana
tabacum] unidentified [115]BAA33810.1 243/317 (76%) 229
GSVIVT00016518001 salt responsive protein 2 [Solanum lycopersicum]
drought and heat combination [116]ACG50004.1 309/464 (66%) 165
GSVIVT00024884001 S-adenosyl-L-methionine:salicylic acid carboxyl
methyltransferase [Chimonanthus praecox] biotic and abotic stresses
[117]ABU88887.2 191/377 (50%) 97
GSVIVT00024408001 potein-binding protein, putative [Ricinus communis]
unidentified [118]EEF27653.1 393/605 (64%) 87
GSVIVT00028930001 ubiquitin-protein ligase, putative [Ricinus communis]
senescence [119]EEF42248.1 357/602 (59%) 72
GSVIVT00014730001 cytochrome P450 [Populus trichocarpa] senescence;
drought and heat combination [120]EEE73840.1 261/453 (57%) 70
GSVIVT00000988001 9-cis-epoxycarotenoid dioxygenase 1 [Vitis vinifera]
senescence; defence [121]AAR11193.1 602/610 (98%) 62
GSVIVT00023009001 ATPP2-A2, putative [Ricinus communis] unidentified
[122]EEF38353.1 114/158 (72%) 56
GSVIVT00014704001 putative integral membrane protein [Cyanothece sp.
CCY0110] unidentified [123]EAZ88012.1 53/176 (30%) 51
GSVIVT00018424001 tropinone reductase, putative [Ricinus communis]
senescence; drought and heat combination [124]EEF38138.1 194/264 (73%)
48
GSVIVT00032938001 aspartic proteinase nepenthesin-1 precursor, putative
[Ricinus communis] phosphorus deficiency; salt stress [125]EEF29846.1
306/441 (69%) 39
GSVIVT00024072001 protein phosphatase 2c, putative [Ricinus communis]
senescence [126]EEF41194.1 254/393 (64%) 37
GSVIVT00015200001 putative phosphate-induced protein [Capsicum
chinense] unidentified [127]BAG16530.1 186/289 (64%) 37
GSVIVT00022245001 f-box family protein [Populus trichocarpa] senescence
[128]EEE87327.1 139/345 (40%) 37
GSVIVT00016166001 ATP-dependent DNA helicase [Brevibacillus brevis] DNA
repair [129]BAH41662.1 16/45 (35%) 36
GSVIVT00024387001 nucleic acid binding protein, putative [Ricinus
communis] oxidative; ABA; abiotic stresses [130]EEF29282.1 102/164
(62%) 34
GSVIVT00024235001 protein phosphatase 2C [Nicotiana tabacum] senescence
[131]CAC10358.1 257/429 (59%) 34
GSVIVT00035825001 ubiquitin-protein ligase, putative [Ricinus communis]
senescence [132]EEF40124.1 572/719 (79%) 32
GSVIVT00019233001 TPA: isoflavone reductase-like protein 3 [Vitis
vinifera] putative defence [133]CAI56332.1 301/319 (94%) 31
GSVIVT00014029001 TPA_exp: cellulose synthase-like D1 [Oryza sativa]
unidentified [134]DAA01752.1 999/1171 (85%) 31
GSVIVT00007984001 serine acetyltransferase [Nicotiana plumbaginifolia]
oxidative stress [135]AAR18403.1 179/307 (58%) 30
GSVIVT00036225001 Beta-expansin 1a precursor, putative [Ricinus
communis] osmotic stress [136]EEF28288.1 207/259 (79%) 27
GSVIVT00017518001 spotted leaf protein, putative [Ricinus communis]
hypersensitive response; cell death; senescence [137]EEF38265.1 243/402
(60%) 27
GSVIVT00007452001 wound-induced protein WIN2 precursor, putative
[Ricinus communis] antifungal [138]EEF31100.1 142/197 (72%) 26
GSVIVT00002450001 UDP-glucose:glucosyltransferase [Lycium barbarum]
drought and heat combination [139]BAG80556.1 293/464 (63%) 24
GSVIVT00036349001 glucose-1-phosphate adenylyltransferase, putative
[Ricinus communis] drought and heat combination [140]EEF49428.1 412/531
(77%) 24
GSVIVT00028839001 spotted leaf protein, putative [Ricinus communis]
hypersensitive response; cell death; senescence [141]EEF52025.1 385/674
(57%) 24
GSVIVT00009741001 f-box family protein [Populus trichocarpa] senescence
[142]EEE86166.1 93/182 (51%) 24
GSVIVT00019669001 galactinol synthase [Solanum lycopersicum] oxidative
stress; drought; salinity; chilling; heat shock [143]BAH98060.1 231/316
(73%) 24
GSVIVT00030537001 senescence-associated protein, putative [Medicago
truncatula] Senescence; drought and heat combination [144]ABD32641.1
99/144 (68%) 23
GSVIVT00001432001 protein phosphatase 2c, putative [Ricinus communis]
senescence; drought and heat combination [145]EEF34881.1 319/389 (82%)
23
GSVIVT00033193001 galactinol synthase [Capsicum annuum] oxidative
stress; drought; salinity; chilling; heat shock [146]ABQ44212.1 239/315
(75%) 21
GSVIVT00023109001 ATEXO70H4 (exocyst subunit EXO70 family protein H4);
protein binding [Arabidopsis thaliana] unidentified [147]NP_187563.1
331/585 (56%) 21
various functions
GSVIVT00017533001 PREDICTED: hypothetical protein [Vitis vinifera]
unidentified [148]XP_002279648.1 500/500 (100%) 20
GSVIVT00020834001 CW14 [Arabidopsis thaliana] unidentified
[149]BAA87958.1 300/533 (56%) 23
Downregulated genes
Defence
GSVIVT00016961001 Immunoglobulin/major histocompatibility complex
[Medicago truncatula] disease resistance [150]ABP03850.1 426/672 (63%)
-164
GSVIVT00014282001 pathogenesis-related like protein [Arabidopsis
thaliana] defence [151]AAM66077.1 117/215 (54%) -67
Metabolism
GSVIVT00027449001 (-)-germacrene D synthase [Vitis vinifera] wounding;
methyl jasmonate [152]AAS66357.1 500/553 (90%) -164
GSVIVT00027451001 (-)-germacrene D synthase [Vitis vinifera] wounding;
methyl jasmonate [153]AAS66357.1 503/557 (90%) -150
GSVIVT00027450001 (-)-germacrene D synthase [Vitis vinifera] wounding;
methyl jasmonate [154]AAS66357.1 274/319 (85%) -53
GSVIVT00027456001 (-)-germacrene D synthase [Vitis vinifera] wounding;
methyl jasmonate [155]AAS66357.1 454/545 (83%) -22
GSVIVT00014725001 cytochrome P450 [Populus trichocarpa] pathogen
induced [156]EEE73840.1 299/511 (58%) -41
GSVIVT00014727001 cytochrome P450 [Populus trichocarpa] pathogen
induced [157]EEE73840.1 269/447 (60%) -35
GSVIVT00007099001 thioredoxin x [Populus trichocarpa] defence; abiotic
stresses, senescence [158]EEE90516.1 98/117 (83%) -39
GSVIVT00008711001 beta-cyanoalanine synthase [Betula pendula] cyanide
metabolism [159]AAN86822.1 311/352 (88%) -36
GSVIVT00037489001 non-specific lipid transfer protein [Vitis vinifera]
defence related [160]ABA29446.1 119/119 (100%) -28
GSVIVT00029445001 expansin [Vitis labrusca x Vitis vinifera] defence
related [161]BAC66695.1 252/252 (100%) -22
GSVIVT00006300001 UDP-glucosyltransferase, putative [Ricinus communis]
defence related [162]EEF47681.1 268/466 (57%) -22
various functions
GSVIVT00005678001 male sterility-related protein [Linum usitatissimum]
unidentified [163]ACA28679.1 260/503 (51%) -23
GSVIVT00032599001 hypothetical protein [Vitis vinifera] unidentified
[164]XP_002284962.1 368/368 (100%) -22
[165]Open in a new tab
Fifteen DEGs were less abundant in the INF library. Those present
twenty fold or more in the CON library were also listed in Table
[166]3, in which 13 genes were classified as defense (2) and metabolism
(11), including genes encoding cytochrome P450 and PR proteins. The
greatest differences between INF and CON DEGs were (-)-germacrene D
synthase and immunoglobulin/major histocompatibility complex that both
were present 164-fold lower in the INF library than in the CON library.
Real-time RT-PCR analysis
In order to validate Solexa expression profiles, the steady-state
transcript levels of 12 "defense related" genes were analyzed. Among
them, seven genes (CHI4D, TL3, PR10, TIP2;1, CYSP, ERF4, STS5) were
upregulated and five genes (THX, SHM1, HypP, GLO, ClpP) were
downregulated (Figure [167]5). Actin, tested to be stable in our
previous work, was chosen as a reference gene for data normalization.
The trend of RT-PCR based expression profiles among these selected
genes was similar to those detected by Solexa-sequencing based method.
However, the scales of difference between the INF and CON were
generally smaller in Real-time PCR (1-18 fold differences) than in
those detected by the Solexa-sequencing based method (2 - 57 folds)
(Table [168]4).
Figure 5.
[169]Figure 5
[170]Open in a new tab
Real-time RT-PCR analysis for twelve differentially expressed genes.
Real-time RT-PCR analysis for twelve transcripts in control (white) and
infected (gray) samples, including (a) seven more abundant in the INF
library and (b) five less prevalent in the INF library as identified by
Solexa expression profile. All data were normalized to the actin
expression level. Data represent fold change of RQ (relative
quantification) in infected vs. control samples. Bars represent RQ
standard deviation calculated from three biological replicates.
Table 4.
Genes selected for Real-time RT-PCR.
Gene Description Forward primer Reverse primer Target size Solexa fold
RT-PCR fold
CHI4D V. vinifera class IV chitinase (gb|[171]AF532966.1)
TCCCACGTTCCCCCTTCT GTAGCTTGGCTGCCATTTTTG 59 11 4
TL3 V.vinifera thaumatin-like protein (gb|[172]AF532965.1)
ACCCCACTCCAACCATCAAG GATTTTGCAGAGGCCCATTG 59 57 4
PR10 Tamnara Tam-RP10 pathogenesis-related protein 10
(dbj|[173]AB372561.1) GGTCAGGCCTCAAGCTATCAA CAGGGCCTCCGTCTCCTT 56 10 3
TIP2;1 V. vinifera aquaporin TIP2;1 (gb|[174]EF364439.1)
GCATCATTGCACCCATTGC GCCTGCAGCCAGGATGTT 59 6 1
CYSP V. vinifera cysteine protease (gb|[175]EU280160.1)
CCTCGCAGGAGGAGCACGAT CCGGCGCAGGTTTGC 54 2 1
ERF4 V. aestivalis putative ethylene response factor 4
(gb|[176]AY484580.1) TCATCACTGCAACTCATCCA TTACAATCTTCGGCCTCTGA 101 11 4
STS5 V. vinifera stilbene synthase5 (gb|[177]AY670312.1)
CGCTCAAGGGAGGAAAGACA AGCCAAACAAAACACCCCAATC 58 12 18
THX thioredoxin x [Populus trichocarpa] ([178]XP_002310066.1)
TGCTCAGGAATACGGGGACAGA TCGCGGGTTTGCATCAT 61 -39 -2
SHM1 A. thaliana serine hydroxymethyl transferase 1
(ref|[179]NM_119954.3) TGTTCATCAGGTCAGCCAGTTT TGCGTCGAATTGCAGCAAGAT 63
-2 -2
HypP Hypothetical protein LOC100264849 TGCCCCTACCCTTGTGACA
GATCAAAATGGCTCATCGGAA 58 -5 -3
GLO V. pseudoreticulata glyoxal oxidase (gb|D201181.1)
TCCCAACGCCGGTATAGC ACCGTGCCGTAACGTGTGA 54 -5 -1
ClpP Carica papaya ATP-dependent Clp protease proteolytic subunit
(gb|[180]DQ159405.1|) GGGCGCCGGACAAGA TTTGCAAATCATCCCTAATGGA 55 -2 -2
[181]Open in a new tab
Pathway enrichment analysis of DEGs
The PV affected biological pathways were evaluated by enrichment
analysis of DEGs. Significantly enriched metabolic pathways and signal
transduction pathways were identified. A total of 115 pathways were
affected by up- and 107 were affected by down-regulated DEGs,
respectively (additional file [182]2 and [183]3). DEGs with pathway
annotation were listed according to enrichment priority (additional
file [184]4 and [185]5). The first ten enriched pathways were reported
in Table [186]5. Pathways with Q value < 0.05 are significantly
enriched.
Table 5.
List of first ten pathways for up- and downregulated EDGs.
Pathway term Pathway ID DEGs tested P value Q value
Pathways for upregulated DEGs
Ribosome ko03010 53 (4.36%) 0.0004 0.0406
Amino sugar and nucleotide sugar metabolism ko00520 25 (2.06%) 0.0010
0.0563
Glycolysis/Gluconeogenesis ko00010 28 (2.3%) 0.0043 0.1660
Biosynthesis of alkaloids derived from histidine and purine ko01065 31
(2.55%) 0.0126 0.3636
Biosynthesis of alkaloids derived from ornithine, lysine and nicotinic
acid ko01064 35 (2.88%) 0.0207 0.4459
Starch and sucrose metabolism ko00500 49 (4.03%) 0.0233 0.4459
Biosynthesis of alkaloids derived from shikimate pathway ko01063 39
(3.21%) 0.0361 0.5868
N-Glycan biosynthesis ko00510 10 (0.82%) 0.0528 0.5868
Fructose and mannose metabolism ko00051 14 (1.15%) 0.0560 0.5868
Selenoamino acid metabolism ko00450 11 (0.91%) 0.0587 0.5868
Pathways for downregulated DEGs
Photosynthesis ko00195 20 (3.14%) 9.9613e-06 0.0011
Photosynthesis - antenna proteins ko00196 6 (0.94%) 4.2252e-05 0.0023
Folate biosynthesis ko00790 5 (0.78%) 0.0002 0.0064
Nicotinate and nicotinamide metabolism ko00760 5 (0.78%) 0.0007 0.0125
Fructose and mannose metabolism ko00051 13 (2.04%) 0.0007 0.0125
Carbon fixation in photosynthetic organisms ko00710 13 (2.04%) 0.0007
0.0125
Pyruvate metabolism ko00620 14 (2.2%) 0.0014 0.0210
Polyketide sugar unit biosynthesis ko00523 4 (0.63%) 0.0016 0.0210
Purine metabolism ko00230 21 (3.3%) 0.0018 0.0215
Biosynthesis of alkaloids derived from histidine and purine ko01065 21
(3.3%) 0.0025 0.0270
[187]Open in a new tab
Ribosomal-associated proteins constituted the only significantly
affected pathway for the upregulated DEGs (Q <0.05). Other
non-significant enriched pathways with large number of upregulated DEGs
included amino sugar and nucleotide sugar metabolism, starch and
sucrose metabolism, secondary metabolism, plant hormone biosynthesis,
and splicesome associated proteins. There were more significantly
enriched pathways (10) for the downregulated DEGs, which were involved
in photosynthesis, as well as metabolism of folate, nicotinate,
nicotinamide, fructose, mannose, pyruvate, polyketide sugar unit, and
purines, along with alkaloids from histidine and purines.
Discussion
In this report Solexa sequencing technology, a high-throughput DNA
sequencing approach, was utilized to estimate gene expression in
libraries prepared from infected and control tissues. The results
(Figure [188]2) provided estimates of gene expression as determined by
the frequency that any given tag (representing a transcript) is
sequenced. The data indicate that there is sufficient coverage depth to
reach saturation, that is, a complete assessment of all transcripts
present in the libraries. Theoretically, the rate of novel tag
discovery should equal zero if all unique tags of the initial sample
had been sequenced. However, this number might be slightly higher
because new tags may be added due to the accumulation of sequencing
errors as the size of the library increased [[189]27]. Strict filtering
and conservative matching allows recognition of erroneous tags, which
are then disregarded. All of these precepts may contribute to a loss of
substantial sequence information. However, loss of some data
potentially made the results more conservative, revealing only robust
and bona fide differences. Moreover, the total number of tags after
stringent filtering was sufficient for annotation to the reference
genes in the grape genome sequence. Theoretically, tags should be
generated by NlaIII from the 3'-most ends of transcripts, but almost
50% of tags from other NlaIII sites were also generated in our result.
Since only one tag could be generated in each transcript from any
NlaIII site in a cDNA, these other NlaIII tags represented a given gene
redundantly in the expression profile. This phenomenon accounts for the
inflated number of unique tags generated (about 200,000) relative to
that of the annotated grape genome (about 30,000). These other tags may
also arise because of alternative splicing or incomplete enzyme
digestion.
The results represent the first large-scale investigation of the gene
expression in DM analysis of grapevine. Polesani et al [[190]28]
reported 804 transcripts identified in PV infected leaves of
susceptible cultivar "Riesling" using cDNA-AFLP. Figueiredo et al
[[191]29] found 121 transcripts, representing 29 unique gene
differentially expressed between two V. vinifera cultivars "Regent" and
"Trincadeira" (resistant and susceptible to fungi, respectively) by
cDNA microarray. In the current study, 15,249 putative genes were
identified among the Solexa sequencing tags for the INF library and
14,549 for the CON library.
The steady-state transcript level for a set of selected genes was
confirmed by Real-time RT-PCR. Although the differences in gene
expression did not match the magnitude of those detected by
Solexa-based sequencing method, the trends of up- and down- regulation
were similar. The lower expression level detected by Real-time RT-PCR
could be due to the difference of sensitivity between the two
technologies. Solexa sequencing has been documented to be more
sensitive for estimation of gene expression, especially for
low-abundance transcripts compared to microarrays and Real-time RT-PCR
[[192]30]. The difference could also be attributed to different
inoculation seasons and developmental stages of the grapevines. The
materials used for the Solexa sequencing method were obtained from
materials inoculated and harvested in September, while materials used
for the Real-time RT- PCR analyses were obtained from plants inoculated
and harvested in June.
Due to the sensitivity of Solexa sequencing technology, many rare
transcripts were detected. Among 536 transcripts present predominantly
(<2-20 fold) in the INF library, 89 were not detected in the CON
library at all. These genes were predicted to be involved in many plant
biological processes, including defense. For example, genes encoding
cinnamyl alcohol dehydrogenase, lipase-like protein, glutathione
synthetase, GDSL-motif lipase, ankyrin repeat family protein, serine
hydrolase, proline-rich cell wall protein and multicopper oxidase were
previously described as plant defense-related genes. Other rare
transcripts detected by Solexa technology were predicted to function in
signal transduction (protein kinase, calcium ion binding protein,
wall-associated kinase), transport (type IIIa membrane protein, ATP
binding protein, D-galactonate transporter, peptide transporter),
transcription (ccaat-binding transcription factor, AP2/ERF
domain-containing transcription factor, mutator-like transposase-like
protein), and protein metabolism (ubiquitin-protein ligase, 50S
ribosomal protein, S-locus-specific glycoprotein S13 precursor,
Rab5-interacting protein). Two novel genes (nectar protein 1,
vernalization-insensitive protein) and some genes encoding hypothetical
proteins (LOC100244011, LOC100258240, LOC100249110) were also
identified from the PV-induced rare DEGs. Among the 608 rare
transcripts present more in CON than INF, 69 were not detected at all
in the INF library. Most of these transcripts have predicated
biological functions in growth regulation (growth regulator protein,
A-type cyclin, auxin response factor 8), transport (ATP-binding
cassette transporter, AWPM-19-like membrane family protein,
copper-transporting atpase p-type), signal transduction
(serine-threonine protein kinase, leucine-rich repeat family protein,
calcium-binding EF hand family protein, calcium-dependent phospholipid
binding ), and metabolism (galacturonosyltransferase 6,
methylenetetrahydrofolate dehydrogenase, iron ion
binding/oxidoreductase, trehalose-6-phosphate synthase,
senescence-associated protein).
Pathway enrichment analysis revealed the most significantly affected
pathways during the PV infection in "Zuoshan-1". It is not surprising
that the "ribosome-related" pathway was the most affected for the DEGs
more common in INF library. This finding implies that the grapevine
utilizes new ribosomes or changes in ribosome components to help
synthesize additional proteins, such as PR proteins, to protect itself
from the pathogen attack. The second affected pathway was the "amino
sugar and nucleotide sugar metabolism" pathway. In this pathway genes
encoding chitinase were more prevalent in the INF than the CON library.
In addition, genes required for cell wall biosynthesis were also
affected, such as D-xylan synthase, UDP-glucose dehydrogenase, and
UDP-glucose 4,6-dehydratase. These enzymes are involved in the
interconversion of nucleotide sugars, and may regulate glycosylation
patterns in response to pathogen, thereby linking signaling with
primary metabolism and the dynamics of the extracellular matrix. The
other noticeable pathways with a large amount of DEGs associated with
PV infection were starch and sucrose metabolism, secondary metabolism,
plant hormone biosynthesis, and splicesome-associated proteins. For
DEGs less prevalent in infected vs. control libraries, there was
significant enrichment for transcripts associated with photosynthesis.
This result was similar to the reports of Polesani et al
[[193]28,[194]31]. Photosystem I proteins (PsaA, PsaB, PsaC),
photosystem II proteins (PsbB, PsbD, PsbO, PsbP, PsbS), cytochorme b6/f
complex (PetD, PetN) and F-type ATPase (beta, alpha, delta, a, b) were
all substantially lower in abundance in INF libraries compared to CON
libraries. The reduction of photosynthesis was possibly due to the
increase of invertase activity in nucleotide sugar metabolism pathway.
Invertase would cleave sucrose into hexose sugars and their
accumulation inhibits the Calvin cycle.
It was observed that 251 tags identified in INF library were homologous
to the oomycete, indicating that they may belong to PV transcripts,
predictably noting the presence of the pathogen. Many of these putative
PV transcripts corresponded to genes involved in protein metabolism
(16S, 18S, 26S, 28S and 60S ribosomal protein subunits) as a
requirement for protein synthesis in the pathogen during the
plant-pathogen interaction. Many housekeeping genes (alpha-tubulin,
elongation factor 1 alpha, ubiquitin and heat shock protein 70) and
genes related to immune response (spike 1 protein and cyclophilin) were
also detected. Several PV transcripts showed similarity to enzymes
involved in carbohydrate and amino acid metabolism (chlorophyll
apoprotein, aspartate aminotransferase, glutamine synthetase and
hyaluronoglucosaminidase-4), energy production (ATP synthase subunit B,
glyceraldehyde-3-phosphate dehydrogenase, phosphoenolpyruvate
carboxykinase and nitrate reductase), and cellular transport
(transportin 1, K^+ channel protein and calmodulin).
Transcripts more abundant in infected leaves
A set of transcripts were clearly more abundant in tissue arising after
PV infection compared to control. This group possibly contains elements
that confer resistance to the spread of the pathogen in "Zuoshan-1".
Among these transcripts, those expressed at a relatively high level in
infected tissue are of the most interest. These transcripts likely
encode genes responding to the pathogen or genuine factors that
underlie genetic resistance, which were broadly grouped into the
following categories based on their known roles in other plant systems.
Defense response genes
Among defense response genes, thaumatin-like protein [[195]17],
polygalacturonase-inhibiting protein (PGIP) [[196]32,[197]33],
harpin-induced protein-related [[198]34,[199]35], glutaredoxin
[[200]36,[201]37] and beta-glucosidase [[202]38,[203]39] have been
widely studied in plant pathogen resistance. Thaumatin-like protein,
like many other disease resistant proteins [[204]40], is also induced
by abiotic stresses, which may indicate existence of a crosstalk
between pathogen and abiotic stresses. In this category, tobacco mosaic
virus (TMV) response -related protein (+32 fold in INF vs CON) is
associated with TMV attack and may also play an important role in DM
resistance of grape.
Transport
Three transcripts were associated with transport function. Multidrug
resistance pump proteins (+121 fold in INF vs CON) and multidrug
resistance ABC transporter (+25 fold in INF vs CON) are well known
transporters in clinical study for bacteria infection of human
[[205]41]. Such transporters also have been isolated from plants, such
as Coptis japonica [[206]42]. They transport several compounds
associated with multidrug (antibiotic) resistance which can inhibit
pathogen infection in animal model [[207]41,[208]43]. Another gene
identified to be transport related is mitochondrial dicarboxylate
carrier protein (+38 fold in INF vs CON) which might be involved in the
excretion of organic acids and rhizotoxic aluminum tolerance [[209]44].
Signal transduction
There were fourteen transcripts in our results associated with signal
transduction. Two came from genes (GSVIVT00030628001,
GSVIVT00030574001) encoding leucine-rich repeat receptor-like protein
kinases which were more prevalent (145 and 20 fold) in the INF library
than in control. Molecules that indicate the presence of pathogen
(elicitors) activate host receptors and that rapidly generate an
internal signal that triggers early defense responses [[210]45].
Various signals presented in our results, including phytohormones like
ABA and ethylene, as well as intracellular messengers like calcium,
phosphoinositide and kinases, have been proposed to regulate plant
responses in adverse environmental conditions and thus contribute to
the coordination of plant stress physiology [[211]46]. Transcripts
representing three kinase-encoding genes (GSVIVT00030628001,
GSVIVT00006178001, GSVIVT00019504001) were present 52-145 fold higher
in INF than CON, and have been widely documented as signaling factors
in many stresses [[212]47-[213]50] and senescence [[214]51]. Four
transcripts (GSVIVT00002706001, GSVIVT00020989001, GSVIVT00036549001,
GSVIVT00002973001) were found to be more abundant (27 to 39 fold) in
INF than CON, and were associated with calcium signaling pathway. All
of these are also induced by senescence [[215]52] and many stresses
[[216]53,[217]54]. Nodulin-like protein (+23 fold in INF vs CON)
induced in fungal pathogen treatment [[218]55] and drought/heat
combination stress [[219]40] has been shown to be involved in salicylic
acid (SA) signaling pathway [[220]56]. A RING-H2 gene (+22 fold in INF
vs CON) has demonstrated regulatory function in ABA signaling
[[221]57], drought tolerance [[222]57], regulation of growth and
defense responses against abiotic/biotic stresses [[223]58].
Ethylene-regulated transcript 2 (ERT2) (+34 fold in INF vs CON) is
involved in ethylene response 'circuit' including ethylene synthesis,
perception, signal transduction and regulation of gene expression
[[224]59]. The PAR-1a (photoassimilate-responsive) protein (+22 fold in
INF vs CON) is a serine/threonine kinase with diverse phosphorylation
targets and has been reported to be induced by infection with potato
virus Y [[225]60,[226]61].
Transcription
Eleven transcripts associated with transcription were 21 to 60 fold
more abundant in INF than CON libraries. Transcripts annotated as
zinc-finger protein 1, DREB protein, AP2 domain class transcription
factor, basic helix-loop-helix protein, CBF4(C-repeat binding factor
4), jasmonate ZIM domain 1, GRAS family transcription factor, and WRKY
transcription factor 21 were all present at higher steady state levels
in infected tissue. They have been documented to play important roles
in responding to phytohormone stasis, pathogen attack and environmental
stresses [[227]62-[228]69].
Metabolism
Synthesis of the hormones
S-adenosyl-L-methionine (GSVIVT00024884001) and 9-cis-epoxycarotenoid
dioxygenase 1(NCED1) (GSVIVT00000988001) are transcripts related to
synthesis of plant hormones, and were found more frequently (97 and 62
fold, respectively) in the INF library. S-adenosyl-L-methionine is the
precursor of ethylene [[229]70] which participates in regulation of
growth, development, and responses to stress and pathogen attack in
plants [[230]71]. NCED is an important enzyme in synthesizing the
phytohormone ABA which plays a central role in responses to pathogen
attack [[231]72].
Protein metabolism
Twelve transcripts related to protein metabolism were more abundant in
the INF library, 21 fold to 72 fold. Among them, ubiquitin-protein
ligase (GSVIVT00028930001, GSVIVT00035825001), spotted leaf protein
(GSVIVT00017518001, GSVIVT00028839001) and f-box family protein
(GSVIVT00022245001, GSVIVT00009741001) were identified, and represent
proteins involved in ubiquitination and subsequent degradation of
target proteins. Aspartic proteinase nepenthesin-1 precursor
(GSVIVT00032938001) is expressed at higher level in "Nipponbare" in
response to phosphorus deficiency [[232]73] and isolated from
salt-stress wild rice "Porteresia coarctata" [[233]74]. Protein
phosphatase 2c (GSVIVT00024072001, GSVIVT00024235001,
GSVIVT00001432001) regulates numerous ABA responses [[234]75,[235]76].
Nucleic acid binding proteins (GSVIVT00024387001) control genes
expression in response to oxidative stress [[236]77], ABA treatment
[[237]78] and abiotic stresses [[238]79]. Exocyst subunit EXO70 family
protein H4 (GSVIVT00023109001) has been shown to be involved in the
exocytic pathway, which sorts newly synthesized proteins from the
endoplasmic reticulum to their final destination at the lysosome,
vacuole or plasma membrane [[239]80].
Secondary metabolism
This subcategory contained 4 genes, including a higher level of
tropinone reductase (GSVIVT00018424001, +48 fold in INF vs CON)
transcript in infected leaves, consistent with previous reports showing
it to be more abundant after pathogen infection [[240]81]. Isoflavone
reductase-like protein 3 (GSVIVT00019233001, +31 fold in INF vs CON)
also has a potential pathogen resistance role because it is involved in
biosynthesis of isoflavonoid phytoalexins [[241]82], an important
product in resistance to pathogen infection [[242]83,[243]84].
UDP-glucose glucosyltransferase (GSVIVT00002450001, + 24 fold in INF vs
CON) and galactinol synthase (GSVIVT00019669001, + 24 fold in INF vs
CON) are reported to be induced by abiotic stresses [[244]85,[245]86].
Cell wall organization
Three genes were classified into this subcategory. Cellulose
synthase-like D1 (GSVIVT00014029001, + 31 fold in INF vs CON) and
beta-expansin 1a precursor (GSVIVT00036225001, + 27 fold in INF vs CON)
contribute to cell wall synthesis and modification [[246]87,[247]88].
The wound-induced protein (WIN2) (GSVIVT00007452001, + 26 fold in INF
vs CON) with anti-fungal activity [[248]89] possesses a domain that
binds PAMP (pathogen-associated molecular patterns) elicitors (e.g.,
chitin) [[249]90] and is induced in response to pathogen. In addition,
other highly expressed metabolic genes in the INF samples were
glucose-1-phosphate adenylyltransferase (GSVIVT00036349001, + 24 fold
in INF vs CON), cytochrome P450 (GSVIVT00014730001, + 70 fold in INF vs
CON) and serine acetyltransferase (GSVIVT00007984001, + 30 fold in INF
vs CON). These transcripts are related to carbohydrate metabolism,
photosynthesis and cysteine synthesis. Cysteine synthesis has reported
to respond to oxidative stress by calcium signaling [[250]91].
Even though most of these genes have been reported to be biotic or
abiotic stresses related, seven high expressed genes in the infected
leaves have not been previously reported being associated with stress.
They were noted as protein-binding protein (GSVIVT00024408001, + 87
fold in INF vs CON), ATPP2-A2 (Arabidopsis thaliana phloem protein
2-A2) (GSVIVT00023009001, + 56 fold in INF vs CON), putative integral
membrane protein (GSVIVT00014704001, + 51 fold in INF vs CON), putative
phosphate-induced protein (GSVIVT00015203001, + 229; GSVIVT00015200001,
+37 fold in INF vs CON), ATP-dependent DNA helicase (GSVIVT00016166001,
+36 fold in INF vs CON), CW14 (GSVIVT00020834001, +23 fold in INF vs
CON), and a hypothetical protein (GSVIVT00017533001, +20 fold in INF vs
CON).
Transcripts less abundant in infected leaves
The most striking functions for transcripts less abundant in infected
tissue were those associated with metabolism and defense response to
pathogen attack. Fifteen DEGs were detected to be less prevalent in the
INF libraries more than 20 fold compared to CON, most of which, such as
(-)-germacrene D synthase [[251]92], non-specific lipid transfer
protein [[252]93], major histocompatibility complex [[253]94],
thioredoxin [[254]95], beta-cyano-alanine synthase [[255]96], expansin
[[256]97] and UDP-glucosyltransferase [[257]98] are reported to be
positively associated with plant defense responses to pathogen attack.
However, our data indicated that the expression level of these
transcripts was lower in infected tissues.
Another two transcripts that were less prevalent in infected tissue
(GSVIVT00014727001, -35 fold in INF vs CON; GSVIVT00014725001, -41 in
INF vs CON) belong to cytochrome P450 family with oxidative function.
Interestingly, a novel gene encoding male sterility-related protein was
also identified in this group, and its function associated with DM
response has not been clarified.
Conclusions
Solexa-based sequencing can be used for analyzing variation in gene
expression between two samples. The gene expression level in
"Zuoshan-1" leaves infected with PV changed significantly in comparison
with control leaves. Analysis of differentially-expressed genes
involved in the pathogen infection allows delineation of candidate
genes potentially relevant to DM resistance in grapevines.
Methods
Plants material and pathogen infection
One-year-old, certified virus-free seedlings of "Zuoshan-1" were grown
and maintained in the greenhouse under a 16-h light/8-h dark
photoperiod at 25°C, 85% relative humidity. Control plants were
maintained under the same conditions. P. viticola was collected from
sporulated field leaves and used for the artificial inoculations of
surface-sterilized leaves. Infections were conducted by dipping the
fourth grapevine leaves in a suspension of 10,000 sporangia per ml pure
water. The leaves were covered with plastic bags for one night to
ensure high humidity. The fourth unfolded leaf from the shoot apex was
harvested from each of three vines, and the three leaves were combined
to represent one replicate. Three independent replicates were collected
for each sample. Infected leaves were collected every 24 h for 9 days.
Control samples were harvested from water-treated leaves incubated
under the same conditions.
Preparation of Digital Expression Libraries
Samples from infected leaves from 4 d to 8 d were pooled for RNA
isolation and library construction. Comparable control leaves were
treated identically and in parallel. Total RNA was isolated from the
leaf mixture using a modification of the CTAB method as presented by
Murray and Thompson [[258]99]. Sequence tag preparation was done with
the Digital Gene Expression Tag Profiling Kit (Illumina Inc; San Diego,
CA, USA) according to the manufacturer's protocol (version 2.1B). Six
micrograms of total RNA was extracted and mRNA was purified using
biotin-Oligo (dT) magnetic bead adsorption. First- and second-strand
cDNA synthesis was performed after the RNA was bound to the beads.
While on the beads, double strand cDNA was digested with NlaIII
endonuclease to produce a bead-bound cDNA fragment containing sequence
from the 3'-most CATG to the poly (A)-tail. These 3' cDNA fragments
were purified using magnetic bead precipitation and the Illumina
adapter 1 (GEX adapter 1) was added to new 5' end. The junction of
Illumina adapter 1 and CATG site was recognized by MmeI, which is a
Type I endonuclease (with separated recognition sites and digestion
sites). The enzyme cuts 17 bp downstream of the CATG site, producing 17
bp cDNA sequence tags with adapter 1. After removing 3' fragments with
magnetic bead precipitation, the Illumina adapter 2 (GEX adapter 2) was
ligated to 3' end of the cDNA tag. These cDNA fragments represented the
tag library.
Solexa sequencing
Sequencing was performed by "HuaDa Gene" [[259]100] with the method of
sequencing by synthesis. A PCR amplification with 15 cycles using
Phusion polymerase (Finnzymes, Espoo, Finland) was performed with
primers complementary to the adapter sequences to enrich the samples
for the desired fragments. The resulting 85 base strips were purified
by 6% TBE PAGE Gel electrophoresis. These strips were then digested,
and the single-chain molecules were fixed onto the Solexa Sequencing
Chip (flow cell). Each molecule grew into a single-molecule cluster
sequencing template through in situ amplification. Four color-labeled
nucleotides were added, and sequencing was performed with the method of
sequencing by synthesis. Image analysis and basecalling were performed
using the Illumina Pipeline, and cDNA sequence tags were revealed after
purity filtering. The tags passing initial quality tests were sorted
and counted. Each tunnel generates millions of raw reads with
sequencing length of 35 bp (target tags plus 3'adaptor). Each molecule
in the library represented a single tag derived from a single
transcript.
Sequence annotation
"Clean Tags" were obtained by filtering off adaptor-only tags and
low-quality tags (containing ambiguous bases). Comparison of the
sequences by blastn was carried out using the following databases: NCBI
[[260]101], Genoscope Grape Genome database [[261]25] and VBI Microbial
Database [[262]26]. All clean tags were annotated based on grape
reference genes. For conservative and precise annotation, only
sequences with perfect homology or 1 nt mismatch were considered
further. The number of annotated clean tags for each gene was
calculated and then normalized to TPM (number of transcripts per
million clean tags) [[263]30,[264]102]. Sequences were manually
assigned to functional categories based on the analysis of scientific
literature.
Identification of differentially expressed genes (DEGs)
A rigorous algorithm to identify differentially expressed genes between
two samples was developed [[265]103]. P value was used to test
differential transcript accumulation. In the formula below the total
clean tag number of the CON library is noted as N1, and total clean tag
number of INF library as N2; gene A holds x tags in CON and y tags in
INF library. The probability of gene A expressed equally between two
samples can be calculated with:
[MATH: P(y|x)=(N2N1)y(x+y)!x!y!(1
+N2N1)<
/mrow>(x+y+<
/mo>1) :MATH]
FDR (False Discovery Rate) was applied to determine the threshold of P
Value in multiple tests and analyses [[266]104]. An "FDR < 0.001 and
the absolute value of log2Ratio ≥ 1" was used as the threshold to judge
the significance of gene expression difference.
Real-time RT-PCR analysis
Samples were prepared using the same method mentioned above and total
RNA was isolated from the leaf mixture. Experiments were carried out on
three independent biological replicates each containing three technical
replicates. First-strand cDNA was synthesized from 650 ng DNase
(Promega, Madison, Wisconsin, USA) -treated total RNA using "ImProm-II
TM Reverse Transcriptase" (Promega, Madison, Wisconsin, USA) and
diluted 20 fold as template. Specific primer pairs of twelve randomly
selected genes were designed (Table [267]4) using Primer Express 3.0
and tested by Real-time RT-PCR. Primers specific for V. vinifera actin
(Forward: AATGTGCCTGCCATGTATGT; Reverse: TCACACCATCACCAGAATCC) were
used for the normalization of reactions. Experiments were carried out
using Power SYBR Green PCR Master Mix (Applied Biosystems, Warrington,
UK) in a StepOne™ Real-Time PCR System (Applied Biosystems). The
reaction volume was 20 μl, including 10 μl Power SYBR Green PCR master
mix, 0.9 μl 10 mM primer, 2.0 μl cDNA sample and 6.20 μl dH2O. The
following thermal cycling profile was used: 95°C 10 min; 40 cycles of
95°C for 15 s, 59°C for 1 min; 95°C for 15 s, 60°C for 1 min, 95°C for
15 s. Data were analyzed using StepOne™ Software Version 2.0 (Applied
Biosystems). Actin expression was used as an internal control to
normalize all data. The fold change in mRNA expression was estimated
using threshold cycles, by the ΔΔCT method [[268]105].
Pathway Enrichment Analysis of DEGs
Pathway enrichment analysis based on KEGG [[269]106] was used to
identify significantly enriched metabolic pathways or signal
transduction pathways in differentially-expressed genes comparing with
the whole genome background. The calculating formula is:
[MATH: P=1−∑i=0m−1<
mrow>(Mi)(N−Mn−i)(Nn) :MATH]
where N is the number of all genes that with KEGG annotation, n is the
number of DEGs in N, M is the number of all genes annotated to specific
pathways, and m is number of DEGs in M. Q value was used for
determining the threshold of P Value in multiple test and analysis
[[270]107]. Pathways with Q value < 0.05 are significantly enriched in
DEGs.
Abbreviations
AFLP: Amplified Fragment Length Polymorphism; BLAST: Basic Local
Alignment Search Tool; cDNA: Complementary DNA; CTAB:
Hexadecyltrimethylammonium bromide; DEGs: differentially expressed
transcripts; NCBI: National Center for Biotechnology Information.
Authors' contributions
JW and YLZ carried out the plant material preparation, PV infection,
RNA extraction, preparation of digital expression libraries, sequence
analysis, and contributed to data interpretation and manuscript
writing. HQZ participated in PV infection and RNA extraction. HH
contributed to sequence analysis. KMF participated in data
interpretation and manuscript modification. JL conceived the study, led
the experiment design and coordinated all the research activities,
contributed to interpretation of the data, manuscript writing and
modification. All authors read and approved the final manuscript.
Supplementary Material
Additional file 1
Complete list of transcripts attributed to P. viticola.
[271]Click here for file^ (67.5KB, XLS)
Additional file 2
Complete list of involved pathways for upregualted DEGs. Pathways with
Q value < 0.05 are significantly enriched for upregulated DEGs.
[272]Click here for file^ (228.5KB, DOC)
Additional file 3
Complete list of involved pathways for downregualted DEGs. Pathways
with Q value < 0.05 are significantly enriched for downregulated DEGs.
[273]Click here for file^ (213KB, DOC)
Additional file 4
List of "Zuoshan-1" transcripts upregulated for at least 2 fold in INF
library. Two fold and more upregualted genes with pathway annotation in
INF library were listed in different categories.
[274]Click here for file^ (283KB, XLS)
Additional file 5
List of "Zuoshan-1" transcripts downregulated for at least 2 fold in
INF library. Two fold and more downregualted genes with pathway
annotation in INF library were listed in different categories.
[275]Click here for file^ (67.2KB, XLSX)
Contributor Information
Jiao Wu, Email: jiaolong722@gmail.com.
Yali Zhang, Email: olivia.yl.zhang@gmail.com.
Huiqin Zhang, Email: foreverjiaxin@gmail.com.
Hong Huang, Email: huanghon2003@gmail.com.
Kevin M Folta, Email: kfolta@ufl.edu.
Jiang Lu, Email: j.lu.cau@gmail.com.
Acknowledgements