º

THE TRANSCRIPTOME OF

QUIESCENCE AND DORMANCY IN

SUBTROPICAL AND MEDITERRANEAN

GRAPEVINE

Presented by:

Sandra Patricia Agudelo Romero, PhD.

MASTER OF SCIENCE IN BIOINFORMATICS

AND COMPUTATIONAL BIOLOGY

NATIONAL HEALTH RESEARCH INSTITUTE

INSTITUTE OF HEALTH CARLOS III (ISCIII)

2014-2015

UNIVERSITY OF WESTERN AUSTRALIA

Professor Dr. Michael Considine

2nd of February of 2015

º

i

CONTENTS

DEDICATION ii

ACKNOWLEDGEMENT iii

1. ABSTRACT 1

2. THE APPROACH TO THE PROBLEM 2

3. OBJECTIVES 3

4. INTRODUCTION 4

5. MATERIALS AND METHODS 6

5.1. Sample collection 6

5.2. RNA extraction, Illumina library construction and sequencing 6

5.3. Data processing analysis 6

5.4. Functional enrichment analysis 7

6. RESULTS AND DISCUSSION 8

6.1. Dormancy and Quiescent Gene Expression Profiles 8

6.2. Transcriptional Bases for Bud Dormancy and Quiescent

Differentiation Between Climates. 12

6.2.1. Pre-chilling 12

6.2.2. Post-chilling 18

6.3. Finding Potential Biomarkers 22

7. CONCLUSIONS 26

8. BIBLIOGRAPHY 27

9. ANEXOS 33

9.1. FastQC command line 33

9.2. Trimmomatic command line 33

9.3. Kallisto on hiseq command line 33

º

ii

DEDICATION

For the two great loves of my life,

thank you so much for existing.

º

iii

ACKNOWLEDGEMENT

I was supported by an Australian Research Council grant (ARC: LP130100347). The

research was supported by the ARC grant: LP0990355. This project was carried out at

the University of Western Australia in the ARC Center of Excellence in Plant Energy

Biology.

º

1

1. ABSTRACT

Vine physiology is dependent on climate for orderly transitions between vegetative and

reproductive growth. Productive viticulture requires a temperate/Mediterranean climate,

while in warmer, low latitude climates. Low latitude viticulture is only viable for table

grapes if development is intensively managed with chemical and physical stress

treatments, such as deficit irrigation to force a ‘rest’ period and cyanamide to force bud

burst. Even with intensive management, vine growth is disorderly and yields are

considerably lower and more variable, as the seasonal cues that grapevine relies on for

developmental transitions are lacking. To gain insight into how differences in the

temperature due to climate features can modify grapevine bud dormancy, a RNA-seq

study was performed to investigate differences between subtropical and Mediterranean

climates in table grapes (Flame Seedless).

For this, gene expression changes in buds from two adjacent vineyards in subtropical

Western Australia (25°S latitude) were compared against one vineyard in a

Mediterranean climate (32°S). Buds were collected for differential expression analysis

at the end of summer (March; henceforth termed pre-chilling) and in mid-winter (June

henceforth termed post-chilling), over two successive years (2012 and 2013). Principal

Components Analysis (PCA) of RNA-seq data revealed that the main factor explaining

the global gene expression differences was between consecutive years.

Differential expression analyzes of subtropical and Mediterranean climates comparison

in pre-chilling and post-chilling conditions were carried out (1% FDR and FC |3-fold|).

Cluster and functional enrichment analyzes were then performed to each condition. In

the comparison performed during pre-chilling, WRKY family transcription and

oxidative stress (Glutathione S-transferase) categories showed differences between

climates. Whereas in the post-chilling condition was detected ethylene-mediated

signaling pathway and C2C2-YABBY family transcription factor categories.

This work provides a global view of major transcriptional changes taking place in

Australian subtropical and Mediterranean climates, highlighting those molecular and

biological functions that showed differences between climates, suggesting a main role

of those functional categories during regulation of bud dormancy.

º

2

2. THE APPROACH TO THE PROBLEM

Grapevine cultivation in subtropical climates is a fragile system requiring intensive

management, for which there are no biological markers. Miss-timing intervention can

result in phytotoxic effects, as in the case of the dormancy-releasing hydrogen

cyanamide. In temperate-grown grapevine, the reproductive and metabolic cycles are

regulated by environmental signals, particularly the induction of dormancy during

autumn and the subsequent re-activation of growth during spring (Lavee et al., 1997).

Like many woody perennials, energy reserves accumulate in the perennial tissues prior

to the onset of dormancy and their mobilisation entirely supports the initial stages of

vegetative and reproductive growth in spring (Lebon et al., 2005). Hence, the dormant

phase is necessary for coordinated, productive and sustainable growth (Lavee et al.,

1997). In grapevine, molecular investigations of dormant axillary buds have also

revealed coordinated profiles, including reprogramming of carbohydrate metabolism,

but these have been under temperate conditions and confined to dormancy release (bud

break), a single event in a complex reproductive cycle (Mathiason et al., 2008). Here,

the reprogramming of buds during dormancy and quiescent (latent) stages can be

studied using RNAseq approach. For this, buds sampled from a subtropical Western

Australian climate (Carnarvon) were compared to buds from a Mediterranean climate

(Swan Valley, Perth) (Figure 1).

Figure 1. Map of Australian grape growing regions and temperature differences. A. Subtropical Western

Australian climate (Carnarvon) and Mediterranean climate (Swan Valley, Perth) are highlight.

Subtropical climate is represented by Bumbak (B) and Condo (C) sites from Carnarvon. Mediterranean

climate is represented by Nuich (N) from Swan Valley (Perth). B. Graphics showed the average of the

differences in temperature of both climates provided by the airports (Carnarvon and Perth respectively) in

the last decades.

º

3

3. OBJECTIVES

The aim of this project is to determine the relationship between climates in grapevine

buds grown in a subtropical region (Carnarvon, WA) and a Mediterranean climate

(Swan Valley) in Australia during two consecutive seasons (2012 and 2013) by using of

RNAseq technology.

Specifically:

To dissect the effects and interactions between climate conditions through their

gene expression profiles.

To finding potential gene candidates in order to be used as biomarkers for

anticipate actions in warm-temperate or stressed conditions (i.e. water stress).

º

4

4. INTRODUCTION

Grapes (Vitis spp.) are economically the most important fruit crop worldwide with a

global production of around 67 million t in 2012 (Food and Agriculture Organization

Corporate Statistical Database (FAOSTAT, 2014,

http://faostat.fao.org/site/567/default.aspx#ancor). Table grapes rape and processed

products such as: wine, juice, jam and dried fruit, represent an important sector in the

global market. Moreover, their consumption has an important added value in a healthy

diet by the polyphenolic compounds with antioxidant and anticarcinogenic properties

found in them (Ali et al., 2010).

Therefore, it is important to understand how improve the production of grapes in

Australian subtropical climate since theoretically, this region lacks sufficient

temperature to regulate dormancy correctly. This fact is a critical environmental

requirement for sustainable table grape production of cultivars as Flame Seedless.

Dormancy induction is problematic and the normal reproductive cycle is perturbed, with

a shifted and condensed phenological cycle. It is only management intervention that

sustains this cycle; vines would otherwise continue vegetative growth throughout

winter, limiting resource storage and resulting in variable yields and very short vine life

(Possingham 2004). In subtropical climates, sustainable production thus relies on

management intervention to supplement for environmental signals; e.g. water stress and

chemical application (concentrated nitrate) to force leaf fall, imposing a winter “rest,”

and pruning/ chemical application (hydrogen cyanamide) to stimulate vines to

recommence vegetative and reproductive growth. Even with interventions, disorders are

common, as described in the predominant subtropical viticulture regions; Carnarvon

(WA) and Rockhampton (Qld) in Australia, Coachella Valley (California), Mexico,

Northern Chile and Orange River (South Africa):

• Slow and erratic bud burst and extreme dominance of the apical buds.

• Delayed foliation following bud burst, often characterised by a period of

chlorotic growth.

• Inflorescence disorders, resulting in partial or complete abortion or abscission.

º

5

Each of these disorders may co-occur. The result is an industry faced with high

prospects but equally high input costs and yield and quality variation. To date, R&D

into production of temperate fruit crops in subtropical or tropical climates has focussed

heavily on improving bud burst, principally through optimal use of chemicals or

through manipulating the microclimate during autumn (Possingham 2004). There is a

distinct lack of research on the stages of bud development preceding winter, so-called

dormancy onset.

In this project, to gain insight into how differences in the temperature due to climate

features can modify grapevine bud dormancy, a RNA-seq study was performed to

investigate differences between subtropical and Mediterranean climates in table grapes

(Flame Seedless). Differential expression in pre-chilling and post-chilling conditions

were carried out (1% FDR and FC |3-fold|) accompanied of a cluster and enrichment

analysis.

º

6

5. MATERIALS AND METHODS

5.1. Sample collection

Field collection in Western Australia (WA) of grape table, Flame seedless, was done at

two sub-tropical vineyards (B and C) in Carnarvon (24.9ºS and 113.7ºE) and a

Mediterranean vineyard in the Swan Valley (31.8ºS and 116ºE). Sampling was

performed between the end of March and early April for the pre-chilling condition and

in the middle of June for the post-chilling condition. Every sample was composed of

two buds randomly selected from healthy canes, they were immediately frozen in dry

ice and stored at -80ºC. At the time of sampling, buds were cut from the cane with a

scalpel, visually assessed for indications of necrosis from beneath the bud; buds with

obvious visible signs of necrosis within were discarded, however this assessment cannot

determine less dramatic levels of necrosis within the bud. For each time point and place

three/four biological replicates of buds were sampled for the RNA-seq analyses.

5.2. RNA extraction, Illumina library construction and sequencing

Buds were ground under liquid nitrogen to a fine powder. Total RNA extraction was

performed using the Spectrum Plant Total RNA kit with an on-column DNase treatment

according to the supplier’s instructions (Sigma-Aldrich, Castle Hill, Australia),

followed by an isopropanol/acetate precipitation. The quality and integrity of the

isolated RNA was tested using a NanoDrop 100 spectrophotometer and agarose gel

electrophoresis. Only RNA with an Abs260 nm/Abs280 ratio above 1.95 was used

further. RNA-seq libraries were prepared with the TruSeq Stranded Total RNA with

Ribo-Zero Plant kit according to manufacturer's instructions (Illumina, Scoresby,

Australia). Sequencing was performed on an Illumina HiSeq1500 instrument as 100bp

single-end runs.

5.3. Data processing analysis

Resulting reads were aligned to the whole 12X V1 Vitis vinifera PN40024 reference

genome (Jaillon et al., 2007) with Kallisto (Bray et al., 2015). Gene expression profiling

was carried out using edgeR (Robinson et al., 2010) and limma (Ritchie et al., 2015)

Bioconductor packages. The counts matrix obtained with Kallisto was read using

º

7

edgeR, it was converted into a DGEList data class and a TMM normalization method

was applied to obtained the log2 Counts-Per-Million (logCPM). Then, a Voom

transformation was applied to convert it into a EList object to can use limma pipelines

for differential expression under a linear model. To identify differentially expressed

genes, a multiple testing correction via False Discovery Rate (FDR) was performed

(P<=0.01) along with fold change (FC 3-fold). Principal component analysis (PCA) was

performed with the full TMM dataset using Acuity 4.0 (Axon Molecular Devices,

http://www.moleculardevices.com). FastQC, Trimmomatic and Kallisto command lines

used to generate the count matrix are detailed in Anexos.

5.4. Functional enrichment analysis

Gene lists were analysed further with FatiGO (Al-Shahrour et al., 2004) to identify

significant functional enrichment in Babelomics 5 (http://babelomics.bioinfo.cipf.es/)

following a grapevine-specific functional classification of 12X V1 predicted transcripts

(Grimplet et al., 2012). Fisher’s exact test was carried out in FatiGO to compare each

study list with the list of total non-redundant transcripts housed in the grapevine 12X

V1 gene predictions (Grimplet et al., 2012). Significant enrichment was considered for

P<0.01 after Benjamini and Hochberg correction for multiple testing.

º

8

6. RESULTS AND DISCUSSION

6.1. Dormant and Quiescent Gene Expression Profiles

As a first approach to analyze the complexity of the gene expression dataset of the

present research, a principal component analysis (PCA) was performed over the

expression data of the dormancy and quiescent stages in two climates (Subtropical and

Mediterranean) during two consecutive seasons. The first, second and third principal

components (PC1, PC2 and PC3) explained together 45.06% of the variability in gene

expression (19.31%, 14.26% and 11.49%, respectively) (Fig. 2A). The results of the

PCA plot showed consistency across biological replicates and seasons, therefore, the

experiment was considered highly reliable for further analysis. Experimental

consistency allowed for exploring PC3 further in a hypothesis-free approach, as this

component discriminated the two consecutive years (Fig. 2B).

To further analyze the biological basis of the patterns observed in the PCA plot,

transcripts that mostly contribute to component PC3 were identified. This was done by

considering the PCA loading scores (LS) for all the transcripts in PC3 (Table SX). The

LS absolute value cut-off of five was considered to identify transcripts dominating PC3.

In this manner, PC3 was dominated by transcripts up-regulated (PC3 LS > 5) more than

down-regulated (PC3 LS < −5), 192 and 122 transcripts, respectively (Table SX).

This difference suggests that seasonality (annual variation) is the major factor between

climates and dormant stages (pre- vs post-chilling). The top three of positive ranking

(PC3 LS > 5) was formed by genes that coding for DNA-directed RNA polymerase

subunit beta (VIT_11s0103g00390), Kinesin family member 5 (VIT_14s0128g00090)

and MAP3K delta-1 protein kinase (VIT_00s0230g00140). They belong to RNA

polymerase, Microtubule-driven movement and MAPK cascade processes, respectively.

On the other hand, the negative ranking top three (PC3 LS > -5) was formed by genes

that coding for Pentatricopeptide (PPR) repeat-containing protein

(VIT_17s0000g06390), MAPK (MPK6) (VIT_05s0094g00900) and Brassinosteroid

insensitive 1-associated receptor kinase 1 (VIT_00s0472g00020). Those transcripts

correspond to Pentatricopeptide domain family, Ethylene-mediated Signaling pathway

and Brassinosteroid-mediated

º

9

A. B. C.

Figure 2. PCA plot of ‘Flame Seedless’ bud samples according to their expression data in 2012 and 2013 seasons, buds were harvested from two adjacent vineyards in

subtropical Western Australia (25°S latitude) and one vineyard in a Mediterranean climate (32°S). A. PCA plot of buds samples according to their TMM normalized

expression data. The first (PC1), the second (PC2) and the third (PC3) principal components are represented. Each season is formed by two stages of dormancy (pre- and post-

chilling) with three or four replicates. Green, 2012 season and orange, 2013 season. B. Stage averaged PC3 loading scores. Color code is the same as in A. Lines represent

standard errors (SE). C. Functional categories over-represented in PC3 (B). Absolute values of log10 transformed P-values were used for the bar diagram representing

statistical signification, only categories with P-values < 0,01 are shown. Clear blue, Primary metabolism and dark blue, secondary metabolism.

º

10

Signaling pathway, respectively. Therefore these transcripts summarize the major

differences between years.

Functional enrichment analysis was carried out using FatiGO (Al-Shahrour et al, 2004)

to assess the biological significance of the transcripts showing a higher contribution to

the expression variability. The lists of transcripts most contributing to PC3 were

compared to the rest of the transcripts represented in the 12X V1 predicted transcripts

(Grimplet et al., 2012), highest scored (LS > |5|) transcripts were analyzed together.

This functional analysis discriminated just two functional categories: Primary

metabolism and Transport overview (Fig. 2C). Four processes associated to Primary

metabolism were found: ‘respiratory-chain phosphorylation’, ‘nucleic acid metabolism’,

‘protein processing in endoplasmic reticulum’ and ‘ribosome’. Two processes were

detected in Transport overview: ‘proton-translocating NADH Dehydrogenase’ and

‘proton-translocating Quinol:Cyt c Red’.

Among all processes, ribosome-related processes had the most highly significant adj. P

value (8,38E-11). The eukaryotic ribosome is a complex structure formed for four

rRNAs and about eighty ribosomal proteins. It represents a crucial piece of the cell

machinery, responsible for protein synthesis, and as such plays a major role in

controlling cell growth, division, and development. Several studies have reported that

genetic defects in ribosomal components can produce deleterious effects on the

development and physiology of drosophila, mice, humans and plants (Barakat et al.,

2001). On the other hand, it was also reported a positive correlation between the level of

r-protein gene transcript accumulation and cell division in suspension culture cells and

tissues such as auxin-treated hypocotyls, apical meristems, young leaves, and lateral

roots (Barakat et al., 2001). Here, two Ribosomal RNAs were detected: 23S

(VIT_01s0010g01260; VIT_11s0037g01180 and VIT_12s0035g02010) and 16S

(VIT_13s0101g00220) along with fifteen ribosomal proteins.

To dissect the influences of consecutive seasons, PCA analysis was performed

separately for each year. Each analysis represents two sites: Subtropical site (B -

Bumbak and C - Condo) from Carnarvon and Mediterranean climate (N - Nuich) from

Swan Valley, they represent subtropical and Mediterranean climates, respectively.

Additionally, two stages of grapevine bud development were compared, pre-chilling

º

11

and post-chilling, these stages correspond to dormant and quiescent buds, respectively

(Fig. 3A and 3B.). Two different signatures in the TMM normalized expression data

resulted. In 2012, transcriptomes segregated by climates but not by time-of-sampling

(Fig. 3A), while 2013, transcriptomes segregated by time-of-sampling rather than

climate (Fig. 3B. In 2012, PC1 and PC2 explained together the 43.26% of the variability

in gene expression (23.66%, and 19.60%, respectively); whereas in 2013, they

explained together the 45.22% of the variability in gene expression (PC1 - 28.06%, and

PC2 -17.16%) (Fig. 3A and 3B).

Figure 3. Bi-dimensional loading score plot resulting from PCA analysis detailed by seasons (A and B)

along with their differentially expressed genes (DEGs) (C and D). A. and B. PCA plots showing

transcriptional discrimination in places of harvest and developmental stages for 2012 and 2013,

respectively. Percent of variation explained by each PC are shown in brackets. Replicate samples for the

same time-point are in the same color. Letter B, C and N refer to the site of harvest, subtropical sites B

and C, and Mediterranean site N. Pre refers to the pre-chilling stage and Post to post- chilling. C. and D.

bar plots display the distribution of DEGs under a FDR (0,01) and FC |3-fold| cut-off. Green, lower

expression; magenta color, higher expression.

Gene expression analyses were then performed to study the differences between

climates corresponding to two different latitudes, subtropical Western Australia climate

(25°S latitude; Carnarvon; B - Bumbak and C - Condo) against Mediterranean climate

(32°S; Swan Valley; N - Nuich). The two subtropical sites were individually compared

º

12

to the Mediterranean site. In parallel, differences in the stage of bud development;

dormancy (pre-chilling) and in quiescence (post-chilling) were considered.

Differential expression analysis was performed with the Bayes t-statistics from the

linear models for microarray data (limma) (1% FDR and FC |3-fold|). Functional

annotation was assigned to the core set of genes that matched a putative molecular

function (Grimplet et al., 2012), 2238 transcripts in the case of 2012 and 1889

transcripts in the case of 2013. The distribution of the differentially expressed genes

(DEGs) in 2012 and 2013 are shown in the Figures 3C and 3D. Illustrated this way, it

was seen that the two subtropical sites bear quite different profiles in relative gene

expression, despite belonging to the same climate, as they are geographically adjacent.

In 2012, subtropical site B showed more up-regulated genes than down-regulated, both

pre- and post-chilling, by reference to the Mediterranean site N. In contrast, subtropical

site C showed more down-regulated than up-regulated genes both pre- and post-chilling

(Fig. 3C). Looking at the 2013 profiles, a reasonably consistent profile was seen for

subtropical site B, but subtropical site C bears a pattern that differs to 2012, albeit more

consistent with site B (Fig. 3D). Therefore, the difference in the number of differentially

expressed genes suggests that an unusual event influenced vine physiology in

subtropical site C in 2012 (Fig. 3C and 3D). This change in the gene expression profile

is likely to be a product of management interventions, as the sites were managed by

different farmers, e.g. chemical, fertilizer or irrigation strategies.

6.2. Transcriptional Bases for Bud Dormancy and Quiescent Differentiation

Between Climates.

The significant transcripts were grouped according to their developmental stages,

dormant buds (pre - chilling) and quiescent buds (post - chilling) and comparisons were

done between climates (Subtropical vs Mediterranean). Expression patterns following a

self-organizing map (SOM) analysis (Fig. 4 and Fig. 5). This clustering analysis

indicated a considerable influence of annual variation and crop management in the gene

expression of pre-chilling and post-chilling; although changes due to climate were also

evident. Functional enrichment analyses were performed on each of these clusters to

assess the biological significance underlying in each pattern.

6.2.1 Pre - chilling

º

13

The Figure 4 summarizes six profiles product of the comparisons between climates in

2012 and 2013 in pre-chilling. There, it can be observed as SOM1, SOM4, SOM5 and

SOM6 are related to changes between years. In addition, SOM2 presents differences in

subtropical site C between 2012 and 2013 whereas subtropical site B showed the same

profile in both seasons; and only SOM3 reflects the same pattern in both locations of

subtropical climate against Mediterranean climate during the two seasons, although the

expression in Bumbak was higher than in Condo. SOM2 and SOM3 in pre-chilling had

the greatest number of genes, 523 and 526, respectively, while SOM5 and SOM6 in pre-

chilling had the lowest numbers among the clusters with 253 and 261, respectively.

The SOM3 (Pre) profile identified six functional categories that were induced in the

subtropical climate (Figure 4; SOM3). These categories were: ‘Diverse functions’,

‘Metabolism’, ‘Regulation overview’, ‘Response to stimulus’, ‘Signaling’ and

‘Transport overview’. Moreover, within ‘Metabolism’ genes related to three main

subcategories, ‘Cellular metabolism’, ‘Primary metabolism’, and ‘Secondary

metabolism’. The category of ‘Diverse functions’ included several genes coding for

NBS-LRR superfamily. Among them were genes encoding HcrVf1 protein

(VIT_01s0010g03210 and VIT_01s0010g03230), Disease resistance

(VIT_09s0054g00300 and VIT_16s0013g01700) and Leucine-rich repeat family

(VIT_09s0070g00690). These transcripts present a fold change >30-fold in subtropical

site B in 2012 (Figure 4; SOM3). The nucleotide binding site-leucine-rich repeat (NBS-

LRR) is a well-known class related with biotic stress leading pathogen resistance;

although recently it was also linked to light signals produced by neighbor plants. This

developmental strategy is known as shade-avoidance syndrome and caused acceleration

in stem growth and flowering as well as epinasty, as reported in the Arabidopsis

constitutive shade-avoidance1 (csa1) mutant (Faigón-Soverna et al., 2006). The csa1 is

a T-DNA mutant of a Toll/Interleukin1 (TIR) NBS-LRR gene (Faigón-Soverna et al.,

2006)

º

14

Figure 4. Self-organization maps (SOMs) showing different patterns of gene expression during Pre-

chilling in two consecutive seasons. Letters B, C and N refer to the site of harvest, subtropical sites B and

C, and Mediterranean site N. A. Gene expression patterns are organized into SOMs (labeled as 1 to 6).

Comparisons were performed: B, C, and N against N. The number of Unigenes belonging to each SOM

category is indicated in parenthesis. Green represents down-regulated expression; magenta represents up-

regulated expression and black no changes in the expression, relative to site N. The brighter the color, the

larger the difference in gene expression. Functional annotation was assigned to the genes that had

matches to a putative molecular function. Enrichment analysis was carried out using FatiGO.

º

15

In ‘Cellular metabolism’ two category were detected enriched the ‘Oxidation reduction’

and ‘Phytoalexin biosynthesis’ processes. Within ‘Oxidation reduction’, all transcripts

belonged to Cytochrome P450 oxidoreductase-family genes, including CYP82M1v3

(VIT_12s0035g00610), CYP89A6 (VIT_16s0039g00840), CYP714A1

(VIT_18s0089g00700) (Figure 4; SOM3). In plants, cytochrome P450 genes are

involved in biosynthetic and detoxification pathways. Cytochrome P450 genes are

involved in the phenylpropanoid biosynthetic pathway (lignins, antioxidants, flower

pigments, defense chemicals), fatty acids, hormones, and signaling molecules.

Additionally, Cytochrome P450 proteins participate in the breakdown of endogenous

compounds and toxic compounds encountered in the environment (Schuler et al 2003).

Furthermore, Cytochrome P450 has multiple functions under stress conditions and

multiple ways exist to regulate these functions in hypoxic tissues, and their differential

upregulation under hypoxia can indicate ROS production (Blokhina et al., 2010).

In the ‘Phytoalexin biosynthesis’ category, transcripts coding for Stilbene synthase gene

(STS) were found (VIT_10s0042g00870, VIT_16s0100g00830 and

VIT_16s0100g00940). A wide range of abiotic stress treatments lead to stilbene

biosynthesis, such as mechanical damage, exposure to UV-C light, treatment with

chemicals, such as aluminum ions, cyclodextrins, and ozone, and the application of

plant hormones like ethylene and jasmonates (Höll et al., 2013; Wang et al., 2010).

Moreover, a transcript coding for Resveratrol synthase (RS1) (VIT_16s0100g01110)

was found. The up-regulation of RS1 (VIT_16s0100g01110) was also reported in V.

riparia (Fennell et al., 2015) and Tempranillo (Díaz-Riquelme et al., 2012) during bud

dormancy. Additionally, a Stilbene synthase transcript was differential expressed in

poplar in the comparison of terminal bud and cambium tissue (Fennell et al., 2015).

Here, it was observed STSs and RS1 expressed in Mediterranean site N, but

comparatively more induced in both subtropical site B and C.

Amino sugar metabolism processes from ‘Primary metabolism’ presented several genes

related to Chitinase (VIT_05s0094g00320 and VIT_16s0050g02230) and Acidic

endochitinase (CHIB1) (VIT_15s0046g01570 and VIT_16s0050g02220). Chitinases in

plants play a main role in defence against pathogen attack, although, it has been

reported that chitinases are also involved in general stress, growth and development

processes (Kasprzewska et al., 2003).

º

16

‘Phenylpropanoid metabolism’ was also altered in Lignin process (adj. P value 6.415E-

012), including sixteen Laccases, three of which were 70-fold induced in subtropical

site B in 2012 (VIT_00s1212g00020, VIT_18s0001g01280 and VIT_18s0075g00590).

Although the FC of these laccases values was comparatively less in 2013, they were still

higher than in subtropical site C. The laccases in plants have been reported to be

involved in varieties of biological processes such as wound healing, iron metabolism

and maintenance of cell wall structure and integrity. In addition, plant laccases have

been reported in responses to environmental stresses e.g. salinity and heavy metal stress

by lead (review by Wang et al., 2015). The marked signature of laccases in the pre-

chilling stage of subtropical site B suggested a high incidence of necrosis, particularly

in 2012.

In ‘Secondary metabolism’ category a prominent role for Stilbenoid biosynthesis was

indicated. This process included transcripts coding for Secoisolariciresinol

dehydrogenase (VIT_08s0007g02630), Cinnamoyl alcohol dehydrogenase

(VIT_13s0064g00270), Coniferyl-alcohol glucosyltransferase (VIT_18s0001g12040),

as well as, STR1 and STS genes that also are part of Phytoalexin pathway. This

indicates the induction of genes encoding several phenylpropanoid-related enzymes

controlling the key step for the synthesis of stilbene and lignin compounds.

Within the ‘Regulation overview’ category, the WRKY family transcription factor was

induced (SOM3, Pre). Six WRKY family members were differentially expressed;

WRKY DNA-binding protein 21 (VIT_00s2547g00010), 27 (VIT_02s0025g00420), 51

(VIT_04s0069g00970), 53 (VIT_16s0050g02510), 70 (VIT_13s0067g03140) and 75

(VIT_14s0068g01770). Several studies have implicated prominent roles of WRKY

factors in plant processes such as germination, senescence and responses to abiotic

stresses such as drought and cold (review by Rushton et al., 2010), as well as wounding

and salinity (review by Chen et al., 2012). Nevertheless, very little known about WRKY

TFs role in bud dormancy. Recently, several transcripts of grapevine VvWRKY TFs

were differentially expressed during the short-day induction of grapevine bud dormancy

(Fennell et al., 2015). Authors reported induction of a transcript coding for WRKY

DNA-binding protein 65 (VIT_10s0003g01600) during the perception phase, one

transcript during induction phase WRKY DNA-binding protein 71

(VIT_12s0028g00270) and three transcripts during dormancy maintenance, WRKY

º

17

DNA-binding protein 48 (VIT_05s0077g00730), 65 (VIT_10s0003g01600) and 75

(VIT_01s0010g03930) (Fennell et al., 2015).

Within the ‘Response to stimulus’ category (SOM3, Pre), both abiotic (Oxidative stress

response) and biotic (Plant-pathogen interaction) stress were (P value 8.41E-8 and

2.45E-11, respectively). Many genes were altered in ‘Oxidative stress response’

process, among them were six Glutathione S-transferases, such Glutathione S-

transferase 8 GSTU8 (VIT_06s0004g05700), 10 GSTU10 (VIT_01s0026g02400), 22

GSTU22 (VIT_19s0093g00110), 25 GSTU7 (VIT_07s0005g04880 and

VIT_08s0040g00920) and 25 GSTU25 (VIT_19s0093g00320). Additionally, transcripts

coding for Lactoylglutathione lyase (VIT_11s0016g05010), L-ascorbate oxidase

(VIT_07s0031g01010) Peroxidase (VIT_08s0058g00990) along with Laccases were

detected. All these genes are directly related to ROS including to cytochrome P450

family proteins, they are involved both in H2O2 elimination and detoxification

(Blokhina et al., 2010).

Several transcripts of biotic stress were found induced in ‘Plant-pathogen interaction’

process coding for R protein disease resistance protein (VIT_10s0071g00150), R

protein L6 (VIT_18s0041g00190), R protein MLA10 (VIT_00s0144g00120) and R

protein PRF disease resistance protein (VIT_03s0017g00920). The hypersensitive

response (HR) is a form of cell death often associated with plant resistance to pathogen

infection. It is a multicomponent response involving increased expression of defence-

associated genes (pathogenesis-related or PR genes) and a form of localised cell death

(LCD) at the site of infection to restrict the advance of microorganisms. Although the

HR has been widely reported during biotic interactions, some of its features, including

LCD and induction of PR genes, are shared by plant responses to a number of abiotic

stresses such as excess of excitation energy (EEE), and exposure to ozone (Review by

Zurbriggen et al., 2010). Additionally, Morel et al., (1997) reported that phytoalexins

and pathogenesis related (PR) proteins are also induced by abiotic treatments and

physical stresses. Hence the signature of PR proteins is in agreement with the necrotic

symptoms that present the buds.

In the category of ‘Hormone Signaling’, Jasmonate Signaling and Salicylic acid-

responsive genes were altered (SOM3, Pre). These two pathways share these genes, the

transcripts found were, Enhanced disease susceptibility 1 (EDS1)

º

18

(VIT_17s0000g07420), Pathogenesis related protein 1 precursor (pr1 gene)

(VIT_03s0088g00700) and Pathogenesis-related protein 1 precursor (PRP 1)

(VIT_03s0088g00810 and VIT_03s0088g00710). Ochsenbein et al., (2006) have

reported that EDS1 gene plays an important role during oxidative stress caused by the

release of singlet oxygen and previously was also reposted in the processing of

hydrogen peroxide/superoxide-derived signals (Mateo et al., 2004). Likewise,

Pathogenesis-related proteins have also been reported in abiotic stresses (Przymusiński

et al., 2004).

Protein kinase, in ‘Signaling pathway’, had the most significant adj. P value

(6.09E-038) in the FatiGo analysis. Here, 110 transcripts in the SOM3 cluster (Pre)

were altered. A selection of transcripts had a 3-fold FC in both seasons in subtropical

sites B and C; Clavata1 receptor kinase (CLV1; VIT_04s0008g00300), Receptor kinase

homolog LRK10 (VIT_16s0050g02700), Receptor kinase (TRKe;

VIT_11s0052g01460), S-receptor protein kinase (VIT_17s0053g00400) and Wall-

associated kinase 2 (WAK2) (VIT_17s0000g04420). The CLV1 gene encodes a

putative receptor kinase required for the proper balance between cell proliferation and

differentiation in Arabidopsis shoots and flower meristems (Stone et al., 1998). CLV1

has been postulated to either inhibit proliferation of undifferentiated cells at the

meristem or promote the transition of these cells toward differentiation (Stone et al.,

1998).

In ‘Transport overview’ category Oxidase-dep Fe2+

Transport process (Figure 4; SOM3)

was indicated, which included transcripts coding for Laccase (VIT_00s1212g00020,

VIT_18s0001g01280 and VIT_18s0075g00590) and L-ascorbate oxidase

(VIT_07s0031g01010). Laccases and ascorbate oxidase belong to blue oxidases, they

are multi-copper enzymes. They can be classified by their substrate specificity; for

example, ascorbate oxidases oxidize ascorbate, and laccases oxidize aromatic substrates

such as diphenols (Hoegger et al., 2006). McCaig et al., (2005) reported a classification

of plant Laccase-like multicopper oxidase (LMCO) providing evidence that most

LMCOs from A. thaliana may play a role in iron or other metal metabolisms.

6.2.2. Post - chilling

º

19

Seven SOMs were defined in the post-chilling cluster analysis from the comparisons of

the two subtropical sites B and C against the Mediterranean site N in 2012 and 2013

(Figure 5). SOM2 showed the highest numbers of genes (415), while SOM1 and SOM5

had the least, 181 and 191 transcripts on them, respectively. The profiles of SOM2,

SOM3 and SOM7 indicate differential regulation between the two seasons considered.

In addition, SOM4 indicates differential regulation in subtropical site C between 2012

and 2013; relative repression versus induction, respectively, while these genes were

induced in both seasons in subtropical site B. Moreover, SOM1 and SOM6 indicated

differences in magnitude of change between seasons; genes of SOM1 were more

repressed in 2012 than in 2013, those of SOM6 were more induced in subtropical site B

in 2012, relative to 2013, although subtropical site C was reasonably consistent. By

contrast, genes of SOM5 showed consistent profiles between seasons, and relative

consistency between sites.

In SOM1 (post-chilling) one functional category was found, ‘Regulation overview’,

corresponding to C2C2-YABBY family transcription factors. Also, in SOM5 (Post),

genes of the ‘Signaling’ category related to Protein kinases and the Ethylene-mediated

Signaling pathway. Functional analysis in SOM6 (post-chilling) highlighted enrichment

of five functional categories; ‘Diverse functions’, ‘Metabolism (Secondary

metabolism)’, ‘Response to Stimulus’, ‘Signaling’, and ‘Transport Overview’.

The C2C2-YABBY family transcription factor of the ‘Regulation overview’ category

obtained a significant adj. P value (1.44E-03; Figure 5 SOM1). Three transcripts were

found coding for Axial regulator YABBY TFs; the Axial regulator YABBY1 (also

called Abnormal floral organs or Protein FILAMENTOUS FLOWER (FIL))

(VIT_15s0048g00550), YABBY2 (VIT_08s0032g01110) and YABBY5

(VIT_11s0016g05590). These genes were relatively repressed in both subtropical sites,

compared to the Mediterranean site, in both seasons at the post-chilling stage. The

magnitude of repression was greater in 2012, most markedly for subtropical site B.

º

20

Figure 5. Self-organization maps (SOMs) showing different patterns of gene expression during post-

chilling in two consecutive seasons. Letters B, C and N refer to the site of harvest, subtropical sites B and

C, and Mediterranean site N. A. Gene expression patterns are organized into SOMs (labeled as 1 to 7).

Comparisons were performed: B, C, and N against N. The number of Unigenes belonging to each SOM

category is indicated in parenthesis. Green represents down-regulated expression; magenta represents up-

regulated expression and black no changes in the expression, relative to site N The brighter the color, the

larger the difference in gene expression. Functional annotation was assigned to the genes that had

matches to a putative molecular function. Enrichment analysis was carried out using FatiGO.

The YABBY (YAB) family of TFs participates in a diverse range of processes that

include leaf and floral patterning, organ growth, and the control of shoot apical

º

21

meristem organization and activity. Siegfried et al., (1999) described that three members

of YABBY (FIL, YAB2 and YAB3) were expressed in abaxial domains of all above-

ground lateral organ primordia (except the ovules) and act to specify abaxial cell fate in

lateral organs. Also, authors hypothesized that the loss of polar expression results in loss

of polar differentiation of lateral organs. YABBY2 gene appears to have conserved

roles in specifying abaxial cell fate in leaves, floral organs and ovules. It is expressed in

a polar manner in all lateral organs produced by the apical and flower meristems

(Bowman et al., 2000). Additionally, YABBY2 and YABBY5 genes are expressed in

leaves and have been termed 'vegetative YABBYs'.

In SOM5 (post-chilling) two processes altered in the category of ‘Signaling’ were

indicated; Protein kinase (1,15E-04) and Ethylene-mediated Signaling pathway (7,68E-

03). This cluster grouped genes that were relatively induced in both subtropical sites in

both years, by comparison to the Mediterranean site N (Figure 5; SOM5).

Within this cluster, 28 transcripts that coded for several genes of Protein kinases were

found. Among them were, Calcium-dependent protein kinase (VIT_17s0053g00710),

FRK1 (FLG22-induced receptor-like kinase 1, also known as Senescence-induced

receptor-like serine/threonine-protein kinase) (VIT_07s0005g06500), Protein kinase

family (VIT_07s0129g00880), Receptor serine/threonine kinase (VIT_16s0148g00300),

S-locus lectin protein kinase (VIT_00s0420g00010), Wall-associated kinase 3 (WAK3)

(VIT_10s0003g05160) and Wall-associated receptor kinase 5 (VIT_10s0003g05130).

Calcium-dependent protein kinases (CDPKs) have been implicated in perceiving

intracellular changes in Ca2+

concentration and translating them into specific

phosphorylation events to initiate further downstream signaling processes. They have

been mainly characterized in rapid abiotic stress and immune signaling response and

less extended in long-term adaptive processes or plant development (Schulz et al.,

2013). CDPKs are positive regulator of abiotic stress responses and their induction in

plants can enhances stress tolerance, they have been reported in abiotic stress stimuli in

the context of salinity, drought, and cold; in particular, isoforms were implicated in

ABA-mediated signaling (Schulz et al., 2013). Likewise, CDPKs participate in the

translation of pathogen signal-induced changes in the Ca2+

concentration into plant

defense reactions. Among the reactions reported are ROS synthesis, changes in

phytohormone synthesis and signaling, and cell death (Schulz et al., 2013). However,

º

22

their activation by pathogen stress and subsequent deactivation in the early stage of

infection, suggests a role for CDPKs in the onset of plant immune reactions death

(Schulz et al., 2013).

In Glycine max was demonstrated that Senescence-Associated Receptor-Like Kinase

(GmSARK) plays an important role in the regulation of soybean leaf senescence (Li et

al., 2006). It was also suggested that two SARK genes regulate leaf senescence through

synergistic actions of auxin and ethylene and that the SARK-mediated pathway may be

a widespread mechanism in regulating leaf senescence (Xu et al., 2011). Moreover,

evidence is emerging that WAKs serve as pectin receptors during pathogen exposure or

wounding and cell expansion during plant development (Kohorn et al., 2012). Wak3

and Wak5 have been reported to be expressed primarily in leaves and stems of

Arabidopsis; also they both can be greatly induced by salicylic acid or INA (2,2-

dichloroisonicotinic acid), the SA analog ( He et al., 1999).

In the Ethylene-mediated Signaling pathway, several transcripts within SOM5 were

identified (Figure 5), coding for 1-aminocyclopropane-1-carboxylate oxidase (ACO)

(VIT_00s2086g00010 and VIT_03s0091g01080), Ethylene-responsive transcription

factor ERF105 (VIT_16s0013g00900 and VIT_16s0013g01070), Ethylene-responsive

transcription factor related to APETALA2 11 (VIT_08s0007g07250), among others.

Ethylene is a hormone involved in numerous aspects of growth, development, and

responses to biotic and abiotic stresses in plants. ACC oxidase (ACO) is involved in the

final step of ethylene production in plant tissues (Ruduś et al., 2013). Also, Chao et al.,

(2013) reported an increase in ACO levels and activities during paradormancy release of

leafy spurge (Euphorbia esula) buds. On the other hand, ERF105 and Ethylene-

responsive transcription factor related to APETALA2 11 belong to the AP2/ERF

family. They have been reported likely to regulate the developmental, physiological and

biochemical responses of plants to a variety of environmental stress conditions,

including those occurring in combination with other abiotic and biotic stresses (Mizoia

et al., 2012; Mishra et al., 2015).

6.3. Finding potential biomarkers

The next step was looking for potential candidate genes that could be used as

biomarkers, to identify the gene profiles in each stage and thus to help to make better

º

23

decisions related with the crop management. For this, it was selected the genes that

presented the same profile (up- and down-regulated) during each stage of dormancy.

The Venn diagram that is represented in the Figure 6, summarize the up-regulated and

down-regulated genes during the two consecutive years in pre and post chilling, they

were product of the comparison between Subtropical climate, places B and C, against

Mediterranean climate, place N. From the 2238 transcripts that were differentially

expressed in pre-chilling just 37 presented the same profiles, 32 transcripts up-regulated

and 5 down-regulated. Whereas in post-chilling just 60 transcripts from the 1889 that

were differentially expressed, 50 up-regulated and 10 down-regulated.

Figure 6. Venn diagrams and functional analysis of differentially expressed genes (1% FDR in limma

and ≥3-fold change). A. and B. Venn diagram of transcripts DEGs during pre-chilling (right) and post-

chilling (left), respectively, in 2012 and 2013 seasons. Green represents down-regulated genes and

magenta represents up-regulated genes. Comparisons were performed: B, C, and N against N. Summary

of functional categories significantly enriched (5% FDR in a Fisher’s exact test) within up- and

downregulated transcripts in each developmental stage.

º

24

Following a functional enrichment analysis was performed to each core set of genes

with the same profile found in pre- and post-chilling (FatiGO, P=0.05). Pre-chilling was

enriched in the ‘Signaling’ category in the Protein kinases process, 10 transcripts coding

to Clavata1 receptor kinase (CLV1; VIT_04s0008g00300), Receptor kinase homolog

LRK10 (VIT_16s0050g02700), Receptor kinase (TRKe; VIT_11s0052g01460), S-

receptor protein kinase (VIT_17s0053g00400) and Wall-associated kinase 2 (WAK2)

(VIT_17s0000g04420) were found among others. CLV1, LRK10 and WAK2 are

members of Plant receptor-like kinases (RLK) family and have been related with plant-

microbe interaction and stress responses (Shui and Bleecker, 2001).

In post-chilling, 20 transcripts were detected coding for genes that belong to ‘Signaling’

category (Figure 6). Among them are Calcium-dependent protein kinase

(VIT_17s0053g00710), CLL1B clavata1-like receptor S/T protein kinase

(VIT_16s0013g01990), FRK1 (FLG22-induced receptor-like kinase 1)

(VIT_07s0005g06500), S-locus lectin protein kinase (VIT_00s0420g00010), Wall-

associated kinase 3 (WAK3; VIT_10s0003g05160), Wall-associated receptor kinase 5

(VIT_10s0003g05130), 1-aminocyclopropane-1-carboxylate oxidase (ACO;

VIT_03s0091g01080), Ethylene-responsive transcription factor (ERF105;

VIT_16s0013g00900), Ethylene-responsive transcription factor related to APETALA2

11 (VIT_08s0007g07250). Here, genes belong to Protein Kinases process along with

Ethylene-mediated Signaling pathway were also identified.

The genes, that presented the same profile during two consecutive years in the

comparison Subtropical vs Mediterranean climates, could be used as potential

biomarker for anticipate actions.

Finally, in the Figure 7 are shown the 97 genes that presented the same profile during

pre- and post-chilling (37 and 60, respectively). Within this core set of genes, 3 genes

were differentially expressed in both, pre- and post-chilling. Two genes were up-

regulated, Ovate family protein 12 OFP12 (VIT_05s0020g03520) and EREBP-4

(VIT_09s0002g08960); while one was down-regulated, it was Gibberellin –regulated

protein (GASA1; VIT_18s0001g14270).

The OVATE gene was first identified as an important regulator of fruit shape in tomato.

Ovate Protein Family genes (OFPs) were also characterized in Arabidopsis (AtOFPs)

º

25

and demonstrated to regulate plant growth and development (Hackbusch et al., 2005; Li

et al., 2011; Wang et al., 2011). Over-expression of OVATE reduces the size of floral

organs and leaflets; therefore, OVATE is considered to be a negative regulator of plant

growth (Liu et al., 2002). Therefore, this data suggests that analyzing the gene

expression profile of the transcript that encoded OFP12 (VIT_05s0020g03520) is a

good candidate to evaluate the stage of buds during pre- and post-chilling.

Figure 7. Expression profile of genes with 1% FDR and FC => |3-fold| during pre-chilling and post-

chilling in the comparisons B vs N and C vs N in 2012 and 2013 from Figure 6. Fold change (FC) is

display as green to down-regulated expression; magenta to up-regulated expression and gray to not

expression. Different colors represent the functional categories.

º

26

CONCLUSIONS

In the present work, differential expression analysis and functional enrichment of

RNAseq data identified numerous transcripts associated with necrosis within the buds

from subtropical sites, which was not visibly detectable during sampling.

Notwithstanding this dominating feature, this stringent statistical comparison identified

three genes, which were differentially expressed in all subtropical conditions relative to

the Mediterranean site, that relate to meristem function and fate, and may thus be useful

markers of the disorderly regulation of vine physiology in subtropical climates.

Further dissecting the pre-chilling and post-chilling conditions of both years considered,

markers unique to these developmental states in the subtropical sites were also

identified. In the pre-chilling condition, genes relating to phytoalexin, phenylpropanoid

and lignin synthesis were prominent, as well as more general reponses to abiotic

(oxidative stress response) and biotic stresses (plant-pathogen interaction). Whether

these signatures were indicative of the climate differences cannot be concluded from

this study, due to the necrosis identified.

Post-chilling data also indicated genes related with biotic and abiotic stresses. However,

genes encoding the YABBY family of transcription factors are highly likely to relate to

bud development, due to their known role in specifying abaxial cell fate in other plant

species. YABBY transcrips were down-regulated in the subtropical sites relative to the

Mediterranean site in both years considered, suggesting that while bud meristems in

Mediterranean climate are beginning to develop and activate cell proliferation, buds in

the subtropical sites are not coordinated regulated.

From the 97 transcripts that presented the same profile during pre- and post-chilling, 37

and 60 respectively, 3 genes were found in both stages. In particular, a gene encoding an

Ovate family protein 12 OFP12 (VIT_05s0020g03520), which was upregulated 14-fold

is prominent, as it has been previously been reported to be a negative regulator of plant

growth. Therefore, this data strongly suggests OFP12 is a potential biomarker to

anticipate measures in grapevine management in subtropical climate.

º

27

BIBLIOGRAPHY

1. Ali, K, Maltese, F, Choi, YH. and Verpoorte, R. (2010) Metabolic constituents

of grapevine and grape-derived products. Phytochem. Rev. 9, 357e378.

2. Al-Shahrour F, Diaz-Uriarte R. and Dopazo J. (2004) FatiGO: a web tool for

finding significant associations of Gene Ontology terms with groups of

genes. Bioinformatics. 20:578–580.

3. Barakat A,, Szick-Miranda K, Chang IF, Guyot R, Blanc G, Cooke R,Delseny

M. and Bailey-Serres J. (2001) The Organization of Cytoplasmic Ribosomal

Protein Genes in the Arabidopsis Genome. Plant Physiology. 127(2):398-415.

4. Blokhina O. and Fagerstedt KV. (2010) Oxidative metabolism, ROS and NO

under oxygen deprivation. Plant Physiol Biochem. 48(5):359-73.

5. Bowman JL. (2000) Axial patterning in leaves and other lateral organs. Curr

Opin Genet Dev. 10(4):399-404.

6. Chao WS, Serpe M, Suttle JC. and Jia Y. (2013) Increase in ACC oxidase

levels and activities during paradormancy release of leafy spurge

(Euphorbia esula) buds. Planta. 238(1):205-15.

7. Chen L, Song Y, Li S, Zhang L, Zou C. and Yu D. (2012) The role of WRKY

transcription factors in plant abiotic stresses. Biochim Biophys Acta.

1819(2):120-8.

8. Faigón-Soverna A, Harmon FG, Storani L, Karayekov E, Staneloni RJ;

Gassmann W- Más P, Casal JJ, Kay SA. and Yanovskya MJ. (2006) A

Constitutive Shade-Avoidance Mutant Implicates TIR-NBS-LRR Proteins

in Arabidopsis Photomorphogenic Development. Plant Cell. 18(11):2919–

2928.

9. Fennell AY, Schlauch KA, Gouthu S, Deluc LG, Khadka V, Sreekantan L,

Grimplet J, Cramer GR. and Mathiason KL. (2015) Short day transcriptomic

º

28

programming during induction of dormancy in grapevine. Front Plant Sci.

4(6):834.

10. Grimplet J, Van Hemert J, Carbonell-Bejerano P, Díaz-Riquelme J, Dickerson J,

Fennell A, Pezzotti M. and Martínez-Zapater JM. (2012). Comparative

analysis of grapevine whole-genome gene predictions, functional

annotation, categorization and integration of the predicted gene sequences.

BMC Research Notes. 5, 213.

11. Hackbusch J, Richter K, Müller J, Salamini F. and Uhrig JF. (2005) A central

role of Arabidopsis thaliana ovate family proteins in networking and

subcellular localization of 3-aa loop extension homeodomain proteins. Proc.

Natl. Acad. Sci. U.S.A. 102: 4908–4912

12. He ZH, Cheeseman I, He D. and Kohorn BD. (1999) A cluster of five cell wall-

associated receptor kinase genes, Wak1-5, are expressed in specific organs

of Arabidopsis. Plant Mol. Biol. 39(6):1189-96.

13. Hoegger PJ, Kilaru S, James TY, Thacker JR. and Kües U. (2006) Phylogenetic

comparison and classification of laccase and related multicopper oxidase

protein sequences. FEBS J. 273(10):2308-26.

14. Höll J, Vannozzi A, Czemmel S, D’Onofrio C, Walker AR, Rausch T, Lucchin

M, Boss PK, Dry IB. and Bogs J. (2013) The R2R3-MYB Transcription

Factors MYB14 and MYB15 Regulate Stilbene Biosynthesis in Vitis

vinifera. The Plant Cell. 25:4135–4149.

15. Jaillon O, Aury JM, Noel B, Policriti A, Clepet C, Casagrande A, Choisne N,

Aubourg S, Vitulo N, Jubin C, Vezzi A, Legeai F, Hugueney P, Dasilva C,

Horner D, Mica E, Jublot D, Poulain J, Bruyère C, Billault A, Segurens B,

Gouyvenoux M, Ugarte E, Cattonaro F, Anthouard V, Vico V, Del Fabbro C,

Alaux M, Di Gaspero G, Dumas V, Felice N, Paillard S, Juman I, Moroldo M,

Scalabrin S, Canaguier A, Le Clainche I, Malacrida G, Durand E, Pesole G,

Laucou V, Chatelet P, Merdinoglu D, Delledonne M, Pezzotti M, Lecharny A,

Scarpelli C, Artiguenave F, Pè ME, Valle G, Morgante M, Caboche M, Adam-

Blondon AF, Weissenbach J, Quétier F, Wincker P. and French-Italian Public

º

29

Consortium for Grapevine Genome Characterization. (2007) The grapevine

genome sequence suggests ancestral hexaploidization in major angiosperm

phyla. Nature. 449(7161):463-7.

16. Kasprzewska A. (2003) Plant chitinases-regulation and function. Cell Mol

Biol Lett. 8(3):809-24.

17. Kohorn BD. and Kohorn SL. (2012) The cell wall-associated kinases, WAKs,

as pectin receptors. Front Plant Sci. 8;3:88.

18. Lavee S. and May P. (1997) Dormancy of grapevine buds - facts and

speculation. Aust J Grape Wine Res. 3(1):31–46.

19. Lebon G, Duchêne E, Brun O. and Clément C. (2005) Phenology of flowering

and starch accumulation in grape (Vitis vinifera L.) cuttings and vines. Ann

Bot. 95(6):943-8.

20. Li XP, Gan R, Li PL, Ma YY, Zhang LW, Zhang R, Wang Y. and Wang NN.

(2006) Identification and functional characterization of a leucine-rich

repeat receptor-like kinase gene that is involved in regulation of soybean

leaf senescence. Plant Mol. Biol. 61: 829–844

21. Liu J, Van Eck J, Cong B, and Tanksley SD. (2002) A new class of regulatory

genes underlying the cause of pear-shaped tomato fruit. Proc. Natl. Acad.

Sci. U.S.A. 99(20):13302-6.

22. Mateo A, Mühlenbock P, Rustérucci C, Chang CC, Miszalski Z, Karpinska B,

Parker JE, Mullineaux PM. and Karpinski S. (2004) Lesion simulating disease1

is required for acclimation to conditions that promote excess excitation

energy. Plant Physiol. 136(1):2818-30.

23. Mathiason K, He D, Grimplet J, Venkateswari J, Galbraith DW, Or E. and

Fennell A. (2009) A. Transcript profiling in Vitis riparia during chilling

requirement fulfillment reveals coordination of gene expression patterns

with optimized bud break. Funct Integr Genomics. 9(1):81-96.

º

30

24. McCaig BC, Meagher RB. and Dean JFD. (2005) Gene structure and

molecular analysis of the laccase-like multicopper oxidase (LMCO) gene

family in Arabidopsis thaliana. Planta. 221(5):619-36

25. Mishra S, Phukan UJ, Tripathi V, Singh DK, Luqman S. and Shukla RK. (2015)

PsAP2 an AP2/ERF family transcription factor from Papaver somniferum

enhances abiotic and biotic stress tolerance in transgenic tobacco. Plant

Mol. Biol. 89(1-2):173-86.

26. Mizoia J, Shinozakib K. and Yamaguchi-Shinozaki K. (2012) AP2/ERF family

transcription factors in plant abiotic stress responses. Biochim. Biophys.

Acta. 1819(2):86-96.

27. Morel JB. and Dangl JL. (1997) The hypersensitive response and the

induction of cell death in plants. Cell Death Differ. 4(8):671-83.

28. N. Bray, H. Pimentel, P. Melsted and L. Pachter, Near-optimal RNA-Seq

quantification with kallisto to the arXiv. http://pachterlab.github.io/kallisto/

29. Ochsenbein C, Przybyla D, Danon A, Landgraf F, Göbel C, Imboden A,

Feussner I. and Apel K. (2006) The role of EDS1 (enhanced disease

susceptibility) during singlet oxygen-mediated stress responses of

Arabidopsis. Plant J. 47(3):445-56.

30. Possingham, JV. (2004) On The Growing Of Grapevines In The Tropics

ISHS Acta Horticulturae 662: VII International Symposium on Temperate Zone

Fruits in the Tropics and Subtropics. Acta Hortic. 662: 39-44.

31. Przymusiński R, Rucińska R. and Gwóźdź EA. (2004) Increased accumulation

of pathogenesis-related proteins in response of lupine roots to various

abiotic stresses. Environ. Exp. Bot. 52(1):53–61.

32. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W. and Smyth GK. (2015)

limma powers differential expression analyses for RNA-sequencing and

microarray studies. Nucleic Acids Research, 43(7):e47.

º

31

33. Robinson MD, McCarthy DJ. and Smyth GK. (2010) edgeR: a Bioconductor

package for differential expression analysis of digital gene expression data.

Bioinformatics. 26(1):139-40

34. Ruduś I, Sasiak M. and Kępczyński J. (2013) Regulation of ethylene

biosynthesis at the level of 1-aminocyclopropane-1-carboxylate oxidase

(ACO) gene. Acta Physiol. Plant. 35:295–307.

35. Rushton PJ, Somssich IE, Ringler P. and Shen QJ. (2010) WRKY transcription

factors. Trends Plant Sci. 15(5):247–258.

36. Schuler MA. and Werck-Reichhart D. (2003) Functional Genomics Of P450s.

Annu. Rev. Plant Biol. 54:629–67.

37. Schulz P, Herde M. and Romeis T. (2013) Calcium-dependent protein

kinases: hubs in plant stress signaling and development. Plant Physiol.

163(2):523-30.

38. Shiu SH and Bleecker AB. (2001) Plant receptor-like kinase gene family:

diversity, function, and signaling. Sci STKE. 2001(113):re22.

39. Siegfried KR, Eshed Y, Baum SF, Otsuga D, Drews GN. and Bowman JL.

(1999) Members of the YABBY gene family specify abaxial cell fate in

Arabidopsis. Development. 126(18):4117-28.

40. Stone JM, Trotochaud AE, Walker JC. and Clark SE. (1998) Control of

Meristem Development by CLAVATA1 Receptor Kinase and Kinase-

Associated Protein Phosphatase Interactions. Plant Physiol. 117(4):1217–

1225.

41. Wang J, Feng J, Jia W, Chang S, Li S. and Li Y. (2015) Lignin engineering

through laccase modification: a promising field for energy plant

improvement. Biotechnol Biofuels. 8:145.

42. Wang S, Chang Y, Guo J, Zeng Q, Ellis BE. and Chen JG. (2011) Arabidopsis

ovate family proteins, a novel transcriptional repressor family, control

multiple aspects of plant growth and development. PLoS One. 6(8):e23896.

º

32

43. Wang W, Tang K, Yang HR, Wen PF, Zhang P, Wang HL. and Huang WD.

(2010) Distribution of resveratrol and stilbene synthase in young grape

plants (Vitis vinifera L. cv. Cabernet Sauvignon) and the effect of UV-C on its

accumulation. Plant Physiol Biochem. 48(2-3):142-52.

44. Xu F, Meng T, Li P, Yu Y, Cui Y, Wang Y, Gong Q. and Wang NN. (2011) A

soybean dual-specificity kinase, GmSARK, and its Arabidopsis homolog,

AtSARK, regulate leaf senescence through synergistic actions of auxin and

ethylene. Plant Physiol. 157(4):2131-53.

45. Zurbriggen MD, Carrillo N. and Hajirezaei MR. (2010) ROS signaling in the

hypersensitive response When, where and what for? Plant Signal Behav.

5(4): 393–396.

º

33

9. ANEXOS

9.1. FastQC command line

for i in {1..n}

do

fastqc -t 4 <path to fastq file>/"$i"*.fastq.gz --outdir=<path to output

folder>/fastqc_reports

done

9.2. Trimmomatic command line

for i in {1..n}

do

java -jar <path of the program>/Trimmomatic-0.33/trimmomatic-0.33.jar SE -threads 4

-phred33 -trimlog <path of a folder to save the files>/trimmed_reads/trimlog_"$i" /dd_

<path to fastq file>/"$/Sample_"$i"/"$i"*.fastq.gz <path to save fastq

file>/"$/Sample_"$i"_trimmed.fastq.gz

ILLUMINACLIP:/usr/local/packages/Trimmomatic-0.33/adapters/TruSeq3-

SE.fa:2:30:10 LEADING:20 TRAILING:20 SLIDINGWINDOW:4:20 MINLEN:25

done

9.3. Kallisto on hiseq command line

# 1st make index file

/usr/local/packages/kallisto/build/bin/kallisto index -i Vitis.idx

/dd_groupdata/Vitis.fa

# run quant

for i in {1..n}

do

<path of the program>/kallisto quant -i <path of the index>/Vitis.idx -o =<path

to output folder>/kallisto_out/sample_"$i" --single -l 270 <path to fastq

file>/Sample_"$i"_trimmed.fastq.gz

done