Review articles

Integration of omics and system biology approaches to study grapevine (Vitis vinifera L.) response to salt stress: a perspective for functional genomics - A review


The ability of plants to modify their behavior appropriately in response to salt stress is a major factor in their adaptation to this specific constraint. To date, environmental constraints, including salinity, become more and more unfavorable especially for glycophytes such as grapevines. Salt tolerance is a complex physiological and multigenic trait. Studying the functional networks of transcriptome, proteome and metabolome of grapevine plants subjected to salinity may help to identify candidate genes associated with salt tolerance mechanisms. Thus, the integration of omics tools (i.e., genomics, proteomics and metabolomics) with physiological approaches allows better understanding of the grapevine plant response and developing efficient markerassisted selection strategies in order to generate salt stress resistant grapevine varieties. In this review, research progress in grapevine responses to salt stress is discussed, highlighting the importance of the system biology approach for identifying molecular regulatory networks leading to a better adaptation ability of grapevine to salt stress.


Plants are permanently subjected to various types of stresses: osmotic, ionic, water and salt (Munns et al., 2006; Chadli and Belkhodja, 2007). Salinity affects about 10% of the land in the world (Cheong and Yun, 2007). The salinization registered in the arid and semi-arid ecosystems results from high soil water evaporation (Munns et al., 2006), irregular and insufficient rainfall (Mezni et al., 2002), as well as the use of poor quality water. Consequently, crop production with an appreciable yield becomes a challenge under these conditions. Therefore, a global understanding of plant mechanisms involved in salt stress adaptation is required. Plant response to salt stress occurs at various levels: molecular, cellular and physiological (Yamaguchi-Shinozaki et al., 2002). Tolerance to abiotic stresses is a complex feature influenced by the coordinated and differential expression of a group of genes (Chen et al., 2002). In general, several modifications are expected to be activated as a response to abiotic stresses (Jain et al., 2001). Recently, progress has been made in the functional genomics of grapevine following the whole genome sequencing and assembling of Vitis vinifera PN40024 reference genome (Jaillon et al., 2007). Global analyses have become possible with the development of high throughput genomic technologies which facilitated the identification of putative gene function. In parallel, methods have been developed for quantitative data acquisition: microarrays are used to quantitatively assess the transcriptome (Schena et al., 1995). However, the recent advent of high throughput-based sequencing technologies has revolutionized the analysis of transcriptomes (Morozova and Marra, 2008). In fact, RNA sequencing (RNA-Seq) involves direct sequencing of complementary DNAs (cDNAs) followed by mapping of the sequencing reads to the reference genome. It allows for the precise quantification of exon expression, generating absolute rather than relative gene expression measurements, providing greater insight and accuracy than microarrays (Cloonan et al., 2008; Mortazavi et al., 2008; Wang et al., 2009). Furthermore, it can detect and measure rare transcripts with frequencies as low as 1 to 10 RNA molecules per cell (Mortazavi et al., 2008). In this context, Next-Gen sequencing technologies have emerged, such as 454 (Margulies et al., 2005) or Illumina (Bennett, 2004) technologies. For example, the Illumina RNA-Seq method was successfully used by Zenoni et al. (2010) to analyze the global grapevine transcriptome during berry development. In proteomics, two-dimensional gels have routinely been used for proteome studies (O’Farrell, 1975). Recently, gel-free technologies have emerged, such as ICAT (Gygi et al., 1999) or iTRAQ (Ross et al., 2004). Metabolome studies are performed with a variety of tools such as gas chromatography or high performance liquid chromatography for the separation of the metabolites and mass spectrometry and nuclear magnetic resonance for the identification and quantification of the metabolites (Fiehn, 2002). This progress opened up a new investigation field, omics, from which many transcriptomic (Tattersall et al., 2007; Daldoul et al., 2010), proteomic (Vincent et al., 2007; Jellouli et al., 2008; Grimplet et al., 2009a; Cramer, 2010; Cramer et al., 2013), interactomic (Čarná et al., 2012), metabolomic (Cramer et al., 2007; Deluc et al., 2009; Hochberg et al., 2013) and candidate gene approaches (Hanana et al., 2007; Hanana et al., 2008) were developed. The present review underlines the integration of the different omics tools with physiological and eco-physiological approaches and their subsequent incorporation into functional networks in order to better understand the mechanisms involved in grapevine salt tolerance.

Physiological responses of grapevine to salt stress

Grapevine (Vitis vinifera L.) is a glycophyte classified as being moderately tolerant to salt (Maas, 1990). It is worth noting that salt-tolerant glycophytes are able to simultaneously limit Na+ and Cl- ion accumulation into the leaves and their efficient compartmentation. Thus, grapevine can avoid the toxic effects of Na+ and Cl- ions. Some experiments conducted by Fisarakis et al. (2001) on ungrafted grapevines showed that salt tolerance traits are significantly correlated with the capacity to exclude Cl- ions from leaves and maintain a high level of growth, photosynthetic activity and stomatal conductance. Besides, Walker et al. (1981) demonstrated that the salt tolerance of the Sultana grapevine variety was related to its capacity to maintain cell turgor, which is associated with the decrease in leaf osmotic potential at high NaCl concentrations (>100mM). Troncoso et al. (1999) demonstrated by in vitro studies conducted on grapevine rootstocks that their salt tolerance depends on their ability to maintain high levels of K+ ions in their tissue. Salt tolerance in grapevine is related to an efficient sequestration of the toxic ions at the root level and, more precisely, to the restriction of their transport towards the aerial parts through the xylem (Storey et al., 2003).

Short term studies have proven the toxic effect of Na+ and Cl- ions in grapevine grown under salt stress (Garcia and Charbaji, 1993; Shani et al., 1993). Paradoxically, a Tunisian wild grapevine specimen called Vitis vinifera subsp. sylvestris var. 'Séjnène' adopted the sodium inclusion strategy at the leaf level concomitantly with Cl- ions exclusion (Hamrouni et al., 2011). Moreover, the wild Vitis sylvestris Khédhayria accession showed a good regulation of sodium transport and restriction in its leaves (Askri et al., 2012). In addition, Shani and Ben-Gal (2005) reported that grapevines responded to salt constraint by a reduction in transpiration rate (Shani and Dudley, 2001) and vegetative growth due to a reduction in osmotic potential, which is considered as early response to salinity. On the other hand, foliar senescence was reported to be associated with an accumulation of Na+ and Cl- ions in the leaves and dependent on the duration of salt stress exposure (Shani and Ben-Gal, 2005). Other examples of specific and non-specific effects on grapevine were assessed by Walker et al. (1981), who reported that stomatal closure and photosynthesis are strongly affected by salinity. In Cabernet Sauvignon cultivar, Garcia and Charbaji (1993) observed specific changes in the Na-K balance and their antagonism under salt stress. Regarding osmolytes, salt tolerance in grapevine is related to an important reduction in sucrose and starch content along with increased levels of reducing sugars (Ashraf and Harris, 2004).

Genomic approach and characterization of salt stress in grapevine

Grapevine is the fourth plant, after Arabidopsis, rice and poplar, to have its genome decrypted. A highly homozygous line (97%), PN40024, was obtained at the INRA center of Colmar (France) by cross-breeding of the Pinot Noir cultivar used for sequencing. Currently, a high coverage (12X), i.e., 12 genome equivalent, is available (; The grapevine genome is small (475 MB), diploid and composed of about 30,000 genes. Grapevine genome sequencing represents a major progress which opens up new perspectives in terms of varietal improvement and gene-function knowledge. Genome assembling and annotation allowed determining the exact physical position of all genes on the chromosomes. It also allowed to access promoter regions. Recently, grapevine genome sequencing allowed the development of high density DNA filters. These DNA microarrays (Qiagen/Operon, Combimatrix, Affymetrix, NimbleGen) allowed (i) for the simultaneous study of the expression of thousands of genes, leading to the identification of gene networks differentially regulated during grape berry development (Goes da Silva et al., 2005; Terrier et al., 2005; Waters et al., 2005; Grimplet et al., 2007; Cramer, 2010; Guillaumie et al., 2011) and (ii) investigating the molecular mechanisms of salt stress (Daldoul et al., 2010). The sequencing of the grapevine genome raised numerous questions about the potential function of all the identified genes. In this respect, the integrative study of metabolite, protein and transcript profiles is recommended and could provide reliable models for prediction of the function of genes with regard to salt stress tolerance. In addition, these approaches aim also to develop efficient strategies for the selection of varieties that are more resistant to abiotic stress.

Differential expression of genes under salinity

Several studies tackled the identification of genes expressed differentially in the context of abiotic stresses. Many candidate genes, which are susceptible to be associated with tolerance to salt in different species, were then identified. These studies are summarized in Table 1.

Table 1. Examples of differentially expressed genes in response to abiotic stress.

Plant species Gene name Abiotic stress Technique used References
Capsicum annuum Ca LEAL1 Salt Stress, Water deficit, ABA mRNA Differential display (Park et al., 2003)
Camellia sinensis PR-5 Water deficit mRNA Differential display (Sharma and Kumar, 2005)
Pinus pinaster Ait. PR-10 Water deficit cDNA-AFLP (Dubos and Plomion, 2001)
Triticum durum AtGSK1 Salt stress cDNA-AFLP (Chen et al., 2003)
Zea maize DRE1/Rab17 Water deficit SSH (Zheng et al., 2004)
Cicer arietinum MIP Water deficit SSH (Boominathan et al., 2004)
Tetragonia tetragonioides Alpha galactosidase Water deficit, Salt stress mRNA Differential display (Hara et al., 2008)
Tobacco ASR1 Salt stress SSH (Kalifa et al., 2004)
Vitis vinifera RD22 Salt stress Candidate gene approach (Hanana et al., 2008; Jardak-Jamoussi et al., 2014)

ABA=Abscisic Acid; AFLP=Amplified Fragment Length Polymorphism; SSH=Suppressive Subtractive Hybridization.

The grapevine response to salt stress was also studied. In fact, Cramer et al. (2007) reported that the majority of genes whose expression is induced by salt stress encode for transcription and protein synthesis factors. Moreover, Tattersall et al. (2007) were able to isolate genes that commonly respond to salt, water and cold stress. Daldoul et al. (2010) elaborated and screened suppressive subtractive hybridization (SSH) cDNA libraries (Fig. 1). These libraries were obtained from leaves of Razegui, a Tunisian salt-tolerant variety, harvested after a short duration of salt stress (6 and 24 hours). After differential screening of the SSH libraries, various cDNA clones were sequenced. Most of the isolated expressed sequence tags (ESTs) corresponded to homologous genes previously described in other plant species as being solicited under salt stress (Daldoul et al., 2012a). Moreover, the study was complemented by microarray hybridizations for a more extensive analysis of the SSH libraries and seven genes were identified as over-expressed (˃1.9 fold) in this salt-tolerant variety (Table 2).

Table 2. Microarray analysis of salt responsive genes in grapevine leaves after 6 h and 24 h of salt stress in the contrasting cultivars Razegui (salt tolerant) and Syrah (salt sensitive).

    Razegui - [salt stress / control] Syrah - [salt stress / control]
  6 h 24 h 6 h 24 h
Clone ID GenBank accession no. Cribi Grape Genome
annotation identifier
putative identity
  Ratio     P-value   Ratio     P-value   Ratio     P-value   Ratio     P-value
SS-24H2-D1 GO238735 VIT_08s0007g08310 Alkaline alpha-galactosidase-Seed
imbibition protein
3.46 5.84E-06 2.92 6.78E-03 2.17 5.84E-06 3.14 0.001185
SS-24H3-F7.1* GH717871 VIT_11s0016g05770 Alkaline α-galactosidase2 2.8 1.75E-04 2.17 2.10E-03 - - 1.95 2.37E-02
SS-24H3-F7.2* GH717872 VIT_01s0150g00190 Transaldolase-like protein 2.8 1.75E-04 2.17 2.10E-03 - - 1.95 2.37E-02
SS-24H5-D12 GH717875 VIT_12s0028g03270 Ethylene-responsive
transcription factor 4
2.32 3.53E-05 2.01 0.001925 - - - -
SS-24H5-G6 GH717873 VIT_04s0023g01430 Zinc finger (C2H2 type) family protein 2.87 1.60E-04 2.58 1.45E-02 - - 2.80 5.78E-03
SS-6H1-D4 GH717874 VIT_03s0063g02110 Ubiquitin conjugating enzyme 2.45 1.74E-05 2.41 1.25E-02 - - 3.16 6.55E-03
SS-6H3-F3 GH717876 VIT_04s0023g00580 Unknown protein 2.98 4.43E-04 3.04 4.33E-03 - - - -

Genes are selected based on a statistical analysis of their differential expression, as described in Daldoul et al. (2010). These seven genes are also specifically selected because they are differentially expressed (threshold >1.9 and P-value <0.05) in at least two of the performed comparisons (6 h Raz stress vs. 6 h Raz control, 24 h Raz stress vs. 24 h Raz control, 6 h Sy stress vs. 6 h Sy control, 24 h Sy stress vs. 24 h Sy control). The grapevine genome accession number and the putative protein identity associated with these sequences are given. They are identified by using blast algorithms to compare the selected genes against the CRIBI Grape genome server and the NCBI non-redundant protein database, respectively. The following eight columns report the microarray experiment results (i.e., 6 h Raz stress vs. 6 h Raz control, 24 h Raz stress vs. 24 h Raz control, 6 h Sy stress vs. 6 h Sy control, 24 h Sy stress vs. 24 h Sy control) with relative standard deviations and P-values. Ratio values shown are expression ratios. A ratio of 2.0 indicates a two-fold up-regulation compared to control. Changes smaller than 1.90-fold are marked as (-) for non significant changes. *cDNA clones were double band; Raz=Razegui; Sy=Syrah.

The functional annotation of these genes was based on sequence similarities with other heterologous genes listed and annotated in GenBank. Among the seven genes, the presence of a Zn-finger transcription factor (TF) (accession no. GH717873) was denoted by Daldoul et al. (2010). In this context, many studies demonstrated that the expression of Zn-finger TF is regulated differentially by various environmental stresses. Besides, these TFs allowed regulating the expression of several genes associated with the response to abiotic stresses (Kasuga et al., 1999; Kim et al., 2001). In Cabernet Sauvignon, the expression of a gene coding for the transcription factor "ethylene-responsive transcription factor 4" also proved to be induced by salt and water deficit (Cramer et al., 2005). The early induction of this transcript (accession no. GH717875) was also observed in the tolerant variety Razegui (Daldoul et al., 2010). The transaldolase protein (accession no. GH717872) that was isolated from the SSH libraries was also identified through screening the normalized library constructed from rice seedlings subjected to water deficit (Reddy et al., 2002). However, the role of this protein remains unknown. Recently, the functional characterization of an alkaline α-galactosidase of rice (Lee et al., 2009, accession no. Q8W2G5) revealed that this enzyme is involved in the hydrolysis of the glycolipid digalactosyldiacylglycerol (DGDG), hence releasing monogalactosyl diglyceride (MGDG) molecules (Lee et al., 2004; Lee et al., 2009). DGDG was largely described in the context of abiotic stress response and more specifically as a response to water deficit in various species such as Arabidopsis thaliana (Gigon et al., 2004) and Vitis vinifera (Toumi et al., 2008). These lipid molecules can also be a second messenger involved in the signal transduction pathway as a response to salt stress (Munnik et al., 1998; Lee et al., 2009). Studies conducted by Daldoul et al. (2010) demonstrated that the closest homologue of Vv-α-gal/SIP (accession no. GO238735) was the α-galactosidase alkaline from melon. The alkaline activity of this enzyme was extensively characterized by Carmi et al. (2003). Based on this sequence homology, Vv-α-gal/SIP may have the same function and the expression of the gene coding for this enzyme is speculated to be involved in abiotic stress tolerance mechanisms (Daldoul et al., 2012b). Bioinformatic analysis of the Vv-α-gal/SIP gene showed several regulatory elements involved in abiotic stress signaling (Daldoul et al., 2012c). The differential expression of this gene was also observed in Vitis sylvestris grapevines and preferentially in the salt-tolerant accession Khédhayria (Askri et al., 2012). In this way, the Vv-α-gal/SIP gene could be used as a selection marker for tolerance to salt stress in grapevine. The screening results of the salt-stressed SSH libraries also identified the cDNA clone encoding for MAP kinase (accession no. GH717878.1). Interestingly, VvMAP kinase transcript showed a differential expression towards salt and drought treatment in the salt-tolerant cultivar Razegui (Daldoul et al., 2012d). The VvMAP kinase gene could be classified as an osmotic stress responsive gene as its expression was induced by salinity and drought (Daldoul et al., 2012e). This transcript provides the basis for future research on the diverse signaling pathways mediated by MAPKs in grapevine.

Figure 1. Strategy for identifying genes involved in salt stress response in grapevine (Vitis vinifera L.), as described by Daldoul et al. (2010). SSH=Suppressive Subtractive Hybridization; QC-PCR=quality check PCR.

The above work provided useful candidate genes for genetic improvement in grapevines and suggested that the dynamic expression changes observed reflect the integrative control and transcriptptional regulation networks in this species.

Based on the hypothesis that a difference in gene regulation is an indicator of an adaptive response, many differentially-expressed genes under abiotic stress conditions were used to transform model or agronomically-interesting plants (Zhang and Blumwald, 2001; Figueras et al., 2004; Mukhopadhyay et al., 2004) in order to improve abiotic stress tolerance and productivity. In this context, a salt-induced VvRD22 gene from grapevine was recently characterized to be involved in salt stress tolerance in tobacco plants (Jardak-Jamoussi et al., 2014).

Integration of omics approaches to identify molecular regulatory networks of salt stress tolerance in grapevine

The plant response to salt stress corresponds to a multigenic character (Pardo, 2010). It is therefore necessary to have reliable markers that would characterize tolerance behaviors. Furthermore, with the emergence of genome sequencing, organisms are now seen as complex interactive systems. Various transcriptomic approaches have been developed in grapevines especially after the completion of its genome sequencing (Jaillon et al., 2007). The development of biotechnological tools dedicated to transcriptomic analysis in grapevine constitutes a valuable opportunity to elucidate or dissect the basis of salt stress tolerance at the molecular level (Cramer et al., 2005). It also allows the analysis of genes induced by salt stress (Cramer, 2010; Daldoul et al., 2010). However, these approaches, essentially based on the synthesis of RNA transcripts, are insufficient to dissect the molecular mechanisms of tolerance to salt stress. In fact, the information at the mRNA level provides an idea about the regulation of gene expression in a cell but must be combined with data at the protein level (i.e., proteomics data), which are often more informative. Knowing when and where a gene is transcribed then translated into a protein constitutes an important clue to determine its biological function (Bouchez and Höfte, 1998). Bulk analysis of coding transcripts requires a comparison with the corresponding proteins for better characterization. Accordingly, to compare the expression of a gene and its protein level within the same organism and in contrasting physiological states can provide response elements on the function of transcripts and their effective involvement in a particular character. Gygi et al. (1999) reported that the abundance of transcripts is not a criterion of prediction of the abundance of cellular proteins. Paradoxically, the work of Futcher et al. (1999) demonstrated a good correlation between the transcripts and their corresponding proteins. It appears, therefore, that there exists in certain cases a partial correlation between the level of transcripts and their corresponding proteins (Greenbaum et al., 2002). Protein investigations in grapevine using proteomic techniques have significantly improved our knowledge in this field (Giribaldi and Giuffrida, 2010). However, Čarná et al. (2012) highlighted that interactomics, a discipline that describes the whole set of molecular interactions in cells, must be combined with proteomics. In fact, interactomes (i.e., the full set of protein family interactions within a proteome) of different species can provide information about the evolutionary mechanisms leading to organism diversity. The study of grapevine and five other species interactomes (yeast, Drosophila, worm, Arabidopsis and human) allowed the identification of 16 protein families that were similar and had the same function (Repka and Baumgartnerova, 2008). Thus, comparative interactomics provides molecular evidence that grapevine cells were originated from heritable alterations in the pattern of gene expression. On the other hand, the metabolite profile does not tell exactly whether the related metabolic pathway is up- or down-regulated since both up-regulation of upstream reaction and down-regulation of downstream reactions can lead to the accumulation of a metabolite. This can be solved by comparing the metabolomic data with those from transcriptomic, or proteomic, and enzyme activity analysis (Cramer et al., 2011). Thus, the integration of transcriptomic, proteomic, interactomic and metabolomic approaches, which is referred to as "system biology" (Fig. 2), remains a necessity in order to better understand the molecular mechanisms of abiotic stress tolerance in plants (Cramer et al., 2011). In this regard, there is a need for common databases and bioinformatic tools to allow a wide and deep use of these important resources. In this way, several databases were built, such as VitisExpDB (Doddapaneni et al., 2008) which provides grapevine genomic resources for the functional analysis, annotation and identification of genes. The VitisNet database ( focuses on the molecular networks occurring in grapevine. This database can be used to visualize changes in transcriptome, proteome and metabolome within molecular networks during a given experiment (Grimplet et al., 2009b; Grimplet et al., 2012). Although networks in systems biology might not completely represent the dynamic biological system, the proper application of these techniques will provide significant insight into the mechanisms of plant abiotic stress (Gupta et al., 2013). The development of grapevine bioinformatic tools could allow transcript, protein and metabolite omics data to be displayed on molecular pathways. The integration of the grapevine databases with a system biology approach will greatly facilitate the understanding of gene function and improve production efficiency under adverse environmental conditions. Integrative functional genomics has successfully demonstrates connections between genes and metabolites. Moreover, the presence/absence and relative accumulation of certain metabolites along with gene expression data provides accurate markers for tolerant crop selection in breeding programs (Arbona et al., 2013). The combination of these omics analyses was used to confirm that water deficit up-regulated the phenylpropanoid pathway in Cabernet Sauvignon berry skin in a tissue-specific manner (Grimplet et al., 2007; Deluc et al., 2009; Grimplet et al., 2009a). In addition, Zamboni et al. (2010) identified stage-specific functional networks of linked transcripts, proteins and metabolites, providing important insights into the key molecular processes that determine wine quality. Thus, this multi-targeted approach could lead to the development of efficient strategies of marker-assisted selection of resistant varieties. The integration of these strategies also allowed the development of models that could predict gene function in plants (Cramer, 2010). In this way, several studies were undertaken to investigate and understand the functions of salt stress proteins in grapevines via transcriptomic (Cramer et al., 2007; Daldoul et al., 2010), proteomic (Vincent et al., 2007; Jellouli et al., 2008) and metabolic analysis (Cramer et al., 2007). All of these differential approaches have opened up many application perspectives in terms of plant improvement and better tolerance to non-favorable environmental conditions.

Figure 2. The omics cascade and System biology approach for the identification of mechanisms of salinity tolerance in grapevine.

Grapevine functional genomics

The present challenge aims to explore the biological function of candidate genes and proteins which may be used to improve the tolerance phenotype in grapevines. In this perspective, transient expression systems by agro-infiltration (Zottini et al., 2008) were developed in grapevine as a rapid way to evidence protein activity. These transient systems of expression may contribute to study grapevine gene function at various levels: cellular, tissue, and even whole-plant (Vidal et al., 2010). In grapevine, many biological (via Agrobacterium) and physical techniques (biolistics, electroporation and protoplasts) were developed to transfer genes and offer even more basic approaches for functional genomics. Therefore, the first transgenic grapevines were obtained by Mullins et al. (1990). Several transgenic lines of different genotypes of Vitis were successfully obtained from embryogenic tissue following inoculation with Agrobacterium tumefaciens (Li et al., 2006) or by bombarding with DNA-coated microprojectiles (Vidal et al., 2006). Although no functional characterization of genes related to salt stress tolerance has yet been reported in grapevine, the transgenic approach is of capital importance to explore the function of these genes.


Most of the morphological and physiological features that make a plant tolerant to an abiotic constraint have been identified. However, the mechanisms that are implemented remains unclear with regard to grapevine, in particular. As stated in this review, the nature and complexity of salt stress responses in grapevines supports the use of global, integrative and multidisciplinary approaches to understand the different levels of regulation of salinity responses. The combination of physiological, transcriptomic, proteomic, interactomic and metabolomic approaches facilitate the identification of genes that are mostly involved in tolerance to salt stress. The large number of publications on grapevine physiology, transcriptome, proteome, and metabolome profiling published over the last few years has highlighted that in addition to its economic role, grapevine is considered more and more as a model plant, especially after the sequencing of its entire genome. Hence, omics and system biology approaches will open up large application perspectives in terms of viticulture improvement/development of plants with a better resistance/tolerance to salt stress. Therefore, understanding the function of genes remains a major challenge for post-genomic research.

Acknowledgments: The authors would like to acknowledge Prof. Serge Delrot and Dr. Eric Gomès from INRA, Bordeaux (France) for their valuable suggestions to improve the quality of the manuscript. This work was funded by the International Centre for Genetic Engineering and Biotechnology (ICGEB -CRP/TUN06-01), a research grant from the German Academic Exchange Service (DAAD) and a short term scientific mission grant from COST action 858 “Abiotic stress in grapevine”.


  • Arbona V., Manzi M., de Ollas C. and Gómez-Cadenas A., 2013. Metabolomics as a tool to investigate abiotic stress tolerance in plants. Int. J. Mol. Sci., 14, 4885-4911. doi:10.3390/ijms14034885
  • Ashraf M. and Harris P.J.C., 2004. Potential biochemical indicators of salinity tolerance in plants. Plant Sci., 166, 3-16. doi:10.1016/j.plantsci.2003.10.024
  • Askri H., Daldoul S., Ben Ammar A., Rejeb S., Jardak R., Rejeb M.N., Mliki A. and Ghorbel A., 2012. Short-term response of wild grapevines (Vitis vinifera L. ssp. sylvestris) to NaCl salinity exposure: changes of some physiological and molecular characteristics. Acta Physiol. Plant., 34, 957-968. doi:10.1007/s11738-011-0892-8
  • Bennett S., 2004. Solexa Ltd. Pharmacogenomics, 5, 433-438. doi:10.1517/14622416.5.4.433
  • Boominathan P., Shukla R., Kumar A., Manna D., Negi D., Verma P.K. and Chattopadhyay D., 2004. Long term transcript accumulation during the development of dehydration adaptation in Cicer arietinum. Plant Physiol., 135, 1608-1620. doi:10.1104/pp.104.043141
  • Bouchez D. and Hofte H., 1998. Functional genomics in plants. Plant Physiol., 118, 725-732. doi:10.1104/pp.118.3.725
  • Carmi N., Zhang G., Petreikov M., Gao Z., Eyal Y., Granot D. and Schaffer A.A., 2003. Cloning and functional expression of alkaline alpha-galactosidase from melon fruit: similarity to plant SIP proteins uncovers a novel family of plant glycosyl hydrolases. Plant J., 33, 97-106. doi:10.1046/j.1365-313X.2003.01609.x
  • Čarná M., Repka V. and Šturdík E., 2012. Proteomics and interactomics in grapevine: the next level in the study. Acta Chim. Slov., 5, 211-219.
  • Chadli R. and Belkhodja M., 2007. Réponses minérales chez la fève (Vicia faba L.) au stress salin. Eur. J. Sci. Res., 18, 645-654.
  • Chen G.P., Ma W.S., Huang Z.J., Xu T., Xue Y.B. and Shen Y.Z., 2003. Isolation and characterization of TaGSK1 involved in wheat salt tolerance. Plant Sci., 165, 1369-1375. doi:10.1016/S0168-9452(03)00365-0
  • Chen W., Provart N.J., Glazebrook J., Katagiri F., Chang H.S., et al., 2002. Expression profile matrix of Arabidopsis transcription factor genes suggests their putative functions in response to environmental stresses. Plant Cell, 14, 559-574. doi:10.1105/tpc.010410
  • Cheong M.S. and Yun D.J., 2007. Salt-stress signalling. J. Plant Biol., 50, 148-155. doi:10.1007/BF03030623
  • Cloonan N., Forrest A.R., Kolle G., Gardiner B.B., Faulkner G.J., et al., 2008. Stem cell transcriptome profiling via massive-scale mRNA sequencing. Nat. Methods, 5, 613-619. doi:10.1038/nmeth.1223
  • Cramer G.R., Cushman J.C., Schooley D.A., Quilici D., Vincent D., Bohlman M.C., Ergul A., Tattersall E.A.R., Tillett R., Evans J., Delacruz R., Schlauch K. and Mendes P., 2005. Progress in bioinformatics - the challenge of integrating transcriptomic, proteomic and metabolomic information. Acta Hort., 689, 417-425. doi:10.17660/ActaHortic.2005.689.49
  • Cramer G.R., Ergül A., Grimplet J., Tillett R.L., Tattersall E.A.R., Bohlman M.C., Vincent D., Sonderegger J., Evans J., Osborne C., Quilici D., Schlauch K.A., Schooley D.A. and Cushman J.C., 2007. Water and salinity stress in grapevines: early and late changes in transcript and metabolite profiles. Funct. Integr. Genomics, 7, 111-134. doi:10.1007/s10142-006-0039-y
  • Cramer G.R., 2010. Abiotic stress and plant responses from the whole vine to the genes. Aust. J. Grape Wine Res., 16, 86-93. doi:10.1111/j.1755-0238.2009.00058.x
  • Cramer G.R., Urano K., Delrot S., Pezzotti M. and Shinozaki K., 2011. Effects of abiotic stress on plants: a systems biology perspective. BMC Plant Biol., 11, 163. doi:10.1186/1471-2229-11-163
  • Cramer G.R., Van Sluyter S.C., Hopper D.W., Pascovici D., Keighley T. and Haynes P.A., 2013. Proteomic analysis indicates massive changes in metabolism prior to the inhibition of growth and photosynthesis of grapevine (Vitis vinifera L.) in response to water deficit. BMC Plant Biol., 13, 49. doi:10.1186/1471-2229-13-49
  • Daldoul S., Guillaumie S., Reustle G.M., Krczal G., Ghorbel A., Delrot S., Mliki A. and Höfer M.U., 2010. Isolation and expression analysis of salt induced genes from contrasting grapevine (Vitis vinifera L.) cultivars. Plant Sci., 179, 489-498. doi:10.1016/j.plantsci.2010.07.017
  • Daldoul S., Mliki A. and Höfer M.U., 2012-a. Suppressive subtractive hybridization method analysis and its application to salt stress in grapevine (Vitis vinifera L.). Russ. J. Genet. (Genetika), 48, 179-185.
  • Daldoul S., Toumi I., Reustle G.M., Krczal G., Ghorbel A., Mliki A. and Höfer M.U., 2012-b. Molecular cloning and characterisation of a cDNA encoding a putative alkaline alpha-galactosidase from grapevine (Vitis vinifera L.) that is differentially expressed under osmotic stress. Acta Physiol. Plant., 34, 891-903. doi:10.1007/s11738-011-0887-5
  • Daldoul S., Hanana M. and Mliki A., 2012-c. Molecular characterization and in silico analysis of an alkaline a-galactosidase gene (Vv-α-gal/SIP) in grapevines (Vitis vinifera. L). Turk. J. Biochem., 37, 368-374.
  • Daldoul S., Hoefer M.U. and Mliki A., 2012-d. Osmotic stress induces the expression of VvMAP kinase gene in grapevine (Vitis vinifera L.). J. Bot.. doi:10.1155/2012/737035
  • Daldoul S., Höfer M.U., Ghorbel A. and Mliki A., 2012-e. Differential expression of osmotic stress-associated ESTs in grapevine cultivars (Vitis vinifera. L) cultivated under salt and drought stresses. In: Grapevines: Varieties, Cultivation and Management. Szabo P.V. and Shojania J. (Eds.), Nova Science, pp. 169-183.
  • Deluc L.G., Quilici D.R., Decendit A., Grimplet J., Wheatley M.D., Schlauch K.A., Merillon J.M., Cushman J.C. and Cramer G.R., 2009. Water deficit alters differentially metabolic pathways affecting important flavor and quality traits in grape berries of Cabernet Sauvignon and Chardonnay. BMC Genomics, 10, 212. doi:10.1186/1471-2164-10-212
  • Doddapaneni H., Lin H., Walker M.A., Yao J. and Civerolo E.L., 2008. VitisExpDB: a database resource for grape functional genomics. BMC Plant Biol., 8, 23. doi:10.1186/1471-2229-8-23
  • Dubos C. and Plomion C., 2001. Drought differentially affects expression of a PR-10 protein, in needles of maritime pine (Pinus pinaster Ait.) seedlings. J. Exp. Bot., 52, 1143-1144. doi:10.1093/jexbot/52.358.1143
  • Fiehn O., 2002. Metabolomics – the link between genotypes and phenotypes. Plant Mol. Biol., 48, 155-171. doi:10.1023/A:1013713905833
  • Figueras M., Pujal J., Saleh A., Savé R., Pagès M. and Goday A., 2004. Maize Rab17 overexpression in Arabidopsis plants promotes osmotic stress tolerance. Ann. Appl. Biol., 144, 251-257. doi:10.1111/j.1744-7348.2004.tb00341.x
  • Fisarakis I., Chartzoulakis K. and Stavrakas D., 2001. Response of Sultana vines (V. vinifera L.) on six rootstocks to NaCl salinity exposure and recovery. Agric. Water Manage., 51, 13-27. doi:10.1016/S0378-3774(01)00115-9
  • Futcher B., Latter G.I., Monardo P., McLaughlin C.S. and Garrels J.I., 1999. A sampling of the yeast proteome. Mol. Cell Biol., 19, 7357-7368. doi:10.1128/MCB.19.11.7357
  • Garcia M. and Charbaji T., 1993. Effect of sodium chloride salinity on cation equilibria in grapevine. J. Plant Nutr., 16. 2225-2237. doi:10.1080/01904169309364682
  • Gigon A., Matos A.R., Laffray D., Zuily-Fodil Y. and Pham-Thi A.T., 2004. Effect of drought stress on lipid metabolism in the leaves of Arabidopsis thaliana (Ecotype Columbia). Ann. Bot., 94, 345-351. doi:10.1093/aob/mch150
  • Giribaldi M. and Giuffrida M.G., 2010. Heard it through the grapevine: proteomic perspective on grape and wine. J. Proteomics, 73, 1647-1655. doi:10.1016/j.jprot.2010.05.002
  • Goes da Silva F., Iandolino A., Al-Kayal F., Bohlmann M.C., Cushman M.A., et al., 2005. Characterizing the grape transcriptome. Analysis of expressed sequence tags from multiple Vitis species and development of a compendium of gene expression during berry development. Plant Physiol., 139, 574-597. doi:10.1104/pp.105.065748
  • Greenbaum D., Jansen R. and Gerstein M., 2002. Analysis of mRNA expression and protein abundance data: an approach for the comparison of the enrichment of features in the cellular population of proteins and transcripts. Bioinformatics, 18, 585-596. doi:10.1093/bioinformatics/18.4.585
  • Grimplet J., Deluc L.G., Tillett R.L., Wheatley M.D., Schlauch K.A., Cramer G.R. and Cushman J.C., 2007. Tissue-specific mRNA expression profiling in grape berry tissues. BMC Genomics, 8, 187, doi:10.1186/1471-2164-8-187
  • Grimplet J., Wheatley M.D., Jouira H.B., Deluc L.G., Cramer G.R. and Cushman J.C., 2009-a. Proteomic and selected metabolite analysis of grape berry tissues under well-watered and water-deficit stress conditions. Proteomics, 9, 2503-2528. doi:10.1002/pmic.200800158
  • Grimplet J., Cramer G.R., Dickerson J.A., Mathiason K., Van Hemert J. and Fennell A.Y., 2009-b. VitisNet: “Omics” integration through grapevine molecular networks. PLoS ONE, 4, e8365. doi:10.1371/journal.pone.0008365
  • Grimplet J., Van Hemert J., Carbonell-Bejerano P., Díaz-Riquelme J., Dickerson J., Fennell A., Pezzotti M. and Martínez-Zapater J.M., 2012. Comparative analysis of grapevine whole-genome gene predictions, functional annotation, categorization and integration of the predicted gene sequences. BMC Res. Notes, 5, 213. doi:10.1186/1756-0500-5-213
  • Guillaumie S., Fouquet R., Kappel C., Camps C., Terrier N., Moncomble D., Dunlevy J.D., Davies C., Boss P.K. and Delrot S., 2011. Transcriptional analysis of late ripening stages of grapevine berry. BMC Plant Biol., 11, 165. doi:10.1186/1471-2229-11-165
  • Gupta B., Sengupta A., Saha J. and Gupta K., 2013. Plant abiotic stress: ‘Omics’ approach. J. Plant Biochem. Physiol., 1, 3. doi:10.4172/2329-9029.1000e108
  • Gygi S.P., Rochon Y., Franza B.R. and Aebersold R., 1999. Correlation between protein and mRNA abundance in yeast. Mol. Cell. Biol., 19, 1720-1730. doi:10.1128/MCB.19.3.1720
  • Hamrouni L., Hanana M., Abdelly C. and Ghorbel A., 2011. Exclusion du chlorure et inclusion du sodium : deux mécanismes concomitants de tolérance à la salinité chez la vigne sauvage Vitis vinifera subsp. sylvestris (var. 'Séjnène'). Biotechnol. Agron. Soc. Environ., 15, 387-400.
  • Hanana M., Cagnac O., Yamaguchi T., Hamdi S., Ghorbel A. and Blumwald E., 2007. A grape berry (Vitis vinifera L.) cation/proton antiporter is associated with berry ripening. Plant Cell Physiol., 48, 804-811. doi:10.1093/pcp/pcm048
  • Hanana M., Deluc L., Fouquet R., Daldoul S., Léon C., Barrieu F., Ghorbel A., Mliki A. and Hamdi S., 2008. Identification et caractérisation d’un gène de réponse à la déshydratation rd22 chez la vigne (Vitis vinifera L.). C.R. Biol., 331, 569-578. doi:10.1016/j.crvi.2008.05.002
  • Hara M., Tokunaga K. and Kuboi T., 2008. Isolation of a drought-responsive alkaline a-galactosidase gene from New Zealand spinach. Plant Biotech., 25, 497-501. doi:10.5511/plantbiotechnology.25.497
  • Hochberg U., Degu A., Toubiana D., Gendler T., Nikoloski Z., Rachmilevitch S. and Fait A., 2013. Metabolite profiling and network analysis reveal coordinated changes in grapevine water stress response. BMC Plant Biol., 13, 184. doi:10.1186/1471-2229-13-184
  • Jaillon O., Aury J.M., Noel B., Policriti A., Clepet C., et al., 2007. The grapevine genome sequence suggests ancestral hexaploidization in major angiosperm phyla. Nature, 449, 463-467. doi:10.1038/nature06148
  • Jain A.K., Basha S.M. and Holbrook C.C., 2001. Identification of drought-responsive transcripts in peanut (Arachis hypogaea L.). Electron. J. Biotechnol., 4, ISSN: 0717-3458.
  • Jardak-Jamoussi R., Elabbassi M.M., Ben Jouira H., Hanana M., Zoghlami N., Ghorbel A. and Mliki A., 2014. Physiological responses of transgenic tobacco plants expressing the dehydration-responsive RD22 gene of Vitis vinifera to salt stress. Turk. J. Bot., 38, 268-280, doi: 10.3906/bot-1301-53. doi:10.3906/bot-1301-53
  • Jellouli N., Ben Jouira H., Skouri H., Gargouri A., Ghorbel A. and Mliki A., 2008. Proteomic analysis of Tunisian grapevine cultivar Razegui under salt stress. J. Plant Physiol., 165, 471-481. doi:10.1016/j.jplph.2007.02.009
  • Kalifa Y., Perlson E., Gilad A., Konrad Z., Scolnik P.A. and Bar-Zvi D., 2004. Over-expression of the water and salt stress-regulated Asr1 gene confers an increased salt tolerance. Plant Cell Environ., 27, 1459-1468. doi:10.1111/j.1365-3040.2004.01251.x
  • Kasuga M., Liu Q., Miura S., Yamaguchi-Shinozaki K. and Shinozaki K., 1999. Improving plant drought, salt, and freezing tolerance by gene transfer of a single stress-inducible transcription factor. Nature Biotechnol., 17, 287-291. doi:10.1038/7036
  • Kim J.C., Lee S.H., Cheong Y.H., Yoo C.M., Lee S.I., Chun H.J., Yun D.J., Hong J.C., Lee S.Y., Lim C.O. and Cho M.J., 2001. A novel cold-inducible zinc finger protein from soybean, SCOF-1, enhances cold tolerance in transgenic plants. Plant J., 25, 247–259. doi:10.1046/j.1365-313x.2001.00947.x
  • Lee R.H., Lin M.C. and Chen S.C.G., 2004. A novel alkaline α-galactosidase gene is involved in rice leaf senescence. Plant Mol. Biol., 55, 281-295. doi:10.1007/s11103-004-0641-0
  • Lee R.H., Hsu J.H., Huang H.J., Lo S.F. and Chen S.C.G., 2009. Alkaline α-galactosidase degrades thylakoid membranes in the chloroplast during leaf senescence in rice. New Phytol., 184, 596-606. doi:10.1111/j.1469-8137.2009.02999.x
  • Li Z.T., Dhekney S., Dutt M., van Aman M., Tattersall J., Kelley K.T. and Gray D.J., 2006. Optimizing agrobacterium-mediated transformation of grapevine. In Vitro Cell. Dev. Biol. Plant, 42, 220-227. doi:10.1079/IVP2006770
  • Maas E.V., 1990. Crop salt tolerance. In: Agricultural Salinity Assessment and Management. Tanji K.K. (Ed.), ASCE Manuals and Reports on Engineering 71, New York, pp. 262-304.
  • Margulies M., Egholm M., Altman W.E., Attiya S., Bader J.S., et al., 2005. Genome sequencing in microfabricated high-density picolitre reactors. Nature, 437, 376-380. doi:10.1038/nature03959
  • Mezni M., Albouchi A., Bizid E. and Hamza M., 2002. Effet de la salinité des eaux d’irrigation sur la nutrition minérale chez trois variétés de luzerne pérenne (Medicago sativa). Agronomie, 22, 283-291. doi:10.1051/agro:2002014
  • Morozova O. and Marra M.A., 2008. Applications of next-generation sequencing technologies in functional genomics. Genomics, 92, 255-264. doi:10.1016/j.ygeno.2008.07.001
  • Mortazavi A., Williams B.A., McCue K., Schaeffer L. and Wold B., 2008. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat. Methods, 5, 621-628. doi:10.1038/nmeth.1226
  • Mukhopadhyay A., Vij S. and Tyagi A.K., 2004. Overexpression of a zinc-finger protein gene from rice confers tolerance to cold, dehydration, and salt stress in transgenic tobacco. Proc. Natl. Acad. Sci. USA, 101, 6309-6314. doi:10.1073/pnas.0401572101
  • Mullins M.G., Tang F.C.A. and Facciotti D., 1990. Agrobacterium-mediated genetic transformation of grapevines: transgenic plants of Vitis rupestris SCHEELE and buds of Vitis vinifera L. Nat. Biotechnol., 8, 1041-1045. doi:10.1038/nbt1190-1041
  • Munnik T., van Himbergen J.A.J., Ter Riet B., Braun F.J., Irvine R.F., van den Ende H. and Musgrave A., 1998. Detailed analysis of the turnover of polyphosphoinositides and phosphatidic acid upon activation of phospholipases C and D in Chlamydomonas cells treated with non-permeabilizing concentrations of mastoparan. Planta, 207, 133-145. doi:10.1007/s004250050465
  • Munns R., James R.A. and Lauchli A., 2006. Approaches to increasing the salt tolerance of wheat and other cereals. J. Exp. Bot., 57, 1025-1043. doi:10.1093/jxb/erj100
  • O’Farrell P.H., 1975. High resolution two-dimensional electrophoresis of proteins. J. Biol. Chem., 250, 4007-4021.
  • Pardo J.M., 2010. Biotechnology of water and salinity stress tolerance. Curr. Opin. Biotechnol., 21, 185-196. doi:10.1016/j.copbio.2010.02.005
  • Park J.A., Cho S.K., Kim J.E., Chung H.S., Hong J.P., Hwang B., Hong C.B. and Kim W.T., 2003. Isolation of cDNAs differentially expressed in response to drought stress and characterization of the Ca-LEAL1 gene encoding a new family of atypical LEA-like protein homologue in hot pepper (Capsicum annuum L. cv. Pukang). Plant Sci., 165, 471-481. doi:10.1016/S0168-9452(03)00165-1
  • Reddy A.R., Ramakrishna W., Chandra Sekhar A., Ithal N., Babu P.R., Bonaldo F.M., Soares B. and Bennetzen J.L., 2002. Novel genes are enriched in normalized cDNA libraries from drought-stressed seedlings of rice (Oryza sativa L. subsp. indica cv Nagina 22). Genome, 45, 204-211. doi:10.1139/g01-114
  • Repka V. and Baumgartnerova I., 2008. Grapevine habituation: understanding of factors that contribute to neoplastic transformation and somaclonal variation. Acta Agron. Hung., 56, 399-408. doi:10.1556/AAgr.56.2008.4.4
  • Ross P.L., Huang Y.N., Marchese J.N., Williamson B., Parker K., et al., 2004. Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents. Mol. Cell. Proteomics, 3, 1154-1169. doi:10.1074/mcp.M400129-MCP200
  • Schena M., Shalon D., Davis R.W. and Brown P.O., 1995. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science, 270, 467-470. doi:10.1126/science.270.5235.467
  • Shani U., Waisel Y., Eshel A., Xue S. and Ziv G., 1993. Responses to salinity of grapevine plants with split root systems. New Phytol., 124, 695-701. doi:10.1111/j.1469-8137.1993.tb03860.x
  • Shani U. and Dudley L.M., 2001. Field studies of crop response to water and salt stress. Soil Sci. Soc. Am. J., 65, 1522-1528. doi:10.2136/sssaj2001.6551522x
  • Shani U. and Ben-Gal A., 2005. Long-term response of grapevines to salinity: osmotic effects and ion toxicity. Am. J. Enol. Vitic., 56, 148-154.
  • Sharma P. and Kumar S., 2005. Differential display-mediated identification of three drought-responsive expressed sequence tags in tea [Camellia sinensis (L.) O. Kuntze]. J. Biosci., 30, 231-235. doi:10.1007/BF02703703
  • Storey R., Schachtman D.P. and Thomas M.R., 2003. Root structure and cellular chloride, sodium and potassium distribution in salinized grapevines. Plant Cell. Environ., 26, 789-800. doi:10.1046/j.1365-3040.2003.01005.x
  • Tattersall E.A.R., Grimplet J., DeLuc L., Wheatley M.D., Vincent D., Osborne C., Ergül A., Lomen E., Blank R.R., Schlauch K.A., Cushman J.C. and Cramer G.R., 2007. Transcript abundance profiles reveal larger and more complex responses of grapevine to chilling compared to osmotic and salinity stress. Funct. Integr. Genomics, 7, 317-333. doi:10.1007/s10142-007-0051-x
  • Terrier N., Glissant D., Grimplet J., Barrieu F., Abbal P., Couture C., Ageorges A., Atanassova R., Léon C., Renaudin J.P., Dédaldéchamp F., Romieu C., Delrot S. and Hamdi S., 2005. Isogene specific oligo arrays reveal multifaceted changes in gene expression during grape berry (Vitis vinifera L.) development. Planta, 222, 832-847. doi:10.1007/s00425-005-0017-y
  • Toumi I., Gargouri M., Nouairi I., Moschou P.N., Ben Salem-Fnayou A., Mliki A., Zarrouk M. and Ghorbel A., 2008. Water stress induced changes in the leaf lipid composition of four grapevine genotypes with different drought tolerance. Biol. Plant., 52, 161-164. doi:10.1007/s10535-008-0035-2
  • Troncoso A., Matte C., Cantos M. and Lavee S., 1999. Evaluation of salt tolerance of in vitro-grown grapevine rootstock varieties. Vitis, 38, 55-60.
  • Vidal J.R., Kikkert J.R., Donzelli B.D., Wallace P.G. and Reisch B.I., 2006. Biolistic transformation of grapevine using minimal gene cassette technology. Plant Cell Rep., 25, 807-814. doi:10.1007/s00299-006-0132-7
  • Vidal J.R., Gomez C., Cutanda M.C., Shrestha B.R., Bouquet A., Thomas M.R. and Torregrosa L., 2010. Use of gene transfer technology for functional studies in grapevine. Aust. J. Grape Wine Res., 16, 138-151, doi: 10.1111/j.1755-0238.2009.00086.x
  • Vincent D., Ergul A., Bohlman M.C., Tattersall E.A.R., Tillett R.L., Wheatley M.D., Woolsey R., Quilici D.R., Joets J., Schlauch K., Schooley D.A., Cushman J.C. and Cramer G.R., 2007. Proteomic analysis reveals differences between Vitis vinifera L. cv. Chardonnay and cv. Cabernet Sauvignon and their responses to water deficit and salinity. J. Exp. Bot., 58, 1873-1892. doi:10.1093/jxb/erm012
  • Walker R.R., Torokfalvy E., Scott N.S. and Kriedemann P.E., 1981. An analysis of photosynthetic response to salt treatment in Vitis vinifera. Aust. J. Plant Physiol., 8, 359-374. doi:10.1071/PP9810359
  • Wang Z., Gerstein M. and Snyder M., 2009. RNA-Seq: a revolutionary tool for transcriptomics. Nat. Rev. Genet., 10, 57-63. doi:10.1038/nrg2484
  • Waters D.L., Holton T.A., Ablett E.M., Lee L.S. and Henry R.J., 2005. cDNA microarray analysis of developing grape (Vitis vinifera cv. Shiraz) berry skin. Funct. Integr. Genomics, 5, 40-58. doi:10.1007/s10142-004-0124-z
  • Yamaguchi-Shinozaki K., Kasuga M., Liu Q., Nakashima K., Sakuma Y., Abe H., Shinwari Z.K., Seki M. and Shinozaki K., 2002. Biological Mechanisms of Drought Stress Response. JIRCAS Working report 1-8.
  • Zamboni A., Di Carli M., Guzzo F., Stocchero M., Zenoni S., Ferrarini A., Tononi P., Toffali K., Desiderio A., Lilley K.S., Pe M.E., Benvenuto E., Delledonne M. and Pezzotti M., 2010. Identification of putative stage-specific grapevine berry biomarkers and omics data integration into networks. Plant Physiol., 154, 1439-1459. doi:10.1104/pp.110.160275
  • Zenoni S., Ferrarini A., Giacomelli E., Xumerle L., Fasoli M., Malerba G., Bellin D., Pezzotti M. and Delledonne M., 2010. Characterization of transcriptional complexity during berry development in Vitis vinifera using RNA-Seq. Plant Physiol., 152, 1787-1795. doi:10.1104/pp.109.149716
  • Zhang H.X. and Blumwald E., 2001. Transgenic salt-tolerant tomato plants accumulate salt in foliage but not in fruit. Nature Biotechnol., 19, 765-769. doi:10.1038/90824
  • Zheng J., Zhao J., Tao Y., Wang J., Liu Y., Fu J., Jin Y., Gao P., Zhang J., Bai Y. and Wang G., 2004. Isolation and analysis of water stress induced genes in maize seedlings by subtractive PCR and cDNA macroarray. Plant Mol. Biol., 55, 807-823. doi:10.1007/s11103-005-1969-9
  • Zottini M., Barizza E., Costa A., Formentin E., Ruberti C., Carimi F. and Lo Schiavo F., 2008. Agroinfiltration of grapevine leaves for fast transient assays of gene expression and for long-term production of stable transformed cells. Plant Cell Rep., 27, 845-853, doi:10.1007/s00299-008-0510-4


Samia Daldoul

Affiliation : Centre de Biotechnologie de Borj cédria, Laboratoire de Physiologie Moléculaire des Plantes, B.P. 901, 2050 Hammam-Lif, Tunisia

Anis Ben Amar

Affiliation : Centre de Biotechnologie de Borj cédria, Laboratoire de Physiologie Moléculaire des Plantes, B.P. 901, 2050 Hammam-Lif, Tunisia

Sabine Guillaumie

Affiliation : Université de Bordeaux, Institut des Sciences de la Vigne et du Vin (ISVV), Ecophysiology and Functional Genomics of the Vine (EGFV), UMR 1287, 33140 Villenave d’Ornon, France; INRA, Institut des Sciences de la Vigne et du Vin (ISVV), Ecophysiology and Functional Genomics of the Vine (EGFV), UMR 1287, 33140 Villenave d’Ornon, France

Ahmed Mliki

Affiliation : Centre de Biotechnologie de Borj cédria, Laboratoire de Physiologie Moléculaire des Plantes, B.P. 901, 2050 Hammam-Lif, Tunisia


No supporting information for this article

Article statistics

Views: 1545


PDF: 510