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Water and salinity stress in grapevines: early and late changes in transcript and metabolite profiles

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Abstract

Grapes are grown in semiarid environments, where drought and salinity are common problems. Microarray transcript profiling, quantitative reverse transcription-PCR, and metabolite profiling were used to define genes and metabolic pathways in Vitis vinifera cv. Cabernet Sauvignon with shared and divergent responses to a gradually applied and long-term (16 days) water-deficit stress and equivalent salinity stress. In this first-of-a-kind study, distinct differences between water deficit and salinity were revealed. Water deficit caused more rapid and greater inhibition of shoot growth than did salinity at equivalent stem water potentials. One of the earliest responses to water deficit was an increase in the transcript abundance of RuBisCo activase (day 4), but this increase occurred much later in salt-stressed plants (day 12). As water deficit progressed, a greater number of affected transcripts were involved in metabolism, transport, and the biogenesis of cellular components than did salinity. Salinity affected a higher percentage of transcripts involved in transcription, protein synthesis, and protein fate than did water deficit. Metabolite profiling revealed that there were higher concentrations of glucose, malate, and proline in water-deficit-treated plants as compared to salinized plants. The metabolite differences were linked to differences in transcript abundance of many genes involved in energy metabolism and nitrogen assimilation, particularly photosynthesis, gluconeogenesis, and photorespiration. Water-deficit-treated plants appear to have a higher demand than salinized plants to adjust osmotically, detoxify free radicals (reactive oxygen species), and cope with photoinhibition.

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Acknowledgments

This work was supported by grants from the American Viticulture Foundation, the USDA Viticulture Consortium, the National Science Foundation (NSF) Plant Genome program (DBI-0217653) to G.R.C. and J.C.C. and the Bioinformatics program (DBI-0136561) to K.A.S., a graduate student fellowship to E.A.R. Tattersall from the NSF EPSCoR Integrated Approaches to Abiotic Stress program (EPS-0132556), and a TUBITAK–NATO B1 fellowship to Dr. A. Ergul. The Nevada Genomics Center is supported by grants from the NIH Biomedical Research Infrastructure Network (NIH-NCRR, P20 RR16464) and NIH IDeA Network of Biomedical Research Excellence (INBRE, RR-03-008).

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Authors and Affiliations

  1. Department of Biochemistry and Molecular Biology, MS200, University of Nevada, Reno, NV, 89557-0014, USA

    Grant R. Cramer, Jerome Grimplet, Richard L. Tillett, Elizabeth A. R. Tattersall, Marlene C. Bohlman, Delphine Vincent, Justin Sonderegger, Jason Evans, David Quilici, David A. Schooley & John C. Cushman

  2. Biotechnology Institute, Ankara University, 06500, Besevler-Ankara, Turkey

    Ali Ergül

  3. Department of Animal Biotechnology, University of Nevada, Reno, NV, 89557-0014, USA

    Craig Osborne

  4. Boston University School of Medicine, Department of Genetics and Genomics, Boston University, E632, Boston, MA, 02118, USA

    Karen A. Schlauch

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  1. Grant R. Cramer
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Correspondence to Grant R. Cramer.

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Grant R. Cramer and John C. Cushman contributed equally to this paper.

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Supplemental Table S1

Pearson and Spearman correlation coefficients across biological triplicate microarray experiments for each experimental state (JPEG 576 kb)

Supplemental Table S2

Genes of water-deficit-treated plants with significantly different expression relative to control plants on day 8 (XLS 26 kb)

Supplemental Table S3

Genes of water-deficit-treated plants with significantly different expression relative to control plants on day 12 (XLS 1 mg)

Supplemental Table S4

Genes of salt-stressed plants with significantly different expression relative to control plants on day 12 (XLS 540 kb)

Supplemental Table S5

Genes of water-deficit-treated plants with significantly different expression relative to control plants on day 16 (XLS 3 mb)

Supplemental Table S6

Genes of salt-stressed plants with significantly different expression relative to control plants on day 16 (XLS 3 mb)

Supplemental Table S7

The top 10% genes of water-deficit-treated plants with significantly different expression relative to control plants on day 12. The gene order is arranged according to hierarchical clustering (XLS 137 kb)

Supplemental Table S8

The top 10% genes of salinity-treated plants with significantly different expression relative to control plants on day 12. The gene order is arranged according to hierarchical clustering (XLS 73 kb)

Supplemental Table S9

The top 10% genes of water-deficit-treated plants with significantly different expression relative to control plants on day 16. The gene order is arranged according to hierarchical clustering (XLS 384 kb)

Supplemental Table S10

The top 10% genes of salinity-treated plants with significantly different expression relative to control plants on day 16. The gene order is arranged according to hierarchical clustering (XLS 381 kb)

Supplemental Table S11

All genes with significantly different expression between water-deficit-treated and salinity-treated plants on day 16. The gene order is arranged according to hierarchical clustering (XLS 522 kb)

Supplemental Fig. S1

Boxplots of PM probe-level intensity levels of all 44 oligonucleotide microarrays after RMA preprocessing and normalization (JPEG 79 KB)

Supplemental Fig. S2

RNA degradation plots of PM hybridization signal intensities across probe sets for all 44 microarrays. The log-transformed preprocessed values of the 10,251 probes having 16 probe pair sets on the arrays are presented in the 5′ to 3′ orientation. Note that differences on the y-axis are relatively small (JPEG 599 KB)

Supplemental Fig. S3

Scatterplot of log-transformed, normalized expression values of biological replicates of water-deficit-treated plants harvested on day 8. The correlation between all three pairs of replications was 0.998, as seen in Supplemental Table S1 (JPEG 466 KB)

Supplemental Fig. S4

Comparison of gene expression levels determined by real time RT-PCR and the GeneChip microarray for five genes assessed in this study. Left: transcript abundance as measured by real time RT-PCR; right: real time RT-PCR transcript abundance correlation with microarray results (JPEG 161 KB)

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Cramer, G.R., Ergül, A., Grimplet, J. et al. Water and salinity stress in grapevines: early and late changes in transcript and metabolite profiles. Funct Integr Genomics 7, 111–134 (2007). https://doi.org/10.1007/s10142-006-0039-y

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