Dataset Information

Relationships between drought, heat and air humidity responses revealed by transcriptome-metabolome co-analysis.

ABSTRACT: Elevated temperature and reduced water availability are frequently linked abiotic stresses that may provoke distinct as well as interacting molecular responses. Based on non-targeted metabolomic and transcriptomic measurements from Arabidopsis rosettes, this study aims at a systematic elucidation of relevant components in different drought and heat scenarios as well as relationships between molecular players of stress response.In combined drought-heat stress, the majority of single stress responses are maintained. However, interaction effects between drought and heat can be discovered as well; these relate to protein folding, flavonoid biosynthesis and growth inhibition, which are enhanced, reduced or specifically induced in combined stress, respectively. Heat stress experiments with and without supplementation of air humidity for maintenance of vapor pressure deficit suggest that decreased relative air humidity due to elevated temperature is an important component of heat stress, specifically being responsible for hormone-related responses to water deprivation. Remarkably, this "dry air effect" is the primary trigger of the metabolomic response to heat. In contrast, the transcriptomic response has a substantial temperature component exceeding the dry air component and including up-regulation of many transcription factors and protein folding-related genes. Data level integration independent of prior knowledge on pathways and condition labels reveals shared drought and heat responses between transcriptome and metabolome, biomarker candidates and co-regulation between genes and metabolic compounds, suggesting novel players in abiotic stress response pathways.Drought and heat stress interact both at transcript and at metabolite response level. A comprehensive, non-targeted view of this interaction as well as non-interacting processes is important to be taken into account when improving tolerance to abiotic stresses in breeding programs. Transcriptome and metabolome may respond with different extent to individual stress components. Their contrasting behavior in response to temperature stress highlights that the protein folding machinery effectively shields the metabolism from stress. Disentangling the complex relationships between transcriptome and metabolome in response to stress is an enormous challenge. As demonstrated by case studies with supporting evidence from additional data, the large dataset provided in this study may assist in determining linked genetic and metabolic features as candidates for future mechanistic analyses.

SUBMITTER: Georgii E

PROVIDER: S-EPMC5504741 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature

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Relationships between drought, heat and air humidity responses revealed by transcriptome-metabolome co-analysis.

Georgii Elisabeth E Jin Ming M Zhao Jin J Kanawati Basem B Schmitt-Kopplin Philippe P Albert Andreas A Winkler J Barbro JB Schäffner Anton R AR

BMC plant biology 20170710 1

<h4>Background</h4>Elevated temperature and reduced water availability are frequently linked abiotic stresses that may provoke distinct as well as interacting molecular responses. Based on non-targeted metabolomic and transcriptomic measurements from Arabidopsis rosettes, this study aims at a systematic elucidation of relevant components in different drought and heat scenarios as well as relationships between molecular players of stress response.<h4>Results</h4>In combined drought-heat stress, t ...[more]

PMID: 28693422

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Project description:BackgroundChinese kale (Brassica alboglabra) contains high nutritional elements and functional molecules, especially anticarcinogenic and antioxidant glucosinolates (GS), which was highly affected by environment temperature. To investigate the link of GS biosynthesis with heat stress response in Chinese kale, global transcription profiles of high-GS line (HG), low-GS line (LG), high-GS line under heat stress (HGT) and low-GS line under heat stress (LGT) were analyzed.ResultsBased on three biological replicates of each RNA sequencing data, 3901, 4062 and 2396 differentially expressed genes in HG vs HGT, LG vs LGT and HGT vs LGT were obtained, respectively. GO annotation, KEGG pathway analysis and a comprehensive analysis of DEGs showed a strong correlation between the GS biosynthesis and heat stress response. It was noticed that 11 differentially expressed genes tied to the GS biosynthesis were down-regulated, 23 heat shock transcription factors and 61 heat shock proteins were up-regulated upon the heat treatment. Another two Chinese kale varieties Cuibao and Shunbao with high- and low- GS content respectively, were used to validate the relationship of GS content and heat-response, and the results showed that high-GS content variety were more thermotolerant than the low-GS content one although GS significantly decreased in both varieties under heat stress. In addition, HSP100/ClpB, HSP90, HSP70 and sHSPs were differentially expressed in high- and low-GS varieties. Notably, HSP90 and sHSPs showed an obviously early response to heat stress than other related genes.ConclusionThe higher heat resistance of high-GS Chinese kale and the sharp decrease of glucosinolate content under heat stress indicated a strong relationship of GS accumulation and heat stress response. Combined with the previous report on the low expression of HSP90 at elevated temperatures in GS-deficient mutant TU8 of Arabidopsis, the differential expression pattern of HSP90 in high- and low- GS varieties and its early heat response implied it might be a key regulator in GS metabolism and heat-resistance in Chinese kale.

Project description:Drought is the major abiotic stress threatening maize (Zea mays L.) production globally. Despite recent scientific headway in deciphering maize drought stress responses, the overall picture of key genes, pathways, and co-expression networks regulating maize drought tolerance is still fragmented. Therefore, deciphering the molecular basis of maize drought tolerance remains pertinent. Here, through a comprehensive comparative leaf transcriptome analysis of drought-tolerant hybrid ND476 plants subjected to water-sufficient and water-deficit treatment conditions at flared (V12), tasseling (VT), the prophase of grain filling (R2), and the anaphase of grain filling (R4) crop growth stages, we report growth-stage-specific molecular mechanisms regulating maize drought stress responses. Based on the transcriptome analysis, a total of 3,451 differentially expressed genes (DEGs) were identified from the four experimental comparisons, with 2,403, 650, 397, and 313 DEGs observed at the V12, VT, R1, and R4 stages, respectively. Subsequently, 3,451 DEGs were divided into 12 modules by weighted gene co-expression network analysis (WGCNA), comprising 277 hub genes. Interestingly, the co-expressed genes that clustered into similar modules exhibited diverse expression tendencies and got annotated to different GO terms at different stages. MapMan analysis revealed that DEGs related to stress signal transduction, detoxification, transcription factor regulation, hormone signaling, and secondary metabolites biosynthesis were universal across the four growth stages. However, DEGs associated with photosynthesis and amino acid metabolism; protein degradation; transport; and RNA transcriptional regulation were uniquely enriched at the V12, VT, R2, and R4 stages, respectively. Our results affirmed that maize drought stress adaptation is a growth-stage-specific response process, and aid in clarifying the fundamental growth-stage-specific mechanisms regulating drought stress responses in maize. Moreover, genes and metabolic pathways identified here can serve as valuable genetic resources or selection targets for further functional validation experiments.

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Relationships between drought, heat and air humidity responses revealed by transcriptome-metabolome co-analysis.

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Relationships between drought, heat and air humidity responses revealed by transcriptome-metabolome co-analysis.

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