Data from: Mycorrhization and chemical seed priming boost tomato stress tolerance by changing primary and defence metabolic pathways
Data files
Dec 14, 2024 version files 100.77 MB
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README.md
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Supplementary_Dataset_S31.csv
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Supplementary_Dataset_S32.csv
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Supplementary_Dataset_S33.csv
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Supplementary_Dataset_S34.csv
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Supplementary_Dataset_S35__.csv
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Supplementary_Dataset_S36.csv
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Supplementary_Datasets_S1.csv
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Abstract
Priming modulates plant stress responses before the stress appears, increasing the ability of the primed plant to endure adverse conditions and thrive. In this context, we investigated the effect of biological (i.e. arbuscular mycorrhizal fungi, AMF) agents and natural compounds (i.e. salicylic acid applied alone or combined with chitosan) against water deficit and salinity on a commercial tomato genotype (cv. Moneymaker). Effects of seed treatments on AMF colonization were evaluated, demonstrating the possibility of using them in combination. Responses to water and salt stresses were analysed on primed plants alone or in combination with the AMF inoculum in soil. Trials were conducted on potted plants by subjecting them to water deficit or salt stress. The effectiveness of chemical seed treatments, both alone and in combination with post-germination AMF inoculation, was investigated using a multidisciplinary approach that included ecophysiology, biochemistry, transcriptomics, and untargeted metabolomics. Results showed that chemical seed treatment and arbuscular mycorrhizal symbiosis modified the tomato response to water deficit and salinity triggering a remodelling of both transcriptome and metabolome, which ultimately elicited the plant antioxidant and osmoprotective machinery. The plant physiological adaptation to both stress conditions improved, confirming the success of the adopted approaches in enhancing stress tolerance.
README: Data from: Mycorrhization and chemical seed priming boost tomato stress tolerance by changing primary and defence metabolic pathways
https://doi.org/10.5061/dryad.dbrv15fb9
Description of the data and file structure
Results reported in Datasets S1-S30 were generated following elaboration of raw RNA-seq data, while results listed in Datasets S31-S36 are the output of data elaboration from untargeted metabolomics analyses. RNA-seq raw reads were submitted to NCBI Sequence Read Archive (SRA) under the BioProject accession number PRJNA1108677.
Datasets related to elaboration of RNA-seq data (Datasets S1-S30) **are a series of tables reporting sequencing statistics, read counts, list of differentially expressed genes (DEGs) with the related expression value for each analysed sample (i.e. **CTRL_NMYC_NS, CTRL_MYC_NS, SA_NMYC_NS, SA_MYC_NS, CHI+SA_NMYC_NS, CHI+SA_MYC_NS, CTRL_NMYC_WS, CTRL_MYC_WS, SA_NMYC_WS, SA_MYC_WS, CHI+SA_NMYC_WS, CHI+SA_MYC_WS, CTRL_NMYC_SS, CTRL_MYC_SS, SA_NMYC_SS, SA_MYC_SS, CHI+SA_NMYC_SS, CHI+SA_MYC_SS).
Datasets related to elaboration of untargeted metabolomics (Datasets S31-S36) **are a series of tables reporting altered mass chromatographic characteristics, annotation of significantly-altered mass chromatographic features and selection of significantly altered masses for each analysed sample (i.e. **CTRL_NMYC_NS, CTRL_MYC_NS, SA_NMYC_NS, SA_MYC_NS, CHI+SA_NMYC_NS, CHI+SA_MYC_NS, CTRL_NMYC_WS, CTRL_MYC_WS, SA_NMYC_WS, SA_MYC_WS, CHI+SA_NMYC_WS, CHI+SA_MYC_WS, CTRL_NMYC_SS, CTRL_MYC_SS, SA_NMYC_SS, SA_MYC_SS, CHI+SA_NMYC_SS, CHI+SA_MYC_SS).
Files and variables
VARIABLES:
WS = water stress
SS = salt stress
NS = not stressed
MYC = AMF-inoculated
NMYC = not inoculated
CTRL = control (untreated)
SA = salicylic acid-treated
CHI+SA = chitosan + salicylic acid-treated
File: Supplementary Dataset S1
*Description: **Summary Statistics of RNA-Seq alignment of tomato reads.*
The table reports, for each sample name, treatment (CTRL, SA, CHI+SA), inoculation (NMYC, MYC) and condition (NS, WS, SS), the number of reads (Reads (million)) and the aligned reads on the genome expressed as percentage (% Aligned) and as millions.
File: Supplementary Dataset S2
Description: Raw RNA-seq plant reads count. The table reports for each gene locus (Gene ID) the corresponding read count in each analysed sample. Sample name: CTRL_NMYC_NS, CTRL_MYC_NS, SA_NMYC_NS, SA_MYC_NS, CHI+SA_NMYC_NS, CHI+SA_MYC_NS, CTRL_NMYC_WS, CTRL_MYC_WS, SA_NMYC_WS, SA_MYC_WS, CHI+SA_NMYC_WS, CHI+SA_MYC_WS, CTRL_NMYC_SS, CTRL_MYC_SS, SA_NMYC_SS, SA_MYC_SS, CHI+SA_NMYC_SS, CHI+SA_MYC_SS.
File: Supplementary Datasets S3-S19
Description: These datasets report the lists of Differentially Expressed Genes (DEGs) obtained in each considered comparison (e.g. CTRL_MYC_NS vs CTRL_NMYC_NS) with indication of the expression level (expressed as log2FoldChange) for each gene (Gene ID), statistics (p-value, padj, e-value), gene annotation and gene ontology descriptions (GO). Lists of DEGs retrieved in each comparison are reported in separate datasets (named from S3 to S19); description of each dataset is reported following:
Supplementary Data Set S3. Significant (Padj<0.05) DEGs in CTRL_MYC_NS.
Supplementary Data Set S4. Significant (Padj<0.05) DEGs in SA_NMYC_NS.
Supplementary Data Set S5. Significant (Padj<0.05) DEGs in SA_MYC_NS.
Supplementary Data Set S6. Significant (Padj<0.05) DEGs in CHI+SA_NMYC_NS.
Supplementary Data Set S7. Significant (Padj<0.05) DEGs in CHI+SA_MYC_NS.
Supplementary Data Set S8. Significant (Padj<0.05) DEGs in CTRL_NMYC_WS.
Supplementary Data Set S9. Significant (Padj<0.05) DEGs in CTRL_MYC_WS.
Supplementary Data Set S10. Significant (Padj<0.05) DEGs in SA_NMYC_WS.
Supplementary Data Set S11. Significant (Padj<0.05) DEGs in SA_MYC_WS.
Supplementary Data Set S12. Significant (Padj<0.05) DEGs in CHI+SA_NMYC_WS.
Supplementary Data Set S13. Significant (Padj<0.05) DEGs in CHI+SA_MYC_WS.
Supplementary Data Set S14. Significant (Padj<0.05) DEGs in CTRL_NMYC_SS.
Supplementary Data Set S15. Significant (Padj<0.05) DEGs in CTRL_MYC_SS.
Supplementary Data Set S16. Significant (Padj<0.05) DEGs in SA_NMYC_SS.
Supplementary Data Set S17. Significant (Padj<0.05) DEGs in SA_MYC_SS.
Supplementary Data Set S18. Significant (Padj<0.05) DEGs in CHI+SA_NMYC_SS.
Supplementary Data Set S19. Significant (Padj<0.05) DEGs in CHI+SA_MYC_SS.
Variables
- CTRL: untreated, SA: treated with salicylic acid, CHI+SA: treated with chitosan + salicylic acid, NMYC: not-inoculated with AMF, MYC: inoculated with AMF, NS: not-stressed, WS: water stress, SS: salt stress.
File: Supplementary Dataset S20.
Description: This file report the list of all statistically significant DEGs in all the considered conditions. It thus represents a summary of significant DEGs resulting from the diverse comparisons.
Supplementary Dataset S20. Significant (Padj<0.05) DEGs in different conditions.
Variables
- CTRL: untreated, SA: treated with salicylic acid, CHI+SA: treated with chitosan + salicylic acid, NMYC: not-inoculated with AMF, MYC: inoculated with AMF, NS: not-stressed, WS: water stress, SS: salt stress.
File: Supplementary Datasets S21-S30
Description: Datasets from S21 to S30 report the list of up-regulated DEGs in each considered condition. For each list, the Gene ID, the fold change of expression in each sample name (CTRL_NMYC, CTRL_MYC, SA_NMYC, SA_MYC, CHI+SA_NMYC, CHI+SA_MYC), gene annotation, annotation statistics, gene ontology (GO) terms are reported. Captions for each dataset are following reported:
Supplementary Data Set S21. Significant (Padj<0.05) up-regulated DEG shared under water stress (WS).
Supplementary Data Set S22. Significant (Padj<0.05) up-regulated DEG shared under salt stress (SS).
Supplementary Data Set S23. DEGs dataset (Solyc) compared with DEGs dataset SOL3.1 (LOC). In light blue the genes that maintain the same trend.
Supplementary Data Set S24. Significant (Padj<0.05) up-regulated DEGs shared in mycorrhizal plants under water stress (MYC_WS).
Supplementary Data Set S25. Significant (Padj<0.05) up-regulated DEGs shared in mycorrhizal plants under salt stress (MYC_SS).
Supplementary Data Set S26. Comparison of DEGs up-regulated in SA_MYC_NS with DEGs up-regulated in CTRL_MYC_NS.
Supplementary Data Set S27. Significant DEG (Padj<0.05) up-regulated exclusively in mycorrhized plants, treated with salicylic acid (SA), under water stress (SA_MYC_WS).
Supplementary Data Set S28. Significant DEG (Padj<0.05) up-regulated exclusively in mycorrhizal plants, treated with salicylic acid (SA), under salt stress (SA_MYC_SS).
Supplementary Data Set S29. Significant DEG (Padj<0.05) up-regulated exclusively in mycorrhized plants, treated with salicylic acid (CHI+SA), under water stress (CHI+SA_MYC_WS).
Supplementary Data Set S30. Significant DEG (Padj<0.05) up-regulated exclusively in mycorrhized plants, treated with salicylic acid (CHI+SA), under salt stress (CHI+SA_MYC_SS).
Variables
- CTRL: untreated, SA: treated with salicylic acid, CHI+SA: treated with chitosan + salicylic acid, NMYC: not-inoculated with AMF, MYC: inoculated with AMF, NS: not-stressed, WS: water stress, SS: salt stress.
File: Supplementary Dataset S31
Description: This dataset reports the altered mass chromatographic characteristics (positive separation; H+). For each metabolite (V1), the mass to charge ratio (mz), retention time in seconds (rt), isotopes, adduct, pcgroup, p-value (ANOVA), and npeaks are indicated.
Variables
- CTRL: untreated, SA: treated with salicylic acid, CHI+SA: treated with chitosan + salicylic acid, NMYC: not-inoculated with AMF, MYC: inoculated with AMF, NS: not-stressed, WS: water stress, SS: salt stress.
File: Supplementary Dataset S32
Description: This dataset reports the altered mass chromatographic characteristics (negative separation; H-) of the predicted metabolites in each sample. For each metabolite (V1), the mass to charge ratio (mz), retention time in seconds (rt), isotopes, adduct, pcgroup, p-value (ANOVA), and npeaks are indicated.
Variables
- CTRL: untreated, SA: treated with salicylic acid, CHI+SA: treated with chitosan + salicylic acid, NMYC: not-inoculated with AMF, MYC: inoculated with AMF, NS: not-stressed, WS: water stress, SS: salt stress.
File: Supplementary Dataset S33
Description: This dataset reports the annotation of significantly-altered mass chromatographic features (positive separation; H+). For each annotated metabolite (Annotation), the mass to charge ratio (mz), retention time in seconds (rt), isotopes (ion), MS/MS, and p-value (anova) are indicated together with the peak intensity value for each sample.
Variables
- CTRL: untreated, SA: treated with salicylic acid, CHI+SA: treated with chitosan + salicylic acid, NMYC: not-inoculated with AMF, MYC: inoculated with AMF, NS: not-stressed , WS: water stress, SS: salt stress.
File: Supplementary Dataset S34
Description: This dataset reports the annotation of significantly-altered mass chromatographic features (negative separation; H-). For each annotated metabolite (Annotation), mass to charge ratio (mz), retention time in seconds (rt), isotopes (ion), MS/MS, and p-value (anova) are indicated together with the peak intensity value for each sample.
Variables
- CTRL: untreated, SA: treated with salicylic acid, CHI+SA: treated with chitosan + salicylic acid, NMYC: not-inoculated with AMF, MYC: inoculated with AMF, NS: not-stressed, WS: water stress, SS: salt stress.
File: Supplementary Dataset S35
Description: This dataset reports the annotation of significantly-altered mass chromatographic features (positive and negative separation; H+, H-). For each significantly-altered mass chromatographic feature, the adjusted annotation, the original annotation, the class and the sub class of the correponsing predicted metabolite are reported together with values of peak areas in each analysed sample. The peak areas were normalized to internal standard area and actual sample weight.
Variables
- CTRL: untreated, SA: treated with salicylic acid, CHI+SA: treated with chitosan + salicylic acid, NMYC: not-inoculated with AMF, MYC: inoculated with AMF, NS: not-stressed, WS: water stress, SS: salt stress.
File: Supplementary Dataset S36
Description: This dataset reports the selection of significantly altered masses (positive and negative separation; H+, H-). For each metabolite reported in the first column of the table are indicated the corrisponding values of peak ares in the analysed samples. Peak areas were normalized to internal standard area and actual sample weight.
Variables
- CTRL: untreated, SA: treated with salicylic acid, CHI+SA: treated with chitosan + salicylic acid, NMYC: not-inoculated with AMF, MYC: inoculated with AMF, NS: not-stressed, WS: water stress, SS: salt stress.
Code/software
.csv
Methods
Results reported in Datasets from S1 to S36 were generated after bioinformatic elaboration of RNA-seq (Datasets from S1 to S30) and untargeted metabolomics (Datasets from S31 to S36) data.
Data elaboration procedures are following reported:
RNA-seq data analysis
For alignment, reads were mapped onto the reference genome GCF_000188115.5_SL3.1 (Hosmani et al., 2019) using STAR v. 2.7.10 (Dobin et al., 2013), a splice junction mapper designed for RNA-Seq reads, under default parameters. The software htseq-count v. 2.0.2 (Anders et al., 2015) was utilized to count the overlapping of reads with genes. The data were then used to identify differentially expressed genes (DEGs) using the DESeq2 package v1.34.0 (Love et al., 2014). The variance on read count was calculated based on three biological replicates per condition by applying a negative binomial distribution to model the count data, therefore identifying genes showing significant expression changes among the different tested conditions. The DEG identification was performed after normalization of the count data and correction for multiple testing, both accounted by DESeq2, through the Wald test. During DESeq2 analysis, the shrinkage estimation of effect size (LFC estimates) was used, to generate more accurate Log2 foldchange estimates and considering the variability among replicates. A cut-off of the p-adjusted value < 0.05 was used to classify a gene differentially expressed (DEG) in comparison with the reference (i.e. untreated (CTRL) in not-stressed condition (NS) and without AM fungal inoculation (NMYC). Both the identified DEGs and all transcripts of the tomato (Solanum lycopersicum L.) transcriptome were annotated through Blast2GO v5.2.5 (Conesa et al., 2005) to obtain an updated functional annotation and to assign the corresponding Gene Ontology (GO) terms. A gene class functional enrichment analysis was then conducted using Blast2GO to reveal the biological processes, pathways, or other functional categories that are enriched among the identified DEGs.
Non-targeted polar metabolite profiling
Polar metabolites were separated using hydrophilic interaction liquid chromatography (HILIC) coupled to hybrid quadrupole-time of flight mass spectrometry (QTOF-MS) according to Andrade et al. (2021). HILIC separation was performed on a 2.1×100 mm InfinityLab Poroshell 120 HILIC-Z, 1.9 µm (Agilent Technologies, Inc., Santa Clara, CA, USA) using acetonitrile:water, 95:5 (v/v) (solvent A) and acetonitrile:water, 2:98 (v/v) (solvent B), both supplemented with ammonium formate at 0.063% and 0.126%, respectively, as solvents and at a flow rate of 0.3 ml min−1. During chromatographic runs, column temperature was at 40°C. Tomato leaf samples (10 mg of dry weight) were extracted in triplicate by ultrasonication in 300 µl of 80% aqueous methanol supplemented with kinetin (1 mg l−1) as internal standard for relative quantitation. After extraction, samples were centrifuged at 10,000 rpm and 4ºC for 10 min and the supernatants recovered. Subsequently, supernatants were diluted 1:4 with acetonitrile (LC/MS grade) and filtered through 0.2 µm PTFE syringe filters directly into chromatography vials. Mass chromatographic data were acquired in positive and negative ionization modes within the 50–1000 amu mass range. Nitrogen was used both as nebulization and desolvation gas (60 and 800 l h−1 and 350°C temperature, respectively). During measurements, capillary and cone voltages were set at 3.5 kV and 30 V for positive electrospray and 2.3 kV and 30 V for negative electrospray, respectively. An additional acquisition function to obtain collision-induced dissociation (CID) information was set by performing a voltage ramp between 6–40 eV. To ensure accurate mass data acquisition, a lockmass reference (Leu-Enkephalin, [M+H]+ 556.2771 and [M-H]- 554.2614) was regularly infused during runs. Data files from each ionization mode were converted to mzML with msconvert (Chambers et al. 2012) and processed independently with XCMS (Smith et al., 2006). Mass chromatographic features were annotated and grouped with CAMERA (Kuhl et al., 2012). Peak areas were normalized to internal standard area and actual sample weight before statistical analyses. Significantly altered mass chromatographic features were subsequently identified as individual compounds by matching mz and retention time values with those of authentic standards or tentatively annotated by matching experimental mass spectra in public databases (Metlin, Massbank or HMDB).