Transcriptomic analysis of CO2-treated strawberries (Fragaria vesca) with enhanced resistance to softening and oxidative stress at consumption

Frontiers in Plant Science 01 frontiersin.org Transcriptomic analysis of CO2-treated strawberries (Fragaria vesca) with enhanced resistance to softening and oxidative stress at consumption Ivandel Olmo , IreneRomero , MariaDoloresAlvarez , RosaTarradas , MariaTeresaSanchez-Ballesta , MariaIsabelEscribano and CarmenMerodio * Laboratory of Biotechnology and Postharvest Quality, Department of Characterization, Quality and Security, Institute of Food Science, Technology and Nutrition (ICTAN-CSIC), Madrid, Spain One of the greatest threats to wild strawberries (Fragaria vesca Mara des Bois) after harvest is the highly perishability at ambient temperature. Breeders have successfully met the quality demands of consumers, but the prevention of waste after harvest in fleshy fruits is still pending. Most of the waste is due to the accelerated progress of senescence-like process after harvest linked to a rapid loss of water and firmness at ambient temperature. The storage life of strawberries increases at low temperature, but their quality is limited by the loss of cell structure. The application of high CO2 concentrations increased firmness during cold storage. However, the key genes related to resistance to softening and cell wall disassembly following transference from cold storage at 20°C remain unclear. Therefore, weperformed RNAseq analysis, constructing a weighted gene co-expression network analysis (WGCNA) to identify which molecular determinants play a role in cell wall integrity, using strawberries with contrasting storage conditions, CO2-cold stored (CCS), air-cold stored (ACS), non-cold stored (NCS) kept at ambient temperature, and intact fruit at harvest (AH). The hub genes associated with the cell wall structural architecture of firmer CO2-treated strawberries revealed xyloglucans stabilization attributed mainly to a down-regulation of Csl E1, XTH 15, Exp-like B1 and the maintenance of expression levels of nucleotide sugars transferases such as GMP and FUT as well as improved lamella integrity linked to a down-regulation of RG-lyase, PL-like and PME. The preservation of cell wall elasticity together with the up-regulation of LEA, EXPA4, and MATE, required to maintain cell turgor, is the mechanisms controlled by high CO2. In stressed air-cold stored strawberries, in addition to an acute softening, there is a preferential transcript accumulation of genes involved in lignin and raffinose pathways. Non-cold stored strawberries kept at 20°C after harvest are characterized by an enrichment in genes mainly involved in oxidative stress and up-expression of genes involved in jasmonate biosynthesis. The present results on transcriptomic analysis of CO2-treated strawberries with enhanced resistance to softening and oxidative stress at consumption will help to improve breeding strategies of both wild and cultivated strawberries. TYPE Original Research PUBLISHED 19 August 2022 DOI 10.3389/fpls.2022.983976 OPEN ACCESS EDITED BY Isabel Lara, Universitat de Lleida, Spain REVIEWED BY Patricio Ramos, Universidad Católica de Maule, Chile Lida Fuentes-Viveros, Pontifical Catholic University of Valparaíso, Chile *CORRESPONDENCE Carmen Merodio [email protected] SPECIALTY SECTION This article was submitted to Crop and Product Physiology, a section of the journal Frontiers in Plant Science RECEIVED 01 July 2022 ACCEPTED 28 July 2022 PUBLISHED 19 August 2022 CITATION del Olmo I, Romero I, Alvarez MD, Tarradas R, Sanchez-Ballesta MT, Escribano MI and Merodio C (2022) Transcriptomic analysis of CO2-treated strawberries (Fragaria vesca) with enhanced resistance to softening and oxidative stress at consumption. Front. Plant Sci. 13:983976. doi: 10.3389/fpls.2022.983976 COPYRIGHT © 2022 del Olmo, Romero, Alvarez, Tarradas, Sanchez-Ballesta, Escribano and Merodio. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. del Olmo et al. 10.3389/fpls.2022.983976 Frontiers in Plant Science 02 frontiersin.org KEYWORDS cell wall, CO2, firmness, H2O2, lignin, RNA-seq, xyloglucans, strawberries Highlights - Gene co-expression network analysis of the transcriptome in CO2-treated strawberries identifies genes involved in softening resistance at consumption. - Identification of molecular determinants underlying the protection against senescence-like process by high CO 2 . - Oxidative stress in non-cold stored strawberries kept at 20°C after harvest. - Softening and up-regulation of genes involved in lignin and raffinose pathways in stressed air-cold stored strawberries. - Preservation of cell wall elasticity, xyloglucans stabilization and improved lamella integrity in CO2treated strawberries. Introduction Strawberries undergo intense breeding selection for new cultivars based on fruit quality traits (Barbey etal., 2020). Breeders have successfully met the demands of consumer preference in taste and nutritional values, but the prevention of waste after harvest in fleshy fruits is still pending. Most of the waste is due to the accelerated progress of senescence-like process after harvest linked to a loss of texture of the fruit. However, genetic studies conducted on texture at consumption after postharvest storage have been limited, partly due to the complexities associated with quantifying it (Cockerton etal., 2021). Hence, understanding of the molecular biology underlying the regulation of fruit texture after harvest is a strategy to tackle eating quality and fruit loss prevention. The softening of strawberries during developmental ripening process is well-studied (Harrison etal., 2001; Park and Cosgrove, 2015; MoyaLeón etal., 2019; Witasari etal., 2019). Different transgenic strategies have been applied to assess the role of specific genes in controlling fruit softening and in some cases, down-regulating the genes involved in pectin solubilization and depolymerization (SantiagoDoménech etal., 2008; Molina-Hidalgo etal., 2013; Jara etal., 2019). Alternatively, the information gained on wild strawberry genes from the application of beneficial postharvest treatments that enhance firmness may betranslated into useful and straightforward ways to improve fruit eating quality. Specifically, wild strawberry Fragaria vesca is an important model system for the increased demand of fruit quality traits (Diamanti etal., 2012) in cultivated strawberry because of its diploid genome, availability of extensive transcriptome data, and a range of molecular genetic tools (Hawkins etal., 2017). A problem with wild strawberries is that at ambient temperature they experience a quick softening, fungal attack and water loss. Low temperature storage around 0°C is one of the most important factors to extend storage strawberry life and to delay softening. Although strawberries are classified as not susceptible to chilling injury, they are prone to producing exudate and application of technologies improving water retention is needed. Short-term high CO2 treatments help strawberries to face temperature shifts at 0°C (Blanch etal., 2015). In cultivated strawberries, the beneficial effect of high CO2 concentrations (15–20% CO2) to reduce softening during cold storage is well-known (Harker etal., 2000). The effect of high CO 2 in the delay of softening during cold storage depends on cultivar., maturity and storage length (Ponce-Valadez etal., 2009), and in some cultivars, including Fragaria vesca Mara des Bois, high CO2-induced firmness enhancement (Smith and Skog, 1992; Larsen and Watkins, 1995; Harker etal., 2000; Blanch etal., 2019). However, little is known whether the firming effect induced by high CO2 concentrations at low temperature was maintained after transfer at 20°C. Since firmness at the time of consumption is crucial to achieve desirable organoleptic characteristics and to reduce waste, it is of importance to understand the effect of high CO2 after transference at 20°C following cold storage. Additionally, while the effect of low temperature and short-term high CO2 in postharvest storage of berries on transcriptome profiling has been analyzed (PonceValadez etal., 2009; Rosales etal., 2016; Bang etal., 2019) less attention has been paid to the molecular mechanisms in response to the temperature shifts from cold storage at 20°C, which greatly affects fruit quality. Moreover, it cannot be assumed that fruit responses at 20°C after transfer from different cold storage conditions will beidentical to that of non-stored fruit, and it is possible that the activation of cold-stress responses to temperature shifts at 0°C might accelerate the loss of firmness at the moment of consumption. Clearly, the regulation of texture in response to temperature shift at 20°C from cold storage is quite complex and new approaches are needed, including a better understanding of the relationship between changes in the textural properties and senescence-like process. The crucial role of turgor, the force exerted on the cell membrane by intracellular fluid for cell separations in the textural properties of fruit, is well recognized (Toivonen and Brummell, 2008; Castellarin et al., 2016). For the contribution to osmotic adjustment for turgor maintenance and cell wall structure, the metabolism of sugars, especially sucrose, the main reserve Abbreviations: GDR, Genome database for rosaceae; H 2 O 2 , Hydrogen peroxide; JA, Jasmonic acid; RFOs, Raffinose family oligosaccharides; RNA-seq, RNA sequencing; RPKM, Reads per kilobase Million; SEA, Singular enrichment analysis; TEG, Triethylene glycol; TE, Trolox equivalents; WGCNA, Weighted gene co-expression network analysis. del Olmo et al. 10.3389/fpls.2022.983976 Frontiers in Plant Science 03 frontiersin.org carbohydrate in strawberries, takes on particular importance. Wepreviously reported that in the diploid Mara des Bois strawberries, other beneficial responses were found concerning high CO 2 treatment, primarily sucrose accumulation and water retention (del Olmo etal., 2020). The aim of the present work was firstly to determine which molecular mechanisms participate in senescence-like responses and degradative process controlled by high CO2 levels. Secondly, to identify the key genes related to resistance to softening and cell wall disassembly following transference from cold storage at 20°C. For this, weundertook a transcriptomic approach on Mara des Bois strawberries with storage conditions, with CO 2 -cold stored (CCS), air-cold stored (ACS), non-cold stored (NCS) and intact fruit at harvest (AH) acting as quality control samples. We applied weight gene co-expression network analysis (WGCNA) to explore candidate biomarkers of firmness from the perspective of a weighted network. Furthermore, generating the whole genome transcriptome of Fragaria vesca serves as a powerful tool for unraveling cross-talk molecular mechanisms of softening and senescence, which may have negative impacts on quality of wild and cultivated strawberries. Materials and methods Plant material Mara des Bois strawberries (Fragaria vesca) were harvested from a commercial crop in San Sebastian de los Reyes (Madrid, Spain). The ripe strawberries immediately after harvest (2 h) were used as the control (AH). 40 boxes of 200 g of fruit placed directly in a chamber at 20°C and 80% RH for 2 days were the non-cold stored (NCS). Another group of 80 boxes of fruit were randomly divided into two lots and stored in two containers at 1°C. One container was stored in air for 7 days and then transferred at 20°C for 1 day (conditions employed to simulate a time in transit) and used to analyze the air-cold stored fruit (ACS). The other container was pretreated for 2 days with a gas mixture containing 18% CO2 + 18% O2 + 64% N2, then air-ventilated for another 5 days and thereafter transferred at 20°C for 1 day and used to analyze the CO2-cold stored fruit (CCS). Five boxes (90 strawberries) from each storage condition (control, non-cold stored, air-cold stored and CO2-cold stored) were collected. 45 strawberries were assessed for texture and quality, while another 45 were divided into three batches of 15 berries and frozen in liquid nitrogen and stored at-80°C until analysis. Glutathione, lignin, H2O2 and major soluble sugars Glutation content was determined following the extraction of 100 mg of frozen strawberry in 0.5 ml of 5% 5-sulfosalicylic acid (SSA), After centrifugation at 8000 x g for 10 min, the supernatant was recovered and diluted (1:10) with ddH 2 O. Glutathione content (GSH + GSSG, reduced plus oxidized glutathione forms) was established by using the commercial kit from Sigma-Aldrich, with the change in absorbance at 405 nm recorded over 10 min using a BioTek PowerWave XS microplate reader (BioTek, France). Glutathione concentrations were extrapolated from standard curves of GSSG y = 0.0366x–0.0121; R2 = 0.9988 and Glutathione total y = 0.0251x–0.0159; R2 = 0.9997. Lignin content was determined using activated triethylene glycol (TEG) with modifications (Edwards, 1973). One L of TEG was activated with 6.3 ml of HCl (37%). One g of frozen strawberry powder was suspended in 7 ml of activated TEG and left for 1 h at 121°C. The samples were centrifuged at 4,000 rpm for 10 min, and the supernatant was recovered. The residue was extracted a second time by adding 7 ml of activated TEG, agitated in vortex and centrifugated at 4,000 rpm for 10 min. The supernatants were combined and diluted at 1:50, and their absorbance was measured at 280 nm using a BioTek PowerWave XS microplate reader (BioTek, France). A calibration curve was prepared in the concentration range from 0 to 100 mg l−1 of lignin (Sigma-Aldrich; y = 0.0284x–0.0216; R2 = 0.9931). Hydrogen peroxide was measured photometrically after reaction with KI. 500 mg of frozen strawberry powder was suspended in 1.5 ml of ice-cold 0.1% TCA. The samples were centrifuged at 12,000 rpm for 15 min at 4°C and the supernatant was recovered. The reaction mixture consisted of 0.2 ml supernatant, 0.2 ml H2O, 12 μl EDTA 10 mM, and 0.8 ml KI 1 M. The reaction was developed for 30 min in darkness and absorbance measured at 390 nm using a BioTek PowerWave XS microplate reader (BioTek, France). A calibration curve of H2O2 (Sigma-Aldrich) from 0 to 150 μM was prepared (y = 0.0011x– 0.0015; R 2 = 0.9997). For sugar analyses, 2 g of frozen strawberry powder (wet) were extracted with 5 ml of distilled water and soluble glucose, fructose, sucrose and xylose were determined as described by del Olmo etal. (2020). Experimental data represent the mean and SD of the three replicates. Each biological replicate was composed of 15 pooled strawberries. Texture measurement Strawberry firmness was analyzed using a TA.HDPlus Texture Analyzer (Stable Micro Systems, Ltd., Godalming, United Kingdom) provided with Texture Exponent software (version 6.1.13.0) and equipped with a 30 kg load cell. Volodkevich tests were carried out using the upper Volodkevich Bite Jaw probe, which performs an imitative test by simulating the action of an incisor tooth biting through the strawberry. Each fruit was placed on the stationary plate, and the biting action was given by the compressive movement of the upper jaw shearing into the equator of the sample to a curve, from which weobtained the first force peak (N), the average shearing force (N), and the shearing energy (J). del Olmo et al. 10.3389/fpls.2022.983976 Frontiers in Plant Science 04 frontiersin.org RNA extraction and quality assessment The total RNA of three biological replicate samples was extracted from 0.5 g of fruit powder in accordance with Yu etal. (2012). Each biological replicate sample contained a fruit mixture of at least 50 strawberries. A TURBO DNase™ enzyme (Ambion, Austin, TX, UnitedStates) treatment was used to degrade the contaminant DNA. RNA quantity and purity were measured with the NanoDrop ND-1000 spectrophotometer (Thermo Scientific). RNA integrity was determined by the RNA integrity number (RIN), using a 2,100 Bioanalyzer (Agilent) at the CNAG-CRG (Centro Nacional de Análisis Genómico, Barcelona, Spain). RNA library construction and RNA sequencing Samples were sequenced at the CNAG-CRG. The mRNA from each biological replica was employed to prepare the RNA-Seq libraries with the reagents provided in the Illumina®TruSeq™ Stranded Total RNA kit protocol (Illumina Inc., San Diego, CA, UnitedStates). The size and quality of the libraries were quality controlled in Agilent DNA 2100 Bioanalyzer assay (Agilent). Each library was sequenced using Illumina HiSeq™ 2,500 (Illumina Inc.), in paired-end mode with a read length of 2 × 50 bp, generating minimally 39 million paired-end reads per replica and passing filter for each RNA-Seq library in a fraction of a sequencing lane of the sequencer following the manufacturer’s protocol (max. 200 M/lane). Weobtained the following million paired-end reads in each replica: 45, 47, and 64 for AH; 39, 34, and 50 for SL; 39, 45, and 57 for ACS; and 41, 45, and 51 for CCS. Image analysis, base calling, and base quality scoring of the run were processed by Sequencer Software HiSeq Control Software 2.2.58—Real-Time Analysis (RTA 1.13.48), followed by the generation of FASTQ sequence files by CASAVA 1.8. The base quality of the sequences obtained was also checked with the FastQC tool,1 and SeqMonk Mapped Sequence Data Analyzer (version: 1.46.0; Babraham Bioinformatics, UnitedKingdom). Likewise, FASTQ Groomer (Version 1.1.5) and Cutadapt (Version 1.16.6) were used to optimize the reads, converting FASTQ quality formats and removing adapter sequences. Sequence alignment with the reference genome The aligned files were generated with the HISAT 2.1.0 program (Langmead etal., 2009) that was employed for aligning the sequences against Fragaria vesca Whole Genome v4.0.a2 (Li etal., 2019). 1 http://www.bioinformatics.babraham.ac.uk/projects/fastqc/ Differential expression analysis DESeq2 was used to identify differentially expressed genes (DEGs) based on the negative binomial distribution that is generalized for linear models (Love etal., 2014). This program allows us to select our triplicates with at least two data stores in each and will identify probes whose representation differs significantly in the two sets. The RNA-Seq quantitation pipeline tool of SeqMonk Mapped Sequence Data Analyzer (version: 1.46.0; Babraham Bioinformatics, UnitedKingdom) was used to analyze RNA-Seq expression data. The DESeq2 stats filter was applied in all probes where each comparison (AH vs. SL, AH vs. ACS, AH vs. CCS, NCS vs. ACS, NCS vs. CCS, ACS vs. CCS) had a significance below 0.05 after Benjamini and Hochberg correction was used with independent intensity filtering. Quantitation was performed through RNA-Seq pipeline quantitation counting reads over exons as raw counts, assuming a non-strand specific library. Therefore, each DEGs of each comparison yielded the following number from the 34,008 total probes: (6,167, 7,587, 6,831, 7,796, 7,876, and 3,900 probes, respectively). Finally, the DEGs were delimited based on an absolute log 2 -fold change (Fc) value ≥1.5 for activated genes and ≤ −1.5 for repressed genes which in turn have a False Discovery Rate ≤ 0.05 (FDR≤ 0.05). Venn diagrams comparing the different sets of DEGs were plotted with the Venn Diagram Plotter.2 Weighted gene co-expression network analysis Co-expression network analysis was performed using the WGCNA package in R (Version: 1.68; Langfelder and Horvath, 2008). The raw data was prepared before running the WGCNA package by carrying out the following steps. The raw counts for 34,008 genes were normalized on Reads Per Kilobase Million (RPKM). Transcript features over each gene were performed using SeqMonk Mapped Sequence Data Analyzer (version: 1.46.0; Babraham Bioinformatics, United Kingdom). Thus, the RPKM quantification for existing probes was performed with the Read Count Quantitation, using all reads corrected for total count per million reads, which were then corrected by the probe length. The normalized count file was adjusted to match the format that the WGCNA package needed, checking gene outliers and removing them until the cuts were passed. Next, weexamined the sample network based on squared Euclidean. The whole network connectivity was calculated, with samples designated as outlying if their Z.k value was below the threshold (thresholdZ.k = −2.5). Therefore, the outlying samples were removed from expression and trait data, and a set of soft-thresholding powers of 20, powers = c (1:20), were selected. Wewere then able to use the pickSoftThreshold function that analyses network topology to choose a proper 2 http://omics.pnl.gov/software/venn-diagram-plotter del Olmo et al. 10.3389/fpls.2022.983976 Frontiers in Plant Science 05 frontiersin.org soft-thresholding power (sft, power = 8). Firstly, we employed automatic module detection via dynamic tree cutting, where the function blockwiseModules automatically implements all steps of module detection. Afterward, wecalculated a one-step network and defined the gene significance variable (GS.value) for each module color. An intramodular analysis was then carried out, identifying genes with high GS and the color member module, and calculating the gene relationship to trait and important modules. In this way, the gene significance (GS) and module (kME) correlation for each module color were obtained. Wealso performed stepwise manual module detection to represent the relationships between the modules and trait. Thus, weprecisely determined which color module should beselected, defining a dissimilarity based on the topological overlap with the dissTOM = TOMdist (A) function. The cutting method for selecting the module is detailed in Langfelder etal. (2008). Finally, in order to construct and analyze a network with such a large number of nodes, weused the “bwnet” function to construct a network with the blockwiseModules function calculating the topographical overlap matrix (TOM). Finally, the network was visualized using Cytoscape_v3.7.2. Functional annotation of unigenes and annotation enrichment analysis We used the ontological annotations described in The Genome Database for Rosaceae (GDR), which comprises about 52% of all Fragaria vesca genome annotations, and enriched them with those obtained in similarity sequence analysis from Arabidopsis ontology databases (23% of the annotations) and UNIPROT databases (13% of the annotations). Thus, it was possible to cover 88% of ontologies from the total (≈ 30,000 ORF) of the annotations of Fragaria vesca genome. A Singular Enrichment Analysis (SEA; Du etal., 2010) was performed to iteratively test the functional category functionally enriched (FDR ≤ 0.05) deemed as being enriched and therefore representative of each condition. These ontological analyses have been represented schematically, using descriptions that include the GO categories collected in databases with tools such as REVIGO (Supek etal., 2011), making it possible to visualize long lists of Genetic Ontology terms. Validation of differentially expressed genes by RT-PCR The cDNA was prepared by reverse transcription of 1 μg of total RNA using the Maxima cDNA Kit with the dsDNase kit (Thermo Fisher Scientific, Waltham, MA, United States) following the manufacturer’s instructions. Quantification was performed by realtime quantitative RT-PCR (qPCR) using iCycler iQ™ Real-Time PCR Detection System (BIORAD) and quantified using Real-Time Detection System Software (version 2.0). The amplification reactions were carried out in a final volume of 12 μl containing 6 μl of NZY qPCR Green Master Mix (2×; NZYTech, Ltd), 1 μl of each primer (10 μM), and 1 μl of the cDNA. The PCR profile used was 2 min at 50°C, 95°C for 10 min, followed by 40 cycles of 20 s at 95°C and 30 s at 55 or 60°C. Three technical replicates were made from each of the genes studied. Gene expression was determined by the 2−ΔΔCT method using the F. vesca Actin-97-like (XM_004307470; FvH4_7g22410; gene26612) as a housekeeping gene. The Fragaria vesca eFP Browser (Darwish etal., 2013) provided us with a basis from which weselected FvACT as a housekeeping gene (Hollender et al., 2014), since it shows less variability in expression for all developmental stages studied than other housekeeping genes. The specific primers used are described in Supplementary Table S1 and PCR amplicons were sequenced to confirm specificity. Statistical analysis Data were analyzed by ANOVA (one-way analysis of variance), and Duncan’s multiple range test was used (IBM Corp. SPSS Statistics version 22.0. Armonk, NY, United States). Statistical significance was assessed at the level p ≤ 0.05. Results Strawberries with contrasting cold storage exhibiting different senescence-like responses and oxidative stress markers Strawberries immediately after harvest (AH) as a control, those placed directly in a chamber at 20°C for 2 days (NCS) and strawberries transferred at 20°C for 1 day following the 7-day storage at 1°C in air (ACS) or CO2-cold stored fruit (CCS) were analyzed. Weselected the hexoses/sucrose ratio as indicator of senescence-like process During stress and in senescing leaves, hexose sugars often accumulate, resulting in an increased hexose/ sucrose ratio (Wingler and Roitsch, 2008). In addition, wepreviously reported that CO2-treated strawberries reduced sucrose degradation primarily linked to a down-regulation of vacuolar invertase (FvVINV2) and cell wall invertase (FvCWINV1) transcripts (del Olmo etal., 2020). Although little is known about senescence-specific marker in fruit, wesuggest that the relative ratio between hexoses and sucrose rather than the absolute concentration of sugars may bea good marker of senescence-like process in fruit. The content of H2O2 and glutathione pool was selected to monitor fluctuations in oxidative stress (Figure1). The content of lignin and the soluble xylose were also determined. The results indicate that the overall ratio of hexoses (glucose and fructose) to sucrose as well as the content of xylose within CCS was the lowest in comparison with ACS and even NCS. The highest content of H2O2 was found in NCS. Furthermore, NCS samples showed a significant decrease in the glutathione pool and a shift in the GSH redox status, with the glutathione pool del Olmo et al. 10.3389/fpls.2022.983976 Frontiers in Plant Science 06 frontiersin.org becoming more oxidized. The highest amount of lignin was quantified in ACS. These results suggest that strawberries under the three storage conditions are ideal for studying the molecular mechanism of cold storage and the effect of high CO2 pretreatment during cold storage to overcome oxidative stress and to repress senescence-like process at consumption. Differential expression analysis and gene ontology analysis of strawberries with contrasting cold storage In the differential expression analysis, the gene expression levels of strawberries with contrasting cold storage conditions: non-cold stored (NCS), air-cold stored (ACS), and CO2-cold stored (CCS) were compared using a pairwise analysis with fruit at harvest (AH). Out of the total of 34,000 genes annotated in Fragaria vesca, 1,681 genes changed their expression in NCS, 2230in ACS and 1762in CCS, respectively. Gene expression changes observed at a global level in NCS, ACS, and CCS only represent 4.9, 6.6, and 5.2% of the total strawberry transcriptome (Figure2A). In CCS, 77.3% (4% of the total) of the genes were activated, with 22.7% (1.2% of the total) being repressed. Venn diagrams summarize the number of overlapping differentially expressed genes in NCS, ACS, and CCS (Figure2B). The smallest number of differently expressed genes was observed in AH versus CCS. A Singular Enrichment Analysis (SEA) was performed (FDR ≤ 0.05) for each of the three differential expressions initially analyzed (AH vs. NCS; AH vs. ACS; AH vs. CCS), obtaining a total enrichment (TE) and a specific enrichment (SE) for each of them. FIGURE1 Strawberries with contrasting cold storage conditions exhibiting different senescence-like responses and oxidative stress markers including: ratio of hexoses/sucrose, content of xylose, H2O2 and glutathione (GSH + GSSG reduced plus oxidized glutathione forms), ratio of GSSG/GSH and lignin content. Strawberries immediately after harvest were used as the control sample (AH). Those placed directly in a chamber at 20°C were used as non-cold stored samples (NCS), while the strawberries transferred at 20°C following cold-stored fruit (ACS) or CO2-cold stored fruit (CCS). Graphs represent the average of at least three independent replicates (each one contains 15 fruits). Mean values (n = 3 ± standard deviation). (a-c) For each parameter, mean values with different letters are significantly different (p < 0.05) according to the Tukey’s multiple range test. del Olmo et al. 10.3389/fpls.2022.983976 Frontiers in Plant Science 07 frontiersin.org Figures 3A,B show how metabolic and response to stimulus processes that were enriched in NCS seem to beclosely related to response to oxidative stress processes. As can beseen in Figure4 there is an enrichment mainly of genes included in the categories with redox and transferase activity. Categories of molecular functions that are unique and specifically enriched (Supplementary Figures S1, S2 in red) include primarily genes encoding proteins with peroxidase activity, transferases that transfer hexosyl groups, together with hydrolases. In AH vs. ACS, an increase in the differential expression of genes plays a role in the response to oxygen levels and the multiorganism process (Supplementary Figure S3). The set of differentially expressed oxygen response genes (Figure5) appears to bemediating in response to hydrogen peroxide and reactive oxygen species, as well as to decreased oxygen levels and hypoxia, and as a cellular response to fermentative alcohol levels (Figure5 and Supplementary Figure S3 in red). Molecular function categories focus on the regulation of catalytic and transporter activities. Specifically, there is a unique and characteristic differential enrichment (Supplementary Figures S4, S5 in red) in activities such as peroxidase, along with jasmonates. In the transcriptional reconfiguration that occurs in AH vs. CCS, a single-organism process, stimulus response and signaling have key roles with differential expression of genes involved in xyloglucan (XG) metabolism (Figure 6; Supplementary Figure S6). Specific enrichment also continues to be primarily linked to A B FIGURE2 Differentially expressed genes (DEGs) analysis among after harvest (AH) and different cold storage conditions. (A) Percentage of differentially activated or repressed genes at global level or among the comparisons: After harvest (AH) vs. non-cold stored (NCS), AH vs. air-cold stored (ACS) and AH vs. CO2-cold stored (CCS). (B) Venn diagram showing the number of DEGs in the comparisons, AH vs. NCS, AH vs. ACS and AH vs. CCS. del Olmo et al. 10.3389/fpls.2022.983976 Frontiers in Plant Science 08 frontiersin.org A B FIGURE3 Total gene ontology enrichment. DEGs obtained between after harvest (AH) and non-cold stored (NCS). A Singular Enrichment Analysis (SEA; FDR ≤ 0.05) showing the GO for the most significant biological processes categories obtained comparing AH vs. NCS. (A) Scheme of the total enrichment (TE). (B) Bar chart of overrepresented terms in biological process category. The Y-axis shows the definition of each of the processes included in the first three GO terms annotated. The X-axis is the percentage of genes mapped by the term, and represents the abundance of the GO term. The percentage for the input list is calculated by the number of genes mapped to the GO term divided by the number of all genes in the input list. The same calculation was applied to the reference list to generate its percentage. response to hydrogen peroxide and reactive oxygen species. However, some functional categories enrichment is only observed between both experimental conditions, such as the positive regulation of activation mediated by abscisic acid and in response to alcohol (Figure6 in red). Regarding the molecular function categories (Supplementary Figure S7), a reorganization occurs in the expressions of some gene categories participating in the control of pectinesterases. A unique and significant differential enrichment can beseen in gene categories of transferase activity, transferring hesoxyl groups (Supplementary Figure S8 in red). Genes differentially expressed controlled by high CO2 To identify the genes involved in deteriorative processes controlled by CO2, gene expression levels of CCS were compared using a pairwise analysis with NCS and ACS. A total of 1,066 genes were differentially expressed in CCS vs. NCS, corresponding to 3.2% of the total genes. This comprised 2.4% genes activated and 0.8% repressed (Figure7). The majority of genes, 93.8% (0.3% of the total), are repressed between ACS and CCS, with only 6.2% activated. A total of 120 genes were obtained from the intersection data of NCS and CCS (−1.5 ≥ fold change ≥ 1.5), together with those obtained of ACS and CCS (−1 ≥ fold change ≥1). Figure8A shows the heatmap of the RPKM of these 120 genes placed by Fc between NCS and CCS, and expressed as a relative percentage, mainly including repressed genes (71/120) and activated ones (43/120) in both DESeq analyses, respectively (NCS vs. CCS and ACS vs. CCS). Also to benoted, a minority of genes are activated between NCS and CCS and repressed between ACS and CCS (6/120). These genes, partially or specifically controlled by CO 2 , were significantly classified exclusively in the molecular function category: enzyme regulator activity (GO 0030234). But to highlight the genes that undergo the greatest change, weanalyzed the 21 genes resulting from the DEseq intersection between NCS and CCS (−3 ≥ fold change ≥3), together with those obtained from DEseq analyses between ACS and CCS (−1 ≥ fold change ≥ 1; Figure8A, right). Similarly, the majority of genes weobtained were exclusively repressed (12/21) or/and activated only in DESeq analyses (9/21). As can beseen in Figure8B, wecan highlight genes that encode proteins involved in cell wall modification processes, in the regulation of ROS and in redox processes. This analysis also includes different regulators of gene del Olmo et al. 10.3389/fpls.2022.983976 Frontiers in Plant Science 09 frontiersin.org expression, and, to a lesser extent, effectors of JA-mediated responses and factors involved in DNA repair and chromosome stability. Weused RT-qPCR to analyze the expression of two genes that are transcriptionally activated (FvH4_3g36410 and FvH4_6g38980) encoding an Expansin A4 and a MATE efflux family protein, respectively (Figure8C), and two that repress their expression in response to CO2 (FvH4_2g39441 and FvH4_3g40160), which encode a JA carboxyl-methyltransferase and a peroxidase superfamily protein, respectively (Figure8D). Effect of high CO2 on texture Different empirical tests have been used to measure texture in fruits and vegetables comprising of living plant cells. However, measured parameters from empirical tests are generally poor measures of perceived texture. In contrast, between imitative methods, Volodkevich bite jaws probe record the force of biting on a piece of food as function of the deformation applied. By using this fixture, it has been shown that the characteristic peaks of the force–distance curve of different fruits and vegetables corresponded well to specific tissue parts and softening-related changes (Alvarez etal., 2020; Kim et al., 2020). In order to analyze the effect of high CO 2 on texture during cold storage and after transfer to 20°C, mechanical parameters derived from Volodkevich force-distance curves, including first peak force, shearing average force and shearing energy were examined (Table1). The yield point and the shear energy required to cause an irreversible deformation were the highest at the end of 2 days of high CO2 treatment, even 60% more than at harvest time in the case of yield point. Although the force value decreased 5 days after transfer to air, the yield point value was 85% higher than that of air-stored strawberries and similar to fruit at harvest time. After transfer to 20°C, the shearing average force and shearing energy values were higher in CCS than in ACS and similar to fruit at harvest time (AH). FIGURE4 Specific gene ontology enrichment. DEGs obtained between after harvest (AH) and non-cold stored (NCS). Bar chart showing in detail the most significant biological processes terms overrepresented obtaining by a Singular Enrichment Analysis (SEA; FDR ≤ 0.05) in AH vs. NCS comparison. The Y-axis shows the definition of GO terms. The X-axis is the percentage of genes mapped by the term, and represents the abundance of the GO term. The percentage for the input list is calculated by the number of genes mapped to the GO term divided by the number of all genes in the input list. The same calculation was applied to the reference list to generate its percentage. These two bars are classificated by a color key showing a green gradient scale for the frequency between both percentages represented. del Olmo et al. 10.3389/fpls.2022.983976 Frontiers in Plant Science 16 frontiersin.org β-glc and XTH in CO2-treated fruit and the steady state of xylose content, can belinked with maintaining the potentially important functions of XG in the cell wall remodeling and flexible accommodation to conserve turgor pressure. Notably, in the selected genes cluster (Figure 12), the expression of genes encoding glycosyltransferases (GTs), mainly nucleotide-diphospho-sugar transferase (NDT) family protein that catalyze the transfer of nucleotide sugars are weakly associated with many other genes involved in cell wall metabolism. In the case of GDP-mannose, our results indicate down-regulation of the gene (mannose-1-phosphate guanylyltransferase (GMP), FvH4_3g23310) in ACS (Figure11B). The Arabidopsis mutant cyt1 deficient in GMP showed severe phenotypes, including deficiencies in cell wall (Lukowitz etal., 2001). GDP-mannose is one of the sources of GDP-β-L-fucose, and its addition carried out by fucosyltransferases (FUTs; Park and Cosgrove, 2015). Our results demonstrate that the expression of FUT genes (FvH4_3g01440, FvH4_6g37210) and (GGalPP; FvH4_4g27370) did not decrease in CCS. Therefore, wepropose that having a similar CO2-treated fruit expression of GMP, FUT A B C D E FIGURE11 Expression analysis of a group of 28 genes co-expressed by Weighted Gene Correlation Network Analysis. Relative expression analysis through qPCR (black bars) vs. RPKMs obtained in RNAseq (gray bars) for genes involving: (A) metabolism of cellulose and hemicellulose, (B) N-glycosylation processes and cell wall simple sugars and polysaccharides metabolism, (C) pectins metabolism, (D) cell expansion and plant growth (E) and lignin metabolism. Graphs represent the average of at least three independent replicates quantified. Error bars indicate ± SD; One-way ANOVA for relative expression of AH versus NCS, ACS, or CCS value. Adjusted p value (*p ≤ 0.03, **p ≤ 0.002; ***p < 0.001). del Olmo et al. 10.3389/fpls.2022.983976 Frontiers in Plant Science 17 frontiersin.org and GGalPP to freshly harvested ones could be essential for preserving the polymerization of XGs. GDP-mannose and GDP-galactose are also involved in the first steps of the ascorbic acid biosynthesis (Badejo et al., 2008). The metabolism of activated sugars is closely related to sucrose catabolism, which wealready reported as being controlled by high CO2 treatment (del Olmo etal., 2020). UDP-glucose can beconverted to other activated sugars and it is also an entrance step to the synthesis of RFOs. The most representative genes that participate in RFOs metabolism are indicated in Figure11B. Biosynthesis of RFOs begins with the conversion of uridine diphosphate-galactose (UDP-Gal) and myo-inositol to galactinol catalyzed by galactinol synthase (GolS). Raffinose is synthesized by raffinose synthase through the transfer of a galactosyl moiety from galactinol to sucrose. Galactose can beremoved from RFOs through the action of α-galactosidase. The present findings in air-cold stored (ACS) samples show an up-regulation of FvH4_4g20790 and FvH4_1g02620, which are encoding a galactinol synthase 2 (GolS2) and a raffinose synthase (RafS) family protein, respectively. Also, there is a decrease in the expression of FvH4_2g12370, encoding an α-galactosidase 1. The present results follow the quantitative determination of raffinose family of oligosaccharides (RFOs; raffinose and stachyose) in strawberries (del Olmo et al., 2020), indicating a sustained synthesis of RFOs in stressed air-cold stored strawberries. Accumulation of trisaccharide raffinose has been also described as a cold-inducible biosynthetic route in other fruit and vegetables (Bustamante et al., 2016; Blanch et al., 2017) and different functional roles have been suggested for RFOs (Elsayed etal., 2014). Considering the selected genes cluster our results evidence that RFOs are responsive responses to cold-induced cell wall disassembly. By contrast, CO2-treated strawberries are able to FIGURE12 Weighted gene co-expression network for firmness of Mara des Bois strawberries at 20°C controlled by high CO2. Network for the genes coexpressed in turquoise and green modules which have a high weight over firmness. The inverse relationship clusters connecting fruit firmness with biotic stress and involving cell wall homeostasis are occupying a central place in the gene expression network. The close-up is highlighted a representative group of genes for both modules analyzed by qPCR. del Olmo et al. 10.3389/fpls.2022.983976 Frontiers in Plant Science 18 frontiersin.org maintain cell wall integrity and up-regulation of RFOs biosynthesis does not happen. CO2-treated fruit showed a down-regulation of genes encoding rhamnogalacturonate lyase (RG-lyase), and pectin esterase (PME/PMEI; Figure11C). The gene (FvH4_1g00290, PL) was considerably repressed in CCS, resembling data found in fruit at harvest, while they increased in ACS, which underwent rapid softening. In transgenic strawberries, suppression of PL resulted in firmer fruit (Santiago-Doménech etal., 2008). The marked low expression levels of RG-lyase, PME and PL-like genes in CCS could avoid pectin degradation and agree with the well-known reported presence of a pectin-rich middle lamella by effect of high CO2 treatment. The lowest expression of EXP-like B1 (encoded by FvH4_5g21120) in CCS (Figure11D) could beassociated with a possible reduced modification of xyloglucans in response to high CO2. Expansins seem to contribute to cell wall disassembly, weakening the hydrogen bonds between cellulose microfibrils and xyloglucan, and thus increasing the accessibility of wall polymers to hydrolytic enzymes and the incorporation of new polymers into the expanding cell wall. By contrast, as wementioned above, in the case of EXPA4, the highest level of expression was found in CCS samples. The marked increase in the expression of EXPA4 remains an active area of research. Several studies have also provided evidence that expansins are associated with enhanced tolerance to abiotic stress and influence the activity of cell wall-bound peroxidase (Han et al., 2015), improving the tolerance of transgenic tobacco plants to oxidative stress. In agreement with this idea, it is likely that the marked transcript abundance for EXPA4 in CO 2 -treated fruit becomes more tolerant to oxidative stress and deterioration after harvest, while the lowest values were found in NCS results in enhanced oxidative stress (amount of H2O2). H2O2 and JA-based signaling processes have been suggested in the regulation of lignin production by cell wall damage (Denness et al., 2011). Our results indicate that lignin accumulation is particularly relevant in ACS (Figure1), even though they become completely softened. Lignin in ACS was linked to a markedly enhanced expression of CAD (FvH4_1g16790), F5H (FvH4_1g12210), as well as a set of laccases (FvH4_1g24380, FvH4_6g12430, and FvH4_6g12410; Figure11E). At the last step of lignin, CAD enzyme converts three types of hydroxycinnamoyl aldehydes into their corresponding hydroxycinnamoyl alcohols (monolignols). The cell wall stiffening by lignification is considered a terminal process in specific cells and deposition of lignin in response to different kinds of stresses and cell wall damage (Denness etal., 2011; Le Gall etal., 2015). Hence, wesuggest that lignification in ACS may bea compensatory protective barrier in response to cell wall polymers degradation caused by stress during cold storage in air. This cold stress has been overcome in CCS by pretreatments with high CO2 levels maintaining cell wall integrity, and so no lignification has happened. Conclusion We performed a global transcriptome analysis in Fragaria vesca using RNA-seq for an integrative study of texture at consumption, an obvious target for preventing fruit loss and to gain quality, as well as to identify the molecular determinants underlying the protection triggered by high CO2 to avoid deteriorative processes. Through exploring sets of DEGs for higher-level biological and molecular themes based on GO and pathway assignments, strawberries kept at 20°C after harvest are characterized by an acute enrichment in genes mainly involved in oxidative stress and up-expression of genes involved in jasmonate biosynthesis that mediate the senescence process in strawberries after harvest. WGCNA of the comparative transcriptome identified key genes which might beassociated with cell wall elasticity of firmer CO2-treated strawberries. The decrease in the expression of Csl E1, XTH30, EXP-like E1 and the maintenance of initial steady-state expression levels of genes involved in transfer of activated sugars such as GMP and FUT may bethe cause of the beneficial effect of high CO 2 primarily maintaining the crosslinks as well as stabilizing xyloglucans. The genes involved in rhamnogalacturonan Iand homogalacturonan, mainly RG-lyase and PL-like are considerably down-regulated in firmer CO2-treated fruit, resembling the expression of fruit at harvest and may explain the improved middle lamella integrity. Furthermore, the up-expression of EXPA4, LEA, and MATE genes in CO2 treated strawberries, indicating their ability to preserve turgor pressure through sucrose and solutes accumulation and transport into the vacuole. By contrast in soft stressed air-cold stored samples there is a decrease in XG crosslinking along with highly disrupted pectins. Furthermore, the transcriptome data underline the preferential transcript accumulation of genes involved in lignin synthesis and raffinose pathway that could beconsider as responsive responses to coldinduced cell wall disassembly. The present results on transcriptomic analysis of CO2-treated strawberries with enhanced resistance to softening and oxidative stress at consumption will help to improve breeding strategies of both wild and cultivated strawberries. Data availability statement The original contributions presented in the study are publicly available. This data can befound at: https://www.ncbi.nlm.nih. gov/geo/query/acc.cgi?acc=GSE207254. Author contributions CM conceived and designed the experimental setup. IO, IR, and MS-B designed the RNA analysis. RT conducted technical support. MA conducted the texture analyses. ME conducted biochemical analysis. IO carried out the del Olmo et al. 10.3389/fpls.2022.983976 Frontiers in Plant Science 19 frontiersin.org transcriptome analyses. IO and CM analyzed the results and wrote the manuscript. IR assisted in writing the manuscript. All authors contributed to the article and approved the submitted version. Funding This research was supported by the Spanish National R&D&I Plan of the Ministry of Science and Innovation [grants AGL2017-85291-R (ERDF) and PID2020-113965RB-I00/ AEI/10.13039/501100011033]. Acknowledgments We are grateful to L. Bravo-Merodio for the valuable assistance and the critical revision of the manuscript. Weare grateful to AEI (Ministry of Science and Innovation) for the funding support. Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could beconstrued as a potential conflict of interest. Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. Supplementary material The Supplementary material for this article can befound online at: https://www.frontiersin.org/articles/10.3389/fpls.2022.983976/ full#supplementary-material SUPPLEMENTARY FIGURE S1 Total gene ontology enrichment. DEGs obtained between AH and NCS. Bar chart showing in detail the most significant molecular function terms overrepresented obtaining by a Singular Enrichment Analysis (SEA; FDR ≤ 0.05). SUPPLEMENTARY FIGURE S2 Specific gene ontology enrichment. DEGs obtained between AH and NCS. SUPPLEMENTARY FIGURE S3 Total gene ontology enrichment. DEGs obtained between AH and ACS. Total gene ontology enrichment. A Singular Enrichment Analysis (SEA; FDR ≤ 0.05) showing the GO for the most significant biological processes categories. SUPPLEMENTARY FIGURE S4 Total gene ontology enrichment. DEGs obtained between AH and ACS. Bar chart showing in detail the most significant molecular function terms overrepresented obtaining by a Singular Enrichment Analysis (SEA; FDR ≤ 0.05). SUPPLEMENTARY FIGURE S5 Specific gene ontology enrichment. DEGs obtained between AH and ACS. SUPPLEMENTARY FIGURE S6 Total gene ontology enrichment. DEGs obtained between AH and CCS. A Singular Enrichment Analysis (SEA; FDR ≤ 0.05) showing the GO for the most significant biological processes categories. SUPPLEMENTARY FIGURE S7 Total gene ontology enrichment. DEGs obtained between AH and CCS. Bar chart showing in detail the most significant molecular function terms overrepresented obtaining by a Singular Enrichment Analysis (SEA; FDR ≤ 0.05). SUPPLEMENTARY FIGURE S8 Specific gene ontology enrichment. DEGs obtained between AH and CCS. SUPPLEMENTARY FIGURE S9 Total gene ontology enrichment obtained for firmness using Weighted Gene Correlation Network Analysis. Bar chart showing in detail the most significant biological processes categories obtaining by a Singular Enrichment Analysis (SEA; FDR ≤ 0.05) in WGCNA. SUPPLEMENTARY FIGURE S10 Specific gene ontology enrichment obtained for firmness using Weighted Gene Correlation Network Analysis. SUPPLEMENTARY FIGURE S11 Total gene ontology enrichment obtained for firmness using Weighted Gene Correlation Network Analysis. Bar chart showing in detail the most significant molecular function terms obtaining by a Singular Enrichment Analysis (SEA; FDR ≤ 0.05) in WGCNA. References Alvarez, M. D., Velarde, C., Barrios, L., and Herranz, B. (2020). Understanding the crispy-crunchy texture of raw red pepper and its change with storage time. J. Texture Stud. 51, 120–133. doi: 10.1111/JTXS.12443 Badejo, A. A., Tanaka, N., and Esaka, M. (2008). 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